Societal Development in Rural China [1st ed.] 978-981-13-8081-5;978-981-13-8082-2

Table of contents :
Front Matter ....Pages i-xxviii
Front Matter ....Pages 1-1
Introduction (Wenrong Qian et al.)....Pages 3-48
Survey Sampling Design (Wenrong Qian et al.)....Pages 49-63
Front Matter ....Pages 65-65
Basic Structure of Rural Households (Wenrong Qian et al.)....Pages 67-85
Employment of Rural Households (Wenrong Qian et al.)....Pages 87-111
Income and Expenditure of Rural Households (Wenrong Qian et al.)....Pages 113-136
Fortune of Rural Households (Wenrong Qian et al.)....Pages 137-146
Front Matter ....Pages 147-147
Social Security of Rural Households (Wenrong Qian et al.)....Pages 149-165
Education of Rural Households (Wenrong Qian et al.)....Pages 167-178
Community Environment of Rural Households (Wenrong Qian et al.)....Pages 179-207
Governance and Public Services of Rural Communities (Wenrong Qian et al.)....Pages 209-228
Front Matter ....Pages 229-229
Conclusions (Wenrong Qian et al.)....Pages 231-251

Citation preview

Societal Development in Rural China Wenrong Qian et al.

Societal Development in Rural China

Wenrong Qian et al.

Societal Development in Rural China

Wenrong Qian et al. Zhejiang University Hangzhou, China

The print edition is not for sale in the Mainland of China. Customers from the Mainland of China please order the print book from: Zhejiang University Press. Based on a translation from the Chinese language edition: 中国农村家庭发展报 告 (2016) by 浙江大学中国农村发展研究院 (CARD) Copyright © Zhejiang University Press, 2017 All Rights Reserved ISBN 978-981-13-8081-5    ISBN 978-981-13-8082-2 (eBook) https://doi.org/10.1007/978-981-13-8082-2 © Zhejiang University Press 2019 Jointly published with Zhejiang University Press 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, express 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 Palgrave Macmillan imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-­01/04 Gateway East, Singapore 189721, Singapore

Preface

China is a country with large population, big agricultural sector and ­thousands of villages. The analysis of the current situation and changing trend of China’s rural society is of great significance to the understanding of China’s modernization process. China’s rural society has undergone tremendous changes since the reform and opening up in 1978. In these circumstances, rural areas have experienced fast economic growth and most peasants have got rid of poverty. Meanwhile, the number of impoverished people calculated according to China’s existing standard, the poverty line of 2300 Yuan per capita (2010 constant price), has dropped from 770 million in 1978 to 30 million in 2017, so that the incidence of poverty dropped from 97.5% to 3.1%. In consideration of the natural population growth, the impoverished people have reduced by nearly 800 million. With the rapid advancement of urbanization and industrialization in rural areas, the local civilization in traditional rural China has begun to transform into modern civilization: the production and life of traditional rural farmers in China are basically carried out in villages, so farmers have a strong dependence on land, which also forms the continuation of local civilization in China from generation to generation. However, this situation is changing rapidly in recent years. Relevant changes to this transformation such as rural household structure, gender structure, age structure, educational background structure and social stratum are also significant. In addition, ideological concept, production methods and employment patterns of farmers are becoming more and more diversified. Rural society presents new characteristics and development trends. Basic public services v

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PREFACE

in rural areas have been significantly improved, and the social security ­system has gradually perfected. At the same time, China’s rural areas are vast, the situation in different areas varies greatly, and the changes of rural society in different areas have appeared as quite different logic. Generally speaking, the developed eastern coastal areas took the lead in completing industrialization and gradually integrating into the coastal urban economic belt, becoming the areas where the population flowed in. Not only did the local villagers seldom move out of the villages, but also a large number of migrant workers from other parts of the country flowed in for work and business. Due to the lack of employment opportunities provided by industrialization in the central and western regions, a large number of rural labor force flowed out of the countryside to work and do business in the cities, resulting in the “hollowing out” of rural villages. In such a changing and complex rural society, it is difficult to describe it systematically and analyze it scientifically. The China Rural Household Panel Survey (CRHPS) launched by Zhejiang University could scientifically record and analyze the transition of China’s rural households and integrate the multidimensional information of society through rural families at the micro level. It could also help us understand the development of rural China in the dimensions of society, economy, politics, culture and resources and environment, as well as the basic features of rural consumption and demand, their production factors and the changes in their supply from a micro level. By continuously tracking and investigating all aspects of China’s rural households and regularly recording their all-directional transitions using micro-statistics, the objective reality of China’s rural households could be thoroughly understood, and the inner mechanism of the various kinds of social problems could be probed into. By doing so, the development and change of rural society could also be explained and predicted from their causal relationship so that the decision-making ability could be elevated, modernization of the national governance system and ability could be promoted, and an in-time and overall statistics foundation and security could be achieved for major national strategic demands. This comprehensive survey involved complete information on China’s rural households, including their basic household structure, employment, income and expenditure, household wealth, social security, education and social environment; it also involved the analysis of rural governance and public services based on the survey data at the village level.

 PREFACE 

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At the final chapter of this report, we further summarized and refined what we think is important and put forward our own viewpoint and the conclusion. Readers may not have to agree to all of our point of view, but we hope it will have a certain reference value for the people who are researchers on the Chinese rural issues and concerned about China’s rural development. The authors of this book are Wenrong Qian, Shaosheng Jin, Jianqing Ruan, Rui Mao, Binlei Gong, Qing Yuan, Yangang Yu, Shu Xu, Tengjiao Liao, Xin He, and Sitong Chen. Due to the limited knowledge of the author, coupled with the time and energy constraints, there may be unavoidable errors in this book; please let us know if you have any questions. Hangzhou, China

Wenrong Qian et al.

英文版序

《Societal Development in Rural China》 中国是一个人口大国, 也是一个农业农村大国, 剖析中国农村社会的 现状及其变化趋势, 对于理解中国的现代化进程具有重要意义。 1978 年改革开放以来, 中国农村社会发生了巨大变化。农村经济高 速发展, 大部分农民摆脱了贫困束缚, 按中国现有的标准即人均年收入 2300 元 (2010 年不变价)的贫困线计算的贫困人口从 1978 年的 7.7 亿下降到 2017 年的 0.3 亿, 贫困发生率从 97.5% 下降到 3.1%, 若考虑 到人口自然增长因素, 相当于减少贫困人口近 8 亿人。随着农村城市 化、工业化的快速推进, 中国传统农村的乡土文明开始向现代文明转型: 中国传统农村农民的生产生活等社会活动基本都是在村落进行的, 因此 农民对土地有强烈的依附性, 也由此形成了中国世代延续的乡土文明, 但这种情况近年来正在发生快速的变化。与这种转型相关的变化是农村 家庭结构、性别结构、年龄结构、学历结构和社会阶层也都发生着重大 变化,农民的思想观念、生产方式、就业方式日趋多样化, 农村社会呈现 出新的特点和发展趋势; 农村基本公共服务显著提升, 社会保障体系逐 步健全。同时, 中国农村地域广阔, 不同地区情况千差万别, 不同地区农 村社会的变化出现了相当不同的逻辑。总体来讲, 东部沿海发达地区率 先完成工业化并逐步融入沿海城市经济带中, 成为人口流入地区, 不仅 本地村民很少搬出村庄, 而且有大量外地农民工流入务工经商; 中西部 地区因为缺少工业化提供的就业机会, 大量农村劳动力流出农村进城务 工经商, 导致农村出现了“空心化”。中国农村社会这种复杂的变化, 使 得对其进行系统的描述和科学的分析并非易事。 浙江大学中国农村家庭研究创新团队启动实施的中国农村家庭调查 (CRHPS), 试图对中国农村家庭状况变迁进行科学记录和分析, 将社会多 维度的信息在微观层面上通过农村家庭关联在一起, 以期了解中国农村 ix

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英文版序

社会、经济、政治、文化、资源环境等各个维度的发展历程, 从微观层 面全面摸清我国农村消费与需求的基本面貌、生产要素及其供给的基本 状态变化。通过连续跟踪调查中国农村家庭的方方面面, 定期用微观数 据记录中国农村家庭全方位的变迁, 以真正了解中国农村家庭的客观现 实, 探究各类社会问题的内在机制, 从因果关系上解释和预测农村社会 的发展变化。本书在系统、连续的调查基础上, 对中国农村社会发展进 行科学分析, 既有以农村家庭调查数据为基础的对农村家庭的基本结 构、就业情况、收支情况、财富、社会保障、教育和社会环境的分析; 也有以农村社区调查数据为基础的对农村社区治理与公共服务的分析。 本书的最后, 对我们认为较重要的内容做了进一步的概括和提炼, 提 出了我们自己的观点和结论, 各位读者可能不会也不必同意我们的全部 观点, 但我们希望对于中国农村社会问题的研究者和关心中国农村发展 的其他读者有一定的参考价值. 由于笔者水平有限, 加上时间和精力的限制, 本书一定存在诸多不足 之处, 敬请各位读者批评指正! 钱文荣

Contents

Part I Survey Background and Sampling Design   1 1 Introduction  3 1.1 History of Related Rural Household Panel Surveys  4 1.1.1 Rural Surveys During the Period of the Republic of China  5 1.1.2 National Population Census of the People’s Republic of China  8 1.1.3 China Rural Surveys 10 1.1.4 National Fixed Point Survey (NFPS) 11 1.1.5 Rural Household Survey (RHS) 13 1.1.6 China Household Income Projects (CHIP) 14 1.1.7 China Health and Nutrition Survey (CHNS) 15 1.1.8 China Health and Retirement Longitudinal Study (CHARLS) 16 1.1.9 China Family Panel Studies, CFPS 17 1.1.10 Chinese General Social Survey, CGSS 18 1.1.11 China Labor Force Dynamics Survey, CLDS 19 1.2 Historical Background of the China Rural Household Panel Survey 22 1.3 The Purposes of China Rural Household Panel Survey 27 1.4 Contents of This Report 29 References 47

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2 Survey Sampling Design 49 2.1 Overall Sampling Plan 49 2.1.1 The Sampling Design of 2011  50 2.1.2 The Sampling Design Plan of 2013  51 2.1.3 The Sampling Design Plan of 2015  52 2.2 Terminal Sampling Design 54 2.2.1 Residence Mapping 54 2.2.2 Terminal Sampling Plan 55 2.3 Survey Sample Weights 56 2.4 Size and Distribution of Samples Over the Years 56 2.5 The Tracking Condition of Each Year 57 2.5.1 The Tracking Condition of Rural Families in Rural Area 57 2.5.2 The Tracking Condition of the Migrant Workers’ Families 57 2.6 Data Collection and Quality Control 58 2.6.1 Data Collection 58 2.6.2 Data Quality Control 61 Part II Basic Characteristics of Rural Households  65 3 Basic Structure of Rural Households 67 3.1 Household Structure 68 3.2 Gender Structure 72 3.3 Age Structure 73 3.4 Educational Attainment Structure 76 3.5 Marital Status 77 3.6 Physical Status 81 3.7 Political Status 84 4 Employment of Rural Households 87 4.1 Economically Active Population and the Working Population 88 4.2 Composition of the Economically Active Population and the Working Population 90 4.2.1 Gender Structure 90 4.2.2 Age Structure 92 4.2.3 Educational Attainment Structure 93

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4.3 Nonagricultural Population and Its Structure 95 4.3.1 Employment Structure 95 4.3.2 Gender Structure 97 4.3.3 Age Structure 98 4.3.4 Education-Level Structure 98 4.3.5 Industry Structure 99 4.4 Industry Distribution of Different Populations104 4.4.1 Industry Distribution Based on Gender104 4.4.2 Industry Distribution Based on Age Groups105 4.4.3 Industry Distribution Among People with Different Educational Backgrounds107 4.5 Industry Composition of the Working Population in Different Communities108 4.6 Family Employment Types and Their Compositions110 5 Income and Expenditure of Rural Households113 5.1 Income and Its Structure115 5.1.1 General Situation of Income115 5.1.2 Changes of Income117 5.1.3 Average Wage of Various Industries118 5.2 Income and Its Composition of Different Groups118 5.2.1 Gender118 5.2.2 Age119 5.2.3 Education Level119 5.2.4 Experience as Migrant Workers121 5.3 Income Inequality123 5.3.1 General Situation of Income Inequality123 5.3.2 Changes of Income Inequality125 5.4 Income and Income Inequality in Different Communities126 5.4.1 Community Expenditure126 5.4.2 Confirmation on the Right to the Contracted Management of Land128 5.5 Expenditure and Its Composition129 5.5.1 General Situation of Expenditure129 5.5.2 Nonproduction Expenses130 5.5.3 Transfer Expenditure131 5.5.4 Changes of Expenditure132

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5.6 Expenditure of Different Groups of People and Its Composition134 5.6.1 Salary134 5.6.2 Age135 5.6.3 Experience as Migrant Workers136 6 Fortune of Rural Households137 6.1 Rural Family Assets138 6.1.1 Introduction of Family Assets in Rural Areas138 6.1.2 Structure of Family Assets in Rural Areas138 6.1.3 Rural Household Property and Land Value141 6.2 Liability of Rural Households144 6.2.1 Introduction of Liability in Rural Households144 6.2.2 Liability Structure of Rural Households145 6.3 Net Worth of Rural Households145 Part III Public Service of Rural Households 147 7 Social Security of Rural Households149 7.1 Endowment Insurance149 7.1.1 Endowment Insurance Penetration149 7.1.2 Endowment Insurance Reception Proportion151 7.1.3 Contributions to Social Endowment Insurance Versus Individual Income151 7.1.4 Account Balance of Social Endowment Insurance152 7.1.5 Implementation of Unifying the Two Separate Pension Systems153 7.2 Medical Insurance154 7.2.1 Coverage of Social Medical Insurance154 7.2.2 Premium of Medical Insurance157 7.2.3 Personal Account of Social Medical Insurance157 7.2.4 Social Pool for Major Disease Treatment159 7.2.5 Medical Insurance and Medical Expenses159 7.3 Unemployment Insurance, Maternity Insurance and Work-Related Injury Insurance160 7.4 Public Housing Fund160

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7.5 Commercial Insurance162 7.5.1 Commercial Insurance Coverage162 7.5.2 Commercial Life Insurance163 7.5.3 Commercial Health Insurance164 7.5.4 Other Types of Commercial Insurance164 8 Education of Rural Households167 8.1 Education Level168 8.2 Education Condition171 8.3 Education Expectation174 8.4 Education Expenses176 9 Community Environment of Rural Households179 9.1 Village Profile and Population180 9.1.1 Village Profile180 9.1.2 Demographic Characteristics181 9.2 Community Infrastructure185 9.2.1 Communities’ Water and Electricity185 9.2.2 Communities’ Transport186 9.2.3 Community Education188 9.2.4 Other Infrastructure191 9.3 Overview of Rural Industries192 9.4 Collective Assets and Debts196 9.5 Community Spending and Income199 9.5.1 Community Spending199 9.5.2 Community Income200 9.5.3 The Implementation of Community Projects203 9.6 Agricultural Land, Land Expropriation and Demolition203 9.6.1 Overview of Agricultural Land203 9.6.2 Land Expropriation and Demolition206 10 Governance and Public Services of Rural Communities209 10.1 Main Governance Body and Mechanism210 10.1.1 Main Governance Body210 10.1.2 Governance Mechanism213 10.2 Governance Conditions214 10.3 Disputes and Settlement in Communities215

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Contents

10.4 Social Organizations and Religious Belief216 10.4.1 Overview of Social Organizations216 10.4.2 Public Services Provided by Social Organizations216 10.4.3 Religious Belief218 10.5 Community Cadres and College Graduate Village Officials220 10.5.1 Community Cadres220 10.5.2 College Graduate Village Officials220 10.6 Social Security221 10.6.1 Unified Urban and Rural Health Insurance221 10.6.2 Minimum Standard of Living222 10.6.3 Maternity Insurance223 10.6.4 Pension Insurance223 10.6.5 Other Social Security Programs224 10.7 Environmental Protection226 10.8 Community Training227 Part IV Research Conclusions 229 11 Conclusions231 11.1 The Rural Gender Imbalance Is Serious and Gender Selection Is Widespread231 11.2 The Aging Trend in Rural Area Intensifies, as the Elderly Make Up 15.2% of the Population234 11.3 The Proportion of Rural Unmarried Population Gradually Increased, “Left Men” Problem Particularly Prominent237 11.4 The Achievements of Nine-Year Compulsory Education Are Obvious, but There Are Still 9.5% of the Post-1995 Generation Who Have Not Completed Middle School240 11.5 The Education Levels of the Rural Population Are Low, but the Improvement Trend Is Obvious242 11.6 Rural Income Inequality Is Ever Increasing, 20% of Households with the Highest Income Account for 53% of Total Household Income244

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11.7 The Shares of Rural Consumer Spending Are Concentrated, with Food and Other Three Categories Accounting for 82%247 11.8 The Number of Left-Behind Children and Elderly People Is Large but on the Decline248

List of Figures

Fig. 2.1 Fig. 2.2 Fig. 3.1 Fig. 3.2

Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 4.4 Fig. 4.5 Fig. 4.6 Fig. 4.7

Steps of residence mapping Refusal rate of past surveys Family size composition (unit: %) Family type (unit: %). (Note: Conjugal family is a family composed of a couple. Nuclear family consists of a husband, wife and unmarried children. Stem family is a family where parents live with a married child and his or her spouse and children, with no more than one couple in each consecutive generation. Joint family is a family that has two or more couples in any generation. Single-parent family is a family where at least one child lives with his or her single parent. Family of grandparents with grandchildren is a family that lacks one generation. (Families of other types are not listed in the figure)) Proportions of agricultural and nonagricultural populations in the working population (%) Proportions of females in agriculture and nonagricultural populations (unit: %) Age structure of male and female, (a) agricultural and (b) nonagricultural populations (%) Proportions of people aged 45 and above in the agricultural population (%) Proportions of people who finished junior high school or below in all age groups (%) Proportions of people who finished junior high school or below (unit: %) Industry distribution of male and female (unit: %)

54 61 69

70 90 91 93 93 95 96 104 xix

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List of Figures

Fig. 4.8 Fig. 4.9 Fig. 4.10 Fig. 5.1 Fig. 5.2 Fig. 5.3 Fig. 5.4 Fig. 5.5 Fig. 5.6 Fig. 5.7 Fig. 5.8 Fig. 6.1 Fig. 6.2 Fig. 6.3 Fig. 6.4 Fig. 6.5 Fig. 6.6 Fig. 6.7 Fig. 6.8 Fig. 8.1 Fig. 8.2 Fig. 9.1 Fig. 9.2 Fig. 9.3 Fig. 9.4 Fig. 9.5 Fig. 9.6

Industry distribution of each age group (unit: %) 106 Age structure of the agricultural population (unit: %) 106 Industry distribution of different education-level groups (unit: %) 107 Total household income and its composition 116 Basic wage of various industries 118 Income of peasant families and migrant workers’ families and its composition (unit: yuan) 123 Gini coefficients of various incomes of rural households and the composition of the total income Gini coefficient 125 Average total expenditure per household and its composition 132 Average nonproduction expenses and its composition 133 Average nonproduction expenses of different income groups and its composition 134 Different age groups’ nonproductive household expenditure and its composition (yuan, %) 135 Structure of family assets in different regions (unit: %) 139 Nonfinancial assets of rural households 140 Financial assets of rural households 141 Regional differences of rural households’ property-owning rate (unit: %) 142 Asset ratio of property in rural household’s family asset (property owned) (unit: %) 143 Regional differences of rural households’ landowning rate (unit: %) 143 Asset ratio of land in rural household’s family asset (land owned) (unit: %) 144 Liability structure of rural households (unit: %) 145 Relationship between education expectation and gender as well as comparison with actual education level (unit: %) 175 Expenses for extra courses and activities 178 Average number of natural villages contained in one administrative village 180 Age structure of rural population 182 The proportion of people with an educational level of Junior College or above 183 The proportion of labor forces going out for work in rural areas184 The proportions of main fuel sources in rural area 187 The number of roads connecting to the county center in rural areas 187

  List of Figures 

Fig. 9.7 Fig. 9.8 Fig. 9.9 Fig. 9.10 Fig. 9.11 Fig. 9.12 Fig. 9.13 Fig. 9.14 Fig. 9.15 Fig. 9.16 Fig. 9.17 Fig. 9.18 Fig. 9.19 Fig. 9.20 Fig. 9.21 Fig. 9.22 Fig. 9.23 Fig. 9.24 Fig. 10.1 Fig. 10.2 Fig. 10.3 Fig. 10.4 Fig. 10.5

xxi

The average distance between a village and its nearest agri-product market 188 The average distance between the village committee and its nearest primary school and junior high school 190 The average salaries for rural primary school teachers and junior high school teachers 191 The proportion of households who have installed broadband in rural areas 192 The percentage of rural communities with special industries 193 The average output value of special industries in rural communities194 The percentages of various local resources in the special industries of rural communities 194 The number of special industries in rural communities in different years (unit: per 1000 villages) 195 The founding models of special industries in rural communities196 The operating models of special industries in rural communities197 The objects of collective debts in rural communities 198 The main reasons for borrowing money in rural communities 198 Main capital sources to repay the collective debts in rural communities199 The expenditure of rural communities (unit: 10,000 yuan) 200 The situation of income in rural communities (unit: 10,000 yuan)202 The structure of main incomes in rural communities 202 The percentages of machine being used in various sections in rural communities 204 The average compensation standard for communities having land expropriation 207 Gender composition of party branch secretaries in rural communities210 The number of times of villager’s representative meeting held in rural communities in 2015 214 Types of social organizations in rural communities nationwide217 Expenditure of rural communities to support the development of social organizations 218 Situation of service purchase from social organizations in rural communities 219

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List of Figures

Fig. 10.6 Fig. 10.7 Fig. 10.8 Fig. 10.9 Fig. 10.10 Fig. 10.11 Fig. 10.12 Fig. 10.13 Fig. 11.1 Fig. 11.2 Fig. 11.3 Fig. 11.4 Fig. 11.5 Fig. 11.6 Fig. 11.7 Fig. 11.8 Fig. 11.9 Fig. 11.10 Fig. 11.11 Fig. 11.12 Fig. 11.13 Fig. 11.14 Fig. 11.15 Fig. 11.16 Fig. 11.17 Fig. 11.18

Funds invested by rural communities to purchase services provided by social organizations 219 Coverage rate of unified urban and rural health care in rural communities222 Subsidy standard for people insured in the new cooperative medical care 222 Minimum standard of living in rural communities 223 Percentage of rural communities with unified maternity insurance system for urban and rural residents 224 Subsidy for people insured in new rural pension insurance 224 Percentage of rural communities with home-based care facilities and programs for the elderly 225 Treatment of stalks in rural communities 228 The sex ratio (male to female) in urban and rural areas 232 Sex ratios according to the birth order 233 The sex ratio in families with a second child 233 Distribution regarding age and gender (unit: %) 234 Urban and rural rate of aged population (unit: %) 236 Distribution of families with elderly people according to regions236 Chronic disease incidence among elderly people aged over 65 years237 Male to female ratio among urban unmarried population aged 30 and above 239 Educational attainment and unmarried rate (unit: %) 239 Educational attainments of different age groups 241 Education levels in urban and rural areas 242 The dissection of the Gini Coefficients of year-by-year rural household income in each area and total household income 245 The dissection of Gini coefficients of various household incomes246 Influencing factors of various income Gini coefficients 246 Average rural household consumption expenditure and its composition247 Year-by-year average household consumption expenditure and its composition 248 The change of proportion of rural left-behind elderly people in the total local permanent residents 250 The change of proportions of rural left-behind children in the total local permanent residents 250

List of Tables

Table 1.1 Table 1.2 Table 2.1 Table 2.2 Table 2.3 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 3.6 Table 3.7 Table 3.8 Table 3.9 Table 3.10 Table 3.11 Table 3.12 Table 3.13 Table 3.14 Table 3.15 Table 3.16

China rural surveys during the 1920s and 1930s Surveys in relevance with China’s rural families Sample size of past surveys The tracking condition of rural families in rural areas each year Tracking condition of migratory workers’ families of each year Relationship between household size and generation (unit: %) Distribution of households with the elderly in different regions (unit: %) Age and gender structure Age structure (unit: %) Family population burden (unit: %) China’s rural population aging rate in 2013–2015 (unit: %) Region and educational attainment structure (unit: %) Gender and Education Level (unit: %) Region and marital status (unit: %) The distribution of unmarried people aging 30 and more (unit: %) Changes in the proportions of unmarried people aging 30 and more (unit: %) The proportions of unmarried people and educational background (unit: %) Chronic disease situation (unit: %) Prevalence of chronic diseases in elderly people aging 65 and more (unit: %) Assessment of physical status for the rural population (unit: %) Assessment of physical status in elderly people aging 65 and more (unit: %)

6 20 57 58 58 71 71 72 73 74 75 76 77 78 79 79 80 81 82 83 84 xxiii

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List of Tables

Table 3.17 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 4.5 Table 4.6 Table 4.7 Table 4.8 Table 4.9 Table 4.10 Table 4.11 Table 4.12 Table 4.13 Table 4.14 Table 4.15 Table 4.16 Table 4.17 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 5.5 Table 5.6

The regional distribution and gender distribution of Communist Party members (unit: %) General situation of the rural economically active population and working population Gender structure of rural economically active population and working population (unit: %) The age structure of the rural economically active population and the working population (unit: %) Education level of rural economically active population and working population (unit: %) Employment structure of the nonagricultural population (unit: %) Gender structure of the nonagricultural population (unit: %) Age structure of the nonagricultural population (unit: %) Education-level structure of the nonagricultural population (unit: %) Industry profile of the nonagricultural population (unit: %) Gender structure of the nonagricultural population in various industries (unit: %) Age structure of the nonagricultural population in various industries (unit: %) Education structure of the nonagricultural population in various industries (unit: %) Industry structure of community working population and landownership (unit: %) Industry structure of community working population and infrastructure status (unit: %) Profiles of families of different employment types (unit: %) Average age of households of each type (unit: %) Average schooling years of households of each type (unit: %) Various kinds of income of rural households (%, yuan) Average household income based on the gender of the householder (unit: yuan) Average household income based on age groups of the householder (unit: yuan) Average income based on different education levels of the householder (unit: yuan) Average income of families with different working experience (unit: yuan) Gini coefficients of various kinds of income of rural families in different regions

85 89 91 92 94 96 97 97 97 99 100 101 102 108 109 110 111 111 117 119 119 120 121 124

  List of Tables 

Table 5.7 Table 5.8 Table 5.9 Table 5.10 Table 5.11 Table 5.12 Table 5.13 Table 6.1 Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 6.6 Table 6.7 Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table 7.5 Table 7.6 Table 7.7 Table 7.8

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Relationship between community’s expenditure and income (unit: yuan) 127 Gini coefficients of various community expenditure 127 Incomes and Gini coefficients in different situations of the confirmation on the right to the contracted management of land 128 Average expenditure of rural households and its composition130 Average nonproduction expenditure of rural households and its composition 131 Transfer expenditure of rural household families and its composition131 Average expenditure and its composition of families with different migrant working experience 136 Regional differences in family asset of rural households (unit: 10,000 RMB) 138 Financial assets and nonfinancial assets of rural households (unit: 10,000 RMB) 138 Proportions of risk-free assets and risk assets of rural households (unit: %) 141 Property value of rural households (unit: 10,000 RMB) 142 Land value of rural households (unit: 10,000 RMB) 143 Regional differences of rural households’ liabilities (unit: 10,000 RMB) 145 Regional differences of rural households’ net worth (unit: 10,000 RMB) 146 Distribution of different pension schemes for rural residents (unit: %) 150 Distribution of different social endowment insurance for rural residents (unit: %) 150 Proportion distribution of rural residents aged 60 and above who have started receiving pension benefits (unit: %) 151 Comparison between rural residents’ personal contribution to social endowment insurance and individual income 151 Rural residents’ personal account balance of different kinds of social endowment insurances (unit: yuan) 152 Gender difference of rural residents in account balance of social endowment insurance (unit: yuan) 153 Situation of pension scheme integration in working units for rural residents (unit: %) 153 Coverage of rural residents’ medical insurance (unit: %) 154

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List of Tables

Table 7.9 Table 7.10 Table 7.11 Table 7.12 Table 7.13 Table 7.14 Table 7.15 Table 7.16 Table 7.17 Table 7.18 Table 7.19 Table 7.20 Table 7.21 Table 7.22 Table 7.23 Table 7.24 Table 7.25 Table 7.26 Table 8.1 Table 8.2 Table 8.3 Table 8.4 Table 8.5

Medical insurance coverage of rural residents of different age groups (unit: %) 155 Gender difference in rural residents’ coverage of medical insurance (unit: %) 156 Types of medical insurance engaged by rural residents (unit: %) 156 Premium of different kinds of medical insurance (unit: yuan per year) 157 Penetration of personal medical insurance accounts among rural residents (unit: %) 158 Average balance of medical insurance accounts among rural residents who have an account (unit: yuan) 158 Balance of medical insurance account among rural residents whose account balance is not zero (unit: yuan) 158 Social pool for major disease treatment engagement among rural residents 159 Medical expenses of rural residents who have been engaged in medical insurance (unit: yuan per year) 159 Coverage of unemployment, maternity and work-related injury insurances (unit: %) 160 Provident fund engagement of rural residents 161 Reasons of rural residents’ withdrawal of provident fund (unit: %) 162 Coverage of commercial insurance among rural residents (unit: %) 163 Gender difference in coverage of commercial insurance among rural residents (unit: %) 163 Coverage of commercial insurances among rural residents of different age groups (unit: %) 163 Rural residents’ engagement in commercial life insurance 164 Rural residents’ engagement in commercial health insurance164 Rural residents’ engagement in other commercial insurances165 Educational level (unit: %) 168 The education received by people aged 6–23 in last semester (unit: %) 169 The choice of universities and colleges in different regions (unit: %) 170 The type of schools students studied in before higher education (unit: %) 171 The type of public primary and middle schools (unit: %) 172

  List of Tables 

Table 8.6 Table 8.7 Table 8.8 Table 8.9 Table 8.10 Table 8.11 Table 9.1 Table 9.2 Table 9.3 Table 9.4 Table 9.5 Table 9.6 Table 9.7 Table 9.8 Table 9.9 Table 9.10 Table 9.11 Table 9.12 Table 9.13 Table 9.14 Table 9.15 Table 10.1 Table 10.2 Table 10.3 Table 10.4

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Education quality of public primary and middle schools (unit: %) 173 The distance from home to school (unit: km) 173 The situation of studying faraway and boarding at school (unit: %) 174 Education expectations of parents (unit: %) 175 Expenses for primary school, junior high school and senior high school 176 Expenses for extra courses and activities 177 Structure of village surnames 181 Average size of the permanent resident population, registered population and rural residents 182 Proportions of households that have lost their only child, left-behind elderly and left-behind children in rural areas 184 Proportions of migrant population, immigrant families and emigrant families in rural areas 185 The proportions of the main sources of domestic water in rural ­communities (unit: %) 186 The coverage rate of infrastructure in rural communities (unit: %) 189 The numbers of teachers and students in primary school 190 The basic situation of collective asset and debt in rural communities (unit: 10,000 yuan) 197 The main expenditure projects in rural communities 201 The implementation of rural community projects 203 The situation of land approval and land circulation in rural communities204 The percentages of economic crops cultivated in rural communities and the average output value 205 Cultivated acreage and unused agricultural acreage in rural communities205 The basic situation of land expropriation in rural communities206 The situation of land expropriation and resettlement in rural communities 207 Basic situation of main governance body in rural communities (unit: person) 210 Composition of education background of party branch secretaries (unit: %) 211 Monthly subsidy and education background of staff in two committees211 Situation of party members in rural communities 212

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List of Tables

Table 10.5 Table 10.6 Table 10.7 Table 10.8 Table 10.9 Table 10.10 Table 10.11 Table 10.12 Table 10.13 Table 10.14 Table 10.15 Table 10.16 Table 10.17 Table 11.1 Table 11.2 Table 11.3 Table 11.4 Table 11.5 Table 11.6

Basic situation of rural community governance mechanism (coverage rate) (unit: %) 213 Basic situation of rural community governance conditions nationwide and in the Eastern, Central and Western Regions214 Situation of rural communities’ participation in meditation (unit: %) 215 Percentage of professional correction services provided by rural villages (unit: %) 216 Situation of social organizations in rural communities nationwide and in the Eastern, Central and Western Regions217 Religious belief in rural communities (unit: %) 220 Cadres’ attitude and expectation for monthly subsidy 221 Basic situation of college graduate village officials 221 Measures implemented by rural communities to help the disabled (unit: %) 226 Situation of community service types provided by rural communities (unit: %) 226 Percentages of rural communities with ecological degradation227 Situations of methods adopted by rural communities to prevent infectious diseases (unit: %) 227 Type of service provided by career guidance institutions in rural communities (unit: %) 228 2011–2015 Domestic rural rates of aged population (unit: %) 235 The changes of rural unmarried population above the age of 30 (unit: %) 238 The proportion changes of urban unmarried population above the age of 30 (unit: %) 238 Educational attainment of different age groups (unit: %) 240 Level of education of different age groups (unit: %) 243 Parents’ expectations for their children’s education (unit: %) 244

PART I

Survey Background and Sampling Design

CHAPTER 1

Introduction

The household is the most basic unit of society. It is also the most fundamental social cell constituted by marital, blood and adoptive relationships. As social resources are very limited, households, the connection between the individual and society, pursue the maximization of efficiency and family welfare by interacting with society. Rural households, on the other hand, are the basic organizational unit of rural society that combines production and social lives. Rural households are the basic units of rural consumption and demands as well as the supply side (including labor, capitals etc.) of production factors. To obtain the maximum satisfaction, rural households not only need to purchase various types of consumptive goods and the needed household’s productive materials from the market but also need to obtain income by utilizing the labor, material and time resources of the whole household. As resources are always scarce and limited, the goal of the decisions made by the entire rural household is to maximize the efficiency of these resources. Therefore, like other rational economic entities, rural households make decisions frequently in terms of production and consumption to maximize the profits or efficiency. The China Rural Household Panel Survey (CRHPS) launched by Zhejiang University (hereafter referred to as ZJU) in 2015 aimed at setting a baseline for investigating rural issues in China. This comprehensive survey involved complete information on China’s rural households, including their basic household structure, employment, income and expenditure, household wealth, agricultural production and management, land utility © The Author(s) 2019 W. Qian et al., Societal Development in Rural China, https://doi.org/10.1007/978-981-13-8082-2_1

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and circulation, migration of population and urbanization, financial behavior, health and social security, education and training and so on. Moreover, the survey covered the basic conditions of China’s rural grass-roots units (village committees), which included information on local public services, social economy, social governance and environmental characteristics, as well as other areas. The CRHPS could scientifically record and analyze the transition of China’s rural households, and integrate the multidimensional information of society through rural families at the micro level. It could also help us understand the development of rural China in the dimensions of society, economy, politics, culture, and resources and environment, as well as the basic features of rural consumption and demand, their production factors and the changes in their supply from a micro level. By continuously tracking and investigating all aspects of China’s rural households and regularly recording their all-directional transitions using micro-statistics, the objective reality of China’s rural households could be thoroughly understood, and the inner mechanism of the various kinds of social problems could be probed into. By doing so, the development and change of rural society could also be explained and predicted from their causal relationship so that the decision-making ability could be elevated, modernization of the national governance system and ability could be promoted and an in-time and overall statistics foundation and security could be achieved for major national strategic demands.

1.1   History of Related Rural Household Panel Surveys The China Rural Household Panel Survey is an integral part of China’s household surveys. In fact, the research on China’s rural household problems has had a long history. According to the research of Li and Deng (2009), in as early as 1914, a foreign scholar, C. G. Dittmer, had already organized a rural survey which targeted 195 farmers around the Tsinghua Campus. However, the earliest rural survey conducted by the Chinese, as commonly accepted by historians, was the survey on Chengfu Village in the western suburb of Beijing in 1923 (Li and Deng 2009). In the 1920s and 1930s, lots of surveys were conducted in rural China. The history of related rural household surveys (RHSs) that had been carried out so far is introduced as follows.

1 INTRODUCTION 

5

1.1.1  Rural Surveys During the Period of the Republic of China During the period of the Republic of China, rural China economy had had an unprecedented crisis and western economics research paradigms had gradually gathered the attention of Chinese scholars who hoped to obtain an in-depth knowledge of the real circumstances in rural society by means of field investigations (Ge 2012). “Field investigation is an important way to understand information, accumulate materials and promote studies. It has a long-standing and well-established history in China, but scientific methods are used in field investigation only recently” (Li 2008). Under the influence of western economics paradigm, many people from all walks of life took part in the survey. The organizers of the survey included Chinese and foreign scholars as well as revolutionaries. Rural construction organizations, research institutions and tertiary institutions also organized and participated in the rural survey. In addition, government agencies carried out various kinds of rural investigation activities for some political purposes. However, although a nationwide rural survey was the highest goal of the scholars during that period, it was not feasible due to the limited technology, personnel and funds. Therefore, Chen Hanqian, Li Jinghan and their partners had to take a step back, select a typical place and conduct a small regional survey. In the first half of the twentieth century, especially in the 1920s and 1930s, the following research and investigations on rural China were of big scales and had made significant impacts: two national rural surveys conducted by John Lossing Buck; rural surveys in the three regions of north China, Yangtze River Delta and Pearl River Delta by Chen Hansheng; rural surveys in Ting Hsien by Li Jinghan, as well as the Mantetsu surveys et cetera. Other representative rural China surveys during the period of the Republic of China are shown in Table 1.1 (Li and Deng 2009). The academic achievements of the American scholar John Lessing Buck in the agricultural economy were derived from the data and information collected from two comprehensive systematic household surveys conducted in rural China from 1921 to 1925 and from 1929 to 1933. During the 1920s and 1930s, he investigated 2866 rural households in 17 places of 7 provinces excluding Northeast China and 38,256 rural households in 16,786 farms of 22 provinces.1 In terms of the investigation method, Buck utilized the sampling survey method2 of which the subjects were mainly students from Jinling University and the main contents were areas such as population, lands, grain output and scale of household agricultural plants.

1923–1924

1921–1925

H. S. Bucklin

John Lossing Buck

1929–1933

1922 Summer

J. B Tayler, C. B. Malene

Residents, families, residence, land possession, succession of predecessors’ career, occupation, economy et cetera Families, religious life, local administration and punishment system, education, business of agriculture, industry and commerce, health and public hygiene, entertainment, housing et cetera 2866 rural households in 17 Focusing on production and technology: places of 7 provinces crop planting, grain yield per mu, planting excluding Northeast Chinab scale and profit of household agricultural crops and the efficacy relations between production factors 38,256 rural households in Population or land way of use or natural or 16,786 farms of 22 common factors that influence land provinces excluding utilization (successful) extent Northeast China

240 Villages in Hebei, Jiangsu, Anhui, Shandong and Zhejiang Song—Ha Kang, Shanghai

1921 Autumn

Lenyon L. Butterfield

Demographic structure and migration, marital state, land, village wealth, religion, language and education Problems of agriculture and agricultural education

Phoenix Village in Chaozhou City, Guangdong Province More than 10 provinces in China

1919–1920

Contents

D. H. Kulp

Sites 195 rural households around Basic conditions such as household scale, the Tsinghua Campus income, expenditure and scale

Time (Year)

C. G. Dittmer 1914–1918

Investigator

Table 1.1  China rural surveys during the 1920s and 1930s

Land Use in China

Chinese Rural Economy

A Social Survey of Song—Ka Hang

Submission on Improving Agriculture and Agricultural Education of China The Study of Chinese Rural Economy

An Estimate of the Standard of Living in Chinaa Country Life in Southern China

Representative achievements

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1931–1932

Sidney D. Gamble

1929–1933

1929–1934

Li Jinghan

Chen Hanshenge

Field areas, houses, land possession and education, utensils, seeds, cattle, household industry, fruits, land property and labor income, labor allocation, tax, fertilizers, foodstuff, earnings and expenditure et cetera Villages in Ting Hsien of Population, households, lands, agricultural Hebei Province production, household industry, commercial trades and social activities et cetera 50 rural households of 4 Household organization, occupations and villages near Yangshupu of income of household members, property Shanghai ownership right (household income and landownership), surplus and deficit of income and expenses, life expenses, farmers who leave villages and enter into factories to work as well as rural economy, education and religions Mainly in areas of Northeast Rural population and economic state, rural China, Northern China and resources and rural productive conditionsd Eastern China Ting Hsien of Hebei General situation of the whole county and Province survey into different villages, including household registration, land, production, tax, markets, education and customs Villages, households, markets, Communication, economic life (land farmhouses in Yangtze River renting patterns etc.) landownership Delta, Pearl River Delta and relations and allocation of means of Northern China production

25 field areas around Xinkaisi on Mount Emei in Sichuan Province and 50 rural households on the Chengdu Plain

b

a

Dittmer (1918) Sheng (2001) c The full name is “the South Manchuria Railways Co. of Japan” d Cao (1991) e Chen (1988)

1933–1945

Mantatsuc

H. D. Lalnson 1933

1926

A. R. Radcliff— Brown

Land Issues of Modern China

Land Survey of Ting Hsien

Agriculture of Zailai, Mantatsu

Influence of Industrialization on Rural Life—Survey of 50 Rural Households in 4 Villages around Yangshupu in Shanghai

Ting Hsien: A North China Rural Community

A Survey of 25 Field Areas in Mount Emei, Sichuan

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Some scholars think that “a complete understanding of studies on modern China agricultural economy by U.S. scholars must be started with John Lossing Buck”. At that time, another representative and a comprehensive rural household survey was the one organized by “Mantetsu”, which utilized the modern economic anthropology theoretical paradigm. Setting a natural village as a survey unit and rural villagers as the respondents, this rural survey was really concrete and complete. “In terms of the factual survey of Mantetsu surveys, a vast area of China was covered, stretching from Heilongjiang in the north to Hainan Island in the south” (Cao 1991). Meanwhile, during the period from 1908 to 1945, the Mantetsu specially established various investigatory institutions focusing on different areas so that the whole survey was well organized and brought a high level of unity and comparability to rural materials of different regions and economic types. Thus, the “Report Materials of Mantetsu Survey” was formed. These considerable book materials and documentaries have been valuable for studies on the rural economy during the period of the Republic of China. These are historical materials in rural household surveys that are worthy of systematical retrospect and analysis. In general, during the period of the Republic of China, although the rural survey method was not mature and the objectivity of survey materials and results were also controversial to some extent, in any case the gained statistics and materials of the rural household survey were obtained after thorough questionnaires or in-depth field investigations by sociologists of that period, and the results also came from systematic standardized processing and comprehensive quantitative analysis (Xia 2007). Today, these surveys have become valuable documents for studying China’s modern history as well as significant sources for social anthropology and economics. Many studies on the agricultural economy during the contemporary Republic of China period have referred to and taken advantage of these materials, which has largely boosted the progress of humanities and social sciences. 1.1.2  National Population Census of the People’s Republic of China China has conducted basic population census since 1953 to obtain the most detailed and accurate population statistics and to understand the basic population situation of China. The National Population Census of the People’s Republic of China is a single-round survey which is carried out nationwide, searching household by household and individual by indi-

1 INTRODUCTION 

9

vidual. It is mainly organized by the State Council and cosponsored by multiple departments. The China population census leadership group oversees its concrete implementation in various parts of China. Six China population censuses have been conducted since the founding of the People’s Republic of China, in the years of 1953, 1964, 1982, 1990, 2000 and 2010, respectively. The China Population Census Regulations published in 2010 by the State Council has further specified that a national census should be conducted in the first year of each decade and that a sample survey on 1% of the population should be launched between the two censuses with an interval of five years since 2010. The standard time for the sixth China population census3 was at 0 o’clock on November 1, 2010, which meant that by this time collecting all the registers by the census takers could gather the total amount of China population and geographic conditions. Conforming with the rule of registering at the current residence in the sixth China population census, everyone should make the registration at his or her current residence. If the census subject did not live in the household register place, his or her relative information would be collected into the household register place. If the subject was working or studying abroad when the census was conducted at the standard time, his or her information should be reported and registered by its former domestic family household or institutional household. In the census, the census takers employed the means of in-­room inquiry as well as fill-out on the spot. The National Population Census of the People’s Republic of China has the characteristics of universality, individuality, standardization, concentricity, unity and periodicity, that is, all the population within a certain area should participate in the census registration; the individual as a unit should fill the census form person by person and item by item; the registration would be the population condition at the standard time, regardless of the actual date when the census taker enters into the household; the population census is under the central leadership and is organized and ­implemented by the deployment of the first level of the Central Census Organization; the national census scheme, form, filling-out method, classification standard and procedures and progress should be strictly uniformed nationwide; and that the population census should be conducted periodically. It is an integral part of the overall population census work to scientifically assess the census statistics. The census has offered detailed and reliable population statistics for the formulation of policies on national population, employment, education, social welfare and nationalities. In terms of the specific content, the population census mainly investigates the

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basic conditions of individuals and households, including gender, age, nationality, level of education, profession, occupation, migration, social security, marriage and fertility, death, housing and so on. Through the census, basic national information such as population, its structure and distribution could be identified, and the social characteristics, household characteristics, educational characteristics, economic characteristics, housing conditions and birth-death conditions a year before the standard census time could also be sorted out. The data is publicly available. In the process, the census takers of the Sixth China Population Census group faithfully registered every census subject onto the census form in the census housing estate through in-door inquiry. Meanwhile, as the time, contents and methods of censuses worldwide tend to be consistent, the materials gathered in the census could be compared globally. However, as the census is generally a static survey, the changes in population could not be reflected. Moreover, since the contents are restricted to the basic population, household migration and social security conditions such as names, genders, ages, nationalities, condition of registered residence and level of education, and information such as household economy and culture are not covered, and thus the academic value of these statistics is discounted to some extent. 1.1.3  China Rural Surveys To dynamically understand the basic condition of China’s rural areas, the Institute of China Rural Studies of Central China Normal University (CCNU) has utilized field trips as its basic research method and has conducted a continuing follow-up survey to study the change in China’s rural areas by researching on 259 tracked observation villages in China since 2009. At the initial stage, the “Observation on a Hundred Villages” project has set 4000 households in 258 villages nationwide as observation subjects and has conducted special research and field trips. The crew of the survey group was mainly composed of young teachers, doctoral students, postgraduates and undergraduates. From the ample individual case observations and thoughts gathered from the field trips, apart from the original 258 villages, the “Observation of a Hundred Villages” project has also investigated nearly 100 forestry villages and 50 poor villages in 2011 to tackle the problems of “forestry reform” and poverty. Fifty counties and 100 villages and towns have been investigated on the basis of rural household surveys. On the foundation of “Observation of a Hundred Villages”, China rural survey was a redesigned

1 INTRODUCTION 

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investigation project, the content of which covered field categories such as villages, households, oral speeches, habits, special topics and documents. It was officially launched in the second half of 2015. Recently, the “new-­ edition rural survey” has taken a further step to systemize and translate the rural survey materials of Mantatsu and Russia villages. It has also planned to set observation sites in several overseas villages outside the mainland. Up to now, the academic achievements of China rural surveys include series of books and atlas such as China Rural Survey, China Rural Studies, Impression of China Rural Farmers and Chinese Peasants Development Report. China Rural Survey, which was first published in 2012, involved characterized villages, rural governance, democracy at the grass-roots level, a logic of land rights, rural society, as well as the construction of new socialist countryside. Policy consultancy Report on Rural China, which was published in 2013, focused on rural water conservancy, rural migrant workers in cities, livelihood issues and rural market. The report was a conclusion and analysis of special topics according to the surveys conducted in the villages and rural households in different provinces in China. Chinese Peasants Development Report 2014 (Politics), which was published in 2014, covered rural engagement in a rural democratic election, decision-­ making, management and surveillance as well as the political development situations of rural party members and cadres, young farmers, elderly farmers, rural women, migrant farmers and minority farmers. Currently, the survey platform has further established 300 survey platforms of “Observation of a hundred villages”, “Observation of a hundred households” and “Observation of a hundred overseas households”. By analyzing the rural individuals and typical events in different villages and the intrinsic mechanisms of incidents or rural governance, theoretical ideas were expected to be put forward in factual narrations. The platform emphasized on studying politics and sociology and did not publicly ­disclose the quantitative statistics and sharing of statistics. Therefore, by only publicizing the project survey report and the realistic conditions of video materials of the “Observation of a Hundred Villages” project, its academic value in its quantitative analysis has been largely affected. 1.1.4  National Fixed Point Survey (NFPS) To deeply understand the dynamic state of rural reform and grasp the basic conditions of rural household production and management, the National Rural Fixed Point Survey (NFPS) system was approved to be established by the Secretariat of the Central Committee of the Communist

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Party of China (CPC) in 1984 and was officially found in 1986. Since 1990, NFPS has been mainly under the organization and implementation of the Policy Research Office of CPC of Rural Economic Research Center of Ministry of Agriculture of PRC and National Fixed Point Office of Ministry of Agriculture of PRC (Rural Social Economy Department of National Bureau of Statistics of China 2010). Since the implementation of the NFPS, the sample distribution expanded from 272 villages in 28 provinces to 360 villages in 31 provinces (districts, municipalities) in China except for Hong Kong, Macao and Taiwan. Abundant rural social economic statistics have been accumulated, which have provided a significant basis for all levels of governments to formulate rural policies and instruct agricultural and rural work. The NFPS has the following five procedures: first, complete the regular survey by collecting and reporting the data of each rural household sample annually in a fixed time; second, carry out special surveys focusing on the important and hot issues surrounding rural development, rural production and farmers’ lives during that period; third, form a dynamic survey by survey institutions of the provincial and county levels reflecting the new circumstances and problems; fourth, organize pieces of training in professional knowledge and rural policies for inspectors of the provincial and county levels; and fifth, make full use of the accumulated survey figures to systematically analyze agriculture, rural villages and rural households. In terms of the specific components of the survey, the NFPS put its emphasis on agricultural production elements, agricultural production and conditions of household income and expenditures. These mainly included the constitution of family members, land conditions, fixed assets conditions, household production and management conditions, the sale of agricultural products, purchase of planting materials, household annual income and expenses, main food consumption, year-end possession of main durable goods and dwelling conditions. Therefore, the NFPS has combined regular research, special research, dynamic inspection and analytical research and boasted the advantages of a broad scope of samples and a considerable number of samples. Meanwhile, abundant and detailed continuous data has been collected in the accumulation of surveys and regular reports during the previous three decades, which has been favorable for dynamic analysis and trend research in various aspects of the rural social economy. However, the main purpose for the NFPS was to provide decision references for all levels of the party and government departments to understand the operation of the rural social economy, formulate rural economic policies and rural economic develop-

1 INTRODUCTION 

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ment strategies as well as deepen the reform of the rural economic system. Reflecting mainly the basic states of rural households, the content of the survey had a minor coverage on finance, health and social security, education, health care and community environment of rural households. Furthermore, the statistics of this survey are not released to the public and are therefore inconvenient to obtain, which to a large extent has limited the use and analysis of the survey statistics. 1.1.5  Rural Household Survey (RHS) The Rural Household Survey (RHS) was a statistical survey project illustrating rural households’ production, income, expenditure, accumulation and social activities organized and was conducted by the Survey Organization of Rural Social Economy of the National Bureau of Statistics (NBS) of China. By investigating on rural households’ economic and social activities and monitoring the changes in their income and quality of life, the RHS aimed at providing basic statistics for national economic accounting and price investigation, as well as providing a basis for people’s governments at all levels and national macroscopic decision-making departments to research and formulate rural economic policies and decisions (Rural Social Economy Department of National Bureau of Statistics of China 2010). The RHS adopted the method of multivariate probability proportional to size (MPPS) sampling where 68,000 rural households in 857 counties from 31 provinces (districts, municipalities) were chosen in 2010. The key contents of the survey were community development, basic household situation, inhabitation condition, the basic condition of population and labor force employment, adjustments of rural production structure and technology applications, and income and expenditure of the rural households. The RHS has gathered statistics on the basic condition of the production and lives of China’s rural households in a relatively scientific and systematic way, but the contents were rather general and the involvement of community units was also limited due to the brief statistical analysis on the rural household level. Meanwhile, the RHS statistics are now available to the public in the years of 1991, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008 and 2009. The figures after 2010 are rather difficult to obtain, so the content of the statistics is outdated to some extent, which could barely satisfy the needs of current academic research in agricultural and economic management.

