The Role of the State in China’s Urban System Development: Government Capacity, Institution and Policy 9813363614, 9789813363618

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Jiejing Wang

The Role of the State in China’s Urban System Development Government Capacity, Institution and Policy

The Role of the State in China’s Urban System Development

Jiejing Wang

The Role of the State in China’s Urban System Development Government Capacity, Institution and Policy

Jiejing Wang Renmin University of China Beijing, China

Published with financial support of the National Natural Science Foundation of China. ISBN 978-981-33-6361-8 ISBN 978-981-33-6362-5 (eBook) https://doi.org/10.1007/978-981-33-6362-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Research Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Research Questions and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Defining the Urban System and the State . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Urban System and Urban System Development . . . . . . . . . . . 1.3.2 The State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Organization of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 4 5 5 6 7

2 Understanding Urban System Development in Different Countries: Theoretical Alternatives and Empirical Evidence . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Shifting Approaches of Urban System Study . . . . . . . . . . . . . . . . . . . 2.2.1 Central Place Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Spatial Diffusion Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Stochastic Growth Theories . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Endogenous Growth Theories . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Development of Urban Systems in Different Countries . . . . . . . . . . . 2.3.1 The “Old World” Countries: UK and France . . . . . . . . . . . . . 2.3.2 The “New World” Countries: USA and Canada . . . . . . . . . . . 2.3.3 The Former Colonies: India and Brazil . . . . . . . . . . . . . . . . . . 2.3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Studies on Urban System Development in China . . . . . . . . . . . . . . . . 2.4.1 Pre-Reform Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Post-Reform Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 A Critique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9 9 10 10 12 14 17 19 20 21 24 27 29 30 31 35 38 39

3 China’s Urban System Development: Basic Concepts, Historical Development, and Changes of the State Policies and Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

43 43 v

vi

Contents

3.2 Clarification of the Key Concepts and Issues for China’s Urban System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 City and Designation of City in China . . . . . . . . . . . . . . . . . . . 3.2.2 National Urban System Policy . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Urban Administrative Level . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.4 Urban Size, Urban Growth, and Classification of City Sizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.5 Geographical Regions and Temporal Periods . . . . . . . . . . . . . 3.3 Development Patterns of China’s Urban System . . . . . . . . . . . . . . . . . 3.4 Changing Policies and Institutions and the Development of China’s Urban System in the Post-reform Period . . . . . . . . . . . . . . 3.4.1 Introducing the Market Mechanism and Redefining the Role of the State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Shifting of the Focus of State Policies . . . . . . . . . . . . . . . . . . . 3.4.3 Reforms on the Hukou System and Population Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.4 Restructuring the Urban Administrative System (UAS) . . . . 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conceptualizing the Role of the State in China’s Urban System Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Understanding the Role of the State in Chinese Context . . . . . . . . . . 4.2.1 Theories on the State and State-Market Relations . . . . . . . . . 4.2.2 Theorization of China’s State in Economic Transition . . . . . 4.3 Conceptualizing the Role of the State in China’s Urban System Development Through the Political Hierarchy Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 The Political Hierarchy and Organization of State Power Among Chinese Cities . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Conceptual Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Analytical Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.3 Research Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

44 44 45 48 50 52 54 65 65 67 69 72 74 77 77 77 77 80

85 85 90 90 90 92 93 94

5 Identifying the Development Patterns of China’s Urban System: Effects of the National Urban System Policy . . . . . . . . . . . . . . 97 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.2 Examining the Urban Growth Processes and Evolution of City-Size Distribution in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.2.1 Growth Processes of Chinese Cities . . . . . . . . . . . . . . . . . . . . . 98 5.2.2 Evolution of City-Size Distribution . . . . . . . . . . . . . . . . . . . . . 104 5.2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

Contents

5.3 Modelling China’s Urban System Development in the Unregulated Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Modelling Strategy and Assumptions . . . . . . . . . . . . . . . . . . . 5.3.2 Models and Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Analysis on the State’s Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Effects of Urban Government Capacity on Urban System Development in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Decentralization of State Power, Urban Government Capacity, and Urban Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Model and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Conceptualization of Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Measuring Urban Government Capacity and Other Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Models and Estimation Methods . . . . . . . . . . . . . . . . . . . . . . . 6.4 Results of Urban Size Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Results of Urban Growth Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Effects of Urban Administrative System on Urban System Development in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Administrative Level Upgrading and Urban Growth . . . . . . . . . . . . . 7.2.1 The Importance of Administrative Level for Chinese Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Effect of Administrative Level Upgrading on Cities . . . . . . . 7.3 Methodology and Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 A Quasi-Experiment with PSM and DID . . . . . . . . . . . . . . . . 7.3.2 Estimation Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Upgrading of a County-Level City to a Prefecture-Level City . . . . . 7.4.1 PSM Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 DID Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 County to County-Level City Upgrading . . . . . . . . . . . . . . . . . . . . . . . 7.5.1 PSM Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.2 DID Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Major Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Theoretical and Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Limitations and Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

vii

108 108 110 116 121 130 133 133 134 136 136 138 143 146 152 158 161 161 162 162 165 170 170 172 173 173 175 178 178 180 183 187 187 193 197

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

Abbreviations

CCP CGC DID FDI GMM Hukou NBS NICs PCA PRD PSM SOEs TSS TVEs UAS WTO YRD

Chinese Communist Party City-Governing-County Difference-in-Difference Foreign Direct Investment Generalized Method of Moments Household Registration System National Bureau of Statistics Newly Industrializing Countries Principal Component Analysis Pearl River Delta Propensity Score Matching State-Owned Enterprises Tax-Sharing System Township and Village Enterprises Urban Administrative System World Trade Organization Yangtze River Delta

ix

List of Figures

Fig. 1.1 Fig. 2.1

Fig. 2.2

Fig. 3.1

Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 3.5 Fig. 3.6 Fig. 3.7 Fig. 3.8 Fig. 3.9 Fig. 3.10 Fig. 3.11 Fig. 3.12 Fig. 3.13 Fig. 3.14 Fig. 3.15

Different strands of literature on urban system development . . . . Schematic Comparison of Population Distribution across the Settlement System in China and Japan (Source Rozman [1973, p. xv]) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . China’s macroregional systems, 1893, showing major rivers and the extent of regional cores (Source Skinner [1985, p. 273]) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Demarcation of Chinese cities, 2010 (Source Author’s work based on the list of cities by the Ministry of Civil Affairs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . China’s administrative system (Source Ma 2005, p. 479) . . . . . . Three regions of China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seven sub-regions of China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Growth of per capita GDP and urbanization level: a 1952– 1977; b 1978–2012 (Source NBS 2013, 2010) . . . . . . . . . . . . . . . Geographic distribution of Chinese cities, 1953 (Source NBS 1954) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Geographic distribution of Chinese cities, 1982 (Source NBS 1985) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Geographic distribution of newly designated cities between 1953 and 1982 (Source NBS 2010) . . . . . . . . . . . . . . . . . Geographic distribution of newly designated cities between 1982 and 2010 (Source NBS 2011) . . . . . . . . . . . . . . . . . Urban population growth of cities in three regions . . . . . . . . . . . . Spatial distribution of migration population, 2000 . . . . . . . . . . . . Spatial distribution of migration population, 2010 . . . . . . . . . . . . Geographic distribution of Chinese cities, 1990 (Source NBS 1993) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Geographic distribution of Chinese cities, 2000 (Source NBS 2002) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Geographic distribution of Chinese cities, 2010 (Source NBS 2012) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

32

33

46 48 53 54 55 57 58 59 60 61 62 62 64 64 65 xi

xii

Fig. 3.16 Fig. 3.17 Fig. 3.18 Fig. 4.1 Fig. 4.2 Fig. 4.3 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. 5.9 Fig. 5.10 Fig. 5.11

Fig. 5.12

Fig. 5.13 Fig. 5.14 Fig. 5.15

Fig. 5.16

Fig. 6.1

List of Figures

Change of employment in different ownership sectors (Source Yeh et al. 2015, p. 2833) . . . . . . . . . . . . . . . . . . . . . . . . . . Geographical distribution of SEZs and COCs in 1980s (Source Author’s summary) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Changes in the number of county-level units since 1978 (Source NBS 2010, 2014) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Political hierarchy and its relationship with urban system . . . . . . Conceptual framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analytical procedure of empirical studies in this book . . . . . . . . Non-parametric estimates of the growth rate and its variance of China by different periods . . . . . . . . . . . . . . . . . . . . . . Non-parametric estimates of the growth rate and its variance of China by different periods . . . . . . . . . . . . . . . . . . . . . . City–size distributions of cities in China, 1953–2010 . . . . . . . . . China’s city-size distributions and Zifp’s law: 1953, 1982, 1990, 2000 and 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Illustration of how to identify the impacts of the state based on the differences between urban system of 2010 and simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weight of cities plotted against the market potential of cities (a) 1980s; (b) 1990s; and (c) 2000 . . . . . . . . . . . . . . . . . Weight of cities plotted against the distance to (a) the nearest national central city; and (b) its provincial capital . . . Scenarios and models of simulating urban system development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seven sub-regions of China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distributions of simulated urban systems and the actual urban system in 2010 . . . . . . . . . . . . . . . . . . . . . . . Comparison of simulated urban systems with actual urban system in 2010 regarding the urban population in difference sizes of cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of simulated urban systems with actual urban system in 2010 regarding the number of cities in difference sizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Differences of urban sizes between the actual urban system in 2010 and simulated urban systems . . . . . . . . . . . . . . . . . . . . . . Spatial pattern of policy capacity index of 2010 . . . . . . . . . . . . . . Relationship between policy index and difference of population between 2010 and simulated results: (a) scenario 2; (b) scenario 3; (c) scenario 5; and (d) scenario 6 . . . City-size distributions of simulated urban systems and the comparisons with the actual city-size distribution in 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Relations of major concepts in urban size and urban growth models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

66 69 74 88 91 93 102 103 105 106

109 114 114 115 116 118

122

123 124 126

126

128 137

List of Figures

Fig. 6.2 Fig. 6.3 Fig. 6.4 Fig. 6.5 Fig. 7.1 Fig. 7.2 Fig. 7.3 Fig. 7.4 Fig. 7.5 Fig. 7.6 Fig. 7.7 Fig. 7.8 Fig. 7.9 Fig. 7.10

Scatter plot association: urban population and urban government capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scatter plot association: urban population and market potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scatter plot association: market potential and urban government capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interaction effects of government capacity with market potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Changes in number of Chinese cities from 1978 to 2013 (Source NBS 2010, 2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Merging a prefecture-level city with a prefecture: Suzhou’s case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Upgrading of a county-level city to a prefecture-level city: Dezhou’s case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Upgrading of a county to a prefecture-level city: Qingyuan’s case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Illustration of the basic concept of the PSM and DID approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seven sub-regions of China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Geographical distribution of treatment and control cities for the county-level to prefecture-level upgrading . . . . . . . . . . . . Trends in the urban growth of the treatment and control groups for the county-level to prefecture-level upgrading . . . . . . Geographical distribution of treatment and control cities/counties for the county to county-level upgrading . . . . . . . Trends in the urban growth of the treatment and control groups for the county to county-level city upgrading . . . . . . . . . .

xiii

141 142 142 152 166 166 167 168 170 173 174 178 179 183

List of Tables

Table 2.1 Table 2.2 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 3.6 Table 4.1 Table 4.2 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 5.5 Table 5.6 Table 5.7 Table 6.1 Table 6.2 Table 6.3 Table 6.4

Three major styles of urban systems in the world . . . . . . . . . . . Spatial trend of population distribution in the UK during the past century . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Minimum requirements for city designation issued in 1986 and 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Changes in the number of China’s administrative units at three levels: 1978–2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Number and average annual growth rate of Chinese cities from 1953 to 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Top 10 largest cities in 1953 and 1982 . . . . . . . . . . . . . . . . . . . . Major reforms and policies regarding the hukou system . . . . . . Migration population in China, 1990, 2000, and 2010 . . . . . . . Theories explaining the role of state in China’s economic reforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Main data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Parameter regression of urban growth rate and urban size . . . . . Parameter regression of growth rate and city sizes by different quartiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of fitting China’s city-size distributions by Zipf’s law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Total population and number of cities in different sizes of simulated results and urban system of 2010 . . . . . . . . . . . . . . Proportions of cities located in different regions of the simulated results and urban system of 2010 . . . . . . . . . . . Population of top ten cities of 2010 and simulated results (in million) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zifp coefficients and scaling ranges of simulated urban systems and the actual urban system in 2010 . . . . . . . . . . . . . . . Eigenanalysis of the correlation matrix . . . . . . . . . . . . . . . . . . . . Principal components’ coefficients and results of test . . . . . . . . Definition of Dependent and Independent Variables . . . . . . . . . Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

20 22 45 49 56 57 70 71 84 95 99 100 106 117 119 120 129 140 140 143 144 xv

xvi

Table 6.5 Table 6.6

Table 6.7 Table 6.8 Table 6.9 Table 6.10 Table 7.1 Table 7.2 Table 7.3 Table 7.4

Table 7.5 Table 7.6 Table 7.7 Table 7.8

List of Tables

System GMM estimation results for urban size model . . . . . . . System GMM estimation results for the urban size model with different components of GOVCAP and interaction terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . System GMM estimation results for the urban growth model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Decomposition of urban growth by quartiles of urban size . . . . Decomposition of urban growth by different regions . . . . . . . . . Decomposition of urban growth by different administrative levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Number of national-level development zones for cities at different administrative levels in 2006 . . . . . . . . . . . . . . . . . . Incomplete lists of benefits of being a county-level city over counties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Balancing tests for the treatment and control groups . . . . . . . . . Changes in the urban sizes and growth rates of the treatment and control groups for the county-level to prefecture-level upgrading . . . . . . . . . . . . . . . . . . . . . . . . . . . . DID estimation of the effect of county-level to prefecture-level upgrading on urban size . . . . . . . . . . . . . . . . Balancing tests for the whole sample . . . . . . . . . . . . . . . . . . . . . Changes in urban size and growth rate of upgraded cites and control group cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DID estimation of the effect of the county to county-level city upgrading on urban size and urban growth . . . . . . . . . . . . .

148

150 153 155 156 157 164 169 175

176 177 180 181 182

Chapter 1

Introduction

1.1 Research Background Due to the introduction of market-oriented reforms and implementation of openingup policies in 1978, China has witnessed a rapid urban system development in the past three decades. The urbanization level reached 50% in 2011, increased from 17.9% in 1978 to 50% in 2011. In 1978, there were only 193 cities in China, and this number has increased to 658 in 2013 (NBS 2013). According to the Sixth National Population Census in 2010, there were 4 cities with urban population larger than 10 million, and 14 cities with urban population larger than 5 million, and 84 cities with population larger than 1 million (NBS 2012). In 2008, the McKinsey Global Institute predicted in a report titled “Preparing for China’s Urban Billion” that there will be about one billion urban population in China in 2025, and 8 cities with urban population larger than 10 million, and 23 cities with urban population larger than 5 million, and 221 cities with urban population larger than one million. Rapid urban system development has increasingly attracted the interests of the government, scholar, media, and the general public in China. On the one hand, they pay more attention to rapid urban system development and its consequences such as pollution, congestion, low living quality, et al. On the other hand, urban system development provides one of the most important driven forces for China’s economic development in the future decades. Therefore, the considerable significance of urban system development has raised some critical theoretical and practical questions that merit further investigations. In conventional wisdom, there are mainly two strands of literature to explain urban system development (Fig. 1.1). The first strand of literature uses the geographical factors to explain urban system development. The natural endowments, location, distance, and historical and cultural factors play essential roles in shaping urban system development (Whebell 1969; Berry 1972; Berry et al. 1970; Harris and Ullman 1945; Pred 1980). The alternative strand of literature focuses on the economic or market forces to explain urban system development, which is considered as the mainstream explanation. Factors including capital flow, trade, transport cost, knowledge spillovers, and agglomeration economies are the major factors in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J. Wang, The Role of the State in China’s Urban System Development, https://doi.org/10.1007/978-981-33-6362-5_1

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1 Introduction

Fig. 1.1 Different strands of literature on urban system development

affecting urban system development (Abdel-Rahman and Anas 2004; Henderson 1974, 1987, 1988; Fujita et al. 1999; Black and Henderson 1999a; Duranton 2007). Except for Bourne (1975), few scholars have taken into account the state as a major force in affecting urban system development. However, urban system development in China is still regulated by the state in the post-reform period, showing different patterns and features compared with the Western advanced countries in which market forces are dominant. It has been argued by many scholars, as the Chinese economy is inherently political, the continuously powerful role of the state should not be underestimated in the study of Chinese cities (Lin 1999; Ma 2002; Pred 1980; Wu et al. 2007; Yeh et al. 2015). The development of urban system are rooted in Chinese political economy contexts and are likely to be associated with the role of the state although economic reforms and opening-up policies have been adopted since 1978. In this regard, the conventional wisdom which sheds little light on the role of the state has theoretical deficiency in explaining China’s urban system development. In sum, there are two major deficiencies in the existing literature on China’s urban system. First, relatively little scholarly attention has been paid to the country-specific theorization of China’s urban system development that emphasizes the structure and organization of the political system. Second, there is a lack of modelling approach in existing literature which takes the state as endogenous factor of urban system development.

1.1 Research Background

3

China is one of the few countries in the world that has a national urban system policy, which is known as “strictly control the scale of large cities, rationally develop medium and small cities”. This policy that was adopted since the early 1980s is implemented to prevent the urban problems that plagued the large cities in the developing countries from happening in China. A series of policies and institutional arrangements, such as the Household Registration System (hukou) and promotion of the promotion of Township and Village Enterprises (TVEs),1 are used to achieve the goal of controlling the scale of large cities and facilitate the development of medium and small cities. With the market-oriented reforms, globalization, and decentralization of state power, the national urban system policy may become less strong as before and the ways in which the policy is implemented are changing. A new conceptual framework needs to understand the role of the state and its changes in affecting China’s urban system development in the post-reform period. The majority of scholars have a consensus that the state plays an important role in China’s urban system development, but few studies have examined how the state intervenes in China’s urban system development processes. In the literature on China’s urban development, most scholars consider China as a unitary entity (Wu 2010; He et al. 2008; He and Wu 2009; Naughton 1996; Sit and Yang 1997; Zhao and Zhang 1999). Driven by the processes of decentralization, globalization, and marketization, the state power is likely to transform from a single unitary power to a power matrix in geographical space. The economic transition, institutional fix and reorganization of state power have profoundly changed the ways in which the Chinese state intervene in urban development. There are three major changes concerning the state power structure. First, the decentralization of the state power from the central government to local governments represents a change of the state of China from a single unitary power into a new power matrix in geographical space. Second, significant variations are observed in the government capacity among cities because reforms have empowered cities to extend their capacities unevenly. Third, cities are organized by the Urban Administrative System (UAS) which hierarchically differentiates and reorders the government capacities of cities at different administrative levels. Therefore, an alternative framework that unfold the “black box” of the state is necessary to understand the role of the state in China’s urban system development. Moreover, there is a lack of quantitative studies which empirically examine how the state affects urban growth and how the interplay of the state and the market shapes the growth patterns across cities. Especially, limited empirical research has tried to establish the micro-foundation of the dynamics of China’s urban system development. The difficulty of measuring the effect of the state is one important limitation of the empirical study. To address this issue, one needs to assess the effects with sound methods and systematic data. The significance as well as the salient inconsistencies between theory and reality have entailed a new conceptual framework and empirical analysis on the role of the 1 TVEs

refer to the enterprises set up in the rural areas and established by the township or village governments, household(s), or Chinese and foreign partnership through shareholding mechanisms or cooperative systems (Liang 2006).

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1 Introduction

state in China’s urban system development. Furthermore, the empirical study needs to treat the state endogenously, as a concept that represents the interventions, institutions, policies related to the state. Since 1978, China’s urban system development is regulated by the national urban system policy. Thus, this book evaluates the effects of this policy first, and then examines how this policy is implemented to affect urban system development in the post-reform period on the basis of quantitative analyses.

1.2 Research Questions and Objectives There are considerable studies on the urban system development in China. Although most scholars have recognized the important role of the state, the ways in which the state intervenes in the urban system development have not been well addressed in existing literature. Some key questions deserve further examination. This book attempts to answer the general research question—how has the state affected the urban system in China in the post-reform period? More important, it focuses on the “how”, not just the “whether”. To answer this general question, this book will investigate and explore four sets of specific research questions as follows: First, what are the development patterns and distinctive features of China’s urban system in the post-reform period? Second, has China’s national urban system policy achieved its goal of regulating the development of urban system in the post-reform period? What would China’s urban system be if state regulation was reduced or largely eliminated since the beginning of economic reforms? Third, how can we understand the role of the state in urban system development in China? To what extent are urban government capacity responsible for the sizes of cities and the uneven growth of cities within the urban system? How does the state interact with market forces in affecting urban growth? Fourth, how does the UAS influence the urban system development? In sum, the major objective of this book is to understand how the state affect China’s urban system development in the post-reform period. This will be achieved by accomplishing the following specific objectives: 1. To review the theoretical and empirical studies in terms of urban system development both in China and different types of countries; 2. To identify the development patterns and distinctive features of urban system in the post-reform period; 3. To examine to what extend has the national urban system policy achieved its goal of regulating the development of China’s urban system; 4. To develop a conceptual framework that is rooted in the political and economic context of post-reform China to understand how the state intervenes in urban system development;

1.2 Research Questions and Objectives

5

5. To examine the micro-level dynamics of urban system development, and to quantitatively estimate the effect of the state on the development of urban system; 6. To examine the interaction of the state and market forces, and to estimate its influence on urban size; 7. To evaluate the causal relationship between administrative level upgrading and urban growth, and to discuss the impacts of the UAS on the urban system development in China; 8. To offer policy suggestions on urban system development on the basis of research findings.

1.3 Defining the Urban System and the State 1.3.1 Urban System and Urban System Development Urban system is one of the most classic concepts in urban geography and urban studies. However, it is a really broad concept with various definitions for scholars focusing on different research topics. Following Pred (1977), the concept of urban system is defined as a set of cities in a country or large region which are economically linked to one or more other individual cities in the same county or large region. More specifically, urban system is a national or regional set of cities which are interdependent in a way that any significant change in the economic and social activities of one member city will directly or indirectly bring about some modification in the economic and social activities of one or more other set of members (Bourne 1975). All cities within an urban system are inter-connected by visible or invisible communication networks. Cities and sets of cities never develop as separated entities, but are characterized by the properties, constructs and models of other social systems (Berry 1964). In a broad perspective, urban system study is a way of understanding the process of urbanization operating at different spatial scales (Bourne 1995). It implies that, the urbanization process is in fact a systematic growth and structural transformation of population from rural to urban. This perspective provides an incisive framework designed to understand the structural, geographic and temporal urban development. Urban system have three basic dimensions: temporal, hierarchical and spatial (Bourne 1975). All analyses in this book are within the narrow confines of one or two or all of these three dimensions. Urban systems are organized at different spatial levels. According to Bourne (1975), at least three levels may be summarized from large to small scale: “national system”, “regional sub-systems” and “daily urban systems”. For the national urban system, it is relatively easy to define its geographic boundary, based on the political boundary. At the regional level, linkages between cities and regional centers may be helpful to define the boundary. At the local level, the daily urban systems may be defined by the movements of commuters and distributions of daily activities. In this book, I mainly focus on urban system at the national level—China’s urban system, which is comprised of all officially designated cities in the corresponding years.

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In a narrow sense, “urban system development” in this book is defined as growth in population size of individual city within the urban system. In a broad sense, urban system development refers to the structural growth of cities measured by population. The growth of cities within the urban system is interdependent, and all cities are coevolving in a complex way. Put it simply, “urban system development” refers to not only the growth of city sizes but also the evolution of spatial organization, city-size distribution and urban functional structure.

1.3.2 The State The second core concept is the state. The state is defined depending on the objective of each study in the existing literature (MacLeod and Goodwin 1999; Jessop 1990; Brenner 2004). To explore the relationship between the state and urban system development, the state is defined from a power relations perspective. Unlike the concept of “development state” which was defined to highlight the pivotal role played by the state as a whole in guiding the industrialization and economic take-off, the definition of the state in this book needs to investigate in the internal structure of China’s state, by emphasizing its internal power relations across scales and its interactions with other actors. As argued by Lin and Ho (2005, p. 414), Chinese state is “characterized not only by its frequent temporal institutional changes but also by an internal structural diversity of power relations”. By synthesizing the insights of Jessop (1990) and Lin and Ho (2005), the Chinese state in this book is defined as an active and dynamic institutional ensemble which is spatially and temporally contingent on the structure of cross-scalar power relations. In order to investigate the role of the state in urban system development, the institutions that bind the various constituents of the state, the power relations among these constituents, and the actions taken by the state to affect urban development need to be examined. Moreover, the state apparatus includes a set of organizations and institutions through which state power is exercised. In practice, the “central government” mainly refers to the State Council including all its departments. In Chinese administrative system, the State Council has the top authority in making rules, policies, strategies and decisions in China’s territory. From a broad perspective, the central government also includes the Central Committee of China Communist Party, National People’s Congress, and other central executive branches. By contrast, “local government” refers to the governments at provincial, prefecture, county and township levels. The local government is the public administration responsible for the administrative affairs within its jurisdiction. The urban government is the government of city including prefecture-level and county-level. The central government is the most powerful decision maker, while local governments at different levels are de facto actors in affecting urban development.

1.4 Organization of the Book

7

1.4 Organization of the Book This book is organized into 8 chapters. Chapter 1 introduces the background of this research, research questions and objectives, core concepts definitions. Chapter 2 systematically and critically reviews the existing theories and empirical studies on urban system development. The chapter includes three parts. The first part is to revisit four approaches and models of urban system studies. The second part is a literature survey on urban system development in different types of counties. The third parts reviews the studies on China’s urban system development in the prereform and post-reform period. This chapter concludes with a critique on the existing literature, pointing out the strengths and weaknesses in application to understand the role of the state in China’s urban system development. Chapter 3 examines the development of China’s urban system and its relationship with the changing state policies and institutions in the post-reform period. This chapter includes three parts. First, some critical concepts and issues that are important to understanding China’s urban system development are clarified. Second, the development patterns of China’s urban system in both pre-reform and post-reform periods are analyzed. The third part explores how the changes of policies and institutions lead to the changes of the ways in which the state intervenes in development of China’s urban system in the post-reform period. Considering the significant inconsistencies between theory and reality identified in Chapters 2, 3, and 4 develops a conceptual framework for this book. With theoretical insights drawn from research from various disciplines such as political sciences, economics, and sociology, a conceptual framework based on political hierarchy perspective is developed to dissect the “black box” of the state. This framework emphasizes that Chinese cities are organized by a hierarchical political system. Cities at different administrative levels have different urban government capacities to affect urban development. Moreover, this chapter also presents the research design for the empirical analyses based on the conceptual framework, including hypotheses, analytical procedure, research methods, and data. Chapters 5–7 are empirical analyses. Chapter 5 attempts to identify the development patterns of China’s urban system and examine the effects of the national urban system policy on urban system development in the post-reform period. After examining the growth processes of Chinese cities and the evolution of the city-size distribution, this chapter performs a series of simulations of China’s urban system development under several scenarios with no or less state interventions. Seven models ranging from the standard stochastic growth model to a more deterministic growth model are developed in this chapter. By comparing the simulated non-state intervened urban systems with the actual urban system in 2010, this chapter explore to what extent the development patterns and distinctive features can be attributed to the national urban system policy. In addition, this chapter also investigates that what would China’s urban system be if the state’s influences were reduced or largely eliminated since the beginning of economic reforms.

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1 Introduction

Chapter 6 performs a series of econometric models to estimate the effects of the Chinese state on urban system development. The empirical analysis can establish the micro-foundation of the dynamics of China’s urban system development. The variable of urban government capacity as a proxy for the ability of urban government to intervene in urban development is developed to measure the state interventions in urban development. In order to address the problems of endogeneity and the presence of heteroscedasticity, the system generalized method of moments (system GMM) estimator is employed to estimate coefficients of the models. This chapter can examine the ways in which the state intervenes in the development of urban system from the perspective of hierarchical organization of state power among Chinese cities. Chapter 7 investigates the relationship between administrative level upgrading and urban growth. UAS lays the foundation for the hierarchical organization of the state power among Chinese cities. Administrative level of cities is correspondent with urban government capacities. In order to examine the causal effects between administrative upgrading and urban growth, a quasi-experimental approach with propensity score matching (PSM) and difference-in-difference (DID) methods is employed. By evaluating the effects of administrative level upgrading on urban growth, this chapter emphasizes the importance of UAS that is implemented to achieve the goal of the national urban system policy and state regulation. Chapter 8 concludes the book, with a summary of the main findings, discussion of theoretical and policy implications, limitation of this book, and directions for future research.

Chapter 2

Understanding Urban System Development in Different Countries: Theoretical Alternatives and Empirical Evidence

2.1 Introduction The goal of this chapter is to systematically review the theoretical and empirical studies on urban system development. This chapter includes four parts. First, various approaches concerning urban system development are revisited. Four prevailing approaches are found in the existing literature on urban system development, that is, Central Place Theory, Spatial Diffusion Approach, Stochastic Growth Theory, and Endogenous Growth Theory. Second, this chapter reviews studies on the development and evolution of urban systems in different types of countries, covering not only the advanced market economies, but also those former colonial countries and other “New World” countries. Third, this chapter provides a review of the studies on China’s urban system development which is divided into pre-reform period and post-reform period. Finally, based on the literature review, this chapter concludes with a critical review on urban system development in general, and presents several research gaps of these studies on China’s urban system development in particular, which leads to the elaboration of the conceptual framework of this dissertation in Chapter 4. As most approaches and theories on urban system are rooted in different discipline domains and derived from the context of Western market economies, this book is not planned to examine their verifiability. Instead, the emphasis is to draw insights from them based on which a country-specific theoretical framework can be developed to examine China’s urban system development. Therefore, special emphasis is placed on dynamic mechanisms, or driving forces, of urban system development in different countries. In particular, the roles of the state and the interactions between the state and the market that influence urban system development are the main points to be highlighted.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J. Wang, The Role of the State in China’s Urban System Development, https://doi.org/10.1007/978-981-33-6362-5_2

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2.2 Shifting Approaches of Urban System Study Many models have been advanced to explain the urban system development. They vary in theoretical foundations, methodologies, and analysis levels. To better understand these models, this chapter arranges them into four general approaches: Central Place Theory, Spatial Diffusion Approach, Stochastic Growth Theory, and Endogenous Growth Theory. These models are mainly performed in two general manners. The first one is a more deductive approach that aims to derive mathematically certain type of urban system based on some dynamic processes. The second approach is mainly based on econometric methods, which uses socioeconomic variables to model the urban system development. Since the primary concern of this review is to examine their explanations on urban system development, the emphasis is placed on the conceptions and perceptions of the four approaches regardless what methodologies they take.

2.2.1 Central Place Theory The classical location theory aims to address the questions that where the economic activities are located, through which a typical optimal spatial pattern of settlements can be obtained. The literature could date back to the work of Weber (1909), Christaller (1933) and Lösch (1940) in the early twentieth century. The classical location theory illustrates a hierarchical structure of regional settlements system, where the position in the hierarchy determines the settlement’s economic function in the system. Based on this theory, a general framework was provided to analyze the spatial patterns, size, and number of settlements within a region, relying upon the historic reasons and the locational patterns of areas today. The industrial location theory, firstly developed by Weber (1909), attempts to find out the general factors in affecting the location for industries. He wanted to answer the question that what is the best location for a certain firm given the locations of all other firms. The least cost approach was employed to solve the optimal problem. The optimal location of a firm is defined as a search for transport-cost minimizing and profit maximization location. With this objective, Weber shows the so-called “Weberian Triangles”. Given two inputs located at two different points and one product market located at the third point, Weber found that the optimal location was at the triangle formed by linking the product market point with two input points and two input points with each other. Christaller developed the so called “Central Place Theory” explaining the general laws that determine the number, size, function and spacing of settlements. He provided a top-down explanation for the emergence of a cascading system of central places. In essence, the central place theory consists of a set of assumptions explaining why hierarchically tiered centers are found at certain preferred locations on the economic landscape. This theory also empirically describes the spatial structure of cities within a defined region based on the market

2.2 Shifting Approaches of Urban System Study

11

area of each city. Christaller showed that a system of hexagonal markets would emerge based on the theoretical assumptions and the real pattern was observed in South Germany. Lösch (1940), on the other hand, adopted a much more complex bottom-up central place system whose conceptual foundation is a triangular lattice of discrete farmsteads. He argued that Christaller’s rigid hierarchy failed to provide the reasons why economic activities tend to concentrate into certain centers within a featureless plain. As Beckmann (1955) has stressed, Lösch mainly concerned with the theoretical bases of the hierarchical spatial structure of hexagons, and the location equilibrium of the balance of spatial and other forces as a consequence of which the spatial structure takes on explicit patterns. Central place theory, as one significant theoretical model of classical location theories, seeks to explain the observed regularities and relationships of a spatial hierarchy of centers regarding the location of economic functions. This theory was widely employed by geographers to study the hierarchy of urban systems. Numerous empirical studies, as summarized by Berry and Pred (1965), have shown the evidence of functional hierarchy of cities based on the central place system. However, there is criticism of the central place theory which points out that it is static observation in nature, lacking dynamics. This may be because central place theory was derived from static observation and only limited interest for understanding the urban hierarchy of empirical cases. Various efforts have been devoted to develop the central place theory by using more sophisticated techniques. Beckmann and McPherson (1970) attempted to develop an analytical approach to construct the relationship between Christaller’s central place structures with rank-size rule. White (1974, 1977) attempted to investigate how cost and revenue function change with time, and went beyond to see the effect of changing interaction patterns. Similarly, Allen and Sanglier (1979) developed a dynamic model of the urban growth that makes reference to central place theory, contributing to simulation the distribution of the growth of activities and employment among cities. They predicted the pattern of central places would be emerged spontaneously in the way of self-organizing evolution. In addition to these dynamic models, there were also another strand of studies relying upon the comparative-static analysis which connected the static Christaller’s approach and dynamic modelling (Parr 2002). Three questions were considered in these studies. First, they consider the evolution of the urban system, from the top downward or firm the bottom upward or in a more complex manner. Vance (1970) and Huff (1976) provided the empirical evidence regarding this question. Second, the manner in which a given function become assigned to a particular level of the hierarchy and the reasons. They were both empirical and theoretical explorations in terms of this issue (Stabler and Williams, 1973; Mulligan, Partridge and Carruthers 2012). Finally, they are concerned with the processes of the structure of the hierarchy becoming modified. For example, the formation of a new hierarchical level, and changes of numbers of centers in a level (Parr 1980, 1981). The classical location theory, in particular the central place theory, might provide a foundation upon which other interdisciplinary studies on urban system could be relied on. But it is difficult to regard the central place theory as a general theory of the urban system. In this regard, urban system need further theoretical refinements

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2 Understanding Urban System Development …

and empirical extensions. It is important to note that central place theory is rooted in explicitly spatial. As pointed out by Mulligan et al. (2012), the main shortcomings of central place theory are the lack of micro-level foundations and a general equilibrium approach. Therefore, there is a need to develop its micro-foundation based on a more general equilibrium fashion and abstract modelling strategy, along with assessing how well the central place theory fits empirical regularities, or, whether it can inform the forecasts of the evolutionary pattern of urban and regional systems.