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1.1.6   China Household Income Projects (CHIP) As a component of the subject “Research on Income and Inequality in China” (Li Shi, Zhao Renwei et al.), China Household Income Projects (CHIP) were mainly organized by Chinese and foreign researchers and conducted in collaboration with the National Bureau of Statistics of China. It primarily aims at tracking the dynamic condition of China’s household income distribution. Currently, CHIP has already collected relative statistics in 1988, 1995, 2002, 2007 and 2013. Take the statistics of 2013 as an example, the survey was co-organized and implemented by China Institute for Income Distribution and experts from home and abroad, and supported by the National Natural Science Foundation of China and the NBS. The features of CHIP lie in the data which contained the investigation targeted at both urban and rural households. Before 2002, the CHIP data had contained data of two major groups of samples of urban and rural households. Since the subsamples of these groups had not fully covered all the floating population, and considering the fact that people from rural areas were migrating into urban areas, the floating population have been added into CHIP in 2002. Since then, CHIP had contained three parts, which were the urban household survey, rural household survey and floating population survey. The statistics gathered in 2013 illustrated that by systematic sampling, among the 18,948 household samples and 64,777 individual samples in 15 provinces, there were 7175 urban household samples, 11,013 rural household samples and 760 migrant household samples. In view of the concrete content of the survey, CHIP focused on the income, expenditure, employment and production situations of China households including the basic information of household individuals and families, income and assets, loans, land expropriations, land and agricultural management, household income and expenditure, et cetera. As we can see, the data of CHIP is now accessible to the public and the survey focused on the income and expenditure of China’s households and tracked the dynamic changing patterns of income and distribution of China. Meanwhile, with its data covering the sample structure of urban, rural and floating population, it has reflected the urban-rural segmentation and the population migration in recent years more realistically. For rural households, CHIP statistics put the household income and expenditure as its focal point and further included migration and land utilization. However, it lacked the detailed investigation of agricultural production and management, health and social securities, social environment and governance of

1 INTRODUCTION 

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rural households. In addition, 15 provinces have been included in the survey, but there is still room for expansion; with the investigation interval of around five to seven years each time, the continuity and accuracy of data could be further improved when compared to relative rural household surveys with higher frequencies such as the National Rural Fixed Observation Point Survey and the China Health and Nutrition Survey (CHNS). 1.1.7   China Health and Nutrition Survey (CHNS) The China Health and Nutrition Survey (CHNS) was cofounded by the Carolina Population Center at the University of North Carolina at Chapel Hill (NINH) and the National Institute of Nutrition and Food Safety of the Chinese Center for Disease Control and Prevention (CCDC). It aimed at inspecting the influence of policies such as health, nutrition and birth control implemented by the national and local governments, as well as understanding how social and economic reforms in China have affected the health and nutritional status of the population. The CHNS utilized multistage cluster sampling to obtain samples. Since the first-round survey in 1989, the coverage of CHNS samples has gradually broadened. Between 1989 and 1993, there were 190 primary sampling units, including 32 urban communities, 30 outskirts, 32 towns (counties) and 96 villages. Since 2000, the primary sampling units have amounted to 216 urban communities, 36 outskirts, 36 counties and 108 villages. The currently accessible survey data of 2011 has already covered 12 representative provinces, consisting of Liaoning, Heilongjiang, Jiangsu, Shandong, Henan, Hubei, Hunan, Guangxi, Guizhou and three of the province-level municipalities, which were Beijing, Shanghai and Chongqing. The CHNS mainly involved aspects of geography, economic development, public resources and health. At the community level, it emphasized on the grain market, health care facilities, birth control and other social services. The survey data is suitable for conducting researches on fields related to health, population, social economy and nutrition policies. Meanwhile, the specific content of the CHNS included families, communities, adults, children, nutrition, growth status of boys, and so on, the family survey of which involved aspects such as demographic background, jobs and income, expenditures, potable water, sanitary facilities and property. The CHNS put its emphasis on nutrition and health conducts, family and individual economic status, as well as changes in population and society. In the meantime, it has realized a dynamic tracing of urban and rural

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samples, thus forming a complete dynamic data which was kept open. The main advantage of the CHNS was in the aspect of health and nutrition, where it revealed the dynamic changes in the development of rural China to some extent, but it lacks a profound understanding of agricultural economic production. Furthermore, compared with other rural household surveys, the sample coverage of the CHNS was not as competitive. 1.1.8  China Health and Retirement Longitudinal Study (CHARLS) Under the management of the National School of Development, the Institute of Social Science Survey (ISSS), Peking University, and Peking University Communist Youth League Committee organized and implemented the China Health and Retirement Longitudinal Study (CHARLS) in order to analyze China’s aging problem in depth and provide scientific basis for formulating and perfecting relative policies in China. CHARLS adopted the method of multistage sampling, where the county (district)-village (resident) samplings both utilized the probability proportional to size (PPS) sampling method. The sample subjects were households and individuals of middle-aged and elderly Chinese over 45. After the pilot survey in 2008, a national baseline survey has been ­conducted since 2011. The survey has been traced once every two years and has covered an area of 28 provinces in China. In the national followup in 2015, 12,400 rural households have become the samples. The questionnaire design of CHARLS has learnt widely from others’ strong points, absorbing international survey experience from the Health and Retirement Survey (HRS) in the US, English Longitudinal Study of Ageing (ELSA) in Great Britain and Survey of Health, Ageing and Retirement in Europe (SHARE) in Europe. The main contents included basic personal information, family structure and economic support, health status, physical measurement, the use of medical services and insurances, work, retirement and pensions, income, consumption and assets, as well as the basic condition of the community. CHARLS not only had a scientific and rational questionnaire design, but it also uniquely adopted the electronic mapping software technology (CHARLS-GIS) which could produce village-level sampling frame using mapping-in sampling. Meanwhile, the CHARLS data maintained a two-­ year tracking frequency. It was free and open to the academic community one year after the survey, boasting obvious dynamics and openness. However, as the CHARLS survey subjects were mainly households of

1 INTRODUCTION 

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middle-aged and elderly people over 45 and the survey emphasized on the relative research on aspects of rural labors and health, the data collection and analysis of CHARLS in other aspects involving rural development and agricultural production and management were lacking. 1.1.9  China Family Panel Studies, CFPS The China Family Panel Studies (CFPS) was a nationwide follow-up study initiated by the Institute of Social Science Survey (ISSS) of Peking University, and aimed to investigate from three levels: individual, household and community. This panel study was mainly focused on the economic and noneconomic welfare of Chinese inhabitants, as well as economic activities, education and educational achievements, family relation, migration of population and health. The main purpose of the survey was to conclude and exhibit changes in China’s society, economy, population, education and health. The CFPS was first initiated across the country in 2010 (except for nine provinces including Tibet and Qinghai) in which 16,000 households were surveyed. The adapted questionnaire consisted of questions on both rural and urban areas; the sampling was based on probability proportional to size (PPS) and the sampling method was designed to be multistage with implicit stratification. The latest survey conducted was its third round of full sample survey (CFPS 2014). As for the efficiency of CFPS 2014, comparing with the survey results of CFPS 2012, the tracing rate on households was 89.9%, and on individuals was 84.8%; and comparing with the survey results of CFPS 2010, the tracing rate on households was 83.0% and on individuals was 79.3%. In CFPS 2014, the data of all three levels (individual, household and community) were collected, and the investigation covered contents such as household income and spending, assets, demographic characteristics, fundamental environment of community, social security and food price, staffing, housing, transportation and natural environment, productivity and income of labor force, health care, finance, et cetera. Five databases, comprised of community, household economics, adult, teenager and family relation, were established upon the samples. According to the CFPS 2014 statistics, the database of Community had 621 samples, Household Economy had 13,946 samples, Adult had 37,147 samples, Teenager had 8617 samples and Family Relation had 57,739 samples. The ISSS has published a report of China Family Panel Studies based on the CFPS annually since 2009. In the 2015 report, Family and Social Inequality were represented as the dual topics. In terms of family, this survey

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investigated the changes in marriage and household during the period of societal transition, which included the distribution of marital assets, social supports from offspring, family environment and the economic/labor role of floating population in a family. In terms of social inequality, this report focused on the imparity in the fields of distribution of property (gender inequality), education inequality due to the backgrounds of society and family, maldistribution of health care policies (health inequality) and the different public opinions led by the perception of social wealth disparities. 1.1.10  Chinese General Social Survey, CGSS The National Survey Research Center at Renmin University of China (NSRC) organized and implemented the Chinese General Social Survey (CGSS) to systematically observe the interactions and transitions in social structure and quality of life, and understand the trend of social changes in China. As the first national academic research program, this comprehensive and continuous survey focused on topics of great scientific importance and real significance. It has improved the opening and sharing ­mechanisms of domestic scientific research, provided data information for international comparative studies, and served as a multidisciplinary platform for economic and social data acquisition. The CGSS has been conducting this continuous cross-sectional survey of over 10,000 households from all provinces across the Chinese mainland annually since 2003. The sampling method of the CGSS had changed three times; however, a stratified multistage PPS random sampling method endured being the foundation. In the case of the 2010 survey, 100 counties and 5 metropolises were chosen, including 480 residents/village committees and 12,000 individuals. The investigation mainly covered three aspects, namely the social structure, quality of life and the correlation mechanism in between the two. For the first aspect, methods of localization and relation were used to analyze the social structure of China’s society. For the second aspect, measurements were made based on five factors, including health, population, psychology, social economy and politics/community. For the third aspect, the internal correlation mechanism was studied from the individual level, interpersonal level, organizational level and institutional level. In addition, the CGSS had launched the East Asian Social Survey (EASS) jointly with the Japanese General Social Survey (JGSS), Korean General Social Survey (KGSS) and Taiwan Social Change Survey (TSCS) in 2006, and has so far

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conducted five featured surveys with the topics of Family (2006), Culture (2008), Health (2010), Social Network and Social Capital (2012), and Working Life (2014). The CGSS systematically and comprehensively collected data related to society, community, household and individual to analyze the trend of social transition. In addition, the CGSS pioneered in the data opening and sharing of major academic surveys in China. These data have contributed to multiple subjects, including economy, sociology, politics, history, et cetera, and have benefited domestic researchers as well as foreign researchers. The CGSS mainly focused on researching China’s social structure and quality of life, which included some information on rural migration and rural household finance. However, it did not conduct an in-depth investigation and analysis on rural and agricultural economic management and was particularly lacking in terms of agricultural production and management. 1.1.11   China Labor Force Dynamics Survey, CLDS The China Labor force Dynamics Survey (CLDS) was a national follow­up study conducted by the Center for Social Survey (CSS) of Sun Yat-sen University, which focused on the status and transition of China’s labor force. The mission was to investigate and trace the basic situation of labor individuals and households, in order to analyze the interactions between social structure and labor family or individuals, and serve as a reference for relevant theory research and policymaking. The CLDS adopted a stratified multistage PPS sampling method, with a household sample database covering 29 provinces and municipalities. This survey is conducted biennially (three rounds of surveys have been conducted so far, which were in 2011, 2012 and 2014, respectively), and the investigation mainly covered education, work, migration, health, social participation, economic activities and the duty of inhabitant/village committees. Each labor force in the sample household was to be interviewed. As the CLDS mainly evolved around the theme of the labor force, it had limitations in terms of content, in which issues relating to the income and expenditures of rural households as well as agricultural production and management were hardly investigated. Thus, it was incapable of comprehensively displaying the development trend of rural areas and the basic situation of production and management in rural households. In addition, since the CLDS only started in 2011, the database was less persuasive for the lack of continuity. Table 1.2 shows surveys in relevance with China’s rural families.

NFPS

RHS

CHIP

Rural Fixed Observation Point Survey

China Rural Household Survey

Chinese Household Income Project Survey

Rural Economy Investigation Headquarter of National Bureau of Statistics Beijing Normal University

Research Center for Rural Economy

Abbreviation Institution

Name

Sampling Method

Province (being surveyed in the latest survey)

Cross-­ section Study

31

Samples of 15 Inhabitants Income Survey from National Bureau of Statistics

Follow-up MPPS Study Sampling

Follow-up National Rural 31 Study Fixed Point

Genre

Table 1.2  Surveys in relevance with China’s rural families

Individual/ Household

Individual/ Household

Individual/ Household/ Community

Unit

Agricultural production elements, agricultural production and conditions of household income and expenditures Production, income, expenditure and assets of China’s rural inhabitants Income Status

Focus

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China Labor force Dynamics Survey China Family Panel Studies

CFPS

CLDS

China Health CHARLS and Retirement Longitudinal Survey Chinese CGSS General Social Survey

China Health CHNS and Nutrition Survey

Institute of Social Science Survey (PKU)

Follow-up Stratified Study Multistage PPS Sampling

Cross-­ section Study

Follow-up Multistage PPS Study Sampling

Follow-up Multistage Study Cluster Sampling

Stratified Multistage PPS Random Sampling Center for Social Survey Follow-up Stratified of Sun Yat-sen Study Multistage PPS University Sampling

Sociology Department, Renmin University

Carolina Population Center at the University of North Carolina at Chapel Hill, NINH, National institute of nutrition and food security, Chinese Center for Disease Control and Prevention National School of Development at Peking University

25

29

28

28

12

Individual/ Household/ Community

Individual/ Household/ Community

Individual/ Household

Individual/ Household

Individual/ Household/ Community

Economic and Social Welfare and Its Changes of China’s Individuals and Households

Status and Changes of China’s Labor Force

Social Transition

Health and Retirement Longitudinal

Demographic Health and Nutrition Status

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In the review of these relevant surveys, it can be seen that ever since the period of the Republic of China, researchers on rural families have always attracted great attention from domestic and foreign institutions. Particularly in recent years, with the rapid development and transformation of the social economy in China, various follow-up studies relating to rural families have been put up by domestic institutions and universities. These surveys have not only helped to demonstrate the development progress and trend of rural families, but have also provided important database support and highly valued groundwork for the academic community to carry out normative academic research. Yet, upon a closer analysis of the above-mentioned research, it is easy to spot that except for some research conducted during the Republic of China, all investigations were carried out by different institutions for different purposes, and each database possesses its own unique features accordingly. In this manner, some concerns of Agriculture and Forestry Economic Management scholars cannot be fulfilled by the data, and certain defects were, therefore, being noticed. For some databases, the samples were only of certain ranges; some have remained confidential; some lack continuity; while a majority of the databases focused only on certain aspects of rural households. Naturally, for scholars of agriculture and forestry economic management, these data were not of enough integration, and surveys hereinbefore have failed to comprehensively and systematically illustrate the major concerns of the agriculture and forestry economic management disciplines.

1.2   Historical Background of the China Rural Household Panel Survey On the basis of the surveys aforementioned, Zhejiang University has launched the China Household Panel Survey (CHPS) since 2015. The CHPS was a national sampling survey project that was fundamental as part of Zhejiang University’s Double First-rate Layout Plan. Due to the large scale of the CHPS project, the China Rural Household Panel Survey (CRHPS) was the first to be initiated. To maintain continuity, data before 2015 were collected and analyzed based on the surveys conducted by the China Household Finance Survey (CHFS) of the Southwestern University of Finance and Economics. The CHFS was launched in 2011, conducted biennially and has so far been executed for three rounds. The 2017 CRHPS would be led by Zhejiang University and the survey will be implemented in mid-2017.

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To demonstrate the current development status of China’s rural families comprehensively and systematically, the CRHPS aims to establish the baseline of China’s rural issues by conducting an overall survey on rural households, including the basic structure of household, employment, income and expenditure, family assets, agriculture production and management, rural land circulation, migration and urbanization, finance behaviors, health and social security, education and training, et cetera. The CRHPS also deals with the basic situation of China’s grass-roots rural administration unit (village committee) and investigates the local status of public services, community economy, social governance, natural environment, et cetera. This survey addresses the issues of China’s rural development and urbanization and offers firsthand information on the poverty reduction and development, labor force mobility, local governance, environment renovation, land reform, et cetera, of rural areas in particular. Zhejiang University has a long history in conducting rural household surveys. As early as the 1930s, the faculty and students of agricultural sociology4 were already devoted to rural surveys concerning the issues of agriculture, countryside and farmers. In the 1940s, Zhejiang University moved westward to Meitan. Under the motto of “seeking truth”, the faculty and students of the agricultural economics major made an elaborate rural investigation during the summer vacation for the good of social services. A number of scientific research achievements based on rural investigations were made, including Rural Economy in Meitan, A Summary of Meitan’s Rural Credit Cooperatives, and Introduction to the Capital Needs in After-War Reconstruction. These research reports were presented at symposiums and published in newspapers and journals such as the Journal of Agricultural Economics of Zhejiang University, China Agriculture Monthly and Statistics of China’s Agricultural Economics. Since the 1980s, with the recovery and enhancement of the qualities in teaching, researching and talent cultivation, the discipline of agriculture and forestry economic management5 was entrusted with various rural household surveys. Particularly after 1998, Zhejiang University has conducted several illustrious research programs with the national and international class as well as cross-sectional studies by mainly relying on the agricultural economy and management disciplines and in cooperation with various related disciplines such as economics, management and public administration. These projects were also carried out by being in line with the international development trend of the agricultural economics discipline and by linking closely with the practices of China’s “Three Rural” issues. As there is a large

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number of scientific research achievements, due to the limit of text length, they shall not be listed here. At this stage, the launching of CRHPS is attributable to the following two historical backdrops: 1. Social economic backdrop In the long run, China is the biggest developing country in the world, and the “three rural” issues concerning agriculture, countryside and farmers remain as the fundamental problem of China’s modernization and the most significant concern of our party and country. Currently, China’s situation of the “three rural” issues is still grim. For example, the dual ­structure in urban and rural economies has not yet been completely eliminated. The trend of increasing inequalities between labor forces for primary production and secondary production, between urban and rural areas, among different regions, and among different classes has not yet been reversed. The backwardness of the agricultural industry, rural areas and farmers has not improved. However, the modernization of agricultural and rural areas and the resolution for the hereinabove issues are essential to a rapid and sound development of China’s economy and significant to the stability of China’s society. The development of rural areas is also related to China’s comprehensive construction of a well-off society, and it is the foundation for China’s modernization and peaceful rising in the world. Therefore, attaching great importance in addressing the “three rural” issues, the party and the government have introduced a series of policies on agriculture and rural matters, including the structural reform on the agricultural supply side, the steady reform of collective property relations in rural areas and the improvement in poverty alleviation policies in rural areas (the poverty elimination responsibility mechanism, for instance). The policies introduced also consisted of the cancellation of agricultural tax, furthering the implementation of agricultural subsidies, improvements in the basic living subsistence allowance system for rural residents, an integrated reform for the compulsory education of urban and rural areas, as well as accelerating the health care reform by integrating the basic medical insurance for urban residents and the new rural cooperative medical system. These series of policies and measurements have greatly increased rural income, lifted up the livelihood of farmers and promoted the development in rural areas. Naturally, as the basic unit of the rural social economy, rural households have changed greatly.

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In addition, as the national economy continues to develop, the development environment for China’s rural society and the economy is also changing rapidly and profoundly. According to the National Bureau of Statistics, China’s urbanization process is accelerating. The 2016 statistical bulletin shows that in 2016, the ratio of urban registration population was 41.2%, which is 1.3% higher than the figure at the end of 2015. The migration rate of the rural and agricultural labor force has increased as well. In 2016, the national floating population had reached 245 million (excluding the urban floating population), and the aggregation of migrant workers across China was 281.71 million, which has increased by 1.5% compared with 2015. Among these migrant workers, 169.34 million were outgoing, which has increased by 0.3% compared with 2015, and 112.37 million worked locally, which has increased by 3.4% compared with 2015. Furthermore, the allocation of production factors has changed significantly. For example, in terms of the financial capital, the loan balance of China’s major rural financial institutions (rural credit cooperatives, rural cooperative banks, rural commercial banks) at the end of 2016 was 13.4219 trillion RMB, which has increased by 1.3895 trillion compared with the figure at the beginning of 2016. In the meantime, technology and information are causing an enormous impact on rural areas and the concept of Internet Plus is changing the lives of rural inhabitants. According to the Research Report on China’s Taobao Villages (2016), 1311 villages from 18 provinces or municipalities were listed as Taobao Village by the end of August 2016. A quantity and quality survey in China’s rural areas conducted by Ericsson Consumer Lab between the end of 2010 and the beginning of 2011 indicated that the owning rate of landline telephone in rural areas declined to 43%, while the owning rate of mobile phones in rural areas rose to 90%. Moreover, the owning rate of personal computers in rural areas was 31%, and 41% of the respondents thought that it is important for them to have a connection to the Internet anytime and anywhere. Hence, a comprehensive survey on China’s rural households would favor the understanding of the basic situations and dynamic development of China’s rural households. Moreover, this survey demonstrates the current situation and transition trend of China’s rural society and provides sufficient information for the consultation of policymaking on agricultural and rural matters.

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2. Subject development backdrop Management of agriculture and forestry economics is a typically applied economics subject which takes the “three rural” issues as the study object. At the beginning of the twentieth century, Professor Xu Xuan returned to National Peking University and started teaching courses related to the agricultural economy, thus beginning the history of China’s agricultural economics. In the 1930s, scholars of agricultural economics launched a great debate on the social nature of China’s rural areas. The debate was mostly based on theories of the agricultural economy, but lacked facts supported by systematic household surveys. In the 1960s–1970s, surveys such as the people’s commune survey and the rural economic survey were gradually implemented, providing firsthand facts and figures for rural ­policymaking on the development of the rural society and economics (Wang 2005). After the restoration of the college entrance examination in 1977, fruitful results have been achieved in the area of China’s agriculture and forestry economic management, but those academic researchers mainly focused on the macro policy level and lacked research at the micro level, and the method of quantitative analysis lagged far behind the international standards (Lin 2004). However, with the rapid progress of internationalization, domestic academic circles of agriculture and forestry economic management have shown a distinctive international characteristic since 2000. On the one hand, as an important country in transition, the “three rural” issues of China have attracted great interests and discussions from foreign researchers. On the other hand, more and more domestic scholars have begun to study abroad and communicate with the international circle, which has brought out tremendous contributions that are unique with Chinese features. In this manner, the traditional method of agricultural economic research will be quickly replaced by the normative and modern mathematical methods from economics and econometric analysis that is based on large-scale samples and micro-level database (Huang 2004). Therefore, in order to carry out research on issues concerning agriculture and forestry economic management in a scientific and comprehensive way, the support of a rural household survey database with a large scale of samples is urgently needed.

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1.3   The Purposes of China Rural Household Panel Survey The purposes of the CRHPS are as follows: I. Understanding the basic characteristics and developing trend of China’s rural households The CRHPS aspires to have an objective understanding of the basic characteristics and developing the trend of China’s rural households. Through confluence comparison and further statistical analysis, these basic data would illustrate the current situation and dynamic patterns of China’s rural households and therefore provide a scientific reference for policymaking. II. Providing the basic database support relevant to China’s rural households for academic research purposes Although the quality standards in the scientific research of agriculture and forestry economic management are relatively strict, the data and figures from the CRHPS were still able to meet the international academic standards. The large-scale samples have covered 29 provinces (municipalities and autonomous regions included), and the sampling methods according to the academic criterion can ensure its randomness and representativeness. Additionally, the elaborate design of the questionnaire has enabled it to reflect the current issues comprehensively, and typical issues could be thereof studied with these facts and figures. As a follow-up study, the CRHPS has formed a dynamic database system that can trace the figures of China’s rural survey. Therefore, the CRHPS is capable of providing a solid data ground for scientific research. CRHPS adheres to three principles: 1. The principle of comprehensiveness The comprehensiveness of the CRHPS was embodied in two aspects. Firstly, it constructed a representative sample-base that exhibited the development and status of China’s rural households with comprehensiveness and objectivity. Conducted using the stratified three-stage and probability proportionate to size sampling method, the 2015 CRHPS implemented the third round of investigation upon 29 provinces (municipalities and autonomous

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regions included), 363 counties, 1439 inhabitant/village committees, among which 76,685 individuals from 22,535 households were rural samples. The rural samples living in rural areas were distributed across 28 provinces (municipalities and autonomous regions included) and 259 counties, while the sample size was 11,654 households from 586 village committees, with a total of 43,952 persons. Secondly, the contents of the CRHPS were composed of the basic structure of household, employment, income and expenditure, family assets, agriculture production and management, land utilization and circulation, migration and urbanization, finance, health and social security, education and training, et cetera. The CRHPS also dealt with the basic situation of China’s rural administration unit (village committee), and investigated the local status on public services, community economy, social governance, natural environment, et cetera. This survey addresses the issues of China’s rural development and urbanization, and offers firsthand information on the poverty reduction and development, labor force mobility, local governance, environment renovation, land reform, et cetera, of rural areas in particular. This investigation is comprehensively concerned with Agriculture and Forestry Economic Management. 2. The principle of openness The openness of the CRHPS was that, on the one hand, the questionnaire is open for review, and we welcome both foreign and domestic scholars to conduct research at Zhejiang University as well as to participate in the questionnaire design, sampling methods and even the implementation of the CRHPS according to the needs of research cooperation. Through these cooperations, we would be able to continuously improve the survey and exploit the utmost of the CRHPS Database. On the other hand, the CRHPS Database is open to all registered accounts. Any individual or institution with a research need may apply through http://ssec.zju.edu.cn and the information would be accessible after the registration succeeds. 3. The principle of dynamism Aimed at making an objective reflection of the basic situation of development in agriculture and rural areas, the CRHPS also focused on analyzing the current development characteristics of rural households. For example, in consideration of the impacts brought by modern information technology upon the production and livelihood of rural households, the

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questionnaire has included questions on the impact of e-commerce on agricultural production. This survey concerned not only the basic conditions of survival and development of rural household, but also the pulse of rural development. In addition, the CRHPS reflected the dynamics of data through the combination of long-term follow-up surveys and special questionnaires on hot topics. In other words, this survey will be conducted every two years and will continue to make breakthroughs in terms of the number of samples, coverage and method design on the basis of the 2015 CRHPS.  In each round of the investigation, a thematic questionnaire would be added to research on the newly emerged hot topics in rural development.

1.4   Contents of This Report This report of China Rural Household Panel Survey is comprised of five parts, including the background and sampling method of this survey, the basic characteristics of rural households, the economic activities of rural households, the public services and rural households, and the conclusion. Part I is the background and sampling method of this survey, and it consists of two chapters (Introduction and Design of Sampling Method). Part II is the basic characteristics of rural households, and it consists of four chapters (Basic Structure, Employment, Income and Expenditure, and Wealth). Part III is the public services and rural households, and it consists of four chapters (Social Security, Education, Community Environment, and Community Governance and Public Services). Part IV is the conclusion. It should be noted that the data involved in this report covers not only rural households living in rural areas, but also rural families living in urban areas. Taking into account of the situation of migrant workers working in urban areas, this report focused on both rural issues concerning agriculture and farmers, as well as issues concerning life in modern cities. The contents of each part and chapter are as follows: In Part I, Chap. 1 works as introduction of the whole book and describes the background information and related issues. To be more specific, the content can be divided into four parts: the history of related rural household panel surveys, historical background of the China Rural Household Panel Survey, the purpose of China Rural Household Panel

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Survey and the contents of this report. More than ten historic surveys are introduced in detail in Chap. 1, including the National Population Census of the People’s Republic of China, China Rural Studies conducted by Central China Normal University (CCNU), National Rural Fixed Point Survey System (NFPS) conducted by the Secretariat of the Central Committee of the Communist Party of China (CPC), The Rural Household Survey (RHS) conducted by the Survey Organization of Rural Social Economy of the National Bureau of Statistics of China (NBS), China Household Income Projects (CHIP) organized by Chinese and foreign researchers and conducted in collaboration with the National Bureau of Statistics of China and so on. In summary, to the best of our knowledge, so far there are no national-wide survey that could contain all the important aspects that researchers of agricultural economics care about. Referring to the historic background of the China Rural Household Panel Survey, we have to consider two important historical backdrops. One is about the Social economic backdrop of China. China, the biggest developing country in the world, not only is facing the “three rural” issues concerning agriculture, countryside and farmers, but also is also faced with rapid growth of rural economy and transformation of rural society. Thus, the CRHPS could help to understand the basic situations and dynamic changes of China’s rural households. The other backdrop is about the management of agriculture and forestry economics subject development in China. In order to get valid results, the researchers of the subject need to use normative and modern mathematical methods from economics and econometric analysis that is based on large-scale samples and micro-level database. The CRHPS in that way has provided valuable data for numerous researches and promoted the development of the management of agriculture and forestry economics subject in China. As for the purpose of our survey, in fact, China Rural Household Panel Survey aims at continuous tracking family all aspects of the information in our country, recording the changes of the family, exploring the internal mechanism of all kinds of social problems, and explaining and predicting the development of the society from the causal relationship changes. Meanwhile, the survey also helps improve the level of decision-making and accelerate the modernization of management system and management ability. This report contains five parts: the first part illustrates the background and sampling method of this survey, the second part describes about the basic characteristics of rural households, the third part talks about economic activities of rural households, the fourth part is about the public services

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and rural households, and in the last part we draw a conclusion as a summary of the whole report. Chapter 2 aims at explaining the survey sampling design; the whole chapter contains six main topics, which are overall sampling plan, terminal sampling design, survey sample weights, size and distribution of samples over the years, the tracking condition of each year, data collection and quality control. The first part is overall sampling plan, which describes the detailed sampling design of year 2011, 2013 and 2015. The sampling units were collected through three stages. During the first stage of sampling, the county-level samples were mostly collected using the sampling stratification and PPS methods. As for the committee/village-level samples, in 2011 and 2013, the residents’ committee/village committee ratio in each sampled county was based on the proportion of the nonagricultural population, while in 2015 equal-space sampling was used to replace the former method. Correspondent samples were selected from each sampled resident committee and village committee using sampling stratification and PPS methods, same as first-stage sample selection. Generally, 20 samples were selected in each sampled village and residents committee. The second part of this chapter talks about terminal sampling design, which includes residence mapping part and terminal sampling plan part. The residence mapping process is achieved by using remote sensing, Global Positioning System (GPS) and Geographical Information System (GIS) technology. Geographic information recorded is also corrected by manual check to avoid any mismatch of the geographic information in the system and the one in the real world. After residence mapping process, the samples are randomly selected from the household list using equal-space sampling method. By the way, in the sampling, we have different definitions for different kinds of households, such as the division of multiperson households and single-person households. For the survey sample weights part, the sampling weights are calculated based on three different sample stages, respectively, considering the probability of each county being selected, the probability of that community (village) being selected in their respective district or counties and the probability of survey samples being selected in their respective communities (villages). As for the part about size and distribution of samples over the years, 8438 household samples and 29,324 individual samples were collected in the survey of 2011, while the second round of the 2013 survey collected 28,141 household samples and 97,916 individual samples, and in 2015 survey 11,654 households totaling 43,952 individual samples were investigated. Referring to the data

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collection and quality control part, the investigators use internationally recognized computer-assisted personal interviewing (CAPI) system to conduct their survey using tablet computer; in that way, errors caused by human factors such as the preset range problems, data entry errors and logic errors could be avoided. Investigators of this survey are mainly undergraduate students who have received systematic training. Each survey team has a full-time supervisor. By using a quality monitoring system, we are able to reduce human error problems After the interview, the data quality is checked through many ways, including giving a feedback call, examining the recording and so on. Actually, the system could achieve nearly real-time monitoring of each interviewer, through recording every interviewer’s operation on mouse or keyboard faithfully in great detail. In Part II, Chap. 3 describes basic structure of rural households and is made up of seven parts, including household structure, gender structure, age structure, educational attainment structure, marital status, physical status and political status. In summary, the household size in Chinese rural areas is getting smaller. Nearly a third of the family are nuclear family, which consists of husband, wife and unmarried children. In terms of generation, two-generation household accounts for the main part of the household. Nearly half of households in western region have more than one elderly person aged 65 and above. The population of males is significantly higher than that of females, and the difference becomes larger in child population, which refers to the population aged 14 and under. The working-age population comprises the major part of the population; they are actually suffering great burden of raising children and taking care of the elderly parents. The education level is still low on average, while there are one-fourth of females who have never attended school. Three quarters of population have got married. The elderly people suffer the most from chronic diseases. The majority of the communist party members are males. To be more specific, the report has shown that in 2013 the average household size was of 4.02 persons, which got smaller in 2015 to 3.77 persons. To be more specific, comparing with data of 2011 and 2013 surveys, the proportions of one-person households and two-person households are increasing, the ratio of three-person households remains similar, while the proportion of households with four persons and above is decreasing over time. In China, households with one elderly person account for 26.9% in western areas, 38.6% in central areas and 36.7% in eastern areas. The result also shows that 4.1% of the households in rural areas are families consisted of a single elderly person, while 7.3% households are composed of an

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elderly couple. Gender imbalance exists in rural China: while the overall gender imbalance in the western region is the most serious, with the sex ratio reaching 111.7:100, for the central and eastern regions the sex ratios are 110.3:100 and 109.4:100, respectively. Referring to the child population, the gender ratio is 132.5:100 in central region, which is experiencing the most serious gender imbalance situation. The sex ratios of the eastern and western regions are 122.5:100 and 122.1:100, respectively. As for the age structure, the result shows that working-age population faces considerable pressure to support children and the elderly. In rural areas, total dependency ratio is 44.5%, while child dependency ratio and the old-age dependency ratio of rural areas are 22.5% and 22.0%, respectively. As for the society aging problem, the data has shown that this situation is worse, with the rural aging rate increasing to 15.2% in 2015 compared to 12.4% in 2013. In 2015, the aging rate for eastern country was 15.4%, while for central region and western region the aging rates were 14.7% and 15.7%, respectively. The overall level of education is relatively low in rural areas. There are 17.7% of rural population aged over 16 years who have had no schooling, only 17.1% adults have completed high school or above, and the proportions for those who have completed primary school and middle school are 31.7% and 33.5%, respectively. Those who have completed a bachelor’s degree or higher accounted for only 2.8% of the population. In the survey, 77.0% of the total samples reported their marital status, namely 33,832 people. Among all, 75.5% were married, while 17.6% were unmarried, 5.8% were widowed and 1.1% were in other marital situations including cohabitation, separation and divorce. Looking at different regions, the central region tops the proportion of married population (77.7%), followed by the eastern (75.0%) and western regions (73.5%). The number of leftover men and women comprise 3.4% of the total population in China’s rural areas; besides, the proportion of leftover men is significantly higher than that of leftover women. Between 2011 and 2015, the proportion of leftover men in rural areas rose from 4.4% to 5.4%, while the proportion of leftover women increased from 0.7% to 1.2%. Considering the relationship between educational background and marital status, the result found that the unmarried rate for those with a bachelor’s degree or higher is as high as 61.7% (since a majority of these are young people, the rate of nonmarriage is comparatively high). And the proportions of the unmarried women who have only finished middle school or below are all lower than those of men in the same educational level. By comparison, for people who have finished high school or above,

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there is higher proportion of unmarried women than unmarried men. As for the physical status, proportion of women with chronic diseases is 38.1%, much higher than men, which is 32.2%. The percentages of women who suffer from extremely severe and relatively severe chronic diseases are also higher than men, with 2.3% and 2.9% higher, respectively. The percentage of people with chronic diseases is much higher in elderly population than in average population. Elderly women with chronic diseases are 70.4%, compared with 59.7% of elderly men. And nearly 60% of the elderly population suffer from extremely severe or relatively severe chronic diseases. In the elderly population, 39.1% think they are in bad health and in very bad health. Referring to the political status, the proportion of male Communist Party members is 11.9% and female members is 2.2%. Chapter 4 describes the employment of rural households through many aspects. The first part is the overall description of the whole rural economically active population and the working population. The second part illustrates the gender structure, age structure and educational attainment structure of those economically active and working population. Next, employment structure, gender structure, age structure, educational attainment structure and industry structure of nonagricultural population are analyzed. Besides, industry distribution based on gender, age and educational backgrounds is also included. The final two parts illustrate industry composition of the working population in different communities, and family employment types and their compositions. To be more specific, the average rate of economically active population in rural population is 55.8%, while the proportion of working population in the economically active population of China is 97.8%, and the proportions of agricultural population and nonagricultural population are 55.3% and 44.7%, respectively. In 2011, the proportion of agricultural population in the working population is 55.6%, while in 2013 this rate increased to 57.8%, and then in 2015 it dropped to 55.3%. Of the economically active population 57.1% are males, and the percentages of males in working population, agricultural population, nonagricultural population are 57.0%, 50.1% and 65.5%, respectively. In 2011, proportion of females in nonagricultural population was 37.2%; in 2013, it dropped to 28.3%, and in 2015 it increased to 34.5%. From 2011 to 2015, the proportion of females in agriculture population decreased from 52.7% to 49.9%. The majority of rural economically active population are in the four groups of ages 25–34, 35–44, 45–54 and 55–64 years. Among them, the proportion of people aged 45–54 is the largest, up to 24.8%. The agricultural population shows an obvious trend

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of aging compared with the nonagricultural population. For agricultural population, groups of people aged 45–54, 55–64 and over 64  years account for 29.4%, 28.4% and 18%, respectively. As for the nonagricultural population, the groups of people aged 25–34, 35–44 and 15–24  years account for 33.1%, 23.2% and 16.2%, respectively. Of the rural economically active population and working population, 44.5% and 44.8%, respectively, have never attended primary school. Of the population engaged in agriculture, 59.7% have an education level of primary school or below, nearly two times higher than the percentage of population not engaged in agriculture. Analyzing education level in different age groups, the results show that in both the agricultural and nonagricultural populations, the education level of the working population under the age of 35 is ­significantly higher than that of the population aged 35 or over. In the nonagricultural population with various jobs, the proportion of males is consistently higher than that of females. More than half of the nonagricultural population are employed with temporary work, of which 66.9% are male. Of the nonagricultural population, 12.8% are self-employed, of which 61.2% are female. Among various kinds of nonagricultural population, the proportion of junior high school graduates is always the highest and accounts for 49.5% in self-employment population. Two most concentrated nonagricultural industries for nonagricultural population are the construction industry and manufacturing industry, attracting up to 27.1% and 24.7% of the nonagricultural population respectively. The gender structure is nearly balanced in the manufacturing sector, while it is imbalanced in primary and secondary sectors, with the proportion of men being higher than that of women. In the vast majority of industries, the nonagricultural population is mainly distributed between the ages of 25–54. In primary and secondary sectors, the nonagricultural population is most concentrated in the two age groups of 45–54 and 35–44  years. People with an education level of junior school or below are the major components of all the industries except for finance, real estate, science and education, health care and social work industry. In 2011, 2013 and 2015, there were more women working in agriculture than men. And the proportion of women working in the manufacturing sector rose from 12.7% in 2013 to 13.6% in 2015. According to the results of industry distribution of each age group, the proportion of agricultural population decreases first and then increases with the increasing of age. The results also show that with the improvement of education level, the proportion of farmers declines gradually, from 74% in the people of primary or lower level to 34.5% in the

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junior college/undergraduate or higher-level group, while the confirmation of community landownership has no significant impact on the population structure of various industries. In rural China, households engaged in both agricultural and nonagricultural productions account for 45.5% of the population, while households engaged in agricultural production is account for 30.3% of the population; the rest are households in which all work in nonagricultural production. The average age for households engaged in agricultural production, nonagricultural production and with combined occupations are 53  years, 40  years and 43  years, respectively. The average education level for households with combined occupations are higher than other two types of households. In average, households with combined occupations receive nine years of education. Chapter 5 aims at analyzing the income and expenditure of rural households. The whole chapter has six main parts. The first part describes the rural households’ income and its structure, including the general situation of income, changes of income and average wage of various industries. The second part illustrates income and its composition of different groups such as gender, age and education level. The third part talks about the situation of income inequality, wherein both general situation of income inequality and changes of income inequality are included. The fourth part compares the income and income inequality of households in different communities. The fifth part is about households’ expenditure and its composition, including the general situation of expenditure, nonproduction expenses, transfer expenditure and changes of expenditure. The last part divides households into different groups, compares their expenditure and describes its composition. The average income for Chinese rural households is 31,910 yuan, of which the proportion of agricultural net income is 26.1%, while the average wage income is 17,398 yuan. The total household income of rural households increased from 27,764 yuan in 2011 to 30033.1 yuan in 2013, then to 31,910 yuan in 2015. The share of wage income first rose from 53.1% in 2011 to 2013 in 56.5%, and then declined to 54.5% in 2015. The proportion of property income remains small, with no big changes of the proportion of transfer income. Among all industries, online wholesale and retail, as well as science and education, enjoy the highest average wage of more than 40,000 yuan per year, while the average wages of business service, accommodation, catering, residents, tourism and public management are the lowest, with less than 20,000 yuan per year. Total income of families with a male household head is 8088 yuan, about 32% higher than that of those with a female household head, which

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can be explained by higher agriculture, wage, industrial and commercial income in male-headed families. For the majority of the age groups, the main sources of income are agricultural net income and wage income, except for those whose household head is aged over 64 years. For peasant households, the main income comes from wage income with 55% and agricultural net income with 26%. However, peasant families with migrant working experience depend more on wage income, which accounts for 65% of the total income and 22% of the net agricultural income. For those migrant workers’ families, their income mainly sources from wage income as well as industrial and commercial income, which account for 62% and 20%, respectively. The Gini coefficient of total household income per household is 0.505  in rural China, while the 50.1% of the difference is resulted from intra-provincial difference. The income inequality situation was worsening in rural areas, which can be reflected by the largely increase of Gini coefficient in rural households’ income, which was 0.450 in 2011 and then to 0.505 in 2015. Communities with high levels of social assistance expenditure also have high-income levels. Compared to communities without social assistance expenditure, communities with certain social assistance expenditure are 2% lower in agricultural incomes, 28% higher in nonagricultural incomes and 20% higher in total income. The average expenditure for households in rural areas is 33,472 yuan in 2015, of which nonagricultural expenditure accounts for 82.3%, agriculture production expenditure accounts for 11.3% and transfer expenditure accounts for 6.4%. Among all nonproduction expenses, food expenditure takes up 43.3%, transportation and telecommunication expenditure takes up 14.3%, living expenses take up 12.7% and health care spending takes up 11.6%. Among all types of transfer expenditure, wedding and funeral expenses account for the highest proportion, which are 71.4% in rural China. Comparing the data of 2011, 2013 and 2015, the results show that the total expenditure per household increased year by year, from 25,433 yuan in 2011 to 33,472 yuan in 2015. Among all types of expenditures, food expenditure remains accounted for the largest part from 2011 to 2015. Dividing households into different groups by income, the results show that nonproduction expenses per household have a positive correlation with the income level, from 19,531 yuan of the low-income group to 34,837 yuan of the high-income group. The amount of nonproduction expenses first increases and then decreases with the increase of the age of householder, with a 35–44-year-old householder holding the largest expenditure, which is 34,773 yuan. Chapter 6 describes the wealth of rural

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households. It contains descriptions of the rural household asset, rural household liability and rural household net worth. It also draws a detailed picture of the current rural household wealth status by conducting a regional analysis, structure analysis and risk profile. Chapter 6 describes the current status of rural households’ fortune on account of three aspects, including the asset, liability and net worth of rural households. This analysis is made up of the databases of the CRHPS and CHFS. Statistics show that the assets of rural households are mainly made up of nonfinancial assets, the proportion of which is 89.2% and that of financial assets is 10.8%. Property and land are the two primary components of rural households’ nonfinancial assets, which share a proportion of 80.6% in the total nonfinancial assets, and the financial assets of rural households are mainly savings (both fixed deposit and demand deposit, accounting for 45.9% of the total financial assets) and the balance of social security account (accounting for 12.5%). And rural households have a considerably high house owning rate which is 94.4%, and there is no significant regional difference. In the eastern region, this rate is 94.7%, in the central region 95.3%, and in the western region 92.9%. As for the ratio of rural land contractual management right owned by rural households in different regions, this rate is 87.8% nationwide, 83.4% in the east, 91.7% in the central region and 88.6% in the western region. The rural land assessment value in China is 79,000 RMB, and the average value is 76,000 RMB in eastern, 53,000 RMB in central and 11,200 RMB western regions. As for the different regions, the share of land assets of rural households in the western region is the highest with 15.5%, followed by the central region with 12.4%. Secondly, the average liability of China’s rural households is 70,000 RMB. In the eastern region, the average liability is 108,000 RMB, and that in central region is 54,000 RMB. In the western region, the average is 56,000 RMB. Clearly, rural households in the eastern region have higher liabilities than those of the central and western regions, while the central region is the lowest. As for the composition of liabilities in rural households, housing liabilities account for 33.7%, operating liabilities for 24.5%, medical liabilities for 18.7%, education liabilities for 8.7% and other liabilities for 8.4%. Thirdly, the net worth of rural households shows a distinctive difference among these three regions, as the national average net worth is 290,000 RMB, the eastern average is 398,000 RMB, the central average is 239,000 RMB and the western average is 218,000 RMB.