2.2.2 Spatial Diffusion Approach The spatial diffusion approach has gradually formed since the early 1960s when there was a flourishing of studies on urban system in the disciplines of urban geography and planning. Basically, the focus of this approach is to explore the underlying logics and mechanisms of organizing human activities over cities, and describe the resulted growth patterns across cities. This approach is concerned with, at least, the five dimensions of human activities: production, distribution, consumption, circulation and control (Bourne 1997). In addition, this approach is largely space-related and views the urban system as an aggregate of cities because cities can only be examined in relation to others. When growth and development processes are considered, the interdependencies and interactions between cities within a system are regarded as principal determinant of urban growth (Pred 1973). Empirical studies that are based on the spatial diffusion model see city growth as a process of general hierarchical diffusion (Berry 1972). In this model, the growth-inducing factors, such as innovations, information, capital, labor forces, firms, powers, and development strategies or policies, are “filtering” or “spreading” within the urban systems from large or central cities to small or periphery cities (Beckmann 1970; Conzen 1975; Berry 1972; Friedmann 1973). Actually, the diffusion theory mainly focuses on the spread or diffusion of such factors which is perceived as a dynamic process in shaping the geographical patterns of cities (Hudson 1969). Cities in the central area or at top positions of the city size distribution receive these development opportunities firstly, and then shift to peripheral areas or cities in the bottom of urban system. Specifically, three possible mechanisms were identifies to explain the diffusion process: (1) “market-searching” in which firms and individuals look up for development opportunities sequentially from large to small cities; (2) “trickle-down” which means the spin-off of declining industries from large centers to small cities;(3) “imitation effects” which encourage the entrepreneurs and policy makers in small cities to learn from those in large centers (Berry 1972). In a study on the USA case, Pred (1975, 1977) emphasized the role of the hierarchical diffusion of job-providing organizations (e.g., multi-locational establishments) and growth-inducing innovations in urban system development. Two fundamental processes of diffusion were highlighted: interurban information circulation and the multiplier effects of locational decisions of large organizations. He developed a model to explore how the locational patterns of such job-providing organizations

2.2 Shifting Approaches of Urban System Study

13

and the inter-city circulation of growth-inducing innovations interact to affect the process of urban system development in advanced economies. Based on this, he can explain the stability of the rank-size among national urban systems or the leading centers of major regional urban systems. According this framework, national or regional interventions were difficult to achieve their goals that aim to alter diffusion patterns of urban systems, owing to the high degree of interdependence typical of “post-industrial” countries. He wrote: Unless some accurate insights into the process of city-system development in advanced economies are established, there is a great danger that growth pole policies and other policies designed either to discourage metropolitan concentration or to aid lagging and depressed regions will continue to prove counterproductive in terms of governmental goals. The need for process insights to aid in the formulation of goal-consistent policies is no small matter. (Pred 1975, p. 267)

Geographical proximity is a key factor that determines the direction and degree of hierarchical diffusion within urban systems. According to the diffusion theory, diffusion is consequence of the interactions and independencies among cities, and the rates of diffusion among urban systems could explain the variations of city growth rates. Industrial shift, population movement, and technology diffusion provide cities and regions with both new opportunities and challenges for economic growth. Capability to gasp the new opportunities and rise to the challenges will determine convergence or divergence of cities development (Storper 1997). As a result, major urban centers play primary role in receiving and redistributing urban growth-inducing factors, which are perceived as “growth centers or poles” of the urban system (Richardson 1976). As Berry (1972) has pointed out, “growth occurs as a consequence of the filtering of innovations downward through the urban hierarchy and the spread of use of the innovation among consumers residing within the urban fields of the adopting centers”. Thus, the general pattern of growth within urban system is from “polarization” in the early stage to “dispersion” in the late stage. Literature in development economics describes the regional convergence trajectory as an inverted-U curve (the Kuznet’s curve) (Myrdal 1957; Simon 1955b). The inverted-U represents the early increase, then stabilization, and finally decline process of regional growth rate (Alonso 1980; Hirschman 1958; Williamson 1965). Furthermore, Richardson (1980) developed a conceptual framework based on empirical cases that attempted to delineate more detailed general process of urban system evolution as a result of the spatial diffusion. He concluded that the urban system, which was embedded in a country’s space economy, evolved through three major phases: convergent primate city urbanization, polarization reversal, and counter-urbanization. In this framework, urban development begins in one or two core cities and then decentralize to some periphery cites, and finally lead to counter-urbanization in the last phase. Subsequently, this framework has been refined, producing a variety of related concepts, and also is widely used to explain periodic

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features of urban system evolution in different countries.1 For example, Geyer and his colleague (Geyer 1996; Geyer and Kontuly 1993) extended the three phases to six phases as a more detailed process named “differential urbanization”. Though the process of urban system evolution presented a general sequence, phases may not necessarily occur or follow chronological. Specifically, based on cross-country comparison, Wheaton and Shishido (1981) argued that population will start to decentralize from large cities to small cities when per capita GNP reaching a certain level. A dynamic model that aimed to associate city size distribution with the stage of economic development in time was built by El-Shakhs (1972). Country-specific empirical studies indicated various factors including state policies, political changes, global influences, natural endowment, and even accidents could cause departure from common track (Geyer 2003; Tammaru 2000; Richardson 1981). The spatial diffusion approach is rather broad, and investigate the development of urban system based on a spatial perspective. Scholars consider the development of urban system as a result of economic restructuring, which implies that the patterns of urban system development are exogenous and determined by reorganization of economic factors over cities. Thus, research efforts are devoted to describe the general process of urban system development. However, the general process lacks its dynamic mechanisms. The structure of urban system considered as a given condition is largely ignored in this approach. Instead of being an exogenous spatial distribution of cities, urban system can affect the organization of population and economic activities in an intricate way. This shortcoming has limited the explanation power of the spatial organization approach.

2.2.3 Stochastic Growth Theories Generally, the stochastic theories mainly attempt to explain the dynamic process which results in the city rank-size distributions. In empirical studies, scholars found that city distribution in a country or region complies with the rank-size rule or, Zipf’s law (Gabaix 1999; Zipf 1949). According to this law, if the product of the population of a city Pk multiplied by its rank k to the power of q equals the population of the largest city P1 , it is said that the city size distribution follows Zipf’s law. However, as an empirical regularity, Zipf’s law lacks theoretical foundation. The stochastic growth theory can provide both empirical evidence and theoretical justifications for Zipf’ law. Theoretical speaking, variations in growth rate among cities determine the evolutionary process of urban system. The stochastic growth theory views urban growth as a random process, or proportionate grow process labeled as Gibrat’s law (“proportionate effect” law) (Gibrat 1931), which suggests that the mean of urban growth rates is independent on the size of city and follows a random walk with 1 For

polarization reversal, subsequent studies could be found in Townroe and Keen (1984), Lo and Salih (1979), and Geyer (1990). Selected notable studies about counter-urbanization include Berry (1976), Frey (1987), Fielding (1989), and Champion (1992).

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constant mean and common variance (Gabaix 1999). The city-size distribution in a steady country would follow Zipf’s law if the growth of cities follows Gibrat’s law (Eeckhout 2004; Ioannides and Overman 2004). According to the random growth theory, the size of city is determined by its historical rank in urban system. External shocks only have temporary impacts on the city size distribution, and most cities will return to their relative positions within a certain period after the shocks. For example, it has successfully demonstrated that the powerful shocks of World War II on Germany and Japan’ urban systems only have temporary influence (Bosker, Brakman, Garretsen and Schramm 2008; Davis and Weinstein 2002). A variety of empirical models based on stochastic growth theory have been developed to study the random growth process of urban systems. Simon (1955a) extended the basic Gibrat’s model by relaxing the closed system assumption, not only to allow number increase of cities over time, but also allow entry and exit of cities. In Simon’s model, the expected growth rate of the cities of each size stratum is independent of stratum. Simon assumes that out migration and birth and death rates are all uncorrelated with city size. Only inter-city migration and rural-to-urban migration are related with city size. The probability of new migration choose to either form a new city with probability p or go to an existing city with probability 1-p. The probability of choosing a particular size class is proportional to the total urban population cities within that size class following the modified rule in Gibrat’s law. Simon’s model is able to generate a power law city distribution of the upper tail with an exponent of 1/1-p. When p is very small the model would lead to formation of a Zipf distribution with an exponent close to 1. Another strand of stochastic growth models was developed by Berry and Garrison (1958) and Berry (1961) with ideas from system theory and concept of entropy (complexity). They argued that entropy is a steady status resulted from many forces that act randomly. The city size distributions are determined by numbers and degrees of complexity (simplicity) of forces in shaping the urban development in countries. When many forces act in complex ways, the rank-size distribution is likely to be found. While, few forces and simple acting way seem generate primate distribution. In empirical studies, low economic development level is likely to be associate with simplicity and thereby generates primate distribution, whereas high economic development level may increase the complexity of urban system that leads to rank-size distribution. Berry summarized: Simplicity was associated with indigenous political and administrative controls exercised from orthogenetic primate cities, with dual or multiple colonial economies and controls exercised from heterogenetic primate cities, and with empire capitals, in all cases also combined with small countries. Complexity was associated with specialized economies, but also with countries which have strong urban traditions and long histories of urbanization, and was found in countries of every size. (Berry 1961, p. 587)

Following this idea, many scholars attempted to derive the rank-size distribution through entropy-maximizing methods (such as Curry (1964), Fano (1969), and Olsson (1967), etc.). Recently, Chen (2012b) presented new derivations based on the self-similar hierarchy of cities to demonstrate the relation between rank-size distribution and entropy maximization, and suggested that entropy maximizing reflected the

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greatest equilibrium between equity for parts/individuals and economic efficiency of the entire urban system. A variety of related empirical studies tested the validity of Gibrat’s law and provided conflicting evidence, but also laid the foundations of the stochastic growth theory. Several studies have found that the observed patterns in urban growth were not consistent with Gibrat’s law. For example, study based on a case of England in nineteenth century, Robson (1973) found that both the mean growth rate and its variance changes with city size. Moreover, Rozenfeld et al. (2008) explored the impacts of city definition on Gibrat’s law and city size distribution. They defined the “City Clustering Algorithm” (CCA) based on geographic boundary of the population instead of the traditional scope of administrative boundaries. Using this definition, they find that the mean growth rate of CCA deviates from the Gibrat’s law and the standard deviation decreased as power law of the city size. On the other hand, there are also many studies confirmed the validity of Gibrat’s law, based on the case of the USA (Glaeser, Scheinkman and Shleifer 1995), Spain and Italy (González-Val, Lanaspa and Sanz-Gracia 2014). Eeckhout (2004) confirmed Gibrat’s law, but found the proportionate growth will result in lognormal distribution of all cities, rather than Zipf distribution. These studies provided conflicting empirical evidence, implying the stochastic growth theory has deficiencies. The first deficiency is the selection of the truncation point of city size distribution which lead to different urban growth patterns and city size distribution. For example, Eeckhout (2004), using the Census Designated Places (CDP) of the USA in 2000 as the unit of analysis, verified that the coefficient of Zipf’s law is sensitive to the selection of the truncation point and is consistently increasing in the truncation. The upper tail of city size distribution follows Zipf’s law well, but the entire city size distribution is shown to be lognormal. Similarly, using entire cities of the USA, Spain and Italy, González-Val et al. (2014) found that Gibrat’s law is only valid in the upper-tail distribution, and does not hold in the long-run urban growth. Also, they demonstrated the city size distribution in the long term would follow the lognormal well if there is no truncation point. The second deficiency of the Gibrat’s law is that the variations of the city size distributions are not addressed (Carroll 1982). Although the stochastic growth process can lead to the general rank-size distribution, the stochastic growth theory fails to explain the variations in the slops of the distributions. This may be because that the stochastic growth theories only explain the observed urban growth pattern with respect to its past growth trajectories. Any factors that potentially affect the independencies of urban growth are taken as exogenous in the stochastic growth theory, which limits its explanatory power.

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2.2.4 Endogenous Growth Theories The endogenous growth theories, in sharp contrast to the stochastic growth theory, believe in the city growth is the outcome of some factors related to changing socioeconomic contexts. The endogenous growth theory allows us to rethink the driving forces of variations of city growth rates, and to explain how does a specific city-size distribution emerge and how does it evolve in the process of economic development. Scholars from different disciplines investigate this issue in various ways. In general, this strand of literature predicts a deterministic growth process of cities, meaning that, variations of urban growth rates across cities are determined by different forces. As such, scholars can explore the evenness of the population distribution over cities. The essential question is whether the deterministic growth of cities leads to a more even distribution of cities as we see more and more medium-sized cities are formed, or to a more uneven city size distribution as people might concentrate in large cities and smaller cities might decline gradually? In contrast to the approaches summarized above, the endogenous growth theory seeks to unfold the black box that dominates the evolution processes of urban system, by establishing relationship between the city-size distributions and economic development. In the early models, the objective is to relate the rank-size or primate city distributions to economic development levels at the national level.2 El-Shakhs (ElShakhs 1972) observed the change from primate to city-size distribution is attributed to economic development from high disequilibrium at the early stage to equilibrium at the late stage, finding the relationship between primacy and economic development is curvilinear. Stewart (Stewart 1958) associated primacy with the type of economy, that is, the agricultural economies are related to primate distribution, and industrial economies are related to ran-size distributions. Although these studies have tried to offer the causal relationship between city size distributions and economic development based on cross-country comparison, they failed to provide solid evidence if they cannot look into the city-size distribution to explore the growth dynamics of individual city. Thus, disaggregate models are needed to probe into the endogenous growth processes of cities. A series of economic models are developed by economists to explore the issue of disaggregate urban growth and the resulted evolution of urban systems. In these models, economic factors are key to urban growth. The driven forces such as knowledge spillovers, human capital accumulation and other local externalities, are taken to explain the variations of city growth. Evans (1972) developed a city growth model consisting of firms and urban residents. City is described as coalition of firms which provide different costs and benefits for residents. Both the firms and residential is rational to choose cities which can maximize their own returns. According to Evans’s 2A

systematic study on the primate distributions was conducted by Jefferson (1989). Jefferson observed, in more than fifty countries, the largest city were often three times larger than the second largest city. National city-size distributions with such a dominated largest city are referred as primate distributions. The largest city in such urban system is the primate city, and a country’s primacy indices are calculated as the ratios of second and third largest cities over the largest city.

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model, a stable hierarchy will emerge when market opportunities and input costs are assumed to vary in cities with different sizes. But Evans’s model is too general and is not sufficiently specified to capture the complexity of urban growth and evolution of city-size distribution (Richardson 1973). The beginning of endogenous growth theory within modern urban economics can be traced back to Henderson (1974). Henderson developed a neo-classical economic model to explain the structure of city size distributions through modeling growth of individual cities of different specialization types. The basic idea of Henderson model is that the tension between external economies associated with geographic concentration of industry within a city, and diseconomies related to factors such as commuting costs, congestion. The net effects of this tension represents as an inverted U-curve, and the optimal size of each city equals the value of the top of that U-curve. This model could explain why city sizes distributions are characterized by a hierarchical structure and the formation of a large variety of specialized cities having quite different sizes. In short, the general hypothesis of these neo-classical models is that urban size is the optimal equilibrium of forces promoting city growth and forces hindering city growth. The new growth theory (NGT) stresses the role of technical progress and knowledge spillovers in generating growth. Thus, the interests on agglomeration economies have been renewed (Hanson 2001). When applied to issues associated with geography and space, the NGT consistently assumes the mechanism of increasing returns is spatially embodied in agglomeration economies, because the knowledge spillovers and technological diffusion tend to be localized, and geographically concentration may be an important source of positive externalities (McCann and van Oort 2009). Moreover, Glaeser et al. (1992) suggested the knowledge spillovers occurs between different industries, meaning that, growth of cities may be attributed more to the urbanization economies consistent with the theories of Jacobs (1969). In a more advanced model, Black and Henderson (1999a) also postulated that localized information spillovers and human capital accumulation foster endogenous urban growth, and demonstrated growth rates of sizes of different types are proportional to the their growth rates of human capital accumulation. (For models of this literature stream, one can also see related work contributed by, Dobkins and Ioannides [2001], Duranton and Puga [2001, 2004], Eaton and Eckstein [1997], and Gabaix and Ioannides [2004]). Another tradition of endogenous growth theory is the New Economic Geography (NEG), which aims to answer the question of how the interaction of economies of scale and transport costs produce a spatial economic landscape described by the central place theory (Fujita et al. 1999). In the literature of this tradition, the models are developed based on monopolistic competition and increasing returns through which the hierarchical central place patterns can emerge (Fujita and Krugman 1995; Krugman 1991). A general equilibrium model with adjustment dynamics, has shown that urban systems can evolve into a “central place-style” hierarchical system through self-organization mechanism as population increases (Fujita 1999; Fujita and Mori 1996). The initial conditions (first nature) of locational conditions that shape the formation and evolution of the city size play a crucial role in NEG models. Effects

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of the first nature may be persistent as a result of the path dependence effect of selfreinforcing agglomeration forces creating the so-called secondary natural advantages (Krugman 1993). Based on these assumptions and dynamic mechanisms, the NEG models resurrect the central place theory as an economic model in space. Furthermore, unlike the main stream of urban economics, this deductive approach emphasizes endogenous emergence of spatial economy through interactions (e.g. trade) among cities (Abdel-Rahman and Anas 2004). However, despite the NEG approach claims that space is resurrected in their models, which is criticized as the main shortcoming in Henderson-type models (Fujita 1999), their cities are only nonrepresentational units of economic activities and seldom related to actual distribution of cities.

2.2.5 Summary The aforementioned four approaches reflect the changing paradigms of urban system development and evolutionary. The models of central place theory specify a spatial hierarchical distribution of cities, and attempt to explain the dynamics of this structure. Although many subsequent research have taken the formal deductive modelling approach (e.g. Beckman [1958]), these attempts fail to directly relate the specified hierarchical distribution to potential causal factors. In contrast, the spatial diffusion approach uses a dynamic process to explain the formation of the hierarchical distribution of cities. It considers the development of urban system as a diffusion process over space, rather than specifies an exogenous distribution structure. Different from the central place theory and spatial diffusion approach which place the emphasis on the entire urban system, both the stochastic growth theory and endogenous growth theory focus on individual city growth and attempt to relate to the evolution of urban system. However, the stochastic growth models explain the variations of urban growth rate based on the urban size instead of the factors which causes the stochastic process, and this is the critical weakness of the stochastic growth models to account for the size distribution problem. Moreover, the endogenous growth theory is attributed mainly to economic factors. In classical theories, physical geography—rivers, coasts, and mountains—may have played a crucial role in early settlements, whereas, for the modern economics, the evolution of the population across geographic locations is an extremely complex amalgam of incentives and actions taken by millions of individuals, businesses, and organizations. Most scholars agree that economic factors are the principal determinant of the dynamics of city populations (Eeckhout 2004). The agglomeration and mobility and urban population between different geographic locations are associated with economic activity. For these four approaches, an underlying prerequisite is that urban system development is driven spontaneously by bottom-up economic activities. Both Zipf’s law and rank-size rule have an underlying prerequisite: urban system is a pure self-organized biological system. Therefore, these theories put emphasis on the dominated role of market, and the development of urban system is conceived as

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a changing equilibrium process, based on the market mechanisms. It is thus easy to understand why these theories have explanatory power in Western market economies. However, even in these economies, institutional favoritism and local policies are important factors in determining city size (Henderson and Wang 2007; Jonas 1991). In socialist and transitional countries, such as China, where state intervenes directly into urban development, non-market factors play even more important roles. It should be admitted that seeking for general theory is of great importance, because it is useful tool to observe features and properties of urban system. But as a complex social system, urban system is shaped partly by self-reorganization mechanisms, partly by planning, institutions and regulations. Cities are inherently associated with politics, history, culture and society in a nation, hence it is reasonable to argue for country-specific theorization for urban system studies. This view provides supports to Berry’s (1961, p. 584) argument that, “explanations of city size must be mediated by the size of the country, the history of urbanization, and the complexity of forces affecting the urban system”.

2.3 Development of Urban Systems in Different Countries The evolutionary nature of the urban system, widely recognized by scholars, are rooted in “historical context”, and determined by historical-locational factors (Berry and Pred 1965). The development of urban systems have different or even contradictory trajectories across countries, because there are considerable differences in their physical, cultural and historical contexts. In this section, three major styles of urban system evolution trajectories have been examined, that is, the “Old World”, the “New World”, and the former colonies (Table 2.1). The classification is mainly based on Berry (1981) and Bretagnolle, Pumain, and Vacchiani-Marcuzzo (2009). These three styles of urban systems are differentiated by their divergent development paths and evolutionary characteristics. Bretagnolle et al. (2009) figured out two major features of the hierarchical and spatial configurations that are significant different between these three types of urban systems. The first feature is morphology or, the pattern of Table 2.1 Three major styles of urban systems in the world Styles

Region

Selected example

“Old World” has long-standing urbanization processes and mature urban system with slow and regular evolution

Europe

UK, France

“New World” where cities were successively created by settlers

North America, Australia, South Africa;

USA, Canada

The former colonies where urbanization was influenced by exogenous forces

Asia, Africa, Latin America;

India, Brazil

Note Summarized and classified based on Berry (1991) and Bretagnolle et al. (2009)

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their occupation of space. The second is the regimes of city growth. In the following sections, the development of urban systems of these three styles will be revisited. Attentions will be paid to the dynamics and the forces that promote urban system development and restructuring, in particular, and the role played by the state in these countries. Typical countries of the three types are selected to be illustrated, that is, the UK and France for the “Old World”, the USA and Canada for the “New World”, and India and Brazil for the former colonies.

2.3.1 The “Old World” Countries: UK and France In these “Old World” countries where urban development has a long-standing and continuous history, the urban systems are characterized by the long-standing nature of their urbanization, and by the regularity of their development over time (Bretagnolle et al., 2009). Driven by the long-range inter-city exchange networks, cities emerged simultaneously all over their territories. Stability seems the most marked feature of the urban systems in the “old world”. The first stability refers to the persistence of the distribution of settlements over geographical space. During hundreds of years, there were no significant alterations of the spatial distribution of cities, despite the strong effects of external shocks such as two world wars. A similar stability occurs to the top of the urban hierarchy. The majority of the largest cities in these countries already established hundreds years ago, and continued to play dominant role in the urban system. London, Paris, Madrid, and Berlin are the typical cases. Due to the long-standing nature, the development of these urban systems exhibit spontaneous evolutionary trajectory characterized by relatively even occupation of territories. UK is a good illustration of this type of urban system. It is the first country of the world to undergo mass urbanization. The urbanization rate of UK passed the 50% in the middle of the nineteenth century. The urban system of UK is rather stable that can be even traced back to the eleventh century. The urban system of UK is a typical primacy distribution, in which the primate city is much larger than other cities (Jefferson 1989). The size of London was more than seven times as large as the second city, Liverpool in the 1930s. The entire urban system is strongly dominated by London. Meanwhile, the position of London has been enhanced by positive migration gain in recent decades. During the past half century, a number of powerful forces have also reorganized the urban system in UK. A variety of forces shaping the British urban system since 1950s has been summarized (Champion 2002, p. 90): 1. Counter-urbanization, that is, population redistribution down the urban hierarchy, underway since the 1960s, in terms of overall population change and internal migration; 2. De-industrialization, involving a massive shake-out in manufacturing and mining employment, with strong effects on the places that had the greatest specialization in these sectors;

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3. An “urban-rural shift” that results in urban employment deconcentration occurred since the early 1970s; 4. Decline of employment in traditional rural industries, as a result of deep depression of the farm-related economic activities in the late 1990s; 5. Net immigration from outside the UK, which in recent years emerged as a major element of national population growth (over 50%) in marked to the net emigration of the 1950s. 6. A social-demographic transformation involves rising female participation in the workforce and changes in household composition; 7. A continuing revolution in transport and communications; 8. The rise of the conservation movement, which brought to an end the era of mass slum clearance and redevelopment in British cities during the 1970s. It is clear that these forces have produced broad influences on the urban system of UK. In summary, two dimensions have dominated: the north-south drift and the urban-rural shift. For example, the “North-South divide” has been reinforced by the north-south drift, with population and jobs becoming more concentrated in the southern part of UK (Turok and Edge 1999). Similarly, Hall, Marshall and Lowe (2001) observed a notable north–south shift and a coastal-inland shift of the urban system in England and Wales. They concluded that these important urban centers have enhanced their position at the expense of smaller cities. The urban-rural drift has resulted in the rapid growth of the more rural districts, especially the most accessible areas around important cities and towns which are also smaller urban centers and rural settlements in regions. Therefore, population has shown an apparent tendency to decentralize from the cores of metropolitan area to the ring (Table 2.2). Of the total metropolitan population of England and Wales, 71% was in the core areas in 1931, and this proportion became 60% in 1966, and 59% in 1971 (Hall 1974). More importantly, urban system in Britain is recognized as being influenced by urban planning, regional policy, and social welfare system (Bourne 1975). In other words, the state plays an important role in the urban system. Specifically, these policies include legislative packages that attempt to rationalize competition among land use, programme for new towns, green belts, and environmental preservation, slum clearance, and public housing and social service systems. These policy tools are mainly proposed to solve urban problems facing in different periods. The social and Table 2.2 Spatial trend of population distribution in the UK during the past century Stage/approximate dates

Population

Employment

1.Pre-1900

Centralizing

Centralizing

2.1900–1950

Decentralizing

Centralizing

3.1951 or 1961 onwards in large areas

Decentralizing absolutely

Decentralizing relatively

4. 1961 onwards, in London and Loss in both core and ring; Manchester only gain in peripheral areas Source Hall (1974)

Loss in both core and ring; gain in peripheral areas

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spatial consequences of the sets of strategies and policies for regulating urban development were summarized by Bourne (1975) as: (1) limiting the geographic spread of cities through land-use controls—planning permissions, green belt, and conservation policies—and the relocation of some of this growth into new and expanded towns; (2) limiting the location of new jobs—industrial and office—in already congested areas and their redirection to depressed areas through capital and employment incentives and government decentralization; and (3) on the local level, limiting private redevelopment of the existing urban landscape in combination with massive incentives for public slum clearance and environmental improvement. France is another typical case of the “Old World”, because it also has a longstanding urbanization history which can go back to Roman times and exhibits original features compared with British experience. Different from Britain which has an isolate territory, French urban system appears to be influenced by adjacent countries. The main stage of accelerated urban growth began during the Industrial Revolution in nineteenth century. During that period, the average annual growth rate of the urban population in France was about 1%, whereas it was 2% in UK (Pumain 2002). This might be attributed to the relative prosperity of the countryside and relative slowness in the pace of industrialization, especially in the southern and western parts that are traditionally agricultural regions. The urbanization rate passed 50% at around 1930, whereas the time was 1850 in UK and 1880 in Germany. In summary, France was a long agricultural country compared with other developed countries in Western Europe. After the Second World War, France exhibited a rapid urban development pace, and the overall urbanization rate reached 73% in 2000. This may partly because the post-war-Baby-Boom and social policy of financial supports for the families with three or more children. It is also partly attributed to the economic cycles with manufacturing and mechanics booming. Similarly, there was a decentralization trend of population distribution since 1975 (Pumain 2002). The urban hierarchy of France has persisted over more than one hundred years. It is demonstrated that there was a tendency of reinforcement in the growth of the medium-sized cities which verified the decentralization trend of the population distribution (Guérin-Pace 1995). Paris maintains the primacy (the ratio exceeds 7 between Paris and the second city). The high level of primacy may be explained by the very centralized political and administrative model of organization stemmed from the French monarch in the fifteenth century and even earlier (Pumain 2002). The distribution pattern can be enhanced by the cumulative processes of accumulation and concentration which have occurred in the past. Again, the immigration is an important factor leading to population redistribution in France, similar to what occurred in Britain. Two major shifts have occurred owing to the net migration since the 1950s. First shift is the Paris region attracted 25% of the migration population increase. The second shift is a reversal in attraction from the industrial regions of Northern and North-eastern France, which is once very attractive before 1960s (Pumain 2002). Migration population is very crucial factor for the reorganization of urban system in the “Old World” countries like UK and France. As the prior studies have shown, migration population has changed the persistent structure of among cities after the decline of the birth rate since 1960s.

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The urban system of France has also influenced by the policies which devised to remove the urban development obstacles, which have significant influence on urban system development (Pumain 2002): (1) reducing political fragmentation in decision-making by moving the decision-making power on town and transport planning matters to inter-communal structures, and (2) reducing the dominant role of the private car in urban transportation by developing policies that favor public transport systems. The planning system of France is also challenged by the articulation between policies coming from different agencies at various level of authorities. Nicholls (2005) developed a theoretical framework which links the both distributional and functional forms of power to explain the restructuring of French governance system leading to decentralization of population distribution. The organizational hierarchy of the metropolitan governance system has changed from the structure that privileged the central city to current structure that characterized by several territorial authorities that are actively engaged in shaping their metropolitan areas. Therefore, the urban systems of the “Old World” exhibits some common features. First, the cities occupy the space of territory in a way of path dependence. Most cities in these countries depend on initial agricultural settlements. Generally, the average bilateral distance of cities are on average shorter than that in other types of countries, and the average density of cities is also much smaller than the latter. Second, the dynamics of urban growth in the “Old World” was evenly distributed across all cities of the country in a manner that is proportional to the size of the towns and cities, following the Gibrat’s law (Bretagnolle et al. 2009). Finally, the state in these countries also exert important impacts on the urban system development. On the one hand, the structure of the urban system was influenced by past political system, like that occurred in France. On the other hand, the policies, programs, and government actions can also reorganized the urban system, like what happened in UK.

2.3.2 The “New World” Countries: USA and Canada The urban systems in the “New World” countries, where cities were imported by settlers and had a long history of colonial relations with the “Old World”, were expanding in successive waves of penetration or occupation of space (Johnston 1982). During the colonial period, some settlements were firstly built as the nodes that closely connected with the European countries, such as New York, Boston and Philadelphia in the USA, Toronto and Montreal in Canada, and Sydney and Melbourne in Australia. These cities were served as intermediary trade centers between staple producing hinterland and foreign market. When the new countries began to break their colonial ties with the Europe after independence, these early settlements have become dominated metropolitan areas and the urban systems began to expand to hinterland areas following waves of settlements establishment with high rate of newly created cities. For example, in the USA, the first urban frontier moved from east coast to west, reached the Mississippi in the 1850s, the Rockies in the

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1870s, and the western coastline in the end of eighteenth century (Bretagnolle et al. 2009). Driven by large-scale immigration from abroad and high rate of natural growth, the urban growth of the new world is rather fast compared with that in the “Old World”, Dynamics of urban system in the new countries was promoted by economic development and wealth accumulation, which directly related to major changes in technology and economic cycles. Taking the United States as an example, Borchert (1967, 1972) concluded that two interrelated factors, great migrations and technological changes (especially for transportation technology and industrial energy), have repeatedly influenced historical patterns of urban growth and decline, and has identified four periods since the first national census in 1790: (1) Sail-Wagon, 1790–1830; (2) Iron Horse, 1830–1870; (3) Steel Rail, 1870–1920; (4) Auto-Air-Amenity, since 1920. In each of the epochs, crucial technological breakthrough was able to promote a new wave of land exploitation. He argued: Major changes in technology have resulted in critically important changes in the evaluation or definition of particular resources on which the growth of certain urban regions had previously been based. Great migrations have sought to exploit resources ranging from climate or coal to water or zinc-that were either newly appreciated or newly accessible within the national market. Usually, of course, the new appreciation or accessibility had come about, in turn, through some major technological innovation. (Borchert 1967, p. 324)

Similarly, Hall and Preston (1988) have also emphasized the impacts of innovations, especially for the New Information Technology, on the geography of urban development in USA. Three eras of urban system evolution have been divided by them: (1) Mechanical (1846–1895); (2) Electrical (1896–1947); (3) Electronic (1948–2003). They have also predicted the fourth wave after year 2000 will be driven by new information technologies. More comprehensively, Yeates (1997) extensively illustrated four eras to describe urban system development in the United States, which turned out to be corresponding to “Long Waves Theory”, initiated by Kondratieff (1979) and ameliorated by Mager (1987) and Berry (1991). The four eras are: (1) Frontier Mercantile Era (17901845); (2) Early Industrial Capitalism (1845–1895); (3) National Industrial Capitalism (1895–1945); (4) Maturing Industrial Capitalism (1945–1973). He believed the structure and organization of urban system in USA has reflected the major socioeconomic features of each era. In the first era, there were only several ports in the east coastline which were urban gateways between hinterland and oversea markets. The flow of staples and agricultural products to the foreign market, and the flow of information and industrial products to the hinterland, were both through these gateways. During the second era (1845–1895), the national urban system was emerging, as a result of development of indigenous industrial base as well as the hierarchical organization of business activities, capital flows and migration flows. Although there was the Great Depression, the United States had experienced a full-scale industrialization in the third era (1895–1945). The outcome was a phenomenal growth in leading urban areas and in cities with specialized manufacturing and finance. The urban

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system became more hierarchical and more highly interconnected, and bank correspondent linkages revealed the trend of being mature of the urban system Conzen (1977). Since the end of the World War II in1945, the United States has entered the period of mature industrial capitalism according to the Long Waves Theory. There were some quite different trends occurring in terms of the organization of urban system. One of the emergent features was the decentralization trend occurring not only at the national urban system level but also at the local level. Old manufacturing cores began to be contracting, while some growing cores and incipient cores, such as Los Angeles and Gulf Coast, have emerged as national or regional centers. The phenomena of counter-urbanization in the USA was firstly observed in 1970s by Berry (1976). Decline of natural growth and immigration, the inter-city migration has increasingly become crucial determinant of urban growth variation Geyer and Kontuly (1993). A population shift from Snow-Belt to Sun-Belt has significantly sharped the structure of urban system. As a result, the rank-size distribution began to be less steep in 1970s. To sum up, as Yeates (1997) argued, “the United States urban system has become a highly interlinked metropolitan society of considerable complexity, containing 83% of the population, in which the advantages of urban agglomeration with respect to production, innovation, and consumption can be realized in all regions and in parts of most urban areas”. Urban system in Canada has many features in common with United States in terms of bases of development, immigrant populations, similar trajectories of urbanization and suburbanization and sprawl, et, al. Similarly, development of urban system in Canada reflects the shift of a colonial economy providing raw materials to a more industrial base (Yeates 1997). However, there are also some distinctive characteristics owing to the historical and territorial factors (Bourne and Simmons 2002). In Canada, urban development has a long history of colonial relations and intense external dependence on, first France and British, and then the USA. The result has been highly specialized urban economies, the so-called “Canadian regionalism”, as well as a distinct “core-periphery” pattern of urban system (Davies and Donoghue 1993; Coffey and Shearmur 1998; McCann and Simmons 2000). Brodie (1997) attributed the “core-periphery” divide to immensity of the territory and the diverse geographical characteristics that resulting in Canadian regionalism. Innis (1995) exerted that, according to his “staple book”, the early unbalanced political and economic relations between the colonies and the colonizer had determined the current formation of Canada’s urban system. The “core-periphery” divide was initially presented by the empire/colony divide, then found as the “core (east)–periphery (west)” divide during the development of the west of Canada. Toronto and Montreal as twin hubs had emerged to serving the whole country, and the west region was developed based on the need of the east. This is why Lalanne (2013) questioned the unity of Canadian urban system and distinguished two systems of cities. In summary, there are some identical features regarding urban system development in the New World countries. First, the evolution of urban systems in those countries were characterized by a set of processes operating in space. Their evolutionary nature reflects the geographically expansions and in particular economic

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development. Second, their urban systems are very “open”, that is, the demographic, economic and political systems are almost borderless. Flows of people, capital, information and products across borders have few restrictions. Third, as countries with free-market mechanism, governmental regulations are of less importance for their urban development.

2.3.3 The Former Colonies: India and Brazil The third type of countries is the former colonies which are now the developing countries. This type of countries are more diversified than the other two because they have different cultural, historical, and geographical backgrounds, and experienced different urbanization paths. This section mainly focus on the former colonies which once have long urban development history and now are the developing countries with ongoing urbanizing processes. The examples of India and Brazil are illustrated to show features of this type of countries. India has a very long urban development history which can go back to two thousand years ago. Its urbanization remains low during the long development period. The urban development of India can be divided into several different phases corresponding historical periods (Bretagnolle et al. 2009). The first phase is in Antiquity and a second phase in the Middle Ages with Muslim expansion. Some important was in existence in these period, such as Agra and Delhi, which were located inland instead of along the coasts because their main functions were related to domestic trade and territorial control. The second phase is until the sixteenth century. A variety of major cities were set up but they did not exert significant impacts on the urban patterns in India. The third phase is from sixteenth to the first half of twentieth century. Two disturbances existed in this period in terms of urban development in India, that is, the arrival and the departure of the Europeans, especially the British colonizer, reorganized the urban framework (Querci and Oliveau 2013). The administrative and economic structure has changed radically since the year of 1847 when India officially became colony of the Britain. This changes have altered the distribution of Indian cities. A set of new cities were established or developed along the coasts and the main rivers, in order to facilitate linkages with Britain by water conveyance. After the independence in 1947, India experienced a take-off during the second half of twentieth century, increased from 16.5% in 1951 to 32.1% in 2011 (Querci and Oliveau 2013). A quick growth of the number of cities and towns is also observed during the same period. After 1950s, India was involved in the globalization process which made a completely evolution in the urban development logics, and made the country to open its economy. The influences of globalization and external changes have resulted in the rapid growing of the size and number of cities, and restructuring of the urban system, which are essential for the understanding of the future of urban India. Prior study find that India’s city-size distribution does not follow the Zipf’s law (the Zipf coefficient is significantly under 1), characterized by the presence of a large number of small cities and some very large metropolises (Schaffar and Dimou

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2012). In other words, the urban system of India is rather hierarchical than other large developing counties like China. In addition, Schaffar and Dimou (2012) also found parallel growth patterns of small cities and large metropolises within their initial groups. The features of urban system is closely related to Indian urban policies and the entire political system which believe in less state interventions and neoliberalism. Brazilian urban system is somewhat different from that of India because they varied in urban development paths and historical backgrounds. Brazil has a rather short urban development history compared with India. On the other hand, Brazil has a strong military regime during 1960s and 1980s, and this country prevailed the developmentist model in the 1970s and then implemented liberal reforms. Comparatively, the state played a more important role in urban system development in Brazil than that in India. Combination of the cases of India and Brazil may lead to understanding of the urban system in the former colonial countries. Urban development in Brazil can be divided into three broad historical phases (Fernandes and Negreiros 2001). Development of cities in Brazil really began with the advent of Portugal explorers around fourteenth century. Major Brazilian cities, including Sao Paulo and Rio de Janeiro, were established for the purpose of exporting agricultural products to the European markets. During this long period, production were mainly located in rural areas, and the majority of population lived in coastal villages that provided basic facilities. Thus, the distribution of Brazilian cities is characterized by concentration along with coast and river, which provides the original pattern of urban system in Brazil. The second phase started after Brazil became an independent country in early nineteenth century, and lasted until the 1970s. Two important reforms have led to a rapid urban development in Brazil: 1850 Land Law and the formal abolishment of slavery in 1888. During the second phase, urban development of Brazil was primarily promoted by immigration before 1930s, but fostered by rapid industrialization after that. In the latter period, the state played an important role mainly through region-preferential development plans and strategies. Fast industrialization and participating in international trade have led to improvement of urban infrastructure to meet industrial production needs and thereby urbanization expansion towards inland areas. Urban development in the third phase experienced a stagnation after the collapse of the developmentist model since the late-1970s. After the implementation of neoliberal reforms and a minimal state, these competitive and stimulated polices never prevailed in Brazil. Nevertheless, interior tendency of urbanization continued in 1990s as a results of effects of the former developmentist investments and to deal with the severe crisis in the large cities and coastal metropolitan areas. Investments were made in inter regional transport infrastructure in order to integrate the national economy by lowing the business costs in peripheral regions. Improvement of transportation is demonstrated to stimulate urban growth for both coastal and inland regions (Da Mata et al. 2007). One of the marked features of Brazilian urban system is the geographically and hierarchically concentration pattern. The top cities account for large share of economic activities and population. For example, Sao Paulo’s share of national industry increased from 15.9% in 1907 to 31.5% in 1919, and to 55% in 1950

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(Fernandes and Negreiros 2001). The Southeast accounted for over two-thirds of the national GDP in 1949, accounted for 60% of the national urban population by 1970. The concentration that inherited in historical and geographical factors proceeded until mid-1970s, when the industrialization in Brazil started to spread to inland areas such as the Center-West. The importance of medium-sized cities and urban centers in the west were raised. As a result, urban population distribution of Brazil has become much more balanced during 1990s. As a Latin country, Brazil now exhibits common features of the over-urbanization (Ordóñez and García 2010). Lacking of urban infrastructure and social welfares is a serious problem that limited future urban development in Brazil. In summary, colonialism exerts important influences on urban system development in these former colonial countries. Majority of cities were established along coast and river in order to be conveniently linked with European countries. After independence, urban growth in these countries experienced a transformation which may significantly alter the structure of their urban system. The urban systems in these countries are likely to be concentrated geographically and hierarchically, because population tend to concentrate in largest cities and small cities but the medium-sized cities are lacking. In addition, the state plays a more important role in these countries than that in the “Old World” or “New World”. In the Brazilian case, the government launched a variety of actions to improve the urbanization and spread the employment throughout the country, which follow the developmentist state model. During the oil crisis, the foreign sector constraints and import substitution policies were launched in Brazil. State-led investments that has made to the west and north induced the decentralization of economic and urban resources from the coastal colonial cities to inland newly founded cities (Fernandes and Negreiros 2001). As such, it is worth noting that the historical “style” of urban system is likely to be enhanced or reorganized after independence of these former colonial countries, but the influences of colonialism are deep and long lasting.

2.3.4 Summary Urban system development in different countries are not only differentiated by their historical situations alone, but also by certain political and economic reforms. Two major differences are identified in the literature review: the patterns and trajectories of urban system development and the features concerning with the regimes of city growth (driven forces). It should be noted that such marked differences can result in diversified hierarchical and spatial configurations and dynamics of urban systems, and each major type of urban system thus which evolve in a specific way. In the “Old World” countries, path dependence and stability are main features of urban systems. As countries with long-standing urban development history, the urban systems are characterized by a marked persistence of the distribution over geographical space. In contrast, urban systems in the “New World” countries spread in successive waves of

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space occupation, driven by construction of transportation facilities. Such a penetration process is accompanied by the extension of human activities in the new countries. Furthermore, for the former colonial countries, urban system development is driven by dual forces: external colonial legacies and internal forces after the independence. The combination of these two forces has generated distinct features with respect to the urban system hierarchical and spatial configurations. The state more or less influences urban system development in the three types of countries. In both the “Old World” and “New World” countries, there are almost fewest governmental regulations and interventions regarding the urban system development. These countries share some common societal values, such as liberalism and small government. As country who advocate the philosophy of free-market economy, the government is expected to provide the public services that market ignored, and to protect public interests, and market forces are major drivers of urban development. Although government of these countries may use policies and programs tools to solve urban problems faced in certain period, the basic organization of their governments has determined that the form of their state interventions are much more flexible than those in the developing countries. For example: Since the Great Depression of the 1930s and World War II, the ‘state’ has had an increasing effects on urban development in North America. The ‘state’ refers to the myriad legislative and regulative actions of federal governments (United States and Canada), the states and province, and local governments (cities, municipalities, counties, special districts and so forth). These three tiers of government have different roles. In general, the degree of direct intervention decreases with increasing levels of government: Local government has the greatest direct impact; then follows the state/provincial government; the federal governments, finally, have the least direct impact. Indeed, the Canadian constitution specially allocates responsibility for municipalities to the provincial governments. The situation in the United States is, however, much more flexible, though there are perpetual tussles between the states and the federal government concerning jurisdiction and responsibilities. (Yeates 1997, p. 11)

In sharp contrast to countries of the two types with mature market mechanisms, the former colonial countries are generally developing countries in which the states have strong effects on urban system development. A national policy is especially important for the developing counties (Renaud 1981). As shown above, Brazil once prevailed developmentist model and the state was quite active in promoting urban development. It is rational to summarize that the state is stronger in the former colonial than that in the first two types of countries. But countries may also exhibit unique features regarding the state intervention in urban development, because they have different structure of regimes (such as the legal definition of cities, rules about private property, and legal systems) that can better explain the ways in which the state intervenes in urban systems development.

2.4 Studies on Urban System Development in China Considerable studies have attributed development of China’s urban system to causal factors that are different from those identified in Western advanced countries or other

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developing countries. It is not only because of the large area of the country and long history of civilization, but in particular because of China has a powerful state. As one of the few countries in the world that has an official national urban system policy, China implements a series of state policies and institutional arrangements to regulate the development of urban system even in the post-reform period. With the market-oriented reforms, globalization, and decentralization of state power, although the role of state is changing but it continues to affect the development of the whole country. This section attempts to revisit the existing studies on the development of urban system in China. This review is based on two different periods: the pre-reform and post-reform, which are divided by the year 1978 when economic reforms and opening-up policies were adopted. Special emphasize is placed on the issue on how the state intervenes in urban system development.

2.4.1 Pre-Reform Period The most salient feature of China’s urban system development in the pre-reform period is that it experienced quite a distinctive trajectory under the socialist planned economy. China’s urban system evolves from the pre-socialist era. When a country as old as China is discussed, the development of urban system cannot be understood in isolation from the historical urban patterns though the socialist government was so strong to influence urban development trajectory. China has the world’s longest urban development history, offering a challenge to the theoretical insights drawn upon the modern western experiences. As a traditional agrarian society with long history, the majority of its population was tied to farmland and living in countryside. Cities and towns functioned largely as political centers rather than as production or consumption centers like Western industrial or commercial society. Several important studies by western scholars have presented an entire picture of the condition of urban system in imperial China. Rozman (1973) highlighted the distinctive feature of China’s system of settlements in the imperial era, that is, the “bottom-heavy” distribution of population and resources, with relatively few medium sized cities lying between rural towns and villages and the largest cities at the peak of the hierarchy. According to his estimation, there were about 4000 settlements with a population of less than 3000 in Qing China. He believed the “bottom-heavy” pyramid was the major reason of the low urbanization rate in traditional China. To illustrate his argument, he provided a comparison with Japan that was “narrow-based” pyramid despite the low urbanization rate (4% of China and 13% of Japan in early eighteenth century) (Fig. 2.1). Rozman also argued that, without the well-developed middle-range urban centers, the state was very difficult to mobilize resource effectively and to consolidate the tax bases. Rozman failed to explain the cause of the “bottom-heavy” pyramid, while Skinner’s research offered one possible explanation for dispersal of population (Skinner 1977). He pointed out that the urban system of China was constituted

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Fig. 2.1 Schematic Comparison of Population Distribution across the Settlement System in China and Japan (Source Rozman [1973, p. xv])

of a set of physiographic macroregional systems in the imperial times. He argued that, at least until the late nineteenth century, a nationally integrated urban system has not emerged. This is attributed partly to the natural geographical factors such as uneven distribution of plains, mountains, and rivers, and partly to the limitations of the inter-regional transportation systems which restrict the movement of population and goods. These regional urban systems were developed based on regional marketing networks in relation to the localized transportation systems. Thus, for the entire urban system, the small settlements have long been relatively balanced dispersed among these nine macroregions (Fig. 2.2). While, only few centers of the macroregions have developed as large cities at the hierarchy of cities. Since the late nineteenth century, foreign forces started to influence China’s urban development because several treaty ports were set up. But, neither Skinner nor Rozman attempted to analyze the role of the treaty ports in China’s urban system. Murphey has viewed China’s urban system as a dual system, one was the coastal foreign-influenced treaty ports and the other was the indigenous cities most of which were located in interior region (Murphy 1974). This dichotomous view further supported by Chang (1976) who saw the dual system was comprised as two sets of

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Fig. 2.2 China’s macroregional systems, 1893, showing major rivers and the extent of regional cores (Source Skinner [1985, p. 273])

cities—one inherited from the late imperial China with administrative functions and the other were commercial and industrial centers which were mainly constituted by former treaty ports. In his opinion, the old administrative cities appeared to gradually transform into a hierarchical urban network of local industrial production centers to serve agriculture, while the former treaty ports assisted China’s early economic and technological modernization. Scholars used to believe the Chinese imperial state was powerful enough to extend direct control over all settlements, but the literature reviewed above may present a challenge to this assertion. By contrast, the imperial state, at least during the nineteenth and twentieth centuries, was a relatively weak bureaucracy system in regulating urban system development (Mann 1984b). However, a real strong socialist state was set up in 1949 which have profound implications for the nature of evolving

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system of Chinese cites. Since the “new China” was founded in 1949, the country adopted a centrally planned economic system which was stemmed from Marxism ideology. In the Marxist view, market and private property rights are inherently the roots of class contradiction and inequality. The market was replaced by the hierarchical distribution system, and all production factors were strictly controlled by the state in the socialist planned economic system, or, in other word, the socialist state internalized virtually all types of market to achieve socialist industrial development. Labor, capital, urban land and other materials were administratively redistributed through this planned allocation mechanism. Through the “Socialist Transformation” in early 1950s, the new-established state successfully ceased the existence of the private property right, and essentially internalized the market into the command economy. Since then, the socialist state has broken its foreign ties and ceased the existence of market mechanisms. Based on the socialist ideology, policies were made to place emphasis on promoting industrialization, in particular the city-based heavy industries, and developing new industrial centers near natural resources most of which were located in interior centers (Kirkby 1985; Young and Deng 1998). The urbanization in pre-reform China was felicitously concluded as “urbanization from above” because the development of Chinese cities was attributed purely to causal factors controlled by the state (Ma and Fan 1994) or “single-track state-sponsored urbanization” (Shen 2006a). Moreover, most scholars believe that China was characterized as “underurbanization” or “industrialization with controlled urbanization” because the state was showing a rapid industrialization, but simultaneously slow urbanization because of strict controls on rural-urban migration (Lin 1998).3 In sum, state or, more appropriately, the central government was naturally the unique regulator of urbanization and the derived policies were the most essential, if not the only, guidance of urban development. Chang (1976) summarized three characteristics of China’s urban system development during the first three decades of Communist leadership. The first characteristic is that the state attempted to integrated the all the cities into one system, aiming at changing the former “dual system” pattern. There is essential progress regarding the emergence of a truly national urban system during thirty years under the Communist administration. Pannell (1982) argued that the nationally integrated urban system was reflected in the linear smoothing of urban rank size figure for large cities in late 1970s. It suggests that the integration of nation-wide urban system may be attributed to several causal factors, including the political regulation system, the national urban policies and investment in the transportation systems. Second, China’s urban growth was regulated or influenced by the state policies, leading to departure from the “law of proportionate effect”. He pointed out that that large cities have grown at a smaller rate than the law of proportionate effect. In other words, argued by Pannell (1982)

3 The

Concept of “under-urbanization” was first defined by Konrad and Szelenyi (1977), which was used to explain the phenomenon of high industrial growth with unparalleled urban population growth especially in socialist economies.

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that, the city policy sought to “restrict the growth of large cities generally, and especially to refocus new development away from the coastal consumer oriented cities to newer, developing cities in the interior”. Lastly, there was a uniform distribution of the small and medium-sized cities all over the country, though China is characterized as high diversity of the physical environment and the resource endowment in different parts of the country. It is resulted from the Maoist ideology that makes industry serve agriculture and urban centers serve rural areas.

2.4.2 Post-Reform Period With respect to the post-reform period, the perspective of political economy, which incorporates both the roles of the state and market, is imperative of understanding China’s urban development and urbanization (Ma 2002; Wu 1997). After the implementation of economic reforms and opening-up policies in 1978, China experienced a short period of rapid urban growth. To prevent the urban problems that occurred in the large cities of other developing countries, the Chinese government adopted a national urban system policy of “controlling the size of large cities, rationally developing medium and small cities”. Rural industrialization, which allowed peasants to “leave their farmlands without leaving their villages (litu bu lixiang)” was proposed by Fei (1994), was used to divert the massive rural surplus laborers to the small cities and towns. The preferential policies and geographic location of the eastern region allowed them to attract a large amount of overseas investment during the freer markets of the opening-up policies. These policies generated new urbanization patterns which are different from the “urbanization from above” in the pre-reform period. Scholars developed the concept of “urbanization from below” to explain the rapid development of small cities and certified towns in mid-1990s (Ma and Fan 1994). Local governments, TVEs, and foreign investors were major forces. Similarly, concept of “exo-urbanization” was developed by Sit and Yang (1997) to understand the pattern of urban growth in the Pearl River Delta (PRD) where foreign investment inflow emerged as a new driving force of urbanization. Furthermore, Shen (2006a) provided an interpretative framework labeled “dual-track urbanization”, which consists of key components such as the mode of industrialization, central and local states, urban and rural economies, urban and rural citizens, the hukou system and global linkages. Changes and interactions of these components are the divers of urbanization in post-reform China. Influenced by the changing and complicated approaches of urbanization, China’s urban system has been restructured during the post-reform era. Considerable research efforts have been devoted to understand the development of China’s urban system in the new period. The first focus is the evolution of the city-size distribution. It is generally accepted that China’s city-size distribution has become flatter during prereform period, owing to the policies that strictly controlled the size of large cities. However, it appears debatable in terms of the city-size distribution in the post-reform period. Evidence has found to support that China’s city-size distribution was evolving

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towards a ‘rational’ distribution (e.g. following the rank-size rule or the Zipf’s law) driven by the market-oriented reform and opening-up policies (Ye and Xie 2012; Yeh and Xu 1990; Chen and Zhou 2004; Gangopadhyay and Basu 2009). Based on a regional approach, Ye and Xie (2012) showed the Zipf’s law can capture main trends of urban system emergence in China. They further found that the slopes of Zipf’s curves were flattened over time, implying the urban population concentration was decreasing in general. In addition, there has been a fast population increase for the cities at the bottom and thus the diminishing long dropping tail. Chen and Zhou (2004) developed three indications of self-organized criticality (SOC), and demonstrated that China’s urban system exhibited some features of self-organizing structure. Gangopadhyay and Basu (2009) performed a comparison of China and India, showing the city-size distributions for both the two countries follow Zipf’s law. Nevertheless, there are also opposite arguments showing that China’s city-size distribution exhibited some distinct features that were different from the Western countries. Xu, Ouyang and Zhou (1995) found that the share of super-large cities (such as Beijing, Shanghai, Guangzhou et al.) and small cities increased significantly during 1978 and 1990, whereas the share of the medium and large cities decreased significantly, creating a ‘saddle shaped’ patter of city size distribution. Similarly, some scholar argued that, although control on the growth of large cities has never loosened, the development of large cities prevailed since 1980s, which were mainly fueled by the forces of globalization (Zhao, Chan and Sit 2003; Zhao and Zhang 1995). Evidence is also found to support that smaller cities still grew faster than larger cities that led the Chinese city size distribution to become flatter and resulted in undersize development of large cities in the 1990s (Song and Zhang 2002; Xu and Zhu 2009). Quatantative models have domenstrated that a large fraction of Chinese cities were undersized, but it was more likely to relate to their economic types rather than sizes categries (Au and Henderson 2006b, 2006a). The second research issue is related to the dynamics of urban growth. According to Fan (1999), the expansions of Chinese urban system are including two dimensions: vertical (population growth of cities) and horizontal (addition of new cities). She argued that institutional factors (including state policies, urban administrative system adjustment, and local government interests and so on) were more important than economic factors in explaining the growth of China’s urban system. Based on time-series econometric techniques, Chen, Fu and Zhang (2013) test the parallel growth among certain group of cities with similar characters from 1984 to 2006. They find a number of cities, with similar location advantages or policy regime, have parallel growth. For example, the two top cities of Guangzhou and Shanghai have parallel growth; the north-eastern traditional industrial cities of Anshan and Liaoyang have parallel growth; and two earliest open cities with special economic zones of Shenzhen and Xiamen, also have parallel growth. Their finding suggests that locational fundamentals may have a persistent impact on city growth. In an empirical analysis, Anderson and Ge (2004) investigated the determinants of urban growth in China, with a special focus on the impact of economic reform. They argued that the degree of the state economy (represented as the share of share of the state sector in

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total industrial output) that reflects the role of economic transition has a negative effect on urban growth and the openness of cities to foreign direct investment has a significantly positive effect on urban growth. The third research theme is much more space-related, which involves the changing and restructuring of spatial patterns of Chinese cities under influences of the economic transformation. Since early 1990s, a large number of studies have been devoted to understanding the emerging “city-region” of China’s urban system. As Pannell (Pannell 2002) pointed out, although the spatial patterns of Chinese urban system inherently followed historical precedents, new trends have emerged particularly in coastal metropolitan regions since late-1980s. The rapid economic growth that driven by industrialization and globalization has fueled urban growth around the traditional central cities, leading to their spatial extension in the form of a new type of mega-city region—the extended metropolitan region (EMR). Research on EMRs in China have explored the structure of space-economy and their functions as global nexuses, which mainly involves regions of Pearl River Delta, Yangzi River Delta and Beijing-Tianjin (Sit 2001; Lin 2001). For example, Sit (2001) has argued that economic globalization in the form of foreign direct investment (FDI) in labor-intensive, export-oriented industries was the primary force underlying the EMR’s formation in Guangdong. Lin (2001) found that the focus of economic activities and population agglomeration, and land-use transformation were mainly in the surrounding are of metropolitan centers, suggesting an on-going process and evolving pattern of “urban–rural integration” and emerging of structure of EMRs. Additional research efforts on EMRs were contributed by Yeung and Hu (1992), Tang (1997), Wang (1998), and so on. In addition, another strand of literature (c.f. McGee, Lin, Marton, Wang and Wu [2007]; Tang and Chung [2000]; Xie, Ward, Fang and Qiao [2007]) focuses on the internal structure of EMRs mainly relied on the model developed by McGee (1991) that divided them into three parts: city core, peri-urban and “desakota”. Another similar concept to EMR is the city-regions that is enlighted by the studies of “global cities” or “global city-regions” in Western advanced countries (Friedmann and Wolff [1982]; Sassen [1991]; Scott, Agnew, Soja and Storper [2002]). Zhou (1991) recognized the distinct nature of urban clusters in China and thereby suggested the Chinaspecific concept of “Metropolitan Interlocking Regions (MIR)”. This concept adopt some features of Gottmann’s widely spread concept of “Megalopolis” (Gottmann 1957), but is firmly rooted in Chinese context. A series of criteria of MIR are provided: (1) Two or more large cities with more than 1 million population as growth poles, (2) important ports, (3) convenient main lines of communication that act as a development corridor between growth poles and between poles and ports, (4) numerous small and middlesized cities and towns along both sides of the corridor, and (5) intensive economic linkages between urban and rural areas. (Zhou 1991, p. 95)

Four MIRs at the beginning of 1990s were also suggested: the Nanjing-ShanghaiHangzhou in the Yangtze River Delta (YRD), Hong Kong-Guangzhou-Macau in the PRD, Beijing–Tianjin–Tangshan and Shenyang–Dalian in central and southern Liaoning Province. In line with these studies, further modifications and interoperation have provided in many studies.

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These studies have enriched our understanding of China’s urban system development. In the post-reform era, China’s urban system development exhibits many features that are much more complex than that of the pre-reform period. These features may be attributed to the rapid urbanization and newly emerging forces that influence urban development. Another important reason is related to China’s state, because urban development is still regulated by the policies and institutional factors that associated with the state during the post-reform era, despite the adoption of the reform and opening-up policies. As Fan (1999) stated: China’s city system clearly exhibits profound impacts of institutional factors. Not only have urban and regional development policies explicitly encouraged or discouraged growth of cities of certain sizes, they also have enabled the growth and birth of cities in certain parts of the country more than in others. Most importantly, these policies have not been stagnant but have changed especially in relation to the economic reforms and economic transformation. Through population growth of cities and designation of new cities, China’s city system has expanded tremendously, which is key to understanding processes of urban growth and the role of institutional factors in that growth. (Fan 1999, p. 495)

The role of the state is crucial in understanding urban system development in China. China is undergoing transformation from a plan economy to a market economy. The theme of China’s transition is adjustment of relations between state and market by gradual institutional reforms. Although state interventions have largely declined, it could not be simply understood as totally retreat from economic and social life (Wu et al. 2007). It is widely accepted that China’s state maintains a vital role in the reform process, as policy maker, resource distributor, market builder and regular, as well as direct market actor. Therefore, it is impossible to capture the essence of urban system development if there is a lack of the state’s role.

2.4.3 Summary China’s urbanization in the pre-reform era started with a period of “normal urbanization”, followed by a stage of “spurious urbanization”, and then experienced “antiurbanization”, ended with a period of “stagnant urbanization”. No matter what type of urbanization it was, China’s urbanization was disrupted by ideological debates, movements and campaigns. Rural-urban migration was completely constrained by the state through the hukou system. Thus, urban system development was not directly associated with economic development like what has occurred in Western countries, but strongly influenced by the state’s intentions. Under such conditions, the cities were growing but the paces were not fast and unstable. Especially, the large cities and coastal cities were under-development due to the policy containments. However, a nationally integrated urban system has continued to be enhanced as a consequence of the strong unified regime and construction of national transportation system since 1949. In sharp contrast to the situations in pre-reform period, China’s urban system development in the post-reform period exhibits many complex features as a result of

2.4 Studies on Urban System Development in China

39

the market-oriented reforms since 1978. First of all, urban growth has been accelerated and is equalities of urban growth increased, which significantly changed the organizing rules of the urban system by bringing in bottom–up market forces. Although there are debates in existing literature, China’s city size distribution evolves towards a more self-organizing system promoted by bottom-up market forces. Moreover, spatially integrated metropolitan areas have emerged in the coastal region. As mentioned in the existing literature, the majority of scholars have a consensus that the state plays an important role in China’s urban system development, but few studies have examined how the state intervenes in China’s urban system development processes. Although undergoing transformation from a centrally planned economy to a market economy, the role of the state remains crucial in shaping urban system development in China. It is widely accepted that China’s reform is a state-led process and the effect of the state on urban system development is considerable large. The changing roles of state are presented as policy maker, resource distributor, market builder and regulator, as well as direct market actor. Therefore, it is impossible to capture the essence of urban system development if there is a lack of the role of the state.

2.5 A Critique In the earlier sections of this chapter, three lines of research has been revisited, including the approaches of urban system, studies on urban system in three different types of countries, and literature on China’s urban system. It has been shown that the paradigms of urban system studies have been changing over time. Moreover, evolutionary patterns and driven forces of urban systems vary in countries with different histories and political and economic contexts. China’s urban system exhibits completely different features in the pre-reform period and post-reform period. More importantly, China is believed to have constituted a distinctive and controversial case for evaluating the effects of the state on urban system development. Because urban development in China is still regulated by the state power during the post-reform era, showing different growth dynamics compared with other countries despite the implementation of market-oriented reforms and opening-up policy. As such, there is an urgent need for country-specific theorization of urban development in China based on political economy, as argued by Ma (2002). It should be admitted that geographical perspective research on urban system in Western advanced countries is on the wane (it reached a peak at 1960s and 1970s). This is partly due to the stable status of urban system in most of the developed economies, and partly due to a lack of breakthroughs in theorization and methodology in this field. For example, though Zipf’s law and Gibrat’s law have attracted a great deal of research efforts, their theoretical foundations are still unfulfilled (see a solid review from Carroll [1982]). We may have known some countries obeyed the ranksize rule, while others did not, but so what? Admittedly, economists have stimulated the research interest again. However, on one hand, these models paid more attention

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2 Understanding Urban System Development …

to individual city growth and search for optimal city size, rather than the system of interactive cities (like Henderson-type models). On the other hand, ignoring of the local institutional environmental and contingent conditions was a striking deficiency of these approaches. Therefore, these models and approaches can be used to describe spatial configurations and growth patterns of China’s urban system, but cannot be directly applied (without any modifications) to explain the realities of China. The development and restructuring of China’s urban system did not occur in a process similar to what has taken place in any other types of country reviewed above. China’s dramatic transformations and resulted rapid urbanization since 1978 suggest a number of issues regarding urban system development that merit scholarly research. The explanations on urban system development derived from existing literature are inadequate to explain urban system development of China. Existing studies on China’s urban system although have enriched our understanding of urban system development in China, they fail to systematically unravel the role of the state in shaping urban system development because of two major deficiencies. First, relatively little scholarly attention has been paid to the country-specific theorization of China’s urban system development that emphasizes the structure and organization of the political system. Urban system development involves three dimensions: spatial, temporal and hierarchical. Each dimension is closely related to the structure of political power in China’s context. Thus, in order to understand the role of the state in China’s urban system development, scholars need to relate to the effects of institutional structure and restructuring on urban system development. Although many studies have recognized that the state is the key to understand urban development in China, their focus has been mainly on urbanization and urban development process at the national level analysis, seeing the state as a whole (see for example Zhu [1999], Lin [2002], Lin, Yang and Hu [2012]). With respect to the issue of urban system, organizational mechanisms and hierarchical distributions of the state power are responsible for variations of urban growth to a large extent. Therefore, studies need to unfold the “black box” of the China’s state in order to explore its role in urban system development. The decentralization of the state power from the central government to local governments represents a change of the state of China from a single unitary power into a new power matrix in geographical space. Moreover, significant variations are observed in the government capacity among cities because reforms have empowered cities to extend their capacities unevenly. Also, Chinese cities are organized by the UAS which hierarchically differentiates and reorders the government capacities of cities at different administrative levels. Second, in existing literature, there is a lack of modelling approach which takes the state as endogenous factor of urban system development. Even in the endogenous growth theory reviewed above, economic factors are taken as the most important forces in promoting urban development. Scholars believe that the state still plays an important role in affecting urban system development in post-reform China, although the role is less influential compared with that in the pre-reform period. However, state forces have seldom been explicitly factored in city size and city growth models. Economists take state forces for granted and do not consider them as endogenous growth factors (e.g., Henderson-style models and NEG models), so the detailed

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cause–effect relationship remains poorly understood. Models without endogenous state forces are difficult to explain urban system development in the case of China. The existing models and approaches offer imperative modeling foundations, and the lack is explicitly factoring the state forces which enable the models to probe into China’s political origins, and thus to understand the role of the state in China’s urban system development. In view of both strength and weakness of the existing theories and models, it needs to understand the development of China’s urban system from the perspective of institutional changes. Thus, the next chapter attempts to examine the development of China’s urban system in the post-reform period, placing the emphasis on investigating the relationship between the changes of policies and institution and urban system development. Based on the analyses, we may be able to develop a new conceptual framework to understand the development of urban system in China.

Chapter 3

China’s Urban System Development: Basic Concepts, Historical Development, and Changes of the State Policies and Institutions

3.1 Introduction This chapter aims to provide a historical review of China’s urban system development and its relationship with the changing state policies and institutions. Since 1978, China has witnessed the rapid development and dramatic restructuring of its urban system that are closely associated with economic reforms and resulting changes in policies and institutions. China’s economic reforms are implemented pragmatically and experimentally and are achieved by gradually changing its old policies and institutions. The institutional driving forces behind the historical development of China’s urban system can be understood clearly by examining the changes in policies and institutions and their effects on urban system development. This chapter is organized into five sections. Following this introduction, Sect. 3.2 clarifies the concepts and issues critical for understanding China’s urban system development, including the definitions of city, city designation, the national urban system policy, urban administrative level, urban size, and urban growth, the classification of sizes of cities, and the geographical regions and time periods employed to analyze urban system development. Section 3.3 revisits and investigates the historical development of China’s urban system since 1949. Section 3.4 examines the changes in some of the most influential policies and institutions that have dramatically restructured China’s urban system. Four dimensions of changes are involved, namely, the introduction of market mechanism and redefinition of the role of the state, the shift of the focus of state policies, reforms on the hukou system, and the restructuring of the UAS. Finally, Sect. 3.5 summarizes the entire chapter.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J. Wang, The Role of the State in China’s Urban System Development, https://doi.org/10.1007/978-981-33-6362-5_3

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3.2 Clarification of the Key Concepts and Issues for China’s Urban System 3.2.1 City and Designation of City in China A city (shi) in China is an officially approved urban administrative entity that satisfies certain minimum criteria (Zhang and Zhao 1998). In China, although a place has clear urban characteristics, it should first be officially approved by the state before it can become a legal city. This approval is known as city designation. The National People’s Congress or the State Council is empowered by the nation’s constitution to approve city designation and its boundary changes, and the Ministry of Civil Affairs is the de facto regulator of city designation (Shen 2007). The first criterion of city designation was made by the State Council in 1955, and it stipulated that a settlement with more than 100,000 inhabitants can be designated as a city. When the number of inhabitants is less than the population threshold, either the seat of provincial administration, the important industrial base, or the existence of a large commercial center can be considered to establish a city.1 This precedent was in effect until 1986 when a new criterion was stipulated. The 1986 criterion included the minimum requirement of inhabitants and the requirements of the share of agricultural population and GDP. Nonetheless, this benchmark seemed significantly facile, in which approximately 180 new cities were designated from 1986 to 1993. The newly issued criterion in 1993 set high standards for city designation, covering the requirements of industrialization, urbanization, and fiscal (for details, see Table 3.1). Since 1997, the central government has suspended the massive designation of city. Hence, the number of city is stable since the late 1990s. In this book, the urban system consists of all officially designated cities (i.e., legal cities in China) by the Ministry of Civil Affairs. The number of cities in 1982, 1990, 2000, and 2010 is 234, 460, 666, and 651, respectively. In China, a city is a political unit. These is a lack of the concept of physical or geographical city (urban area) in China (Zhou and Shi 1995). The boundary of a Chinese city is defined by its administrative area which is close to the concept of “city proper” in some other countries. In the post-reform period, some cities are empowered to govern subordinate counties.2 Considering these cities, this book only regards the area of urban districts (shiqu) as the areas they directly govern.3 In other words, the subordinate counties and county-level cities are excluded from the boundary of these superior cities. When the socio-economic and demographic characteristics of these cities are measured, the subordinate counties are not considered because only 1 The

1955 criteria were reported in the notice labeled as Decisions by the State Council Regarding the Establishment of Cities and Town issued on 9th June, 1955. 2 This condition is a result of the “City-Governing-County” system that has been implemented since the early 1980s. This book investigates this institutional arrangement in the subsequent sections. 3 Cities with urban districts are prefecture-level cities, but not all prefecture-level cities have urban districts. Four cities are without urban districts in 2010, that is, Dongguang, Zhongshan, Danzhou, and Jiayuguan. This book discusses the administrative level of cities in the next section.

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Table 3.1 Minimum requirements for city designation issued in 1986 and 1993 Minimum requirements of 1986 Total population (person)

500,000

Urbanization level

Non-agricultural population (person)

100,000

120,000

Economic level

GDP (million yuan)

300

400

Minimum requirements of 1993 Population density (person/km2 )

400

Industrialization level

Industrial output value (million yuan)

800

1,200

1,500

Share of industrial output value in gross output value (%)

60

70

80

100,000

120,000

150,000

Share of non-agricultural population (%)

20

25

30

Total fiscal revenue (million yuan)

40

50

60

Per capita fiscal revenue (yuan)

60

80

100

Urbanization level

Fiscal level

Non-agricultural population (person)

Source Summarized by the author based on “The Report on Adjusting the Criteria for the Designation of New Cities and the City-Administering-County” in 1986 and “The Report on Adjusting the Criteria for the Designation of New Cities” in 1993 by the Ministry of Civil Affairs

these urban districts are close to the functional urban areas of cities in which urban functions such as employment, residence, commerce, and transportation facilities are concentrated. Figure 3.1 shows the demarcation of Chinese cities in 2010 and illustrates that the entire administrative areas (light gray) of the cities with subordinate counties cannot represent the “real” urban areas. The dark gray areas are urban districts, and the dot areas are county-level cities. Some county-level cities are undergoverned by prefecture-level cities, whereas others are administered by prefectures. Thus, the dark gray and dot areas are considered the urban areas of Chinese cities.4

3.2.2 National Urban System Policy China is one of the few countries in the world that has a national urban system policy. As early as in the National City Planning Conference in 1980, the National 4 Some

county-level cities in the western region have exceedingly large administrative areas, but the urban districts are not identified because they have the same administrative level of the urban district. This problem should be avoided when urban size or other indictors of county-level cities are measured.

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Fig. 3.1 Demarcation of Chinese cities, 2010 (Source Author’s work based on the list of cities by the Ministry of Civil Affairs)

Construction Committee put forward the urban development guideline as “control the size of big cities, rationally develop medium-size cities and actively develop smallsized cities and towns”. Four years later such guideline was written in the Town Planning Act issued by the State Council. It became the formal urban system policy in 1989 when it was written in the Urban Planning Act. The statement was changed as “strictly control the scale of large cities, rationally develop medium and small cities”. During the post-reform period, this urban system policy was the official guideline for urban system development in China until it was deleted in the new Urban and Rural Planning Act in 2008. The major objective of the national urban system policy is to avoid the urban problems that plagued the large cities in the developing countries during the post-WWII period (Xu 1984). Such urban problems are characterized as “psuedo-urbanization” which is the result of population expansions in the urban areas without concomitant increase in employment. Population explosion cause serious housing and infrastructure burden, and urban poverty problems in large cities of these countries (McGee 1971, 1967; Yeh and Laquian 1979; Drakakis-Smith 1986). In the pre-reform period, the socialist state exerted a strong control on urban growth and urban system development owing partly to the political ideology of the Chinese Communist Party (CCP) and partly because of necessity (Chang 1976; Xu 1984; Yeh and Xu 1984). Both the development strategy of strictly controlling the size of large cities and the industrial policy of dispersing industries from the coast increased the urbanization level of cities in the interior region and led to the development of medium and small cities and a balanced urban system. In the post-reform period, the state fears that the population

3.2 Clarification of the Key Concepts and Issues …

47

in Chinese cities, especially the large cities, would grow too fast for the cities to handle. The concern was that the “housing facilities and municipal infrastructure in the large cities are already so heavily burdened they can barely meet the demands of the growing urban population itself” (Ma and Lin 1993, p. 586). Therefore, the state inherited the strategy of strictly controlling large cities and wrote it into the Urban Planning Act soon after the commencement of economic reforms in 1978. The national urban system policy can regulate urban system development through a series of institutional arrangements. Among them, the hukou (household registration) system which was established in 1954 is served as an important institutional instrument to control migration from the countryside to the cities. The government divided the population into agricultural (nongye renkou) and non-agricultural (fei nongye renkou), a division which was used in conjunction with the food-rationing system to regulate the monthly quotas of foodstuffs, consumables, and consumer durables. Each person in China was required to register in one and only one place of regular place, and movement from registered place to another place without proper registration was strictly controlled by the government (Chan and Zhang 1999). Without a proper hukou registration, one had no access to jobs, education, and many highly subsidized and otherwise unavailable consumer necessities such as grain, cloth, oil, pork, bean curd, and soap. Through this system, the state can control rural–urban migration, labor transferring across cities, and thereby can control the sizes of large cities. Although the hukou control has been largely relaxed in the postreform period, it has not been abolished but remains potent and intact in controlling the rural–urban migration, especially for migration to large cities. The national urban system policy has generated the boom of small cities and town in the 1990s driven by the development of TVEs and rural urbanization. TVEs helped to absorb large amount of surplus laborers in rural areas, and prevented them from flooding into large cities. This strategy of “leaving the land but not the village” (litu bulixiang) and “entering the factory but not the city” (jinchang bujincheng) (Fei 1994) was a result of the national urban system policy and the resulting development of TEVs in 1990s. Consequently, small cities and towns experienced a phenomenal development in this period. However, the power of this policy seems to be weaken since the mid-1990s. The influence of globalization has strongly stimulated the growth of mega-cities in China. In addition, the decentralization of state power from the central government to local government has empowered local governments to be primarily responsible for their own development. The Chinese state has gradually transformed from a single unitary power to a power matrix in which Chinese cities are hierarchically organized by the administrative system. All Chinese big cities are trying to optimize their economic structure and upgrade their infrastructure, in an attempt to enhance their status in the urban system (Xu and Yeh 2005). The state has increasingly recognized that the national urban system policy cannot be effectively implemented to achieve the goal of strictly controlling the size of large cities. Nevertheless, the national urban system policy has profound influence on China’s urban system development in the post-reform period. The influence of the national urban system policy may become less strong as before and the ways in which the policy is implemented are changing. Although this policy has been deleted in the

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new issued Urban and Rural Planning Act in 2008, its related institutional arrangements have not eliminated yet. The hukou system continues to limit the population movement to large cities. Thus, we need an alternative framework to understand the state regulation on urban system development in addition to paying attention to the policy itself and the relevant hukou system and TVEs. In this book, we attempt to examine the effects of the national urban system policy on China’s urban system in the post-reform period, and construct a framework to investigate the changing ways in which the national urban system policy is implemented and its changes as a result of economic reforms and changing institutions and policies.

3.2.3 Urban Administrative Level China’s territory is governed administratively. Its current administrative system is more complex than before and also than those in other countries. China’s urban administrative level can be categorized into four basic types, namely, provincial, prefecture, county, and township and town levels (Fig. 3.2). Each territorial unit (e.g., city, county, autonomous Zhou, Meng, and Districts) falls under one particular

Fig. 3.2 China’s administrative system (Source Ma 2005, p. 479)

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49

level, and the entire administrative system is hierarchically structured. This system integrates the geographical divisions with the administrative system of the bureaucrat organization. The administrative level of a territorial unit is equivalent to the administrative level of its government, and it determines the political level of its governor. When a territorial unit has high administrative level, it has great political power. Thus, the administratively governed spatial divisions system can be seen as a way to territorialize the state power. China’s administrative units are tightly and hierarchically structured (Ma 2005). A unit at a low level should be a subordinate to its superior units to which it “administratively belongs” (lishu). In the hierarchical structure, the vertical interactions are enhanced, whereas the horizontal interactions are greatly limited because units often interact directly with those that are immediately above and below them. One of the most distinctive features of Chinese cities is that as a territorial unit, each of them has a certain administrative level. In addition, Chinese cities fall under different administrative levels. Hence, they have varying levels of political power, resulting in complex power relations among cities. This book defines the UAS as the system that administratively organizes all Chinese cities. Table 3.2 presents the number of administrative units at different levels in selected years. In 2010, China Table 3.2 Changes in the number of China’s administrative units at three levels: 1978–2010 1978

1990

2000

2010

Provinces

21

22

22

22

Autonomous regions (zizhi qu)

5

5

5

5

Centrally administered cities

3

3

4

4

Total

29

30

31

31

Vice-provincial-level cities

0

0

15

15

Prefecture-level cities

99

185

244

268

Province-level

Prefecture-level

Prefectures (diqu)

206

113

37

17

Autonomous prefectures (zizhi zhou)

28

30

30

30

Leagues (meng)

7

8

7

3

Total

340

336

333

333

1,990

1,731

1,509

1,461

County-level Counties County-level cities

91

279

400

370

Urban districts (shiqu)

408

651

787

853

Autonomous counties (zizhi xian)

65

121

116

117

Banners (qi)

83

51

49

55

Total

2,328

2,825

2,861

2,856

Source NBS (2010, 2011)

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3 China’s Urban System Development …

had 4 centrally administered cities (provincial-level), 15 vice-provincial-level cities, 268 prefecture-level cities, and 370 county-level cities. When a city has high administrative level, it has great political power. The centrally administered cities have the same political power as provinces. Vice-provincial level cities were previously known as “central economic cities.” In 1995, these central economic cities were officially confirmed as vice-provincial administrative level, which is half-level higher than other ordinary prefecture-level cities. The number of cities at the top two levels remains the same, and only Chongqing has been upgraded to a provincial-level city during the post-reform period. Most cities fall under prefecture- or county-level, and the number of cities at these levels continuously increases since 1978, thereby causing a rapid “horizontal expansion” of China’s urban system (Fan 1999). Compared with small cities, large cities tend to be found at higher administrative levels, and high administrative-level cities tend to govern larger areas than low-level cities (Chan and Zhao 2002). As a country-specific regulation intuition, UAS exerts salient effects on China’s urban system development and may distinguish China’s urban system from those in a largely “unregulated” environment. This book analyze the influence of UAS on the urban system in China.