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In Part III, Chap. 7 analyzes rural households’ engagement in the social security system based on the data from the 2015 China Rural Household Panel Survey (CRHPS) by Zhejiang University. Firstly, it is found that the pension security of rural residents is still far from satisfying. There are 33.3% of rural residents without any pension scheme. Across the nation, the proportion of rural residents engaged in retirement pay pension is quite low, averaging at 1.4%, with the eastern region being the highest (1.6%), followed by the central region (1.4%) and the western region (1.1%). As for the proportion of rural residents aging 60 and above who have started receiving pension contributions, and among people aged 60 and above who are engaged in endowment insurance, 87.7% of females and 86.2% of males have started to receive pension benefits. Some facts are found from the comparison between rural residents’ personal ­contributions to social endowment insurance and their individual income. The average personal contribution is 950.4 yuan per year, while the average individual income is 3297.1 yuan per year. When it comes to the average balance of social endowment insurance, results show that the gap between various social endowment insurances for rural residents is relatively huge. And there is still a certain gap between the endowment insurance account balance of males and females. As for the implementation of unifying the two separate pension systems (combining the former Urban Residents’ Endowment Insurance and New Rural Endowment Insurance into the Urban and Rural Residents’ Social Endowment Insurance), there are 23.5% working units on average that have implemented pension scheme integration. Secondly, some facts are found about the medical insurance. The proportion of medical insurance engagement is relatively high (91.7%), of which 92.89% of rural residents participate in the New Rural Cooperative Medical Insurance, while only 48.5% of those rural residents have private health care accounts, with an average account balance of 397.4 yuan. The engagement of the social pool for major diseases treatment is quite low, accounting for only 0.3%. As for the medical expenses of rural residents who have been engaged in medical insurance, the average hospitalization expense for rural residents who have been in hospital in 2015 amounts to 12256.1 yuan. Thirdly, the results also show that the coverage of rural residents’ unemployment insurance, maternity insurance and work-related injury insurance is quite limited. At the national level, there are 7.6% rural residents aged 16 years and above who have unemployment insurance, while the figures for the eastern region, central region and western region stay at 11.2%, 4.4% and 6.6%, respectively. Meanwhile,

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at the national level, 6.1% rural residents aged 16 years and above have maternity insurance and 9.2% rural residents aged 16  years and above enjoy work-related injury insurance. The eastern region has seen a higher coverage than other regions in these respects. Fourthly, the proportion of rural residents who enjoy public housing fund is only 1.9%. The average housing fund contribution in 2014 stays at 428.7 yuan per month and the average balance of provident fund account amounts to 18957.5 yuan. Among rural residents who enjoy housing provident fund, 7.8% of them have used the provident fund in 2014. The average withdrawal of provident fund of rural households in 2014 is equivalent to 77355.3 yuan. The data also show some facts about commercial insurance. The figure of rural residents participating in commercial insurance is also low. Of the rural residents, 96.3% do not participate in any commercial insurance. Commercial life insurance coverage takes up 1.8%, health insurance coverage 0.9%, while coverage of commercial insurance of other kinds amounts to 1.0%. The insured amount per capita of rural residents for commercial life insurance nationwide in 2014 was 53306.0 yuan, that figure for commercial health insurance is 2031.3 yuan, and for other commercial insurances is 1653.6 yuan. Chapter 8 describes the education of rural households. This chapter analyzes the basic situation of the education and training received by rural residents using the samples of rural households living in rural areas from the China Rural Household Panel Survey (CRHPS). Facts can be found that the overall education level of rural residents remains relatively low and that their educational level is closely related to regions and gender. In terms of region difference, the educational level of residents in the eastern region is generally higher than that in the central and western regions, while the same pattern applies in terms of the popularization of higher education. There is a striking difference between the education of males and females. Generally, males are better educated than females. The proportion of  Males who have received junior high school education and above is far higher than that of females. Besides, the educational level of male residents is higher than that of their female counterparts. The latter, however, is becoming better educated. However, as the general education level improves, the gap between male and female is narrowing. Given that people aged 6–23 ought to receive education, those who do not enroll in schools are primarily dropouts apart from students who start their studies late. It is evident that the dropout rate in the eastern region is much lower than that of the central region and the western region. Accordingly, females

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have behaved better than their male counterparts in almost every educational phase. With regard to education fees, the policy of compulsory education has played a certain role in reducing students’ fees and expenses during compulsory education. Apart from that, a few people are willing to pay for extracurricular courses and activities, with more courses for boys and more activities for girls. The statistics of education and training within rural households is made upon four aspects, namely the degree of education, conditions of education, the expectation of education and the expenses on education. It makes an objective description of rural households’ education expenditure and education level. For rural students, they may attend primary schools in the countryside, but go somewhere faraway for junior and senior high school. The huge gap between the average number and the median number results from some relatively high extremum. With the popularization of education, more attention has been paid to education quality and conditions. In popularizing basic education, only by improving the education quality and conditions will the nation be able to improve its education standards. Because of the differences in economic and educational conditions, there are also disparities in the quality of public primary and middle schools. Although there are no significant differences among China’s different regions, gaps can still be observed. When comparing the education expectations in different regions, little difference can be found among the eastern, western and central regions. Parents in the eastern region have a bit higher expectation than those in the central and western regions. Generally, females are prone to be treated unfairly in terms of education, especially in underdeveloped areas. However, it can be found that parents’ expectations for their children are not notably influenced by the gender, especially in terms of low education level, which illustrates that the concept of gender equality has found its way into the current education. In the relatively high education expectation, sons are expected to attain a doctoral degree, whereas daughters are expected to receive a bachelor’s or a master’s degree. The possible reason may be that parents are concerned about the negative influence of education level on marriage, especially for the females. The higher their educational level, the lower is the marriage rate. Compared with the education expectation, the actual education level of rural areas in China is extremely low, where most of the population have not got higher education. In rural households, parents’ expectations for their children are rather high, far above their own educational level, which illustrates a popular concept that knowledge can change destiny. This has a positive impact on the advancement of universal

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compulsory education in rural areas, dissemination of higher education and the development of national education level. Chapter 9 describes the community environment of rural households. The overview was made upon the data of community sector on six aspects, including the coverage area and population of the village, the infrastructure, industrial situation, collective assets and liabilities, income and expenditure of community, as well as agricultural land, land expropriation and house demolition. The statistics shows that the number of rural left-­ behind elderly and children is considerable. China’s rural communities are experiencing huge demographic changes in recent years. A large number of middle-aged labor forces flow into cities to engage in nonfarming production, leaving the elderly, children and relatively low-skilled labor forces in the rural areas. Our research investigated the permanent resident population and registered population in rural areas. An administrative village is generally composed of multiple natural villages. The demographic structure of village surnames reflects the situation of rural clan forces. The proportion of people with junior college degree or above to some extent reflects the educational level of the rural population. However, the differences between regions are obvious. The educational level in the eastern region is much higher than those in the central and western regions. With the progress of urbanization, a large number of rural labor forces move out for work. In accordance with research data, nationally, nearly 30% of rural labor forces go out for work on average. There is a huge gap among different regions. This gap may be due to the significant differences among the three regions’ nonagricultural industries development. The nonagricultural industries in the eastern region develop faster; hence, many rural labor forces choose to work at home. However, a large number of labor forces in the central and western regions flow into the eastern region. The operating situation of rural industries reflects the economic situation of rural communities, which provides a strong reference and support for understanding relevant information on rural industries, benefiting the development of special industries in rural communities and facilitating further development of the communities’ economy. The database centers on the overview of rural industries. The development of special industries in rural areas is not achieved overnight. As early as the 1950s, some rural communities had already established special industries to improve the development of communities’ economy. When it comes to the main sources of domestic water in rural communities, well water and mountain spring accounts for a large proportion. Firewood, the main fuel source in

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rural areas, accounts for 40.5% of all energy resources. Apart from water, energy and roads, rural communities also have other infrastructure including kindergartens, primary schools, junior high schools and other education facilities. In general, the distance between the village committee and its nearest primary school is less than that of the village committee and its nearest junior high school. The information on the collective assets and debts in rural communities reflects the operating situation of communities’ assets. Analyzing the basic situation and the forming reason of collective assets and debts provides useful information to help increase the overall production efficiency and improve the collective well-being. Indicators including community spending and cash flow show more intuitively how capital is currently disposed in rural communities, since clear spending is conducive for rural communities to increase income and reduce expenditure in order to better use capital to invest in major expenditure projects. Besides, indicators including community income and its source introduce more intuitively the situation of the current income in rural communities. Diversified capital inflow channels have guaranteed the implementation of various tasks in rural communities. The implementation of rural community projects focuses on resolving some urgent problems facing rural communities and facilitates the proceeding of other tasks. Agricultural land is the basic carrier of production and living for farmers in rural areas. It is beneficial for farmers to improve production efficiency and make better use of land by understanding the information about land approval, land circulation, agricultural land output value and land vacancy. Most rural communities across the nation are concerned about land expropriation and demolition. In the process of land expropriation, the scale of land acquired, the compensation standard as well as the resettlement are among the most concerned issues. In terms of land expropriation and demolition, the compensation standard in rural communities varies with different regions. Apart from compensation, the resettlement is also in the interest of a large number of farmers. Chapter 10 describes the community governance and public services. The current governance upon rural communities was demonstrated through the statistics of seven aspects, including the subject and mechanism of governance, the conditions of governance, the disputes and corrections of community, social organizations and religions, cadres of committees and college-graduated village officials, social security, as well as environmental protection and community training. The village committee and other staff are the main governance body and undertaker of

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supervision in rural communities. They are responsible for decision-­ making, organizing, commanding, coordinating and supervising, and thus critical to achieve village self-governance. Some important indicators of governance mechanism improvement in rural communities include the establishment of standing deliberative institutions, self-governance regulations, grid management and villager’s representative meeting system. As we all know, sound governance conditions are conducive to the governance improvement in rural communities. It not only helps standardize the process of various tasks and improve working efficiency of community staff, but also contributes to the reduction of disputes caused by delaying services and inconvenience. The level of communities’ governance directly affects the overall performance of social governance. Social rule of law will act well only when social governance is enhanced and innovated, the level of the rule of law in rural communities is promoted, all affairs in communities are directed into the orbit of the rule of law, and standardized operation is achieved. According to the research, rural communities across the nation had fewer incidents of sudden crises in the past five years. Social organizations are nongovernmental and nonprofitable enterprises founded by civil societies for the public good. Although the number of social organizations is still low in rural communities, their role in grass-roots government, public services, et cetera cannot be overlooked. Although people’s demand for public services is increasing, many prominent issues in various sectors still remain, including low-quality, small-scale and imbalanced development. By making use of market mechanisms, government procures services from social forces in order to directly hand over a part of public services to eligible social forces in a certain way. Payment shall be made in accordance with the quantity and quality of services. The monthly subsidy for communities’ cadres, to some extent, affects the attitude and effect of community governance. A monthly subsidy that lives up to the cadres’ expectation can stimulate work vitality, improve working efficiency and promote the advancement of rural communities’ governance. There are an increasing number of college-graduate village officials joining in rural communities in recent years. Unified urban and rural health insurance is aimed to abandon the old concept and practice which emphasize urban region over the rural region and advocate administrating city and village separately. Structural reform and policy adjustment are conducted to narrow and gradually eliminate the gap between urban and rural region and promote urban-rural integration. Rural communities are the important main body of conducting rural social security work. Communities’

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care and support for its members directly affect the result of social security. The sanitation of rural communities directly affects the health conditions, production and living of rural residents. Community training can create an atmosphere of learning society, enlighten people’s mind and enable community members to share the fruits of reform and opening up as well as cultural development. Skills can be improved and rights and interests can be protected. According to the result, communities which have career guidance only account for 6.7%. Religious belief, a type of various faiths, is the unwavering belief and mental and physical devotion derived from worship and identification among a group of people who believe in religion and a sacred object (including specific doctrines et cetera). Such thought, faith and devotion are shown and incorporated in specific ­religious rituals and activities to guide and standardize people’s behavior in society. Religious belief is a special social ideology and cultural phenomenon. In Part IV, Chap. 11 is the conclusion. Based on the statistics of the CRHPS, it summarizes the newly emerged characteristics of China’s rural households. The main results are as follows: 1. The rural gender imbalance is serious and gender selection is widespread. In our report, the sex ratio of male to female in rural areas is seriously imbalanced, reaching 110.4: 100. This gender imbalance is mainly found in children and people of working age. 2. The aging trend in rural area intensifies. The distribution of the age and gender shows a “big in the middle but small at the two sides” spindle-shaped structure, and has some obvious “pits” in the age groups of 30–44 and 55–59. 3. The proportion of rural unmarried population gradually increased, “left men” problem being particularly prominent. With the development of society, the proportion of unmarried people who are above the average age for marriage in rural areas is gradually increasing. 4. The achievements of Nine Year Compulsory Education are obvious, but there are still 9.5% of the post-1995 generation who have not completed middle school. This shows that although the compulsory education system has made a significant contribution to Chinese people’s education levels, it has not yet achieved full coverage for the time being.

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5. The education levels of the rural population are low, but the improvement trend is obvious. A large number of rural population have given up continuing their education once they got junior high school diploma. However, the education level of the rural population has improved significantly during the recent decades and even more obviously after 1980. 6. Rural income inequality is ever increasing, 20% of households with the highest income account for 53% of total household income. There are significant differences of income inequalities in different regions. 7. The shares of rural consumer spending are concentrated, with food and other three categories accounting for 82%. Rural household consumption expenditure is mainly concentrated in food, living, health care, and transportation and communication. There are significant differences in the structure of consumption expenditure in different regions. 8. The number of left-behind children and elderly people is large but on the decline. After the reform and opening-up policy was implemented, the rural labor force has been gradually moving into cities, and especially in the recent decades, a large number of rural labor force went to work in urban areas. With this big wave of labor force migrating, two special groups emerged in rural areas, which are left-­ behind children and elderly people.

Notes 1. Chen, Y. X. (2001). Study of American scholars on modern China agricultural economy. Researches in Chinese Economic History, 1, 118–124. 2. If any doubt occurs, please refer to the following two passages, and details are omitted here: Yang and Ren (2011, pp. 242–248), Wang and Yang (2016, pp. 98–104). 3. Of detailed information about the sixth China population census, the report mainly referred to http://www.stats.gov.cn/ztjc/zdtjgz/zgrkpc/dlcrkpc/ 4. The discipline of agricultural sociology at Zhejiang University was founded in 1927; it was the original basis of the discipline of agriculture and forestry economic management.

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5. Approved by the State Council Academic Degrees Committee, the discipline of agriculture and forestry economic management of Zhejiang University was valid of declaring master’s degree gift in 1981, and the declaring doctor’s degree gift in 1990 along with a postdoctoral research center. In 2000, it was approved of Primary Discipline Doctoral Programs. In 1994, it was listed as one of the first Provincial-level Key Disciplines in Zhejiang and was elected into the national 211 Project. In 1999, the Center for Agriculture and Rural Development (CARD) of Zhejiang University was established, and was approved as the National Key Research Institutes of Humanities and Social Science in Universities by the Ministry of Education of the People’s Republic of China. This institution was later developed into an innovation center for rural development, and was classified into the level­A list of innovation research centers of 985 Project. In 2007, this discipline was selected as a National Key Discipline. In 2016, Zhejiang University put an emphasis on the construction of this discipline and listed it into the Summit Discipline Construction Support Project.

References Cao, X.  S. (1991). Overview of the China Rural Factual Survey by the South Manchuria Railways Company of Japan. The Journal of Chinese Social and Economic History, 4, 104–109. Chen, H.  S. (1988). Me in Four Historical Periods. Beijing: China Culture and History Press. Dittmer, C. G. (1918). An Estimate of the Standard of Living in China. Quarterly Journal of Economics, 33(1), 107–128. Ge, H. J. (2012). A State About the China Rural Survey of the Intelligentsia in the Republic of China (1925–1935). Unpublished Master Dissertation, Hunan Normal University, Hunan, China. Huang, Z. H. (2004). About the Studies of Agricultural and Economic Management in China: A Retrospect on the Tenth Five-Year Plan Period and the Development Trend During the Eleventh Five-Year Plan Period. Unpublished Master Dissertation, Hangzhou, Zhejiang. Li, J. Z. (2008). The Survey of Ting Hsien: A Milestone of China Rural Social Survey. Sociological Studies, 2, 165–191. Lin Jinzheng and Deng Hong. Another view: overseas scholars and Chinese rural surveys during the Republic of China. Journal: Journal of History, Literature and Philosophy, 2009(3):23–26. Lin, J. (2004). The Evolution and Future Development of the Discipline of Agriculture and Forestry Economic Management in China. Unpublished Master Dissertation, Hangzhou, Zhejiang.

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Rural Social Economy Department of National Bureau of Statistics of China. (2010). 2010 China Yearbook Rural Household Survey. Beijing: China Statistics Press. Sheng, B. Y. (2001). A Recognition of L. J. Buck’s China Rural Social Surveys. Academia Bimestris, 2, 119–123. Wang, X. Q. (2005). A Century of Great Changes in China’s Agriculture Economy. Issues in Agricultural Economy, Beijing, China, 11, 6–10. Wang, J., & Yang, X. (2016). Investigation of Rural Economy and Society in Pingxiang, Hebei Province in the 1920s. Journal of Hebei University: Philosophy and Social Sciences, 41(3), 98–104. Xia, M. F. (2007, August 1). Social Survey in the End of Qing Dynasty and Republic of China and the Rise of Chinese Social Science. China Reading Weekly. Yang, X., & Ren, H. (2011). Economic and Social Survey of 150 Rural Households in Yanshan County, Hebei Province. China Rural Research, 8(1), 242–248.

CHAPTER 2

Survey Sampling Design

The survey sampling design of the China Rural Household Panel Survey (CRHPS) took consideration of the needs of being scientific, controlled and trackable. To ensure the randomness and representativeness of the samples, the sampling plan design included both overall sampling plan and terminal sampling plan. Generally, the overall sampling scheme was carried out in three stages, where in each stage the stratification sampling and probability proportional sample (PPS) methods were adopted. The terminal sampling scheme, on the other hand, adopted systematic sampling by using sampling frames made from the “geographic information of housing distribution”. Our sampling not only covered the rural households in rural areas, but also rural families living in urban areas, so that we could have a comprehensive survey of China’s rural households, guarantee the lateral comparability and figure out how urbanization has impacted rural households by comparing samples of different areas. In addition, to ensure that changes over the years were looked through so that vertical comparability can be maintained, high traceability was pursued in the sampling by using the same survey samples in the past as the basis when the sampling was to be expanded.

2.1   Overall Sampling Plan The China Rural Household Panel Survey has hitherto been carried out for three rounds, all of which shared the same design but with a slight difference in practice. © The Author(s) 2019 W. Qian et al., Societal Development in Rural China, https://doi.org/10.1007/978-981-13-8082-2_2

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2.1.1  The Sampling Design of 2011 The objective of the 2011 sampling design was to ensure that the data was nationally representative. 2.1.1.1 The First Stage of Sampling The sampling units of the first stage were taken from 2585 counties (including districts and county-level cities, hereinafter referred to as “counties”) all over the country except Tibet, Xinjiang, Inner Mongolia, Hong Kong, Macao and Taiwan. The goal was to select 80 counties out of 2585 counties around the country. The survey required that these 80 sampled counties were evenly distributed across the country. In order to meet this requirement, we divided the 2585 sampling units into ten strata according to their gross domestic product (GDP) per capita and selected 8 counties from each level, totaling 80 counties and covering 25 provinces (municipalities or districts) in China. The ratio of the 80 sampled counties in the eastern, middle and western part of China was 32:27:21. 2.1.1.2 The Second Stage of Sampling The goal of the second stage of sampling was to collect residents ­committee/village committee samples from the 80 sampled counties. The residents’ committee/village committee ratio in each sampled county was based on the proportion of the nonagricultural population, and the number of residents committee/village committee of each county in total was always four. 1. Firstly, we divided counties into five groups according to the percentile of their nonagricultural population. The percentiles were 20%, 40%, 60% and 80% of the nonagricultural population of counties. 2. In the county group which had the largest proportion of the nonagricultural population, the sample ratio of the residents’ committee and village committee was 4:0. 3. In the county group which had the second largest proportion of the nonagricultural, the sample ratio of the residents’ committee and village committee was 3:1. 4. Keeping in line with this rule, in the county group which had the lowest proportion of the nonagricultural population, the sample ratio of the residents’ committee and village committee was 3:1.

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Based on the sample distribution method mentioned above, an urban and a rural sampling frame were formed in these counties (districts or county-level cities). Because the numbers of the sampled residents committee and village committee were fixed, we could adopt PPS sampling in accordance with the households of the residents committee and village committee. 2.1.1.3 The Third Stage of Sampling Correspondent samples were allocated in each sampled residents committee and village committee. In the rural areas, we collected a fixed sample of 20 households. For each urban community, the higher the housing price was, the greater was the number of households being surveyed, to a certain extent, and thus in each community there were 25–50 households being surveyed. We interviewed the family members who met the survey requirements. 2.1.2  The Sampling Design Plan of 2013 The 2013 sampling design was to guarantee its representativeness of data at the provincial level. Therefore, the sampling quantity was largely expanded. The primary sampling unit (PSU) covered cities and counties all over the country excluding Tibet, Xinjiang, Hong Kong, Macao and Taiwan. 2.1.2.1 The First Stage of Sampling All counties in each province were listed according to their GDP per capita and then sampled symmetrically on the basis of the per capita GDP in 2011. For example, for a province with 100 counties, if the sampled county ranked as 15th in terms of GDP per capita in 2011, the new sample of 2013 would have to be symmetrically ranked as 85th. On this basis, if the number of samples of a province was too few to be provincially representative, we then adopted the PPS method to increase the samples (see specific implementation). For the newly sampled provinces of Ningxia, Inner Mongolia and Fujian, we also used the probability proportional sample (PPS) method. Specifically, a province’s counties, ranked by per capita GDP, were dealt with systematic sampling and weighted by population.

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2.1.2.2 The Second Stage of Sampling A different sampling method was adopted to collect samples from the newly sampled cities/counties. The subdistricts (townships) and residents committees (village committees) of sampled counties were listed according to the proportion of the nonagricultural population. Four village and resident committees were then selected using the PPS systematic sampling method which is weighted by population. The sampling ultimately covered 262 cities and counties as well as 1048 residents committees and village committees across 29 provinces. 2.1.2.3 The Third Stage of Sampling The same method as that of the first stage of 2011 was adopted at this stage, in which correspondent samples were allocated for each selected residents committee and village committee. We collected 20 household samples in the rural areas. For each urban community, the higher the housing price was, the greater was the number of households being surveyed, and, thus, in each community there were 20–50 households being surveyed. We interviewed the family members who met the survey requirements to guarantee the samples’ nationwide representativeness. 2.1.3  The Sampling Design Plan of 2015 The sampling design of 2015 guaranteed both the sampling’s representativeness at the provincial level and its representativeness in 15 sub-­ provincial cities (including Chengdu and Wuhan). To increase the sampling’s representativeness at the provincial level, we revisited the 28,141 sampled households of 2013 and added new samples from 13 provinces/municipalities/autonomous regions, namely Anhui, Fujian, Hainan, Hebei, Hunan, Jilin, Jiangsu, Shanxi, Sichuan, Yunnan, Zhejiang and Chongqing. In addition, we carried out the sampling in 15 sub-provincial cities including Chengdu and Wuhan. 2.1.3.1 The First Stage of Sampling The provincial sampling was carried out in three stages, and in each stage, samples were collected using the sampling stratification and PPS methods. In contrast to 2013, we adopted data interpolation in order to guarantee the even distribution of samples in each province.

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1. We listed provinces which needed more samples according to their per capita GDP. 2. By applying population weighting, we calculated each county’s population correlation, the population correlation interval and the average interval based on the statistics of 2013. 3. If the interval was more than 80% of the average interval, then one sample would be added. If the interval is more than 1.5 times of the average interval, then two samples would be added. 4. We worked out the random number seed and used equal-space sampling to get new sampled counties. To guarantee the representativeness at the sub-provincial level, based on the samples of 2013 we listed the counties in the administrative area of each province and used the probability proportional sample (PPS) method. Specifically, a province’s counties, ranked by per capita GDP, were dealt with systematic sampling and weighted by population. We collected one sample county in every two million people. Counties in the administrative area of 80 sub-provincial level cities were collected and the proportion was 44.69%. 2.1.3.2 The Second Stage of Sampling Because the information of the committee was updated, during the second stage sampling of 2015, we used the codes for the administrative divisions released by the National Bureau of Statistics of the People’s Republic of China and used equal-space sampling to collect four new sampled counties out of the counties of the first stage. This method was different from those of 2011 and 2013. 2.1.3.3 The Third Stage of Sampling The method was the same as the ones used in 2011 and 2013. For each sampled village and residents committee, the fixed quantity of rural samples, in general, was 20. However, for urban samples, the higher the housing price was, the greater was the number of households being surveyed, and thus in each committee there were 25–50 households being surveyed. We interviewed the family members who met the survey requirements to guarantee the samples’ nationwide representativeness.

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2.2   Terminal Sampling Design During the terminal sampling, we used on-site sampling based on mapping the residential areas and collecting lists of households. The “geographic information of housing distribution” was adopted as the sampling frame of the terminal sampling. The specific means are as follows. 2.2.1  Residence Mapping The CRHPS has adopted a geographic sampling system by using 3G technology (remote sensing, GPS and GIS) to collect the geographic information of the targeted areas. The digital image with high precision and vector maps provided by the Institute of Geographic Information in the Chinese Academy of Sciences. In the field, our mapping technicians used an e­ lectronic measuring instrument and a GPS system to collect accurate electronic data which is automatically transferred to computers to create high-quality vector maps. Considering the timeliness of geographic data, we decided to manually check and record any changes that appear at following stages. This ensured that the geographic information in the system would match the one in the real world. The fundamental working procedure is illustrated in Fig. 2.1. Fig. 2.1  Steps of residence mapping

Prepare the Vector Maps

Determine the Reference Map

Do the Amendments

Input the Residence Information

Draw the Residence Map

GIS data of Boundaries

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2.2.2  Terminal Sampling Plan The equal-space sampling was adopted to draw households from the household list collected during the previous mapping stage. The details are as follows. 1. First, we calculated the sampling interval, that is, the number of households from which one sample was collected, using the following formula: Sampling interval = total number of households in the community/ number of households to be selected (rounded up to the closest integer). For example, if we plan to draw 30 households from a community of 100 households, we get 100/30  =  3.33. Then the sampling interval is 4. 2. Second, the random starting point was decided by the final digit of the clock time when the procedure was carried out. For example, if the clock time is 15:34, then 4 would be the starting point; if the clock time is 12:03, then the 3 would be the starting point. 3. Third, the households were collected. The first selected household was the one whose position on the list corresponds to the random starting point. Using the above example again, if 3 was the starting point and 4 is the sampling interval, the 3rd household on the list would be the first one chosen in the sample, followed by the 7th, 11th, 15th, 19th and so on until all 30 households were drawn. In the sampling, households are defined as follows: households can be divided into multiperson households and single-person households. Multiperson households are constituted by a husband and wife, parents, children, siblings and so on, with members who can be interviewed directly. Single-person households are categorized into the following two conditions: one that can be interviewed directly without any other household member and if there are household members in other places but they are economically independent of each other, then other household members are not counted as household members. Moreover, the household must have at least one person who has Chinese nationality and has been living in that region for at least six months. In general, the principle to identify a household is that the family members either share revenues or share expenses.

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2.3   Survey Sample Weights According to our sampling design, since each household was drawn with different probabilities, the number of members per Chinese household was different. In general, to make correct inferences about the population, it is required to accurately reflect the family size as represented by each sample household by adjusting the weights. All calculations were subject to sampling weights adjustment. Sampling weights are calculated as follows: based on the sampling of each stage, we calculated the probability of each county being selected (P1), the probability of that community (village) being selected in their respective district or counties (P2) and the probability of survey samples being selected in their respective communities (villages) (P3) respectively. Thus, the weighting of the three-stage sampling would be calculated by: W1 = 1/P1, W2 = 1/P2, W3 = 1/P3, and the final survey sample weighting would be W = W1 × W2 × W3.

2.4   Size and Distribution of Samples Over the Years During the first round survey of 2011, samples were distributed to 320 residents committees and village committees, 80 counties, and 25 provinces (municipalities, districts) across the country. A total of 8438 household samples and 29,324 individual samples were collected, which were all nationally representative. A total of 17,276 individuals from 5120 households were collected as rural samples. Among these households, 3244 households from 159 village committees totaling 12,569 individuals lived in rural areas. These rural household samples were distributed in 62 counties and 22 provinces (municipalities, districts) across the country. During the second round of the 2013 survey, samples were distributed to 1048 residents and village committees, 262 counties and 29 provinces (municipalities, districts) across the country. A total of 28,141 household samples and 97,916 individual samples were collected, and the data was representative of the national and provincial level. A total of 49,060 individuals from 16,511 households were collected as rural samples. Among these households, 8932 households totaling 35,921 individuals lived in rural areas, which were distributed in 446 village/neighborhood committees, 194 counties and 28 provinces (municipalities, districts) across China. During the third round of the 2015 survey, samples were distributed in 1439 residents committees and village committees, 363 counties and 29 provinces (municipalities, districts) across the country. A total of 37,289

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Table 2.1  Sample size of past surveys Type

2011

2013

2015

Family sample (household) Individual sample (person)

8438 29,324

28,141 97,916

37,289 125,400

Among all samples:  Family sample (household)  Individual sample (person)

Rural household samples living in rural areas 3244 8932 11,654 12,569 35,921 43,952

Among all samples:

Rural household samples living in urban areas (migrant workers’ families) 1876 7579 10,881 4707 13,139 32,723

 Family sample (household)  Individual sample (person)

household samples and 125,400 individual samples were obtained. The data was representative on the national, provincial even sub-provincial level. A total of 76,675 individuals from 22,535 households were collected as rural samples. Among these households, 11,654 households totaling 43,952 individuals lived in rural areas, which were distributed to 586 residents and village committees, 259 counties and 28 provinces (municipalities, districts) across China. Table 2.1 shows the sample size of past surveys.

2.5   The Tracking Condition of Each Year 2.5.1  The Tracking Condition of Rural Families in Rural Area According to Table 2.2, in 2013 and 2015 we revisited the rural samples surveyed in 2011 and found that for rural families living in rural areas, the tracking rate of households was 87% and the tracking rate of individuals was 78% in 2013, while in 2015 the tracking rates of households and individuals were 79% and 64%, respectively. In 2015, the tracking rate of the newly sampled families (including the 2011 samples) was 89% while that of individuals (including the 2011 samples) was 79%. 2.5.2  The Tracking Condition of the Migrant Workers’ Families According to Table 2.3, in 2013 we revisited the samples collected in 2011. For rural families living in urban areas (migrant workers’ families), the tracking rate of households was 78% and the tracking rate of individuals was 74%. In 2015, the tracking rate of households and that of individuals were 63%

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Table 2.2  The tracking condition of rural families in rural areas each year 2011

2013

2015

Household Individual Household Individual Household Individual Rural samples of 2011 Rural samples of 2013 Rural samples of 2015

3244

12,569

2837

9766

2549

8280

–

–

8932

35,921

7989

28,298

–

–

–

–

11,654

43,952

Table 2.3  Tracking condition of migratory workers’ families of each year Year

2011

2013

2015

Household Individual Household Individual Household Individual Samples of 2011 1876 Samples of 2013 – Samples of 2015 –

4707 – –

1464 7579 –

3469 13,139 –

1184 6707 10,881

2664 11,998 32,723

Notes: The numbers in the table refer to the numbers of people of the tracking families; the people of the untracking families are excluded

and 57%, respectively. In 2015, the tracking rate of the newly sampled families (including the samples of 2011) was 88% while that of the individuals (including the samples of 2011) was 91%.

2.6   Data Collection and Quality Control 2.6.1  Data Collection 2.6.1.1 Means of Survey The CRHPS project adopted the sophisticated and internationally recognized computer-assisted personal interviewing (CAPI) system to realize digital access with the support of computers. With CAPI, the inner logic of the questionnaire would be verified automatically and the system would inform the interviewer if any anomalies were discovered. In this way, nonsampling errors caused by human factors such as the preset range problems, data entry errors and logic errors would be effectively reduced. CAPI could better meet the requirements of data confidentiality and real-­time accessibility, which would significantly improve the quality of survey data.

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2.6.1.2 Interviewer Selection and Training The interviewers were mainly outstanding undergraduate and graduate students across China who have received a good level of education. Prior to conducting the official “door-to-door interviews”, the selected students all underwent systematic training, including the knowledge, techniques and attitudes of surveys. Full-time supervisors or Ph.D. students who acted as on-site supervisors have also received relevant training. All CRHPS interviewers as well as the supervisors have received the following training before going into the field: 1. First, basic training of knowledge and skills. This part of training aimed at enabling the trainees to do preparation before interviews, get to the exact starting places of the surveys or samplings in a speedy manner, plan the filed routes and recognize the available rooms. Another important part of the training was learning how to prevent accidental injuries and other safety precautions. 2. Second, they were trained in interviewing skills. The training included how to select qualified interviewees, build trust and cooperate with them; how to question in a precise and neutral way and deal with emergent issues during an interview; and how to save and transmit data while preserving the confidentiality of the respondents’ information. 3. Third, training about the questionnaire contents. The interviewers were given training to first ensure that they understood the structure and content of the questionnaires as well as the content of the interviewers’ handbooks. They need to ensure that they knew the meaning of the relevant questions and definition of relative concepts and that they understood the main logic and conversed logics. We then divided the interviewers into small groups to help them familiarize with the questionnaires. During the training, PPTs, video clips and other multimedia technologies were employed to make the process more exciting and engaging. Trainees were also involved in roleplays where they took turns to play the interviewer and the respondent. By conducting mock interviews in the classroom during this period, trainees would be able to enhance their interviewing abilities and have a more comprehensive understanding of the questionnaire. For the parts in which they were not familiar with, further training would be received.

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4. Fourth, trainees were trained to use the computer-assisted personal interviewing (CAPI) system and the corresponding survey management system. Each interviewer was given a PAD with both the CAPI system and the survey management system preinstalled. An appropriate amount of time was spent teaching the interviewers to become efficient users of both the equipment and the software systems. During the training, trainees specifically learnt about how to use the PAD, CAPI system and management system, including the distribution of download quota and sample information as well as the uploading of data. They were specially trained to make remarks and use various shortcuts during interviews, which effectively controlled and reduced the interview time. 5. Finally, the interviewers were trained to conduct trial interviews in the field. In addition to classroom training, trainees were given the chance to conduct trial interviews in surrounding neighborhoods and to test their acquisition of interview skills and questionnaire content. They were then each given extensive and detailed feedback to help them further improve their skills. The terminal sampling framework was highly dependent on the accuracy of the mapping. Therefore, the mapping technicians were under higher expectations, and they also needed to receive mapping training in addition to the interviewer training. They needed to be adept at using the geographic sampling system, mapping the residential areas on the digital maps directly, as well as recording the external information of housings and information on the residents. After the completion of the training, the interviewers would be scored based on their training performance. Those who received lower scores would get a second training or lose the opportunity to be involved in this project. The instructors and supervisors, on the other hand, received more intense training. In addition to the interviewing training, they needed to be adept in the Monitor Management System, Samples Distribution System and CAPI Questionnaire System. The intense training and strict assessment ensured that our project is supported by a group of excellent instructors and interviewers who were helpful in collecting the high-quality data. The statistics given in Fig. 2.2 illustrate the refusal rate of rural samples.

2  SURVEY SAMPLING DESIGN 

3.5

61

3.2

Refusal Rate

3 2.5 2 1.5

1.2

1

0.9

0.5 0

2011

2013 Year

2015

Fig. 2.2  Refusal rate of past surveys

2.6.2  Data Quality Control 2.6.2.1 Quality Control During the Interviews In addition to using computer-assisted personal interviewing (CAPI), the project was also incorporated into a quality monitoring system in order to reduce problems caused by human errors. The system includes the following: 1. Strict management of the samples Management of the samples was mainly implemented by the sample management system, of which the main features included sample building, sample distribution, sample allocation, sample maintenance, sample tracking, management of interviews, management of supervisors, and the inspection of dissemination and implementation of samples. The CAPI system, incorporated with an excellent management system, is a key element for sample management. The management system would ensure that the management, distribution and allocation of the information be under control. Although interviewed samples were arranged on the computer, the interviewer could not replace the samples at will to reduce the difficulties of interviewing, thus the maximum representativeness and randomness of the samples would be guaranteed. Interviewers must be refused or unanswered six times before they can ask the supervisor about overseeing a replacement sample. Only after the supervisor confirms that it was impossible to interview the sample could the interviewer replace the sample.

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The specific process is as follows: 1. Build the sample data based on the sampling results obtained during the third stage of sampling (housing ID number, map drafting ID number, information about housing address, etc.). 2. Input the information of interviewers, field interview supervisors and so on, to establish correlations between interviewers and supervisors. 3. Confirm the sample frame and set the rules and regulations of sample release according to the sample target goals, and then send this information to the on-site interviewers or supervisors. 4. Each client has his or her data returned from the PAD in real time, in accordance with the established rules for data presentation. Parts of the collected data include the time frame for receiving sample data, the time frame for delivering sample data and changing the sample’s data history. 2. Details of the Interview Management System Detailed interview management was automatically conducted by the interview management system. There were four main functions of the interview management system: first, receiving the sample data released by the sample management system; second, collecting the contact information of the interviewees; third, collecting the survey data; and fourth, returning the collected data in a timely manner. Data acquired in this part included survey data, survey contact information and data (contact time, contact result, contact manner, contact environment, reservation period, reservation manner etc.), interviewers’ behavioral data (time it took to answer, intervals between answers, sequence of the answers, keyboard and mouse operations to record, etc.), the time it took for the sample data to be returned and so on. 3. Timely uploading of data In the above-synchronized data, VPN client users access data through the server under the premise of a stable line of communication. We use the host server and distribute the work in order to synchronize data with a

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subscription model to satisfy supporting staff’s access to continued real-­ time analysis and verify the quality requirements. 2.6.2.2 Data Inspection The data inspection had several rounds of check-ups in various forms. The system would check the on-site data automatically, make an automatic judgment of the “paradata” (behavior data), give a feedback call after the interview, examine the recording and cleanse the data and so on. When getting in touch with the interviewees, the interviewer should take down specific information, including the time of the interview, the accompanying group and the responses of the interviewees. This will benefit the refusal rate analysis, provide support to the new solution plan and also prevent the interviewers changing the samples. With the aid of CAPI’s data logging system, all paradata (“behavior data”) in the interview process, from the recording of interviews to every interviewer’s operation on mouse or keyboard, was recorded faithfully in great detail. In an area where network conditions are allowed, this recording could be promptly returned to the project server, thus enabling near real-time monitoring. The specific tasks include: 1. Analysis of the “paradata” (behavioral data): This was to check and statistically analyze the collected behavioral data. 2. Telephone interviews: A certain proportion of households with a telephone would receive a feedback interview on the telephone to confirm whether they had received an interview. 3. Inspection of recordings: The inspection of the recording was done by replaying the recording and displaying the video on the screen. 4. Data cleansing: This was to have a basic analysis of the statistics, examine the questionnaires which had with questionable answers, analyze the interviewing data of the questionnaires and give a follow-­up phone call to households that had a suspicious questionnaire and a recording recheck.

PART II

Basic Characteristics of Rural Households

CHAPTER 3

Basic Structure of Rural Households

This chapter analyzes the basic structure of rural households using the sample from the 2015 China Rural Household Panel Survey (CRHPS) by Zhejiang University and compares it with the data collected in 2011 and 2013 in some aspects. In 2015, CRHPS collected 11,654 valid samples of rural households, including 43,952 samples of individuals. The study found that the average size in a rural household was 3.77, while the weighted average household size in China was 3.71. About half of the households were nuclear families or conjugal families, while 39.1% of the households had members aged over 65 years. The sex ratio in rural areas was seriously imbalanced. The overall ratio of male to female was 110: 100, of which the ratio in the underage population was 126: 100. Such significant gender imbalance in the underage population reflects that the situation of gender imbalance in rural areas has been deteriorating rapidly in recent years. The average age of China’s rural population was 40.5 years, and the median age was 42 years. The underage population, working-age population and aging population accounted for 15.6%, 69.2% and 15.2% of the overall population respectively, which appeared as a “spindle-­ shaped” structure on the whole. In the future, it will gradually transform into a senescent-age structure, which will look like an “inverted triangle”. At that time, China’s rural areas will face a very serious problem of sub-­ replacement fertility and an aging population. As nearly half of the rural residents did not complete their middle school education, the overall level of education in rural areas is not high. Moreover, the differences among © The Author(s) 2019 W. Qian et al., Societal Development in Rural China, https://doi.org/10.1007/978-981-13-8082-2_3

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regions and genders are significant. In the eastern, central and western regions of China,1 the proportion of people who have attained secondary or higher education was 55.3%, 49.2% and 46.8%, respectively. In the male and female population, this proportion was 58.8% and 41.6%, respectively. Nevertheless, this figure had increased rapidly for people born in the 1980s and 1990s, which reflects the great contribution of China’s nine-­ year compulsory education policy. Among the rural residents in their legally marriageable age, 75.5% had been married. Among the three regions, the western region was the highest in terms of unmarried rate. The proportion of unmarried people who were above the average age for marriage2 had been increasing year by year, from 2.6% in 2011 to 3.1% in 2013, and to 3.4% in 2015. There were significant gender differences, as 5.4% of these people were rural males, which was four times the proportion of rural females. The proportion of unmarried people also increased as the degree of education improved. Of the rural residents, 35% reported that they were suffering from chronic diseases. This ratio is 65% for the elderly population aged over 65 years. It is thus clear that chronic diseases are quite common in China’s rural areas. This situation was more serious in the central and western regions as well as in the female population. The proportion of Communist Party members in rural areas was 7.1%. Differences in this proportion were not obvious in region, but significant in gender. The proportion of male party members in rural areas was 11.9%, which was more than five times the proportion of female party members.

3.1   Household Structure The 2015 CRHPS rural sample shows the average household size is 3.77 persons, which is smaller than the average household size of 4.02 in the 2013 survey. The finding of a smaller household size is consistent with the one reported in China Family Development Report 2015 published by National Health and Family Planning Commission of the People’s Republic of China. After data weighting, the national average household size is 3.71 persons. Household size is affected by social, economic, cultural, demographic and other factors altogether. The postponement of marriage age, decrease in marriage rate, increase in divorce rate, lower fertility rate, longer average life span and migration to cities may all lead to the reduction of rural household size. Figure 3.1 shows, according to the household size, one-person households in rural areas represent 6.5% of the total rural households, ­two-­person households’ proportion is 24.7%, three-person households 18.9%, four-

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69

30.0 24.7

Percentage %

25.0

18.9

20.0

19.0 14.5

15.0 10.0

10.2 6.5 3.4

5.0 0.0

1

2

3

4

5

6

Number of Person

7

1.3

1.5

8

9 and more

Fig. 3.1  Family size composition (unit: %)

person households 19.0%, five-person households 14.5%, six-person households 10.2%, seven-person households 3.4%, eight-person households 1.3% and households with nine and more persons 1.5%. Compared with the distribution in the 2011 and 2013 survey data, the proportions of one-person households and two-person households are increasing, the ratio of three-person households remains similar, while the proportion of households with four persons and above is decreasing over time. This result also implies the aforementioned decline in average household size. Figure 3.2 shows that, according to household types, nuclear family is the main type in rural areas and accounts for 27.4% of the total households, conjugal family comprising of a couple without children or with children living away from home accounts for 21.9%, and stem family accounts for 14.7%. In addition, in all household types, single-parent family accounts for 3.7% of the total surveyed sample, and family of grandparents with grandchildren (with the absence of the parental generation) accounts for 2.9%. These two types of households are relatively vulnerable and the absence of one or both parents may lead to adverse effects on the education and life of the younger generation (children) and due attention should be paid to the issue. Finally, the joint family has the smallest percentage, accounting for only 1.7%. Compared with data in the 2013 ­survey, the proportion of conjugal family has increased while the proportions of all other types have decreased.

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30

Percentage %

25

27.4 21.9

20 14.7

15 10 5 0

1.7 Conjugal Family

3.7

2.9

Nuclear Family Stem Family Joint Family Single Parent Family of Family Grandparents with Grandchildren Family Type

Fig. 3.2  Family type (unit: %). (Note: Conjugal family is a family composed of a couple. Nuclear family consists of a husband, wife and unmarried children. Stem family is a family where parents live with a married child and his or her spouse and children, with no more than one couple in each consecutive generation. Joint family is a family that has two or more couples in any generation. Single-parent family is a family where at least one child lives with his or her single parent. Family of grandparents with grandchildren is a family that lacks one generation. (Families of other types are not listed in the figure))

As is shown in Table 3.1, when classified by household structure and household population in terms of generation, two-generation households account for the main part (35.8%), followed by three-generation households (32.9%). One-generation households also takes up a considerable proportion (29.1%), while the proportion of four-generation and above is relatively low (2.2%). In the one-generation households, households with two persons (mainly married couples) take up the majority, accounting for 22.3%. The proportions of one person, three persons and four persons are 6.3%, 0.3% and 0.2%, respectively. As for two-generation households, the main household sizes are three persons and four persons, representing 16.4% and 12.9%, respectively; 2.1% households have two persons, 3.2% households have five persons, 0.9% households have six persons and 0.3% households

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Table 3.1  Relationship between household size and generation (unit: %) Household size Household ratio One person Two persons Three persons Four persons Five persons Six persons Seven persons Eight persons Nine persons Ten persons or above Total

One generation

Two generations

Three generations

Four generations or above

6.3 24.4 19.0 19.2 14.7 10.3 3.3 1.3 0.7 0.8

6.3 22.3 0.3 0.2 0.0 0.0 0.0 0.0 0.0 0.0

0.0 2.1 16.4 12.9 3.2 0.9 0.2 0.1 0.0 0.0

0.0 0.0 2.3 5.9 11.2 8.6 2.6 1.1 0.6 0.6

0.0 0.0 0.0 0.2 0.3 0.8 0.5 0.1 0.1 0.2

100.0

29.1

35.8

32.9

2.2

Table 3.2  Distribution of households with the elderly in different regions (unit: %) Region Total

China East Central West

39.1 36.7 38.6 42.7

One elderly person Subtotal

Single elderly person

23.2 21.7 21.7 26.9

4.1 4.3 3.6 4.3

Two elderly persons Other Subtotal

19.1 17.4 18.1 22.6

15.6 14.7 16.6 15.4

One elderly couple 7.3 7.8 7.5 6.4

Three elderly persons or Other above

8.3 6.9 9.1 9.0

0.3 0.3 0.3 0.4

Note: The elderly refers to people aged 65 and above

have seven to eight persons. Three-generation households are mainly composed of families with four, five and six persons, accounting for 5.9%, 11.2% and 8.6%, respectively; other sizes are less represented, which are three-person households (2.3%), seven-person households (2.6%), eight-­ person households (1.1%), nine-person households (0.6%), and households with ten and more persons (0.6%). Households with four and more generations account for 2.2%, mainly composed of six and seven persons (0.8% and 0.5%, respectively) while the proportion of households with more than ten persons is 0.2%. As is shown in Table 3.2, 39.1% of the total households in China have at least one person aged 65 or above. Families with only one elderly per-

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son, two, and three and above elderly persons account for 23.2%, 15.6% and 0.3%, respectively. It is particularly worth noting that 4.1% of the households in rural areas are families consisted of a single elderly person. Households composed of an elderly couple account for 7.3%. In the western region of China, households with one elderly person account for 26.9%, among which families consisted of a single elderly person take up 4.3%. The rate of households consisted of a single elderly person in the eastern region is very close to that of the west and higher than that of the middle (3.6%). The proportion of households with two elderly persons is higher in the central region, accounting for 16.6%, among which 7.5% is composed of an elderly couple. Households with two elderly persons in the east take up 14.7%, relatively lower than that of the central and western regions. The proportion of households composed of one elderly couple, however, is 7.8%, surpassing that of the central and western regions. Households that have elderly people account for 15.4% in the west, slightly higher than that of the east. The rate of households composed of only an elderly couple is 6.4%, significantly lower than that of the east and the middle. Moreover, around 0.3% of the households in the rural areas have three elderly persons and above. The proportions of such households in different regions are very close. In the eastern region, these types of households account for 0.3%, the same with that of the central region and lower than that of the western region (0.4%).