3.2.4 Urban Size, Urban Growth, and Classification of City Sizes In this book, urban size is measured by the urban population (Chengzhen Renkou) of cities mainly drawn from the National Population Census data. However, the lack of well-defined concept of physical urban area has caused significant confusion of urban population and makes it difficult to construct comparable urban population measurement over time. The comparability of the urban population of the involved population censuses should be verified because of the changing definitions of urban population in different censuses and the changing criteria for designating cities and towns. The Third National Census (1982) defined the urban population of a city as the total population of the city, including the agricultural and non-agricultural population within its direct administrative area. This definition exaggerated the real size of the city because it enumerated the inhabitants in the countryside within the administrative area (Ma and Cui 1987). However, the direct administrative areas of most cities were small in 1982 because most of them were established by “carving out a block of geographical scope” (qiekeuai sheshi). The 1982 census reported two kinds of caliber of urban population. The “small caliber” (xiaokoujing) of urban population excludes the population of suburban townships (xiang) and appropriately reflects the real urban size (NBS 1985). As such, this book adopts the definition of the “small caliber” of urban population stipulated in the 1982 census.5 5 In

the regression models in Chapter 6, I use the year of 1984 to substitute the year of 1982, so the urban size of 1984 is also measured by the “urban non-agricultural population”, which is consistent with the year of 1990.

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51

The Fourth National Population Census (1990) adopted two sets of criteria for the enumeration of the respective populations of cities, towns, and counties (Zhou and Ma 2003). The first criteria defined urban population as the total population of the urban districts within the administrative area of a city, excluding the subordinate counties. The second criteria defined urban population as the total population of cities that have urban districts and as the total population of the sub-districts (jiedao banshichu) for the cities that do not have urban districts. Nonetheless, the administrative areas of many cities have been largely extended since the early 1980s to cover large rural areas. Hence, using the total population of the urban districts to measure urban population overcounts the real urban size. Similar to the first criteria, the second criteria undercounts the actual urban size of the cities without urban districts. The definition of the urban population in the Fourth National Population Census (1990) is not comparable with that of the 1982 population census. Thus, this book employs the “urban non-agricultural population (chengshi feinongye renkou)” as a proxy to the urban population for cities of 1990 because urban administrative boundary extension may not influence the non-agricultural population calculated based on hukou status instead of geographical area.6 The “non-agricultural population” under-estimates the urban size because it does not enumerate the agricultural population who work and live in urban areas. However, the differences should not be significant because the number of migration population in 1990 was small. Two major changes are observed in the criteria for enumerating urban population in the Fifth National Population Census (2000) and the Sixth National Population Census (2010). First, urban population refers to the “resident population (changzhu renkou)” who had resided there for more than six months. In other words, the migration population is also enumerated as the urban population regardless of their hukou status. Second, the criteria use population density in which the general threshold is 1,500 person per square kilometer to separate the administrative area into urban area and others (for detailed criteria, see Zhou and Ma [2003] and Shen [2006b]). Although the criteria of 2000 and 2010 have few differences, the definitions of urban population are comparable and better capture the real urban sizes. This book uses the resident population reported in the two years’ censuses to measure urban size. The urban sizes of cities with urban districts are defined as the total resident population of their urban districts (shiqu changzhu renkou). In sum, this book tries to make the definitions of urban size of different years comparable. Inconsistencies generally remain unaddressed because of the low quality of datasets. To avoid these conflicting definitions, the year dummy variable is included in the regression models in Chapters 6 and 7, and all potential biases of the analysis results generated by inconsistent urban size definitions are discussed individually in the latter part of this book.

6 The

urban population for the cities that have urban districts is defined as the total non-agricultural population of these urban districts, and the urban population for the cities that do not have urban districts is defined as the total non-agricultural population of the whole city.

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Urban growth in this book refers to the growth of the urban population defined above. As such, this book does not classify the urban growth into natural and migration growth. Urban growth rate is the combined rate of natural and migration growth. With regard to urban system development, natural growth rate has less explanation power for the differences of growth rates among cities. Migration population is the major factor of urban growth since the early 1990s. Migration population will be easy to become the permanent residents as China has increasingly relaxed the hukou control. Therefore, the development of China’s urban system is the result of the combination of natural and migration growth. This book focuses on the gross growth of cities than on the differences of natural growth and migration population growth. The size of Chinese cities has official classification, which is associated to a variety of state policies and urban planning. The first version of criteria for the classification of city sizes was promulgated in the Urban Planning Act in 1989. Based on the urban non-agricultural population, Chinese cities are classified into four categories, namely, extra-large cities (larger than 1 million), large cities (500,000–1 million), medium cities (200,000–500,000), and small cities (smaller than 200,000). However, a new set of criteria were promulgated by the State Council in 2014 that raise the minimum requirements of classification. In the new criteria, cities are classified into seven categories, namely, super-large cities (larger than 10 million), extra-large cities (5 million–10 million), type-I large cities (3 million–5 million), type-II large cities (1 million–3 million), medium cities (500,000–1 million), type-I small cities (200,000–500,000), and type-II small cities (smaller than 200,000). This book mainly uses the old criteria because the analysis period begins from 1982. When mention the extra-large, large, medium, and small cities, the size classification refers to the old criteria. However, in the analysis in Chapter 5, we use the new criteria to analyze the differences of actual urban system in 2010 and simulated urban systems because the new criteria is better to capture the classification of city sizes in 2010 than the old criteria.

3.2.5 Geographical Regions and Temporal Periods Two issues regarding China’s urban system development are yet to be addressed. The first issue is the division of China’s geographical regions, and the second is the division of development period. This book divides the territory of the country into different regions to further analyze the regional difference of urban system development. Two types of spatial divisions adopted are particularly adopted: to divide the country into three (i.e. eastern, middle, and western regions) and seven sub-regions. The former division is used to reflect the inter-regional differences in economy and geography. Figure 3.3 illustrates the eastern, middle, and western regions of China. In addition, the coastal region is the equivalent of the eastern region and the interior region is equal to the plus of the middle and western regions. However, division of China into three regions overlooks some variations within the regions as each region because each of them covers a huge land are. Therefore, this

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53

Fig. 3.3 Three regions of China

book adopts the seven sub-region division to appropriately determine the regional difference of urban development. The seven sub-region and the province/provincial cities include North China (i.e., Beijing, Tianjin, Hebei, Shanxi, and Inner Mongolia), Northeast China (i.e., Heilongjiang, Jilin, and Liaoning), East China (i.e., Shanghai, Shandong, Jiangsu, Zhejiang, Jiangxi, Anhui, and Fujian), Central China (i.e., Hubei, Hunan, and Henan), Southwest China (i.e., Chongqing, Sichuan, Guizhou, Yunnan, and Tibet), Northwest China (i.e., Shaanxi, Gansu, Ningxia, Xinjiang, and Qinghai), and South China (i.e., Guangdong, Guangxi, and Hainan). Figure 3.4 illustrates the seven sub-regions of China. Another analytical issue with regard to China’s urban system development is the time period. Yeh et al. (2015) specified that the urbanization of China has experienced four waves since 1949. Nevertheless, the fundamental transformation of the county’s urban development occurred in 1978 when economic reforms and opening-up policies implemented.7 This book divides the process into two periods, namely, the preand post-reform periods, to examine the difference of urban system development over time. The year 1978 is selected as the turning point because the development ideology has completely changed after this year. The “pre-reform period” in this book refers to the time period from 1949 to 1977, and the “post-reform period” refers to the time period after 1978.

7 The

significant event is the pivotal meeting of the third plenary session of the 11th Central Committee of the Communist Party of China from December 18 to December 22, 1978.

54

3 China’s Urban System Development …

Fig. 3.4 Seven sub-regions of China

3.3 Development Patterns of China’s Urban System China has long-standing urban traditions and one of the oldest urban systems in the world. Since the establishment of “New China” and the reunification of the whole country in 1949, China’s urban development has experienced several waves accompanied by the fluctuation of political economy. However, 1978 was a turning point for such development when economic reforms and opening-up policies were introduced. The urban development in China in the pre-reform period (1949–1977) was strictly controlled by the state through a centrally planned economy. Socialist ideology oriented the urban policies, and development strategies generated a fundamental shift in urban system development. Without the existence of a market, China’s urban development purely fluctuated with the changing state polices and political movements. As such, the country experienced a relatively stagnant period of urbanization with an increase of only 7.3 percentage points (increased from 13.26% in 1953 to 17.6% in 1977) (NBS 2010).8 Figure 3.5a shows the growth of per capita GDP index and urbanization index from 1952 to 1977. The urbanization level was not correspondent with economic growth but was greatly facilitated by political movements and policies, including the First Five-year Plan (1952–1957), the “Great Leap Forward” (1958–1960), the Great Famine (1961–1963), and the Great Cultural Revolution (1966–1976). In the post-reform period (since 1978), Chinese cities have 8 The

annual growth rate of urban population was not significantly low during this period. Zhou (1995a) stated that the average annual growth rate of urban population was about 3.3% from 1950 to 1980 and the annual growth rate of the total population was 1.92% during the same period. Because of the high growth rate of the total population, the urbanization ratio was growing relatively slowly. In other words, natural growth was the major component of urban population growth.

3.3 Development Patterns of China’s Urban System

55

Fig. 3.5 Growth of per capita GDP and urbanization level: a 1952–1977; b 1978–2012 (Source NBS 2013, 2010)

rapidly developed, and the ideological change has fundamentally altered the political economy of China, which led the former socialist state to “grow out of the plan” (Naughton 1996). Market forces became the major driving forces of urbanization during this period. Figure 3.5b shows a clear trend of parallel growth between the urbanization level and per capita GDP during the post-reform era. The urbanization level was increased from 17.9% in 1978 to 52.5% in 2012, with an increase of nearly 1 percentage point each year (NBS 2013). Table 3.3 shows the increase in the number of cities and the change of the average annual urban growth rate from 1953 to 2010. The growth rate of cities in the prereform period was quite slow, and the number of cities was almost stagnant. By contrast, the number of cities rapidly grew in the post-reform period, and the average annual urban growth rate during this period was significantly higher than that in the pre-reform period. The First National Population Census in 1953 reported that the total urban population was 77,257,282, which accounted for 13.3% of the total population of 582,603,417 (Shabad 1959). At this time, China had 164 officially designated cities. Table 3.4 lists the top 10 cities of 1953 and 1982. In 1953, Shanghai was China’s largest city with a total population of 6,204,417. This condition implies that one of 12 China’s urban residents lived in Shanghai then. The second and third largest Chinese cities during the same year were Beijing (2,768,149) and Tianjin (2,693,831). Figure 3.6 maps the geographic distribution of the 164 Chinese cities in 1953. The spatial pattern of China’s urban system during this year reflected the influences imposed by external forces in the pre-1949 period. Most large cities were previously treaty ports created by foreign counties after the mid-nineteenth century. These cities were located along the coastal line and the Yangtze River and in the Northeast region, which was once occupied by Japan. Since 1949, China’s urban development has been redirected toward interior cities through targeted and preferential investment plans. However, the 1953s basic geographic pattern of urban system is still in effect until today. The development and structural changes of the urban system in the pre-reform period can be identified by comparing the First National Population Census in 1953

27

110

Medium (0.2–0.5 million)

Small (1 million) 9

All cities

City size

279

185

3

93

189

178

283

118

28

31

460

400

244

4

121

247

298

211

304

91

60

666

370

268

4

125

246

283

131

305

136

82

654

2010

–

–

–

2.07

2.98

2.01

2.80

1.99

1.52

1.43

2.43

5.48

1.74

2.34

3.93

3.92

3.94

6.45

2.64

1.27

1.18

3.93

1982–1990

Average annual urban growth rate (%) 2000

1953–1982

1990

1953

1982

Number of city

Table 3.3 Number and average annual growth rate of Chinese cities from 1953 to 2010

9.27

7.69

5.77

7.24

7.14

9.91

9.78

7.31

4.98

6.21

8.59

1990–2000

2.93

3.54

5.19

2.99

2.89

3.61

3.51

2.81

3.06

4.17

3.19

2000–2010

5.02

3.55

4.11

3.92

3.34

4.57

6.15

4.42

3.54

3.47

4.08

1982–2010

56 3 China’s Urban System Development …

3.3 Development Patterns of China’s Urban System

57

Table 3.4 Top 10 largest cities in 1953 and 1982 1953

1982

City name

Population

Rank

Population

Rank

Shanghai

6,204,417

1

6,320,829

1

Beijing

2,768,149

2

4,674,921

2

Tianjin

2,693,831

3

4,119,086

3

Shenyang

2,299,918

4

2,981,695

4

Chongqing

1,772,486

5

1,902,601

10

Guangzhou

1,598,882

6

2,349,490

7

Wuhan

1,427,291

7

2,739,173

5

Ha’erbin

1,162,962

8

2,542,832

6

Nanjing

1,091,575

9

1,707,861

11

Qingdao

916,846

10

1,173,872

23

Source NBS (1954)

Fig. 3.6 Geographic distribution of Chinese cities, 1953 (Source NBS 1954)

with the Third National Population Census in 1982 (Figs. 3.6 and 3.7).9 The number of cities quickly increased to 207 in 1961 during the “Great Leap Forward” campaign, which was the peak of the pre-reform period. Many new established cities were eliminated from the list of the central government in 1962, and the number of cities was 9 Data

of the first and third census are comparable, and the urban system in 1982 kept the basic structure of the pre-reform period because the economic reform was just beginning.

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Fig. 3.7 Geographic distribution of Chinese cities, 1982 (Source NBS 1985)

reduced from 207 to 168 in 1965. Only 17 new cities were designated during the “Great Cultural Revolution (1966–1976).” The period since 1978 has been characterized by the rapid growth of both the number of cities and urban population. The Third National Population Census in 1982 reported that the total number of cities was 238, and the total urban population of these cities was 206,588,582. The urbanization level during this period was 20.55%. However, the growth rate of cities during the three decades was quite low. If the newly designated cities are not considered, then the average annual growth rate of the cities is only 2.43, which is roughly equal to the natural growth rate of the overall population (2.32%) (Zhou 1995a). This evidence supports the argument of “anti-urbanism” in the pre-reform period (Lin 1998). In the pre-reform period, the average annual growth rate was negatively related to urban size (Table 3.3). The average annual growth rates of extra-large, large, and medium cities were all lower than the average value (i.e., 1.43, 1.52, and 1.99%, respectively), whereas the rate of small cities was 2.82%, which was higher than the average growth rate. The eastern cities had the lowest average growth rate of only 2.01%, and the cities within the middle region had an average annual growth rate of 2.98%, which was the highest value. The western cities had an average annual growth rate of 2.07%. It indicates that Chinese state was remarkably successful in using population control and resources allocation to shape the urban system according to their inland-oriented and small-oriented city development policies. The growth of large cities was successfully curtailed, small cities were developing rapidly, and the balance of both cities and population was successfully shifted from the coast to interior and border locations. The national development policy emphasizes the role of small- and medium-sized cities as industrial centers to link agriculture with

3.3 Development Patterns of China’s Urban System

59

Fig. 3.8 Geographic distribution of newly designated cities between 1953 and 1982 (Source NBS 2010)

industry (Chang 1976), and the inland-oriented policy is associated with national security. The development of interior cities is a strategy to reduce the vulnerability of attacks on coastal industrial bases and population centers (Wu 1967). As a result, many factories and their workers were moved from the eastern region to western or middle mountainous regions during the Third Front Construction (sanxian jianshe) period (1965–1971) (Naughton 1988). The strategies mostly benefited the interior centers (i.e., Xi’an, Chengdu, Chongqing, Guiyang, Lanzhou, Baotou, and Kunming) and several newly established cities (i.e., Shizuishan, Shiyan, and Panzhihua). The spatial pattern of the newly designated cities also reflected the policy preference and ideology of that time. The urban development policy favored the traditional centers and had a spatial bias that focused on the interior regions (Pannell 1982). Figure 3.8 shows the locations of the 90 newly designated cities between 1953 and 1982, which continued to exist by 1982.10 The figure also shows that 69 of the newly designated cites were located in the west and middle, and only 11 eastern cities were established during this period. The government’s policy of new city designation was based on national strategies. Many cities were established in the southwest (i.e., Guizhou, Hubei, Sichuan, and Yunnan) and northwest (i.e., Shanxi, Gansu, Qinghai, and Ningxia) provinces, which were established as important regional centers such as Panzhihua, Liupanshui, Anshun, and Erlianhaote. 10 A total of 15 cities of 1953 degraded to counties or incorporated into other cities in 1963. The cities that were once designated but then dropped during the period of 1953 and 1976 are not shown in the figure.

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In the post-reform period, there were a surge of new cities and a rapid growth of existing cities in China, which were labeled as the horizontal and vertical expansion, respectively (Fan 1999). In 1982, China only had 238 cities, which increased to 654 in 2010 (Table 3.3). The number of cities grew extremely quickly between 1982 and 2000 because the central government relaxed the control on the designation of cities. Nonetheless, this growth trend discontinued after 1997, and the number of designated cities became relatively stable. According to the Sixth National Population Census in 2010, China’s total urban population dramatically increased from 206 million in 1982 to 665 million in 2010 (NBS 2012). The urbanization level was at 49.68% in 2010 and has reached 50% in 2011, presenting a significant increase from 17.9% in 1978. The average annual growth rate of all Chinese cities in the post-reform period was 4.08%, which was significantly higher than that in the pre-reform period (Table 3.3). In 2010, four cities had populations larger than 10 million and 10 cities had population varying between 5 and 10 million. The focus of urban development has gradually shifted from the interior to the coastal region in the post-reform period, reflecting the geographical preference of state policies. This transition was evidently observed through the location of the newly designated cities, which were mostly in the east region such as the PRD, YRD, and North China (Fig. 3.9). These cities had a higher average annual growth rate than the interior cities in the post-reform period, which is different from that of the pre-reform period (Table 3.3). The overall average annual growth rate of the eastern cities during 1982 and 2010 was 4.57%, which was higher than that of the central and western cities (3.34 and 3.92%, respectively). The growth rates of cities

Fig. 3.9 Geographic distribution of newly designated cities between 1982 and 2010 (Source NBS 2011)

3.3 Development Patterns of China’s Urban System

61

Fig. 3.10 Urban population growth of cities in three regions

in these regions were roughly equal during 1982 and 1990, but the eastern cities had higher growth rates in the next two decades (9.91 and 3.61%). Figure 3.10 clearly shows the rapid urban population growth of the cities in the eastern region compared with that in western and middle regions since 1990. In recognition of the inherent comparative advantages of the eastern cities, the central government granted these cities with numerous preferential policies. To attract foreign investment and allow market forces to play strong roles, the central government has set up special economic zones (SEZs) and coastal open cities (COCs) in the early period of reform (Lin 2002). Most of these SEZs and COCs are located in the eastern region and have become new growth centers that lead the whole eastern region to outperform its interior counterparts. The increasing migration population is another result of the shift of development focus. After the Chinese government began to relax its control on population flow, a large number of migration workers moved to the coastal cities which shaped the distribution of the population over cities. Thus, the coastal region has abstracted increasing migration population than the interior region since the early 1990. Figures 3.11 and 3.12 show the spatial distribution of the inter-county migration population of 2000 and 2010, respectively. Most migrants that came from the interior rural area concentrated in metropolitan areas of the east region, especially in YRD, PRD, Beijing-Tianjin-Hebei, Fujian, and Shandong. Shen (2006a) argued that the migration population can reflect the effects of a new track of spontaneous urbanization in the post-reform era. The rural surplus labors in 1980s moved to TVEs

62

3 China’s Urban System Development …

Fig. 3.11 Spatial distribution of migration population, 2000

Fig. 3.12 Spatial distribution of migration population, 2010

3.3 Development Patterns of China’s Urban System

63

near their hometown. However, the situation was completely changed since 1990s, and coastal large cities became the most important destinations of migrants. The hierarchical structure of the urban system was reorganized because of the variations in urban growth rates in cities of different sizes. Table 3.3 shows that although the proportion of the number of small and medium cities (smaller than 0.2 million) decreased, their annual growth rates (6.15% for small-sized cities and 4.42% for medium-sized cities) were higher than those of large and extra-large cities (3.54 and 3.47%) during the post-reform period. From 1982 to 1990, small cities experienced a high growth process with an average annual growth rate of 6.45% that was significantly higher than the average rate (3.93%) and the rates of large cities. The reason behind this occurrence is the fact that China’s reform initially took place in the countryside since the early 1980s that triggered the rapid industrialization and economic growth of small cities and counties (Ma and Cui 2002). However, the development focus was gradually shifted to medium and large cities in the 1990s. The cities whose population was larger than one million rapidly grew since 1990 with average annual growth rates of 6.21 and 4.17%. Nonetheless, the average growth rates of medium-sized cities were always low in the post-reform period. Table 3.3 also shows the variations in the growth rates of cities at different administrative levels. The county-level cities had high average growth rates at the early stage of economic reforms, and then the prefecture-level and centrally administered cities had high growth rates at the latter stage. Since 1978, the spatial pattern of China’s urban system has significantly changed, and metropolitan regions have extensively experienced spatial restructuring (Figs. 3.13, 3.14, and 3.15). One critical feature of these development is emergence of several city-regions. FDI, labor forces, land, and the global shift of technologies were altogether contributed to the increasing concentration of manufacturing industries and services in coastal cities. These cities stood out as the centers of capital investment, human capital accumulation, and technological innovation through the advantages of agglomeration economies. With the rapid urbanization, industrialization, and globalization, massive land expansion and population concentration led to the formation of interconnected high density metropolitan areas. For example, PRD and YRD are two regions that were considered emerging “global city-regions” (Wu 2000; Lin 2001). In sum, the level of urbanization in the pre-reform period remained relatively low, and the urban system development was characterized as stagnated and fluctuated as influenced by unstable policies and political movements. However, economic reforms and opening-up policies have led to the rapid development and dramatic structural change in China’s urban system since 1978. The difference in the urban system between the pre- and post-reform periods should be principally attributed to the transformation of ideology, which has led to the developmental strategies that are favorable to cities and has significantly accelerated the development of China’s urban system. Scholars have accepted the fact that the urban development in China in the pre-reform period was strictly controlled by the state. However, state control has been relaxed by introducing market mechanisms. A series of deregulation reforms have changed the role of the state in China’s urban system development, which has

64

3 China’s Urban System Development …

Fig. 3.13 Geographic distribution of Chinese cities, 1990 (Source NBS 1993)

Fig. 3.14 Geographic distribution of Chinese cities, 2000 (Source NBS 2002)

3.3 Development Patterns of China’s Urban System

65

Fig. 3.15 Geographic distribution of Chinese cities, 2010 (Source NBS 2012)

resulted in a different development process in the post-reform period analyzed in this section.

3.4 Changing Policies and Institutions and the Development of China’s Urban System in the Post-reform Period 3.4.1 Introducing the Market Mechanism and Redefining the Role of the State China is one of the few counties in the world that has a national urban system policy. However, economic reforms adopted in 1978 in China have introduced the market mechanism into the country’s economy and have redefined the role of the state and readjusted the power relations between the central and local governments. The state and the market are basic regulating mechanisms in economic development. In the pre-reform period, the socialist state internalized the market mechanism into the centrally planned economy system by controlling most economic units and resource allocations. The product market was largely established in the early 1980s, and different types of resource markets were successively established or externalized by the state in the subsequent two decades (Yeh et al. 2015). A series of major reforms in policies and institutions greatly influenced the establishment of markets and the redefinition of the role of the state. The first reform aimed to gradually eliminate the

66

3 China’s Urban System Development …

mechanisms of product distribution and production input allocation, which were the major obstacles of non-state sector development. The commodities allocated by the state were reduced from 256 categories in 1978 to 17 in 1989, and the dual-track price system was eliminated by the end of 1991. Since the early 1990s, the non-state capital has been allowed to invest in most production fields, and the rural labor has been allowed to move to these non-state sectors. The state has also broken the “iron rice bowl” (tiefanwan) and “fraternal cooperation” of the state-owned enterprises (SOEs). Hence, the share of SOEs in the total employment decreased rapidly in 1990, accounting for less than 20% after 2008 (Fig. 3.16). The collective units were reduced to less than 5% in 2002, and the non-state sector became the major actors to absorb labor forces in the post-reform period. To satisfy the labor needs of these non-state sectors, the state eliminated the food grain rationing system in 1992 and allowed the rural labor force to enter the cities. Another key reform in China’s policies and institutions is the land reform or the establishment of the land market. The land reform was initiated in the early 1980s but during this period, it was only a tentative experiment in paid land-supply methods for foreign investors in some early opened cities. The paid transfer of land-use rights was formally legitimized by the State Council only in 1990, which marked the establishment of the land market in China (Yeh and Wu 1996). The establishment of the land market and the introduction of two new policies (i.e., the tax-sharing system (TSS) in 1994 and the housing reform that began in the early 1990s) emerged as a new force that promoted China’s urban development toward a new stage. The fiscal reform

Fig. 3.16 Change of employment in different ownership sectors (Source Yeh et al. 2015, p. 2833)

3.4 Changing Policies and Institutions and the Development of China’s …

67

altered the incentive of the urban governments from supporting local enterprises to facilitating land-centered development to obtain large extra-budget revenues, that is, the rise of “land developmentalism” (Tao et al. 2010). The house reform released large demands for housing and facilitated the boom of the real estate market. The revenues collected from lands enabled urban governments to develop large projects. Thus, the emphasis of China’s urban development was shifted gradually from rural to urban sectors and became a city-based and land-centered urbanization (Lin 2007). The third reform in China’s policies and institutions was related to the capital market. For the domestic non-state capital, the State Council issued the Tentative Stipulation of Private Enterprise in 1988 that was the first legitimization of private capital. In 1990, the state explicitly defined the sectors that allowed the participation of private investment, including manufacturing, construction, transportation and communication, commerce, retailing, and consumer services. The foreign capital was officially allowed to access China in 1979 when the State Council issued the Law of the People’s Republic of China on Chinese-Foreign Equity Joint Ventures, and the Constitution was amended to allow the use of foreign capital in 1982. The foreign capital was limited to the eastern coastal region in the early period but was later extended to cover the middle and western regions as well. China joined the World Trade Organization (WTO) in 2001 that greatly extended its market access for foreign investment. The Chinese state eliminated its regulation on the product market soon after the implementation of economic reforms in 1978, and began to gradually externalize the labor, land and capital markets. The externalization of the market mechanism has redefined the role of the state in China such that it insignificantly and indirectly intervened in urban and economic development. The importance of the market mechanism in China’s economic development was increased because of economic reforms. The experience of China’s urban system development is embedded in the country-specific state–market interplay and may vary from that in the pre-reform period and from those of other developing countries.

3.4.2 Shifting of the Focus of State Policies The focus of state policies had two important shifts since the beginning of economic reforms in 1978, that is, from rural areas to urban areas and from the interior region to the coastal region. These shifts indicate that China has largely dropped the strategy balanced development in urban and rural areas and in interior and coastal regions. The balanced development strategy in the pre-reform period was partly due to the political ideology of the CCP and partly because of necessity (Yeh and Xu 1984). According to the Marxist viewpoint, the socialist ideological commitment is achieved by eliminating unacceptable injustice and by reducing poverty. National security was another necessary concern for Chinese state in the pre-reform period because the coastal region and large cities were significantly vulnerable at that time. Therefore,

68

3 China’s Urban System Development …

the strategy of balanced development occupied a dominant place throughout the prereform period. However, it began to change since the beginning of economic reforms in 1978. First, the development focus was shifted from countryside to cities, a development strategy known as “urban-biased,” to achieve the effectiveness of economic growth; this strategy took the cities as the engine of economic development in the post-reform period (Oi 1993; Yang 1999). China entered the stage of city-based development in the early 1990s (Lin 2007). At the fifth session of the Fifth National People’s Congress in 1982, then-premier Zhao Ziyang suggested to create economic centers at every level, which is labeled as the start point of “a central city-based regional planning strategy” (Kirkby 1985, p. 230). This strategy was then officially validated in the Twelfth Party Central Committee in 1984. A set of “central economic cities” (jihu danlie shi) were then set up successively since 1984.11 These cities were granted economic power that is equal to those of provinces and listed separately within the state plan instead of being treated as only ordinary prefecture cities under provincial jurisdiction. Central economic cities were designated to free several cities from the hierarchy to serve as economic centers of regional economies. In addition, although the state in China continues to control the scale of large cities and develop medium and small cities based on the national urban system policy, it has increasingly realized that this policy has caused the loss of some economic efficiency. Small cities and towns are criticized for diseconomies of scale, scattered distribution and duplication of production, and wasteful use of resources. The central government cannot deny the role of large cities played in economic development. To face these challenges, the state began to emphasize the coordinated development of metropolises, medium- and small-sized cities, and small towns since the early 2000s. While large cities receive a greater amount of investment, the rapid growth of TVEs and small cities and towns has stopped. In super-large cities like Beijing, Shanghai, Shenzhen and Guangzhou, the size of population is growing rapidly. Second, the focus of the state policies was then shifted from the interior region to the coastal region. Chinese state tried to attract foreign capital and technology to drive economic development after they introduced opening-up policies. The coastal cities received most of the preferential policies because of location advantages. The central government set up 4 SEZs in 1979 and designated 14 COCs in 1984. All these cities were located along the coastal line with improved transport conditions (Fig. 3.17). Various types of development zones were also built in the central cities in the eastern region. Cities in Guangdong, Jiangsu, Shandong, Zhejiang, and Fujian received most of the preferential policies and played as the pioneers in economic reforms. These pioneer cities became pivots that contributed to the emergence of the export-oriented economy and the so-called “world factory.” After China joined the WTO in 2001, the influences of external forces began to extend to interior regions, and the whole country benefited from the “global shift” of manufacturing activities 11 The seven cities established in 1985 were Chongqing, Wuhan, Shenyang, Dalian, Guangzhou, Harbin, and Xian. Additional seven cities were added, including Qingdao and Ningbo in 1987; Xiamen and Shenzhen in 1988; and Nanjing, Chengdu, and Changchun in 1989.

3.4 Changing Policies and Institutions and the Development of China’s …

69

Fig. 3.17 Geographical distribution of SEZs and COCs in 1980s (Source Author’s summary)

from “global north” to “global south.” Nevertheless, the effects of foreign forces were greatly stronger in the eastern region than in the western region because of its better location conditions and inherent socio-economic advantages. The shifts of the focus of state policies imply that China tried to change the balanced development strategy. As Deng Xiaoping stated, China’s market-oriented reforms were implemented by allowing some people and some regions to become rich first and then drive other regions and help other people to gradually achieve common prosperity. Therefore, the development priority and privilege were granted to the cities with location advantages and economies of scale. This shift has triggered the uneven growth of cities and resulted in the structural transformation of the entire urban system in the post-reform period.

3.4.3 Reforms on the Hukou System and Population Mobility As stated above, the hukou system is the most important institutional arrangement for the state to implement the national urban system policy. With the market-oriented reforms, globalization, and decentralization of state power, the state began to relax its strict control on population mobility in response to the increasing demand for labors. The reforms on the hukou system can be divided into two phases (Table 3.5). The first phase was initiated in 1984. The State Council issued an official notice to allow people with an agricultural hukou to work in market towns as long as they can take care of their food and lodging and they are labeled as “self-supplied grain populations.”

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Table 3.5 Major reforms and policies regarding the hukou system Year Reforms and policies Phase 1: Top-down relaxation of state’s control on non-hukou migration when hukou migration was still required the state’s approval

1984 The State Council allowed the rural population to work in market towns as long as they can take care of their own livelihood; 1993 The State Council introduced the “blue-stamp” hukou to migrants by providing them with the right of residence and certain benefits in urban areas 1997 The agricultural population living and working in small cities and towns were allowed to obtain the non-agricultural hukou if they can satisfy the requirements

Phase 2: Bottom-up experiments of 2001 The State Council issued a notice to classification division between agricultural eliminate the quota limitation for offering and non-agricultural hukou status when the hukou status in small cities and towns state encouraged the localities to determine 2006 By 2006, there are 12 provinces unified the criteria for offering local hukou hukou system by eliminating the classification division between agricultural and non-agricultural hukou; 2008 Major cities such as Shanghai, Shenzhen and Guangzhou began to experiment offering local hukou by scoring 2013 The central government promulgated a plan to promote further hukou system reforms and to establish the Residence Permit System to eliminate the hukou classification Source Author’s summary

This reform primarily aimed to solve the problem in the increase of surplus of farm labors, which was generated by the decomposition of the commune system and the establishment of the household responsibility system that significantly increased rural productivity.12 The relaxation of the strict control on population mobility directly resulted in the rapid development of TVEs, which led to the boom of small counties and towns, especially in South Jiangsu and Zhejiang. The “South Jiangsu model” and “Wenzhou model” were typical development models of small towns. The relaxation of hukou system in the 1980s was a national strategy to absorb the rural surplus labor and promote industrialization in rural areas. This strategy of “leaving the land but not the village” (litu bulixiang) and “entering the factory but not the city” (jinchang bujincheng) (Fei 1994) played a crucial role in facilitating the development of rural industries, but preventing them from flooding into large 12 Taylor and Banister (1989) estimated that each year between 1982 and 1987, the number of surplus rural workers exceeded 100 million and the surplus rural labor rates, ranging between 33.5 and 42.5%.

3.4 Changing Policies and Institutions and the Development of China’s …

71

cities which was experienced at the early stage of urbanization in other developing countries. Meanwhile, the national urban system policy aimed to encourage the growth of small cities and control the growth of large cities to avoid the urban problems in other developing countries (Xu 1984). Hence, small cities and towns rapidly developed from the early 1980s to mid-1990s (Table 3.3). The second phase of the hukou system reforms began in the early 2000s. When the economic development center was shifted from small cities and towns to large cities and from the interior region to coastal region since the 1980s, the long-distance migration population rapidly increased. This occurrence implies that people tended to “leave the land and also leave the village” (litu youlixiang). Table 3.6 shows the distribution of migration population among three regions, illustrating the rapid increase of migration population from 1990 to 2010 and the changes in the distribution of this population. The inter-provincial migration population in 1990 was only 11.1 million, which accounted for about one-third of the total migration. However, this number increased to 85.8 million in 2010, which accounted for more than a half of the total migration population. Moreover, the eastern region had 49% of the total migration population in 1990 but had more than two-thirds migration population in Table 3.6 Migration population in China, 1990, 2000, and 2010 Total migration population Intra-provincial cross-county migration population

Inter-provincial migration population

1990

Absolute values Percent (million)

Eastern

16.7

Central

10.1

Western China 2000

Absolute values (million)

Percent

Absolute values (million)

Percent

49

10.2

44.4

6.5

58.7

29.6

7.5

32.5

2.6

23.5

7.3

21.4

5.3

23.1

2

17.8

34.1

100

23

100

11.1

100

Absolute values Percent (million)

Absolute values (million)

Percent

Absolute values (million)

Percent

Eastern

54.4

68.8

19.9

54.8

34.5

80.8

Central

12.7

16.1

9.5

26.2

3.2

7.4

Western

11.9

15.1

6.9

19

5

11.8

China

79

100

36.3

100

42.7

100

2010

Absolute values Percent (million)

Absolute values (million)

Percent

Absolute values (million)

Percent

Eastern

116.7

68.5

45

53.2

71.7

83.6

Central

27.8

16.3

21.8

25.7

6

7

Western

25.9

15.2

17.8

21.1

8.1

9.4

China

170.4

100

84.6

100

85.8

100

Source NBS (1993, 2002, 2012)

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3 China’s Urban System Development …

2000 and 2010, and the inter-provincial migration population tended to concentrate in the eastern region from 1990 to 2010. To deal with the increase of migration population, the Chinese state began to further relax its control on population mobility. In 2001, the State Council issued a notice to eliminate the quota limitation to offer hukou status among small cities and towns. By 2006, a total of 12 provinces claimed that they have unified the hukou system by eliminating the classification division between agricultural and non-agricultural hukou. Large cities, including Shanghai, Shenzhen, and Guangzhou, began to experiment by offering local hukou in 2008 through scoring. The central government promoted the hukou reforms in 2013 by encouraging the relaxation of hukou control in small and medium cities and planned to establish a residence permit system to gradually substitute the hukou system. In sum, the hukou system reforms had two phases. The first phase was the topdown relaxation of the control on non-hukou migration, and the second phase was promoted by the experiments of local governments. China has not completely abolished the hukou system, but devolves the responsibility for hukou policies to local governments (Chan and Buckingham 2008). However, the restriction of the hukou system has become rather flexible. The reforms on the hukou system have significantly restructured China’s urban system. The huge migration population is the most important driving force for urban system development. Continuous reforms on the hukou system can make the migration population permanent urban residents. The reforms on the hukou system seem to “turn on the tap” for population movement, but other factors will determine where the population will move to.

3.4.4 Restructuring the Urban Administrative System (UAS) Another important institutional change is the restructuring of the UAS which as a response to the market-oriented reforms. Every Chinese city falls under a certain administrative level. Most cities in the pre-reform period were prefecture-level and directly governed by the provincial government. Only few cities were governed by the prefecture governments. In addition, most cities were established by “carving out a block of geographical scope (qiekeuai sheshi)” with urban characteristics that met the criteria for the designation of cities. However, the UAS was greatly reformed in the early 1980s to empower the central cities to play a leading role in driving national and regional economic development (Ma 2005). The restructuring of the UAS in post-reform China can be characterized as the reshuffling of power relations of different territorial units to make the adjustment to new political and economic contexts in the post-reform period. UAS had two major changes, vertical scaling and horizontal territorialization, which can take place simultaneously (Shen 2007). Vertical scaling was manifested by the implementation of the “City-GoverningCounty” (CGC) system, which has re-organized the power structure among cities. This system can be accomplished by placing counties under the administrative control of a neighboring large city at or above the prefecture-level. The CGC system makes

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73

the prefecture-level city a hybrid territorial and administrative entity that consists of a set of county-level entities. This system was promulgated in the 1978 constitution and has been widely implemented since 1982.13 By the end of 1999, 97% of the cities at and above the prefecture-level had subordinate counties or county-level cities under their administrative control (Liu et al. 2012). The CGC system was an important strategy that allowed the prefecture-level cities to become central places to organize and manage economic activities and to stimulate rural economy growth (Tang and Chung 2000). This system has also shaped the relations among cities. Before the adoption of the system, prefecture-level cities, county-level cities, and counties were relatively independent and had minimal interaction. Nevertheless, after the adoption of the CGC system, these cities and counties developed a superiorsubordinate relationship in the administrative system, and their vertical interactions were largely increased. Hence, the political power of prefecture-level cities was expanded, and the political power of the subordinate counties and county-level cities was significantly reduced. The second change in the UAS was urban territorialization in which cities expanded their territories to administer a large area. Urban territorialization is an important tool that strengthens a city’s ability to acquire as many land as it can to gain revenue from land leasing and to control the resource of its subordinate areas (Shen 2007). The key premise of this strategy is that China has dropped the way of “carving out a block of geographical space to establish a city,” which was implemented in the pre-reform period, and adopted a set of new mechanisms, such as “converting county to city” (chexian sheshi), “abolishing prefectures and establishing prefecture-level cities” (chedi sheshi or di gai shi), “merging prefecture-level cities with prefectures” (dishi hebing) (Ma 2005). The most important difference between the old and new mechanisms was the boundaries of cities. For example, the mechanism of “converting county to city” was accomplished by merging the prefecture-level cities with prefectures in which the cities were located and the counties that used to be under a prefecture were placed under the newly established prefecture-level city. After merging, the boundaries of the prefecture-level cities covered the entire administrative boundaries, including a set of counties or county-level cities. This mechanism allowed the cities at high administrative levels to directly control the whole administrative areas, benefit the integration of urban and rural areas, facilitate the industrialization of suburban areas, and extend the urban built-up areas. “Abolishing county and establishing district” (chexian shequ) was another strategy often used by the prefecture-level and above cities to extend their scale. This annexation of suburban counties was operated by placing the counties under the jurisdiction of a prefecture-level and above city. Hence, the number of urban districts was increased from 408 in 1978 to 872 in 2013, and the number of county-level cities also increased from 91 in 1978 to 368 in 2010 (Fig. 3.18). Many counties became urban districts or county-level cities because of the restructuring of UAS.

13 In

1982, the State Council issued a notice termed as “The Notification of Reform on Prefecture System and Implementation of City-Governing-County Policy.”

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Fig. 3.18 Changes in the number of county-level units since 1978 (Source NBS 2010, 2014)

The restructuring of UAS was mainly development-oriented, but it substantially influenced the urban system development. Owing to the low levels of both the urban and economic developments at the early stage of economic transition, China attempted to grant additional authoritative power to large and medium cities, which expanded their abilities to lead the development of their hinterland (Solinger 1991). Hence, these cities were empowered to administer their nearby counties, and their scope was extended to cover large areas that were largely rural in appearance and function. However, the restructuring of UAS significantly changed the power relations among administrative units at different levels. China has rebuilt a hierarchical authoritarian political system among cities within the new market economy environment and shaped the structure of the urban system by reorganizing its UAS (Naughton 2010).

3.5 Summary China’s urban system development was characterized as state-regulated in the prereform period because all production input factors were strictly controlled by the state in the centrally planned economy or the state internalized the market. The urban system development of China exhibited a development trajectory that is different from that in the pre-reform period owing to the economic reforms and opening-up

3.5 Summary

75

policies. The transformation of ideology has altered the urban development policies from “anti-urbanism” into “urban-biased.” In the post-reform period, China’s urban system was developed and restructured in three dimensions, namely, temporal, hierarchical, and spatial. The surge of new cities and the rapid growth of existing cities in China expanded its urban system both horizontally and vertically. The development focus was gradually shifted from the interior to the coastal region in the post-reform period, which gave rise to the concentration of newly designated cities and high growth cities in the eastern region. Such focus was also shifted gradually from small and medium cities to large cities, especially for the large cities located in the eastern region. Another critical feature is the emergence of several city-regions, such as PRD and YRD. The economic reforms and resulting changes of policies and institutions led to the development and restructuring of China’s urban system in the post-reform period. The first critical change was the introduction of the market mechanism, which redefined the role of the state in China. The Chinese state eliminated its regulation on product market soon after the economic reforms and began to externalize labor, land, and capital markets gradually. The market became a critical driving force of urban system development, whereas the state changed the ways in which it intervenes in urban system development. The second change was the shift of the focus of state policies from rural areas to urban areas and from the interior region to the coastal region. Although the Chinese state continues to control the scale of large cities and develop medium and small cities based on the national urban system policy, it has increasingly realized that this strategy cannot be effectively implemented since the late 1990s. Thus, the strength and the ways in which the state regulates the development of urban system is changing. This change contributed to the restructuring of China’s urban system in hierarchical and spatial dimensions. The third change was the reform on the hukou system as a response to the labor demands of economic development. The restriction of the hukou system became flexible, and the responsibility for hukou policies was decentralized to the local governments. Hence, the reforms significantly changed the distribution of population among cities. Lastly, the UAS was restructured in the post-reform period to primarily empower some large and medium cities to play a leading role in driving national and regional economic development. The restructuring of UAS was characterized as vertical scaling and horizontal territorialization, which reshuffled the power relations of different territorial units to adjust to the new political and economic contexts in the post-reform period. With the changing policies and institutions, the state in China changed the ways in which it intervenes in urban system development. Market-oriented and de-regulation reforms led to the readjustment of the relationship between the state and the market. Hence, the state regulation became less strong than that in the pre-reform period, and the local governments were empowered to be principally responsible for their development. However, regulatory policies and institutional arrangement continuously existed, and the state incessantly exerted relevant effects on the urban system development in the post-reform period. In this regard, the continuously powerful role of the state is important to understand the trajectory of urban system development. A

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new framework built upon the post-reform China’s political and economic contexts is needed to understand the role of the state in the country’s urban system development. Such a conceptual framework is developed in the next chapter on the basis of a critical review of the existing conceptualization of China’s state in a broad social science discourse.

Chapter 4

Conceptualizing the Role of the State in China’s Urban System Development

4.1 Introduction Western studies have shed minimal light on the role of the state in urban system development. Inconsistencies between theory and reality were evidently observed in China because theories are derived largely from the experiences in the West. Most scholars agree that the state plays an important role in the urban system development in China. However, few studies have explained how the state intervenes in China’s urban system development processes. The distinctive development processes of China’s urban system and the unique political economic contexts have entailed a relevant conceptual framework. This chapter attempts to develop an alternative conceptual framework to clearly understand how the state intervenes in China’s urban system development. This chapter is organized into four sections. Following this introduction, Sect. 4.2 reviews the theorization of the role of China’s state from a broad perspective. Research in different disciplines is reviewed to offer an integrated understanding of the role of the state in China. Section 4.3 formulates a conceptual framework based on the perspective of political hierarchy and the organization of the state power in Chinese cities. Finally, Sect. 4.4 provides the research design for the subsequent empirical analyses, including the hypotheses, analytical procedure, research methods, and data.

4.2 Understanding the Role of the State in Chinese Context 4.2.1 Theories on the State and State-Market Relations The state and the market are consistently involved in the operation of a national economy. However, the relative importance and respective roles of these institutions vary greatly in different countries. The role of the state in a nation is inherited from its ideological origins and history and is influenced by the combination of internal and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J. Wang, The Role of the State in China’s Urban System Development, https://doi.org/10.1007/978-981-33-6362-5_4

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external political factors. In contemporary academic discourses, three major theories on the state are relevant to China’s state and state–market relations in the transition since 1978—the neoliberalism discourse, the developmental state model, and the market transition theories. Neoliberalism is a complex term that comprises a set of ideological discourses, policies, and practices with common ideas of economic liberalization and pro-market philosophy (Perreault and Martin 2005). Neoliberalism claims that economic, social, and political relations are best organized through free choices (Jessop 2002), and “neoliberal students” believe in “market ethics” in which relations in a society can be organized in the most efficient way of “contractual relations in the marketplace” (Harvey 2005, p. 3). This term is a theory of political economic practices and is characterized by strong private property rights, free markets, and free trade (Harvey 2005). In a narrow definition, neoliberalism is an economic strategy of market revolution that can be archived through a series of economic policies, such as privatization, deregulation, fiscal devolution, and workfare programs (Moseley et al. 2010). Neoliberalism is a return or resurgence of the classical liberalism, which can be traced back to the Age of Enlightenment. Adam Smith wrote The Wealth of Nations in 1776 when core ideas and concepts, including free market and trade, laissez-faire government with minimal intervention and taxation, a balanced budget, and free market, were incorporated into economics. Liberalism supports the expansion of market economy, that is, the commodification of all factors of production and monetization of exchanges to as many economic practices as possible. Liberalism also claims that the state should be involved with the limited power of economic intervention and maximize the formal freedom of economic actors and freedom of legally recognized subjects in the public sphere (Jessop 2002). The resurgence of liberalism in the form of neoliberalism can be attributed to the Reagan and Thatcher administrations, which successfully managed the crisis-tendencies of Atlantic Fordism due to the oil shock in the 1970s. Neoliberalism was expanded in Western advanced countries and in the developing countries, particularly in most countries in Latin America. A wide-ranging neoliberal reform strategy in Washington Consensus (Williamson 2000), which emphasizes the ideology of “state-retreat” and “market-advance” and the importance of macroeconomic stability and integration into the globalization dominated by multinational enterprises, was applied to these countries to address stagflation and achieve economic take-off (Gore 2000). The core emphasis of neoliberal shift is the deregulation of state control in the context of market-oriented reforms that leads to “denationalization” of the state and “destatization” of the national political system (Jessop 2000). In neoliberalism, open, competitive, and unregulated markets liberated from different types of state regulations represent the optimal mechanisms of economic development. The neoliberal state should play a limited role in the “creation, governance, and conduct of markets” (O Neill 1997) to give the national economy further flexibilities to compete in the global market. This circumstance reduces the state’s direct intervention and control over economic development. The principle of free market is at the center of the neoliberal market-oriented restructuring process. However, a moderate approach of reform and a harmonious relationship between market and non-market forces are adopted

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when the neoliberal shift spreads across the developing countries. An authoritarian state is the key element for the application of neoliberal restructuring in the developing countries without clear property rights, mature market mechanisms, and legal systems. Opposing the origins of neoliberal ideology, Brenner and Theodore (2002, p. 351) argued that neoliberalization is embedded in “path-dependent, contextually specific interactions between inherited regulatory landscapes and emergent marketoriented restructure projects.” In other words, the state in the developing countries is always involved in marketization, Hence, the national state remains absolutely pivotal in the neoliberal formation in the developing countries (Peck 2001). A different type of state-market relation in the Asian Newly Industrializing Countries (NICs)—Japan, South Korea, Thailand, Singapore, Taiwan, which is termed as the developmental state model. This model advocates effective state interventions in late-developing countries, in contrast to the neoliberalism that believes in nonintervention. Although market is the basic instrument in these countries, the state still strongly influences their economic development. The successes of the NICs are mainly based on national capitalisms, which integrate the market forces and other organizations to achieve rapid economic growth (Deyo 1992). In NICs, the state plays a strategic role in guiding the market economy and in taming the domestic and international market forces. The developmental states are skeptical about neoliberalism as well as the principals of the Washington Consensus and aim to intervene in the national economy. “Import substitution” or “export promotion” is designated to protect fledging domestic industries to raise their productivity and gain international competitiveness (White and Wade 1988). The developmental state model also emphasizes market share over profit and the improvement of infrastructure for business (Wade 1990). Developmental states have established a new relationship between the state and the market in which the regressive governments can utilize the different types of market forces to promote rapid economic growth (Xia 2000). Amsden (1990) mentioned that the rapid growth of East Asian countries is attributed less to the free markets than the states that have allowed effective subsidies to domestic productions. The developmental states attempt to restructure their economies by directly influencing investment decisions in targeted industries and funded research programs. The state’s role in Japan is “shared with the private sector, and both the public and private sectors have perfected the means to make the market work for development goals. This pattern has proved to be the most successful strategy of international development” (Johnson 1982, p. 8). Japan’s Ministry of International Trade and Industry has served as an architect of industrial policies. The Korean government has selected certain firms from particular strategic industries (e.g., automotive industry) to allocate subsidies and production volume according to the actual performance of firms and allow specific new firms to enter certain sectors (Kim and Lee 1980). In Singapore, state intervention has been focused on three strategic areas, namely, the labor market, taxation and fiscal incentives, and SOEs, which are central to a developing country in the early stage of economic development (Huff 1995). Market transition theories can interpret many former socialist economies in transformation, emphasizing the restructuring of power and privilege and the changing of

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the role of the state during marketization. Research on market transition societies has focused on trends in income inequality and issues concerning who gains and who loses during market reforms. Market transition theories highlight the “path dependent” process in the formation of post-socialist institutions (Nee and Cao 1999). The top level social stratum loses a part of the sources of power and privilege in the formation of post-socialist institution, and the “direct producers” who are actors at the bottom of the state socialist hierarchy will most likely benefit from power restructuring (Nee 1989). Market transition theories stipulate that market transition in these post-socialist countries leads to inequality. The state also becomes less powerful when the redistributive economy collapses. The expansion of market economy in post-socialist countries diminishes the significance of the state, but the state–market relation involved in the transformation is not a “zero-sum” game; the effects of the state and social structure are important in shaping post-socialist economy institutions (Nee and Cao 1999). Bian and Logan (1996) offered many arguments and emphasized the persistence of state power. The strategic position in state bureaucracy continues to give advantages to the powerful individuals and strata in market transactions. The former political cadres convert their political capital into market capital and make use of their political positions and powers of control on state resources. Hence, “power conversion” is key to understand market transition processes in these post-socialist countries (Szelenyi and Kostello 1996). The permanence and embeddedness of political-based privilege may offer both advantages and disadvantages to market transactions and shape the post-socialist institutions (Bian and Logan 1996). Nee and Cao (1999) argued that the unfairness and pre-existing political control can be overcome gradually because the accumulative change in the stratification order is entailed by market mechanisms.

4.2.2 Theorization of China’s State in Economic Transition Given the fact that the Chinese trajectory of economic transition is different from the experiences of other countries undergoing structural change away from socialism, considerable research efforts have been devoted to conceptualize the role of China’s state in the transition. The central question is whether the existing theories on neoliberalism, the developmental state model, and market transition theories can be applied to explain the role of China’s state in economic transition. The state’s role in China’s economic transition has controversial views. Some scholars have placed China’s transition in the theories of “convergence” with the West (neoliberalism), other East Asia countries (developmental state model), or Eastern Europe (market transition theories) (Sachs and Woo 2001, 1994; Woo 1999). These scholars have ascribed China’s rapid economic growth to its economic structure at the starting point of the reform, which is the high concentration of population in low-wage agriculture favorable for labor-intensive and export-oriented production. Most scholars have recognized that China’s marketization is moving along an experimental approach driven by a “third hand” (Huang 2012), and they attributed China’s rapid economic

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growth to the experimental, pragmatic, and incremental nature of the reform, which is known as “crossing the river by groping stones” (mozhe shitou guohe) (Rawski 1994; Lin 2012b; Naughton 1994, 2010; Nolan and Ash 1995). These researchers argued that no blueprint preexisted when China started its reform. By encouraging the “trial-by-error” experiments, the former socialist economy was allowed to “grow out of the plan” gradually by removing obstacles and avoiding the sudden collapse of the function and the authority of the former political systems, which had occurred in Eastern Europe and Russia (Jefferson 2008; Naughton 1996; Lin et al. 2003). Hence, China is transforming toward a unique system (e.g., the officially claimed “Socialist Market Economy”) than converging with other transitional economies or Western capitalist countries. The distinctions of China’s transition have therefore generated context-based and country-specific theorization and conceptualization. Central to China’s economic transition is the redefinition of the role of the state and the readjustment of the central–local government relationship and state–market relationship (Naughton 1996) that have significantly influenced economic restructuring and urban development. However, the state in China is far from a unitary entity but is characterized as “a mixture of conflict and collaboration” relationships between governments at different levels with market forces (Lin 2000, p. 476). Decentralization and marketization are salient processes of China’s transition in the past three decades. Under marketization, the market mechanism increases its importance in resource allocation in various fields and many types of non-state agents were born at the periphery of the socialist economy. Moreover, the state control has been relaxed in a gradual manner. The Chinese state has essentially externalized different types of markets, including labor, land, housing, and capital, by allowing the national economy to “grow out of the plan” (Yeh et al. 2015). The Chinese state remains to play an active role than completely retreats from national economy although numerous economic fields have been gradually exposed to the market forces and the state exerts less control over the economy in the marketization. Yao (2009) specified that China’s state has moved significantly toward the neoclassical economics doctrines by implementing a series of free-market policies (e.g., prudent fiscal policy, economic openness, privatization, market liberation, and the protection of private rights). However, Yao pointed out that CCP was forced to promote economic development and improve the living standards of citizens to seek “performance-based legitimacy.” As such, the central government played as a disinterested government to take a neutral stance and make correct policies. This disinterested government was only interested in economic growth. He and Wu (2009) observed the emergence of neoliberal urbanism in China and attributed the occurrence of neoliberalization in this country to multiple difficulties/crises and the desire for rapid development than to a deliberate design. The decentralization of decision-making power from the central government to local governments is regarded as the most important sign of neoliberal shift. These researchers also claimed that the local governments are of great importance in conducting neoliberal

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experiments and facilitating marketization. Neoliberalization in China has a responsive and resilient system to cope with the inherent contradictions and imbalances of neoliberalism. Local governments played an active role in China’s reform process as a result of the decentralization of the administrative and fiscal power (Walder 1995). China’s local governments have been granted great incentives and feasibilities to pursue local economic growth since the onset of economic reforms. Hence, local governments have “enduring power” to be responsible for the regional experiments and decision making on fiscal budget, finance, investment, and enterprise management (Wank 1999). The decentralized relationship between the central and local governments is considered a pillar of China’s successful transition, which is labeled as “marketpreserving federalism with Chinese characteristics” (Qian and Weingast 1997; Qian and Roland 1998; Montinola et al. 1995). The central issue of Chinese style “federalism” is a new political system that divides the authority between the central and local governments, in which the latter is primarily responsible for local economic matters within their jurisdictions. Two effects are derived from federalism: local (fiscal) competition and hard budget constraints of local government. Both outcomes provide the local governments with great incentives to promote their economic growth. Another wide-spread theory is the “local state corporatism” developed by Oi (1992, 1995) who assumed that such a corporatism represents the co-operative relationship between the local governments at three levels (i.e., county, township, and village) and the coordinated economic enterprises in their administrative territory (i.e., local enterprise, collectively owned firms, and private firms). The system acted as a business corporation with the same goal, that is, to promote local economic growth. Local officials within local corporations acted as the equivalent of a board of directors, with the top local official as the chief executive officer. Cheung (2008) regarded the relationship between the central and local governments as the relationship between “landlord and share tenancy” based on new institutional economies. Layer-by-layer responsibility contracts exist between these regimes; hence, the central state always has incentives to push local governments, resulting in rapid economic growth. Cheung highlighted that the county (xian) competition system was largely responsible for China’s successful economic reform. The landlord and share tenancy system enables a county to compete against another county to attract investors and increase financial revenue. Given the intensive competition, county governments do their best to improve their local circumstances for investors and are highly motivated to accomplish their responsibilities effectively. Zhu (1999) developed a relevant concept known as “local growth coalition,” adapted from the political economy concepts of “growth machine” (Logan and Molotch 2007; Molotch and Logan 1984) and “urban regime” (Stone 1993), to illustrate the relations of local governments, enterprises, and developers in the context of gradual urban land reforms. Owing to the ambiguous property rights of land and the informal institution of land development right, the local government-enterprise coalitions have significantly improved the urban land and property development, strengthened the central position of the local government, and nurtured local enterprises and developers (Zhu 2002, 2004). Zhang (2002) posited that regime theory

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rooted in Western advanced countries cannot be perfectly applied to explain such phenomena in a transition economy, such as China’s, for the political dimension. This researcher subsequently rendered the “socialist pro-growth coalition” and examined the case of Shanghai by offering several key features of urban regime with Chinese style with a strong local government leading cooperative nonpublic sectors and the absence of community organizations. “Entrepreneurial state”, another influential theoretical model, is elaborated to describe the role of state bureaucracy as a set of profit-seeking and risk-taking businessmen in the early period of China’s market-oriented economic reforms (Duckett 1998, 2001). Although state entrepreneurialism may induce a number of problems, including uneven or insufficient provision of public services, loss of state assets, and reduced government control over departmental finance, it is an effective solution to the politically difficult problem generated in market transition. In the case of China, entrepreneurial state is considered a pragmatic means through which market mechanisms can be introduced to the pre-existing bureaucrat system in the early stage of market-oriented reforms. The entrepreneurial nature of local governments has significantly persisted owing to their great power to dispose the urban land use rights (Shin 2009). However, Huang (2012) insisted that the profit-seeking Chinese state and their firms have been a major driving force for rapid economic growth. In China’s mixed economy, the state has overcome bureaucratic obstacles and enjoyed benefits in mobilizing resources, providing special subsidies and tax breaks, and bending or violating its own laws and regulations on labor and the environment. All these benefits can further make the enterprise profitable. A number of studies have compared the market-oriented reforms of China with those of other countries undergoing structural change away from socialism, such as Eastern Europe and Russia. Coase and Wang (2013) pointed out that China’s institutional foundation of post-reform was a legacy of a pre-reform governance structure. Different from the structure in the Soviet model, the local governments in China were authorized to supervise the SOEs within their administrative preview even in the pre-reform era. As such, these governments became the sole controller of the enterprises when the vertical line of command for SOEs was gradually eliminated by the privatization reform. This instance is the key reason why local governments can be considered responsible for China’s economic growth in the short period after the economic reforms. Lin (2011, 2012a) highlighted the role of China’s state, which has always played an important role in facilitating structural change and helping the private sector sustain it across time, to explain the reasons why China achieved a more successful economic development in transition than other former socialist countries. The “facilitating government” can benefit early industrialization because it provides information, compensate for externalities, and improve both “hard” and “soft” infrastructure according to the dynamic change in the economy’s comparative advantage. Lin argued that the effective government interventions with a “new structural economics” framework are important in promoting structural upgrading for the developing countries in the take-off stage. The role of the state has been regarded as an important perspective to understand the development and transformation in the Third World and in the advanced capitalist

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Table 4.1 Theories explaining the role of state in China’s economic reforms Theories

Contributor(s)

Level of government

Local state corporatism

Oi (1995)

Local governments

Local growth coalition

Zhu (1999)

Local governments

Entrepreneurial state

Duckett (1998, 2001)

Local governments

Share tenancy theory

Cheung (2008)

Central and local governments

State disarticulation

Naughton (1996, 2010)

Central and local governments

Chinese fiscal federalism

Qian and Roland (1998), Qian Central and local governments and Weingast (1997)

Neoliberal urbanism

He and Wu (2009)

Profit-making state firms

Huang (2012)

Local governments Local governments

Socialist pro-growth coalition Zhang (2002)

Local governments

Disinterested government

Yao (2009)

Central government

Facilitating government

Lin (2011)

Central government

Source Author’s summary

countries. The shift from Keynesianism to neoliberalism and the transition from planned economy to market economy involve the readjustment of the role of the state in the national economy. Neoliberalism, the developmental state model, and market transition theories are three most related theories that have yielded many important theoretical insights into the great transformations that occurred in different countries. However, these insights have theoretical and empirical limitations when directly applied to explain the role of the state in China’s transition. Numerous studies have looked into the role of the state and the state–market relations in the Chinese context, providing relevant insights into theorizing China’s economic transition. Table 4.1 summarizes the relevant theories explained in the preceding paragraphs. Several key points cited in the succeeding sentences can be summed up by synthesizing the existing theories and models. First, most scholars believe that the state plays a pro-active role in China’s economic transition although their views regarding the forms of government interventions vary. These scholars believe that the functions of China’s state cannot be understood in “hegemonic neoliberal discourse” (Huang 2012). None of the existing theories derived from the experiences of other countries can be perfectly utilized to explain China’s story. In this regard, a context-based country-specific theorization is needed, and political economy perspective should be centered in the theorization to further understand the complexity of China’s transition (Ma 2002). Second, the state has complicated and changing relations with different market forces in China. Existing theories have placed great emphasis on the importance of the state–market interplay in facilitating economic transition and growth. In other words, the Chinese state intervenes in the national economy in combination with the market forces. China’s market-oriented transition is characterized by a gradual shift from an

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internalized market in the pre-reform period to an externalized market in the postreform period (Yeh et al. 2015). Embedded in such a distinct state–market interplay, Chinese transition can be understood by probing into the complex relations between the state and the market in different periods, regions, and political hierarchies. Third, the role of China’s state always changes as economic reform proceeds. Reform on critical institutions can lead to the readjustment of the relationship between the state and the market. For example, the TSS reform in 1994 is a milestone that shifted the relations between the central and local governments. Hence, some theories can explain certain phenomena before 1994, including the “local state corporatism” and “local growth coalition,” but they cannot account for the stories that ensued after. However, previous interpretations have mostly considered China’s state as a whole (e.g., “disinteresting” and “facilitating” states) or have focused on local states in certain regions (e.g., “local state corporatism” and “profit-making state firms”). Although some scholars have looked into the central–local state relationship (e.g., “Chinese fiscal federalism” and “share tenancy” theories), the power relationships over cities remain underexplored. In this book, the study of urban system development involves cities at different administrative levels, regions, and development stages. The state’s role among the cities should not be regarded as a unitary item or a central–local binary relation. The state needs to be incorporated into a new conceptual framework to understand the dynamics of urban system development in China. The literature, however, is yet to develop a specific theory that can clearly explain the role of the state in China’s urban system development. Nonetheless, the aforementioned theories have provided foundations regarding the conceptualization of the state. The next section conceptualizes the Chinese state and theorizes it based on a perspective situating in the urban system considering the importance of the role of the state in explaining urban system development.

4.3 Conceptualizing the Role of the State in China’s Urban System Development Through the Political Hierarchy Perspective 4.3.1 The Political Hierarchy and Organization of State Power Among Chinese Cities In the pre-reform period, the socialist state can be largely regarded as a unitary power entity because local governments have little autonomy but can merely implement the plans of superior governments. However, state power has been gradually decentralized from the central government to local government because of the marketoriented reforms since 1978. As a unitary power entity, the state can implement a policy such as the national urban system policy through direct command and plans. Most existing literature considers the state as a unitary power and mainly focuses on the nationwide policies, such as the hukou system, and the promotion of TVEs,

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globalization, and so on. However, the state in China is far from a unitary entity but is characterized as a power matrix of governments at different levels in the postreform period. State regulation on urban system development cannot be implemented through direct commands and plans but in a way that is close to that in a market economy. To understand the changes of the ways in which the state regulates China urban system development, this book attempts to dissect the “black box” of the state by constructing a conceptual framework that focuses on the hierarchical organization of China’s state power among cities. There are three reasons for probing into the internal power relations of China’s state. First, the decentralization of the state power from the central government to local governments represents a change of the state of China from a single unitary power into a new power matrix in geographical space. Second, significant variations are observed in the government capacity among cities because reforms have empowered cities to extend their capacities unevenly. Third, cities are organized by the UAS which hierarchically differentiates and reorders the government capacities of cities at different administrative levels. The distinctive feature of China’s urban system is politically hierarchical, implying that Chinese cities at different political levels have different state power status. China’s political system imposed a rigid hierarchical distribution of state power into the urban system whose political hierarchy was largely inherited from the socialist planned economy of the post-reform period (1949–1978). Theories in political anthropology assert that the chief organizing principle of cities in planned economies is hierarchy, which strongly contradicts the network in economies that honor market principles (Leeds 1973). Since 1978, China has been attempting to reorient the relation among its cities at different administrative levels in the transition from plan to market. Reforms have granted urban governments with a variety of authorities by expanding their abilities to promote urban growth. Although these reforms have resulted in a shift of the state power downward to localities at different administrative levels, the intended conversion from hierarchy into network has not fully occurred because of the continuous administrative control of the political system (Solinger 1991). The decentralization of state power may not be simply considered a neoliberal shift but may be regarded as a reorganization of the state power among cities to achieve effective state interventions and give the right incentives to local government to spur economic growth. These endeavors can be achieved by developing a new conceptual framework to dissect the state and understand the redistribution of state powers among cities in the post-reform China. The perspective of political hierarchy can be adopted to understand the organization of state powers among Chinese cities in the post-reform period that is central to the new conceptual framework. Hierarchical authority structure is one of the core concepts in Weber’s ideal–typical bureaucracy that depicts the organization of bureaucratic officials or government agencies in a national society (Weber 1947). Weber’s concept of hierarchical authority structure can be illustrated on a large scale in the operation of an urban system, which is politically organized into a similar hierarchical structure. This perspective primarily argues that Chinese cities are organized into a hierarchical authority structure, and their positions in the hierarchy determine their urban government capacities, which are key to local growth. This conceptual

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framework can access and explain the effects of the state on complex urban growth process and the evolution of the entire urban system in China’s political economic context by considering the variations in urban government capacities among cities at different levels. A city is a political actor in the political system of China and is tied to other cities in a hierarchical manner that is subject to vertical bonds. The city at a particular political level acts on its own to a certain extent but is controlled by its immediate superior authority. The relationship between superiors and subordinates is asymmetrical. The superiors can require their subordinates to accomplish their intentions. In other words, the linkages between cities are primarily vertical and mediated by the political hierarchy. In the ideal typical market economy, cities are free to operate in accord with economic and geographical advantages given that their relationship is organized into networks of connections. Thus, this vertically constructed political hierarchy gives rise to the hierarchical distribution of state power among Chinese cities. The concept of state power in this book, which referred to Friedmann (1973), is defined as the abilities of governments to mobilize and allocate resources (i.e., man power, capital, and information) and intentionally structure the decision-field of others (e.g., to coordinate market forces by policies, rules, and commands). The high hierarchical level implies an important position of the city in the urban system because growth-inducing factors are likely to be diffused from top levels to low levels. Thus, the cities at high hierarchical level can mobilize the developmental resources and coordinate market forces to promote local growth. Although the market-oriented reforms implemented since 1978 have relaxed the vertical bonds of cities, the political hierarchy continues to limit the abilities of these cities to completely break free from the vertically structured state power relations. UAS, fiscal system, preferential policies system, and SOEs production system are four important institutional arrangements that can bind cities into a hierarchical structure. The first and maybe the most important institution is UAS, which lays the foundation for the hierarchical distribution of state power among cities. Chinese cities within the UAS are stratified into different administrative levels (i.e., province-, vice-province-, prefecture-, and county-levels), which correspond to the political system (Fig. 4.1). Each spatial unit (not only city) in the territory has an administrative level, and the subordinate city (e.g., county-level city) (lishu) administratively belongs to its superior unit or city. The administrative rank or level (dengji) determines the abilities of cities to obtain resources related to urban development because of the existence of the “state-bias” resource allocations (Chan and Zhao 2002). UAS can be regarded as a special type of scale that is key to the understanding of the tensions among spatial units (Ma 2005). Although China has restructured UAS by empowering the cities with further authority, the vertically structured administration relationships have not fundamentally changed. Resource allocation and power distribution remain conditional on the administrative levels of cities or other spatial units. Therefore, UAS continues to constitute the basic framework of the hierarchical organization of China’s urban system in the post-reform period and shape the other three institutions or systems.

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Fig. 4.1 Political hierarchy and its relationship with urban system

The second important institution is the fiscal system, which stems from China’s political system and enhances the hierarchical structure. Wong and Bird (2008) stated that China’s fiscal system exhibits a clear feature of “path dependency,” which implies that the fiscal institution depends on the then-current practice of the nation’s political system. The Chinese fiscal system has experienced two reforms since 1978: the change from a unitary system into a relatively decentralized arrangement (i.e., tax contracting between the central government and the provinces) in the early 1980s and the establishment of TSS (fenshuizhi) in 1994 (i.e., recentralization of the tax revenues). The Chinese fiscal system has shaped the relationships between governments at different levels, especially after the TSS reform was stipulated (Wong 2000). In the fiscal system, superior governments have the decisive power on the fiscal schemes of their direct subordinate governments. For example, the provinces specify revenue sharing with their subordinate prefectures, which set the rules with their counties. A significant vertical imbalance has been observed; subordinate governments have fewer financial resources and smaller fiscal power than their superior governments (Jia et al. 2014). The county-level governments, which are the second lowest level in the political hierarchy, have faced greatly expanded expenditure obligations and an increasingly downward trend of revenue. By contrast, high-level governments face an improved fiscal condition. Given that budget is an important policy instrument for urban governments to provide public goods and services and to support economic activities, the fiscal system contributes greatly to the hierarchical organization of state power among cities. The third institutional arrangement is the regional preferential policy system, which is an informal institution that played a critical role in shaping the spatial pattern of urban growth during the post-reform period. In this book, the regional preferential policy system refers to the economic policies that are unequally applied to all regions and are only limited to a few targeted regions. These policies are usually designated by the central government to achieve a certain strategical objective. After implementing the reforms and open door policy, the Chinese government has gradually dropped the socialist ideology of equal development and adopted an uneven development to

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a few regions to become “rich” and ahead of others (Wang and Hu 1999). The early regional preferential policies are related to the open door policy, including the SEZs, COCs, and various types of economic development zones (Démurger et al. 2002). Contrarily, the succeeding preferential policies go beyond the open door policy and include broad experimental policies and institutions that are tailored to a region’s specific conditions (e.g., “national comprehensive reform pilot new areas” labeled as “new special zone”). Given the de-regulation policies, these regional preferential policies provide further authority and freedom to local governments to undertake pilot experiments. Hence, preferential policies have inevitably enhanced the government capacities of cities, and the uneven distribution of preferential policies have resulted in an uneven pattern of government capacities. These regional preferential policies are selective to certain cities/regions with reference to political hierarchy and are granted to high-administrative cities or geographically advantageous cities. The cities granted with important preferential policies can upgrade their administrative level, such as in the case of Shenzhen (i.e., from county to vice-provincial-level city) and Shantou (i.e., from county to prefecture-level city). The fourth institutional arrangement is the SOEs production system, which is a unique system in China. SOEs in the pre-reform period played a dominating role in the national economy and provided a range of social services, education, medical care, and healthcare and retirement protection. Since the beginning of economic reforms and opening-up policies in 1978, the SOEs continuously maintained their crucial role in key and strategic sectors although they have undergone a long gradual and progressive transformation (Fan and Hope 2013). A spatially differentiated pattern of SOEs was observed in the post-reform China; some regions maintained a large proportion of their SOEs, whereas others experienced a significant decline in their SOEs (Hu 2015). The SOEs in China are under the administration of different levels of governments (i.e., central, provincial, or municipal governments). The SOEs owned by the superior government can establish sub-branches in the subordinate regions. The spatial distribution of the SOEs production system also exhibits a hierarchical structure correlated with UAS. For example, the SOEs owned by the central government are likely to establish the provincial headquarters in provincial capital cities. Similarly, province-owned SOEs usually build their headquarters in provincial capital cities and place sub-offices in different prefectures. SOEs provide tax revenue to governments and fulfill the plan and serve the purpose of the government by which they are under. Thus, the proportion and quality of SOEs of a city are one of the important factors in determining the government capacity of such city. The institutional arrangements discussed in the preceding paragraphs comprise the political hierarchy, which contributes to the hierarchical organization of state power among cities. Among these institutional arrangements, UAS lays the foundation for the politically hierarchical organization of cities. The other three institutional arrangements are correlated with UAS, and they restructure and shape this hierarchical organization. Hence, the stratified state power among cities leads to the spatial variations of government capacities in the urban system. Therefore, the effects of cities on urban development can be determined through their respective government capacities.

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4.3.2 Conceptual Framework The above conceptualization of the role of the state in China’s urban system development emphasizes the political hierarchical organization of Chinese cities. The perspective of political hierarchy attempts to dissect the “black box” of the state to understand the role of the state in urban system development. In post-reform China, state power has been decentralized to local governments in an uneven way, which resulted in a hierarchical organization of urban government capacities among cities. As such, the cities at different administrative levels have different government capacities to promote urban growth. A new conceptual framework based on the perspective of political hierarchy of cities is formulated to understand the role of the state in China’s urban system development (Fig. 4.2). In the conceptual framework, the central and local governments at different administrative levels are interconnected by four major institutional arrangements. This organizational structure of the state has resulted in a politically hierarchical organization of cities in China. The state power has been decentralized downward from the central government to the local governments, but in an uneven manner. Urban governments have stronger capacities in some cities than in other cities. Such variations in urban government capacities determine the differences of urban growth to a large extent, because government capacity is associated with the city’s ability to spur market development and urban growth. Urban system development can be examined by investigating the variations of urban growth in three dimensions, namely, spatial, hierarchical, and temporal. The empirical analyses in this book are based on this framework to provide its quantitative evidence.