3.2   Gender Structure As shown in Table 3.3, the overall sex ratio in rural China is 110.4: 100. This gender imbalance is mainly found in the child population and the workingage population. The sex ratio of the working-age population is 109.3:100, Table 3.3  Age and gender structure Total population Child population Working-age population Elderly population China Eastern Central Western

110.4 109.4 110.3 111.7

126.3 122.5 132.5 122.1

109.3 109.4 106.5 112.7

100.9 99.3 104.9 98.2

Note: According to the Chinese Population and Employment Statistics Yearbook, child population refers to the population aged 14 and under. Working-age population is the population aged between 15 and 64, and elderly population refers to the population aged 65 and above. Sex ratio = male population/female population, which refers the ratio of males per 100 females in a given population

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which is relatively close to the overall sex ratio; while the sex ratio of the child population is 126.3:100, which far exceeds the sex ratio of the other two age groups. This serious gender imbalance in the younger age group reflects the worsening of gender imbalance in rural areas in recent years. Compared with the sex ratio of the middle and younger age groups, the sex ratio of the elderly population is 100.9:100. On the whole, it is more balanced, which is mainly because women tend to live longer than men. As some males pass away, the overall sex ratio is more balanced in the older age group. Looking at the different regions, the overall gender imbalance in the western region is the most serious, with the sex ratio reaching 111.7:100, which is slightly higher than the national average. The sex ratios of the central and eastern regions are 110.3:100 and 109.4:100 respectively. In terms of the child population, the gender imbalance in the central region is the most serious, reaching 132.5:100. The sex ratios of the eastern and western regions are close, with 122.5:100 and 122.1:100, respectively. In the working-age population, the gender imbalance in the western region is the most serious, reaching 112.7:100, which far exceeds that of the eastern (109.4:100) and central (106.5:100) regions. Elderly men still outnumber elderly women in the central region, with a sex ratio of 104.9:100; while in the eastern and western regions, the female population exceeds the male population with the sex ratios of 99.3:100 and 98.2:100, respectively. It shows that the elderly male population is smaller than the elderly female population in these two regions.

3.3   Age Structure As shown in Table 3.4, the average age of the rural population in China is 40.5, and the median age is 42. The average and median age of the female population, at 41.5 years and 44 years respectively, are significantly higher Table 3.4  Age structure (unit: %) Average Median Proportion of the population in different age groups (%) age(Year) age(Year) Child Working-age Elderly Total population population population population Male Female Total population

39.7 41.5 40.5

41.0 44.0 42.0

16.6 14.5 15.6

68.9 69.6 69.2

14.5 15.9 15.2

52.5 47.5 100.0

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than those of the male population. While the average age is 1.8  years higher than men, the median age is 3 years higher. In terms of the age structure, the proportion of male and female working-age population is very close, which is 68.9% and 69.6%, respectively. However, the proportion of male child population is 16.6%, a figure that is significantly higher than that of the female child population (14.5%). Correspondingly, the proportion of the elderly male population is lower than that of the female, at 14.5% and 15.9% respectively. Table 3.4 also shows the population burden of Chinese families. The rural child, working-age and elderly population ratios are 15.6%, 69.2% and 15.2%, respectively. The proportion of child and elderly population is quite close, both taking up a small proportion on the two sides. As shown in Table 3.5, the total dependency ratio, child dependency ratio and the old-age dependency ratio of rural areas are 44.5%, 22.5% and 22.0%, respectively. The working-age population faces considerable pressure to support children and the elderly. The total dependency ratios of rural areas in different regions in China vary greatly. The ratios of the central and western regions are relatively close, which are 47.4% and 45.9%, respectively. The total dependency ratio of the eastern region, at 40.4%, is much lower than that of the two other regions. The disparity of the old-age dependency ratio is not obvious among the eastern, central and western regions, which are 21.6%, 21.7% and 22.8% respectively, and the difference in population burden in different regions is mainly due to the child dependency ratio. The child dependency ratio in the eastern rural areas of China is 18.8%, which is much lower than that of the central region (25.7%) and the western region Table 3.5  Family population burden (unit: %) Region National Eastern Central Western

Total dependency ratio 44.5 40.4 47.4 45.9

Child dependency ratio 22.5 18.8 25.7 23.1

Old-age dependency ratio 22.0 21.6 21.7 22.8

Notes: According to the Chinese Population and Employment Statistics Yearbook, child population refers to the population aged 14 and under. Working-age population is the population aged between 15 and 64, and the elderly population refers to the population aged 65 and above. Child dependency ratio refers to the ratio of the children population to the working-age population, and old-age dependency ratio is the ratio of the elderly population to the working-age population. Total dependency ratio refers to the ratio of the children and elderly population to the working-age population

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Table 3.6  China’s rural population aging rate in 2013–2015 (unit: %)

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Location

2013

2015

National Eastern Central Western

12.4 13.4 12.1 11.8

15.2 15.4 14.7 15.7

Note: The aging rate refers to the proportion of population aged 65 and above taking up the total population

(23.1%). This difference may come from the influence of different regional economic levels on the idea of family planning, as the eastern region is relatively developed. As the corresponding cost of raising children is also higher, more and more families would voluntarily forgo childbearing, leading to a much lower level of child dependency ratio in the eastern region than that of the other regions. Since China became an aging society at the beginning of this century, population aging has become a great concern for the people. According to the United Nations, a society enters into the aging stage when its elderly population aged 65 and above makes up more than 7% of its total population. As shown in Table 3.6, China’s rural society is now a serious aging society, and this situation is continuing to worsen with the rural aging rate at 12.4% in 2013 and reaching to 15.2% in 2015. In 2013, the rural aging issue in eastern China was the most serious, with the aging rate reaching 13.4%. The western region was relatively better at 11.8%; however, after two years, the situation in this region has deteriorated sharply, with the aging rate increasing by 3.9%. This figure exceeded that of the eastern and the central regions, and the western region has since become the most serious region of rural aging. As the birth and death rates are relatively stable in recent years, the sharp growth of the rural aging rate is not only attributable to the natural population aging, but also the movement of population among the regions. These movements, for example, include young rural migrant workers settling into urban areas, or young people from the central and western regions settling into urban areas of the eastern region because they have come to work there. All of these are factors that are likely to lead to a rising aging rate in rural areas, especially that of the central and ­western regions.

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3.4   Educational Attainment Structure As shown in Table  3.7, the proportions of the rural population over 16 years who have completed no schooling, primary school and middle school are 17.7%, 31.7% and 33.5%, respectively. Those who have completed high school or above account for only 17.1%, of which 2.8% of the population have completed a bachelor’s degree or higher. The overall level of education is relatively low. There is a clear dividing line between middle and high school education, because while 33.5% of the population have completed middle school, only 9% have completed high school. It can therefore be seen that a large number of rural people gave up continuing their education after finishing middle school. There are also significant differences in the level of education among the population in different regions. The eastern region has the lowest illiteracy rate (no schooling) with only 16.0%, while the levels are 18.9% and 18.2% in the central and western regions, respectively. The proportion of the population receiving secondary and higher education in these three regions are 55.3%, 49.2% and 46.8%, respectively. In terms of the proportion of the population with a bachelor’s degree or higher, the eastern region is the highest at 3.3%, while the central and western regions are 2.6% and 2.5%, respectively. Therefore, on the whole, the eastern rural population has a relatively high degree of education, followed by the central region, while the western region has a population with a relatively lower educational attainment. However, overall, there are more people with a lower educational attainment than those with a higher educational attainment. It can, therefore, be seen that the nation’s rural population generally have a low educational level. Table 3.7  Region and educational attainment structure (unit: %) Educational attainment No schooling Primary school Middle school High school Specialized secondary/vocational high school Junior college/higher vocation school Bachelor’s degree or higher Note: For the population aged 16 and above

National

Eastern

Central

Western

17.7 31.7 33.5 9.0 2.9 2.4 2.8

16.0 28.7 35.6 10.2 3.4 2.8 3.3

18.9 31.9 33.7 8.6 2.3 2.0 2.6

18.2 35.0 30.6 8.0 3.2 2.5 2.5

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Table 3.8  Gender and Education Level (unit: %) Educational attainment No schooling Primary school Middle school High school Specialized secondary/vocational high school Junior college/higher vocation school Bachelor’s degree or higher

Overall

Male

Female

17.7 31.7 33.5 9.0 2.9 2.4 2.8

10.1 31.1 39.1 10.9 3.5 2.5 2.8

25.9 32.5 27.4 6.9 2.3 2.3 2.7

The level of education not only is geographically different, but also significantly varies in gender. As shown in Table  3.8, men are generally more educated than women. The female illiteracy rate (no schooling) is 25.9%, while the male illiteracy rate is only 10.1%. The proportion of men with a secondary or higher education is 58.8%, while that of women is only 41.6%, a figure that is much lower than that of men. However, with the overall increase of education level, the gap between men and women is narrowing. For example, although the gap between the proportion of men and women with high school education is still large (10.9% for men and 6.9% for women), the proportion of men with junior college or higher vocation school education is 2.5%, which is only 0.2% higher than that of women. The proportion of men who have received a bachelor’s degree or higher is 2.8%, and to women, 2.7%, with a gap of only 0.1%.

3.5   Marital Status In the rural sample provided by the CRHPS, the number of people who have reported their marital status is 33,832, accounting for 77.0% of the total samples collected. As shown in Table 3.9, among the rural population in China, 17.6% are unmarried and 75.5% are married, while 5.8% are widowed and 1.1% are in other marital situations including cohabitation, separation and divorce. At the regional level, the proportion of unmarried people in the western region is the highest at 19.1%, in contrast to the 17.9% in the eastern region and 16.0% in the central region. Correspondingly, the central region tops the proportion of married population (77.7%), followed by the eastern (75.0%) and western regions (73.5%). The proportions of other abnormal marital statuses (such as cohabitation, separation and divorce) are not

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Table 3.9  Region and marital status (unit: %) Marital status

National

Eastern region

Central region

Western region

Unmarried Married Cohabitation Separation Divorce Widowed

17.6 75.5 0.1 0.2 0.8 5.8

17.9 75.0 0.1 0.1 0.7 6.2

16.0 77.7 0.1 0.2 0.8 5.2

19.1 73.5 0.1 0.2 1.0 6.1

Note: All the samples listed in this table and any of the following tables on marital status refer to people who have reached the legal marriage age in China (Male ≥22; Female ≥20)

high. Cohabitation accounts for 0.1% in all three regions, and separation respectively holds 0.1% in the eastern region and 0.2% in the central and western regions. The proportion of divorce is comparatively high, with around 0.8% of the Chinese rural people, of which the lowest proportion of divorce is shown in the eastern region (0.7%), followed by the central region (0.8%), and the highest figure is in the western region (1.0%). Compared with the other three abnormal marital statuses, the status of being widowed is more common, which makes up 5.8% of the population in China’s rural areas. On a regional scale, the proportion of the population being widowed in the eastern region is 6.2% at the maximum, which is slightly higher than the western region of 6.1%, while the central region is significantly lower than both the eastern and western regions, at only 5.2%. As the society develops, more and more “leftover men and women”, a special term referring to unmarried people who are above the average age for marriage, have begun to appear, and this situation has aroused widespread concern. As shown in Table 3.10, the proportion of leftover men is significantly higher than that of leftover women. The proportion of ­leftover men and women is 3.4% of the total population in China’s rural areas, of which the proportion of leftover men is 5.4%, which is more than four times the figure of leftover women. The figures are different in various regions. The proportion of leftover men and women in the western region is 3.9%, which is higher than the 3.4% in the eastern region and the 3.1% in the central region. This is mainly because the proportion of leftover men in the western region is as high as 6.4%, a figure that is dramatically higher than the figures of the eastern and central regions (at 5.3% and 4.5%, respectively). By compari-

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Table 3.10  The distribution of unmarried people aging 30 and more (unit: %) Gender

Male Female Total

The proportion of people aging 30 and more

53.7 46.3 100.0

The proportion of unmarried people aging 30 and more National

Eastern region

Central region

Western region

5.4 1.2 3.4

5.3 1.2 3.4

4.5 1.4 3.1

6.4 1.1 3.9

Note: Leftover men and women refer to unmarried people aged 30 and more The proportion of unmarried men aged 30 and more = the number of unmarried men aged 30 and more/ the number of men aged 30 and more The proportion of unmarried women aged 30 and more = the number of unmarried women aged 30 and more/the number of women aged 30 and more

Table 3.11  Changes in the proportions of unmarried people aging 30 and more (unit: %)

Male Female Total

2011

2013

2015

4.4 0.7 2.6

5.0 1.1 3.1

5.4 1.2 3.4

son, in terms of leftover women, the proportions in the three regions have little difference, with the highest being the central region at 1.4%, followed by the eastern region at 1.2%, and then the western region at 1.1%. Such a social phenomenon is becoming more and more common. As shown in Table 3.11, between 2011 and 2015, the proportion of leftover men in rural areas rose from 4.4% to 5.4%; while the proportion of leftover women also saw an increasing trend, from 0.7% to 1.2%. The total proportion of leftover men and women also rose from 2.6% to 3.4%. It has become a common understanding that the number of leftover men and women is increasing. The changes in data are also significant. On the one hand, due to the growth of society, people are under more pressure at work and women are starting to play more important roles in the workplace, and so both men and women are spending less time on their private lives; on the other hand, facing with the increasing cost of living in society and an improved comprehensive capacity, many men and women now have a higher standard for their future partners, and, as a result, the probability of finding a suitable partner starts to fall and many of these people who have yet found their expected partners are viewed as leftover men or women.

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Table 3.12  The proportions of unmarried people and educational background (unit: %) Educational background No schooling Primary school Middle school High school Specialized secondary/vocational high school Junior college/higher vocational school Bachelor’s degree or higher

China

Male

Female

2.6 4.8 8.8 15.3 23.2 43.9 61.7

6.8 7.6 10.1 11.8 22.7 34.3 58.6

0.8 1.9 6.8 21.1 24.0 55.3 65.4

The relationship between educational background and marital status is within people’s concerns. As shown in Table 3.12, in terms of the proportions of unmarried people who have reached the legal marriage age, there are comparatively large differences in different genders and also in different educational backgrounds, which are mainly shown in the following two aspects: On the one hand, compared with women with a low academic qualification, men with a low academic qualification hold a higher proportion of nonmarriage. For instance, the proportion of unmarried men with no schooling is 6.8%, while the figure for women with no schooling is only 0.8%; and the proportion of unmarried men who have completed only primary education is 7.6%, while the figure for women who have completed primary education is 1.9%. On the other hand, compared with men with a high academic qualification, women with a high academic qualification hold a higher proportion of nonmarriage. For instance, the proportion of unmarried men finishing high school is 11.8%, while the figure for women finishing high school is 21.1%; and the proportion of unmarried men entering junior college is 34.3%, while the figure for women with the same degree is as high as 55.3%. In the national rural samples, whether male or female, as the academic qualifications increase, so do the proportions of unmarried people, in which the figure for those with a bachelor’s degree or higher is as high as 61.7% (since a majority of these are young people, the rate of nonmarriage is comparatively high). Overall, the proportions of the unmarried for

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women who have only finished middle school or below are all lower than those of men in the same educational level. By comparison, for people who have finished high school or above, women hold a higher proportion of the unmarried than men.

3.6   Physical Status In the rural samples of CRHPS, the number of people reporting the symptom of chronic disease is 36,429, accounting for 82.9% of the total samples collected, in which 35% are diagnosed with chronic diseases. This indicates that chronic diseases are relatively common in China’s rural areas. As shown in Table  3.13, the prevalence of chronic diseases varies in different regions. The proportion of chronic diseases is 29.8% in the eastern region, which is far lower than the 38.5% in the central region and the 36.9% in the western region. In addition, in terms of the severity of chronic diseases, the proportion of extremely severe or relatively severe chronic diseases is 45.9% in the eastern region, 54.2% in the central region and 58.4% in the western region. Overall, compared with the central and western regions, the proportion of chronic diseases is relatively lower in the eastern region and the chronic disease condition there is less severe. This is possibly due to the better medical and health conditions in the eastern region. Table 3.13  Chronic disease situation (unit: %) Suffers from chronic disease? Yes No Severity Extremely severe Relatively severe Generally severe Less severe Not severe

National

Eastern region

Central region

Western region

Male

Female

35.0 65.0

29.8 70.2

38.5 61.5

36.9 63.1

32.2 67.8

38.1 61.9

16.8 36.3 24.3 17.7 4.9

13.5 32.4 26.8 20.6 6.7

16.1 38.1 24.1 17.5 4.2

20.7 37.7 22.2 15.2 4.2

15.6 34.8 24.1 19.9 5.6

17.9 37.7 24.4 15.7 4.3

Note: Chronic diseases include hypertension, hyperlipidemia, hypolipemia, diabetes, cancer, chronic bronchitis, emphysema, pulmonary heart disease, hepatic disease, cardiopathy, stroke, nephropathy, gastric disease, digestive system disease, mental disease, senile dementia, encephalatrophy, parkinsonism, arthritis, rheumatism, asthma, cervical and lumbar pain et cetera

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Table 3.14  Prevalence of chronic diseases in elderly people aging 65 and more (unit: %) Suffers from chronic disease? Yes No Severity Extremely severe Relatively severe Generally severe Less severe Not severe

National

Eastern region

Central region

Western region

Male

Female

65.0 35.0

59.6 40.4

68.2 31.8

67.3 32.7

59.7 40.3

70.4 29.6

20.4 40.4 20.1 14.9 4.2

17.0 35.8 23.8 17.3 6.1

20.6 42.0 19.5 14.1 3.8

23.4 42.9 17.3 13.6 2.8

18.3 39.9 20.8 16.6 4.4

22.1 40.8 19.5 13.5 4.1

The prevalence of chronic diseases varies in different genders. In China’s rural areas, the proportion of men with chronic diseases is 32.2%, which is significantly less than the figure for women (38.1%). In terms of the severity of chronic diseases, 17.9% of women think that their chronic diseases are very severe and 37.7% think that their chronic diseases are relatively severe in contrast to the 15.6% and 34.8%, respectively, for men. This indicates that women are more prone to chronic diseases and the severity of chronic disease for women is worse than that for men. According to Table  3.14, the proportion of elderly people suffering from chronic disease is dramatically higher than the figures for the other age groups. The chronic disease proportion for elderly people aging over 65 is 65.0%, which is much higher than the average level of 35.0%. But similar to the overall situation, the figure in the eastern region is only 59.6%, which is far less than the 68.2% in the central region and 67.3% in the western region. In the elderly population, the gap between men and women in terms of chronic diseases proportion is widening, with 70.4% of women aged above 65 reporting chronic diseases in contrast to the 59.7% for men in the same age group. In terms of the severity of chronic diseases, 20.4% of the elderly think that their chronic diseases are extremely severe and 40.4% of the elderly think that their chronic diseases are relatively severe, in contrast to the average levels of 16.8% and 36.3%, respectively. This indicates that the severity of chronic disease for elderly people is worse than that of the other age groups. For the elderly people, the relationship between the severity of chronic diseases and gender and region is similar to the over-

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Table 3.15  Assessment of physical status for the rural population (unit: %) Physical status Perfect health Good health General health Bad health Very bad health

National Eastern region Central region Western region Male Female 13.4 32.0 34.3 16.2 4.1

17.0 35.2 31.8 13.2 2.8

13.0 30.2 35.1 17.4 4.3

9.7 30.4 36.4 18.2 5.3

14.9 33.7 34.2 13.8 3.4

11.7 30.2 34.4 18.9 4.8

all situation. In the eastern region, the proportion of the elderly who think that their chronic diseases are extremely severe or relatively severe is 52.8%, which is significantly lower than the 62.6% in the central region and the 66.3% in the western region. In addition, 22.1% of elderly women think that their chronic diseases are extremely severe and 40.8% of them think that their chronic diseases are relatively severe in contrast to 18.3% and 39.9%, respectively, for men. This indicates that women’s assessment of the severity of chronic disease is significantly higher than men’s. In addition to the assessment of chronic diseases, there is a significant difference in the assessment of physical status for people living in rural areas. As shown in Table 3.15, 13.4% of the rural population think that they are in perfect health and 32.0% of them think they are in good health, while 16.2% of them think that they are in bad health and 4.1% of them think they are in very bad health. Overall, the assessment of physical status for the rural population is relatively good. But at the regional level, there is a larger gap in the assessment of physical status. Of people living in the eastern region, 17.0% think that they are in perfect health and 35.2% think they are in good health, which is better than the figures in the central (13.0% and 30.2%, respectively) and western regions (9.7% and 30.4%, respectively). People of different genders have their own assessments of physical status: 14.9% of men think that they are in perfect health, while only 11.7% of women hold the same assessment; 33.7% of men think that they are in good health, which is higher than the figure for women (30.2%). Overall, men’s assessment of physical status is better than women’s, possibly because women are more prone to chronic diseases or that women are more concerned about their physical status.

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Table 3.16  Assessment of physical status in elderly people aging 65 and more (unit: %) Physical status Perfect health Good health General health Bad health Very bad health

National Eastern region Central region Western region Male Female 6.0 19.4 35.5 30.1 9.0

8.9 23.3 36.0 25.3 6.5

5.5 17.7 34.6 32.2 10.0

3.4 17.1 36.0 33.0 10.5

7.6 22.1 33.6 26.0 7.7

4.4 16.8 34.3 34.2 10.3

According to Table  3.16, compared with the other age groups, the elderly hold an unsatisfactory assessment of their physical status, with only 6.0% and 19.4% listed in the levels of perfect health and good health, respectively, and up to 30.1% and 9.0% listed in the levels of bad health and worse health, respectively. In addition, at the regional level, 32.2% of the elderly living in the eastern region think that their physical status is above the generally good level, ranking as the highest region. The figures for the central and western regions are 23.2% and 20.5%, respectively, which is similar to the average situation. In addition, men hold a better assessment of physical status compared to women: 7.6% of the elderly men think that they are in perfect health and 22.1% think they are in good health in contrast to the figures for elderly women (4.4% and 16.8%, respectively).

3.7   Political Status As shown in Table  3.17, in China’s rural areas, the proportion of the population being Communist Party members is 7.1%. At the regional level, this figure for the central region is 7.3%, which is slightly higher than the 7.1% in the eastern region and the 6.8% in the western region. However, it is worth noting that the proportion of male Communist Party members is 11.9%, which is more than five times the figure for females. At the national level, the proportion of female Communist Party members is 24.3%,3 which is only one-third of the figure for males. Hence, in terms of the coverage of Communist Party members, the gender difference is larger in the rural areas than in the urban areas.

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Table 3.17  The regional distribution and gender distribution of Communist Party members (unit: %) Communist Party member Yes No

National 7.1 92.9

Eastern region

Central region

Western region

Male

Female

7.1 92.9

7.3 92.7

6.8 93.2

11.9 88.1

2.2 97.8

Note: The coverage rate of male Communist Party members = number of male Communist Party members/total male population; coverage rate of female Communist Party members = number of female Communist Party members/total female population

Notes 1. The eastern region in this report is divided into 11 provinces and municipalities, including Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan. The central region includes Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei and Hunan. The western region includes Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Ningxia, Qinghai and Xinjiang. 2. This report defines the average age for marriage as 30 years. 3. Data sourced from the white paper on Gender Equality and Women’s Development in China published by the State Council Information Office in September 2015.

CHAPTER 4

Employment of Rural Households

This chapter is to analyze the employment of rural households based on the sample of “rural households living in rural areas”. The results show that the proportion of rural economically active population in rural areas is 58.8%, descending from east to west. The proportion of agricultural population in rural employment population is 55.3%, and the proportion of nonagricultural population is 44.7%. As for the proportion of agricultural population in rural employment population, this ratio is significantly lower in the eastern region than that in the central and western regions. Compared to the statistics in 2011 and 2013, the proportion of agricultural population decreased in 2015, while the proportion of nonagricultural population increased. The proportion of females in the agricultural population is higher than that in the nonagricultural population, which shows “feminization” in agricultural production. The proportion of males in the construction industry and other nonagricultural industries is significantly higher than that of women. The proportions of women in both agricultural and nonagricultural industries are declining. Thirdly, the agricultural population tended to show more signs of aging than the nonagricultural population. Compared with the data of the two surveys in 2011 and 2013, the trend of aging in the agricultural population has strengthened in 2015. The pattern of leaving the farm can be observed among workers of all age groups, but is particularly pronounced for workers aged 54 or below. Most of the economically active population has received as far as only junior school education and the education level of nonagricultural population © The Author(s) 2019 W. Qian et al., Societal Development in Rural China, https://doi.org/10.1007/978-981-13-8082-2_4

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is obviously higher than that of agricultural population. In agricultural and nonagricultural industries, workers under 35 years of age have a higher education attainment than workers aged 35 or above. Compared with 2011 and 2013, the education level of agricultural and nonagricultural populations has been improved. The vast majority of nonagricultural population engaged in temporary work, but the proportion in the eastern region is significantly lower than that in the central and western regions. In the eastern part of China, the proportion of people employed by others or self-employed is higher than that in the central and western regions. In the nonagricultural population, people employed by others and people engaged in temporary work are relatively young, while the self-employed people and freelancers are relatively old. The population engaged in temporary work is the least educated, and the nonagricultural population employed by other people or units is the most educated. The construction industry and manufacturing industry are the two industries with the most concentrated distribution of nonagricultural population across the country. However, the proportion of the manufacturing industry is higher than that of the construction industry in the eastern region, and the proportion of the construction industry is relatively high in the central and western regions. In rural households, proportions of households engaged in both agricultural and nonagricultural production, only the agricultural production and only the nonagricultural production rank, respectively, the first, the second and the third. The composition of the three types of families in the central and western regions are similar. In eastern China, households engaged in agricultural production and in both productions account for a rather low proportion, while households engaged in nonagricultural ­production account for a relatively higher proportion. The average age of all types of families in the eastern region is higher than that in the central and western regions, and the average educational level is lower.

4.1   Economically Active Population and the Working Population Table 4.1 shows the economically active population and working population profile of the country as well as that of the eastern, central and western regions. The general situations of the economically active population and the working population are indicated by the percentage of the economically active population in the rural population and the percentage of

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Table 4.1  General situation of the rural economically active population and working population China Eastern Central Western Economically active population/rural population (%) Working population/economically active population (%) Agricultural population/working population (%) Nonagricultural population/working population (%)

58.8 97.8 55.3 44.7

59.3 97.8 49.8 50.2

58.7 98.0 58.7 41.3

58.4 97.4 57.4 42.6

the working population in the economically active population, respectively. In the working population, the proportions of agricultural and nonagricultural populations are distinguished. Table 4.1 shows that as far as the whole country is concerned, the proportion of economically active population in rural areas is 58.8%. The proportions in the eastern, central and western regions are 59.3%, 58.7% and 58.4%, respectively, descending from east to west. The proportion of working population in the economically active population of China is 97.8%, indicating a high employment rate. The proportions in the east, central and western regions are 97.8%, 98% and 97.4%, respectively, indicating that the employment rates in the eastern and central regions are higher than that in the western region. Nationally, most of the rural working population are engaged in agriculture. Specifically, the proportions of agricultural population and nonagricultural population are 55.3% and 44.7%, respectively. The proportions of agricultural and nonagricultural population in the eastern region are basically the same, while the proportion of agricultural population is obviously higher in the central and western regions. Specifically, the proportions of agricultural population in the eastern, central and western regions are 49.8%, 58.7% and 57.4%, respectively, indicating that the proportion of agricultural population in the rural employment population in the eastern region is significantly lower than those in the central and western regions. Figure 4.1 shows that the proportion of agricultural population in the working population first increased and then decreased, from 55.6% in 2011 to 57.8% in 2013 to 55.3% in 2015, while the proportion of nonagricultural population showed an opposite trend, decreasing from 44.4% in 2011 to 42.2% in 2013, and then increased to 44.7% in 2015. Compared with 2011 and 2013, the proportion of agricultural population was the lowest in 2015, and that of nonagricultural population was the highest.

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70.0

Percentage %

60.0 50.0

57.8

55.6 44.4

40.0

55.3 42.2

44.7

30.0 20.0 10.0 0.0

2011

2013 Year Agricultural Non-agricultural

2015

Fig. 4.1  Proportions of agricultural and nonagricultural populations in the working population (%)

4.2   Composition of the Economically Active Population and the Working Population The following three tables illustrate the three different aspects of the rural economically active population and working population: gender, age and education level, which are respectively analyzed based on the proportions of males and females; the seven age groups of under 15, 15–24, 25–34, 35–44, 45–54, 55–64 and over 64; and the four education attainment groups of primary school and below, junior high school, senior high school/technical secondary school/vocational school and college/undergraduate and above. Again, for the working population, the composition on these three dimensions is distinguished for agriculture and nonagricultural populations. 4.2.1  Gender Structure Table 4.2 shows that the proportions of males in both economically active population and working population are significantly higher than those of females. To be specific, the proportions of males in economically active population and working population are 57.1% and 57% respectively. Since the two proportions are almost equal, there is no distinctive difference between males and females in employment rate. But in terms of agricultural

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Table 4.2  Gender structure of rural economically active population and working population (unit: %)

Economically active population Working population Agricultural population Nonagricultural population

60.0

52.7

Percentage %

Female

57.1 57.0 50.1 65.5

42.9 43.0 49.9 34.5

50.7

50.0 40.0

Male

49.9

37.2

34.5 28.3

30.0 20.0 10.0 0.0

2011

2013 Year Agricultural Non-agricultural

2015

Fig. 4.2  Proportions of females in agriculture and nonagricultural populations (unit: %)

and nonagricultural populations, it is revealed that males account for as much as 65.5% of the nonagricultural population but only account for 50% of the agricultural population, the same as females. Therefore, men tend to engage in nonagricultural production, and women tend to engage in agricultural production. The relatively high proportion of female in the agricultural population shows a trend of “feminization” in agricultural production. However, Fig. 4.2 shows that compared with the data of the two surveys in 2011 and 2013, both proportions of females in agriculture and nonagricultural populations are declining. The proportion of females in the agricultural population drops from 52.7% to 49.9%, and that in the nonagricultural population drops from 37.2% to 34.5%.

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4.2.2  Age Structure Table 4.3 shows that the majority of rural economically active population are in the four groups of 25–34, 35–44, 45–54 and 55–64 years. Among them, the proportion of people aged 45–54 is the largest, up to 24.8%. In addition, 10.6% of the economically active population are over 64 years of age. This age structure of the working population is consistent with the pattern of the economically active population, indicating that there is no distinctive difference in employment rates among age groups. As for the agricultural population, it is revealed that the groups of people aged 45–54, 55–64 and over 64 years account for, respectively, 29.4%, 28.4% and 18% of the agricultural population. As for the nonagricultural population, the groups of people aged 25–34, 35–44 and 15–24 years account for 33.1%, 23.2% and 16.2%, respectively. Therefore, the agricultural population shows an obvious trend of aging compared with the nonagricultural population. To examine the gender differences in age structure, Fig. 4.3 depicts the age structure of males and females in agricultural and nonagricultural populations. The results show that in the agricultural population, the graph of males has two similar peaks, while the left peak of females is lower, and the right peak is higher, which shows that the aging of the agricultural population is more prominent among female workers. In the nonagricultural population, the age structure of males is obviously more concentrated than that of females, which shows that in female workers the proportion of young and old workers is higher. Figure 4.4 shows that compared to 2011 and 2013, the trend of aging in the agricultural population is on the rise. In the agricultural population, the proportion of 45–54-year-olds increased from 27.3% (2011) to 29.3% Table 4.3  The age structure of the rural economically active population and the working population (unit: %) 15–24 years 25–34 years 35–44 years 45–54 years 55–64 years Over 64 years Economically active population Working population Agricultural population Nonagricultural population

9.2

18.9

18.2

24.8

18.3

10.6

8.5

18.9

18.3

24.9

18.5

10.9

2.6

7.3

14.3

29.4

28.4

18.0

16.2

33.1

23.2

19.3

6.2

1.9

4  EMPLOYMENT OF RURAL HOUSEHOLDS 

b

40.0 30.0 20.0 10.0 0.0

0

20

50.0 40.0

Female Male

Percentage %

Percentage %

a

40

60

80 Age/years old

93

Female Male

30.0 20.0 10.0 0.0

0

20

40

60 80 Age/years old

Fig. 4.3  Age structure of male and female, (a) agricultural and (b) nonagricultural populations (%) 35.0

Percentage %

30.0

29.3 28.3

27.0 27.7

27.3 27.6

25.0 18.8

20.0 13.3

15.0

14.1

10.0 5.0 0.0

2011 45–54 years old

2013 Year 55–64 years old

2015 65 years old and above

Fig. 4.4  Proportions of people aged 45 and above in the agricultural population (%)

(2015), the proportion of 55–64-year-olds from 27.6% (2011) to 28.3% (2015) and that of 65-year-olds and above from 13.3% (2011) to 18.8% (2015). 4.2.3  Educational Attainment Structure Table 4.4 shows that most of the economically active population have only received as far as junior school education. Specifically, the proportion of people completing primary education or below is the highest, up to 44.5%,

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Table 4.4  Education level of rural economically active population and working population (unit: %)

Economically active population Working population Agricultural population Nonagricultural population

Primary school or below

Junior high school

High school/technical secondary school/ vocational school

Junior college/ bachelor’s degree or above

44.5

38.5

11.8

5.2

44.8 59.7

38.5 32.6

11.7 7.2

5.0 0.6

26.2

46.0

17.2

10.6

and the next is junior high school, 38.5%. The education level of the working population is close to that of the economically active population. By comparing the structures of the two, we find that the employment rate of people who have only finished primary school or below is slightly higher than the average, and the employment rate of junior high school graduates is slightly below the average. In terms of agricultural and nonagricultural populations, it can be found that 59.7% of the population engaged in agriculture have an education level of primary school or below, 32.6% of the population have finished junior high school education and only 0.6% have a college/bachelor’s degree. In nonagricultural population, people with an education level of or below primary school account for only 26.2% of the population, people who have finished junior high school education account for 46%, and 10.6% of the population reach college/undergraduate or higher education level. Therefore, the education level of the nonagricultural population is obviously higher than that of the agricultural population. The educational attainment of the working population is related to their age. Figure 4.5 depicts the proportions of people with the education level of junior high school or below in agricultural and nonagricultural populations in each age group. The results show that among all the age groups, the proportion of the agricultural population whose education level is junior high school or below is higher than that of the nonagricultural population, which shows that the nonagricultural population has higher education level. Comparing the age groups, we find that the proportions of agricultural and nonagricultural population whose educational level is junior high school or below show a same trend with the improvement of age: at age 15–44, the proportion increases with age; at age

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95

120.0

Percentage %

100.0 80.0 60.0

84.5

79.9 55.2

95.7 84.1

91.5 82.6

90.9

35–44Y

45–54Y

55–64Y

97.6 89.3

77.6

53.6

40.0 20.0 0.0

16–24Y

25–34Y

Years old

Agricultural

65Y and above

Non-agricultural

Fig. 4.5  Proportions of people who finished junior high school or below in all age groups (%)

45–64, the proportion declines slightly; over 65, the proportion rises sharply. As a whole, in the agricultural and nonagricultural populations, the education level of the working population under the age of 35 is significantly higher than that of the population aged 35 or over. Compared with the results of the two surveys in 2011 and 2013, Fig. 4.6 shows that the proportions of agricultural and nonagricultural populations at junior high school or lower level of education are on a downward trend. In the agricultural population, the proportion of people at junior high school or lower education level dropped from 93.2% in 2011 to 92.3% in 2015, and the proportion of nonagricultural population at junior high school or lower education level decreased from 72.6% in 2011 to 72.2% in 2015.

4.3   Nonagricultural Population and Its Structure 4.3.1  Employment Structure By definition, the nonagricultural population is the rural working population that is not included in the agricultural population. Nonagricultural population includes people who are employed by others (with formal labor contracts), with temporary work (without formal labor contracts,

W. QIAN ET AL.

Percentage %

96 

100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0

93.2

92.9 73.1

72.6

2011

92.3 72.2

2013 2015 Year Agricultural Non-agricultural

Fig. 4.6  Proportions of people who finished junior high school or below (unit: %) Table 4.5  Employment structure of the nonagricultural population (unit: %)

Employed by others With temporary work Self-employed Freelancers The miscellaneous group

China

Eastern

Central

Western

22.8 61.9 12.8 1.6 0.9

28.1 52.5 17.2 1.6 0.6

20.0 66.6 10.3 1.7 1.4

19.0 68.9 9.8 1.5 0.8

such as part-time jobs), self-employed (in individual or private business), freelancers (such as operating start-ups or online business), as well as in the miscellaneous group (such as volunteers). Table 4.5 shows the composition of employment characteristics of nonagricultural population in the country, the eastern, central and western regions. Table 4.5 shows that in China the majority of nonagricultural population are engaged in temporary work, accounting for 61.9%. The number of people employed by others ranks the second, accounting for 22.8%. The employment structure of nonagricultural population in the central and western regions are similar, but those in the eastern region are quite different. Specifically, the proportion of people engaged in temporary work in the nonagricultural population in the eastern region is 52.5%, which is significantly lower than those in the central and western regions. The proportions of people employed by others and self-employed are 28.1% and 17.2%, respectively, in the eastern region, which are significantly higher than those in the central and western regions.

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4.3.2  Gender Structure Tables 4.6, 4.7 and 4.8 report the composition of nonagricultural population with various kinds of jobs in three dimensions: gender, age and ­education level. Table  4.6 shows that in the nonagricultural population with various jobs, the proportion of males is consistently higher than that of females, which is consistent with the conclusion of Table 4.2 that males tend to engage in nonagricultural production. The proportion of males is especially high for people with freelancing, miscellaneous and temporary jobs, while the female nonagricultural population is relatively more actively employed by others and self-employed. Table 4.6  Gender structure of the nonagricultural population (unit: %)

Employed by others With temporary work Self-employed Freelancers The miscellaneous group

Male

Female

63.1 44.9 61.2 76.9 73.4

36.9 33.1 38.8 23.1 26.6

Table 4.7  Age structure of the nonagricultural population (unit: %)

Employed by others With temporary work Self-employed Freelancers The miscellaneous group

15–24

25–34

35–44

45–54

55–64

64+

17.5 17.8 6.2 8.2 24.5

38.9 33.7 22.4 25.3 17.8

19.6 23.0 31.5 25.7 16.1

17.0 17.7 29.8 29.3 18.9

6.4 5.7 6.6 10.9 21.0

0.6 2.1 3.5 0.6 1.7

Table 4.8  Education-level structure of the nonagricultural population (unit: %)

Employed by others With temporary work Self-employed Freelancer The miscellaneous group

Primary school or below

Junior high school

High school/technical secondary school/ vocational school

Junior college/ bachelor’s degree or above

13.9 30.4 28.0 25.0 26.1

36.2 49.1 49.5 48.0 29.3

22.1 15.0 17.8 20.4 28.6

27.7 5.5 4.7 6.7 16.0

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4.3.3  Age Structure Table 4.7 shows that the age structure of those employed by other people or units is similar to that of those engaged in temporary work. In the two groups, the 25–34 age group accounts for the highest proportions, with 38.9% and 33.7%, respectively. The proportions of people aged 35–44 are 19.6% and 23% respectively, ranking the second. In addition, the 15–24 group accounts for 17.5% and 17.8%, respectively. This shows that people employed by other people or units and people engaged in temporary work are relatively young. Compared with the above two groups of nonagricultural population, those self-employed in private businesses and freelancers are relatively old. Specifically, in the two groups, the proportions of people aged 35–44 are 31.5% and 25.7% respectively, the proportions of those aged 45–54 are 29.8% and 29.3% respectively, and those over the age of 55 account for 10% and 11.4% respectively. The age structure of the nonagricultural population doing other jobs is polarized, with about one-fourth people aged 24 or below and one-fourth people aged 55 or over. 4.3.4  Education-Level Structure Table 4.8 shows that among various kinds of nonagricultural population, the proportion of junior high school graduates is always the highest, which is consistent with the observations in Table 4.4. The nonagricultural population engaged in temporary work bears the lowest education level, as 30.4% of the population have finished only primary school and 49.1% have completed only junior high school. The education levels of people self-employed in individual or private business and freelancers are slightly higher, as 28% and 25%, respectively, of them have finished primary school and 49.5% and 48%, respectively, have finished junior high education. The nonagricultural population employed by other people or units is the most educated. Among them, the proportion of people who have finished only junior high school education or below is only 50.1%, while 22.1% of them have finished high school/technical secondary school/vocational school and 27.7% college/university degree.

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4.3.5  Industry Structure Table 4.9 shows the industry structure of the nonagricultural population according to the industries in which people engaged. Nationwide, the construction industry and manufacturing industry are the most concentrated nonagricultural industries, attracting up to 27.1% and 24.7% of the nonagricultural population respectively, followed by residential services and other services, lodging and catering, which absorb 5.6% and 4.9% of the population, respectively. This shows that the majority of rural n ­ onagricultural population is distributed in secondary and traditional tertiary sectors. In terms of regions, although the construction industry and manufacturing industry are the two most concentrated sectors of nonagricultural population, the proportion of the population engaged in manufacturing industry in the eastern region is higher than that of the construction Table 4.9  Industry profile of the nonagricultural population (unit: %) China Eastern Central Western Agriculture, forestry, animal husbandry and fishery Mining Manufacturing Production and supply of electric power, gas and water Construction Online wholesale and retail trade Wholesale and retail outlets Transportation Warehousing, logistics and postal services Accommodation and catering Information transmission, software and information technology services Finance/banking business Real estate Leasing and business services Science and education Water conservancy, environment and public facilities Public services and other services Tourism Health care Culture, media, sports and entertainment Public administration and social security Social work and social organizations Others

4.1 2.5 24.7 3.1

5.8 1.7 32.7 3.5

3.0 3.1 19.7 2.7

2.2 3.2 15.6 3.1

27.1 0.1 2.0 3.8 1.2 4.9 0.9

23.3 0.2 2.3 3.7 1.4 3.4 0.4

32.3 0.1 1.6 3.7 1.0 5.1 1.5

28.5 0.0 1.6 4.3 1.4 7.5 1.1

0.5 0.4 0.7 3.6 0.7 5.6 0.1 2.5 0.2 3.6 0.8 6.9

0.3 0.3 0.8 2.4 0.6 4.6 0.2 2.3 0.2 3.2 0.6 6.1

0.0 1.1 0.3 3.0 0.6 5.8 0.0 2.6 0.3 4.7 1.5 6.3

1.2 0.0 1.0 6.5 0.7 7.5 0.0 2.3 0.4 3.2 0.2 8.5

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industry, with 32.7% and 23.3% respectively, while the proportions of people working in the construction industry in the central and western regions are relatively higher. Meanwhile, the proportions of residential services and other services, accommodation and catering industry in the eastern region are 3.4% and 4.6%, respectively, which are significantly lower than those in the central and western regions, and the proportion of online or offline wholesale and retail trade in the eastern region is higher than those in the central and western regions. 4.3.5.1 Gender Structure of Nonagricultural Industries Tables 4.10, 4.11 and 4.12 report the composition of the nonagricultural population in three dimensions: sex, age and education level. Table 4.10 shows that in primary and secondary sectors, the proportion of men is higher than that of women, but the gender structure in the manufacturing sector is more balanced. Table 4.10  Gender structure of the nonagricultural population in various industries (unit: %)

Agriculture, forestry, animal husbandry and fishery Mining Manufacturing Production and supply of electric power, gas and water Construction Online wholesale and retail trade Wholesale and retail outlets Transportation Warehousing, logistics and postal services Accommodation and catering Information transmission, software and information technology services Finance/banking business Real estate Leasing and business services Science and education Water conservancy, environment and public facilities Public services and other services Tourism Health care Culture, media, sports and entertainment Public administration and social security Social work and social organizations Others

Male

Female

63.6 89.8 52.1 91.0 91.0 34.4 38.8 94.4 64.6 32.6 41.3 61.3 83.6 21.1 56.0 74.7 55.2 0.0 49.0 64.9 83.0 45.8 77.2

36.4 10.2 47.9 9.0 9.0 65.6 61.2 5.6 35.4 67.4 58.7 38.7 16.4 78.9 44.0 25.3 44.8 100.0 51.0 35.1 17.0 54.2 22.8

Agriculture, forestry, animal husbandry and fishery Mining Manufacturing Production and supply of electric power, gas and water Construction Online wholesale and retail trade Wholesale and retail outlets Transportation Warehousing, logistics and postal services Accommodation and catering Information transmission, software and information technology services Finance/banking business Real estate Leasing and business services Science and education Water conservancy, environment and public facilities Public services and other services Tourism Health care Culture, media, sports and entertainment Public administration and social security Social work and social organizations Others

5.5 16.4 15.7 9.3 8.2 30.0 16.2 14.1 31.1 22.5 27.7 8.3 0.0 26.7 7.1 7.3 8.9 14.1 5.6 13.3 6.5 3.0 7.9

0.9 4.2 2.3 7.4 0.5 0.0 2.1 1.1 0.0 0.0 3.0 0.0 0.0 0.0 1.6 10.4 0.4 0.0 1.0 0.0 0.6 0.0 1.7

33.1 22.9 57.0 18.5 27.1 17.8 71.4 26.2 5.4 14.1 18.0 25.9

26.1 33.0 35.9 33.1 28.0 59.3 46.7 25.2 26.4 31.5 37.0 15.9 59.8 4.0 48.5 25.4 44.3 4.2 39.8 62.5 37.4 65.6 40.1

45.1 36.1 35.0 35.0 45.8 10.7 19.7 37.5 38.4 33.9 29.1 42.7 6.6 12.3 24.3 26.0 18.5 10.3 24.1 18.2 34.8 12.7 18.0

19.2 9.4 8.9 14.1 14.8 0.0 13.8 18.0 4.1 6.8 3.2

0.0 10.7 0.0 0.0 3.8 10.1 0.0 3.3 0.6 6.6 0.7 6.4

3.2 0.9 2.2 1.1 2.7 0.0 1.5 4.1 0.0 5.3 0.0

16–24 years 25–34 years 35–44 years 45–54 years 55–64 years over 65 years

Table 4.11  Age structure of the nonagricultural population in various industries (unit: %)

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Table 4.12  Education structure of the nonagricultural population in various industries (unit: %)

Agriculture, forestry, animal husbandry and fishery Mining Manufacturing Production and supply of electric power, gas and water Construction Online wholesale and retail trade Wholesale and retail outlets Transportation Warehousing, logistics and postal services Accommodation and catering Information transmission, software and information technology services Finance/banking business Real estate Leasing and business services Science and education Water conservancy, environment and public facilities Public services and other services Tourism Health care Culture, media, sports and entertainment Public administration and social security Social work and social organizations Others

Primary school and below

Junior high school

High school/ Junior college/ technical secondary bachelor’s school/vocational degree or above school

32.6

47.5

16.7

3.2

40.6 40.2 32.4

46.0 42.3 51.0

11.1 15.2 16.4

2.3 2.3 0.2

45.8 74.8

45.1 25.2

8.1 0.0

1.0 0.0

33.6 26.9 38.0

46.2 55.2 45.0

14.3 12.4 17.0

5.9 5.5 0.0

47.5

44.3

7.7

0.5

51.3

25.5

3.7

19.5

0.0 35.4 47.0

14.3 11.1 41.0

47.4 36.0 12.0

38.3 17.5 0.0

0.0 22.1

16.2 63.0

34.1 12.3

49.7 2.6

40.9

46.8

10.6

1.7

10.3 4.8 32.3

75.6 25.6 35.9

14.1 57.6 26.5

0.0 12.0 5.3

17.8

36.6

34.0

11.6

11.6

31.8

29.3

27.3

51.7

31.7

13.4

3.2

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In the tertiary sector, transportation, warehousing and logistics, financial and real estate industry attract more men than women, while online or offline wholesale and retail, accommodation and catering, as well as business services industry show the opposite. 4.3.5.2 Age Structure of Nonagricultural Industries Table 4.11 shows that in the vast majority of industries, the nonagricultural population is mainly distributed between the age of 25 and 54. In primary and secondary sectors, the nonagricultural population is most concentrated in the two age groups of 45–54 and 35–44 years. These two age groups add up to about 70% of the total number of people in all sectors. In the tertiary sector such as wholesale and retail, accommodation and catering, warehousing and logistics, information technology, business services and tourism, the nonagricultural population is more concentrated in the two groups of 25–34 and 35–44 years, and the age structure is relatively young compared with primary and secondary sectors. On the contrary, there is a trend of aging in the age structure of transportation, finance, science and education industries, as more than 20% of the population engaged in these industries are aged 55 or older. 4.3.5.3 Education Structure of Nonagricultural Industries Table 4.12 shows that, except for finance, real estate, science and education, health care and social work, people with an education level of junior school or below are the major components of all the industries. These people account for the highest proportions in mining, construction, online wholesale and retail, accommodation and catering, business services, as well as residential services, respectively 86.6%, 90.9%, 100%, 91.8%, 88% and 87.7%. In financial, science and education industries, the proportions of population show an upward trend with the improvement of education level. In the real estate industry, the two groups of primary school graduates and high school/secondary school/vocational school graduates are in the majority, while the health care sector is dominated by high school/technical secondary school/vocational school graduates.

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4.4   Industry Distribution of Different Populations Figures 4.7, 4.8 and 4.9 describe the distribution of various populations in three dimensions: gender, age and education. Industry distribution is measured by the proportions of the agricultural population and the nonagricultural population engaged in manufacturing, construction and other nonagricultural sectors. 4.4.1  Industry Distribution Based on Gender Figure 4.7 shows the proportions of people engaged in agriculture, manufacturing, construction and other nonagricultural industries for men and women in 2011, 2013 and 2015. Through comparative analysis, it can be found that more women worked in agriculture in any given year. At the same time, except in 2011 when men in the manufacturing sector accounted for 14.9%, which was higher than the 12.9% of women, in the other two years the proportions of women engaged in manufacturing were higher than those of men. On the contrary, the proportions of men in the construction industry and other nonagricultural industries were always higher than those of women. 120.0

Percentage %

100.0 80.0 60.0

21.9 1.3 12.9

25.2 11.4 14.9

40.0 20.0

63.9

48.5

18.4 2.1 12.7

25.9 16.1 8.8

19.3 2.9 13.6

23.3 18.4 9.6

66.8

49.2

64.2

48.7

0.0 Male

Female 2011

Male

Female

Male

2013

Female 2015

Year Agriculture

Manufacturing

Construction

Other non-agricultural

Fig. 4.7  Industry distribution of male and female (unit: %)

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There was no big difference between the proportions of men and women in agriculture from 2011 to 2015. However, it is worth noting that in 2013 the proportions of men and women in agriculture were both higher than those in 2011 and 2015, especially the proportion of women. The proportion of men in the manufacturing sector showed a downward trend, being 14.9%, 8.8% and 9.6% in the three years respectively, while the proportion of women rose slightly, being 12.9%, 12.7% and 13.6% in the years, respectively. The proportion of men in the construction sector continued to expand, from 11.4% in 2011 to 18.4% in 2015. The proportion of women also expanded slightly, from 1.3% in 2011 to 2.9% in 2015. Overall, between 2011 and 2015, males moved away from manufacturing and other nonagricultural sectors into the construction industry, while women left other nonagricultural sectors to enter agriculture, manufacturing and construction. 4.4.2  Industry Distribution Based on Age Groups Figure 4.8 shows the industry distribution of each age group. The results show that with the age increasing, the proportion of agricultural population decreases first and then increases. Of the population of 15–24-year-­ olds, 38.8% engage in agriculture, while in the 25–34 age group the proportion is only 16.4%, and in the population aged 65 or over the proportion is up to 92.3%. The proportion of people in the manufacturing industry shows the opposite, rising first and then fall. In the 15–24 age group, 25.7% engage in manufacturing. This proportion rises to 31.2% in the 25–34 age group, while in the 65 and older group it is only 1.3%. In the construction industry, people aged 15–24, 35–44 and 45–54 years are in the largest number, with a proportion of 22.1%, 14.9% and 14.7%, respectively. Overall, the population of manufacturing and construction industries accounts for about 50% of the nonagricultural population in all age groups, consistent with the results of Table 4.9. Figure 4.9 depicts the proportion of agricultural population in each age group in 2011, 2013 and 2015. The results show that the proportion of agricultural population was similar in all age groups between 2011 and 2013. But in 2015, the proportion of agricultural population in all age groups declined, particularly the proportion of the 15–54  years group, indicating that workers aged 54 or below tend to leave agriculture.

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120.0 100.0 13.4

Percentage

80.0

22.1

24.7

37.5

44.4

8.6 4.1 2.3

4.7 1.3 1.7

14.7

60.0 25.7

14.9

8.0

85.0

40.0

0.0

43.4

38.8

16–24Y

16.4

21.6

25–34Y

35–44Y

45–54Y

55–64Y

Years old

Agriculture

92.3

26.0

31.2

20.0

17.2

Manufacturing

Construction

65Y and above

Other non-agricultural

Fig. 4.8  Industry distribution of each age group (unit: %) 100.0 90.0

Percentage %

80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0

15–24Y

25–34Y

35–44Y

45–54Y

Years old 2011

2013

55–64Y 65Y and above 2015

Fig. 4.9  Age structure of the agricultural population (unit: %)

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4.4.3  Industry Distribution Among People with Different Educational Backgrounds

Percentage %

Figure 4.10 shows the proportions of people with different educational backgrounds in agriculture, manufacturing, construction and other nonagricultural sectors. The results show that with the improvement of education level, the proportion of farmers declines gradually, from 74% in the people of primary or lower level to 34.5% in the junior college/undergraduate or higher-level group. The proportion of people engaged in other nonagricultural sectors increases gradually, up from 10.8% in the primary or lowerlevel group to 54% in the junior college/undergraduate or higher-level group. The proportions of people working in manufacturing and construction industries are the highest in the two groups: junior high school and high school/technical secondary school/vocational school. Among them, the highest proportion of people engaged in the manufacturing industry is in the high school/technical secondary school/vocational school group, reaching 16.4%, and the highest proportion of people working in the construction industry is in the junior high school group, being 15.4%. 100.0% 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0%

34.5 34.5

3.9

7.7

9.5

54.0

15.4

39.6

16.4

47.0 13.2 74.0 Agriculture

6.8

8.4

Manufacturing

Construction

24.4 10.8 Other nonagricultural

Education Junior college/undergraduate and above High school/technical secondary school/vocational school Junior high school Primary school and below Fig. 4.10  Industry distribution of different education-level groups (unit: %)

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4.5   Industry Composition of the Working Population in Different Communities Tables 4.13 and 4.14 compare the industry composition of the working population in communities from the two dimensions of land ownership and infrastructure. Based on the confirmation of land ownership, the communities are divided into confirmed and unconfirmed groups. In terms of infrastructure, the composition is analyzed on the basis of with or without schools, medical points, financial services, broadband and employment training institutions. Table 4.13  Industry structure of community working population and landownership (unit: %)

Agricultural population Nonagricultural population Among them Agriculture, forestry, animal husbandry and fishery Mining Manufacturing Production and supply of electric power, gas and water Construction Online wholesale and retail trade Wholesale and retail outlets Transportation Warehousing, logistics and postal services Accommodation and catering Information transmission, software and information technology services Finance/banking business Real estate Leasing and business services Science and education Water conservancy, environment and public facilities Public services and other services Tourism Health care Culture, media, sports and entertainment Public administration and social security Social work and social organizations Others

Landownership confirmed

Landownership unconfirmed

56.7 43.3

56.2 43.8

3.3 2.8 24.6 3.3 28.5 0.0 2.0 3.6 0.8 5.1 1.1

3.6 1.8 23.8 2.6 32.6 0.2 2.1 4.0 1.5 4.2 0.7

0.2 0.6 0.5 2.4 0.8 5.7 0.0 3.3 0.2 3.1 0.8 7.3

0.2 0.3 0.6 4.1 0.5 4.4 0.2 1.5 0.2 3.9 0.4 6.6

Agricultural population Nonagricultural population Among them Agriculture, forestry, animal husbandry and fishery Mining Manufacturing Production and supply of electric power, gas and water Construction Online wholesale and retail trade Wholesale and retail outlets Transportation Warehousing, logistics and postal services Accommodation and catering Information transmission, software and information technology services Finance/banking business Real estate Leasing and business services Science and education Water conservancy, environment and public facilities Public services and other services Tourism Health care Culture, media, sports and entertainment Public administration and social security Social work and social organizations Others

53.6 46.4 3.7 2.5 28.5 2.6 22.2 0.2 1.7 3.6 1.7 5.9 1.2 0.8 0.3 1.1 3.4 0.6 6.6 0.2 1.9 0.2 3.5 0.8 6.8

4.1 2.6 20.8 3.8 32.6 0.1 2.0 4.0 0.8 4.1 0.6 0.1 0.5 0.4 3.9 0.7 4.8 0.0 3.1 0.2 3.3 0.8 6.7

No

56.4 43.6

Yes

School

0.0 0.0 0.6 2.9 0.9 4.9 0.0 1.1 0.4 3.2 1.5 7.1

10.5 1.9 24.2 4.9 25.7 0.0 2.7 3.5 0.4 3.6 0.0

54.6 45.4

Yes

0.2 0.5 0.5 2.9 0.6 5.2 0.1 2.8 0.2 3.3 0.6 7.5

3.3 2.5 25.2 3.1 29.7 0.1 1.9 3.8 1.0 4.0 1.0

56.8 43.2

No

Clinic

0.8 0.3 0.8 3.7 0.9 6.6 0.2 3.0 0.3 3.1 1.0 6.6

4.6 2.6 26.3 3.5 23.9 0.2 1.2 3.0 1.6 4.9 0.9

48.7 51.3

Yes

0.2 0.6 0.7 3.6 0.5 5.1 0.0 2.2 0.1 3.7 0.6 6.9

3.5 2.4 23.4 2.9 29.8 0.1 2.3 4.3 1.0 5.1 1.0

59.0 41.0

No

Financial service

Table 4.14  Industry structure of community working population and infrastructure status (unit: %)

0.1 0.3 0.6 2.9 0.7 4.9 0.1 3.0 0.2 3.4 0.7 7.0

3.5 2.3 24.8 3.3 29.6 0.1 2.1 3.9 0.9 4.8 0.8

55.8 44.2

Yes

0.4 1.3 0.3 3.0 0.1 7.2 0.0 1.1 0.0 2.6 0.6 6.9

5.0 3.5 26.8 2.4 28.4 0.2 1.4 3.0 1.4 2.9 1.5

61.2 38.8

No

Broadband

0.0 0.0 0.3 6.5 1.3 5.4 0.1 2.4 0.8 3.0 0.4 9.9

2.5 0.4 21.8 4.1 26.3 0.0 1.5 8.1 2.0 2.8 0.4

51.0 49.0

Yes

0.5 0.5 0.7 3.4 0.6 5.8 0.1 2.5 0.2 3.5 0.8 6.4

4.0 2.7 24.8 3.1 27.5 0.1 1.9 3.5 1.2 5.2 1.0

55.5 44.5

No

Employment training institutions

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Table 4.13 shows that overall the confirmation of community landownership has no significant impact on the population structure of various industries. Table 4.14, however, shows that the industry composition of the working population varies among communities with different infrastructure conditions. Specifically, the proportion of nonagricultural population is relatively high in the communities with clinics, financial services, broadband and employment training institutions. To further distinguish nonagricultural industries, the proportion of people engaged in agriculture, forestry, animal husbandry and fishery is relatively high in communities with clinics. People employed in the manufacturing sector account for a relatively high proportion in communities with financial services. In communities with broadband, the construction sector attracts relatively large amounts of people. In communities with education and training institutions, the proportions of people working in transportation, science and education industries are relatively high.

4.6   Family Employment Types and Their Compositions According to the employment of family members, rural families can be divided into three types. Specifically, if all members of a family are employed in agriculture, it is referred to as a household engaged in agricultural production; if all members work in nonagricultural production, it is referred to as a household engaged in nonagricultural production; the remaining families are households with combined occupations, that is, engaged in both agricultural and nonagricultural productions. Table 4.15 shows the structures of the three types of families in China, the east, the central and the west. As far as the whole country is concerned, the proportion of households with combined occupations is 45.5%, nearly half; agricultural production households account for 30.3%; nonagricultural households account for 24.2%. In terms of regions, the compositions of employment types in the central and western regions are Table 4.15  Profiles of families of different employment types (unit: %) China Eastern Central Western Households engaged in agricultural production Households engaged in nonagricultural production Households with combined occupations

30.3 24.2 45.5

26.2 32.0 41.8

32.7 21.2 46.1

31.6 21.2 47.2

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Table 4.16  Average age of households of each type (unit: %) China Eastern Central Western Households engaged in agricultural production Households engaged in nonagricultural production Households with combined occupations

53 40 43

53 41 44

52 39 43

52 38 42

Table 4.17  Average schooling years of households of each type (unit: %) China Eastern Central Western Households engaged in agricultural production Households engaged in nonagricultural production Households with combined occupations

6.3 9.0 7.9

6.6 9.2 8.2

6.2 8.8 7.7

6.9 8.8 7.8

similar, while that in the eastern region is significantly different. Specifically, the proportions of agricultural production households and households with combined occupations in the eastern region are 26.2% and 41.8%, respectively, significantly lower than those in the central and western regions, while the proportion of no-agricultural production households is 32%, higher than those in the central and western regions. Table 4.16 reveals the average age of households of each type of employment. The national average ages of households engaged in agricultural production, nonagricultural production and with combined occupations are 53, 40 and 43 years, respectively, indicating that the average age of agricultural production families is the largest and that of nonagricultural production families is the smallest. The same pattern applies in the eastern, central and western regions. In addition, the average ages of the three types of families in the central and western regions are close, while those in the eastern region are slightly higher. Table 4.17 reveals the average schooling years of households of each type of employment. The national average schooling years of households engaged in agricultural production, nonagricultural production and with combined occupations are 6.3, 9.0 and 7.9 years, respectively, indicating that the average schooling years of agricultural production families are the lowest and that of nonagricultural production families are the highest. The same pattern applies in the eastern, central and western regions. In addition, the average schooling years of those in the eastern region are higher than those of central and western regions.

CHAPTER 5

Income and Expenditure of Rural Households

This chapter analyzes the income and expenditure of rural households using the sample “households living in rural area” from the China Rural Household Panel Survey (CRHPS). In parts of the comparative study, we used the “families living in towns with rural hukou (migrant worker families)” sample. Through the study, we found that wage income and agricultural income are the main sources of total income of rural households, and the average income in the eastern and central regions of China is significantly higher than that in the western region of China. Nonagricultural incomes in the eastern region such as wage, industrial, commercial and property income are relatively high while the agricultural net income in the central region is higher. The proportion of property income in total income is generally low all over the country. Because of the high agricultural net income, wage income, industrial and commercial income, households headed by males have gained a relatively higher income. The total income and all types of income other than transfer income show a general trend of rising at first and then declining as the age of household head increased. Additionally, as the age of household head increased, the transfer income increased. Generally speaking, with the increase of household head’s education level, the total income and other nonagricultural incomes went up, while agricultural net income and industrial and commercial income began to decrease after an increase. Migrant workers’ families hold the highest total income and the lowest agricultural net income. Moreover, peasant families that used to work as © The Author(s) 2019 W. Qian et al., Societal Development in Rural China, https://doi.org/10.1007/978-981-13-8082-2_5

113

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migrant workers gain higher wage income than others. Compared with 2011, the gap between peasant families and migrant workers’ families has narrowed, because the agricultural net income and nonagricultural income of peasant families increased, while the nonagricultural income of migrant workers’ families decreased. The Gini coefficient of total income of rural families is 0.505. The coefficient of nonagricultural income is higher than that of agricultural net income. Among nonagricultural incomes, the Gini coefficients of property and transfer income are relatively high, while the coefficient of wage income is the lowest. Although the Gini coefficient of total income in the western region of China is higher than that in the eastern and central regions, the coefficient of agricultural net income there is the lowest. Intra-provincial differences make little contribution to the Gini coefficient of rural families’ total income, while interprovincial differences have much influence on the Gini coefficients of wage, industrial, commercial as well as agricultural net incomes. As for the Gini coefficients of wage and transfer incomes, the overlapping part is the major contributor. From 2011 to 2015, the Gini coefficient of total income of rural households was basically on the rise, while the Gini coefficient of nonagricultural income continued to rise, and the Gini coefficient of agricultural net income rose at first and then decreased. The contribution of differences within provinces to the Gini coefficient of total income decreased year by year. There is a positive correlation between the social assistance and public affairs expenditure per person and the total income per household, and a negative correlation between the social assistance expenditure per person and the Gini coefficients of every kind of income, especially agricultural net income, wage and industrial income, as well as commercial income. In addition, the public expenditure per person is positively related to the Gini coefficient of agricultural net income and negatively correlated with the Gini coefficient of nonagricultural income. The total household expenditure is the highest in the eastern region and the lowest in the western region. For all regions, the proportion of nonproduction expenses is the highest in total household expenditure, and the proportion in the eastern and western regions is significantly higher than that in the central region. The proportions of household transfer expenditure and agricultural production expenditure in the central region are higher than those in the eastern and western regions. In China, food expenditure, living expenses, health care expenditure, as well as transportation and communication expenditure account for a large

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115

­ roportion in nonproduction expenses. In terms of regions, the food p expenditure in the eastern and western regions is higher than that in the central region. The living expenses in the eastern region are higher than those in the central and western regions. The expenditure on health care in the western region is higher than that in the eastern and central regions. The transportation and communication expenditure in the central and western regions is higher; and the education and entertainment spending in the central region is higher than that in the eastern and western regions. The total household expenditure has been increasing year by year. Nonproduction expenses accounts for the largest part, but the proportion first increased, then declined. The share of transfer expenditure is declining over time. In nonproduction expenses, the shares of expenditures on food, health care and transportation have gradually increased. With the income going up, the average nonproduction expenses have increased generally. The proportions of expenditures on health care, clothing, education and entertainment climbed, while the shares of expenditures on transportation, health care, food, daily necessities and durable goods decreased. The proportion of living expenses in the middle-income group is slightly larger. Nonproduction expenses show an inverted U-turn change with the increase of the head of household’s age. Among them, the proportion of food expenditure increased slightly with age; the clothing expenditure reduced significantly; the living, health care, education and entertainment spending increased at first, and then declined; and the transportation and communication spending declined at first, and then increased.

5.1   Income and Its Structure 5.1.1  General Situation of Income The total income of rural families is made up of agricultural net income (the rest of the agricultural income after deducting the producing cost) and nonagricultural income. Nonagricultural income includes wage income, industrial and commercial income, property income, and transfer income. In this survey, the statistics of wage income was obtained at the individual level, while others were based on household. For consistency, wage income was added at the household level. Therefore, each income source is measured at the household level. Figure 5.1 shows the income of rural households in the country and in the eastern, central and western regions. From the national point of view,

W. QIAN ET AL.

120.0 Percentage %

100.0 80.0 60.0 40.0 20.0 0.0

2011

2013 Year

Agricultural net income Industrial and commercial income Transfer income

2015

33,000.0 32,000.0 31,000.0 30,000.0 29,000.0 28,000.0 27,000.0 26,000.0 25,000.0

Income yuan

116 

Wage income Property income Total income

Fig. 5.1  Total household income and its composition

in 2015, the total income of rural households was 31,910 yuan, of which 26.1% was agricultural net income, and 73.9% was nonagricultural income. In nonagricultural incomes, the proportion of wage income accounted for the largest, reaching 73.8%; transfer income as well as industrial and commercial income ranked the next, with 14.8% and 10.4%, respectively; and property income took up the smallest proportion of 1.0%. In terms of different regions, wage income and agricultural net income are the main sources of income of rural households in all regions. However, the total income of rural households in the eastern and central regions is significantly higher than that of the western region, with an excess of about 20%. If the incomes are differentiated, it can be seen that the proportions of agricultural net income in the eastern and western regions are only 21.5% and 24.9%, respectively, of the total income, obviously lower than the 31.8% in the central region. This indicates that rural areas in the eastern and western regions have similar agricultural net income, but the total household income in the east region is higher because of the higher nonagricultural income. On the other hand, the nonagricultural income of rural households in the central and western regions is similar. However, due to the high net income of agriculture, the central region has a higher total household income. By further studying the composition of nonagricultural income, we can find that due to higher wages, industrial, commercial and property income, the nonagricultural income in the eastern region is much higher than that

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117

Table 5.1  Various kinds of income of rural households (%, yuan)

Total income Agricultural net income Agricultural income The proportion Nonagricultural income Wage income Industrial and commercial income Property income Transfer income

Country

East

Central

West

31,910 8324 12,180 26.1 23,586 17,398 2450 236 3502

33,474 7186 10,807 21.5 26,288 18,689 3729 345 3525

33,623 10,678 15,267 31.8 22,945 17,639 1914 173 3219

28,006 6961 10,246 24.9 21,045 15,544 1520 176 3805

in the central and western regions. However, even in the eastern region, property income accounts for only about 1% of the total household income. In summary, the following conclusions could be drawn from Table 5.1. First, wage income and agricultural net income are the main sources of income of rural households. Second, the total household income of rural households in the eastern and central regions is significantly higher than that in the western region. Third, the higher total income of rural households in the eastern region mainly came from the high nonagricultural income such as wages, industry, commercial and property income, while the higher total income of rural households in the central region is due to the high agriculture net income. Fourth, the central region enjoys the lowest cost of agricultural production and operation. Fifth, the proportion of property income in total income is universally low. 5.1.2  Changes of Income Figure 5.1 shows the total income (corresponding to the right axis) and the shares of various kinds of income (corresponding to the left axis) in the three years of 2011, 2013 and 2015. The results show that the total household income of rural households increased from 27,764 yuan in 2011 to 31,910 yuan in 2015, and the proportion of net agricultural income decreased slightly from 27.4% in 2011 to 26.1% in 2015. The share of wage income first rose and then declined, while the industrial and commercial income showed the opposite trend. The proportion of property income was always small, while the proportion of transfer income did not change much.

W. QIAN ET AL.

90,000.0 80,000.0 70,000.0 60,000.0 50,000.0 40,000.0 30,000.0 20,000.0 10,000.0 0.0

on

lin

e w so scie ho cia n les l w tifi ale cu o c r a ltu rk es nd re an ea re ,m d rch ta in ed org a il fo ia an nd rm , at m phy iza ... io in si tio n ,d ing c an n w iss in d ar e em du ... h pr o od us tra ina str uc ing ns tio y p t m ion , log ort n,.. an a is at . n uf d tic io ac su a n tu p nd rin ply ... gi o n f f i co na re du ... ns nc al str of w tru ial es y fli at ct in tat ne e io d e w r c n us ag me ho ons ind try ric di les er us le ultu cal ale van try as r tr a cy in e,f ea nd ,.. g a o tm r . re e Ne a nd b str ent tail igh cco us y,a an bo mm ine nim d... rh o ss al oo da se ... d tio rvi se n ce rv an pu ice d bl t an ... ic ou m ri d an sm oth ... ag i er em nd s en ust t a ry nd ...

Basic wage yuan

118 

Industry

Fig. 5.2  Basic wage of various industries

5.1.3  Average Wage of Various Industries Figure 5.2 shows the average wage in various industries based on the wage data at the individual level. The results show that online wholesale and retail, as well as science and education enjoy the highest average wage of more than 40,000 yuan per year, while the average wages of business service, accommodation, catering, residents, tourism and public management are the lowest, with less than 20,000 yuan per year.

5.2   Income and Its Composition of Different Groups Families are classified according to the personal information of the householder, and then the income of different groups and its composition are compared. The personal information of householders mainly includes gender, age, education level and migrant working experience. 5.2.1  Gender Table 5.2 divides households into different groups based on the gender of the household head and compares each income source across groups. The results show that the total income of families with a male household head is 8088 yuan, about 32% higher than that of those with a female household head. The higher agriculture, wage, industrial and commercial incomes in male-headed families contribute to a higher total income, and the gaps

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119

Table 5.2  Average household income based on the gender of the householder (unit: yuan)

Total income Agricultural net income Agricultural income Nonagricultural income Wage income Industrial and commercial income Property income Transfer income

Male

Female

32,979 8797 12,916 24,182 17,975 2597 242 3368

24,891 5218 7346 19,673 13,607 1485 198 4383

Table 5.3  Average household income based on age groups of the householder (unit: yuan)

Total income Agricultural net income Agricultural income Nonagricultural income Wage income Industrial and commercial income Property income Transfer income

16–24

25–34

35–44

45–54

55–64

65 and above

30,537 15,219 18,673 15,318 10,994 470

39,686 8004 11,044 31,682 24,899 3984

33,377 8940 13,843 24,437 18,235 3800

41,122 9920 14,915 31,202 24,871 3798

32,709 9136 12,923 23,573 17,795 1803

19,737 5437 7753 14,300 7886 856

131 3724

260 2539

234 2169

242 2290

242 3734

223 5335

between the two kinds of families in the three kinds of incomes are 3579 yuan, 4368 yuan and 1112 yuan, respectively. However, the transfer income of female-headed households is 1015 yuan higher. 5.2.2  Age Table 5.3 divides households into different groups based on the age of the household head and compares each income source across groups. The results show that families with a head aged between 45 and 54 have the highest total income. Overall, the total income increased first and then fell with the age of the household head. In terms of subitems, the net income of agriculture is the highest among families with a 16–24-year-old householder, while nonagricultural income is the highest among families with a 25–34-year-old householder. Other nonagricultural incomes, except for

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transfer income, first rose and then declined as the age of the householder went up. Transfer income kept the rising tendency with the increase of age. If we look through Table 5.3 vertically, it can be found that for the majority of the age groups, the main sources of income are agricultural net income and wage income. However, when the head of household is aged over 64 years, transfer income is as important as agricultural net income; therefore, their main sources of income are agricultural income and transfer income. 5.2.3  Education Level Table 5.4 divides families according to the education level of householders into different groups and compares each income source across groups. The results show that the household income of families whose householder had a college/undergraduate degree or above is 54,084 yuan, and those of primary or below is 26,625 yuan. On the whole, the total income rises as the education level of the household head increases. As the ­education level goes up, the agriculture net income and the industrial and ­commercial income increase first and then decrease, while other ­ nonagricultural incomes generally continue to rise.

Table 5.4  Average income based on different education levels of the householder (unit: yuan)

Total income Agricultural net income Agricultural income Nonagricultural income Wage income Industrial and commercial income Property income Transfer income

Primary school or below

Secondary school

High school/three-year Junior college/ high school/vocational undergraduate high school or above

26,625 7383

37,806 10,764

41,574 8938

54,084 5887

10,676 19,242

19,702 27,042

13,233 32,636

9032 48,197

14,096 1553

19,843 3447

24,679 3859

36,628 2520

195 3398

291 3461

246 3852

584 8466

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In the first three groups, wage income and agricultural net income are the main sources of total income. In the families whose householder had a college/undergraduate or above degree, wage and transfer income are the main sources of income. By comparing the income structure of different groups, it is found that agricultural net income accounts for only 11% of the total income in families with a graduate or above householder, which is much lower than the proportions of 21–28% in the other three types of households; wage income accounted for 68% of the total revenue, higher than the proportions of 52–59% in the other three categories of households; the proportion of transfer income reaches 16%, which is also much higher than that of 9–13% in the other three kinds of families. 5.2.4  Experience as Migrant Workers Table 5.5 compares various incomes of different groups based on the experience as migrant workers of the household head. To be more specific, if the head of household holds a rural hukou or held a rural hukou before obtaining the unified resident hukou, but lives in cities or towns, this household is then defined as a migrant worker family. If the household head is currently a farmer, but had the experience of leaving the current province and working in other provinces, the family is defined as a peasant family with the experience as migrant workers, which is a subset of peasant families. Table 5.5  Average income of families with different working experience (unit: yuan) Farmer Total income Agricultural net income Agricultural income Nonagricultural income Wage income Industrial and commercial income Property income Transfer income

Farmer with migrant working experience

Migrant worker

31,910 8324 12,180 23,586 17,398 2450

41,917 9153 13,025 32,764 27,217 2587

54,312 2360 3230 51,952 33,760 10,941

236 3502

220 2740

1294 5957

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The results show that the total income of migrant workers’ families is the highest, which is 70% higher than that of all peasant households and 30% higher than that of peasant families with the experience as migrant workers. Obviously, migrant workers’ families have the least net income of agriculture. However, if we calculate agricultural production and operation cost percentage, we will find that the lowest cost percentage is in migrant workers’ families, followed by peasant families with the experience as migrant workers. The nonagricultural income of migrant workers’ families is significantly higher than that of other households, among which the industrial and commercial income is particularly high. Compared with peasant families, peasant families with migrant working experience have a significantly higher wage income. From the vertical perspective, the main sources of the income of peasant families are wage income and agricultural net income, which account for 55% and 26%, respectively. Peasant families with migrant working experience depend more on wage income, which accounts for 65% of the total income and 22% of the net agricultural income. The main sources of the income of migrant workers’ families and wage income as well as industrial and commercial income, which account for 62% and 20%, respectively. In the three categories of households, property income accounts for the highest proportion of 2% in migrant workers’ families. In addition, the proportion of transfer income in households with the experience as migrant workers is only 7%, which is lower than the 10% of the other two types of families. Figure 5.3 depicts the total income and its composition of the three kinds of households in the years 2011, 2013 and 2015. Since there were no statistics for peasant families with the experience as migrant workers in 2011 and 2013, only peasant families and migrant workers’ families were compared here. The results show that the various incomes of peasant families have been rising from 2011 to 2015. Among them, the net income of agriculture rose from 7608 yuan in 2011 to 8324 yuan in 2015, and nonagricultural income from 20,156 yuan in 2011 to 23,586 yuan in 2015. The agricultural net income in migrant workers’ families, though low, rose from 623 yuan in 2011 to 2360 yuan in 2015. The nonagricultural income of migrant workers’ families first rose from 53,970 yuan in 2011 to 59,923 yuan in 2013, and then fell to 51,952 yuan in 2015. Thus, in general, the total income gap between peasant households and migrant workers’ families has decreased in 2015 compared to 2011, thanks to the increase of nonagricultural income and agricultural net income of peasant families as well as the decrease of nonagricultural income of migrant workers’ families.

5  INCOME AND EXPENDITURE OF RURAL HOUSEHOLDS 

70,000.0 60,000.0

60,945.8 54,592.4

59,923.3

123

54,312.1 51,951.7

53,969.7

Income yuan

50,000.0 41,917.3 40,000.0

20,000.0 10,000.0

23,585.8

21,791.4

20,155.9

farmer

migrant farmer

2011

2360.4

1022.5

622.7

0.0

9153.0

8324.3

8241.7

7608.3

32,764.3

31,910.0

30,033.1

30,000.0 27,764.2

farmer

migrant farmer

farmer

2013

migrant farmer

farmer with migrant working experience

2015 Year

total income

agricultural net income

non-agricultural income

Fig. 5.3  Income of peasant families and migrant workers’ families and its composition (unit: yuan)

5.3   Income Inequality 5.3.1  General Situation of Income Inequality Table 5.6 reports the Gini coefficients of household income in the eastern, central and western regions and the interprovincial decomposition of the Gini coefficients to characterize income inequality among rural households. The results show that, for the whole country, the Gini coefficient of total household income per household is 0.505, and the Gini coefficient of nonagricultural income is higher than that of agricultural net income. Among each kind of nonagricultural income, the Gini coefficient of wage income is the smallest, 0.469, while the Gini coefficient of transfer income is the largest, which is 0.639. In terms of regions, although the Gini coefficient of total household income in the western region is higher than that of the eastern and central regions, its Gini coefficient of agricultural net income is the lowest. In terms of nonagricultural incomes, the Gini coefficients of transfer and property income in each region are higher than those of wage income as well as

Total income Agricultural net income Agricultural income Nonagricultural income Wage income Industrial and commercial income Property income Transfer income

4.1 3.5 3.5 4.1 4.1 4.1 3.9 4.1

0.549 0.639

Intra-provincial difference (%)

0.505 0.542 0.518 0.599 0.469 0.480

Gini coefficient

59.4 21.2

24.5 50.9 52.3 27.1 37.8 56.8

Interprovincial difference (%)

Whole country

36.7 74.7

71.4 45.6 44.2 68.8 58.1 39.1

Overlapping part (%)

0.539 0.636

0.501 0.524 0.501 0.569 0.455 0.444

Eastern region

Table 5.6  Gini coefficients of various kinds of income of rural families in different regions

0.559 0.640

0.488 0.547 0.521 0.607 0.463 0.463

Central region

0.549 0.632

0.524 0.518 0.504 0.619 0.489 0.542

Western region

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i­ndustrial and commercial income. However, the differences of Gini coefficients of transfer and property income are not very huge among regions, while the Gini coefficients of wage income as well as industrial and commercial income are particularly large in the western region, leading to a significantly higher Gini coefficient of nonagricultural income in the western region. For the whole country, 71.4% of the Gini coefficient of rural household income came from the overlapping part and 24.5% from interprovincial differences, and the contribution of intra-provincial differences is small. In terms of various incomes, the contribution of intra-provincial differences to the Gini coefficients is always small. However, to the Gini coefficients of property income, industrial and commercial income, and agricultural net income, the contributions of interprovincial differences are 59.4%, 56.8% and 50.9%, respectively, higher than the contribution of the overlapping part. As for the Gini coefficients of wage and transfer income, the contribution of the overlapping part is larger than interprovincial differences, which indicates a large part of overlapping in the distribution of the two types of income in each province. 5.3.2  Changes of Income Inequality

100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0

0.650 0.600 0.550 0.500 0.450 2011

2013 Year contribution of interprovincial difference contribution of provincial difference non-agricultural income

2015

Gini coefficient

Percentage %

Figure 5.4 shows the Gini coefficients of rural household income, agricultural net income and nonagricultural income in the three years of 2011, 2013 and 2015 (corresponding to the right axis) and the contribution of

0.400

contribution of lapped difference agricultural net income total income

Fig. 5.4  Gini coefficients of various incomes of rural households and the composition of the total income Gini coefficient

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interprovincial differences, overlapping differences and intra-provincial differences to Gini coefficients (corresponding to the left axis). The results show that the Gini coefficient of rural households’ income was increasing at large, from 0.45 in 2011 to 0.505 in 2015, indicating that the income inequality situation was worsening. The Gini coefficient of nonagricultural income continued to rise, from 0.536 to 0.599. The Gini coefficient of agricultural net income increased from 0.445 in 2011 to 0.552 in 2013, then decreased to 0.542 in 2015. The contribution of intra-provincial differences to the Gini coefficient of total income decreased year by year, from 5.8% in 2011 to 4.2% in 2015. The contribution of interprovincial differences fell from 31.1% in 2011 to 18.4% in 2013 and to 24.5% in 2015. In general, overlapping differences and intra-provincial differences are the main sources of the Gini coefficient of gross income.

5.4   Income and Income Inequality in Different Communities According to public expenditure and the confirmation on the right to the contracted management of land, the income and income inequality of households in different communities are compared. 5.4.1  Community Expenditure Tables 5.7 and 5.8 both conducted a comparative study based on the social assistance expenditure and public affair expenditure of communities. Table 5.7 shows that the per capita social assistance expenditure of a community is positively related to the total household income. Compared to communities without social assistance expenditure, communities with certain social assistance expenditure are 2% lower in agricultural incomes, 28% higher in nonagricultural incomes and 20% higher in total income. This indicates that communities with high levels of social assistance expenditure also have high income levels (especially nonagricultural income). The public affair expenditure of a community is positively related to the total household income. Compared to communities without public affair expenditure, communities with public affair expenditure are 1% higher in agricultural net income, 8% higher in nonagricultural income and 10% higher in total income. This shows that communities with high levels of

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Table 5.7  Relationship between community’s expenditure and income (unit: yuan)

Total income Agricultural net income Agricultural income Nonagricultural income Wage income Industrial and commercial income Property income Transfer income

Social assistance

Expenditure

Public affair

Expenditure

With

Without

With

Without

35,262 8281 11,858 26,981 19,752 3101

29,607 8449 12,519 21,158 15,768 1993

32,342 8398 12,202 23,945 17,522 2571

29,991 8285 12,459 21,706 16,772 1853

276 3853

211 3186

243 3579

211 2869

Table 5.8  Gini coefficients of various community expenditure

Total income Agricultural net income Agricultural income Nonagricultural income Wage income Industrial and commercial income Property income Transfer income

Social assistance

Expenditure

Public affair

Expenditure

With

Without

With

Without

0.494 0.500 0.513 0.578 0.462 0.467

0.512 0.507 0.521 0.612 0.473 0.488

0.505 0.506 0.518 0.595 0.467 0.475

0.505 0.490 0.519 0.612 0.478 0.488

0.548 0.636

0.548 0.635

0.545 0.636

0.560 0.640

public affair expenditure have higher income levels (especially nonagricultural income). Table 5.8 shows that the Gini coefficients of various incomes are generally negatively related to per capita social assistance expenditure, indicating that social assistance contributes to easing income inequality, particularly the inequality in agricultural net income, wage income as well as industrial and commercial income. At the same time, although the Gini coefficient of agricultural net income is higher in communities with public affair expenditure, the Gini

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coefficient of nonagricultural income is lower in such communities, indicating that public affair expenditure is helpful for alleviating nonagricultural income inequality. The Gini coefficient of gross income is not significantly different between the two types of communities. 5.4.2  Confirmation on the Right to the Contracted Management of Land Table 5.9 compares household income and inequality according to the confirmation on the right to the contracted management of land. The results show that the total income of a community that had confirmed its right to the contracted management of land is higher. Among various incomes, although the net agricultural income is low, the nonagricultural income and each type of it are significantly higher than those of the communities that had not confirmed the right to the contracted management of land. This indicates that one of the premises of achieving the confirmation on the right to the contracted management of land may be that the major income source of community residents is nonagricultural income and that the importance of agricultural net income is not significant. From the perspective of Gini coefficient, the results show that the Gini coefficients of various incomes of communities that had confirmed the right to the contracted management of land are lower than those of the communities that had not confirmed, which is especially reflected in the agricultural net income. The Gini coefficient of the agricultural net income Table 5.9  Incomes and Gini coefficients in different situations of the confirmation on the right to the contracted management of land Confirmed

Total income Agricultural net income Agricultural income Nonagricultural income Wage income Industrial and commercial income Property income Transfer income

Unconfirmed

Average income (yuan)

Gini coefficient

Average income (yuan)

Gini coefficient

32,783 8771 12,660 24,012 18,120 2212

0.504 0.508 0.495 0.598 0.464 0.477

31,143 9891 14,506 21,252 15,943 1987

0.507 0.531 0.516 0.604 0.474 0.479

3431 248

0.543 0.631

3094 229

0.555 0.638

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of the communities that had confirmed the right to the contracted management of land is 0.508, while the Gini coefficient of the other type of community is 0.531. This indicates that the confirmation on the right to the contracted management of land is related to the more equal distribution of net agriculture income.

5.5   Expenditure and Its Composition 5.5.1  General Situation of Expenditure The total expenditure of rural households is divided into three parts: nonproduction expenses, transfer expenditure and agricultural production expenditure, according to their usage. Among them, nonproduction expenses refer to daily living expenses, including food expenditure, clothing expenditure, living expenses, daily necessities and durable goods expenditure, health care expenditure, transportation and communication expenditure, education and entertainment expenses as well as other expenses. Transfer expenditure is the cash or noncash expenditure spent for people, or organizations, who are not family members. In terms of expenditure items, transfer expenditure can be divided into holiday expenditure (including New Year’s red packet money), wedding and funeral expenditure (including birthday, etc.), the aid to others in education, medical care and life, as well as other transfers, donations or aid. In terms of the recipients of such expenditures, transfer expenditure consists of expenditures transferred to parents, parents-in-law and other relatives/nonrelatives. Agricultural production expenditure is the cost that occurs in the process of agricultural production and operation, mainly including agricultural production costs and agricultural employment expenditure. The data show that more than 90% of agricultural production expenditure is agricultural production costs, while the proportion of agricultural employment expenditure is small. Table 5.10 displays the expenditure per rural household and its composition in the country and in the eastern, central and western regions. The expenditure per household in the country is 33,472 yuan. The expenditure per household in the eastern region is the highest, while that in the western region is the lowest. In the whole country, nonproduction expenses accounts for 82.3% of the total expenditure. In terms of regions, nonproduction expenses account for 84.0% and 84.7%, respectively, in the eastern and western regions, which are significantly higher than the 78.4% in the central region. In the whole country, the shares of transfer expenditure and

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Table 5.10  Average expenditure of rural households and its composition

Total expenditure (yuan) Nonagricultural expenditure (%) Transfer expenditure (%) Agriculture production expenditure (%)

Whole country

Eastern region

Central region

Western region

33,472 82.3 6.4 11.3

34,386 84.0 5.5 10.5

32,985 78.4 8.2 13.4

32,925 84.7 5.5 9.8

a­gricultural production expenditure are 6.4% and 5.5%, respectively. The proportions of the two types of expenditure in the central region are higher than that in the east and west regions, which are 8.2% and 13.4%, respectively. 5.5.2  Nonproduction Expenses We can find from Table 5.11 that among the eight kinds of nonproduction expenses, food expenditure, living expenses, health care spending as well as transportation and telecommunications expenditure take up a large proportion. Among them, the proportion of food expenditure in nonproduction expenses is 43.3% in the country. The proportions in the eastern and ­western regions are 44.7% and 44.0%, respectively, significantly higher than 40.8% in the central region. The proportion of living expenses is 12.7% in the country, while the eastern region has a proportion of 13.4%, which is significantly higher than 12.4% in the central region and 11.9% in the western region. The proportion of health care expenditure is 11.6% in the country, while the western region has a proportion of 11.9%, which is higher than the 11.5% in the eastern region and 11.3% in the central region. The proportion of transportation and communication expenditure is 14.3% in the country, while the figures are 15.9% and 15.1%, respectively, in the central and western regions, which are higher than the 12.3% of the eastern region. Education and entertainment expenditure account for 8.3% in the country, while this figure is 9.4% in the central region, which is higher than the 7.8% in the eastern region and the 7.6% in the western region. Little regional differences existed in the proportions of the other three categories of nonproduction expenses.

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Table 5.11  Average nonproduction expenditure of rural households and its composition

Nonproduction expenses (yuan) Food expenditure (%) Cloth expenditure (%) Living expense (%) Daily necessities and durable goods expenditure (%) Health care expenditure (%) Transportation and telecommunications expenses (%) Education and entertainment (%) Other expenses (%)

Whole country

Eastern region

Central region

Western region

27,549 43.3 4.5 12.7 5.4

28,885 44.7 4.7 13.4 5.5

25,871 40.8 4.7 12.4 5.3

27,883 44.0 4.0 11.9 5.4

11.6 14.3

11.5 12.3

11.3 15.9

11.9 15.1

8.2 0.0

7.8 0.1

9.4 0.2

7.6 0.1

Table 5.12  Transfer expenditure of rural household families and its composition

Transfer expenditure (yuan) Various items (%) Spring festival, mid-autumn festival, other holiday expenses Wedding and funeral expenses Medical care Education Life Other expenses Donation or aid Various objects (%) Parents Parents-in-law Other relatives/non relatives

Whole country

Eastern region

Central region

Western region

2889

2461

3487

2727

22.7

29.0

18.9

21.3

71.4 1.1 2.0 2.3 0.3 0.2

64.4 0.5 1.8 3.9 0.3 0.2

75.6 1.5 2.0 1.8 0.1 0.0

73.0 1.1 2.4 1.1 0.5 0.6

5.6 3.1 91.3

8.0 4.2 87.8

4.1 2.5 93.4

5.6 3.1 91.3

5.5.3  Transfer Expenditure As Table 5.12 demonstrates, according to the expenditure items, wedding and funeral expenses account for the highest proportion of transfer expenditure. The proportion in the country is 71.4%. Across the country, the eastern region has the lowest proportion of 64.4%, and the central region has the highest proportion of 75.6%.

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The Spring Festival, Mid-Autumn Festival and other holiday expenses account for the second largest proportion. In the whole country, the Spring Festival, Mid-Autumn Festival expenditure account for 22.7% of the transfer expenditure. The proportion in the eastern region is the highest, which is 29.0%, and the central region has the lowest proportion of 18.9%. Grouped by the recipients of expenditure, the proportion of transfer payments to parents in the total transfer expenditure is about 5.6% nationwide, and the proportion of transfer payments to parents-in-law is about 3.1%. The proportion of transfer payments to other relatives/nonrelatives in the central and western regions is significantly higher than that in the eastern region. 5.5.4  Changes of Expenditure

35,000.0 34,000.0 33,000.0 32,000.0 31,000.0 30,000.0 29,000.0 28,000.0 27,000.0 26,000.0 25,000.0

2011

2013

2015

Year

non-productive expenditure transfer expenditure agricultural production expenditure total expenditure

Fig. 5.5  Average total expenditure per household and its composition

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

Percentage %

Expenditure yuan

Figure 5.5 depicts the total expenditure per household (corresponding to the left axis) and its composition (corresponding to the right axis) of rural households in 2011, 2013 and 2015. The total expenditure per household increased year by year, from 25,433 yuan in 2011 to 33,472 yuan in 2015. From the perspective of the different types of expenditure, although

29,000 28,000 27,000 26,000 25,000 24,000 23,000 22,000 21,000 20,000 19,000

133

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

Percentage %

Expenditure yuan

5  INCOME AND EXPENDITURE OF RURAL HOUSEHOLDS 

2011

2013 Year

2015

food expenditure living expenditure

cloth expenditure Daily necessities and durable goods expenses

Health care expenditure Education and entertain expense

Transport and communication expenses Other expenditure

Non-productive expenditure

Fig. 5.6  Average nonproduction expenses and its composition

nonproduction expenses accounted for the largest proportion constantly, its proportion at first rose and then dropped. In contrast, the share of agricultural production expenditure dropped in the beginning and climbed later. Overall, the proportion of transfer expenditure has been decreasing year by year. Figure 5.6 further compares the nonproduction expenses per rural household (corresponding to the left axis) and its composition ­(corresponding to the right axis) in 2011, 2013 and 2015. The results show that the nonproduction expenses per household rose significantly from 19,682 yuan in 2011 to 27,549 yuan in 2015. Among all types of nonproduction expenses, food expenditure accounted for the largest part, but the tendency first rose and then fell. The proportion of living expenses was the second largest, but was generally declining. The proportions of health care as well as transportation and communication spending first dropped and then rose, while the proportion of education and entertainment spending showed a significant decline. Overall, the share of nonproduction expenses shifted to food, health care, as well as transportation and communication spending.

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5.6   Expenditure of Different Groups of People and Its Composition 5.6.1  Salary

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

Low

Below average

Middle Above average Income group

food expenditure living expenditure Health care expenditure Education and entertain expense

High

37,000.0 35,000.0 33,000.0 31,000.0 29,000.0 27,000.0 25,000.0 23,000.0 21,000.0 19,000.0

Expenditure yuan

Percentage

Figure 5.7 divides households into low-income groups, below-average-­ income groups, middle-income groups, above-average-income groups and high-income groups, based on the level of total income, and compares the nonproduction expenses per household (corresponding to right axis) and the composition (corresponding to the left axis). The results show that nonproduction expenses per household generally increased with the rise of income level, from 19,531 yuan of the low-income group to 34,837 yuan of the high-income group. In terms of subitems, shares of transportation expenditure, food expenditure, daily necessities and durable goods expenditure fell with the income level increasing. The proportions of health care expenditure, clothing expenditure, education and entertainment expenditure went up as the level of income increased. Living expenditure accounts for a slightly larger part in the middle-income group.

cloth expenditure Daily necessities and durable goods expenses Transport and communication expenses Other expenditure

Non-productive expenditure

Fig. 5.7  Average nonproduction expenses of different income groups and its composition

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135

5.6.2  Age

36,000 34,000 32,000 30,000 28,000 26,000 24,000 22,000 20,000

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

percentage

Expenditure yuan

Figure 5.8 divides households based on the age of the household head and shows the nonproduction expenses per household (corresponding to the left axis) and the composition (corresponding to the right axis) of each age group. The results indicate that the amount of nonproduction expenses shows an inverted U-shaped tendency with the increase of the age of householder. The nonproduction expenses of families with a 35–44-year-­ old householder is the largest, which is 34,773 yuan, and the expenditures are 22,390 yuan and 22,705 yuan, respectively, of families whose householder is aged 15–24, and 65 and above. In terms of subitems, with the householder’s age increasing, food expenditure always accounts for the largest, but the trend has increased slightly. The share of clothing expenditure has significantly reduced. The proportion of living expenses in the 25–34-year-old householder families significantly increased, and then slightly dropped. Health care as well as education and entertainment expenditure account for significantly higher parts in the 35–44 and 45–54-year-old householders’ families, but account for a lower proportion in families whose householder is younger or older, while the proportion of transportation and communication expenditure is high in those families.