4.4 Research Design 4.4.1 Hypotheses Based on the conceptual framework, three sets of hypotheses regarding the development of China’s urban system are proposed for this book. Hypothesis 1 (H1): China’s urban system development in the post-reform period exhibits several features, thereby distinguishing itself from that of Western advanced countries, and these distinctive features can largely be attributed to state interventions. Hypothesis 1a (H1a): The national urban system policy has achieved its goal in regulating the development of urban system in the post-reform period, leading to the under-development of large cities.

4.4 Research Design

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Fig. 4.2 Conceptual framework

Hypothesis 1b (H1b): The effects of the state on cities are unevenly distributed, which leads to the varying growth of cities in different regions and sizes, and the state can also conduct the market forces to achieve

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a certain degree of balanced development and attain the political objective of equality. Hypothesis 1c (H1c): The effects of the state have reduced the hierarchical urban development disparity which is induced by the market forces. Hypothesis 2 (H2): The size and growth of Chinese cities depend on urban government capacity, and the interplay of urban government capacity and market forces. Hypothesis 2a (H2a): Urban government capacity positively affect urban size. Hypothesis 2b (H2b): The interaction effects of urban government capacity and market forces are positively associated with urban size. Hypothesis 2c (H2c): Urban government capacity and market forces positively affect urban growth rate, but the effects vary in sizes, regions, and administrative levels. Hypothesis 3 (H3): The upgrading of urban administrative level has positive effects on urban size and growth, indicating that the “position” of a city in the Chinese political hierarchy is key to its development. Hypothesis 3a (H3a): Upgrading from a county-level to a prefecture-level city has “long-term” effects on urban growth, because it can fundamentally change the urban political power. Hypothesis 3b (H3b): Upgrading from a county to a countylevel city only provides “one-time” stimulation on urban growth, and a short-term fast growth is then obtained at the cost of slow growth and even stagnation in the next period.

4.4.2 Analytical Procedure To test the hypotheses formulated above, the empirical analyses of this book are organized according to a logical procedure as shown in Fig. 4.3. The empirical analyses

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Fig. 4.3 Analytical procedure of empirical studies in this book

include three logically-connected studies. The conceptual framework developed in this chapter provides theoretical explanations for the three empirical studies. The first study identifies the development patterns of China’s urban system and examines the effects of the national urban system policy on the development of urban system in the post-reform period. The second study establishes the micro-level dynamics mechanism which can estimate the effects of urban government capacity on China’s urban system development. The third study estimates the causal relationship between the administrative level upgrading and urban growth.

4.4.3 Research Methods This book combines qualitative analysis with quantitative and qualitative analysis. Three sets of methods are employed to test H1–H3, respectively. Several quantitative methods, including OLS regression, Kernel regression, Gibrat’s law, and Zipf’s law, are employed to analyze the urban growth processes and evolution of city-size distribution to test H1. A simulation method is then performed to model China’s urban system development under different development scenarios. The starting point of the simulation is the stochastic growth model. There are several hypotheses on the stochastic growth process, including a steady population growth, free labor mobility, bottom-up driving forces, constant returns-to-scale technology,

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and randomly distributed exogenous shocks (e.g., policy shocks, natural shocks and historical shocks) (Gabaix 1999). Seven scenarios ranging from the standard stochastic growth model to a deterministic growth model are proposed to model the urban system development processes without or with less state interventions. The extent to which the distinctive features of China’s urban system are attributed to state interventions can be explored by comparing the simulated urban system with the actual urban system in 2010. To test H2, a series of econometric models to estimate the effects of the Chinese state on urban system development. Principal component analysis is used to construct the variable of urban government capacity as a proxy for the ability of urban government to intervene in urban development. Based on this key explanatory variable, regression models are performed to estimate the relationship of the urban government capacity, urban size, and growth. Given the fact that the hypothesized causality between the independent variables (e.g., urban government capacity, market potential, economic structure, and FAI, as well as other control variables) and the dependent variables (urban size and urban growth ratio) may run in both directions, it can lead to the bias of the OLS estimation. Therefore, system GMM estimator is employed to estimate the coefficients of the models and to address endogeneity and heteroscedasticity. The system GMM estimator uses the lagged values of the differences and levels of endogenous variables as instruments to control for endogeneity. A quasi-experimental approach with propensity score matching and differencein-difference method, which is widely used to capture the dynamic effect of policy adoption, is employed to estimate the causal effect of administrative level upgrading on urban growth and to verify H3. This method offers a wide range of advantages in addressing the causal effect because it can match a control group and conduct a temporal comparison between the treated observations (the cities that adopted administrative upgrading) and the control group (the cities that had the largest likelihood to be upgraded but did not undergo upgrading). The details of the three sets of methods and models are explained in Sects. 5.3, 6.3 and 7.3 respectively.

4.4.4 Data The data used in this book are mainly based on various sets of statistical yearbooks released by the National Bureau of Statistical and the National Population Census (1953, 1982, 1990, 2000, 2010). Table 4.2 lists the main data sources employed in this book. All data sources are open to public. A database for China’s urban system studies is constructed based on these data sets. The overall data on China’s urban development are collected from this database, including urban size and urban growth rate, demographic characteristics, and a series of key economic and social indicators that represent various aspects of urban development. Necessary data cleaning has been made to ensure the reliability of our analysis. For example, we drop data which are obviously inconsistent, eliminate the outliers, test the validity before performing regression analysis, and introduce year dummy variables to control differences across time. As

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Table 4.2 Main data sources Data categories

Main data resources

Population census

1953 Population Census of China Results 1982 Population Census of China Results of Computer Tabulation Tabulations on the 1990 Population Census of the People’s Republic of China Tabulations on the 2000 Population Census of the People’s Republic of China Tabulations on the 2010 population census of the People’s Republic of China

Statistical yearbook

China City Statistic Yearbook, 1985 to 2011 China Compendium of Statistics, 1949–2008 China Statistic Yearbook for Regional Economy, 2001–2011 China County Statistical Yearbook, 2001–2011 China Urban Construction Statistical Yearbook, 2001–2011 China Statistic Yearbook, 1983–2011 Fifty Years of New China, 1999

Archive data

The Report on Adjusting the Criteria for the Designation of New Cities and the City-Administering-County, 1986 The Report on Adjusting the Criteria for the Designation of New Cities, 1993 The Notification of Reform on Prefecture System and Implementation of City-Governing-County Policy, 1982 The Notification of Verification and List of National Development Zones, 2006

shown in Chapter 3, we employ population censuses data to measure urban size and urban growth, because the population censuses report more reliable population of cities than that from the China City Statistical Yearbooks. The empirical analyses in this book are conducted based on this database which covers four years—1984, 1990, 2000, and 2010.1 It should be noted that all variables for cities with urban districts are the sum of all urban districts, which are the same as the measurement of urban size. The prefecturelevel city in China mainly refers to an area-type administrative unit that includes a set of counties, county-level cities, and urban districts. To reduce the influences of ambiguity, we exclude the prefecture-level cities’ subordinate counties and countylevel cities when various variables are measured. Thus, the urban districts are the taken as the “urbanized area” of prefecture-level cities.

1 The

population census data of 1953 and 1982 are used to analyze the nature of urban system development in this chapter. For the regression models in Chapter 5, I use the data of 1984, 1990, 2000 and 2010, because the earlies economic and social indicators are available since 1984.

Chapter 5

Identifying the Development Patterns of China’s Urban System: Effects of the National Urban System Policy

5.1 Introduction Owing to the economic reforms since 1978, China’s urban system has witnessed rapid development and dramatic restructuring. The state may maintain a quite strong role in the urban system development because China’s economy is inherently political (Ma 2002). As stated in Chapter 3, a national urban system policy has put forward to regulate the development of China’s urban system since the early 1980s. A series of institutional arrangements, such as the hukou system and the promotion of TVEs, have been implemented to control the scale of large cities and encourage the development of medium and small cities. This national urban system policy has exerted profound influences on China’s urban system development in the past three decades, giving rise to several distinctive features of China’s urban system that may be different from that in the pre-reform period and those of Western advanced countries and other developing countries. Therefore, the analysis of this chapter pay attention to the effects of the national urban system policy on China’s urban system development. Hypothesis 1 (H1a, H1b, and H1c) is tested in this chapter. This chapter attempts to provide provisional and exploratory answer to three interconnected questions. First, what are the development patterns of China’s urban system in the post-reform period? Second, has China’s national urban system policy achieved its goal of regulating the development of urban system in the post-reform period? Third, what would China’s urban system be if state regulation was reduced or largely eliminated since the beginning of economic reforms? Through investigations of these three questions, the effects of the state in China’s urban system development could be identified. The empirical analyses in this chapter include two parts. The first part will examine the development patterns of China’s urban system using the empirical regularities which are mainly drawn from the experiences of Western advanced countries. The growth process and evolution of the city-size distribution will be examine by the Gibrat’s law and Zipf’s law, respectively. This examination can identify the patterns and distinctive features of China’s urban system development in the post-reform period. The second part involves simulating the development © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J. Wang, The Role of the State in China’s Urban System Development, https://doi.org/10.1007/978-981-33-6362-5_5

97

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of China’s urban system in unregulated or less regulated environments. A series of models will be developed to simulate urban system development processes without or with less state interventions. By comparing the simulation results with actual urban system in 2010, this chapter can evaluate the effects of the state regulation on urban system development in China and identify to what extent the distinctive features of China’s urban system development attributed to the state regulation. Due to the nature of the complexity as well as the limitation of data collection, the objective of the simulation in this chapter is not to exactly predict the development trajectory of individual city. Instead, the main objective is to produce several simulated urban systems with no or less state interventions, and to compare these simulations with actually observed urban system. This chapter is organized into five parts. Section 5.2 will examine the growth process and evolution of the city-size distribution by using two empirical regularities—Gibrat’s law and Zipf’s law, respectively. Section 5.3 will introduce the basic issues for the simulation of urban system development, including the assumptions, models, scenarios, models, rules, and results. The fourth part is the analysis on the effects of the state on urban system development based on the comparisons of the simulated urban systems and the actual urban system in 2010. Finally, the chapter will conclude with a concise conclusion.

5.2 Examining the Urban Growth Processes and Evolution of City-Size Distribution in China 5.2.1 Growth Processes of Chinese Cities According to the stochastic growth theory (proportionate effect), the urban growth rate is expected to be independent on the size of city and follow a random walk with constant mean and common variance, which is known as Gibrat’s law (Gibrat 1931). This empirical regularity has been applied to examine the urban system development in many Western market economies, in which bottom-up market forces are the main organization power of urban growth (Eeckhout 2004; González-Val et al. 2014; González-Val 2010; Clark and Stabler 1991). By contrast, growth of Chinese cities are significantly influenced by the state intervention, state policies and institutions. With these distinctive features, growth of Chinese cities may not follow the stochastic growth process. This section will examine quantitatively whether Chinese city growth processes follow the stochastic growth process or not. Both the parametric regression and nonparametric estimation are employed to examine the relationship between urban growth rate and urban size with corresponding periods (1982–1990, 1990–2000, and 2000–2010). The number of cities involved in each analysis period is the number of base year. In other words, the newly designated cities during this corresponding period are not considered in the estimation. In addition, according to Eeckhout (2004), the estimates

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Table 5.1 Parameter regression of urban growth rate and urban size 1982–1990

1990–2000

2000–2010

1982–2010

α

1.77*** (0.16)

1.15*** (0.24)

0.25 (0.15)

2.45*** (0.31)

β

−0.12*** (0.013)

−0.051** (0.019)

0.002 (0.012)

−0.12*** (0.025)

N

220

434

628

1282

Note *** Significant at 0.01 level; ** Significant at 0.05 level; * Significant at 0.1 level

of growth rates are sensitive to the outliers. Moreover, these smallest cities may have much higher growth rates which may influence the estimate results of all cities. Thus, the cities within the 5 per cent of the sample and with an atypical high growth rates, which could be recognized as outliers, are eliminated. Finally, there are 220 cities in 1982–1990, 343 cities in 1990–2000, 628 cities in 2000–2010. The OLS estimation is used to fit the relation between urban growth rate and urban size. The regression could provide an overall relation between the growth rate and city size within a certain time period. The model is specified as below: gi,t→t+1 = α + βsi,t + εi

(5.1)

where, gi,t →t+1 refers to the growth rate of city i between year t and t + 1, that is gi,t →t+1 = ln (Pi,t+1 /Pi,t+1 Pi,t .Pi,t ), and si,t is the logarithm of the size of city i, that is si,t = log (Pi,t ) (Pi,t is the urban population size of city i at the year t), and β is the coefficient to be estimated. Based on this model, the relationships of urban growth rate and urban size of time within the periods of 1982–1990, 1990–2000, and 2000–2010 will be estimated. In addition, we also investigate the differences of the relationships for various quantiles of the city size. The hypothesis of proportionate growth is that the coefficient β has no significant difference with 0. According to the estimate results of all cities (Table 5.1), the urban growth rates of both 1982–1990 and 1990–2000 have significant negative relationship with urban size (the estimates of parameter β are negative and statistically significant). The evidence suggests that urban growth processes during 1982–1990 and 1990–2000 do not follow the stochastic growth because the smaller cities were growing faster than the larger cities during these periods. However, the estimated parameter β for the period of 2000–2010 does not has significant differences from zero, suggesting the urban growth is independent of the urban size (Table 5.1). This finding supports the fact that urban growth process during 2000–2010 follows the stochastic growth. In order to perform a long-term analysis, urban growth processes within these three periods are pooled together to test the Gibrat’s law. According to the column 4 of Table 5.1, there is a negative relationship between urban growth rate and urban size (the coefficient β = −0.12, which is statistically significant at 0.001 level). Moreover, the relationships of different quartiles within these three periods are estimated in Table 5.2. The results indicate that the negative relationships between growth rates

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Table 5.2 Parameter regression of growth rate and city sizes by different quartiles First quartile

Second quartile

Third quartile

Forth quartile

1982–1990

1990–2000

2000–2010

α

1.35 (1.37)

2.14 (1.44)

1.07 (0.90)

β

−0.80 (0.12)

−0.14 (0.13)

−0.065 (0.076)

N

55

109

157

α

1.28 (1.46)

3.59 (2.88)

−0.28 (2.04)

β

−0.80 (0.12)

−0.26 (0.24)

0.045 (0.17)

N

55

108

157

α

3.03** (1.20)

8.52*** (0.74)

−0.73 (1.55)

β

−0.22** (0.095)

−0.65*** (0.061)

0.074 (0.12)

N

55

109

157

α

−0.28 (0.61)

3.86** (0.48)

−0.43 (0.37)

β

0.028 (0.044)

−0.24*** (0.036)

0.053** (0.027)

N

55

108

157

Note *** Significant at 0.01 level; ** Significant at 0.05 level; * Significant at 0.1 level

and city size are significant in the large cities (third or fourth quartiles). Therefore, the cities at the upper-tail did not follow the Gibrat’s law. The large cities have smaller rates than that of the small cities. Next, the non-parametric analysis is employed to test the Gibrat’s law. Following the previously literature (Rozenfeld et al. 2008; Eeckhout 2004; González-Val et al. 2014; Ioannides and Overman 2003), a non-parameter using Kernel regressions are performed an estimation of the relationship between the growth and size. The basic idea of this technique is to find the non-linear relation between the urban growth rate and urban size and between the variance and urban size through estimating the conditional expectation of urban growth rate and its variance on urban size. The model specification is as follows:   gi,t→t+1 = m si,t + εi

(5.2)

where, gi,t →t+1 is the growth rate of city i between year t and t + 1, which is normalized (subtracting the mean and dividing by the standard deviation), and si,t is the logarithm of the size of city i of the initial year, that is, si,t = log (Pi,t ). Instead of arbitrarily assuming the functional form of m(s), this method will estimate m(s), denoted as m (s), as a local average value around the points, using kernel which is a broadly 

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101 

used continuous weighted function. To estimate m (s), the Nadaraya-Watson method is used to calculate and estimate for the growth rate as: N 

m (s) = i=0 N

K h (s − si )gi

i=0

K h (s − si )

(5.3)



where, Based on the calculated m (s), the variance of the growth rate gi is also estimated by the same method: N 2



σ (s) =

i=0



K h (s − si )(gi −m (s))2 N i=0 K h (s − si )

(5.4)

K h is the Gaussian form kernel, and the bandwidth follows the Silverman’s (1986) rule of thumb, an adaptive measurement of spread. Finally, the bootstrapped 95% confidence bands calculated from 500 random samples with replacement are used to estimate the amount of statistical error of the results (Efron and Tibshirani 1986). If the Gibrat’s law is perfectly fulfilled, the estimated growth rate and its variance will be a straight line on the zero and one, respectively. At least, the line of zero and one should fall within the 95% confidence bands. Two tests are performed to test the Gibrat’s law. First, the same as the parametric regression above, long-term analysis are performed by pooling all cities of the three consecutive periods. Second, urban growth rates of each period are tested individually in order to analyze the differences over time. Our results for the entire post-reform era are shown in Fig. 5.1 (the solid line is the estimated growth rate or its variance, and the dash lines are 95% confidence bands). Generally, the value of zero does not always fall within the confidence bands in term of the estimate of growth rate, suggesting that the hypothesis that growth rates being significantly different from zero should be rejected. Thus, it seems that China’s urban growth did not follow the Gibrat’s law. Especially, there are two crucial characteristics. First, there is a slight negative relationship between the urban growth rate and urban size. Overall, small cities grow faster than the medium and large cities, that is, the cities shows differentiated growth patterns by size. Second, the estimated variance of urban growth rate decreases clearly with the size of the city, and the value one does not fall within the 95% confidence bands. The variance is smaller for cities at both the upper-tail of the size distribution, but larger for cities at the lower-tail of the size distribution. The presence of the decrease relationship indicates the growth rates of small cities are more erratic or divergent. Testing for different periods enables us to examine the temporary tendency of urban growth patterns. Figure 5.2 shows the estimated results of the corresponding three time periods (1982–1990, 1990–2000, and 2000–2010). It indicates that there are differences in the three periods. For 1982–1990 and 1990–2000, the Gibrat’s law can be rejected clearly, because the estimated growth rates are significantly deviated from zero value, and its variances are also deviated from one. For 1982–1990, the estimated growth rates of cities at the lower-tail distribution are clearly higher than

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Fig. 5.1 Non-parametric estimates of the growth rate and its variance of China by different periods

that of cities at the upper-tail distribution. It indicates that the small cities grow faster than the larger cities during this period, which is consistent with the result of the parametric analysis. This growth pattern continued to be shown in the period of 1990–2000, but negative relationship between the estimated growth rate and city size are rather weak. This conclusion is also supported by the parametric analysis above. Not surprising, the estimate for the period of 2000–2010 may support the Gibrat’s law to some extent because the value zero almost falls into the confidence bands. But, the estimated growth rates of large cities are slightly higher than that of the small cities. The estimates of the variance of growth rates for the period of 1982–1990 and 2000–2010 significantly decrease with the size of the city, while the variances for 1990–2000 have an inverted-U relationship with city size. All the evidence suggests that Chinese urban growth process may not follow the Gibrat’s law, and small cities have a higher average growth rate than the large cities, and there are various growth patterns in different periods. To sum up, the results, obtained from both parametric and non-parametric estimates, show that the growth of Chinese cities during the whole post-reform period may be dependent of the initial size of the cities, and thus seems not follow the Gibrat’s. The urban growth should be fundamentally considered as a deterministic process instead of a stochastic process. In the entire post-reform period, the urban growth rate decreases with the urban size. Generally, the small cities grow faster than the large cities over the long time period (1982–2010). However, the negative relationship between growth rate and city size became weaker over time. For the

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Fig. 5.2 Non-parametric estimates of the growth rate and its variance of China by different periods

period of 2000–2010, the growth rate was independent of urban size according to the estimates. The negative relationship between urban growth rate and urban size indicates that the national system policy has achieved its goal of “strictly control the size of large cities, rationally develop medium and small cities”. Small cities grew faster than large cities in the post-reform period. The results show that the national urban system policy was influential in the 1980s and 1990s, but became less influential during 2000 and 2010 which is affected by the continuous economic reforms towards the market economy. The urban growth rate has a significant negative relationship with urban size (the estimates of parameter β are negative and statistically significant) in 1982–1990 and 1990–2000. However, the estimated parameter β for the period of 2000–2010 does not have significant differences from zero, suggesting a stochastic urban growth process in this period. The non-parametric estimation shows large cities have a larger growth rate than the medium and small cities although the difference is not significant in statistics. The growth of Chinese cities do not follow the stochastic process. In other words, the Gibrat’s law seems not to hold exactly true for Chinese cities. Factors associated with the state may be an important reason. The influence of the national urban

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system policy was so strong that can lead to divergent growth pattern of cities in a short term. However, the analyses reflect that there is a tendency of urban growth process evolving towards the Gibrat’s law, which may be largely attributed to the market-oriented reforms. The effect of the national urban system policy was weak since the late 1990s.

5.2.2 Evolution of City-Size Distribution City-size distribution within a sufficiently large region has been demonstrated to follow the power law, which is known as Zipf’s law (Zipf 1949), or rank-size rule in urban geography (Berry and Garrison 1958). This regularity is a useful tool to understand the evolution of urban system. A large number of empirical findings have indicated that city-size distributions in many Western advanced countries follow the Zipf distribution (Berry and Okulicz-Kozaryn 2012; Carroll 1982). However, China may present a challenge to this empirical regularity. There are debates on China’s city-size distribution in existing literature regarding whether Chinese cities follow Zipf’s law (Song and Zhang 2002; Anderson and Ge 2004; Chen and Zhou 2008; Ye and Xie 2012; Xu and Zhu 2009). This section will examine the China’s citysize distribution over the past six decades (the focus is the past three decades), and interpret the results in a qualitative manner that investigates the endogenous factors associated with the state, which may be responsible for the distinctive features of China’s urban system. If the product of the population of a city Pk multiplied by its rank k to the power of q equals the population of the largest city P1 , then we can state that the city-size distribution follows Zipf’s law. The general form of Zipf’s law is as follows: Pk = P1 k −q

(5.5)

where q refers to the Zipf exponent. If q = 1, it will become the pure form of Zipf’s law. If q < 1, then it implies that the large cities are smaller than what is expected in theory (underdeveloped), while the medium and small cities are larger than the predicted values (overdeveloped). By contrast, if q > 1, then it suggests that the large cities are overdeveloped, while the medium and small cities are underdeveloped. In theory, Zifp’s law could be considered as the “ultimate signature” of a self– organization mechanism of a “coherence” system with regard to finite resources (Cristelli et al. 2012). The rank-size pattern indicates a scaling relation and dynamic balance between the equity for parts and the efficient for the entire urban system (Chen 2012a). Therefore, based on analyzing the city-size distribution by Zipf’s law, the evolutionary trends can be figured out to explain the underlying driving forces. Figure 5.3 illustrates China’s city-size distribution since 1953 to 2010. Based on the rank-size patterns, we can see the evolution of the urban system. The city-size distributions do not exhibit straight lines as expected by Zipf’s law, though they have maintained a persistent shape over the years. This feature implies a deviation from

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Fig. 5.3 City–size distributions of cities in China, 1953–2010

Zipf’s law. A phenomenon of droopy tail is quite clear for the years of post-reform. The rank-size distributions of 1982 and 1990 are intertwined together especially for the medium and large cities, because urban growth during 1982 and 1990 were slow. For 2000 and 2010, it can see a parallel growth trend which is consistent with the result of examination on the growth processes. Next, Zipf’s law is used to examine the rank-size patterns. First, it is necessary to determine the scaling range which includes data points that exhibit scaling properties. Cities falling outside the scaling range are considered as outliers. Then, Zipf exponents are calculated by fitting the model to the data points in the scaling ranges (Fig. 5.4). Table 5.3 summarizes the Zipf coefficients, the goodness of fit, and the scaling range. In general, although China’s city-size distribution exhibits some properties of Zipf distribution, the empirical evidence also indicates that the city-size distribution of China deviates from the standard Zipf distribution. The first evidence is the Zipf coefficients were significantly smaller than the expected value 1, except for the 1953. The Zipf coefficient decreased from 1.004 in 1953 to 0.795 in 1982, suggesting the stagnation of large cities and relative growth of medium and small cities during the pre-reform period. Starting from 1982, the Zipf coefficient increased to 0.85 in 2010, but is still significantly smaller than the theoretically expected value of 1. It indicates that the large cities are under-development in China’s urban system, leading to a flat city-size distribution. Second, the scaling range is considerably narrow because many of the cities are significantly small to be included. The largest cities on the top and smallest cities on the bottom of the urban hierarchy are not included in the scaling ranges. For example, only 120 cities are included in the scaling range of 1982, and the goodness of fit is approximately 0.968. For 1990, the scaling range has been widened because of the continuous upgrading of counties to cities. For 2000 and 2010, the scaling ranges cover most of the designated cities (approximately 550 cities). The number is small for China with such a large population.

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Fig. 5.4 China’s city-size distributions and Zifp’s law: 1953, 1982, 1990, 2000 and 2010 Table 5.3 Results of fitting China’s city-size distributions by Zipf’s law Year

Rank–size distribution Zipf coefficient

R2

Total city number

Scaling range

1953

1.004

0.988

164

Rank 3 to 120

1982

0.795

0.968

238

Rank 3 to 120

1990

0.816

0.992

460

Rank 4 to 300

2000

0.805

0.992

666

Rank 7 to 550

2010

0.850

0.999

651

Rank 6 to 550

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The third evidence is that the data points are not aligned in a strict line, indicating imperfect curve fitting. Regarding the city-size distribution in 1953, although the Zipf coefficient was considerably close to 1, the data points on the log-log plot were not aligned in a line and there was no conspicuous scaling range to be present on the log–log plots. The city-size distribution of 1982 has similar feature with that of 1953, i.e., data points on the log-log plot were not aligned in a line and there was no conspicuous scaling range. Therefore, it is difficult to conclude that the city-size distributions of 1953 and 1982 follow the standard Zipf distribution. In summary, as a country with a vast territory, large population, and a long-term urban development history, China is unlikely to have a primate city-size distribution. However, due to the limitation of transportation system and state capacities, China was comprised of several macro-region-wide urban systems that were separately developed into physiographic sub-regions in the long pre-modern agrarian society (Skinner 1977). Even by the end of the Qing Dynasty, a well-integrated national urban system had not emerged. Since 1949, Chinese cities were gradually integrated into a nationally integrated system. However, the development of the urban system has been regulated by the state, in particular, the scale of large cities was strictly controlled owing to both the “anti-urbanism” strategy in the pre-reform period and the national urban system policy in the post-reform. As a result, the Zipf coefficient decreased from 1.004 in 1953 to 0.805 in 2000, suggesting the significant under-development of large cities. With the market-oriented reforms, the national urban system policy that attempts to control the scale of large cities became less strong. Thus, the Zipf coefficient increased from 0.805 in 2000 to 0.850 in 2010. The empirical results indicate the socialist “anti-urbanism” strategy and the national urban system policy has successfully controlled the scale of large cities, leading the city-size distribution of China’s urban system to deviate from the Zipf’s law.

5.2.3 Summary Close examination indicates that the growth of Chinese cities does not follow the stochastic growth process which is an empirical regularity derived from the Western advanced counties. The urban growth rate has a negative relationship with urban size, that is, small cities grew faster than large cities during the early period of reform. This is principally attributed to the national urban system policy which strictly controlled the development of large cities. For example, the control of hukou system is much stricter in the large and extra-large cities than in the small and medium cities. Moreover, although China’s city-size distribution exhibits some properties of Zipf distribution, the empirical evidence also indicates that city-size distribution of China deviates from the standard Zipf distribution. The Zipf coefficient of China’s city-size distribution is small, suggesting the under-development of large cities. The findings also suggest that the national urban system policy has achieved its goal of controlling the large cities. However, the control of large cities was less strong since the late 1990s with further marketization. In order to further identify the effects of

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the national urban system policy, the next section attempts to develop a series of models to simulate the development of China’s urban system with no or less state interventions.

5.3 Modelling China’s Urban System Development in the Unregulated Environments 5.3.1 Modelling Strategy and Assumptions As revisited in Chapter 2, there are two major growth theories regarding the longterm urban system development: the stochastic (random) growth and endogenous (deterministic) growth. The stochastic growth theories see urban growth as a random process in long period, that is, the mean city growth rates are independent on the sizes of cities (Gabaix 1999). On the other hand, the endogenous growth theories assume that the growth rates of cities dependents on advantages and disadvantages of cities which related to economic factors such as agglomeration economies and diseconomies, cumulative human capitals, technological advancements, and localized information spillovers (Eaton and Eckstein 1997; Black and Henderson 1999b). The endogenous growth theories may be better to capture the nature of urban growth in the modern economy. However, the model specification should be quite complex and the quality of data should be very high. Furthermore, the endogenous growth processes are context-based and vary greatly in different economies. By contrast, the stochastic growth theories reflect the natural or classical urban growth process in a free market economy, suggesting a common growth process of urban system development (Córdoba 2008). Therefore, the starting point of the simulation model in this chapter is the stochastic growth theories. Moreover, sequent modifications have improved the explanation power of the stochastic model by incorporating temporal autocorrelation into the model (Simon 1955a; Vining 1976; Ijiri and Simon 1967). The original purpose of these models is to study the development of business firms and their rank-size relationship, but these models are used and modified to investigate urban system by incorporating the spatial autocorrelation (Xu and Harriss 2010, 2014). As such, the stochastic growth model provides a framework which can better simulate the development of urban system in unregulated environments. As shown in Chapters 3 and 4, the present status of China’s urban system is still largely influenced by the state though market mechanism has been introduced in the economy in the post reform era. The basic idea of this chapter is that the differences between the urban system of 2010 and simulations that eliminate the state interventions can reflect the impacts of the state on urban system development (Fig. 5.5). Thus, in order to figure out the influences of the state, we attempt to development several models to simulate China’s urban system development driven

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Fig. 5.5 Illustration of how to identify the impacts of the state based on the differences between urban system of 2010 and simulation results

by other major forces such as the market and geographical factors, and then we can analyze the differences between the urban system of 2010 and the simulation results. In order to develop models that are comparable with the actual urban system in 2010 based on the available data, four assumptions are made for the simulation models: Assumption 1: state forces are largely exogenous to the stochastic urban growth process. There are several restrictive hypotheses of the stochastic growth process, including a steady population growth, free labor mobility, bottom-up driving forces, constant returns-to-scale technology, randomly distributed exogenous shocks (e.g., policy shocks, natural shocks and historical shocks) (Gabaix 1999). These hypotheses imply that development of urban system following stochastic process is primarily driven by bottom-up market forces with less endogenous influences of the state. Empirical studies have also demonstrated that urban systems development in many market economies are proved to follow the stochastic growth process, such as the USA (Glaeser et al. 1995), France and Japan (Eaton and Eckstein 1997), Spain and Italy (González-Val et al. 2014), and Canada (Clark and Stabler 1991). Assumption 2: spatial and temporal autocorrelation is assumed to exist in the process of China’s urban system development.

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One of the major limitations of the stochastic growth theories is that they seldom take into account the influence of spatial and temporal correlation on urban growth (Sheppard 1982). Vining (1976) and Xu and Harriss’s (2010) models provide mechanisms to integrate the stochastic growth process with spatial and temporal autocorrelation in growth rates, and their empirical studies demonstrate that introduction of spatial and temporal autocorrelation can be better to fit the development of urban system in market economies. This suggests that the spatially and temporally autocorrelated growth process can contribute to capture some critical effects of market forces (e.g., agglomeration and spillover effects). Accordingly, spatial and temporal autocorrelation will be introduced to the model in this chapter. Assumption 3: the number of cities and the city entry process is assumed to follow the real developing trajectory of China’s urban system. The number of cities increases over time, because of forming new cities in different years. But the new city entry process has been a persistent challenge for urban system development simulation. In the existing studies, scholars have modified Gibrat’s model to simulate the entry process of new cities with probabilistic models (see for example, Simon (1955a), Vining (1976), and Xu and Harriss (2010), but their results are less than satisfactory. Moreover, new city formation is rather than a random process but regulated by the state (Li 2011). Therefore, I adopt the real city entry process and focus on modelling their variations in growing. As such, the simulated results of urban system have the same number of cities in 2010. Assumption 4: the global growth rate of the whole system of cities is assumed to remain roughly the same as the real developing trajectory, and the local growth rates of individual cities are modeled as functions of other determinants. It is obviously true that urban growth rates would be quite different from the real growth rates if there is no or less state interventions during the past three decades. But, on the one hand, it is nearly impossible to precisely predict the growth of cities without state interventions. On the other hand, this assumption can ensure the comparability of the simulated results with the real urban system in 2010.

5.3.2 Models and Scenarios Based on the stochastic growth theories, this chapter will generate a series of models by incorporating spatial-temporal autocorrelation, geographical factors and market forces into the standard stochastic growth model successively. In the standard stochastic growth model, the size of cities changes from year to year is based on the following process: Pi(t+1) = γit Pit

(5.6)

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111

where Pi(t+1) and Pit denote the size of city i at time t and t + 1, and γ it is the random growth rate which follows a random distribution of π (γ). If the distribution is independent of city size and has a same mean and variance, the growth process is regarded as a stochastic process following Gibrat’s law. This process will lead to the asymptotic power distribution. The stochastic process represents ideal natural growth but has some limits. Therefore, following Vining (1976), we decompose the growth rate into two factors: a global growth factor that affects equally all cities in the nation, ρ, ¯ and a local growth factor applicable to city i only, ρ it : ¯ it γit = ρρ

(5.7)

Moreover, Vining (1976) has also integrated the temporal autocorrelation of urban growth rates with the stochastic growth process. Thus, the local growth factor, ρit , in time t is the product of the growth rate of the same city in the (t-1) period and a random factor, Eit , which is independently and identically distributed random number with mean 1 and variance σ 2 . The local growth factor can be expressed as: α Pi (t) ρit = εit ρi(t−1)

(5.8)

where α is constant ranging from 0 to 1, used to control the degree of influence of temporal autocorrelation. Thus: α ¯ it ρi(t−1) Pi (t) Pi (t + 1) = ρε

(5.9)

when α = 0, this model become the stochastic model. If α > 0, growth rates are positively correlated over time, but this model is still consistent with the Gibrat’s law because growth rates are not associated with current city sizes. Furthermore, Xu and Harriss (2010) introduced the spatial correlation into the model. That is:  exp(−di j D)  , j ∈ Vi (D)  j exp(−di j D)

 ρit =

j ρ jt

(5.10)

where Vi (D) denotes a set of cities that are within the threshold distance D of city i, and di j is the distance between city i and j. The threshold D defines a range within which cities can affect the growth rate of city i. Including the spatial autocorrelation is based on the assumption that cities with higher growth rates are more likely to influence the growth rates of their nearby cities. All these three inter-connected models (standard stochastic model, temporal autocorrelated model, and spatial-temporal autocorrelated model) can be considered as generic stochastic growth model. In order to simulate urban systems development with no or less state interventions, we still needs to incorporate the market forces and geographical factors into the model. Therefore, two weight functions are integrated with the spatial-temporal autocorrelated model. It is assumes that the local growth factor of city i at time t is not only affected by local growth factor at time t-1, but

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also related to market forces and geographical factors. So, the Eq. 5.8 is modified as:  α ρit = εit ρi(t−1) m i(t−1)

(5.11)

 α ρit = εit ρi(t−1) gi(t−1)

(5.12)

where mi(t-1) and gi(t-1) represents the influence of market forces and geographical factors, respectively. Including the two factors implies that the influence of growth rate at time t-1 on growth rate at time t is weighted by the relative strength of market forces and geographical factors. By incorporating these two factors, the models can simulate China’s urban system development under environments dominated by the market forces and geographical factors. In the simulation models, the market weight mi(t-1) is calculated based on the market potential (MP) of each city. Market potential is a widely used concept in the New Economic Geography and Spatial Economics models, measuring the location’s access to markets—as opposed to its fixed geographical characteristics—characterizing the forces that contribute to the geographic concentration of economic activity (Hanson 2005; Fujita et al. 1999). Population and economic activities tend to concentrate into the locations with high market potential, which would be consistent with the home-market effects by Krugman (1980). For empirical measurement, this chapter adopts the same adjustments of Da Mata et al. (2007). Thus, the market potential is defined as: M Pi (t)=

n  y j (t) × POP j (t) τi, j j=1

(5.13)

where, M Pi (t) refers to the market potential of city/county i in year t, y j (t) and P O P j (t) are the per capita GDP and population of city/county j in year t, respectively.1 Also, τi, j represents the transport cost between city/county i and j, and its σ −1  , and di, j is the Euclidean distance between city/county i form is τi, j = A•di, j the distance of own city (di,i ) is the average distance to city center, which and j,2 and  is set as 23 • 2 ar ea/π , and σ is the distance decay parameter, where σ here is assumed σ −1  for the smallest land area to be 2, and the value of A is that make τi, j = A•di, j city (the values of parameters refer to Au and Henderson (2006a). We calculate the market potential of 1984, 1990, and 2000 to represent the market weights for cities in 1980s, 1990s, and 2000s, respectively. 1 In

the study of Da Mata et al. (2007), they use the per capita income. Given the fact that the data of per capita income were not available in early years, this chapter uses per capita GDP instead. When calculating the market potential, all cities and counties are taken into account. But only the market potential of cities are used in the following regression models. 2 A threshold distance need to determine firstly, this chapter set the threshold as 250 km firstly. Then, I test the robustness by selecting alternative threshold values (e.g. 150, 500 et al.), and the results show their no significant differences of the selection of threshold values.