16-24Y

25-34Y

35-44Y 45-54Y Age group

food expenditure living expenditure Health care expenditure Education and entertain expense Non-productive expenditure

55-64Y 65Y and above

cloth expenditure Daily necessities and durable goods expenses Transport and communication expenses Other expenditure

Fig. 5.8  Different age groups’ nonproductive household expenditure and its composition (yuan, %)

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5.6.3  Experience as Migrant Workers Table 5.13 divides households based on the experience as migrant workers of the household head and compares the household expenditure and its composition of peasant families, peasant families with the experience as migrant workers, and migrant workers’ families. The results show that the total expenditure of migrant workers’ families is the highest, followed by peasant families with the experience as migrant workers and then peasant families. The same applies to nonproduction expenses and transfer expenditure, while agricultural production expenditure is the largest in peasant families and the smallest in migrant workers’ families. Further study of the nonproduction expenses structure of the three kinds of families found that the proportions of food expenditure and transportation expenditure are lower in migrant workers’ families than those of the other two types of families, while the proportions of clothing expenditure, living expenses, daily necessities and durable goods expenditure, medical health expenditure are higher. The proportion of educational and entertainment expenditure in peasant families with the experience as migrant workers and migrant workers’ families is obviously higher than that of peasant families. Transportation and communication expenditure accounts for a higher proportion in peasant families than the other two families. Table 5.13  Average expenditure and its composition of families with different migrant working experience

Total expenditure (yuan) Nonproduction expenses (yuan) Food expenditure (%) Cloth expenditure (%) Living expense (%) Daily necessities and durable goods expenditure (%) Health care expenditure (%) Transportation and communication expenses (%) Education and entertainment (%) Other expenses (%) Transfer expenditure Agriculture production expenditure

Farmers

Farmers with migrant working experience

Migrant workers

33,472 27,549 43.3 4.5 12.7 5.4

39,847 33,398 42.0 4.7 14.0 5.2

51,183 47,239 39.8 5.4 17.7 6.5

11.6 14.3

11.2 12.9

13.5 7.0

8.2 0.0 2152 3771

9.9 0.1 2858 3591

9.8 0.3 3196 748

CHAPTER 6

Fortune of Rural Households

This chapter describes the current status of rural households’ fortune on account of three aspects, including the asset, liability and net worth of rural households. This analysis is made up of the databases of the China Rural Household Panel Survey (CRHPS) and China Household Finance Survey (CHFS). Statistics show that the assets of rural households are mainly made up of nonfinancial assets, the proportion of which is 89.2%, and that of financial assets is 10.8%. Property and land are the two primary components of rural households’ nonfinancial assets, which share a proportion of 80.6% in the total nonfinancial assets, and the financial assets of rural households are mainly savings (both fixed deposit and demand deposit, accounting for 45.9% of the total financial assets) and the balance of social security account (accounting for 12.5%). Secondly, rural households have a considerably high house-owning rate which is 94.4%, and there is no significant regional difference. In the eastern region, this rate is 94.7%; in the central region, this rate is 95.3%; and in the western region, this rate is 92.9%. Thirdly, the net worth of rural households shows a distinctive difference among these three regions, as the national average net worth is 2,90,000 RMB, the eastern average is 3,98,000 RMB, the central average is 2,39,000 RMB, and the western average is 2,18,000 RMB.

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6.1   Rural Family Assets Family assets have two components: financial assets and nonfinancial assets. Family nonfinancial assets include the operating assets of agricultural production, operating assets of industrial and commercial production, land and property, vehicle, durable goods, et cetera. Family financial assets include cash, fixed deposit, demand deposit, social security account balance, stock, bond, managed fund, derivative instrument, wealth management product, non-RMB assets, gold, lending, et cetera. 6.1.1   Introduction of Family Assets in Rural Areas Up until 2015, the average number of rural households’ family assets was 3,13,000 RMB and the median was 1,26,000 RMB. Table 6.1 displays the regional differences and distribution of family assets in rural areas. It reflects that the average number of rural households’ family assets in the eastern region is 4,26,000 RMB, and the median is 1,51,000 RMB; the average in the central regions is 2,57,000 RMB, and the median is 1,21,000 RMB; the average in the western regions is 2,39,000 RMB, and the median is 1,13,000 RMB. Clearly, the numbers of the eastern region are much higher than the others, and the last position in the statistics is taken by the western region. 6.1.2   Structure of Family Assets in Rural Areas 6.1.2.1 Financial Assets and Nonfinancial Assets Table 6.2 reflects the asset structure of rural households. The average asset of rural households is 3,13,000 RMB, of which 34,000 RMB are financial assets, while 2,79,000 RMB are nonfinancial assets. On average, the Table 6.1 Regional differences in family asset of rural households (unit: 10,000 RMB)

Table 6.2  Financial assets and nonfinancial assets of rural households (unit: 10,000 RMB)

Region Nationwide East Central West

Average

Median

31.3 42.6 25.7 23.9

12.6 15.1 12.1 11.3

Composition of assets

Average

Median

Nonfinancial assets Financial assets Total

27.9 3.4 31.3

11.0 0.5 12.6

6  FORTUNE OF RURAL HOUSEHOLDS 

100.0

89.2

90.0

90.2

90.1

88.2

139

Proportion/%

80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0

10.8 Total

11.8

9.9

East

9.8

Central

West

Region Financial Asset

Non-financial Asset

Fig. 6.1  Structure of family assets in different regions (unit: %)

­ roportion of financial assets in the whole family assets is 10.8%, while the p proposition of nonfinancial assets is 89.2%. This demonstrates that most of the rural household assets are nonfinancial. Figure 6.1 compares the structure of family assets among different regions. In the eastern region of China, financial assets account for 11.8% and nonfinancial assets for 88.2%. In the central region, 9.9% of the entire assets in rural households are financial assets, and 90.1% are nonfinancial. In the western region, the proportion of financial assets is 9.8% and nonfinancial assets 90.2%. In conclusion, the ratio of financial assets in rural households has declined from east to west. 6.1.2.2 Nonfinancial Assets Nonfinancial assets in this chapter refer to seven items, including the agricultural production machines, the operating assets of industrial and commercial production, land, property, vehicle and durable goods. Figure 6.2 shows that agricultural assets have a proportion of 2.5%, operating assets of industrial and commercial production 8.9%, land 14.4%, property 66.2%, vehicle 4.6% and durable goods 3.4%. In all, property and land, which share a proportion of 80.6% of the total of nonfinancial assets, are of representative importance in the nonfinancial assets of rural households.

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Vehicle 4.6%

AgriDurable cultural Industrial and Goods Asset Commercial 2.5% 3.4% Asset 8.9%

Land 14.4%

Property 66.2% Fig. 6.2  Nonfinancial assets of rural households

6.1.2.3 Financial Assets The financial assets of a household contain cash, fixed deposit, demand deposit, social security account balance, stock, bond, managed fund, derivative instrument, wealth management product, non-RMB assets, gold, lending, et cetera. Figure 6.3 shows the proportions of each type of financial assets. In rural areas, savings (both fixed deposit and demand deposit) account for 54.8% of all financial assets, cash for 12.7%, lending for 13.3% and the balance of social security account for 14.9%. It can be concluded that the main part of financial assets in rural households are savings and the balance of social security account. Based on the risk attributes of assets, financial assets can be divided into two categories: risk assets and risk-free assets. Risk-free assets contain cash, fixed deposit, demand deposit, state treasury bond, local government bond, stock account balance, social security account balance, et cetera. Risk assets contain stock, managed fund, financial bond, corporate bond, derivative instrument, wealth management product, non-RMB assets, gold, lending, et cetera. In Table  6.3, only 8.2% of rural households’ financial assets are risk assets, and 91.8% are risk-free assets, showing that rural households clearly prefer risk-free assets.

6  FORTUNE OF RURAL HOUSEHOLDS 

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Other Financial Assets 4.3% Lendings 13.3%

Balance of Social Security Account 14.9%

Savings 54.8%

Cash 12.7% Fig. 6.3  Financial assets of rural households Table 6.3  Proportions of risk-free assets and risk assets of rural households (unit: %) Region Total

Proportion of risk assets

Proportion of risk-free assets

8.2

91.8

6.1.3   Rural Household Property and Land Value 6.1.3.1 Property Figure 6.4 compares the property-owning rate of rural households in different regions. The overall property-owning rate of rural households in China is 94.4%, and there is no distinctive difference among different regions. In the eastern, central and western regions, the rates are 94.7%, 95.3% and 92.9%, respectively. Table 6.4 reflects the value of rural households’ property in China. As for the whole country, the average number is 2,11,000 RMB, and the median number is 1,00,000. As for each region, the average number in the eastern regions is 3,25,000 RMB and the median number is 1,00,000 RMB; the average in central region is 1,66,000 RMB and the median is 80,000 RMB; and the average number in the western region is 1,33,000 RMB, the median is 75,000 RMB. It is fair to say that the value of property of eastern rural households are far above that of central and western China.

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95.3

Property owning rate/%

95.5 95.0 94.5

94.4

94.7

94.0 93.5

92.9

93.0 92.5 92.0 91.5

Total

East

Central

West

Region Fig. 6.4  Regional differences of rural households’ property-owning rate (unit: %) Table 6.4 Property value of rural households (unit: 10,000 RMB)

Region Nationwide East Central West

Average

Median

21.1 32.5 16.6 13.3

10.0 10.0 8.0 7.5

Figure 6.5 compares the property ratio of rural households with housing among different regions. The nationwide ratio is 51.8%. As for the different regions, the eastern region enjoys the highest rate of 53.6%, followed by the central region with 52.9%, and then the western region with the lowest rate of 48.3%. 6.1.3.2 Land Figure 6.6 compares the ratio of rural land contractual management right owned by rural households in different regions. This rate is 87.8% nationwide, 83.4% in the east, 91.7% in the central region and 88.6% in the western region. Table 6.5 shows that the rural land assessment value in China is 79,000 RMB and the median is 10,000 RMB. The average in the eastern region is 76,000 RMB and the median is 10,000 RMB; the average in the central region is 53,000 RMB and the median is 10,000 RMB; and the average in the western region is 11,200 RMB, and the median is 20,000 RMB. The fact that rural households in the western region enjoy a higher land value is attributed to them owning a larger area of agricultural land on average.

53.6

54.0 53.0

Asset ratio/%

52.0

52.9

51.8

51.0 50.0 49.0

48.3

48.0 47.0 46.0 45.0

Nationwide

East

Central

West

Region Fig. 6.5  Asset ratio of property in rural household’s family asset (property owned) (unit: %) 94.0 91.7

92.0

Ratio/%

90.0 88.0

88.6

87.8

86.0 83.4

84.0 82.0 80.0 78.0

Nationwide

East

Central

West

Region Fig. 6.6  Regional differences of rural households’ landowning rate (unit: %) Table 6.5  Land value of rural households (unit: 10,000 RMB)

Region Nationwide East Central West

Average

Median

7.9 7.6 5.3 11.2

1.0 1.0 1.0 2.0

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18.0

15.5

16.0 14.0 Ratio/%

12.0

12.4

11.6

10.0

7.6

8.0 6.0 4.0 2.0 0.0

Nationwide

East

Central

West

Region

Fig. 6.7  Asset ratio of land in rural household’s family asset (land owned) (unit: %)

Figure 6.7 compares the share of land value over assets of rural households with land among different regions. As for the whole country, land accounts for 11.6% of the total family assets. As for the different regions, the share of land assets of rural households in the western region is the highest with 15.5%, followed by the central region with 12.4%.

6.2   Liability of Rural Households Liabilities of rural households include agricultural, industrial and commercial loans, the loans of housing, vehicle, financial investment, credit card and education, as well as other loans. 6.2.1   Introduction of Liability in Rural Households Table 6.6 presents the regional differences in the liabilities of rural households. The average liability of China’s rural households is 70,000 RMB. In the eastern region, the average liability is 1,08,000 RMB, and the median number is 30,000 RMB.  In the central region, this average is 54,000 RMB, and the median is 30,000 RMB. In the western region, the average is 56,000 RMB, and the median is 20,000 RMB. Rural households in the eastern region have higher liabilities than those of the central and western regions, while the central region is the lowest.

6  FORTUNE OF RURAL HOUSEHOLDS 

Ratio/%

Table 6.6  Regional differences of rural households’ liabilities (unit: 10,000 RMB)

40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0

Region

145

Average

Median

7.0 10.8 5.4 5.6

3.0 3.0 3.0 2.0

Nationwide East Central West

33.7

24.5 18.7 8.7 4.0

8.4 1.1

0.7

0.1

Composition of liabilities Fig. 6.8  Liability structure of rural households (unit: %)

6.2.2   Liability Structure of Rural Households Figure 6.8 reflects the composition of liabilities in rural households, including housing liabilities, operating liabilities, education liabilities, medical liabilities and other liabilities. Housing liabilities account for 33.7%, operating liabilities account for 24.5%, medical liabilities account for 18.7%, education liabilities account for 8.7% and other liabilities account for 8.4%.

6.3   Net Worth of Rural Households Family net worth refers to the difference (mathematical) between family assets and family liabilities. Table 6.7 describes the regional differences in the family net worth of rural households. From the table, it can be seen that the overall average

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Table 6.7 Regional differences of rural households’ net worth (unit: 10,000 RMB)

Region Nationwide East Central West

Average

Median

29.0 39.8 23.9 21.8

11.7 13.6 11.3 10.2

net worth of China’s rural households is 2,90,000 RMB. In the eastern region, this average is 3,98,000 RMB, and the median is 1,36,000 RMB. In the central region, this average is 2,39,000 RMB, and the median is 1,13,000 RMB. In the western region, this average is 2,18,000 RMB, and the median is 1,02,000 RMB. Clearly, the rural households’ family net worth in the eastern region is higher than that in the central and western regions.

PART III

Public Service of Rural Households

CHAPTER 7

Social Security of Rural Households

This chapter analyzes rural households’ engagement in the social security system based on the data from the 2015 China Rural Household Panel Survey (CRHPS) by Zhejiang University. It is found that the pension security of rural residents is still far from satisfying. There are 33.3% of rural residents without any pension scheme. The proportion of medical insurance engagement is relatively high (91.7%), of which 92.89% of rural residents participate in the New Rural Cooperative Medical Insurance, while only 48.5% of those rural residents have private health care accounts, with an average account balance of 397.4 yuan. The engagement of the social pool for major diseases treatment is quite low, accounting for only 0.3%. The figure of rural residents participating in commercial insurance is also low. Of the rural residents, 96.3% do not participate in any commercial insurance. Commercial life insurance coverage takes up 1.8%, health insurance coverage 0.9%, while coverage of commercial insurance of other kinds amounts to 1.0%.

7.1   Endowment Insurance 7.1.1  Endowment Insurance Penetration Table 7.1 shows the coverage of pension security for rural residents in China. Across the nation, there are 33.3% of rural residents without any kind of pension scheme. Of the rural residents, 64.8% are engaged in the form of social endowment insurance, while only 1.4% in the form of © The Author(s) 2019 W. Qian et al., Societal Development in Rural China, https://doi.org/10.1007/978-981-13-8082-2_7

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Table 7.1  Distribution of different pension schemes for rural residents (unit: %) Pension scheme Without any pension scheme Engaged in pension scheme Social insurance Retirement pay Other kinds

National 33.3 66.2 64.8 1.4 0.5

Eastern region Central region 32.8 66.2 64.6 1.6 1.0

30.4 69.1 67.7 1.4 0.5

Western region 37.8 61.8 60.7 1.1 0.4

Table 7.2  Distribution of different social endowment insurance for rural residents (unit: %) Different kinds of social endowment insurance Urban Employees’ Basic Endowment Insurance New Rural Social Endowment Insurance Urban Residents’ Social Endowment Insurance Urban and Rural Residents’ Social Endowment Insurance

National

Eastern region Central region

Western region

5.4

8.4

2.6

5.8

90.7

87.7

94.4

89.2

1.7

1.6

1.2

2.8

2.2

2.3

1.8

2.2

retirement-­pay pension. At the regional level, the western region has witnessed the highest proportion of no endowment engagement, accounting for 37.8%, while this figure for the central region is 30.4%, ranking the lowest among different regions. Across the nation, the proportion of rural residents engaged in retirement-pay pension is quite low, averaging at 1.4%, with the eastern region being the highest (1.6%), followed by the central region (1.4%) and the western region (1.1%). It is safe to conclude that the coverage of pension scheme in rural areas needs further improvement. Table 7.2 shows a detailed picture of the distribution of different kinds of social endowment insurance, among which the New Rural Social Endowment Insurance has witnessed the highest proportion of 90.7% on a national scale, followed by the Urban Employees’ Basic Endowment Insurance as well as the Urban and Rural Residents’ Social Endowment Insurance, while that figure of Urban Residents’ Social Endowment Insurance ranks the lowest. In terms of the New Rural Social Endowment Insurance, the central region enjoys the highest coverage of 94.4%, while the eastern region witnesses the lowest coverage of 87.7%.

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7.1.2   Endowment Insurance Reception Proportion Table 7.3 illustrates the proportion of rural residents aged 60 and above who have started receiving pension contributions. Among people aged 60 and above who are engaged in endowment insurance, 87.7% of females have started to receive pension benefits, while the figure for males is 86.2%. In terms of gender, the proportion of females starting to receive pension benefits is generally higher than that of males, with the only exception being in the central region, where the figure of females is lower than that of males. 7.1.3  Contributions to Social Endowment Insurance Versus Individual Income Table 7.4 suggests the comparison between rural residents’ personal contributions to social endowment insurance and their individual income. At the national level, the average personal contribution is 950.4 yuan per year, while the average individual income is 3297.1 yuan per year. At the regional level, contributions in the eastern region amount to 1490.4 yuan per year, the highest nationwide, followed by that figure for the western region (966 yuan per year), while the figure for the central region is the lowest (484.8 yuan per year). Individual income witnesses the same tendency as the personal contributions, with the eastern region ranking the Table 7.3  Proportion distribution of rural residents aged 60 and above who have started receiving pension benefits (unit: %) Whether pension has been received Yes No

National

Eastern region

Central region

Western region

Male

Female

Male

Female

Male

Female

Male

Female

86.2 13.8

87.7 12.3

84.2 15.8

89.0 11.0

87.4 12.6

85.7 14.3

87.0 13.0

89.0 11.0

Table 7.4  Comparison between rural residents’ personal contribution to social endowment insurance and individual income Items for comparison Contribution (yuan/year) Income (yuan/year)

National

Eastern region

Central region

Western region

950.4 3297.1

1490.4 4139.8

484.8 2191.7

966.0 3657.8

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first (4139.8 yuan per year), followed by that figure for the western region (3657.8 yuan per year). The central region is on the lowest individual income level (2191.7 yuan per year). 7.1.4  Account Balance of Social Endowment Insurance Table 7.5 shows rural residents’ personal account balance of social endowment insurances of main kinds. In terms of the average balance, the gap between various social endowment insurances for rural residents is relatively huge. Across the nation, the average account balance of the Urban Employees’ Basic Endowment Insurance is 14,746.2 yuan, while that figure for the New Rural Social Endowment Insurance is 1228.8 yuan. At the regional level, among the four major social endowment insurances, the account balance of rural residents’ social endowment insurances in the eastern region is generally higher than that of the central and western regions. Judging by the figures for 2013 and 2015, the personal account balance of the New Rural Social Endowment Insurance, Urban residents’ Social Endowment Insurance and Urban and Rural Residents’ Social Endowment Insurance has increased remarkably. On the contrary, the personal account balance of the Urban Employees’ Basic Endowment Insurance has been on the decrease. What is more, that figure for the Urban and Rural Residents’ Social Endowment Insurance in the eastern region has also dropped from 7372.3 yuan in 2013 to 4652.6 yuan in 2015, by 36.9%. Table 7.5  Rural residents’ personal account balance of different kinds of social endowment insurances (unit: yuan) Region

Year

Urban Employees’ Basic Endowment Insurance

New Rural Social Endowment Insurance

National

2013 2015 2013 2015 2013 2015 2013 2015

14,914.4 14,746.2 16,148.4 15,810.0 15,134.4 12,782.7 11,439.1 14,232.5

557.5 1228.8 610.2 1862.9 224.3 759.3 908.7 1245.3

Eastern region Central region Western region

Urban Residents’ Urban and Rural Social Residents’ Social Endowment Endowment Insurance Insurance 2504.7 8192.7 2071.8 11625.5 2459.7 2661.1 2736.0 9587.7

2773.9 3711.7 7372.3 4652.6 219.6 3473.3 61.3 2978.7

7  SOCIAL SECURITY OF RURAL HOUSEHOLDS 

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Table 7.6  Gender difference of rural residents in account balance of social endowment insurance (unit: yuan) Region

Sex

National

Male Female Male Female Male Female Male Female

Eastern region Central region Western region

Urban New Rural Urban Urban and Social Employees’ Social Residents’ Rural Residents’ endowment Basic Endowment Social Social insurances Endowment Insurance Endowment Endowment of other Insurance Insurance Insurance kinds 14,328.7 15,414.5 16,153.5 15,187.1 13,700.4 10,712.5 11,946.5 17,782.7

1353.1 1101.8 2057.3 1669.3 797.1 721.0 1429.6 1050.6

6996.2 9272.4 11,091.3 11,970.0 3044.5 2106.4 8141.0 10,769.0

3554.0 3877.0 4321.1 4999.0 3642.9 3308.1 2724.6 3265.0

8467.4 9014.7 5108.9 3976.1 16,750.4 18,709.9 2687.9 7460.5

Table 7.7  Situation of pension scheme integration in working units for rural residents (unit: %) Having implemented or not Yes No

National

Eastern region

Central region

Western region

23.5 76.5

19.8 80.2

12.2 87.8

43.7 56.3

There is still a certain gap between the endowment insurance account balance of males and females. As is shown in Table  7.6, at the national level, the figures for females in three different insurances, namely the Urban Employees’ Basic Endowment Insurance, Urban Residents’ Social Endowment Insurance and Urban and Rural Residents’ Social Endowment Insurance, are all higher than those for males. However, things vary in different regions. 7.1.5   Implementation of Unifying the Two Separate Pension Systems Table 7.7 suggests the implementation of unifying the two separate pension systems in the workplace of rural residents. Pension scheme integration refers to combining the former Urban Residents’ Endowment Insurance and New Rural Endowment Insurance into the Urban and

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Rural Residents’ Social Endowment Insurance. As branches of social endowment insurances, neither the previous Urban Residents’ Endowment Insurance nor the subsequent Urban and Rural Residents’ Social Endowment Insurance includes other types of insurance. As is shown in Table 7.7, at the national level there are 23.5% working units on average that have implemented pension scheme integration. At the regional level, the implementation in the western region works out the best, which amounts to 43.7%, while that figure for the central region is only 12.2%, the worst among different regions.

7.2   Medical Insurance 7.2.1  Coverage of Social Medical Insurance Table 7.8 indicates the coverage of rural residents’ health insurance, from which we can see that the average coverage of rural residents’ health insurance nationwide reaches 91.7%. At the regional level, the central region has witnessed the widest coverage of 93.9%, while the eastern region has seen the lowest coverage of 89.8%. The figure for the western region is slightly higher than that of the eastern region, which amounts to 91.2%. Table 7.9 analyzes the coverage of social medical insurance for rural residents of different age groups. Across the whole nation, there are 88.6% rural residents aging more than 50 who have basic medical insurance, the figures of which for the eastern region, central region and western region are 86.6%, 90.5% and 88.3%, respectively. At the national level, there are 91.7% rural residents aging from 31 to 50 who have basic medical insurance, with 89.8% in the eastern region, 93.9% in the central region and 91.2% in the western region. Meanwhile, there are 93.5% rural residents aging 30 and below who have basic medical insurance nationwide, with 91% in the eastern region, 96.2% in the central region and 93.4% in the western region. Table 7.8  Coverage of rural residents’ medical insurance (unit: %) Coverage of medical insurance Yes No

National 91.7 8.3

Eastern region Central region 89.8 10.2

93.9 6.1

Western region 91.2 8.8

Yes No

Medical insurance ratio

91.7 8.3

88.6 11.4

91.0 9.0

89.8 10.2

86.6 13.4

≤30 years 31–50 years >50 years

≤30 years 31–50 years >50 years

93.5 6.5

Eastern region

National

96.2 3.8

93.9 6.1

90.5 9.5

≤30 years 31–50 years >50 years

Central region

Table 7.9  Medical insurance coverage of rural residents of different age groups (unit: %)

93.4 6.6

91.2 8.8

88.3 11.7

≤30 years 31–50 years >50 years

Western region

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Table 7.10  Gender difference in rural residents’ coverage of medical insurance (unit: %) Whether having medical insurance Yes No

National

Eastern region

Central region

Western region

Male

Female

Male

Female

Male

Female

Male

Female

91.7 8.3

91.8 8.2

89.8 10.2

90.0 10.0

93.6 6.4

94.1 5.9

91.3 8.7

91.2 8.8

Table 7.11  Types of medical insurance engaged by rural residents (unit: %) Types of medical insurance Urban Employees’ Basic Medical Insurance Urban Residents’ Basic Medical Insurance New Rural Cooperative Medical Insurance Urban and Rural Residents’ Basic Medical Insurance Medical Services at State Expense Commercial Medical Insurance at Working Unit’s Expense Commercial Medical Insurance at Personal Expense Supplementary Medical Insurance by Employers Social Pool for Major Disease Treatment Social Mutual Aid Other types

National Eastern Central Western region region region 2.70 1.44 92.89 1.16 0.16 0.36

4.18 1.43 90.10 1.92 0.24 0.60

1.15 1.05 95.87 0.63 0.16 0.14

3.02 1.92 92.15 0.99 0.09 0.37

0.39 0.02 0.35 0.04 0.49

0.60 0.07 0.24 0.03 0.58

0.34 0.01 0.31 0.01 0.33

0.23 0.01 0.53 0.09 0.60

Table 7.10 analyzes the gender difference in the coverage of rural residents’ medical insurance. At the national level, there is little difference in the coverage of medical insurance between males and females in rural areas. At the regional level, the figures for males and females in the central region are the highest among different regions, reaching 93.6% and 94.1%, respectively. From the gender perspective, female’s coverage of medical insurance is slightly higher than that of male, with the exception in the western region, where the figure for male is above that of female by 0.1%. Table 7.11 gives a picture of rural residents’ engagement in different types of medical insurance, from which we can see that among rural residents engaged in medical insurance nationwide, there are 92.89% of them

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Table 7.12  Premium of different kinds of medical insurance (unit: yuan per year) Types of medical insurance Urban Employees’ Basic Medical Insurance Urban Residents’ Basic Medical Insurance New Rural Cooperative Medical Insurance Urban and Rural Residents’ Basic Medical Insurance Commercial Medical Insurance at Working Unit’s Expense Commercial Medical Insurance at Personal Expense Supplementary Medical Insurance by Employers Social Pool for Major Disease Treatment

National Eastern Central Western region region region 1263.5 313.8 2669.5 135.7 329.2

1706.2 852.4 696.9 109.4 127.4 6722.5 122.0 92.0 477.7 109.6

796.7 145.7 95.0 195.9 181.3

1551.0 183.3 319.5

2015.1 1115.0 1047.5 234.3 113.3 0.0 621.8 414.4 117.1

who have been engaged in the New Rural Cooperative Medical Insurance. At the regional level, the central region has witnessed the highest coverage of the New Rural Cooperative Medical Insurance, which amounts to 95.87%, followed by the western region (92.15%). The eastern region has seen the lowest coverage of only 90.1%. 7.2.2  Premium of Medical Insurance Table 7.12 shows the premium contribution of rural residents to different types of medical insurance. In terms of the New Rural Cooperative Medical Insurance, which is engaged by most rural residents, the average premium contribution at the national level is 2669.5 yuan. However, there is a huge gap among different regions, with 127.4 yuan in the eastern region, 6722.5 yuan in the central region and 95.0 yuan in the western region. The average premium for Social Pool for Major Disease Treatment amounts to 319.5 yuan, with 621.8 yuan in the eastern region, 414.4 yuan in the central region and 117.1 yuan in the western region, respectively. 7.2.3  Personal Account of Social Medical Insurance As is shown in Table 7.13, at the national level, there are 48.5% rural residents who have personal accounts of medical insurance, while 51.5% rural residents do not have personal accounts of medical insurance. The central region has witnessed the highest penetration of personal medical insurance accounts among rural residents, reaching 53.9%, which is followed by the eastern region (46.5%), while the western region has seen the lowest penetration of 43.6%.

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Table 7.13  Penetration of personal medical insurance accounts among rural residents (unit: %) Having personal account or not Yes No

National Eastern region Central region Western region 48.5 51.5

46.5 53.5

53.9 46.1

43.6 56.4

Table 7.14  Average balance of medical insurance accounts among rural residents who have an account (unit: yuan) Region National Eastern region Central region Western region

Average

Median

397.4 603.7 340.5 290.7

0.0 0.0 0.0 0.0

Table 7.15  Balance of medical insurance account among rural residents whose account balance is not zero (unit: yuan) Region

Average

Median

National Eastern region Central region Western region

1306.9 2298.3 1160.1 797.8

300.0 500.0 300.0 200.0

Table 7.14 illustrates the average account balance of rural residents with medical insurance accounts. It is found that the nationwide average account balance of rural residents is 397.4 yuan. The eastern region has witnessed the highest balance of 603.7 yuan, much higher than the average level nationwide. The figure for the central region ranks the second, amounting to 340.5 yuan, while the western region has seen the lowest figure of 290.7 yuan. Table 7.15 shows the account balance among rural residents whose balance is not zero. It can be seen that the average balance of medical insurance accounts among rural residents (ruling out those whose account balance is zero) is 1306.9 yuan. The eastern region has seen the highest of 2298.3 yuan, far higher than the average level nationwide. The central region follows behind, with 1160.1 yuan on average, while the western region has witnessed the lowest of 797.8 yuan.

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7.2.4  Social Pool for Major Disease Treatment Table 7.16 demonstrates some facts of the Social Pool for Major Disease Treatment among rural residents, from which we can see the current situation is far from satisfying. At the national level, there are only 0.3% rural residents on average who have been engaged in the Social Pool for Major Disease Treatment, with the highest proportion in the western region of only 0.5%. The central region follows behind with a proportion of 0.3%, while the eastern region has the lowest figure of 0.2% only. 7.2.5  Medical Insurance and Medical Expenses Table 7.17 shows the medical expenses of rural residents who have been engaged in medical insurance. For rural residents who have been in hospital in 2015, the average hospitalization expenses amount to 12256.1 yuan. That figure for the eastern region ranks the highest, reaching 15824.8 yuan, followed by the central region of 12031.6 yuan. The western region has seen the lowest medical expenses of 10302.6 yuan. At the national level, the average expense coverage by medical insurance is equivalent to 4652.5 yuan, with the highest in the eastern region (6315.3 yuan), followed by the western region of 4330 yuan and the lowest in the central region (4021.1 yuan). Table 7.16  Social pool for major disease treatment engagement among rural residents Engagement situation Proportion of engagement (%)

National 0.3

Eastern region Central region Western region 0.2

0.3

0.5

Table 7.17  Medical expenses of rural residents who have been engaged in medical insurance (unit: yuan per year) Medical expenses

National

Eastern region

Central region

Western region

Hospitalization expenses (yuan per year) Medical insurance coverage (yuan per year) Proportion of medical insurance coverage (%)

12256.1 15824.8 12031.6 10302.6 4652.5 6315.3 4021.1 4330 37.96 39.91 33.42 42.03

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Table 7.18  Coverage of unemployment, maternity and work-related injury insurances (unit: %) Coverage of three insurances Coverage of unemployment insurance Coverage of work-related injury insurance Coverage of maternity insurance

National

Eastern region

Central region

Western region

7.6 9.2 6.1

11.2 12.5 8.7

4.4 5.9 3.2

6.6 8.5 6.0

7.3   Unemployment Insurance, Maternity Insurance and Work-Related Injury Insurance Table 7.18 shows the coverage of rural residents’ unemployment insurance, maternity insurance and work-related injury insurance. At the national level, there are 7.6% rural residents aged 16 years and above who have unemployment insurance, while the figures for the eastern region, central region and western region stay at 11.2%, 4.4% and 6.6%, respectively. Meanwhile, across the nation, there are 9.2% rural residents aged 16  years and above who enjoy work-related injury insurance, while the figures for the eastern region, central region and western region rest at 12.5%, 5.9% and 8.5%, respectively. What is more, at the national level, 6.1% rural residents aged 16  years and above have maternity insurance, with 8.7% in the eastern region, 3.2% in the central region and 6.0% in the western region. In general, coverage of these three insurances among rural residents is quite limited. The eastern region has seen a higher coverage than other regions in these respects.

7.4   Public Housing Fund As is shown in Table 7.19, the proportion of rural residents who enjoy public housing fund is only 1.9%, with 2.7% in the eastern region, 1.2% in the central region and 1.9% in the western region. Among those who enjoy public housing fund, about 93.7% rural residents are still ­contributing to public housing fund. The average housing fund contribution in 2014 stays at 428.7 yuan per month, with 566.2 yuan per month in the eastern region, 305.5 yuan per month in the central region

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Table 7.19  Provident fund engagement of rural residents Engagement of provident fund Proportion of those who enjoy provident fund (%) Proportion of those who are still contributing (%) Cumulative time of contribution (month) Contribution to provident fund in 2014 (yuan per month) Account balance of provident fund (yuan) Proportion of those who make use of provident fund in 2014 (%) Withdrawal of provident fund in 2014 (yuan)

National

Eastern region

Central region

Western region

1.9

2.7

1.2

1.9

93.7

92.7

93.2

95.6

102.0

72.1

67.4

166.3

428.7

566.2

305.5

339.5

18,957.5

12,245.6

17,412.5

26,960.0

7.8

10.7

2.3

7.0

77,355.3

10,7624.3

13,944.9

19,909.1

and 339.5 yuan per month in the western region. The average balance of provident fund account amounts to 18957.5 yuan, with 12245.6 yuan in the eastern region, 17412.5 yuan in the central region and 26960.0 yuan in the western region. Among rural residents who enjoy housing provident fund, 7.8% of them have used the provident in 2014, with 10.7% in the eastern region, 2.3% in the central region and 7.0% in the western region. The average withdrawal of provident fund of rural households in 2014 is equivalent to 77355.3 yuan, with 107624.3 yuan in the eastern region, 13944.9 yuan in the central region and 19909.1 yuan in the western region. Table 7.20 lists reasons of rural residents’ withdrawal of provident fund, among which house purchase accounts for the highest proportion, averaging 71.1%, with 90.4% in the eastern region, 75.4% in the central region and 21.5% in the western region. In the central and western regions, reasons such as house building, major repairing and rebuilding follow behind, while in the eastern region, the second major reason is to repay house-purchasing loan and interests.

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Table 7.20  Reasons of rural residents’ withdrawal of provident fund (unit: %) Reasons House purchase House building/major repair/rebuilding Paying for house purchase loan and interests Paying for house rent For retirement Termination of labor relation with working unit Other reasons

National

Eastern region

71.1 9.2 5.3 0.3 3.2 0.4 10.5

90.4 0.7 4.6 0.4 0.0 0.0 3.9

Central Western region region 75.4 9.2 0.0 0.0 15.4 0.0 0.0

21.5 30.5 8.5 0.0 8.3 1.6 29.6

7.5   Commercial Insurance 7.5.1  Commercial Insurance Coverage As is shown in Table 7.21, the coverage of commercial insurance among rural residents in our country is relatively low. At the national level, 96.3% of rural residents do not insure any commercial insurance. There are 1.8% of rural residents who enjoy commercial life insurance. The coverage of health insurance is 0.9%, while the figure for other types of commercial insurance is 1.0%. At the regional level, there is no obvious gap among different regions in terms of commercial insurance coverage which is generally low. The proportion of rural residents who do not enjoy any commercial insurance is 96.0%, 96.5% and 96.2% in the eastern, central and western regions, respectively. For the eastern region, there are 2.0% of rural residents who enjoy commercial life insurance, higher than that of the central region (1.7%) and western region (1.8%). What is more, there are 1.0% of rural residents in the eastern region who enjoy commercial health insurance. Besides, 1.0% of rural residents in the eastern region are engaged in other types of commercial insurance. In the central region, the coverage of commercial health insurance among rural residents makes up 1.0%, while that figure for other types of accounts is 0.8%. The western region has witnessed 0.7% coverage of commercial health insurance and 1.3% coverage of the other types among rural residents. From the gender perspective, the commercial insurance coverage of male is slightly higher than that of female. As is illustrated in Table 7.22, 95.8% male residents and 96.8% female residents in rural areas do not enjoy any commercial insurance. There are 2.0% of male rural residents who have commercial life insurance and 1.0% of males who enjoy commercial health insurance. Coverage

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Table 7.21  Coverage of commercial insurance among rural residents (unit: %) Coverage of commercial insurance National Eastern region Central region Western region Life insurance Health insurance Other types None

1.8 0.9 1.0 96.3

2.0 1.0 1.0 96.0

1.7 1.0 0.8 96.5

1.8 0.7 1.3 96.2

Table 7.22  Gender difference in coverage of commercial insurance among rural residents (unit: %) Sex

Commercial life insurance

Male Female

2.0 1.5

Commercial health insurance

Other types

None

1.0 0.8

1.2 0.9

95.8 96.8

Table 7.23  Coverage of commercial insurances among rural residents of different age groups (unit: %) Age groups ≤30 years 31–40 years 41–50 years 51–60 years >60 years

Commercial life insurance Commercial health insurance Other types None 1.3 2.0 3.4 2.8 0.7

1.0 1.0 1.6 0.8 0.3

1.2 0.8 1.6 0.9 0.5

96.5 96.2 93.4 95.5 98.5

of other types of commercial insurance among male residents takes up 1.2%. As for female rural residents, the coverage of commercial life insurance is 1.5%, while the figures for commercial health insurance and other types amount to 0.8% and 0.9%, respectively. Table 7.23 shows the coverage of commercial insurance among rural residents of different age groups, from which we can see that of all these commercial insurances, the middle-age group of 41–50 years has the highest coverage of commercial insurances. 7.5.2  Commercial Life Insurance It is clear from Table 7.24 that the insured amount per capita of rural residents for commercial life insurance nationwide is 53306.0 yuan, with

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Table 7.24  Rural residents’ engagement in commercial life insurance Features of life insurance

National

Eastern region

Central region

Western region

Coverage of insurance (yuan per person) Proportion of return (%) Dividend received last year (yuan) Proportion of principal return (%) Insurance payment last year (yuan) Proportion of claim receiving (%) Amount of claim received (yuan)

53,306.0 37.9 790.3 61.5 2620.7 4.4 5209.7

54,627.9 43.8 690.9 69.7 3222.3 6.5 9617.6

58,393.6 29.7 1695.8 62.1 2120.5 1.1 683.9

47,529.5 39.6 396.6 50.8 2473.2 5.5 1451.4

Table 7.25  Rural residents’ engagement in commercial health insurance Features of commercial health insurance

National

Eastern region

Central region

Western region

Insurance payment last year (yuan) Reimbursement amount last year (yuan)

2031.3 302.3

2487.1 610.7

1714.3 123.6

1719.2 92.9

54627.9 yuan in the eastern region, 58393.6 yuan in the central region and 47529.5 yuan in the western region. In terms of insurance dividend ratio, 37.9% of commercial life insurance engaged by rural residents nationwide has yielded returns, averaging 790.3 yuan. That proportion in the eastern region is 43.8%, with an average return of 690.9 yuan. In the central and western regions, the proportion is 29.7% and 39.6% respectively, with a per capita return of 1695.8 yuan and 396.6 yuan, respectively. In terms of capital return ratio, 61.5% of commercial life insurances invested by rural residents nationwide have returned principal, with 4.4% of rural residents receiving their claims of 5209.7 yuan on average. 7.5.3  Commercial Health Insurance As is shown in Table 7.25, the insured amount per capita of rural residents for commercial health insurance in 2014 is 2031.3 yuan, with an average reimbursement per person of 302.3 yuan. At the regional level, the eastern region has witnessed the highest average payment of rural residents for commercial health insurance of 2487.1 yuan, with the largest reimburse-

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Table 7.26  Rural residents’ engagement in other commercial insurances Items of commercial health insurance Insurance payment last year (yuan) Reimbursement amount last year (yuan)

National

Eastern region

Central region

Western region

1653.6 163.4

2385.3 458.8

1976.1 55.6

794.9 7.7

ment of 610.7 yuan. While the figures for the central region and western region are 1714.3 yuan and 1719.2 yuan, respectively, with an average reimbursement of 123.6 yuan and 92.9 yuan per person, respectively. 7.5.4  Other Types of Commercial Insurance As is shown in Table 7.26, the insured amount per capita of rural residents for other commercial insurances in 2014 is 1653.6 yuan nationwide, with an average reimbursement amount of 163.4 yuan. At the regional level, the eastern region has witnessed the highest contribution to other commercial insurances of 2385.3 yuan among rural residents, with the highest reimbursement amount of 458.8 yuan on average. While the figures of contribution to other commercial insurances in the central region and western region are 1976.1 yuan and 794.9 yuan, respectively, with the average reimbursement amount of 55.6 yuan and 7.7 yuan, respectively.

CHAPTER 8

Education of Rural Households

This chapter analyzes the basic situation of the education and training received by rural residents using the samples of rural households living in rural areas from the China Rural Household Panel Survey (CRHPS). The study finds that the overall education level of rural residents remains relatively low and that their educational level is closely related to regions and gender. The educational level of residents in the eastern region is generally higher than that in the central and western regions, while the same pattern applies in terms of the popularization of higher education. The educational level of male residents is higher than that of their female counterparts. The latter, however, is becoming better educated. Most rural students study at public primary, middle and high schools, while very few study at private schools. Of the private schools, 2.3% are for migrant workers’ children. The education quality of these schools is mixed, with a majority providing education of fairly average quality. The average distance from the students’ home to their schools tends to increase with the furthering of their education. Around 7% of the students study in other regions, and 30% to 40% are boarding students. More than half of the parents expect their children to attain a bachelor’s degree or above, with almost the same expectations for both their son and daughter. The expectation for their children’s education is far above their own educational level, which illustrates a common belief among rural households that knowledge can change destiny. This attitude has a positive impact on the advancement of universal © The Author(s) 2019 W. Qian et al., Societal Development in Rural China, https://doi.org/10.1007/978-981-13-8082-2_8

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compulsory education in rural areas, ­dissemination of higher education and the development of national education level. With regard to education fees, the policy of compulsory education has played a certain role in reducing students’ fees and expenses during compulsory education. Apart from that, a few people are willing to pay for extracurricular courses and activities, with more courses for boys and more activities for girls.

8.1   Education Level As is shown in Table 8.1, the overall educational level in China is relatively low. Of the rural population over 16  years, 17.7% have never received education, 31.7% have received only primary school education and 33.5% only junior high school education. Only 17.1% have received high school education and above, and only 2.8% have obtained a bachelor’s degree or above. When compared among the different regions, the illiteracy rate (referring to people who have not received education) in the eastern region remains the lowest (16.0%), while that of the central and western regions are 18.9% and 18.2%, respectively. In the eastern region, people who have only received junior high school education account for 80.3%, which is the lowest, with 84.5% in the central region, the highest, and 83.8% in the western region. Accordingly, people who have achieved the bachelor’s degree and above account for 3.3% in the eastern region, which is the highest, and 2.6% in the central region and 2.5% in the western region. Table 8.1  Educational level (unit: %) Education level

Total

Eastern region

Central region

Western region

Male

Female

Illiteracy Primary school Junior high school Senior high school Secondary school/ professional high school Postsecondary education/ higher vocational education Higher education or above

17.7 31.7 33.5 9.0 2.9

16.0 28.7 35.6 10.2 3.4

18.9 31.9 33.7 8.6 2.3

18.2 35.0 30.6 8.0 3.2

10.1 31.1 39.1 10.9 3.5

25.9 32.5 27.4 6.9 2.3

2.4

2.8

2.0

2.5

2.5

2.3

2.8

3.3

2.6

2.5

2.8

2.7

Note: Targeted at the population aged over 16 years

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There is a striking difference between the education of males and females. Generally, males are better educated than females, with the illiteracy rate being 10.1% and 25.9%, respectively. Males who have received junior high school education and above account for 58.8%, and females account for 41.6%, the rate being far below that of males. However, as the general education level improves, the gap between males and females is narrowing. For example, although there is still a huge gap between males and females in terms of the proportion of those who have received senior high school education (10.9% and 6.9%, respectively), the proportion of males who have received junior college degree or higher vocational degree is 2.5%, which is only 0.2% higher than that of females. The proportion of males who have received their undergraduate education is 2.7%, which is only 0.1% higher than that of females. As is shown in Table 8.2, in last semester, among the rural population of the age to be educated, which means from 6 to 23, 29.0% are not at school, 34.5% are studying in primary schools and 16.0% are studying in junior high school. In last semester, people from 6 to 23 who received undergraduate education and above account for 5.5%, notably higher than the national average educational level of the total population in rural areas. In the comparison among different regions, it is found that people who were not at school account for 31.4% in the western region, which is the highest, 28.5% in the central region, and 26.6% in the eastern region, which is the lowest. Given that people aged 6−23 ought to receive education, those who do not enroll in schools are primarily dropouts apart from students who start their studies late. It is evident that the dropout rate in Table 8.2  The education received by people aged 6–23 in last semester (unit: %) Education received Not at school Primary school Junior high school Senior high school Technical secondary school Higher vocational school/ junior college Undergraduate school and above

Proportion

East region

Central region

West region

Male

Female

29.0 34.5 16.0 9.1 2.1 3.8

26.6 31.4 16.9 9.2 2.6 5.5

28.5 38.5 14.6 9.7 1.5 2.5

31.4 32.4 16.9 8.6 2.3 3.7

29.9 35.7 16.0 8.3 2.2 3.5

27.8 33.0 16.0 10.1 2.0 4.1

5.5

7.8

4.7

4.7

4.4

7.0

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the eastern region is much lower than that of the central region and the western region. In the central region, the proportion of people who are at primary school is 38.5%, much higher than the western region (32.4%) and the eastern region (31.4%). When it comes to higher education, 7.8% of teenagers of the suitable age in the eastern region have universities and colleges degrees, which is much higher than the 4.7% of the central and western regions, showing that higher education is more popularized in the eastern region than in the central and western regions. Males and females who have not received education in the last semester account for 29.9% and 27.8%, respectively, which shows that the dropout rate for the former, to some extent, is higher than the latter. Accordingly, females have behaved better than their male counterparts in almost every educational phase. In the last semester, males who are receiving high school and undergraduate education account for 8.3% and 4.4%, respectively, while the figures for females are 10.1% and 7.0%, respectively, which is far above the average education level of the whole society. It is foreseeable that the education level of females will be much higher than that of males in the near future. As is shown in Table 8.3, among the students who have received a bachelor’s degree, differences can still be seen in terms of the types of universities in various regions. Only 15.2% of graduates studied at “985 Project” and “211 Project” universities, while 22.7% of undergraduates studied at other first-tier universities. Second-tier universities are the main choices of rural students, accounting for 42.9%. Comparatively, there are not many students studying at third-tier universities, accounting for only 19.2%. The proportion of students in the western region who studied at “985 Project” or “211 Project” Universities is 18.1%, which is relatively higher than the 14.5% in the eastern region and 13.6% in the central region. In the eastern region, 24.6% of undergraduate students studied at first-tier universities other than the “985 Project” and “211 Project” universities, while this Table 8.3  The choice of universities and colleges in different regions (unit: %) Types of universities and colleges

Total

Eastern region

Central region

Western region

“985 project” and “211 project” universities Other first-tier universities The second-tier universities The third-tier universities

15.2

14.5

13.6

18.1

22.7 42.9 19.2

24.6 48.3 12.6

21.2 41.0 24.2

21.9 37.5 22.5

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proportion is 21.9% in the western region and 21.2% in the central region. Of rural students in the eastern region, 48.3% chose to study in the secondtier universities, far more than that in the western and central regions. Accordingly, only 12.6% of students in the eastern region chose the thirdtier universities, yet these are the primary choices of students in the central and western regions, with a proportion of 24.2% and 22.5%, respectively.