5.3 Modelling China’s Urban System Development …

113

And, the geographical weight gi(t−1) is measured by the distance of each city to nearest one of the three national central cities—Beijing, Shanghai, and Guangzhou, and the regional central cities—provincial capitals.3 The distance is calculated as the Euclidean distance between the geometric centers of two cities. The assumption is that the national central and regional central cities occupy the central locations in the national economic geography. Thus, the distance to these centers actually reflects the access to the economic geographical centers and the weights of the distance represent the decay of the economic geographical forces. Both the market potential and distance are transformed into weights with mean equals 1. They are first transformed to logarithm, and then standardized and centered with a mean of 1.4 Figure 5.6 represents the relationship between the weight and market potential of cities in 1980s, 1990s, and 2000s. The standard deviations of these three time periods are 0.095, 0.077, and 0.078. Figure 5.7 plots the relationship between the weight and distance. The standard deviations of the two types of weights are 0.11 and 0.18, respectively. The time period of the simulation is from 1984 to 2010, covering about three decades in the post-reform period.5 The simulation is conducted by yearly iteration. The number of cities increases from 291 in 1984 to 651 in 2010. As stated in assumption 4, a new city is added in the simulated urban system according to the year when it officially obtains the city status. The average annual urban growth rate for all cities during the study period is 1.054 per year, which is used as the global growth factor ρ¯ to control macro annual growth rate. Then, the average annual growth rates for each cities in three sub-periods, that is, 1984–1990, 1991–2000, and 2001–2010, are calculated and set as the initial growth rates for cities used in the first iteration (i.e., if a new city is established in during 1991 and 2000, its initial growth rate is set as the average annual growth rate in the sub-period of 1991–2000). Further, the initial size of a city is defined based on its real urban population size of the entry year (for the cities that have already established in 1984, the initial city sizes are the urban population sizes of 1984). At the start of each iteration (except for the first iteration),

3 The

National Central City is a concept proposed by the Ministry of Housing and Urban-Rural Development of the People’s Republic of China in the “National Urban System Plan” in 2005. Five cities are designated as the National Central City—Beijing, Tianjin, Shanghai, Guangzhou, and Chongqing. In the simulation, we only employ three of them—Beijing, Shanghai and Guangzhou. Tianjin is exclude because it is very close to Beijing. Chongqing is excluded because we mainly consider the locational advantages of the coastal region and Chongqing is relative late developing city compared with other cities. For the convenience and consistence, we consider the provincial capital as the central city of each province. 4 There are different standardization techniques in statistics, and we use the so called “0-1 scaling”— the standardized value of distance x is calculated as (x − min x)/(max x − min x). Then, the standardized values are centered by subtracting the mean, so the distance values become the weights with a mean of 1. 5 The beginning of the economic reforms is 1978, and the analysis in previous part of urban system in 1980s is mainly based on the Third Population Census in 198 s, but the earlies year when the National Statistical Bureau published the urban social and economic statistical data is 1984. Therefore, the starting time of the simulation in this chapter is 1984.

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Fig. 5.6 Weight of cities plotted against the market potential of cities (a) 1980s; (b) 1990s; and (c) 2000

Fig. 5.7 Weight of cities plotted against the distance to (a) the nearest national central city; and (b) its provincial capital

the model calculates the growth rate for each city, and then calculates the size of the next year. A set of parameters of the model also needs to be designated exogenously. The first parameter is α which represents the degrees of temporal autocorrelation (in Eq. 5.8). The larger the component, the stronger the temporal autocorrelation. Xu and Harriss (2010) have argued that α = 0.1 can generates the best consistent results of the temporal autocorrelated model and spatial-temporal autocorrelated model.

5.3 Modelling China’s Urban System Development …

115

Therefore, the exponent α is set as 0.1 in this chapter, and robustness check will provided by using alternative values. The second parameter is the distance threshold for spatial autocorrelation D in Eq. 5.10. This threshold value is set as 250 km that is same as the distance threshold used to calculate the market potential. Third, the random number εit with mean 1 and variance σ 2 is introduce to generate a random disturbance for urban growth (in Eq. 5.8). Given the actual standard deviation of annual growth rates during 1984 and 2010 is about 0.03, the parameter σ in the simulation is set as 0.03 which is consistent with the real growth process. As such, a series of models have been proposed to simulate the development of China’s urban system under different scenarios. The multiple scenario analysis generate a series of simulated urban system that used to compare with the actual urban system in 2010. To measure the differences between the simulated and the actual urban systems, this chapter can identify how have the state influenced the urban system development during the past three decades. Figure 5.8 shows the models and corresponding scenarios of the simulation in this chapter. The seven models are logically connected and range from the standard stochastic growth process to more deterministic growth processes. Scenario 1 is the standard stochastic growth model (SGM), and scenario 2 is adopted from Xu and Harriss model which incorporates spatial and temporal autocorrelation (STAM). Starting from scenario 3, geographical

Fig. 5.8 Scenarios and models of simulating urban system development

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5 Identifying the Development Patterns of China’s Urban System …

Fig. 5.9 Seven sub-regions of China

factors and market forces are integrated with the STAM. There are types of geographical weighted functions—weighted by distance to the nearest one of the three national central cities (GWM1) and distance to the provincial capitals (GWM2)—which represent different growth diffusion structures. In scenario 5, only market forces are considered as the weight for the growth of cities, which is the market weighted model (MWM). By incorporating both the market and geographical factors, it therefore generates two models and corresponding scenarios (GMWM1 and GMWM2).

5.3.3 Results The simulation runs for 100 times for each scenario because there is a random number εit in the model. Table 5.4 reports the total population and number of different sizes of cities of the actual urban system of 2010 and the seven simulated urban systems.6 The total urban population of all simulated urban systems are larger than that of the actual urban system of 2010 which is 475 million. This evident suggests that the urbanization level of China is lower than it should be in a free market economy. There are four cities above 10 million in the actual urban system of 2010, but this number 6 In

the analysis of this chapter, we employ the new criteria of city sizes classification that were promulgated by the State Council in 2014. In the new criteria, cities are classified into seven categories, namely, super-large cities (larger than 10 million), extra-large cities (5 million–10 million), type–I large cities (3 million–5 million), type–II large cities (1 million–3 million), medium cities (500,000–1 million), type–I small cities (200,000–500,000), and type–II small cities (smaller than 200,000).

5.3 Modelling China’s Urban System Development …

117

Table 5.4 Total population and number of cities in different sizes of simulated results and urban system of 2010 2010

S. 1

S. 2

S. 3

S. 4

S. 5

S. 6

S. 7

Total population

475

537

554

594

573

588

639

601

> 10,000,000

4

5

6

6

7

6

7

8

500,000–10,000,00

10

9

9

10

10

10

11

9

3,000,000–5,000,00

12

19

19

17

17

19

16

20

1,000,000–3,000,00

56

79

79

79

82

84

83

77

500,000–1,000,000

136

124

131

120

113

124

125

123

200,000–500,000

323

290

286

293

293

288

266

278

< 200,000

110

125

121

126

129

120

143

136

Total number of cities 651

651

651

651

651

651

651

651

ranges from five to eight in the seven scenarios. Furthermore, there are more extralarge cities (above 1 million) in the simulated urban systems than in the actual urban system of 2010. Meanwhile, there are also more small cities (below 200,000) in the simulated urban systems than that in the actual urban system of 2010. The number of medium-sized cities (200,000 to 500,000) in the actual urban system of 2010 is larger than the number of the simulated urban systems. It implies that some of the medium-sized cities may have potentials to become larger if cities are developing in the market economy. Models incorporating both the geographical factors and market forces (scenario 6 and 7) generate more extra-large cities and small cities than the stochastic growth models (scenario 1–3) and models incorporating only the geographical factors (scenario 4 and 5). These findings suggest that the urban growth processes with no or less state interventions suppress the growth of small cities, and enhance the growth of large cities. The gaps between large and small cities of Chinese cities will be larger if the state interventions were completely eliminated since the beginning of the economic reforms in 1978. The simulated urban systems are plotted in the map to show their geographical distributions (Fig. 5.10). In general, simulated sizes of cities located in interior region are larger than that of the actual sizes of cities in 2010, while simulated sizes of the coastal cities are smaller than that of the actual sizes in 2010. For analytical purpose, this chapter calculates the population proportions of the simulated and actual urban systems in the seven sub-regions—Northeast China, North China, Northwest China, East China, Central China, South China, and Southwest China, a spatial division which has introduced in Section 3.2.4 (Fig. 5.9). According to Table 5.5, the actual population proportions of Northeast China and North China in 2010 are 11% and 13.2%, decreasing from 18.5% and 19.8% in 1984, respectively. But the simulated population proportions of these two sub-regions are significantly higher than the proportions of the actual urban system in 2010. Conversely, the population proportions of East China and South China, two rapid growth sub-regions, are 33.2% and 15.7% in 2010, increasing from 26.6% and 6.8% in 1984. But the simulated population proportions of these two sub-regions are significantly smaller than those of

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5 Identifying the Development Patterns of China’s Urban System …

Fig. 5.10 Spatial distributions of simulated urban systems and the actual urban system in 2010

5.3 Modelling China’s Urban System Development …

119

Table 5.5 Proportions of cities located in different regions of the simulated results and urban system of 2010 1984 2010 S. 1

S. 2

S. 3

S. 4

S. 5

S. 6

S. 7

North China

18.5

13.2

15.9

16.6

20.5

16.6

17.2

21.3

17.4

Northeast China

19.8

11.0

20.3

19.5

16.2

18.1

18.3

14.6

17.1

East China

26.6

33.2

27.5

27.8

32.0

27.7

30.9

35.2

30.8

Central China

12.4

12.3

12.7

12.7

11.0

12.8

12.8

10.7

12.8

Southwest China

8.7

9.2

8.4

8.5

5.9

9.0

7.7

5.3

8.0

Northwest China

7.2

5.4

6.7

6.5

4.5

7.6

4.9

3.4

5.7

South China All cities

6.8 100

15.7 100

8.5 100

8.4 100

9.9 100

8.2 100

8.2 100

9.5 100

8.2 100

the actual urban system in 2010. Moreover, the simulated urban systems also have smaller population proportions in two interior regions—Southwest China and Northwest China. Therefore, it is clear that there are differences of the spatial distribution between the actual urban system in 2010 and simulated urban systems. Cities in the East China and South China are growing very quickly mainly because the national policies favor these two regions. But the simulated urban systems driven by market forces and geographical factors may not be able to model the policies-oriented trend. On the other hand, analyzing the regional differences between simulations and actual urban system can identify the impacts of the state, which will be scrutinized in next section. Table 5.6 shows the top ten cities of the actual urban system in 2010 and simulated urban systems of the seven models. Shanghai and Beijing are the first and second largest cities in all simulated urban systems that is consistent with the actual urban system in 2010. However, the estimated population sizes of Shanghai and Beijing are significantly larger than the actual sizes, which implies these two super-large cities may be smaller than they should be in a free market economy. Tianjin is the third largest city in the simulated urban systems, but it is only the sixth largest city in the urban system of 2010. Shenyang is another city which is significantly small in the simulated results. In five of the simulated urban systems, Shenyang is the fourth largest city, but it is not in the top ten cities in the actual urban system in 2010. Tianjin and Shenyang are two traditional heavy industrial cities in the prereform period. But their relative importance has decreased in the post-reform period. Especially, Shenyang and cities in the Northeast China region have suffered loss of population. In contrast, none of the scenarios can simulate the dramatic grow up of Shenzhen, which is the result of the SEZs policy. Chongqing, Guangzhou, Chengdu, Wuhan and Nanjing are long regional central cities and more or less occupy the positions of the simulated urban system. It might note that Chongqing is the third largest city in 2010 which is largely attributed to the upgrading of its political position (Chongqing became the fourth centrally administered city in 1997). In the models which seldom consider the impacts of the state, Chongqing’s relative importance has been actually under-estimated. Based on the comparisons above, the differences

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5 Identifying the Development Patterns of China’s Urban System …

Table 5.6 Population of top ten cities of 2010 and simulated results (in million) Rank

City

2010

City

S.1

City

1

Shanghai

20.2

Shanghai

24.5

Shanghai

S. 2

2

Beijing

16.4

Beijing

19.1

Beijing

22.2

Beijing

35.9

3

Chongqing

10.8

Tianjin

16.8

Tianjin

18.6

Tianjin

26.5

4

Shenzhen

10.4

Shenyang

12.9

Shenyang

12.4

Guangzhou

16.1

5

Guangzhou

9.7

Wuhan

10.9

Wuhan

11.6

Shenyang

11.8

6

Tianjin

9.6

Guangzhou

9.7

Guangzhou

10.3

Wuhan

10.5

7

Wuhan

7.5

Haerbin

8.8

Haerbin

8.6

Nanjing

9.7

8

Dongguan

7.3

Chongqing

7.6

Chongqing

8.3

Haerbin

6.9

9

Foshan

6.8

Nanjing

7.0

Nanjing

7.4

Chongqing

6.1

10

Chengdu

6.3

Xi’an

6.3

Xi’an

6.8

Taiyuan

6.0

Rank

City

S. 4

City

S. 5

City

City

S. 7

1

Shanghai

30.0

Shanghai

34.0

Shanghai

53.1

Shanghai

38.1

2

Beijing

24.1

Beijing

26.5

Beijing

42.8

Beijing

28.4

3

Tianjin

18.9

Tianjin

22.2

Tianjin

31.5

Tianjin

22.2

4

Shenyang

13.7

Shenyang

13.3

Guangzhou

17.8

Shenyang

14.3

5

Wuhan

13.3

Wuhan

12.6

Shenyang

12.2

Guangzhou

14.2

6

Guangzhou

12.4

Guangzhou

11.8

Nanjing

12.1

Wuhan

14.0

7

Haerbin

10.2

Nanjing

9.4

Wuhan

8

Chongqing

8.6

Haerbin

8.6

Haerbin

9

Nanjing

8.6

Chongqing

8.6

10

Xi’an

8.1

Xi’an

6.6

25.9

S. 6

1019

City Shanghai

S.3 40.7

Nanjing

10.6

6.9

Haerbin

10.5

Tangshan

6.8

Chongqing

8.6

Ji’nan

6.5

Chengdu

7.6

between the simulated urban systems and the actual urban system in 2010 can reflect the impacts of policies and state interventions. Moreover, differences also exist among the seven scenarios reflecting various development trajectories of urban system. The stochastic growth model and spatialtemporal autocorrelated model show an gradual evolutionary trend of reconstructing the rank-size distribution, but exhibit the path-dependence of the initial conditions of urban system as well (scenario 1 and 2). Thus the simulations of scenario 1 and 2 are mostly influenced by the initial conditions. Introducing geographical factors gives rise to increase of regional disparity of population distribution. If using the distances to provincial capitals as weights, these high administrative level cities will become much larger (scenario 4). When using the distances to the nearest national central city, coastal cities will grow faster (scenario 3). The market weighted model can simulate unbalanced growth of cities in different sizes, shown as the fast growth of large cities (scenario 5). When incorporating both geographical factors and market forces, both the disparities between regions and city sizes will be further enlarged (scenario 6 and 7).

5.4 Analysis on the State’s Impacts

121

5.4 Analysis on the State’s Impacts This section aims to identify the state’ impacts on China’s urban system through comparing the differences between the simulated urban systems and the actual urban system of 2010. The basic logic is that the differences between the actual urban system of 2010 and simulated urban systems that eliminate the state interventions will be able to reflect the outcome of the state’s influences on urban system (Fig. 5.5). The seven models in this chapter are performed to simulate the developing processes that are primarily driven by market or/and geographical factors. Comparison of the actual urban system of 2010 with these simulated urban systems can reveal the impacts of the state on the development of urban system in the post-reform period. First, the national urban system policy has achieved its goal of “strictly control the scale of large cities” and “rationally develop medium and small cities”. With such a policy, large cities are significantly under-developed. Figure 5.11 shows the simulated urban systems have a larger proportion of cities above 10 million than that of the actual urban system in 2010. The actual urban system in 2010 has a larger proportion of cities with a population between 3 and 10 million than that of the simulated urban systems, indicating that the state regulation has limited these cities to grow larger. In other words, China can have more super-large cities (larger than 10 million) if there is no such a national urban system policy. Meanwhile, the actual urban system in 2010 has larger proportions of the medium and small cities (smaller than 1 million) than that of the simulated urban systems. This reveals that the national urban system policy has achieved its goal of developing the medium and small cities. Similarly, Fig. 5.12 shows the city number of different sizes between the simulated urban systems and the actual urban system in 2010. It indicates the similar pattern that state regulation has limited the emergence of cities with a population larger than 1 million. With the national urban system policy, there are more cities with a population between 200,000 and 1,000,000 that are medium cities as encouraged by the state. Notably, there will be more small cities if the urban system development is mainly driven by market forces. These cities may have no competitiveness in a free market economy, and some of these cities are promoted by the state owing to the national urban system policy that aims to develop the small cities. Furthermore, as listed in Table 5.6, Shanghai has the potential to have a urban population larger than 30 million and Beijing and Tianjin may be able to become cities with a population about 25 million if the national urban system policy does not control the large cities. Several cities such as Chongqing, Wuhan, Guangzhou and Nanjing have potential to become super-large cities with a population larger than 1 million if market forces are allowed to dominate. Therefore, it is clear that the national urban system policy has achieved its goal to a large extent. Second, regional variations in urban population between the actual urban system in 2010 and the simulated urban population can reflect the spatial variations of the impacts of the state on urban system development. As mentioned in Chapter 3, cities located in the coastal region have experienced faster growth than their interior counterparts since the beginning of the economic reform. Such an unbalanced

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5 Identifying the Development Patterns of China’s Urban System …

Fig. 5.11 Comparison of simulated urban systems with actual urban system in 2010 regarding the urban population in difference sizes of cities

growth pattern is attributed to various driving forces related to the state, market and geographical factors. Among them, the state plays an important role. For example, a variety of geographically preferential policies have been implemented to promote the growth of coastal cities after China decided to open the door. These policies are actually de-regulation tools which enable marketization and internationalization of these coastal cities. In the simulation models, the impacts of the state are not taken into account, so the simulated urban system are primarily driven by market forces

5.4 Analysis on the State’s Impacts

123

Fig. 5.12 Comparison of simulated urban systems with actual urban system in 2010 regarding the number of cities in difference sizes

or/and geographical factors. Thus, comparing the regional differences between the actual urban system of 2010 and simulated urban systems can be regarded as the influences of the state on spatial evolution of urban system. For city i, the difference between its actual urban population and simulated urban population is defined as: Di = Pi,2010 − Pi,simulated

(5.14)

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5 Identifying the Development Patterns of China’s Urban System …

Fig. 5.13 Differences of urban sizes between the actual urban system in 2010 and simulated urban systems

Thus, if Di is positive, city i is under-estimated, whereas negative Di value of implies city i is over-estimated. First, the difference is shown in a map to indicate the general spatial variation. Only four simulated results (i.e., scenario 2, 3, 5, and 6) are taken into account because the other three may have similar results. Figure 5.13 shows that the simulated sizes of cities located close to the coast are mostly smaller than their actual sizes in 2010. This is understandable because these cities were smaller than their normal sizes in the pre-reform period and have been granted priorities for economic growth in the post-reform period. The models significantly underestimated their growth rates during the past three decades, though they have considered their locational advantages. Conversely, the simulated sizes of cities in Northeast China are significantly larger than their actual sizes in 2010. As heavy industrial base of China, these cities have lost their ascendant positions during the economic reform. Thus, their actual sizes are smaller than the simulated sizes, which should be largely attributed to relative decline of their positions in China’s political system compared with cities in the coastal provinces such as Guangdong, Jiangsu, Zhejiang and Fujian. In addition, the simulated sizes of cities located in some western provinces, such as Sichuan, Gansu, Yunnan, and Xinjiang, are significantly smaller than the actual urban system in 2010. Especially for two central cities in the West China, namely, Chengdu and Chongqing, the simulated sizes are smaller than their actual sizes in 2010. This is mainly because these western cities have few location advantages if only considering the geographical and economic conditions, but they are important in China’s political system as the central cities in the western region, and thereby can gain many

5.4 Analysis on the State’s Impacts

125

preferential policies from the central government. There are no clear spatial trend in terms of the difference for the cities in the middle region. To further examine the relationship between state’s impacts and regional difference of urban system, this chapter attempts to create a policy capacity index (POLICY) to measure the spatial pattern of state’s impacts. We select these preferential policies that are granted by the central government to local governments. The preferential policies are not evenly distributed to all local governments, but are targeted to certain regions. The preferential policies mainly include Special Economic Zone (SEZ), Coastal Opening City (COC), national New Area (NA), and various types of national development zones (DZs).7 Following Démurger et al. (2002), the index is created by multiplying the number of these policies of a city hosts by a weight that reflects the important of the policy. The weight is proposed as: Weight = 3: SEZ, national NA; Weight = 2: COC; Weight = 1: other types of national level DZs; Weight = 0: no such policies. For analytical purpose, we aggregate the policies to province level to work out the index for each province. For convenience, the index are standardized between 0 and 1. Figure 5.14 shows the index for each province. Guangdong’s index is highest as 1, implying that it has the most preferential policies. The preferential policies from the central government favor the coastal provinces, including Tianjin, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, and Guangdong. Also, provinces without location advantages such as Chongqing, Guangxi, Xinjiang, and Neimenggu have been granted many preferential policies from the central government, mainly owing to their political importance. Shanxi, Guizhou, Ningxia, Gansu, Tibet, and Qinghai have fewest preferential policies. Figure 5.15 examines the relationship between policy capacity index and the difference of the actual urban system in 2010 and simulated urban systems. Generally, there is a positive relation between the index and difference of the actual urban system in 2010 and simulated urban systems. For most provinces with low policy capacity index (within the circle in the upper-left), their differences between the actual urban system in 2010 and simulated urban systems are not too large. This implies that the impacts of the state on these provinces are relatively small, and the models that simulate urban system development driven by market forces and geographical factors can better capture their urban growth processes. For provinces with high policy capacity index such as Guangdong, Jiangsu, Zhejiang, Shandong, and Fujian (within the circle in upper-right), the simulated population sizes are smaller than the actual sizes or the sizes they should be in a market economy. The simulations 7 These development zones includes all types of national-level zones such as Economic and Techno-

logical Development Zones (ETDZs), High-Tech Industrial Development Zones (HTIDZs), Bonded Areas (BAs), Export Processing Zones (EPZs), Coastal Open Economic Zones (COEZs), and other special development zones; the data source is based on “The Notification of Verification and List of National Development Zones” published by the National Development and Reform Commission (http://bgt.ndrc.gov.cn/zcfb/200704/t20070406_500240.html, available until 2015-11-07);

126

5 Identifying the Development Patterns of China’s Urban System …

Fig. 5.14 Spatial pattern of policy capacity index of 2010

Fig. 5.15 Relationship between policy index and difference of population between 2010 and simulated results: (a) scenario 2; (b) scenario 3; (c) scenario 5; and (d) scenario 6

5.4 Analysis on the State’s Impacts

127

report smaller urban population sizes for these regions because of the lack of state’s influences. But then, influences of the state have accelerated the growth in these regions. For the provinces with development priorities in the pre-reform period but with low policy index in the post-reform (e.g., Liaoning, Heilongjiang, Hebei, and Jilin), their simulated urban population sizes are significantly larger that their actual sizes in 2010. The difference between the simulated urban systems and the actual urban system in 2010 is mainly because the models can’t reflect the spatial shift of development focus from these provinces towards the coastal region that have locational advantages. The regional differences between the simulated urban systems and the actual urban system in 2010 and their relationship with index policy reveal that the state has restructured spatial distribution of urban system through the powerful preferential policies. These policies are not evenly distributed but favour the coastal region. Meanwhile, the state also encourages the development of several regional central cities in the less-developed interior region such as Chongqing, Chengdu, Xi’an, and Wuhan. Second, the state’s impacts could be identified by comparing the city-size distributions of the simulated urban system and the actual urban system in 2010. The city-size distributions of the seven scenarios are plotted in Fig. 5.16, which are pairwise compared with the actual city-size distribution in 2010. In general, the simulated sizes of cities in the upper and middle sections of the city-size distributions are larger than the sizes of the actual city-size distribution in 2010. But the simulated sizes of cities in the low tail of the distributions are quite close to the actual sizes in the city-size distribution in 2010. Another salient feature is that the simulated city-size distributions follow the Zipf’s law better than the actual city-size distribution in 2010. The data points in the log–log plots of the simulated city-size distributions are aligned in a straight line (except for these top cities of scenario 3, 5 and 6 which are too large to be aligned with other cities), and the curves of the simulated city-size distributions are steeper than that of the actual city-size distribution in 2010. Furthermore, Table 5.7 reports the estimated Zipf coefficients and the 95% confident intervals, as well as the scaling ranges of both the simulated and the actual city-size distributions. Several features could be identified. The first feature is that Zipf coefficients of the simulated urban systems are all higher than that of the actual urban system in 2010. The Zipf coefficient of 2010 is 0.85, and coefficients of the SGM (scenario 1) and STAM (scenario 2) are 0.909 and 0.937, respectively. After including the geographical factors and market forces, the Zipf coefficients are even larger, and close to the expected value 1. This implies that sizes of cities in the upper tail of the simulated city-size distributions are larger than that of the actual city-size distribution in 2010. The second feature is, as also shown in the plots (Fig. 5.16), the scaling ranges of the simulated city-size distributions are larger than that of the actual city-size distribution in 2010. This implies a better fitting of the log-log linear curve. There are only 544 cities falling within the scaling range of the actual city-size distribution in 2010, while more cities are included in the scaling ranges of the simulated city-size distributions (e.g., there are 600 cities are within the scaling range of the city-size distribution simulated by the stochastic growth model).

128

5 Identifying the Development Patterns of China’s Urban System …

Fig. 5.16 City-size distributions of simulated urban systems and the comparisons with the actual city-size distribution in 2010

City-size distribution could provide more information related to the state as a driving force of urban system development. The smaller Zipf coefficients of the actual city-size distribution in 2010 comparing with the simulations indicates that cities at the top of urban hierarchy are controlled by the state. The underdevelopment of these large cities may be attributed principally to the urban development guideline issued by the central government in the 1980s, which stated, “strictly control the size of large cities, rationally develop medium–sized cities, and actively develop small cities and towns” (Zhou 1995b). Although this policy became less rigid at the national level after the late 1990s, the local governments of these large cities

5.4 Analysis on the State’s Impacts

129

Table 5.7 Zifp coefficients and scaling ranges of simulated urban systems and the actual urban system in 2010 Zipf coefficient

95% Conf. Interval

R2

No. of cities

Scaling range

Actual urban system (2010)

0.850

0.843

0.856

0.99

651

Rank 6 to 550

Scenario 1

0.909

0.901

0.919

0.99

651

Rank 1 to 600

Scenario 2

0.937

0.928

0.956

0.99

651

Rank 3 to 600

Scenario 3

0.942

0.933

0.950

0.99

651

Rank 2 to 575

Scenario 4

0.982

0.974

0.990

0.99

651

Rank 5 to 600

Scenario 5

0.950

0.942

0.959

0.99

651

Rank 2 to 580

Scenario 6

0.969

0.960

0.978

0.99

651

Rank 2 to 560

Scenario 7

0.966

0.958

0.973

0.99

651

Rank 2 to 560

continued to regulate their own growth. As stated in Chapter 3, the hukou control has been largely relaxed in the medium and small cities, but is still rigid in these large cities. Many institutional and policy barriers generated by the hukou system, including the education opportunity, health care, and social welfares, have prevented the population from agglomerating in these large cities. Moreover, the state has also restricted the growth of the cities at the bottom of the city-size distribution which is evidenced by the narrower scaling range of 2010 comparing with the simulations. Consequently, the cities became too small to be included in the scaling range of the hierarchy of cities, resulting in the phenomena of droopy tail and narrow scaling range. Although the phenomena of droopy tail is also shown in the simulated citysize distributions, their scaling ranges are more or less larger than the range of the actual city-size distribution in 2010. Both of these effects have squashed the data points on the city–size distribution log–log plot in 2010, resulting in its departure from the standard Zipf distribution. The findings in this section have shown the state’s impacts on urban system development in the spatial dimension and urban hierarchy. The state’s preferential policies have led to an uneven urban growth pattern, by encouraging the development of the coastal cities. But, on the other hand, the state is important for the growth of cities located in the interior region where market forces may not be able to extend. Therefore, the findings suggest that the state’s impacts are positive for reducing regional disparity in terms of urban development. Meanwhile, it may also lead to low economic efficiency especially for the cities with locational advantages which should be larger than their actual sizes in 2010. Further, state regulations have resulted in the underdevelopment of cities in the upper tail and lower tail of the city-size distribution. The simulated results demonstrate that the market forces and geographical factors could enhance the growth of cities in the upper tail and suppress the growth of cities in the lower tail. In this sense, the gap between large and small cities will be even greater if the state regulations are eliminated as shown in the simulated urban systems.

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5 Identifying the Development Patterns of China’s Urban System …

5.5 Conclusion This chapter investigates the development pattern of China’s urban system in the post-reform period. First, the urban growth process and evolution of the city-size distribution of China’s urban system are examined using the Gibrat’s law and Zipf’s law which are empirical regularities derived from the experiences of Western advance counties. Then, a series of models have been performed to simulate the urban system development in the period from 1984 to 2010. The models ranging from the standard stochastic growth to a more deterministic growth process are used to simulate urban system development processes under environments without or with less state interventions. The spatial-temporal autocorrelation, market forces, and geographical factors are incorporated into the stochastic growth model successively. By comparing the simulated urban system with the actual urban system in 2010, this chapter identifies the effects of the national urban system policy on urban system development during the study period. The findings in this chapter have verified Hypothesis 1 (H1a, H1b, and H1c) formulated in Chapter 4. Furthermore, the finding of this chapter will provide evidence for the following chapters which will examine the dynamics of the state’s effects on the urban system development. The findings suggest that the national urban system policy has achieved its goal of “strictly control the scale of large cities” and “rationally develop medium and small cities”. The direct evidence is that the urban growth rates of Chinese cities have a negative relationship with urban sizes throughout the post-reform period, and the Zipf coefficient of the city-size distribution is significantly small. Based on the comparison between the simulated urban systems and actual urban system in 2010, this chapter finds out that the total urban population of all simulated urban systems are larger than that of the actual urban system in 2010. The actual sizes of large cities are significantly smaller than that of the simulated urban systems with less or no state interventions. Meanwhile, the actual sizes of medium and small cities are larger than that of the simulated urban systems. This suggests that China’s urban system is under-developed because of the national urban system policy. Especially, the growth of large cities is strictly controlled by using the hukou system and other related institutional arrangements. The medium and small cities have experienced rapid growth that promoted by the state policies such as the TVEs and relaxation of the hukou system in the post-reform period. In addition, urban system development in the post-reform period indicates several distinctive features that reflects the effects of the state interventions. The growth of Chinese cities in the post-reform period does not follow the Gibrat’s law. Moreover, the city-size distribution of China deviates from the standard Zipf distribution. Spatially, the simulated sizes of cities located in the interior region are larger than the actual sizes of cities in 2010, and the simulated sizes of coastal cities are smaller than the actual size of cities in 2010. Especially, the simulated sizes of cities in Northeast China are significantly larger than the actual sizes, and the simulated sizes of cities in the PRD and YRD are smaller than the actual sizes in 2010. The evidence indicates that the state has enhanced the shift of urban development focus

5.5 Conclusion

131

from the interior region to the coastal region. Further analysis shows that there is a positive relation between the policy capacity index and the difference between 2010 and the simulations. It implies that state’s impacts on urban growth are unevenly distributed. Preferential policies favor the cities with better locational advantages, but also encourage the development of a few high administrative level cities in interior cities such as Chongqing, Chengdu and Wuhan. Therefore, the state’s impacts are positive for reducing regional disparity in terms of urban development. Hierarchically, large cities will be larger and small cities will be smaller in the simulated urban systems than in the actual urban system in 2010. It reveals the urban development process without state regulations will enhance the growth of large cities and suppress the growth of small cities, confirming the phenomenon of “rich get richer”. This result is also verified by comparing the simulated citysize distributions and the actual city-size distribution in 2010. The smaller Zipf coefficient of the actual city-size distribution in 2010 compared with the simulated city-size distributions indicates that cities in the upper tail of the city-size distribution are particularly controlled by the state. Meanwhile the state has also restricted the growth of the cities in the lower tail of the city-size distribution, which has resulted in the narrower scaling range of the city-size distribution in 2010 compared with the simulated urban systems. In summary, the effects of the state on the development of China’s urban system are twofold. On the one hand, the state tries to control the development of large cities to avoid the urban problems plaguing the primate cities in the urbanization process of the developing countries. Thus, state policies and other institutional arrangements are implemented to encourage the development of the medium and small cities, and strict hukou control has not relaxed for these large cities. On the other hand, the state needs the large cities to play as engine of economic development. Development priorities, such as different types of preferential policies, have been given to the cities with better locational advantages (e.g., cities in the PRD and YRD), and market force are also conducted by the state’s preferential policies to favor these cities. The twofold effects of the state should be altogether attributed to the national urban system policy of “strictly control the scale of large cities, rationally develop medium and small cities”. In order to achieve the goal of this policy, the state also promotes the development of medium and small cities at the cost of economies of scale and locational disadvantages. As a result, China has successfully avoided the urban problems of the primate cities in other developing countries, but this success is achieved at the expense of the economic efficiency. If the state regulations are completely eliminated in the Chinese context, the gap between large and small cities as well as between coastal and interior cities will be further enlarged. The state, as a regulation force, has reduced the gaps between cities at the top and bottom of the urban hierarchy and between cities located in the coastal and interior regions. Thus, regarding the objective of economic efficiency, the effects of the state may be negative. But, in terms of the objective of equality or balanced development, the effects of the state should be positive.

Chapter 6

Effects of Urban Government Capacity on Urban System Development in China

6.1 Introduction As argued above, the Chinese government continues to implement the national urban system policy of “strictly control the scale of large cities, rationally develop medium and small cities” in the post-reform period. However, the state is no longer the monopolistic driving force of urban development as a result of the introduction of market forces. With the influences of marketization, globalization, and decentralization of state power from the central government to local governments, China’s state has transformed from a single unitary power to a power matrix in the territory. The major responsibility for urban development has gradually devolved from the central government to urban governments. All cities are trying to attract private and foreign investors, to optimize their economic structure and upgrade their infrastructure, in an attempt to accelerate their growth. The state has increasingly realized that the national urban system policy is difficult to be effectively implemented owing to these changes in state policies and institutional arrangements. Therefore, we need to examine the importance of the power relations among governments at different levels in postreform China, and examine the relationship between urban government capacity and urban growth. According to the framework developed in Chapter 4, Chinese cities are hierarchically organized by the administrative system. Attention should be paid to variations in urban government capacities among cities that determine their differences in urban growth patterns. In this chapter, we examine the effects of government capacity on urban size and urban growth by using a series of econometric models. Emphasis is placed on the estimation of the effects of urban government capacity across time, spatial distribution, and urban hierarchy. The analyses treat the urban government capacity as an endogenous factor in the urban size and urban growth models. A variable is construct to measure urban government capacity. These empirical models are developed to test Hypothesis 2 (H2a, H2b, and H2c). The remainder of this chapter is organized as follows. The next section provides the theoretical insights for the empirical analyses, and illustrates how the decentralization of state power leads © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J. Wang, The Role of the State in China’s Urban System Development, https://doi.org/10.1007/978-981-33-6362-5_6

133

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to an uneven distribution of urban government capacities across cities and how the urban government capacity affects urban growth. Section 6.3 introduces the variables, models and estimation methods. Sections 6.4 and 6.5 report the results of the urban size models and urban growth models. Section 6.6 presents the major findings and conclude this chapter.

6.2 Decentralization of State Power, Urban Government Capacity, and Urban Growth In pre-reform China, local governments were tightly controlled by the centrally planned economy. With little political power, local governments at different levels merely carried out the plans of the superior governments. For example, no local governments had a separate fiscal budget, and the pre-form fiscal system was labeled as tongshou tongzhi, that is, the central government collected all fiscal revenues and made a consolidated budget for all governments at all administrative levels (Lin and Liu 2000). Another example is the centrally controlled hukou population migration, especially the formal rural–urban migration (nonzhuanfei).1 With the hukou system, the formal rural–urban migration was strictly regulated by the central government, and in this sense, no subnational government had power to determine the criteria and quotas of nonzhuanfei during the pre-reform period (Chan and Zhang 1999). The annual quota of nonzhuanfei was allocated from the central government to the provincial governments and then allocated to the prefecture-level governments, county-level governments, and township-level governments. As such, urban governments faced difficult in determining their growth rate and size. In effects, local governments were seen as a part of the detached agencies of the central government under the centrally regulated political system. By contrast, local governments play an increasingly important role in local development because of the decentralization of state power since the beginning of economic reforms in 1978. In the post-reform period, the shift of state power from the central government has given the local government many incentives to be responsible for local economic and social development. Urban governments at different administrative levels not only provide public services as the governments do in Western advanced countries, but also actively participate in economic activities, such as attracting investments, supporting private firm development, and so on. They have taken on a broad range of power to make differential policies tailored to their specific situations and to undertake bold pilot experiments. Local governments are no longer agencies to that implement the plans of superior administrations as they have assumed primary responsibility for local development. The major jobs of the central government are not to directly regulate the behaviors of local governments

1 Nongzhuanfei

refers to the official sanction of converting the hukou status from agricultural to on-agricultural and changing the place of hukou registration from rural to urban.