8.2   Education Condition With the popularization of education, more attention has been paid to education quality and conditions. In popularizing basic education, only by improving the education quality and conditions will the nation be able to improve its education standards. As is shown in Table 8.4, most rural students attended public primary, middle and high schools, accounting for 90.9%, among which 89.1% are schools for nonmigrant children. Private schools account for only 8.9%, which is about one-tenth of public schools, among which 8.4% are not schools for migrant workers’ children. Although public and private schools for migrant workers’ children account for only 2.3%, they have become a great concern in society. In addition, very few (only 0.01%) rural students studied at international schools. It is obvious that compared with urban students, rural students are at a disadvantage in terms of the internationalization of education. Public schools account for 95.4% in the western region, which is relatively high and might be the result of the sluggish private capital. Among public schools, schools for migrant workers’ children account for 2.6%, far above 0.8% in the eastern region and 1.9% in the central region. However, in the central region, private schools are popular, accounting for 12.1%, while private schools for migrant workers’ children account for only 0.4%. Table 8.4  The type of schools students studied in before higher education (unit: %) Type of school

Total

Eastern region

Central region

Western region

Public school (not specialized for migrant workers’ children) Public school for migrant workers’ children Private school (not specialized for migrant workers’ children) Primary school for migrant workers’ children International school Others

89.1

89.6

85.8

92.8

1.8 8.4

0.8 8.5

1.9 11.7

2.6 4.1

0.5 0.01 0.2

0.6

0.4

0.5

0.2

0.4 0.03 0.07

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Table 8.5  The type of public primary and middle schools (unit: %) Type of school

Total Eastern region Central region Western region

Key schools at the national level Key schools at the provincial level Key schools at the city level Key schools at the district level Nonkey schools

0.4 1.1 5.3 19.0 74.2

0.4 1.2 8.5 17.0 72.9

0.3 1.5 4.9 20.1 73.2

0.4 0.5 3.4 19.4 76.3

The quality of public schools is usually mixed. As is shown in Table 8.5, among public primary and middle schools, most (74.2%) are not key schools. A small part (19.0%) are key schools of the district level, 5.3% of the city level, 1.1% the provincial level and 0.4% the national level. Because of the differences in economic and educational conditions, there are also disparities in the quality of public primary and middle schools. Although there are no significant differences among China’s different regions, gaps can still be observed. In the eastern region, 1.2% of the public primary and middle schools where rural students studied at are key schools of the provincial level, and 8.5% of the city level. While in the central region, the rate of key schools of the provincial level is relatively higher, accounting for 1.5%, while the rate of the city level is 4.9%, which is lower than that of the eastern region. The education quality of primary and middle schools in the western region is relatively poorer. Key schools of the provincial level and of the city level account for 0.5% and 3.4%, respectively. Nonkey public schools in the western, eastern and central regions account for 76.3%, 72.9% and 73.2%, respectively. Since private schools do not have the key school system, assessment on the teaching quality can only be made by comments given by others. As is shown in Table  8.6, among private primary and middle schools where rural students studied, only 6.1% are of the top quality and 33.5% of good quality. The education quality of private schools in the eastern region, among which 9.6% are of top quality and 37.9% of good quality, is notably higher than that of the central and western regions. In the central region, top-quality schools account for 5.5% and good-quality schools 31.1%. In the western region, there are fewer private schools and the overall quality is worse than that in the eastern region and the central region. Among private schools in the western region, top-quality schools account for only 1.7% which is the lowest in the three regions.

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Table 8.6  Education quality of public primary and middle schools (unit: %) Education quality

Total

Eastern

Central

Western

Top Good Average

6.1 33.5 60.4

9.6 37.9 52.5

5.5 31.1 63.4

1.7 34.2 64.1

Central region

Western region

Table 8.7  The distance from home to school (unit: km) Type of school

Total

Eastern region

Average Median Average Median Average Median Average Median Primary school Junior high school Senior high school Total

9.0 17.0 38.3 15.6

2 6 25 3

9.0 10.4 37.7 14.0

2 5 20 3

8.9 22.6 31.9 15.5

2 5 26 3

9.2 17.3 46.7 17.2

2 7.5 25 4

For rural students, they may attend primary schools in the countryside, but go to somewhere faraway for junior high school and senior high school. As is shown in Table  8.7, the distance from school to home increases as education level furthers. The average distance of primary schools to homes is 9.0 km, while the median number is 2 km. For junior high school, the average distance and the median distance are 17.0 km and 6 km, respectively. For senior high school, the average distance is 38.3 km and the median number is only 25. The huge gap between the average number and the median number results from some relatively high extremum. The distance from home to the primary school is around 9.0 km, representing little difference in the eastern, western and central regions. However, there are striking differences in terms of the distance from home to junior high school, with 22.6 km in the central region, 17.3 km in the western region and 10.4  km in the eastern region. As for senior high school, the distance is 46.7  km, 37.7  km and 31.9  km in the western, eastern and central regions, respectively. Generally speaking, people would choose nearby schools. But students might attend school far away from home in pursuit of better education quality or something else. As is shown in Table 8.8, 7.0% of rural students in China study in other regions, while this figure is 7.8% in the eastern region, 7.0% in the central region and 6.2% in the western region. It can

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Table 8.8  The situation of studying faraway and boarding at school (unit: %)

Faraway study rate Resident student rate

Total

Eastern region

Central region

Western region

7.0 37.6

7.8 32.7

7.0 38.8

6.2 40.3

Note: Studying faraway means studying and living in different cities

be seen that rural students in the eastern region would take more factors into consideration. On the other hand, when the school is far away from home, students have to board at the school. Generally, 37.6% of rural students board at school, while this figure is 32.7% in the eastern region, 38.8% in the central region and 40.3% in the western region.

8.3   Education Expectation The education expectations of parents will influence the ultimate education level of their children to some extent. As is shown in Table 8.9, apart from 13.5% of parents who have no expectation for their children, most parents have a relatively high expectation. Parents expecting their children to attain a bachelor’s degree, master’s degree and doctorate account for 54.1%, 3.5% and 18.2%, respectively. When comparing the education expectations in different regions, little difference can be found among the eastern, western and central regions. Parents in the eastern region have higher expectations than those in the central and western regions. Parents in the eastern region expect their children to attain the bachelor’s degree account for 54.6%, which is higher than that in the central (54.0%) and western (53.7%) regions. It is the same with the expectations of master’s degree. It is worth noting that expectations for their children to attain doctorate in the eastern region is notably lower than in the central and western regions. In the eastern region, 16.8% of the parents expect their children to get a doctorate degree, while 19.0% in the central region and 18.3% in the western region have the same expectations. Generally, females are prone to be treated unfairly in terms of education, especially in underdeveloped areas. However, it can be found in Fig. 8.1 that parents’ expectations for their children are not notably influenced by the gender, especially in terms of low education level, which illustrates that the concept of gender equality has found its way into the

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Table 8.9  Education expectations of parents (unit: %) Type of school

Total

Primary school Junior high school Senior high school (technical secondary school and vocational high school included) Vocational college Undergraduate Master’s Doctorate No expectation

60 50

Eastern region

Central region

Western region

0.5 2.7 4.8

0.4 1.6 5.3

0.4 3.1 4.1

0.8 3.1 5.1

2.7 54.1 3.5 18.2 13.5

3.5 54.6 4.3 16.8 13.5

1.8 54.0 3.1 19.0 14.5

3.2 53.7 3.5 18.3 12.3

Actual level Expectation of the total Expectation for the male Expectation for the female

40 30 20 10 0

Fig. 8.1  Relationship between education expectation and gender as well as comparison with actual education level (unit: %)

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current education. In the relatively high education expectation, sons are expected to attain a doctoral degree, whereas daughters are expected to receive a bachelor’s or a master’s degree. The possible reason may be that parents are concerned about the negative influence of education level on marriage, especially for the female. The higher their educational level, the lower is the marriage rate. Compared with the education expectation, the actual education level of rural areas in China is extremely low, where most of the population has not got higher education. In rural households, parents’ expectations for their children are rather high, far above their own educational level, which illustrates a popular concept that knowledge can change destiny. This has a positive impact on the advancement of universal compulsory education in rural areas, dissemination of higher education and the development of national education level.

8.4   Education Expenses Since September 1, 2008, China has waived the tuition and fees for both rural and urban education, which significantly reduced the burden for rural students at the stage of compulsory education. As is shown in Table  8.10, the average expenses of the primary schools in the rural area are 1474.0 yuan and the median expenses are 500 yuan, while these figures of junior high schools are 2738.3 yuan and 1000 yuan, respectively. The figures for senior high schools are 7256.1 yuan Table 8.10  Expenses for primary school, junior high school and senior high school Type of school

Total

Eastern region

Central region

Western region

Average Median Average Median Average Median Average Median Primary school Junior high school Senior high school Total

1474.0

500

1594.5

400

1773.2

700

961.6

400

2738.3

1000

3003.9

1000

3212.5

1650

2045.7

1000

7256.1

5000

6595.3

4000

8512.7

7000

6315.8

4900

2712.5

1000

2870.0

1000

3108.7

1100

2086.0

700

Note: Expenses in this table cover tuition and fees

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and 5000 yuan, respectively. The gap between the average number and the median number results from some relatively high extremum. In spite of the gap, expenses of compulsory education are evidently lower than of noncompulsory education, which shows the positive effect of waiving the tuition and fees. The expenses in the central region are higher than those in the eastern region, while expenses in the eastern region are higher than those in the western region. For instance, the expenses of primary schools in the central, eastern and western regions are 1773.2 yuan, 1594.5 yuan and 961.6 yuan, respectively. With the increasing attention paid to the education of students, expenses for extra courses and activities become an important part of education fees and expenses. As is shown in Table  8.11, within the 5099 ­samples from primary schools to senior high schools, there are 4286 without expenses for extra courses and activities, resulting in a low-average tuition fee, which is 216.4 yuan for primary school, 310.6 yuan for junior high school and 614.4 yuan for senior high school. However, among those who have extra expenses, the expenses are rather high. Expenses on extra courses, junior senior high school students are 953.4 yuan, 3060.7 yuan and 2379.2 yuan, respectively, with an average of 1575.5 yuan. Expenses on extra activities are relatively low. It is 567.7, 156.2 and 959.2 yuan for primary school, junior high school and senior high school, respectively. The average expense is 332.2 yuan. Table 8.11  Expenses for extra courses and activities Type of school Primary school Junior high school Senior high school Total

Total expense Total

male

female

1521.1 1538.4 1494.3

Extra courses Total

male

953.4

975.6

Comprehensive quality

female

Total

male

female

919.2 567.7 562.9

575.0

3217.0 4340.1 1413.8 3060.7 4314.8 1047.4 156.2

25.3

366.4

3338.4 2239.9 3929.1 2379.2 1771.1 2706.2 959.2 468.8 1223.0 1907.7 1862.7 1957.7 1575.5 1646.6 1496.4 332.2 216.0

461.3

Note: Expenses for extra courses refer to the tuition expenses for curricular courses. Expenses for extra activities refer to the cultivation of comprehensive quality in terms of arts and sports

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2500.0

Expenses yuan

2000.0

1862.7

1957.7

1500.0

1646.6

1496.4

1000.0 461.3

500.0 0.0

216 Total

Extra courses Extra activities Types of Extra Courses and Expenses Male Female

Fig. 8.2  Expenses for extra courses and activities

There are some differences in these extra expenses between males and females. As is shown in Fig. 8.2, the expenses for extra courses and training for males are lower than that for females, amounting to 1862.7 yuan and 1957.7 yuan, respectively. In terms of expenses for extra courses, the expenses for males are higher than that for females, amounting to 1646.6 yuan and 1496.4 yuan, respectively. However, the expenses for females on extra activities amount to 461.3 yuan, which is notably higher than the 216.0 yuan for males. It can be seen that parents are willing to spend more on courses for boys and more on comprehensive activities for girls. Although there is little difference in terms of the total investment, they have different emphasis for boys and girls in terms of training.

CHAPTER 9

Community Environment of Rural Households

This chapter analyzes the basic situation of rural communities in China by using the rural community questionnaires of the China Rural Household Panel Survey (CRHPS). The CRHPS rural community questionnaires for 2015 were collected from 578 administrative villages of 257 counties in 28 provinces in China on which the analysis in this chapter and the following are centered. In some parts of the statistical analysis, the number of analysis samples is less than 578 due to missing data or the elimination of outliers. The analysis content in this chapter includes the villages’ demographic structure, villages’ infrastructure, the general situation of rural industries, villages’ collective assets and debts, communities’ spending and income, as well as agricultural land, land acquisition and demolishment. The analysis shows that the number of rural left-behind elderly and children is considerable, accounting for 11.4% of the total rural registered population. Well water and mountain spring, the main sources of domestic water in rural communities, account for 57.6%, while tap water accounts for 36.1%, of which the eastern region holds the highest tap water penetration of 48.6%. Firewood, the main fuel source in rural areas, accounts for 40.5% of all energy resources. The average distance between one village and its nearest farmer’s market is 28.4  km and the figure for the eastern region is less compared with that of the central and western regions. The main spending projects for rural communities are public affairs. Of rural communities’ income, 57.2% comes from superior government’s financial subsidies or © The Author(s) 2019 W. Qian et al., Societal Development in Rural China, https://doi.org/10.1007/978-981-13-8082-2_9

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returns, while 36.3% comes from the returns on collective assets. On the national level, 66.6% of rural communities have conducted the land rights’ confirmation process, of which the western region has the highest proportion, reaching 77.9%.

9.1   Village Profile and Population 9.1.1  Village Profile An administrative village is generally composed of multiple natural villages. Figure  9.1 shows the number of natural villages included in one administrative village. Nationally, one administrative village contains 4.3 natural villages on average. On the regional level, the figure for the central region is 5.7, while the figures for the western and eastern regions are 4.2 and 3.2, respectively. The demographic structure of village surnames reflects the situation of rural clan forces. If we define a popular surname as one that accounts for over 10% of the village’s total population, then 82.6% of rural villages have popular surnames on average nationally. The proportion for villages with one popular surname is 29.4% and the figures for villages with two popular

5.7

6 5

4.3

Number

4

4.2 3.2

3 2 1 0

National

Eastern Region Central Region Region

Western Region

Fig. 9.1  Average number of natural villages contained in one administrative village

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Table 9.1  Structure of village surnames Villages with popular surnames (%) National Eastern region Central region Western region

82.6 88.6 80.2 78.1

Number of popular surnames One

Two

Three

Above

29.4 38.2 24.7 22.7

32.0 32.0 31.2 32.8

20.4 16.9 26.0 18.8

18.3 12.9 18.2 25.7

surnames, three popular surnames or above are 32.0%, 20.4% and 18.3%, respectively (see Table 9.1). 9.1.2  Demographic Characteristics 9.1.2.1 Population Size China’s rural communities are experiencing huge demographic changes in recent years. A large number of middle-aged labor forces flow into cities to engage in nonfarming production, leaving the elderly, children and relatively low-skilled labor forces in the rural areas. Our research investigated the permanent resident population and registered population in rural areas. The permanent resident population is divided into three groups: the first refers to people who live in their native village and whose permanent residence are registered or to be registered in this village; the second refers to people who live in their native village and have been away from their permanent residence for more than half a year; the third refers to people who have registered their permanent residence in their native village and go out for less than half a year or study abroad. The registered population refers to people who have registered their permanent residence in their native village, most of whom are rural residents while a few are urban residents. Table 9.2 shows that, in an administrative village, the number of permanent residents is 856.9 on average nationally, for the registered population the figure is 1058.0 and for the rural resident population is 1021.2. The average population size of one administrative village in the central region is the largest, with 1215.2 registered residents, while the average population size for one administrative village in the eastern region is the smallest, with 858.4 registered residents and the figure for the western

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Table 9.2  Average size of the permanent resident population, registered population and rural residents Permanent resident population National Eastern region Central region Western region

856.9 758.5 956.7 868.1

Registered population

Rural residents

1058.0 858.4 1215.2 1116.7

1021.2 804.3 1209.8 1069.8

Aged 80 or above Aged between 60 and 79

Aged 15 or less

Aged between 60 and 79

Fig. 9.2  Age structure of rural population

region’s registered residents is 1116.7. The permanent resident population shows a similar regularity of distribution. The central region has the largest number of permanent resident population and the eastern region has the lowest. In addition, Table 9.2 also shows that the proportion of nonrural residents in all registered population of eastern rural area is higher than that of the central and western rural regions. 9.1.2.2 Age Structure Figure 9.2 further shows the age structure of national rural residents. In terms of the village population, the proportion of people aged between 16 and 59 is 60.4%, while the figure for people aged 15 or less is 20.2%. If people aged 60 or above are defined as elderly people, then the proportion of aging population in rural areas is as high as 19.4%.

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183

12.0 9.9

Percentage %

10.0 8.0

6.6 5.5

6.0

4.3

4.0 2.0 0.0

National

Eastern Region Central Region Western Region Region

Fig. 9.3  The proportion of people with an educational level of Junior College or above

9.1.2.3 Educational Level The proportion of people with junior college degree or above to some extent reflects the educational level of the rural population. Figure  9.3 shows that, nationally, 6.6% of rural population on average achieve the educational level of junior college or above. However, the differences between regions are obvious. The educational level in the eastern region is much higher than those in the central and western regions. The proportion of people who have completed junior college education or above in the eastern region is 4.4% points higher than the figure for the western rural areas and 5.6% points higher than the figure for the central rural areas. 9.1.2.4 Migrant Workers With the progress of urbanization, a large number of rural labor forces move out for work. In accordance with research data, nationally, nearly 30% of rural labor forces go out for work on average. There is a huge gap among different regions. In the western region, 41.2% of labor forces go out for work, thereby ranking the first compared with the central and eastern regions (25.5% and 20.2%, respectively). This gap may be due to the significant differences among the three regions’ nonagricultural industries development. The nonagricultural industries in the eastern region develop faster; hence, many rural labor forces choose to work at home. However, a large number of labor forces in the central and western regions flow into the eastern region (see Fig. 9.4).

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45.0

41.2

40.0 Percentage %

35.0 30.0

29.8 25.5

25.0

20.2

20.0 15.0 10.0 5.0 0.0

National

Eastern Region Central Region Western Region Region

Fig. 9.4  The proportion of labor forces going out for work in rural areas Table 9.3  Proportions of households that have lost their only child, left-behind elderly and left-behind children in rural areas

National Eastern region Central region Western region

Households that have lost their only child (%)

Left-behind elderly (%)

Left-behind children (%)

0.5 0.3 0.6 0.7

7.8 4.4 11.9 7.5

3.6 2.0 5.7 3.5

9.1.2.5 H  ouseholds That Have Lost Their Only Child and the  Left-­Behind Population Table 9.3 shows the situation of households that have lost their only child, as well as the left-behind elderly and children in targeted villages. Nationally, the average proportion of in rural areas that have lost their only child is 0.5%, which means that 1 out of 200 households had lost its only child. Compared with the eastern region, the central and western regions have more severe situations in terms of the households that have lost their only child. Nationally, the average proportions of left-behind elderly and children in rural areas are 7.8% and 3.6%, respectively. Compared with the eastern region, the central and western regions have more severe situations in terms of left-behind elderly and children.

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Table 9.4  Proportions of migrant population, immigrant families and emigrant families in rural areas Migrant population (%) Immigrant families (%) Emigrant families (%) National Eastern region Central region Western region

2.8 4.4 0.9 2.9

0.3 0.1 0.2 0.5

5.3 5.5 3.9 6.3

The proportion of left-behind elderly in the central rural region is as high as 11.9%. In the mid-term and long term, a large number of left-behind elderly and children in rural areas may cause serious social problems. 9.1.2.6 Population Migration This research also shows the migration situation of the rural population. Emigration in this research refers to changes in the residents’ permanent address, which includes going through the formalities of emigration or not going through the formalities of emigration. Immigration in this research refers to changes in the residents’ permanent address, which includes going through the formalities of immigration or not going through the formalities of immigration. Going-out refers to not working in the residents’ registered place or in their usual place of residence. For the rural population, the destinations of going-out are usually nearby counties or county-level cities. Table 9.4 shows the proportions of migrant population, immigrant families and emigrant families in rural areas. As shown in the table, the population loss is serious in rural areas. Nationally, the average proportion of emigrant families reaches 5.3%, while the proportions of immigrant families and migrant population are 0.3% and 2.8%, respectively, which reflects that China is experiencing a rapid urbanization process.

9.2   Community Infrastructure 9.2.1  Communities’ Water and Electricity 9.2.1.1 Domestic Water As the Table 9.5 shows, on the national level, well water and mountain spring, the main sources of domestic water in rural communities, account for 57.6%, while tap water accounts for 36.1%. The eastern region has the

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Table 9.5  The proportions of the main sources of domestic water in rural ­communities (unit: %) Main sources of domestic water National Eastern region Central region Western region

Rivers and lakes

Well water and mountain spring

Tap water

Purified Rain water

Cellar water

Pool water

Others

3.7 0.0

57.6 50.8

36.1 48.6

0.2 0.0

0.4 0.0

1.0 0.0

0.0 0.0

0.9 0.6

11.4

60.5

23.7

0.6

1.5

0.0

0.0

2.4

1.2

61.5

34.6

0.0

0.0

2.6

0.1

0.0

Notes: In the statistics of domestic water usage, if any changes in the water source occur, then the domestic water refers to water used in the most recent three months

highest tap water popularity rate of 48.6%, compared with 23.7% in the central region and 34.6% in the western region. 9.2.1.2 Energy Consumption In terms of the national average, firewood, the main fuel source in rural areas, accounts for 40.5% of all energy. The proportions of pipeline natural gas and coal gas, coal, electricity, liquefied petroleum gas, and methane are 18.9%, 15.2%, 14.3%, 9.4% and 0.9%, respectively, which illustrates that there is still a huge room for improvement in terms of energy consumption in rural areas (see Fig. 9.5). 9.2.2  Communities’ Transport In terms of the national average, the percentage for villages which only have one road connecting to the county center is 54.5%, while the figures for those that have two and three to five roads connecting to the county center are 29.8% and 14.2%, respectively. Furthermore, 0.7% villages do not have any roads connecting to the county center, which means that 1 in around 140 villages does not have any roads connecting the county center (see Fig. 9.6).

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Electricity 14.3%

187

Others 0.8%

Liquified Petroleum Gas 9.4%

Firewood 40.50%

Pipeline Natural Gas and Coal Gas 18.9%

Methane 0.9%

Coal 15.2%

Fig. 9.5  The proportions of main fuel sources in rural area More than None Five Roads 0.7% Three of Five Roads

One Road Two Roads

Fig. 9.6  The number of roads connecting to the county center in rural areas

Figure 9.7 illustrates the distance between the village and its nearest agri-product market. In general, the average distance between a village and its nearest agri-product market is 28.4 km. The figures for the central and western regions are 30.3 and 29.7 km, respectively, while the eastern region has the shortest distance of 25.2 km.

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The Average Distance kilometers

35.0 30.0

30.3

28.4

29.7

25.2

25.0 20.0 15.0 10.0 5.0 0.0

National

Eastern Region Central Region Western Region Region

Fig. 9.7  The average distance between a village and its nearest agri-product market

9.2.3  Community Education Apart from water, energy and roads, rural communities also have other infrastructure including kindergartens, primary schools, junior high schools and other education facilities. As is shown in Table 9.6, the average percentage of villages that have kindergartens is 20.1% across the nation, while the figures for the eastern and western regions are 16.5% and 17.1%, respectively, which are slightly lower than that of the central region (28.1%). The percentage of villages that have primary schools is 28.0%, which means that one in four administrative villages has one primary school. Figures for the eastern, western and central regions are 22.0%, 26.9% and 36.4%, respectively. By contrast, the average percentage of administrative villages that have junior high schools is low, only representing 3.6% across the nation, which means that 1 in around 30 administrative villages has one junior high school. Figures for the eastern, central and western regions are 2.1%, 3.4% and 5.1%, respectively. In general, the distance between the village committee and its nearest primary school is less than that of the village committee and its nearest junior high school. Nationally, the average distance to the nearest primary school is 3.3 km, while the distance to a junior high school is 7.8 km. For the central region, the distances to both primary school and junior high

National Eastern region Central region Western region

28.0 22.0 36.4 26.9

28.1

17.1

Primary school

20.1 16.5

Kindergarten

School

5.1

3.4

3.6 2.1

Junior high school

78.8

87.2

79.6 73.9

Medical point

3.1

2.1

2.4 1.8

Hospital

Medical service

Table 9.6  The coverage rate of infrastructure in rural communities (unit: %)

1.4

0.7

2.6 5.6

Village bank

31.6

45.2

47.1 66.2

Public fitness facility

Financial service point

48.8

70.8

67.4 85.4

Broadband

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The Average Distance kilometers

9.0 8.0

8.3

7.9

7.8

7.2

7.0 6.0 5.0 4.0

4

3.6

3.3

3.0

2.3

2.0 1.0 0.0

National

Eastern Region nearest primary school

Central Region

Western Region

nearest junior high school

Region

Fig. 9.8  The average distance between the village committee and its nearest primary school and junior high school Table 9.7  The numbers of teachers and students in primary school

National Eastern region Central region Western region

Number of teachers (person)

Number of students (person)

Teacher-faculty ratio

13.7 15.4 14.1 12.0

192.3 211.4 143.5 232.2

7.1:100 7.3:100 9.8:100 5.2:100

school (2.3 km vs. 7.2 km) are less than those for the eastern and western regions (as is shown in Fig. 9.8). As is shown in Table  9.7, the average number of primary school teachers in rural areas is 13.7, while the figure for students is 192.3. The average number of primary school students in the western region (232.2) is higher than the figures for the central and eastern regions. By comparison, the student-faculty ratio in primary schools in the central region is better than the figures for the eastern and western regions, reaching 9.8:100.

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191

5000.0 4420.9

4500.0 The Average Salaries yuan

4000.0 3500.0

3721.3

3820.9

3708.5

3191.4

2999.3

3000.0

3294.5

2475.2

2500.0 2000.0 1500.0 1000.0 500.0 0.0

National

Eastern Region

Central Region

Western Region

The average salary for primary school teacher The average salary for junior high school teacher Region

Fig. 9.9  The average salaries for rural primary school teachers and junior high school teachers

The national average salary for primary school teachers is 3191.4 yuan per month, while the eastern region ranks the highest with a salary of 3820.9 yuan per month, followed by the western and central regions (3294.5 and 2475.2 yuan per month, respectively). The salary for junior high school teachers is 3721.3 yuan per month. The eastern region ranks the highest with a salary of 4420.9 yuan per month, followed by the western and central regions (3708.5 and 2999.3 yuan per month, respectively) (see Fig. 9.9). 9.2.4  Other Infrastructure Table 9.6 reports the situation of medical services, financial services, fitness facilities, broadband and other infrastructure in rural communities. In terms of medical services, the coverage rate of medical centers is 79.6% in rural communities. On the regional level, the central region accounts for the highest proportion with 87.2%, while the eastern and western regions represent 73.9% and 78.8%, respectively. The coverage rate of hospitals in

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35.0

31.3

Percentage %

30.0 25.0

21.8

20.0

16.5 13.4

15.0 10.0 5.0 0.0

National

Eastern Region Central Region Western Region Region

Fig. 9.10  The proportion of households who have installed broadband in rural areas

rural areas is 2.4%, which means that 1 in around 40 administrative villages has one hospital. Figures for the eastern, western and central regions are 1.8%, 2.1% and 3.1%, respectively. In terms of financial services, the average coverage rate of village banks in rural areas is 2.6%. On the regional level, the percentage for the eastern region is 5.6%, which is 3% points higher than the national average. In terms of fitness and entertainment facilities, the coverage rate of public fitness facilities in rural areas is 47.1%. Furthermore, the average coverage rate of broadband in rural areas is 67.4% across the nation, while the figure for the eastern region is 85.4%, higher than that of the central and western regions, which are 70.8% and 48.8%, respectively. Figure 9.10 further illustrates the proportion of households that have installed broadband in rural areas. Nationally, in 2015, one in around five families on average has installed broadband in rural areas and about one-­ third of rural families have installed broadband in the eastern region, ranking the first among the three regions.

9.3   Overview of Rural Industries The operating situation of rural industries reflects the economic situation of rural communities, which provides a strong reference and support for understanding relevant information on rural industries, benefiting the

9  COMMUNITY ENVIRONMENT OF RURAL HOUSEHOLDS 

45.0 40.0

36.3

Percentage %

35.0

38.9

193

37.3 32.1

30.0 25.0 20.0 15.0 10.0 5.0 0.0

National

Eastern Region Central Region Western Region Region

Fig. 9.11  The percentage of rural communities with special industries

development of special industries in rural communities and facilitating further development of the communities’ economy. The database centers on the overview of rural industries. As is shown in Fig. 9.11, the percentage of rural communities that have special industries is over 30% on average nationally. However, there is a huge difference among the three regions. Rural communities in the eastern region have a large number of special industries which make up 38.9% of the total number, followed by the western region with a percentage of 37.3%. By comparison, the central region has a lower percentage of special industries, representing only 32.1%. The gross value of the special industries’ output is an important indicator of benefits brought by community industries. Figure 9.12 illustrates the average output value of special industries in the central and western rural communities in 2015. From the perspective of average output value, eastern rural communities rank the first, reaching 5.348 million yuan, followed by the 3.613 million yuan of western rural communities. By contrast, the central region has the lowest output of 1.793 million yuan. The development of special industries in rural communities makes full use of local resources and produces economic benefits. Figure 9.13 shows the percentages of various local resources in the special industries of rural communities. Among local resources, material products have been developed in a wide range, representing 66.6%. Natural scenery is also one of

The Average Output Value 10,000 yuan

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600.0

534.8

500.0 400.0

380.2

361.3

300.0 179.3

200.0 100.0 0.0

National

Eastern Region

Central Region

Western Region

Region

Fig. 9.12  The average output value of special industries in rural communities Fig. 9.13  The percentages of various local resources in the special industries of rural communities

Non-material Others Products 0.2% 5.2% Natural Scenery 28.0%

Material Products 66.6%

the channels to help improve the economic level of rural communities, representing 28.0%. By comparison, the development of nonmaterial products and human scenery falls behind. The development of special industries in rural areas is not achieved overnight. As early as the 1950s, some rural communities had already established special industries to improve the development of communities’

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195

30 25 20 15 10 5 0

1949 1958 1965 1968 1973 1975 1978 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015

Number per thousand village

35

Year

Fig. 9.14  The number of special industries in rural communities in different years (unit: per 1000 villages)

economy. Figure 9.14 shows the number of special industries established in different years. Before 1980, the number of special industries established in rural communities was small, averaging at 2–3 industries for every 1000 villages. The number of special industries established in rural communities witnessed an increase from 1980 to 1999, during which more than five years saw the figure exceeding 10 industries for every 1000 v­ illages. Rural communities achieved a remarkable economic progress in this stage. The development of special industries in rural communities reached a climax after 2000. In 2000, 2005 and 2010, the number of special industries for every 1000 villages reached 31, 33 and 29, respectively. Special industries have played a dynamic role in rural communities, attracting more and more rural communities to engage in the development of special industries, which shows a positive developing trend. The founding and operating models vary with different natural, economic and sociocultural conditions in rural communities. Figure  9.15 illustrates several main founding models of special industries in rural communities. With the purpose of improving the living standard, the community masses have actively engaged in the exploitation of special industries. The community masses founded 54.5% of special industries in rural ­communities. Since a certain number of special industries needs ­professional

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founded by collectiveassets 3.6%

others 1.7%

guided by superior government 17.3%

founded by community cadre 15.8%

founded by community mass 54.5%

founded by foreign investment 7.1%

Fig. 9.15  The founding models of special industries in rural communities

guidance and prospective planning, superior government and community cadres are of great importance. The percentages of special industries founded with the guidance of superior government and community cadre are 17.3% and 15.8%, respectively, while the figure for those founded by both foreign investment and collective assets is over 10.0%. Figure 9.16 illustrates several main operating models of special industries in rural communities. Of the rural communities, 82.8% chose the family-oriented and individual operating model, while 9.8% chose the community cooperating model. By comparison, only 5.9% of rural communities chose the non-native population to run their special industries.

9.4   Collective Assets and Debts The information on the collective assets and debts in rural communities reflects the operating situation of communities’ assets. Analyzing the basic situation and the forming reason of collective assets and debts provides useful information to help increase the overall production efficiency and improve the collective well-being. As is shown in Table 9.8, the average net income of collective assets in rural communities reaches 28,000 yuan. The net income is on the decrease progressively from east to west. The figure for the western region is 7000

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non-native operation-model 5.9%

197

others 1.5%

community cooperation model 9.8%

family-oriented and individual operation models 82.8%

Fig. 9.16  The operating models of special industries in rural communities Table 9.8  The basic situation of collective asset and debt in rural communities (unit: 10,000 yuan)

National Eastern region Central region Western region

Net income of collective assets

Current collective debts

New collective debts

Collective debts returned

2.8 4.9 3.1 0.7

29.0 55.8 26.3 8.1

5.7 5.7 5.2 6.5

6.0 7.4 3.4 8.4

yuan, which is dramatically lower than the other two regions. In terms of the collective debts of rural communities, the average figure across the nation is 290,000 yuan, with the eastern region ranking first (558,000 yuan), followed by 263,000 yuan of the central region and 81,000 yuan of the western region. Furthermore, the new debt for the western region ranks first among the three regions, with the highest being 65,000 yuan. Figures 9.17 and 9.18 illustrate the objects of collective debts and the reasons for debt in rural communities. Of 68.9% the rural communities, 68.9% owe a debt to individuals and the percentages for those who own a debt to enterprises and superior governments are 14.3% and 7.7%, respectively. The main reason for borrowing money is that rural communities

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enterprises 14.3% superior governments 7.7%

others 9.1%

individuals 68.9%

Fig. 9.17  The objects of collective debts in rural communities payment of taxes in rural communities 10.8%

others 17.9%

payment of workers' salary in rural communities 3.7%

large-scale projects in rural communities 53%

public spending in rural communities 14.6%

Fig. 9.18  The main reasons for borrowing money in rural communities

have to support the establishment of large-scale projects, pay for the ­public spending and taxes in rural communities and offer the salary for communities’ workers. Figure 9.19 reflects the main capital sources of collective debts returned in rural communities. Of the rural communities, 24% rely on the superior

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collective income of communities 12.8%

others 59.3%

support from superior governments 24.0%

various loans 3.9%

Fig. 9.19  Main capital sources to repay the collective debts in rural communities

government to repay their debts and 12.8% use the collective income to refund the borrowed money, while the percentage for those that rely on various loans is 3.9%. In addition, 59.3% of rural communities choose other ways to repay the collective debts.

9.5   Community Spending and Income 9.5.1  Community Spending Indicators including community spending and cash flow show more intuitively how capital is currently disposed in rural communities, since clear spending is conducive for rural communities to increase income and reduce expenditure in order to better use capital to invest in major expenditure projects. As is shown in Fig.  9.20, there is an obvious regional difference in expenditure for rural communities in 2015. The average spending in the eastern region is up to 457,000 yuan, ranking first among three regions, following which are the figures for the western and central regions (209,000 and 162,000 yuan, respectively). Nationally, the average expenditure throughout the year is 281,000 yuan in rural communities. Table 9.9 summarizes the major expenditure projects in rural communities in 2015. In the eastern rural communities, most projects are public

Expenditure 10,000yuan

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50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0

45.7

28.1 20.9 16.2

National

Eastern Region Central Region Western Region Region

Fig. 9.20  The expenditure of rural communities (unit: 10,000 yuan)

affairs with the expenditure averaging at 192,000 yuan which represents 41.9% of the total spending. Projects invested by collective economy have the average expenditure of 142,000 yuan, representing 31.0%. In the central region, the main expenditure projects are public facilities and collective economic investment, although both have relatively lower percentages and spending costs. In the western region, the main items of expenditure are public facilities with the expenditure averaging at 97,000 yuan. The percentage of expenditure on public affairs is the highest, which reaches 40.4% on average nationally. 9.5.2  Community Income Indicators including community income and its source introduce more intuitively the situation of the current income in rural communities. Diversified capital inflow channels have guaranteed the implementation of various tasks in rural communities. As is shown in Fig. 9.21, there is a regional difference of income in rural communities. In 2015, the average income in rural communities across the nation was 251,000 yuan, while the figures for the eastern, central and western regions were 302,000, 307,000 and 162,000 yuan, respectively. Figure 9.22 illustrates the structure of main incomes in rural communities in 2015. It is clear from the chart that in 2015, 57.2% of income in

Dividend issue of communities’ residents Collective economic investment Social assistance of communities’ residents Expenditure on communities’ public affairs

Expenditure projects

3.0 19.0 7.8 40.4

6.7 2.0 11.5

Percentage (%)

0.8

Mean (10,000)

National

19.2

2.7

14.2

1.2

Mean (10,000)

41.9

6.0

31.0

2.6

Percentage (%)

Eastern region

Table 9.9  The main expenditure projects in rural communities

5.3

1.4

3.1

0.7

Mean (10,000)

32.9

8.9

18.8

4.2

Percentage (%)

Central region

9.7

1.8

1.5

0.4

Mean (10,000)

46.4

8.5

7.2

2.1

Percentage (%)

Western region

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35.0

Income 10,000yuan

30.0 25.0

30.2

30.7

25.1

20.0

16.2

15.0 10.0 5.0 0.0

National

Eastern Region Central Region Western Region Region

Fig. 9.21  The situation of income in rural communities (unit: 10,000 yuan) donations from enterprises and other organisations administrative charges or fines 4.7% 1.8%

financial subsidy or return from superior governments 57.2%

benefits of collective asset 36.3%

Fig. 9.22  The structure of main incomes in rural communities

rural communities came from financial subsidies or refunds from superior governments and 36.3% came from the returns of collective assets, while 4.7% was contributed by enterprises, individuals or other organizations. The other 1.8% was composed of administrative charges or fines.

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Table 9.10  The implementation of rural community projects Number of projects National Eastern region Central region Western region

0.8 0.7 0.8 0.9

Project funds (10,000 yuan)

Supporting finance (10,000 yuan)

111.3 99.4 63.5 167.6

18.9 14.1 17.5 24.2

9.5.3  The Implementation of Community Projects The implementation of rural community projects focuses on resolving some urgent problems facing rural communities and facilitates the proceeding of other tasks. As is shown in Table  9.10, in 2015, the average number of projects launched in one rural community is 0.8 and the average fund for the project is 1.113 million yuan, while the figure for the supporting finance is 189,000 yuan. Western rural communities have the largest quantity of projects and project funds, reaching 1.676 million yuan, which is 2.6 times the figure for the central rural communities. In addition, the ­supporting finance in the western region averages at 242,000 yuan, which is 1.7 times the figure for the central rural communities.

9.6   Agricultural Land, Land Expropriation and Demolition 9.6.1  Overview of Agricultural Land Agricultural land is the basic carrier of production and living for farmers in rural areas. It is beneficial for farmers to improve production efficiency and make better use of land by understanding the information about land approval, land circulation, agricultural land output value and land vacancy. Land approval and circulation in rural communities help improve the land operating efficiency. Table  9.11 reflects the basic situation of land approval right and operating-right circulation in rural communities. Of the rural communities, 66.6% across the nation have land approval right, in which the western region ranks first among the three regions with the figure reaching 77.9%. The percentage of noncontractors who cultivate the land by themselves in the western region is relatively high (36.1%), which

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Table 9.11  The situation of land approval and land circulation in rural communities National

Eastern region

Central region

Western region

66.6

69.2

48.7

77.9

27.1

20.5

22.9

36.1

65.7

66.9

73.3

59.0

The percentage of communities having land approvals (%) The percentage of noncontractors who cultivate the land by themselves (%) The percentage of communities in free circulation of land operating-right (%)

70.0

62.8

65.6 66.2

Percentage 100%

60.0

59.3 59.4

57.8 45.8

50.0

52.1

50.8 42.3 44.1

40.0

40.3

30.0 20.0 10.0 0.0

Cultivation Section National

Sowing Section

Eastern Region

Central Region

Harvest Section Western Region

Fig. 9.23  The percentages of machine being used in various sections in rural communities

is 1.8 times the figure for the eastern region. By contrast, the p ­ ercentage of communities in free circulation of land operating-right is lower than the figures for the central and eastern regions, accounting for 59.0%. As is shown in Fig. 9.23, there are obvious regional differences in the percentages of various sections using mechanical equipment. In cultivation and harvest, the percentages for machines being used in the eastern and central regions are higher than the figure for the western region. Especially in harvest, the percentages of the eastern and central regions are both over 59% in contrast to the 40.3% in the western region. Additionally,

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Table 9.12  The percentages of economic crops cultivated in rural communities and the average output value The percentage of economic crops acreage (%) National Eastern region Central region Western region

The average output value (10,000)

34.6 38.0 31.7 34.0

129.1 159.1 91.7 133.7

Table 9.13  Cultivated acreage and unused agricultural acreage in rural communities

National Eastern region Central region Western region

Cultivated acreage (acre)

Unused agricultural acreage (acre)

Vacancy rate (%)

1914.9 1011.4

135.9 68.6

7.1% 6.8%

2327.5

94.8

4.1%

2382.9

226.8

9.5%

in the sowing process, rural communities in the eastern region have an advantage with a proportion of 50.8%, while the figures for the central and eastern regions are 42.3% and 44.1%, respectively. Table 9.12 shows the percentages of economic crops cultivated in rural communities and the average output value in 2015. The average output value of economic crops in 2015 reaches 1.291 million yuan in rural communities. On the regional level, 38% of crop acreage is used to cultivate economic crops in the eastern rural communities where the average o ­ utput is the highest, reaching 1.591 million yuan, while the figure for the central rural communities is 31.7%, the lowest among the three regions with an average output value of 917,000 yuan. As is shown in Table 9.13, nationally, 135.9 acres of land in rural communities on average are left unused and the land utilization rate is 92.9%. On the regional level, 6.8% of lands in the eastern rural communities are left unused and the land utilization rate in the western region is as high as 9.5%, which is more than two times the figure for the central region (4.1%).

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9.6.2  Land Expropriation and Demolition Most rural communities across the nation are concerned about land expropriation and demolition. In the process of land expropriation, the scale of land acquired, the compensation standard as well as the resettlement are among the most concerned issues. Table 9.14 introduces the basic situation of land expropriation in rural communities in 2015. Nationally, in 2015, 0.5% of villages on average have experienced land expropriation. The percentage for the eastern rural communities that have experienced land expropriation is 0.8%, ranking first among the three regions with 343.8 acres of land being required on average, while the figures for the central and western regions are 0.6% and 0.3%, respectively, and the average number of land required in the western region is only 49 acres. In terms of land expropriation and demolition, the compensation standard in rural communities varies with different regions. It is clear from Fig.  9.24 that the compensation standard witnesses a gradual decrease from east to west. On the regional level, the average compensation standard in the eastern rural communities is 45,000 yuan per mu, higher than the 25,000 yuan per mu in the central region and the 18,000 yuan per mu in the western region. Apart from compensation, the resettlement is also in the interest of a large number of farmers. Table 9.15 shows the resettlement situation in surveyed rural communities from July 2013 to 2015. On a national scale, the average number of resettled citizens in every rural community is 54.6. Furthermore, the resettlement ratio inside communities is 28.1% and the figure for unified building is 21.0%, while the average number for those who apply for social security by the means of resettlement is 6. On the Table 9.14  The basic situation of land expropriation in rural communities National The percentage of communities having land expropriation (%) The average area of land required (care)

Eastern region

Central region

Western region

0.5

0.8

0.6

0.3

226.8

343.8

246.7

49.0

Notes: The number of communities having land expropriation refers to the number of communities whose land have been confiscated by governments from 2013 till the survey time

Average Compensation Standard 10000 yuan per acre

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5.0

207

4.5

4.0 2.9

3.0

2.5 1.8

2.0 1.0 0.0

National

Eastern Region

Central Region

Western Region

Region

Fig. 9.24  The average compensation standard for communities having land expropriation Table 9.15  The situation of land expropriation and resettlement in rural communities

National Eastern region Central region Western region

Population

The resettlement ratio inside communities (%)

The resettlement The number of people who ratio for unified apply for social security by buildings (%) the means of resettlement

54.6 6.0

28.1 5.5

21.0 13.8

6.0 0.0

99.0

60.8

23.7

12.3

49.9

8.0

26.1

3.3

regional level, the land expropriation and resettlement perform well in the central rural communities with the average number of resettled citizens reaching 99 and the internal resettlement ratio of 60.8%. In addition, the resettlement ratio for unified building is 23.7% in the central rural communities and the average number for those who apply for social security by the means of resettlement is 12.3.

CHAPTER 10

Governance and Public Services of Rural Communities

This chapter uses the research data on rural communities in the China Rural Household Panel Survey (CRHPS), similar to the preceding chapter, to analyze the basic situation of the governance and public service of rural communities. The main body, conditions and mechanism of governance in rural communities are analyzed. The chapter also includes a synopsis of religious belief in social organizations, social security, environmental protection, community training, as well as other public services. According to the research, rural communities in China now have 7.2 village committee staff on average. Among all party branch secretaries in rural communities, 90.2% are males, with the majority graduating from junior middle school. Nationally, 57.5% of rural communities have established standing deliberative institutions, while 66.4% of them have introduced self-­ governance regulations. The types of dispute meditation in which rural communities get involved in mainly include marital conflict, accounting for 50.5%, followed by property right dispute, 34.5%. Services bought by rural communities from social organizations are mainly elderly care and large-scale social activities. Rural communities have a relatively high proportion of Buddhist belief, accounting for 24.9%. Over a half of rural communities are suffering ecological degradation.

© The Author(s) 2019 W. Qian et al., Societal Development in Rural China, https://doi.org/10.1007/978-981-13-8082-2_10

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10.1   Main Governance Body and Mechanism 10.1.1  Main Governance Body The village committee and other staff are the main governance body and undertaker of supervision in rural communities. They are responsible for decision-making, organizing, commanding, coordinating and supervising, and thus critical to achieve village self-governance. Table 10.1 summaries the basic situation of main governance body in rural committees. Rural communities have 7.2 village committee staff and 2.9 other staff on average. Rural communities in the western region have the largest staff number, with figures for two categories being 8 and 3.7, respectively. The eastern region has fewer staff, with 5.9 village committee staff and 1.9 other staff. Figure 10.1 and Table 10.2 reflect the basic situation of party branch secretaries in rural communities, including gender and education background. On average, 90.2% of them are males and 9.8% are females, representing a relatively vast gender difference. Table 10.1 Basic situation of main governance body in rural communities (unit: person)

Fig. 10.1  Gender composition of party branch secretaries in rural communities

Number of village Number committee staff of other staff National Eastern region Central region Western region

7.2 5.9 7.7 8.0

2.9 1.9 3.0 3.7

Female 9.8%

Male 90.2%

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Table 10.2  Composition of education background of party branch secretaries (unit: %)

National Eastern region Central region Western region

Below primary school

Primary school

Junior high school

High school

0.3 0.0

3.7 5.7

36.6 29.1

31.5 21.8

0.7

2.2

31.7

0.3

3.1

46.9

Secondary school

Junior college

University

7.7 15.2

16.9 24.0

3.3 4.2

43.8

3.6

13.0

5.0

30.7

4.2

13.8

1.0

Notes: High school in the table includes professional high school

Table 10.3 Monthly subsidy and education background of staff in two committees

National Eastern region Central region Western region

Monthly subsidy (yuan)

Number of staff with junior college or above (person)

499.7 492.6 416.5 574.3

0.6 0.7 0.5 0.5

Table 10.2 reflects the composition of education background of party branch secretaries. Generally speaking, only 3.3% of them have university degrees. Secretaries with junior middle school degree are in the majority, accounting for 36.6%. The percentage of those with high (vocational) school degree is relatively high, at 31.5%. Regionally, secretaries with junior college degree or above in the eastern, central and western regions account for 28.2%, 18% and 14.8%, respectively. Table 10.3 reflects the basic situation of staff in two rural committees (rural village committee and party committee). The average monthly subsidy for staff in the two committees is 499.7 yuan. Rural communities in the western region have the highest subsidy (574.3 yuan). Two committees in the eastern region have the largest number of staff with junior college degree or above, averaged at 0.7, while those in the central and western regions have around 0.5 staff with the same education background. Table 10.4 reflects the basic situation of party members in rural communities, with an average of 31.7 members in each community. Party

National Eastern region CentralRegion Western region

31.7 32.2 30.7 32.0

Number of party member

Aged above 60

Junior college or above

Female party members

4.2 4.3 4.3 4.0

13.2 13.4 14.0 12.5

11.8 12.6 12.5 10.5

37.2 39.1 40.7 32.8

2.5 3.3 2.2 2.0

7.9 10.2 7.2 6.3

4.4 4.8 3.3 4.8

13.9 14.9 10.7 15.0

Average Percentage Average Percentage Average Percentage Average Percentage

Aged less than 30

Table 10.4  Situation of party members in rural communities

1.1 1.0 0.9 1.3

Average

3.5 3.1 2.9 4.1

Percentage

Newly recruited party members in 2014

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members, aged less than 30 and above 60, account for 13.2% and 37.2%, respectively. Among them, 7.9% have junior college degree or above and 13.9% are female. The level of education background descends gradually from the east to the west. Rural communities in the eastern region have 3.3 party members with junior college degree or above, while those in the central region have 2.2 members. The western region has 2 members, accounting for 6.3%. 10.1.2  Governance Mechanism Some important indicators of governance mechanism improvement in rural communities include the establishment of standing deliberative institutions, self-governance regulations, grid management and villager’s representative meeting system. As is shown in Table 10.5, concerning the governance mechanism in rural communities, 57.5% of them have established standing deliberative institutions. The percentage in the central region is 65%. Of the rural communities, 66.4% have introduced self-governance regulations and the coverage rate in the eastern region is 78.6%. Rural communities that have implemented grid management, however, only account for 24.7%. The highest coverage rate is in the eastern region, with only 32.1%. Villagers’ representative meeting is an important channel for farmers to express their opinion. As is shown in Fig. 10.2, 6.9 rural community meetings are held on average in the eastern region and about 5 meetings are convened in the central and western regions. The national average is 5.7 times.

Table 10.5  Basic situation of rural community governance mechanism (coverage rate) (unit: %)

National Eastern region Central region Western region

Standing deliberative institution

Self-governance regulation

Grid management

57.5 47.1 65.0 60.8

66.4 78.6 73.8 49.7

24.7 32.1 21.1 21.0

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The Number of Times

8.0

6.9

7.0 6.0

5.7

5.1

5.0

5.0

4.0 3.0 2.0 1.0 0.0

National

Eastern Region Central Region Western Region Region

Fig. 10.2  The number of times of villager’s representative meeting held in rural communities in 2015 Table 10.6  Basic situation of rural community governance conditions nationwide and in the Eastern, Central and Western Regions Communities with independent office

National Eastern region Central region Western region

Communities with one-stop service window or office hall

Number

Percentage

Number

Percentage

533 173 153 207

92.4 90.1 91.1 95.4

221 92 53 76

38.3 47.9 31.6 35.0

10.2   Governance Conditions Sound governance conditions are conducive to the governance improvement in rural communities. It not only helps standardize the process of various tasks and improve working efficiency of community staff, but also contributes to the reduction of disputes caused by delaying services and inconvenience. Table 10.6 reflects the basic situation of governance conditions in rural communities. Of the rural communities in the eastern, central and western regions, 90% have independent offices. Western region has the highest percentage, up to 95.4%. National rural communities with

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215

one-stop service window account for 38.3%. The eastern region has a relatively large number of rural communities with one-stop service, accounting for 47.9%, 10 percentage points higher than those of the central and western regions.

10.3   Disputes and Settlement in Communities The level of communities’ governance directly affects the overall performance of social governance. Social rule of law will action well only when social governance is enhanced and innovated, the level of the rule of law in rural communities is promoted, all affairs in communities are directed into the orbit of the rule of law, and standardized operation is achieved. According to the research, rural communities across the nation had fewer incidents of sudden crises in the past five years. Only 3.6% of rural communities had such incidents and 1.9% had mass disturbances. As is shown in Table 10.7, the types of meditation work in which rural communities have participated in mainly include marital conflict, roughly accounting for 50.5%, followed by property right dispute for about 34.5%. Support and inheritance dispute accounts for 24.7%. According to statistics in the research, the percentage of national rural communities with correction population1 is 8.4%. Communities with specialized correction institutions (including addiction treatment center) account for 2.6%. Table  10.8 further reflects that the percentage of national rural communities providing correction services is relatively high, at 78.6%. Communities providing psychological rescue, relationship restoring and career guidance account for 43.6%, 41.2% and 32%, respectively. Table 10.7  Situation of rural communities’ participation in meditation (unit: %) Property Property Debt Marital Custody and Support Others right dispute credit conflict guardianship and dispute dispute inheritance National Eastern region Central region Western region

34.5 26.1 29.9 45.6

5.3 2.6 9.3 4.5

Notes: This is a multiple-choice question

14.5 11.9 10.7 19.8

50.5 42.8 51.2 57.0

18.2 14.3 17.0 22.7

24.7 21.3 21.8 30.0

18.2 14.3 17.0 22.7

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Table 10.8  Percentage of professional correction services provided by rural villages (unit: %) Career guidance National Eastern region Central region Western region

32.0 2.7 26.6 62.8

Psychological assistance 43.6 9.0 56.0 68.4

Social relation restoring

Behavior modification

Others

41.2 8.2 23.9 82.6

78.6 96.1 45.5 82.3

1.8 4.0 0.0 1.0

10.4   Social Organizations and Religious Belief 10.4.1  Overview of Social Organizations Social organizations are nongovernmental and nonprofitable enterprises founded by civil societies for the public good. Although the number of social organizations is still low in rural communities, their role in grass-­ roots government, public services, et cetera cannot be overlooked. From Table 10.9, the total number of social organizations in communities in various regions across the country is 243 social organizations in 578 administrative villages included in the research. In terms of geographical distribution, the eastern region has the largest number of social organizations (103), followed by the western region (87). The central region has the fewest social organizations (53). A large number of social organizations are founded by community residents, while a few are established by people outside the community. As is shown in Fig. 10.3, in terms of the types of social organizations, the number of hobbies and interests of social organizations is the largest, accounting for 51.1%. There are also many public service social organizations, accounting for 40.0%. The percentage of economic cooperation social organizations is 15.6%, while the figure for rights protection is relatively low, at 5.8%. 10.4.2  Public Services Provided by Social Organizations Although people’s demand for public services is increasing, many prominent issues in various sectors still remain, including low-quality, small-scale and imbalanced development. By making use of market mechanisms,

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Table 10.9  Situation of social organizations in rural communities nationwide and in the Eastern, Central and Western Regions Social organizations founded by community residents

Social organizations founded by people outside the community

Total

235 101 51 83

8 2 2 4

243 103 53 87

National Eastern region Central region Western region

60.0 51.1

Percentage %

50.0 40.0

40.0

30.0 20.0

15.6

10.0 0.0

5.8 Public Service

Hobbies and Rights Protection Economic Interests Cooperation

Fig. 10.3  Types of social organizations in rural communities nationwide

government procures services from social forces in order to directly hand over a part of public services to eligible social forces in a certain way. Payment shall be made in accordance with the quantity and quality of services. From Fig. 10.4, the average expenditure of national rural communities to support the development of social organizations is 3232.8 yuan, with the largest in the eastern region (3927.6 yuan). The central region ranks the second with 3217.2 yuan, followed by the western region with 1697.8 yuan.

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4500.0

3927.6

4000.0 Expenditure yuan

3500.0

3232.8

3217.2

3000.0 2500.0 2000.0

1697.8

1500.0 1000.0 500.0 0.0

National

Eastern Region Central Region

Western Region

Region Fig. 10.4  Expenditure of rural communities to support the development of social organizations

From Fig. 10.5, nationally, elderly care and large-scale social activities are two main fields where rural communities buy services from social organizations, accounting for 40% and 37.7%, respectively. Of the rural communities, 16.4% have bought child care services and 12.4% have bought poor family care service. Communities’ purchase of disabled care accounts for 7.2%. From Fig.  10.6, in terms of funds invested by rural communities to purchase services provided by social organizations, a relatively large difference is found among various regions, with a national average of 98,000 yuan. The central region has the largest number, 231,000 yuan, while funds of the eastern and western regions average at 24,000 and 55,000 yuan, respectively. 10.4.3  Religious Belief Religious belief, a type of various faiths, is the unwavering belief and mental and physical devotion derived from worship and identification among a group of people who believe in religion and a sacred object (including specific doctrines etc.). Such thought, faith and devotion are shown and incorporated in specific religious rituals and activities to guide and standardize people’s behavior in society. Religious belief is a special social ide-

10  GOVERNANCE AND PUBLIC SERVICES OF RURAL COMMUNITIES 

45.0

40.0

40.0

219

37.7

Percentage %

35.0 30.0 25.0 20.0

16.8

16.4 12.4

15.0 7.2

10.0 5.0 0.0

Care for the Teenagers and Care for the Elderly Children Care Disabled

Large-scale Social Activities Types

Poor Household Care

Others

Fig. 10.5  Situation of service purchase from social organizations in rural communities 25.0

23.1

Funds 10,000 yuan

20.0 15.0 10.0

9.8 5.5

5.0 0.0

2.4 National

Eastern Region Central Region Western Region Region

Fig. 10.6  Funds invested by rural communities to purchase services provided by social organizations

ology and cultural phenomenon. From Table  10.10, communities with Buddhist belief have the highest percentage, accounting for 24.9%, followed by Christianity (23.1%). Communities without a religious belief account for 55.3%.

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Table 10.10  Religious belief in rural communities (unit: %)

National Eastern region Central region Western region

Buddhism

Taoism

Christianity

Catholicism

Islam

Others

None

24.9 22.9 20.1 30.5

4.4 3.4 1.1 7.8

23.1 22.3 37.4 12.8

5.8 8.6 4.6 4.2

2.0 0.2 1.4 4.2

0.0 0.1 0.0 0.0

55.3 59.2 51.0 55.1

10.5   Community Cadres and College Graduate Village Officials 10.5.1  Community Cadres The monthly subsidy for communities’ cadres, to some extent, affects the attitude and effect of community governance. A monthly subsidy that lives up to the cadres’ expectation can stimulate work vitality, improve working efficiency and promote the advancement of rural communities’ governance. Table 10.11 reflects the cadres’ attitude and expectation to monthly subsidy (real income). Cadres who think subsidy income is unreasonable take up the largest proportion. The situation is particularly severe in the central region, which is accounting for 70.6%. Rural communities in the western region have the highest expectation for subsidy (3142.4 yuan), followed by 2229.7 yuan in the eastern region. The central region has the lowest expectation (1849.7 yuan). 10.5.2  College Graduate Village Officials There are an increasing number of college graduate village officials joining in rural communities in recent years. Table 10.12 reflects the basic situation of college graduate village officials in 2015, with 0.7 cadres in each rural community. The number of officials in each community who work for a long term is 0.4, with an average service length of 2.7 years. The western region has the largest number of college graduate village officials (0.8 person), the lowest number of cadres who work in communities for a long term (0.3 persons), and the lowest service length (2.2 years).

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Table 10.11  Cadres’ attitude and expectation for monthly subsidy Attitude toward the subsidy

National

Eastern region

Central region

Western region

Unreasonable (%) Neither good nor bad (%) Reasonable (%) Expected subsidy (yuan)

52.1 23.4 24.5 2469.5

48.1 16.4 35.5 2229.7

70.6 15.3 14.1 1849.7

41.1 35.6 23.3 3142.5

Table 10.12  Basic situation of college graduate village officials

National Eastern region Central region Western region

Number of college graduate village officials (persons)

Number of cadres working for long term (persons)

Average service length (years)

0.7 0.6 0.6 0.8

0.4 0.4 0.5 0.3

2.7 2.9 3.1 2.2

10.6   Social Security 10.6.1  Unified Urban and Rural Health Insurance Unified urban and rural health insurance is aimed to abandon the old concept and practice which emphasize urban region over the rural region and advocate administrating city and village separately. Structural reform and policy adjustment are conducted to narrow and gradually eliminate the gap between urban and rural region and promote urban-rural integration. Figure 10.7 reflects that around 69.0% of rural communities have implemented unified urban and rural health insurance system. The eastern region has the highest coverage rate (83.7%), followed by the central region and western region (67.9% and 56.8%, respectively). Figure 10.8 illustrates that the standard for the national average subsidy for people insured in the new cooperative medical care is 185 yuan per person. The standard subsidy for insured people in the central region is lower than that of the nation, averaged at 62.4 yuan. The average standard subsidies in the eastern and western regions are 207.4 and 196.1 yuan per person, respectively.

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90.0

80.0

Percentage %

70.0

83.7

69.0

67.9 56.8

60.0

50.0 40.0

30.0 20.0 10.0

0.0

National

Eastern Region

Central Region Western Region

Region

Fig. 10.7  Coverage rate of unified urban and rural health care in rural communities 250.0

Unit yuan per person

207.4 200.0

196.1

185

150.0 100.0 62.4 50.0 0.0

National

Eastern Region Central Region

Western Region

Region

Fig. 10.8  Subsidy standard for people insured in the new cooperative medical care

10.6.2  Minimum Standard of Living Figure 10.9 illustrates that the minimum standard of living nationwide, in the eastern region and in the central region in 2015 is 842.2, 672.8 and 879.4 yuan per person, respectively. The western region has the highest

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223

1200.0

Unit yuan per person

1000.0

879.4

842.2

800.0

956.1

672.8

600.0 400.0 200.0 0.0

National

Eastern Region Central Region Western Region Region

Fig. 10.9  Minimum standard of living in rural communities

minimum living level with 956.1 yuan per person. According to statistics in the research, payment of minimum living allowance is made in three ways: monthly, quarterly and yearly. The percentage of quarterly payment is the highest, at 52.2%, while those of monthly and yearly payments are 37.1% and 10.7%, respectively. 10.6.3  Maternity Insurance As is shown in Fig. 10.10, most rural communities do not implement unified maternity insurance system for urban and rural residents, with only 28.0% putting it into practice. The percentage of rural communities with maternity insurance system in the eastern region is 44.3%, higher than the national average. The percentage of the central region is 30.9%, slightly higher than the national one. The western region has the lowest percentage (11.4%). 10.6.4  Pension Insurance Figure 10.11 suggests that rural communities in the western region have the highest subsidy for people insured in the new rural pension insurance, which is approximately 307.3 yuan per person. Subsidy in the eastern region is the lowest with around 84.8 yuan per person, while the national average is around 169.2 yuan per person.

Percentage %

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50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0

44.3

30.9

28.0

11.4

National

Eastern Region Central Region Western Region Region

Fig. 10.10  Percentage of rural communities with unified maternity insurance system for urban and rural residents 350.0

307.3

unit yuan per person

300.0 250.0 200.0

169.2

150.0

127.6

84.8

100.0 50.0

0.0

National

Eastern Region

Central Region Western Region

Region

Fig. 10.11  Subsidy for people insured in new rural pension insurance

10.6.5  Other Social Security Programs Rural communities are the important main body of conducting rural social security work. Communities’ care and support for its members directly affect the result of social security. As is shown in Fig. 10.12, there are a few

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225

30.0 24.6

Percentage %

25.0 20.0 15.0

13.5 9.1

10.0

7.1

5.0 0.0

National

Eastern Region Central Region Western Region Region

Fig. 10.12  Percentage of rural communities with home-based care facilities and programs for the elderly

of communities possessing home-based care facilities and programs for the elderly (13.5%). The percentage of the eastern region is relatively higher (24.6%), while those of the central and western regions are only 9.1% and 7.1%, respectively. As is shown in Table 10.13, helping the disabled apply for subsidy is the main supporting measure in national rural communities, accounting for 78.3%. Other support measures are relatively scarce. Rural communities in the eastern and central regions without helping measures for the disabled account for 20.8% and 23.3%, respectively. As is shown in Table 10.14, among all community services across the nation, rural communities with community care services have the highest percentage, at around 67.7%. The percentages of communities with career guidance, career correction and community rehabilitation are 52.3%, 31.6% and 29.5%, respectively. Rural communities providing community rehabilitation in the western region account for 48.2%, relatively higher than other communities. In general, rural communities have accelerated the establishment of social security system covering residents. Progress has been made in social security. The overall situation, however, still has some room for improvement.

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Table 10.13  Measures implemented by rural communities to help the disabled (unit: %) Measures to help the disabled Helping the disabled apply for and receive subsidies Day care and recovery measures provided by communities Public facilities and accessibility facilitiesProvided by communities Braille vote for people with visual impairment Programs to provide employment for the disabled No above service provided

National

Eastern region

Central region

Western region

78.3

72.8

70.9

88.9

17.0

16.9

17.0

17.2

9.1

11.5

13.1

3.8

1.8

2.3

2.7

0.7

14.8

11.8

19.1

13.9

17.3

20.8

23.3

9.3

Table 10.14  Situation of community service types provided by rural communities (unit: %) Types of community service Community care Community inclusion Community correction Community rehabilitation Career guidance Psychological decompression and counseling No service provided Others

National

Eastern region

Central region

Western region

67.7 11.6 31.6 29.5 52.3 8.7

59.5 26.4 39.1 10.0 38.8 7.4

72.5 9.7 33.7 26.7 60.5 4.8

69.8 1.4 23.5 48.2 55.4 13.5

16.1 2.0

17.1 3.3

19.3 0.0

12.2 2.9

10.7   Environmental Protection The sanitation of rural communities directly affects the health conditions, production and living of rural residents. In Table 10.15, rural communities which have ecological degradation issue account for over 50%. Meanwhile, 34.2% of them need to deepen their wells. The percentage of communities with the issue of water and soil loss is relatively high (21.5%). As is shown in Table 10.16, in terms of the ways of preventing infectious diseases, publicizing on the bulletin board, which accounts for 74.7%, is the main prevention method adopted by rural communities at national level.

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Table 10.15  Percentages of rural communities with ecological degradation Existing issues Decline or cutoff of overland runoff Wells remained to be deepened Desertification Soil alkalization Water and soil loss Deforestation Grassland degradation Loss of biodiversity None

National

Eastern region

Central region

Western region

17.7 34.2 8.7 11.1 21.5 6.1 8.4 11.1 40.7

18.8 32.7 2.7 15.0 10.6 2.8 4.9 7.1 45.4

17.8 39.9 10.5 13.8 22.7 8.6 5.7 16.9 35.7

16.7 31.2 12.5 5.7 30.1 7.1 13.6 10.2 40.5

Table 10.16  Situations of methods adopted by rural communities to prevent infectious diseases (unit: %) Methods to prevent infectious diseases Bulletin board publicity Door-to-door publicity New media publicity Periodic disinfection Others

National

Eastern region

Central region

Western region

74.7 56.0 4.0 36.2 7.7

77.6 53.1 5.2 31.5 5.0

70.9 49.8 4.2 34.9 16.4

75.6 62.4 3.1 40.3 3.1

Door-to-door publicity also has a relatively high percentage, at 56.0%. New media is less used in the prevention of infectious diseases in the eastern, central and western regions, 5.2%, 4.2% and 3.1%, respectively. As is shown in Fig. 10.13, in terms of straw treatment, 35.2% of communities choose to return stalks to the field, 35.2% of them still burn stalks and only 0.4% turn stalks into the source of methane.

10.8   Community Training Community training can create an atmosphere of learning society, enlighten people’s mind and enable community members to share the fruits of reform and opening up as well as cultural development. Skills can be improved and rights and interests can be protected. According to the result, communities which have career guidance account for only 6.7%.

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Others 23.6%

Burned 35.2% Turned into Methane 0.4%

Discarded 2.3% Purchased by Others 3.3%

Returned to the Field 35.2%

Fig. 10.13  Treatment of stalks in rural communities Table 10.17  Type of service provided by career guidance institutions in rural communities (unit: %)

National Eastern region Central region Western region

Career guidance

Career planning

Laws and policies

77.0 85.0 46.6 74.7

44.5 75.1 34.3 14.2

54.0 82.4 37.3 27.3

Labor safety Vocational and protection skill 68.0 78.1 21.9 66.6

95.1 96.7 74.3 97.7

Among all types of service provided by career guidance institutions in rural communities, those providing vocational skill have the highest percentage, at 95.1%. The percentage of communities with career guidance is 77.0%. Communities with labor safety and protection guidance account for 68.0%. Those providing law and policy and career planning account for 54.0% and 44.5%, respectively (see Table 10.17).

Note 1. Correction population refers to a group of people who have committed minor offenses with relatively lower subjective malignant and social harmfulness, or those who have repented of their crime and are unlikely to endanger society after jail management and transformation.

PART IV

Research Conclusions

CHAPTER 11

Conclusions

Based on the analysis of previous chapters, this chapter further uses the China Rural Household Survey (CRHPS) data to carry out systematic analysis and comprehensive research, as well as to sum up and refine the new features, trends and issues of China’s rural families.

11.1   The Rural Gender Imbalance Is Serious and Gender Selection Is Widespread The sex ratio of male to female in rural areas is seriously imbalanced, reaching 110.4:100. This gender imbalance is mainly found in children and people of working age. As shown in Fig. 11.1, the sex ratio of male to female in China’s rural population is 110.4:100, which is quite imbalanced. However, the sex ratio in migrant workers’ population and urban population is 97.9:100 and 99.5:100, respectively, with women slightly outnumbering men. In terms of child population, gender imbalance is very serious across the three populations, but the sex ratio of rural population, which has reached 126.3:100, is significantly higher than that of the other two populations. In the working-age population, there are more women than men in the migrant workers’ population and urban population, with the sex ratios being 96.9:100 and 97.5:100 respectively. In the rural population, the sex ratio of men to women is 109.3:100. The differences between urban and rural areas are obvious. In the elderly popu© The Author(s) 2019 W. Qian et al., Societal Development in Rural China, https://doi.org/10.1007/978-981-13-8082-2_11

231

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140.0 120.0

Ratio

100.0

110.4 97.9 99.5

126.3 120.9 118.8

109.3 96.9 97.5

100.9

91.7

78.8

80.0 60.0 40.0 20.0 0.0

In General

Children

Rural Population

Working-age

migrant workers

Elderly People

urban population

Fig. 11.1  The sex ratio (male to female) in urban and rural areas

lation, the gender imbalance of migrant workers is the most serious, with the sex ratio being 78.8:100. The sex ratio of male to female in the urban elderly population is 91.7:100, while the ratio in the rural elderly population is close to 1:1. The one-child policy’s influence on the gender imbalance in our country is mainly reflected in the parents’ deliberate selection of their children’s sex. For some rural families who meet special standards, the one-child policy actually permits a second child, but it is forbidden to have a third child or more. As shown in Fig. 11.2, in the child population born after 1990, firstborn’s sex ratio (male to female) is 117.4:100, but the secondborn’s sex ratio reaches as high as 126.4:100, and the sex ratio of the third-born even reaches 146.3:100. It can be seen from this that the rural population could not give birth without any limitations because of the one-child policy, and in the limited opportunities to have children, people are inclined to have boys. Such bias becomes stronger as the birth order increases, and is clearly manifested in the abnormal sex ratio. Compared to the sex ratio of the firstborns, the deliberate selection of the secondborns is more obvious. There is a much higher proportion of firstborn female children than firstborn male children. As shown in Fig. 11.3, the secondborn sex ratio is 106.1:100 when the firstborn is a boy and 194.3:100 when the firstborn is a girl. It shows that under the background of most rural areas

11 CONCLUSIONS 

160.0

146.3

140.0

126.4

117.4

120.0 Sex Ratios

233

100.0 80.0 60.0 40.0 20.0 0.0

firstborn

secondborn

thirdborn or more

Fig. 11.2  Sex ratios according to the birth order

250.0 194.3

Sex Ratios

200.0 150.0 100.0

106.1

50.0 0.0

the firstborn is a boy

the firstborn is a girl

Fig. 11.3  The sex ratio in families with a second child

implementing the “one and a half children” policy, there are still many families choosing to have a second child when their firstborn is a boy, part of which are against the law. When the firstborn is a boy, the sex ratio of the secondborn is balanced. But when the firstborn is a girl, the sex ratio of the secondborn is abnormally high (194.3100).

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11.2   The Aging Trend in Rural Area Intensifies, as the Elderly Make Up 15.2% of the Population Figure 11.4 shows the distribution of the age and gender analysis of 42,915 samples, which is a “big in the middle but small at the two sides” spindle-shaped structure, and has some obvious “pits” in the age groups of 30–44 and 55–59. The formation of this age structure is influenced by many factors such as socioeconomic situations, natural condition changes and national policies since the founding of the nation. Since the founding of our nation in 1949, the steady economic growth and social stability have led to the first birth peak. However, during 1959–1961, the rare three-year natural disaster greatly affected people’s living standards. The death rate soared and newborns decreased, causing the “increase then fall” situation in total population, resulting in the “pit” in the 55–59 age group. After the three-year disaster, the clearly improved environment and rapidly developing economy led to the “compensatory pregnancy” phenomenon. Our country entered the second-birth peak. However, during this period of time, the rapid growth of population also led to great social and economic pressure. Our country started to promote the one-child policy, resulting in the downward trend of birth rate in the next ten years. At the same time, the people born during the three-year disaster period started to enter their childbearing years. The small number of birth-giving populaFemale

Male >95 90–95 85–89 80–84 75–79 70–74 65–69 60–64 55–59 50–54 45–49 40–44 35–39 30–34 25–29 20–24 15–19 10–14 5–9 0–4 -6

-4

-2

0

2

Percentage of the whole population %

Fig. 11.4  Distribution regarding age and gender (unit: %)

4

6

11 CONCLUSIONS 

Table 11.1 2011–2015 Domestic rural rates of aged population (unit: %)

235

Region

2011

2013

2015

Nationwide Eastern Central Western

11.3 12.6 10.4 11.3

12.4 13.4 12.1 11.8

15.2 15.4 14.7 15.7

Note: The rate of aged population refers to the percentage of the old people above 65

tion and the birth control policy led to the decrease of newly born population, giving rise to the “pit” in the 30–40 age group. Henceforth, the population born in the second-birth peak time started to give birth, and the population started to grow again. Although there are several “pits” in the age structure of the rural population, its overall “large in the middle, small at the two sides” spindle-­ shaped structure will inevitably enter the declining “inverted triangle” age structure with the passage of time. At that time, China’s rural areas will face the problem of aging population and a very serious shortage of newborns. In fact, China’s rural areas are now facing a very serious aging situation, and this situation is rapidly deteriorating. As shown in Table 11.1, China’s rural population in 2011 has reached a rate of aged population of 11.3% (reaching 7% is seen as an aging society), which represents a seriously aging society. By 2013, the rate of aged population further increased to 12.4%, and even reached as high as 15.2% by 2015. From the regional perspective, the eastern rural areas used to be the most aged areas, but by 2015 the western rural areas reached a rate of aged population of 15.7%, exceeding the eastern areas for the first time. The aging population in rural areas was significantly larger than that in urban areas. As shown in Fig. 11.5, the rate of aged population in domestic urban areas in 2011 was 10.1%, significantly lower than the rural rate of aged population at the same year. The rate of aged population in urban areas in 2015 was 14.2%, which was significantly lower than the 15.2% rate of aged population in rural areas. However, it can be seen from the development trend that the aging situation in urban areas is also rapidly ­deteriorating, and its deterioration rate is basically consistent with that in the rural areas. The proportion of families with elderly people in the western region was significantly higher than that in the eastern and central regions. As shown in Fig. 11.6, the proportion of families with elderly people across the country is 39.1%, with the proportion in the western region up to

236 

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15.2

16.0

Percentage %

14.0 11.3

12.0 10.0

14.2

12.4 11.9 10.1

8.0 6.0 4.0 2.0 0.0

2011

2013 Rural areas Urban areas

2015

Fig. 11.5  Urban and rural rate of aged population (unit: %) 45.0 40.0

42.7

39.1

38.6

36.7

35.0 30.0 25.0 20.0 15.0

23.2 15.6

26.9 21.7 14.7

21.7 16.6

15.4

10.0 5.0 0.0 in total

nationwide

eastern

households with one elderly person

central

western

households with two elderly people

Fig. 11.6  Distribution of families with elderly people according to regions

42.7%, significantly higher than those in the eastern region (36.7%) and the central region (38.6%). The proportion of households with two elderly people across the country is 23.2%. From a regional perspective, the western region has the highest proportion, which is 26.9%, and the proportions in the eastern and central regions are both 21.7%. The proportion of households with one elderly people nationwide is 15.6%. In terms of different regions, the proportion in the central region is the highest

11 CONCLUSIONS 

72.0

70.4

70.0

68.2

Percentage %

68.0 66.0

237

67.3

65

64.0 62.0 59.7

59.6

60.0 58.0 56.0 54.0

nationwide

eastern

central

western

male

female

Fig. 11.7  Chronic disease incidence among elderly people aged over 65 years

(16.6%), and the proportions in the eastern and western regions are 14.7% and 15.4%, respectively. It is noteworthy that the proportion of families with two elderly people in each region is higher than the proportion of households with one elderly person. In the population of old people aged 65 and above in our country, women had a higher proportion of getting chronic diseases than men. As shown in Fig. 11.7, the proportion of old people aged 65 or above with chronic diseases was 65%. According to the statistics from different regions, there was 59.6% of old people aged above 65 having chronic diseases in the eastern region, much lower than that in the central region (68.2%) and western region (67.3%). In terms of genders, the proportion of women aged above 65 getting chronic diseases was 70.4%, much higher than men (59.7%).

11.3   The Proportion of Rural Unmarried Population Gradually Increased, “Left Men” Problem Particularly Prominent With the development of society, the proportion of unmarried people who are above the average age for marriage in rural areas is gradually increasing. As shown in Table 11.2, the proportion of unmarried people aged 30 or above in China was 2.6% in 2011, 3.1% in 2013 and 3.4% in 2015,

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Table 11.2  The changes of rural unmarried population above the age of 30 (unit: %)

Table 11.3  The proportion changes of urban unmarried population above the age of 30 (unit: %)

In total Male Female

In total Male Female

2011

2013

2015

2.6 4.4 0.7

3.1 5.0 1.1

3.4 5.4 1.2

2011

2013

2015

1.9 2.6 1.2

2.4 3.5 1.4

2.6 3.7 1.6

showing a clear upward trend. In the previous surveys, the proportion in the unmarried male population is always much higher than that in the unmarried female population. The proportion of unmarried people who are above the average age for marriage in rural areas was significantly higher than in urban areas. As shown in Table  11.3, the proportion in urban areas was 1.9% in 2011, 2.4% in 2013 and 2.6% in 2015, displaying an upward trend which was lower than that in rural areas at the same period of time. In addition, the proportion of unmarried men in urban areas was significantly lower than that in rural areas. The proportion of unmarried men in urban areas was 3.7% in 2015, which was significantly lower than that in rural areas. On the contrary, the proportion of urban unmarried women was significantly higher than that of rural unmarried women. In 2015, the proportion of urban unmarried women above the average age for marriage was 1.6%, while the proportion was only 1.2% among rural women. Thus, China’s sex differences in unmarried population above the average age for marriage was significantly greater in rural areas than in urban areas. The proportion of 30–39-year-old urban unmarried population was much higher than that of other urban age groups. As shown in Fig. 11.8, the proportion of unmarried people aged 30–39  years was 9.7%, well above the proportion of urban unmarried people aged 40–49 (3.1%), 50–59 (1.7%) and over 60 (1.7). The statistical results in terms of gender show that the proportion of unmarried men are higher than unmarried women in all age groups.

11 CONCLUSIONS 

16.0

14.1

14.0 Percentage %

12.0 10.0

239

9.7

8.0 6.0

4

4.0 2.0 0.0

5 3.1 1.7

0.9 30–39

40–49 in total

male Age

2.9 0.4

50–59 female

1.7

2.7 0.6

60 and above

Percentage %

Fig. 11.8  Male to female ratio among urban unmarried population aged 30 and above 20 18 16 14 12 10 8 6 4 2 0

no education primary school

junior high senior high intermediate advanced bachelor's professional professional degree or school school above

Educational Attainment nationwide

male

female

Fig. 11.9  Educational attainment and unmarried rate (unit: %)

China’s rural unmarried rate also increases as the level of education increases. From Fig. 11.9, we can see that the proportion of unmarried women increases obviously with the increase of educational attainment. The unmarried rate among women with a bachelor’s or higher degree reaches as high as 18.2%, which is higher than that of men

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(people with a bachelor’s or higher degree are mostly young people, whose unmarried rate is relatively high). The unmarried rate among males decreases first and then increases again with the increase of educational attainment.

11.4   The Achievements of Nine-Year Compulsory Education Are Obvious, but There Are Still 9.5% of the Post-1995 Generation Who Have Not Completed Middle School China enacted the People’s Republic of China Compulsory Education Law in 1986 and began to implement the nine-year compulsory education program. Therefore, it can be calculated that people born after the 1980s will fully enjoy the rights of compulsory education, while people born in the 1970s or even earlier could not enjoy the rights. As shown in Table 11.4, the educational level of different age groups shows significant differences. The proportion of people not completing middle school education and born in the 1960s was as high as 49.2%, and the proportion of people who received higher education (college/ vocational school and above) was only 0.9%. The proportion of people not completing middle school education and born in the 1970s was significantly lower (46.7%), and the proportion of people with higher education increased slightly to 1.9%. Table 11.4  Educational attainment of different age groups (unit: %) Educational attainment Under compulsory education Middle school High school/intermediate professional school/ advanced professional school Higher education

Overall 1960s 1970s 1980s 1990s a 49.4 33.5 11.9 5.2

49.2 46.7 38.8 43.7 11.1 7.7 0.9

1.9

19.8 51.9 16.1

9.5 38.1 31.9

12.2

20.5

In the “post-90s” generation, people born after 1998 have not entered higher education yet. This report further examines and deduces the education levels of people born between 1990 and 1997. The proportion of people who have not completed compulsory education, middle school, high school/intermediate vocational/vocational high school and higher education are 9.6%, 38.0%, 30.9% and 21.5%, respectively. The four proportions are not much different from those of the “post-90s” generation, so this report only reserves the statistics of the “post-90s” a

11 CONCLUSIONS 

100.0

90.5

90.0

80.2

80.0 Percentage %

241

70.0 60.0 50.0

53.3

50.8

50.6

40.0 30.0 20.0 10.0 0.0

2.8 overall

0.2

0.6

6.5

8.4

60–69 70–79 80–89 90–99 junior high and above bachelor's degree and above

Fig. 11.10  Educational attainments of different age groups

People born after 1980 should theoretically fully enjoy the rights of compulsory education, as shown in Table 11.4 and Fig. 11.10. This group of people’s education level has a great leap compared to the people born in the 1960s and 1970s. The proportion of people who have completed the nine-year compulsory education among the “post-80s”, who were born between 1980 and 1989, is 80.2%, and the proportion among the “post-90s” is 90.5%. On the other hand, the proportion is only 50.8% and 53.3% among those born in the 1960s and 1970s, respectively. The proportion of people who have received higher education increased rapidly from 0.9% (the “post-60s”) and 1.9% (the “post-70s”) to 12.2% (the “post-80s”) and 20.5% (the “post-90s”). It can be seen from this that the implementation of compulsory education not only reduced the illiteracy rate and greatly enhanced people’s basic education level, but also increased the proportion of highly educated population. However, we must also notice that in rural areas there are still nearly 20% of the “post-80s” generation and 10% of the “post-90s” generation who have not finished the nine-year compulsory education. This shows that although the compulsory education system has made a significant contribution to Chinese people’s education levels, it has not yet achieved full coverage for the time being.

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11.5   The Education Levels of the Rural Population Are Low, but the Improvement Trend Is Obvious In China, rural population’s education levels are generally low, as shown in Fig. 11.11. The proportion of people who have not gone to school and are above 16 years is 17.7%; the proportion of people who have completed primary school is 31.7%; junior high education 33.5%. Only 17.1% of the population have obtained high school diploma or above, and only 2.8% of them have obtained a bachelor’s degree or above. The overall education level is quite low. There is a clear dividing line between junior high school and high school: 33.5% of the population have obtained junior high school diploma but only 9% have obtained high school diploma. This shows that a large number of rural population have given up continuing their education once they got junior high school diploma. The education level of the urban population is significantly higher than that of rural population. The illiteracy rate in urban areas (have not been to school) is only 6.8%, significantly lower than that in rural areas. The proportion of the urban population who have only finished primary school 40.0 35.0

31.7

Percentage %

30.0

33.5 27.6

25.0 20.0

17.7

15.0 10.0

17.7 11.8 9

6.8

5.0 0.0

14.5

13.9 7.7 2.9 no education

primary school

2.4

2.8

junior high senior high intermediate advanced bachelor's professional professional degree or school school above rural urban

Fig. 11.11  Education levels in urban and rural areas

11 CONCLUSIONS 

243

Table 11.5  Level of education of different age groups (unit: %) Educational attainment Have not been to school Primary school Middle school High school Intermediate vocational/ vocational high school Advanced vocational Bachelor and above

Overall “post-­60s” “post-­70s” “post-­80s” “post-­90s” 17.7 31.7 33.5 9.0 2.9

11.3 37.9 38.8 10.0 1.1

7.4 39.3 43.7 5.4 2.3

2.3 17.5 51.9 9.6 6.5

1.2 8.3 38.1 23.6 8.3

2.4 2.8

0.7 0.2

1.3 0.6

5.7 6.5

12.1 8.4

and junior high school education is also significantly lower than that in rural areas, and the proportion of the urban population who have finished high school and above is significantly higher than that of rural residents. The proportion of urban residents who have finished higher education is as high as 14.5%, more than five times higher than that of rural residents. It can be concluded that in terms of higher education, urban residents are far better educated than rural residents. Although the overall educational level of rural areas in China is low, it has clearly improved with the improvement of economic and educational conditions in recent years. As shown in Table 11.5, the education level of the “post-60s” and “post-70s” generations is generally low, with an illiteracy rate of 11.3% and 7.4%, respectively. The proportions of people who have not finished junior high school are 59.2% and 46.7%, respectively, and the proportions of people who have a bachelor’s or higher degree are 0.2% and 0.6%, respectively. The education level of the “post-80s” and “post-90s” improved significantly, with an illiteracy rate of 2.3% and 1.2%, respectively. Furthermore, most of the “post-80s” and “post-90s” have completed compulsory education, with the completion rate at 80.2% and 90.4%, respectively. Regarding higher education, 6.5% of the “post-80s” have received university education or above. The proportion is higher among the “post-90s”, reaching 8.4%. The education level of the rural population has improved significantly during the recent decades and even more obviously after 1980. On the one hand, it is the result of the significant improvement of rural economic and education conditions. On the other hand, the universal nineyear compulsory education influenced the education level of the rural

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Table 11.6  Parents’ expectations for their children’s education (unit: %) Education expectations Primary school Middle school High school (including intermediate vocational school vocational high school) Advanced vocational school Bachelor’s degree Master’s degree Doctor’s degree No expectations

Nationwide

Male

Female

0.5 2.7 4.8

0.8 2.8 4.6

0.3 2.5 5.0

2.7 54.1 3.5 18.2 13.5

2.9 52.7 3.2 19.3 13.7

2.5 55.8 4.0 16.8 13.1

population to a great extent. Apart from these outside factors, the expectations of rural parents may have also influenced the education level of their children. As shown in Table 11.6, rural parents’ expectations for their children are very high. Over 54.1% of parents hope their children receive university education, 3.5% hope their children get a master’s degree and 18.2% expect their children get a doctor’s degree. Furthermore, the expectations do not show a big difference due to the gender of their children. But in terms of higher education, parents tend to hope their daughters get a bachelor’s degree (55.8%) or a master’s degree (4%). Only 19.3% of parents hope their children get a doctor’s degree, which may be explained by parents’ concerns over their daughters’ marriage.

11.6   Rural Income Inequality Is Ever Increasing, 20% of Households with the Highest Income Account for 53% of Total Household Income China’s rural household income inequality is becoming increasingly serious. Of households with the highest income, 20% account for 53% of total household income, while the 20% households with the lowest income make up only 2% of the total household income. In terms of Gini coefficient, the inequality of household income increased year by year, from 0.45 in 2011 to 0.505 in 2015. Figure 11.12 shows that the Gini coefficient of nonagricultural income is always higher than the net income of agriculture, but the Gini coefficient of agricultural net income increases more than nonagricultural income. Specifically speaking, the Gini coeffi-

11 CONCLUSIONS 

245

0.65 0.599

Gini Coefficients

0.6

0.575 0.55

0.552

0.536

0.542

0.505

0.5 0.45 0.4

0.498 0.45

0.445

agricultural pure income 2011

non-agricultural income 2013

total household income 2015

Fig. 11.12  The dissection of the Gini Coefficients of year-by-year rural household income in each area and total household income

cient of no-agricultural income increased from 0.536 in 2011 to 0.599 in 2015, while the Gini coefficient of agricultural net income increased from 0.445 in 2011 to 0.542 in 2015. There are significant differences of income inequalities in different regions. Figure 11.13 shows that the Gini coefficient of total household income is the lowest in the central region. Although the Gini coefficient of total income is the highest in the western region, the Gini coefficient of its agricultural net income is the lowest. At the same time, the Gini coefficient of its nonagricultural income reaches as high as 0.619, indicating that uneven nonagricultural income may be the main cause of the income inequality in the western region. Compared with other regions, the eastern region has the lowest Gini coefficient of nonagricultural income, which is mainly due to the more balanced distribution of salaries as well as industrial and commercial incomes (see Chap. 5 for details). The Gini coefficient of agricultural net income in the central region is the highest. As shown in Fig. 11.14, the intra-province differences have little influence on the various income coefficients, and the uneven income is mainly caused by interprovince differences and the overlapping parts. In terms of total rural household income and nonagricultural income, the overlapping parts contribute 71.4% and 68.8%, respectively, significantly higher than

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0.7

Gini Coefficients

0.6 0.5

0.599 0.542 0.505

0.569 0.524 0.501

0.607 0.547 0.488

0.619 0.524 0.518

0.4 0.3 0.2 0.1 0.0

nationwide total income

eastern central western agricultural net income non-agricultural income

Fig. 11.13  The dissection of Gini coefficients of various household incomes 100

Percentage %

80 60 40 20 0

Total income in province differences

Agricultural net income non-agricultural income intra-province differences

overlapping parts

Fig. 11.14  Influencing factors of various income Gini coefficients

11 CONCLUSIONS 

247

the contribution of the intra-province differences. However, the Gini coefficient of agricultural net income is mainly caused by interprovince differences. Interprovince differences contribute 50.9%, and the overlapping part contributes 45.6%.

11.7   The Shares of Rural Consumer Spending Are Concentrated, with Food and Other Three Categories Accounting for 82% Rural household consumption expenditure is mainly concentrated in food, living, health care, as well as transportation and communication. Figure 11.15 shows that these four categories of household consumption expenditure in rural households accounted for 43.3%, 12.7%, 11.6% and 14.3%, respectively, across the country, reaching a total of nearly 82%. There are significant differences in the structure of consumption expenditure in different regions. Compared with the central region, the percentage of food expenditure in the eastern and western regions was significantly higher, and the percentage of education and entertainment expenditure was significantly lower. The percentage of transportation and communication expenditure in the central and western regions was significantly higher than that in the eastern region. The percentage of living costs gradually decreased from the east to the west. 100% 80% 60% 40% 20% 0% National Eastern Central food expenses cloting expenses daily necessities expenses medicial expenses edutainment expenses other expenses

Western living expenses transport expenses

Fig. 11.15  Average rural household consumption expenditure and its composition

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100% 80% 60% 40% 20% 0%

2011

2013 2015 Year Four main consumptions other consumptions

Fig. 11.16  Year-by-year average household consumption expenditure and its composition

Figure 11.16 shows that the percentages of food, living, health care, as well as transportation and communication in the consumption of rural households were increasing year by year. The total percentage of the four categories increased from 77.5% in 2011 to 81.8% in 2015, indicating that the focus of rural consumer spending gradually shifted to food, living, health care, transportation and communication.

11.8   The Number of Left-Behind Children People Is Large but on the Decline

and Elderly

After the reform and opening-up policy was implemented, the rural labor force has been gradually moving into cities, and especially in the recent decades, a large number of rural labor force went to work in urban areas. With this big wave of labor force migrating, two special groups emerged in rural areas, which are left-behind children and elderly people. The two groups were investigated in the CRHPS conducted in 2013 and 2015. The left-behind elderly people surveyed in the CRHPS are parents whose children had left their homes for urban areas and had been working there for quite a long time (generally more than half a year) aged above 60. The left-behind children refer to those aged under 16 whose parents had gone outside of their hometowns for a long period of time, but they themselves

11 CONCLUSIONS 

249

lived in rural areas. According to our analysis, the characteristics of rural left-behind elder people and children are as follows: The proportions of rural left-behind elderly people and children are high and the total number is large. According to the statistics of the CRHPS survey in 2015, the proportion of rural left-behind elderly people in the whole rural population was 7.8% across the country, and the proportion of rural left-behind children in the total rural population was 3.6%, totaling over 11%. According to China Population and Employment Statistics Yearbook in 2014, the total number of agricultural population was 876 million. It can be estimated that the number of rural left-behind elderly people across the country was more than 60 million, and the number of left-behind children in rural areas exceeded 30 million, totaling nearly 100 million, which is an enormous number. The mere number of rural left-behind elderly people equaled the population of Zhejiang province. The regional difference was huge, the proportions of left-behind elderly people and children high in the central and western regions. The proportions of left-behind children and elderly people in rural areas differ across regions. The proportions in the east were relatively low, as leftbehind elderly people and children accounted for 4.4% and 2.0%, respectively. The proportions were the highest in the central region, as left-behind elderly people and children accounted for 4.4% and 2.0%, respectively. The proportions in the west were close to the national average, as left-behind elderly people and children accounted for 7.5% and 3.5%, respectively. From the comparison between 2013 and 2015, it can be seen that the proportions of left-behind elderly people and children are on the decline. According to the statistic of the CRHPS survey in the rural community, left-behind elderly people made up 9.4% of the local permanent resident population, while in 2015 the proportion dropped to 7.8% by 17%. In terms of regions, the proportion of left-behind elderly people in the three regions all decreased, of which the eastern region decreased the most from 7.4% in 2013 to 4.4% in 2015 by 41% (Fig. 11.17). The proportion of rural left-behind children also showed a declining trend. In the national sample, the proportion of rural left-behind children in the registered permanent residents dropped from 4.5% in 2013 to 3.6% in 2015 by 20%. In terms of regions, the proportion of left-behind children in the three regions all decreased (Fig. 11.18).

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16.0

13.8

14.0

11.9

Percentage %

12.0 10.0

9.4 7.8

8.0

7.9

7.4

6.0

7.5

4.4

4.0 2.0 0.0

Nationwide

Eastern

Central

Western

Region 2013

2015

Fig. 11.17  The change of proportion of rural left-behind elderly people in the total local permanent residents 7.0

6.3 5.7

Percentage %

6.0 5.0 4.0

4.7

4.5 3.6

3.5 2.6

3.0

2.0

2.0 1.0 0.0

nationwide

eastern

central

western

Region 2013

2015

Fig. 11.18  The change of proportions of rural left-behind children in the total local permanent residents

11 CONCLUSIONS 

251

The probable reasons for this trend are as follows. First, the proportion of family migration of people who worked outside of their residential areas started to rise, which is closely related to the loosening of registered permanent residential policy and the support for the education of migrant workers’ children. Second, in recent years, some migrant workers might have gone back to their hometowns, resulting in fewer households of left-­ behind elderly people and children.