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135

in the same tight way as that in the pre-reform period, but to provide right incentives to local governments. Under a hierarchical political system, these goals are achieved by relying on administrative authority to create effective incentive mechanisms (Zhuravskaya 2007). In the post-reform Chinese political system, the previous unitary state power has been changed to a power matrix in the territory because of the institutional transition and political decentralization since 1978. The decentralization of state power to local governments has resulted in the differentiation of urban government capacities that affect urban growth. Government capacity is an important concept in political science and public administration that reflects the ability of government to act effectively in pursuit of its objectives. Generally, government capacity determines the aggregated performance pf governments in conducting the functions assigned to them (Gargan 1981). In theory, government capacity covers multiple governmental functions. Mann (1984a) divides government capacity into two functional dimensions: “despotic” and “infrastructural.” The former refers to the ability of governments to act in isolation from the demands of non-state actors, and the latter refers to the ability of states to penetrate society and ensure that its decisions are implemented. Grindle (1996) proposes four functional dimensions of government capacity, namely, institutional capacity, technical capacity, administrative capacity, and political capacity, which constrain governments to take on active functions. Cities with larger government capacities are expected to be highly effective in mobilizing resources to promote urban development. We must note that, given the uneven distribution of state power, cities should have different government capacities that determine their different abilities to affect urban growth. The distribution of government capacities among cities can better represent the organization of state power, and capture the endogenous political hierarchy of China’s urban system. Urban government capacity can affect urban development in different ways. As one of the main objectives of Chinese urban governments is to promote urban development, Chinese urban government capacity is likely to refer to the ability of urban governments to effectively affect urban development. First, cities with larger government capacities are more likely to take on administrative functions, such as providing public goods, addressing externalities, overcoming imperfect information, coordinating private–public partnerships, and developing social capital. Government capacity also reflects the abilities to carry out these functions that are labeled as “operational efficiency.” Second, cities with large government capacities may have a large amount of fiscal resources to intervene in urban economic activities and are highly capable of targeting the right areas of investment and providing fiscal support for urban development programs. Third, these cities are highly capable to employ multiple policy instruments to implement their strategic interventions, and they may effectively structure the policy-making process, carry out policies and enforce rules. This capacity is known as “policy capacity” or “implementation authority” (Polidano 2000). Limited empirical research has tried to establish the link between government capacity and urban development. The basic logic is that the decentralization of state power from the central government to the local governments enables the urban

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government to be primarily responsible for urban development. Due to the hierarchical distribution of the state power among cities, urban government capacities differ across cities at different administrative levels. The differences in urban government capacities determine the magnitude of the state’s effect on urban development. This chapter aims to estimate the effect by employing regression models.

6.3 Model and Methodology 6.3.1 Conceptualization of Issues In this section, a set of regression models are performed to estimate the effect of the state on urban development. Conceptually, urban size and growth are constrained by the availability of different types of resources (Bettencourt et al. 2007). Given the finite nature of resources, the allocation of resources in the urban system could determine urban size and growth. The state and the market are two major regulating mechanisms in allocating resources and directing economic activities. The “positions” of the state and the market in the political system determine their respective roles in affecting urban development. For an individual city, the relative capacities of the state and market are two vital determinants of the city’s power and privilege within the entire urban system and therefore, its ability to obtain sources. Therefore, this chapter assumes that urban size depends mainly on three sets of variables: P = f(STATE, MARKERT, Z)

(6.1)

where P is the size of the city, and STATE and MARKET represent two constructs that explicitly measure the effects of the state and the market, respectively. Z denotes variables that are not included in any of the other two sets. As discussed previously, although market mechanism has been introduced into the Chinese economy since 1978, the state continues to play an important role in affecting urban development. A mixed economy characterized by the dual driving forces of the state and the market has gradually emerged in post-reform China (Nee 1992). Urban system development is also driven by the dual forces stemming from the state and market, which follow different resources allocation mechanisms. Unlike the market, which allocates economic resources subject to the prices, contracts, property rights, and other institutions, the state affects the distributions of inputs and products through a hierarchical political system. Cities in different “positions” in the Chinese political hierarchy have different capacities, resulting in variations in sizes and growth rates. The variable of urban government capacity (GOVCAP) is used to measure the “position” of a city in the Chinese political hierarchy, whereas market potential (MP) is developed to capture the “position” of a city in the Chinese market system is developed. Hence, econometric models in this chapter treat the state and the market endogenously. Notably, the interplay of the state and the market can affect urban

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growth. The complementarities between the state and the market and their joint effects on urban development are examined as well. Apart from the state and the market, other factors, such as the geographical and demographic features of a city, need to be controlled. Cities are heterogeneous in terms of both physical geographical characteristics and economic geographical characteristics (e.g., proximity to market, services, or amenities). Geographical or locational advantages can be regarded as a type of resource allocation over space. The geography literature has long pointed out that cities with location-specific advantages, such as those located in coastal regions with rich natural resources, wide hinterlands, and excellent weather, may grow at a considerable rate (Berry et al. 1970; Whebell 1969). The advantages of initial physical geographical characteristics may persist through the mechanism of “path dependence” or “self-reinforcing effects” (Krugman 1993). The literature also discusses the effects of these so-called demographic drivers (mortality, fertility, migration, age structure, educational attainment, and so on) on urban growth (Guest 1973; Partridge et al. 2009). Figure 6.1 presents the relationships between the major concepts involved in the empirical analysis presented in this chapter. The econometrics models are developed to analyze the micro-level dynamics of urban system development by quantitatively assessing the effects of the state and the market, as well as the complementary effects of the interplay of the state and the market on urban size and growth. Accordingly, two sets of econometric models are employed—urban size models and urban growth models. There are two dependent variables in the econometrics models: urban size, and urban growth. Urban size (P) is measured by the urban population (chengzhen renkou) which has been defined in Chapter 3. Urban size growth (GPOP) is defined as: GPOPi, t→t + 1 =ln(P) = lnPi, t + 1 − lnPi,t = ln(Pi, t + 1 /Pi,t ), which is the growth rate of the urban population of a city within a given time interval.

Fig. 6.1 Relations of major concepts in urban size and urban growth models

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6 Effects of Urban Government Capacity …

6.3.2 Measuring Urban Government Capacity and Other Variables The critical explanatory variable is government capacity (GOVCAP), which is employed to capture the effects of the state on urban development. The indicators of government capacity are widely discussed in the existing literature. The famous work of Kaufmann, Kraay, and Mastruzzi (2009) at the World Bank Institute presents the Worldwide Governance Indicators (WGI) at the country level. These indicators reflect the capacity of the government to effectively govern economic and social development and formulate and implement policies, in particular, the WGIs are derived from the aggregation of 340 variables that ate grouped into six dimensions: voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption (Kaufmann et al. 2009). However, the lack of data at the city level limits the construction of indicators similar to the WGIs in this chapter. In the Chinese context, the government can intervene in economic activities in a variety of ways, thereby increasing the difficulty in measuring government capacity in econometrics models. The country-specific characteristics of China’s government system call for different measurements of government capacity. Different from the majority of urban governments in Western market economies whose main function is to provide public services, China’s urban governments are deeply involved in economic activities. Furthermore, the measurement of government capacity at the city level is different from that at the national level. City-level government capacity can reflect the variances of urban governments’ capacities across cities because all cities within a country are regulated by the same political system. Given the facts mentioned above, this chapter attempts to calculate a composite index instead of a single variable to measure the urban government capacity on the basis of the principal component analysis (PCA) method. In this chapter, urban government capacity contains three functional dimensions: fiscal capacity, policy capacity, and SOEs authority. First, fiscal capacity is important for urban governments in mobilizing resources to promote local development. It is measured using three variables, namely, per capita fiscal revenue (FISREV), per capita fiscal expenditure (FISEXP), and the ratio of local budgetary fiscal revenue over gross domestic product (FISGDP). FISREV and FISEXP have been widely used in extant literature to capture the fiscal capacity of cities. Cities with high per capita fiscal revenue and expenditure should have great potentials to grow because of their high capacities to provide public services and urban infrastructures, and spur market development and economic growth. FISGDP is also a common revenuebased indicator of the capacity of the state in comparative politics and international relations (Hendrix 2010), and is one of the World Development Indicators adopted by the World Bank. Second, the preferential policies that are granted by the central government are, to large extent, considered as de-regulation tools for the urban governments to facilitate the marketization and internalization of urban economies (Démurger et al. 2002) and

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are thus regarded as one dimension of urban government capacity. The preferential policies are not evenly distributed to all cities, but are targeted to certain cities. Cities that are granted more preferential policies have great capacities to affect urban economic growth, attract FDI, and promote foreign trade. Thus, we construct a policy capacity index (POLICY), which is the same as the index created in Chapter 5, to quantify the relative contributions of preferential policies to urban government capacity. The preferential policies mainly include Special Economic Zone (SEZ), Coastal Opening City (COC), national New Area (NA), and various types of national development zones (DZs).2 In the same way of Chapter 5, the POLICY index is created by multiplying the number of the preferential policies of a city hosts by the weight that reflects the importance of the policies. The weight is also the same as that in Chapter 5: Weight = 3: SEZ, national NA; Weight = 2: COC; Weight = 1: other types of national DZs; Weight = 0: no such policies. The policy capacity index is created for each city and calculated for the corresponding year, according to the time when the policies were granted. Then, the index is standardized as 0 and 1 for each year. Third, SOEs authority refers the share of the industrial output of SOEs in the gross industrial output of a city (SOEshare). This SOEs authority measures the influence of SOEs in the urban economy. SOEs serve as an important tool for the Chinese state to intervene in economic activities and generate fiscal revenue for the urban government. In this case, the urban government has great capacity to affect urban growth if the city has a large amount of SOEs within its administrative area. The index of government capacity (GOVCAP) is created with the PCA method on basis of the five variables for measuring the three dimensions of government capacity. Both the first and second component, which account for a total of 69.4% of the cumulative variance of the five variables, are used (Table 6.1). Several tests are employed to examine the validity of the PCA method (Table 6.2). The Cronbach’s α test with a result of 0.634 implies an acceptable level of internal consistency among the five variables comprising the index. The result of the Bartlett test of sphericity indicates that these variables are inter-correlated. The KMO test result of 0.5 is apparently low but remains acceptable because the other tests have already yielded acceptable results. In the PAC, the first component explains the variable of FISREV and FISEXP, and the second mainly explains FISGDP; both components explain POLICY and SOEs (for analytical purpose, the two component are named as fiscal capacity and integrative capacity). Then, a composite index of government 2 These development zones includes all types of national-level zones such as Economic and Techno-

logical Development Zones (ETDZs), High-Tech Industrial Development Zones (HTIDZs), Bonded Areas (BAs), Export Processing Zones (EPZs), Coastal Open Economic Zones (COEZs), and other special development zones. The data source is based on “The Notification of Verification and List of National Development Zones” published by the National Development and Reform Commission (http://bgt.ndrc.gov.cn/zcfb/200704/t20070406_500240.html, available until 2015-11-07).

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Table 6.1 Eigenanalysis of the correlation matrix Component

Eigenvalue

Difference

Proportion

Cumulative

Comp. 1

2.237

1.003

0.447

0.447

Comp. 2

1.234

0.462

0.247

0.694

Comp. 3

0.771

0.111

0.154

0.848

Comp. 4

0.660

0.561

0.132

0.980

Comp. 5

0.099

.

0.020

1.000

Table 6.2 Principal components’ coefficients and results of test Variable (all variables are normalized)

Fiscal capacity

Integrative capacity

Unexplained variance

FISREV (Per capita fiscal revenue) (log)

0.615

−0.164

0.33

FISEXP (Per capita fiscal expenditure) (log)

0.559

−0.420

0.12

FISGDP (ratio of fiscal revenue 0.073 over GDP)

0.727

0.08

POLICY (preferential policy index)

0.397

0.311

0.52

SOEs (gross value of industrial 0.384 output of all) (log)

0.414

0.45

Test Cronbach’s α

0.634

Bartlett test of sphericity

χ2 (df) = 3989.866 (10), p-value = 0.000

Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy

0.500

capacity (GOVCAP) is created as the weighted arithmetic mean of scores of the two components (the weight is the proportion of the two components). Index of GOVCAP =

(Score of Comp.1 ∗ 0.447) + (Score of Comp.2 ∗ 0.247) (0.447+0.247)

Thus, the index of government capacity is developed to capture the influence of the state on urban size and growth. Figure 6.2 plots the relationship between urban population and urban government capacity, which suggests a linear relationship. This figure also indicates that urban government capacity is closely associated with its level in the Chinese political hierarchy, that is, cities at high administrative levels have relatively strong government capacities. Moreover, the following three dummy variables indicating urban administrative levels are introduced in the models—the county-level cities (COUNCITY), prefecture-level cities (PREFCITY), and other high level cities (PROVCITY). These variables are used to measure the effects of

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Fig. 6.2 Scatter plot association: urban population and urban government capacity

the UAS that cannot be captured by GOVCAP. Another related variable measures whether a city has upgraded its administrative level (ADMINUP). This dummy variable carries a value of 1 if the administrative level of the city has upgraded within one time interval; otherwise, it carries a value of 0. The variable of market potential (MP) is included to measure the cross-city differences in the characteristics related to market forces. The measurement of MP is the same as that in Chapter 5: M Pi (t)=

n  yj (t) × POPj (t) j=1

τi,j

(6.2)

where, M Pi (t) refers to the market potential of city/county i in year t, y j (t) and P O P j (t) are the per capita GDP and population of city/county j in year t, respectively.3 In addition, τi, j represents the transport cost between city/county i and j, σ −1  , di, j is the Euclidean distance between and its form is written as τi, j = A•di, j own city/county (di,i ) is the average distance city/county i and j.4 The distance of one’s  to the city center, which is set as 23 • 2 ar ea/π , and σ is the distance decay parameter, 3 Da

Mata et al. (2007) use per capita income. Given that the data of per capita income were not available in the early years, the present study uses per capita GDP instead. When calculating the market potential, all cities and counties are considered. However, only the market potential of cities is used in the following regression models. 4 A threshold distance need to determine firstly, this chapter set the threshold as 250 km firstly. Then, I test the robustness by selecting alternative threshold values (e.g. 150, 500 et al.), and the results show their no significant differences of the selection of threshold values.

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6 Effects of Urban Government Capacity …

σ −1  where σ is assumed to be 2. The value of A makes τi, j = A•di, j denote the city with smallest land area (the values of the parameters are set as the same in the work of Au and Henderson [2006a]). Figures 6.3 and 6.4 show the relationships of the market potential and urban population and the relationship of market potential and urban government capacity, respectively; both relationships imply positive associations.

Fig. 6.3 Scatter plot association: urban population and market potential

Fig. 6.4 Scatter plot association: market potential and urban government capacity

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Table 6.3 Definition of Dependent and Independent Variables Variable

Definition

Dependent variables POP

City size, measured by the urban population

GPOP

City growth rate of urban population of a city within a time interval

Independent variables GOVCAP

Government capacity, defined as a composite index that created by means of principal component analysis base on five variables

COUNCITY

Dummy variable: = 1, if the city is county-level city

PREFCITY

Dummy variable: = 1, if the city is prefecture-level city

PROVCITY

Dummy variable: = 1, if the city is higher than prefecture-level city (including the province capitals, vice-province cities, and province level cities)

ADMINUP

Dummy variable: = 1, a city has upgraded its administrative level during the time interval

MP

Market potential, measuring the strength of market forces

Service/Manu

The ratio of service employment over manufacturing employment

FAI

The magnitude of per capita foreign investment

NGROWTH

Natural growth rate of urban population

In addition, several control variables are included. Economic structure and changes in sectoral composition can affect the pace of urban growth. Genenrally, manufacturing is dominant in the early economic development stage, whereas the service sector is of increasing importance in the late economic development stage. Thus, the ratio of service employment over manufacturing employment (Service/Manu) is used to represent the effects of urban economic structure. The magnitude of per capita foreign investment (FAI) is used to reflect the degree of openness of the urban economy. Foreign investment is critical in boosting urban growth, especially in the early period of the economic reforms in China. The population natural growth rate (NGROWTH) is used to capture the changes of demographic features. All variables are at the city level, and they are used in different econometric models on the basis of theoretical arguments and hypotheses, which are detailed in the following sections. Table 6.3 lists all the variables (Table 6.4).

6.3.3 Models and Estimation Methods Two panel data models are defined as follows: Pit = α + β1 •G O V C A Pit + β2 •M Pit + β3 •F AIit + β4 •Ser vice/Manu it + β5 •Pi,t−1 + β6 •N G R O W T Hi,t−1 + β7 •P R O V C I T Yit + β8 •P R E FC I T Yit + μi + ϕt + εit

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Table 6.4 Descriptive Statistics Variable

Observations

Mean(SD)

Min.

POP

2028

527,092 (1,106,067)

22,700

Max.

GPOP (%)

1377

0.41 (0.38)

−1.05

2.92

GOVCAP

2023

0.31 (0.12)

−4.51E-08

1

GOVCAP

1373

1(1.07)

−2.85

4.83

2.02E + 07

MP

2028

1.67e + 07 (2.84e + 07)

0

MP (log)

1376

1 (0.26)

0.055

2.13

Service/Manu

2025

1.27 (0.86)

0.11

6.79

FAI

2025

57.85 (142.7)

0 −2.14

NGROWTH (‰) 2026

7.53 (5.37)

Variable

Observations

No. of “0”

COUNCITY

2028

843

1185

PREFCITY

2028

1325

703

PROVCITY

2028

1888

140

ADMINUP

1742

1208

534

1.67E + 08

2329 25.5

No. of “1”

G P O Pit = α + β1 •G O V C A Pi,t−1 + β2 •G O V C A Pi,t−1→t + β3 •M Pi,t−1 + β4 •M Pi,t−1→t + β5 •F AIi,t−1 + β6 •Ser vice/Manu i,t−1 + β7 •Pi,t−1 + β8 •N G R O W T Hi,t−1 + β9 •AD M I NU Pi,t−1→t + μi + ϕt + εit For each model, only the basic function is laid out. In the following part, interactions between variables will be included in the models to examine the complementary effects of the interplay of the state and the market. In addition, μi denotes the unobservable individual effect, ϕt is the unobservable time effect, and εit is the remaining stochastic disturbance term. These models both use unbalanced panel data because of the changing number of cities in different years. The observations include all cities with officially designated city status in their respective years. Given the limitation of data availability, the study period covers four years, that is, 1984, 1990, 2000, and 2010; hence the time interval of the panel data is 10 years, except for the first interval, which is 6 years. In this way, we can identify the varying roles of the state in the past three decades. In previous section, the urban system of 1982 are discussed. However, because of the lack of city-level socio-economic data in 1982, the data of 1984 are selected to reflect the urban system in the 1980s.5 Specifically, urban size models capture the “long-run” equilibriums of the urban system development. However, urban growth models estimate the change in urban size every 10 years (6 years for 5 The

first issue of the China City Statistical Year book was published in 1985; hence the earliest city-level data cover the year of 1984.

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the first time interval), which does not impose the “long-run” equilibriums but a dynamic process. For the urban growth models, a dynamic specification is performed to separate the improvements of driving forces from the effects of the base period conditions of the forces. Two sets of variables, namely, the one-order lagged variables (t − 1) and the changes of the variables, are introduced into the urban growth models to capture the two different effects. For example, the urban growth from 2000 to 2010 is assumed to have depended on both the base period government capacity (G O V C A Pt−1 ) and its changes during the same period (G O V C A Pi,t−1→t ). The two effects are considered because urban growth is a dynamic adjustment process (Rappaport 2004). Historical accumulative effects are related to current changes in a dynamic adjustment from the base towards the new equilibrium level. This section describes the econometric estimation strategy employed in the empirical analysis. The proposed regression models are likely to have several problems which may result in a biased estimation of the ordinary least squares (OLS). First of all, the potential for endogeneity is an important concern. As shown in many empirical studies, regressions on urban size or urban growth are consistently troubled by the problem of endogeneity (Au and Henderson 2006b; Da Mata et al. 2007; Henderson and Wang 2007). In the models presented in this chapter, urban government capacity and market potential, as well as the economic structure and FAI may also depend on the scale of city and its change. As a result, the hypothesized causality between the explanatory variables (e.g., urban government capacity, market potential, economic structure, and FAI, as well as other control variables) and the dependent variables (urban size and urban growth ratio) may be reversed. Given that causality may run in both directions, the independent variables are likely to be correlated with the error term. This correlation may in turn lead to the bias of the OLS estimation. The common method for addressing the endogeneity problem is the instrument variable (IV) approach using a two-stage least squares (2SLS) model. However, finding good instruments for the endogenous variables in all the models other than their lags is difficult. Similar to OLS, the 2SLS model would produce biased estimations if weak instruments are used (Wooldridge 2003). More important, the potential presence of heteroscedasticity and serial correlation in the errors, which are two other key concerns of the proposed regression models, can also lead to a significantly biased 2SLS estimation. Hence, the test of Pagan and Hall (1983) is first carried out to detect heteroscedasticity in the IV estimation. The null hypothesis of the test shows no significant heterogeneity across cities. The results (Prob > chi2 (8) = 0.0013, and Prob > chi2 (10) = 0.000 for the two models, respectively) of the two regression models reject the null hypothesis, thereby indicating the presence of heteroscedasticity in these models. Moreover, the presence of serial correlation suggests that the proposed regression models should be dynamic panel data models, in which case the dependent variables depend on their own past realizations. The Wooldridge (2003) test is employed to detect the presence of serial correlations in the proposed models. The test results of the two regression models (Prob > F = 0.000, Prob > F = 0.0036 for the two regression models, respectively) reject the null hypothesis, thereby indicating the absence of any serial correlation. This outcome

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6 Effects of Urban Government Capacity …

also indicates a significant first-order autocorrelation (AR(1)) in the errors. Therefore, OLS and 2SLS estimators are likely to lead to biased estimations for the proposed regression models should the problems stated above emerge. In this chapter, we address the aforementioned problems by using the Arellano– Bover/Blundell–Bond system GMM estimator (Arellano and Bond 1991; Blundell and Bond 1998; Arellano and Bover 1995). The system GMM estimator can make coefficient estimates highly consistent and efficient in the presence of endogenous independent variables, serial correlation in errors, and fixed effects (Amaral et al. 2011). On the basis of the GMM introduced by Hansen (1982), he system GMM estimator builds a system comprising the original equation and the transformed one by differentiating the regressors in the original equation. The lagged values of the differences and levels of endogenous variables are used as instruments to control for endogeneity in the system GMM estimator. As a result of the assumption that the first differences in the instrument variables are uncorrelated with the fixed effects, the system GMM removes the fixed effects (Arellano and Bond 1991). Therefore, additional instruments may be introduced to further improve the efficiency of the estimation. In the following sections, the system GMM estimator is employed to estimate the two proposed panel data models. All likely endogenous independent variables are instrumented with the lagged values of the variables in both levels and their own first differences. Time dummies are included to control for time-related trends and shocks. Specifically, the two-step system GMM estimator which is highly robust to the presence of heteroscedasticity and serial correlation is used. A set of statistic tests are also performed following each regression to ascertain the validity and sensitivity of the estimation results. In the settings of the proposed models, the first year (1984) is mainly used as the instruments because the system GMM estimator needs to use the differences in the independent variables that will eliminate the variables of the first year. Additional details about the operation of the system GMM model are provided in combination with the interpretations of the estimation results.

6.4 Results of Urban Size Models In the urban size models, eight variables are included to capture the effects of the different forces and factors on urban size in the “long-run” equilibrium. As a result of the serial correlation, the first lag of the dependent variable (POP (t-1)) is included in the model to control the historical condition and cumulative effects of urban size. GOVCAP, MP, Service/Manu, FAI, PREFECTURE and POP (t-1) are considered as the endogenous variables and are instrumented with their lagged values. The population natural growth ratio (NGROWTH) is determined by the demographic structure at the regional level, and is thereby taken as the exogenous variable in this model. Different from the PREFECTURE which is considered endogenous, PROVCITY is taken as the exogenous variable. A county-level city with a large urban population is likely to be upgraded to a prefecture-level city; hence urban size may have a positive

6.4 Results of Urban Size Models

147

effect on the prefecture level of city. More than 100 county-level cities and counties have been upgraded to prefecture-level cities in the past three decades. However, the number of cities at top urban administrative levels measured by PROVCITY is almost consistent over time, because it is mainly determined by political and historical factors rather than urban size. Thus, PROVCITY is actually an exogenous variable in the study period, which only covers approximately 30 years. All models include year dummies to control the time trend. Deep lags of the endogenous variables can be specified as instruments in the system GMM estimation. Given the few periods in the dataset and the objective of passing the diagnostic tests, the number of instruments used is reduced by specifying few lags. This chapter specifies the first and second lags of the endogenous variables as instruments. Table 6.5 shows the results of the urban size models. Column (1) provides the baseline estimations of the effects of all the control variables on urban size. Column (2) also presents the estimations that include the GOVCAP variable. Column (3) reports the full estimations that include two dummies measuring urban administrative levels. A comparison of the three models indicates that the estimations are quite robust and consistent. The following interpretations are based on the full model in column (3). Several diagnostic tests are shown in the table to ascertain the validity of the models. As shown in column (3), 19 instruments are used in the system GMM estimation. The Hansen test of overidentification under the null hypothesis, which states that the instruments are orthogonal to the error term, is aimed at testing whether the instruments are exogenous. The results with the p-values of 0.84 cannot reject the null hypothesis. The Sargan test is similar to the Hansen test. Table 6.5 also reports the tests of exogeneity on a subset of instruments. Similarly, the p-values of the difference-in-Hansen test indicate that the additional instruments are exogenous. Although the test on the differences of the instruments for the levels equation is not strictly exogenous, it should not influence the validity of the estimation given the few time periods of the model. Generally, the results of the system GMM estimation are valid and convincing. The variables of GOVCAP, MP, POP (t-1), PROVCITY, and PREFECTURE have positive and significant effects on urban size. The coefficient of GOVCAP, which reflects the effect of government capacity on urban size, is 2.53. The result provides evidence of a substantial effect of government capacity on urban size. Specifically, as the value of GOVCAP is standardized (between 0 and 1), the coefficient of 2.53 implies that an increase of 0.01 in government capacity leads to an increase in urban size by approximately 2.53%. The coefficient of MP is 0.26 in column (1), but it decreases to 0.13 in column (3) with the inclusion of the variables of GOVCAP, PROVCITY, and PREFECTURE, which represent the effects of the state. The decrease in the magnitude of MP can be attributed to the fact that a portion of the effect of MP on urban size is now being captured by GOVCAP, PROVCITY, and PREFECTURE. A 1% increase in the market potential of a city leads to a 0.12% increase in urban size. Moreover, the positive and significant coefficient of urban population (POP (t-1)) indicates the nature of the accumulation process of urban growth. Clearly, the two dummies, PREFECTURE and PROVCITY, have a positively significant association with urban size. In addition, both the FAI and Service/Manu

148

6 Effects of Urban Government Capacity …

Table 6.5 System GMM estimation results for urban size model Dependent variable: log(POP)

(1)

(2)

(3)

−2.87*(1.66)

−0.94(2.70)

MP (log)

0.26***(0.077)

0.17**(0.074)

0.13***(0.043)

Service/Manu

0.39***(0.079)

0.34***(0.12)

0.095(0.10)

FAI (log)

0.097(0.060)

0.079(0.052)

0.01(0.033)

NGROWTH (t-1)

0.011**(0.0042)

0.0061(0.0043)

0.0017(0.0031)

POP (t-1) (log)

0.91***(0.064)

0.87***(0.15)

0.48***(0.13)

Intercept GOVCAP

3.70**(1.77) 2.53***(0.62)

PROVCITY

0.029(0.37) −0.015(0.13)

PREFECTURE

0.51**(0.25) 0.17*(0.10)

Time dummy

Yes

Yes

Yes

F-value (df)

189.04 (7, 640) (p < 0.01)

751.9 (9, 640) (p < 0.01)

491.2 (10, 640) (p < 0.01)

N

1373

1373

1373

Diagnostic tests −3.83 (p < 0.01)

−3.63 (p < 0.01)

−4.04 (p < 0.01)

Sargan test overid. Restrictions χ2 (df)

5,86 (4) (p = 0.21)

19.9 (7) (p = 0.06)

5,25 (8)(p = 0.73)

Hansen test overid. Restrictions χ2 (df)

4.48 (4) (p = 0.35)

13.9 (7) (p = 0.05)

4.18 (8)(p = 0.84)

Arellano-Bond test for AR(1) in first differences (z-stat.)

Difference-in-Hansen tests of exogeneity of instrument subsets GMM instruments Hansen test excluding group χ2 (df)

–

1.33(1) (p = 0.25)

0.93(1) (p = 0.33)

Difference (null H = exogenous) χ2 (df)

4.48 (4) (p = 0.35)

12.54(6) (p = 0.05)

3.24 (7) (p = 0.86)

Hansen test excluding group χ2 (df)

0.16 (1) (p = 0.69)

1.48(3) (p = 0.69)

0.80(4) (p = 0.94)

Difference (null H = exogenous) χ2 (df)

4.32 (3) (p = 0.23)

12.39(4) (p = 0.02)

3.37(4) (p = 0.50)

Exogenous variables

Note *** Significant at 0.01 level; ** Significant at 0.05 level; * Significant at 0.1 level

ratio have no significant effect on urban size, possibly because of the limited influences of the two variables within a short period. Thus, they are not essential forces of urban size in the long-run equilibrium analysis. The empirical results show that urban government capacity and market potential are two dominant factors in determining urban size. As stated above, these two

6.4 Results of Urban Size Models

149

variables are measurements of the state and the market. Thus, both the state and the market have positive associations with urban size. The dual forces determine the capacity of a city to attract workers, firms, capital and other factors which are key to urban growth. Hence, such forces determine the equilibrium level of urban size. The dependent variable is urban size which reflects the accumulation effects of historical urban growth, and the variable of the lag value of urban size control the historical condition. Thus, the results verify that the state still plays a proactive role after the implementation of the economic reforms and opening-up policies since 1978. If the urban government can extract a high ratio of tax over GDP, it can extend its ability to intervene in urban economy and promote urban growth. The market potential of a city determines its access to markets of trade and production, and is thus associated with efficient market mechanisms. Cities with large market potential could grow further in the future. In addition, the two variables PREFECTURE and PROVCITY reflect the importance of administrative levels on urban size, even with the consideration of urban government capacity. Next, the two components of government capacity are included in the model to examine the robustness of the effects of the state. Columns (5) and (6) in Table 6.6 shows that both components have positive and significant effects on urban size. Such result suggests consistency in our estimation of urban government capacity. In addition, the interaction terms between GOVCAP and MP are included in the model to estimate their potential complementarities and the joint effects of the state and the market on urban size. The state is likely to affect urban system development in combination with market forces. Thus, the basic hypothesis here is that the effect of government capacity on urban size may be conditioned by market forces. Introducing the conditional effects of government capacity into the urban size model enables us to understand how the state exerts its influence on cities, and to probe further into the interplay of the state and the market. The interaction term GOVCAP × MP is included in the urban size models. Column (4) is equivalent to column (3), but substitutes all the variables with centered data for the original variables to allow is comparison with other columns. To eliminate the “nonessential ill-conditioning” (Marquandt 1980) produced by serious multicollinearity and make the results easy to interpret, all the variables are centered by subtracting the mean values, which is a common method used in statistics and econometric studies (Aiken et al. 1991; Tate 1984). Column (7) reports the estimate of the interaction term. The result indicates that the interaction effect is positive and significant, with the estimated coefficients being 0.24. The findings verify the existence of complementarity between government capacity and the market potential. In other words, the interaction effect of the state and market is positively associated with urban size. The overall effect of government capacity on urban size can be explained by the sum of its direct effect on urban size and its corresponding interaction effect with market potential. For example, at the mean level of market potential (the mean value is zero because the data are centered), the result of column (7) suggests that an increase of 0.01 in government capacity is associated with a 1.37% increase in urban size. However, this amount increases by 0.24% for every 1% increase in market potential. This result implies that the government capacity of cities whose

150

6 Effects of Urban Government Capacity …

Table 6.6 System GMM estimation results for the urban size model with different components of GOVCAP and interaction terms Dependent variable: (4) log(POP)

(5)

(6)

(7)

Intercept

3.70** (1.77)

4.88** (2.04)

2.51 (2.00)

6.42*** (1.44)

GOVCAP

2.53*** (0.62)

Comp. 1 of GOVCAP

1.37** (0.59) 0.22*** (0.077)

Comp. 2 of GOVCAP

0.21*** (0.05)

GOVCAP*MP (log)

0.24** (0.11)

MP (log)

0.13*** (0.043)

0.12*** (0.043)

0.13*** (0.052)

0.13*** (0.04)

Service/Manu

0.095 (0.10)

0.11 (0.098)

0.15 (0.10)

0.036 (0.089)

FAI (log)

0.01 (0.033)

0.02 (0.04)

0.04 (0.032)

NGROWTH (t-1)

0.0017 (0.0031)

0.0012 (0.0036)

0.0029 (0.0031)

0.0008 (0.00312)

POP (t-1) (log)

0.48*** (0.13)

0.47*** (0.14)

0.62*** (0.12)

0.49*** (0.12)

PROVCITY

0.51** (0.25)

0.60** (0.27)

0.38 (0.29)

0.80*** (0.28)

PREFECTURE

0.17* (0.10)

0.15 (0.10)

0.19* (0.11)

0.27** (0.10)

Time dummy

Yes

Yes

Yes

Yes

F-value (df)

491.2 (10, 640) (p < 0.01)

457.3 (10, 640) 550.3 (10, 640) (p < 0.01) (p < 0.01)

468.6 (11,640) (p < 0.01)

N

1373

1373

1373

1373

−0.0041 (0.033)

Diagnostic tests Arellano-Bond test for AR(1) in first differences (z-stat.)

−4.04 (p < 0.01)

−3.74 (p = 0.000)

−4.39 (p = 0.000)

−4.33 (p = 0.000)

Sargan test overid. Restrictions χ2 (df)

5,25 (8) (p = 0.73)

14.3 (8) (p = 0.073)

4.6 (8) (p = 0.80)

26.08(11) (p = 0.06)

Hansen test overid. Restrictions χ2 (df)

4.18 (8) (p = 0.84)

8.97 (8) (p = 0.35)

3.24 (8) (p = 0.92)

20.2(11) (p = 0.05)

Difference-in-Hansen tests of exogeneity of instrument subsets GMM instruments (continued)

6.4 Results of Urban Size Models

151

Table 6.6 (continued) Dependent variable: (4) log(POP)

(5)

(6)

(7)

Hansen test excluding group χ2 (df)

0.93(1) (p = 0.33)

2.13(1) (p = 0.14)

0.19 (1) (p = 0.66)

1.96(2) (p = 0.38)

Difference (null H = exogenous) χ2 (df)

3.24 (7) (p = 0.86)

6.83 (7) (p = 0.45)

3.04(7) (p = 0.88)

20.7(9) (p = 0.04)

Hansen test excluding group χ2 (df)

0.80(4) (p = 0.94)

1.26 (4) (p = 0.87)

0.97 (4) (p = 0.92)

1.86(7) (p = 0.97)

Difference (null H = exogenous) χ2 (df)

3.37(4) (p = 0.50)

7.71 (4) (p = 0.10)

2.27 (4) (p = 0.69)

18.41(4) (p = 0.01)

Exogenous variables

Note *** Significant at 0.01 level; ** Significant at 0.05 level; * Significant at 0.1 level

market potential are higher than the average has greater effect than those cities whose market potential is lower than the average. To interpret the interaction effect clearly, we present a two-dimensional plot to illustrate the differential effects of government capacity and market potential. First, the sample of cities is split into two subsets on the basis of their government capacities. A city is classified to have “low government capacity” or “high government capacity” depending on whether its government capacity is lower or higher, respectively, than the median government capacity of all cities. A similar approach is used to classify cities into “low market potential” and “high market potential” groups based on the magnitude of their market potential relative to the median of market potential. Figure 6.5 represents the plot of the interaction effect. On the X-axis, government capacity is reported as low and high, whereas on the Y-axis, the log value of urban size is reported as the dependent variable in the urban size models. Then, the mean value of urban size for each subset groups is calculated and represented in the plots. For the purpose of comparison, the mean values of the low and high government capacities of all the cities are also calculated and depicted as a dotted dash line in the figure. Significant differences in the sizes of the cities that fall in different subset groups are observed in these plots. For instance, the slope of the regression of urban size on urban government capacity for the cities with high market potential is larger than that for all cities and for the cities with low market potential. The result suggests that the complementary effect of government capacity and market potential on urban size is stronger for cities with high government capacities and market potential. Therefore, the interplay of the state and the market has a positive effect on urban size. For the interaction effect of urban government capacity and market potential, the positively significant coefficient indicates that the relationship between the state and

152

6 Effects of Urban Government Capacity …

Fig. 6.5 Interaction effects of government capacity with market potential

urban size is strengthened by the market. In other words, the interplay of the state and the market exerts a positive effect on urban size. This finding can be explained by a rationale. One of the substantial functions of urban governments in fostering urban growth is performed by promoting market development. For example, an urban government is likely to encourage the development of nonpublic economy by removing various restrictive policies. This finding implies that the state plays a decisive role in determining where market forces can grow quickly and occupy an important position in the urban economy. Therefore, the interplay of the state and the market, which affects the movements of population, investments, and firms, has positive effects on urban development in post-reform China (Yeh et al. 2015).

6.5 Results of Urban Growth Models Table 6.7 shows the estimated results of the urban growth models using the same econometric estimation methods described in the previous section. The dependent variable is the difference in log urban size; hence, the model actually estimates the effects on the city growth rate for a given time interval. The model involves three intervals: 1984–1990, 1990–2000, and 2000–2010. Similarly, column (1) provides a baseline estimation that only includes all control variables, column (2) presents an estimation that includes the GOVCAP variable, and column (3) reports the full model. As mentioned above, a dynamic specification is constructed to estimate the urban growth model. Both the base period conditions and changes of the state and market forces are included in the model. Except for the variables used in the urban size model, a new variable, that is, ADMINUP, is introduced into the urban growth

−2.08 (2.27)

(2) −1.58(1.53)

(3)

57.97 (8, 639) (p < 0.01) 1371

F-value

N

0.31 (1) (p = 0.58)

Hansen test overid. Restrictions χ2 (df)

0.31 (1) (p = 0.58)

Difference (null H = exogenous) χ2 (df)

Note *** Significant at 0.01 level; ** Significant at 0.05 level; * Significant at 0.1 level

–

Hansen test excluding group χ2 (df)

GMM instruments

Difference-in-Hansen tests of exogeneity of instrument subsets

0.35 (1) (p = 0.55)

Sargan test overid. Restrictions χ2 (df)

Arellano-Bond test for AR(1) in first differences (z-stat.)

−3.41 (p = 0.001)

Yes

Time dummy

Diagnostic tests

0.0039(0.0041)

NGROWTH (t-1)

−0.099***(0.025)

0.015(0.015)

FAI (log) (t-1)

POP (log) (t-1)

2.21***(0.73) 0.095***(0.033)

Service/Manu (t-1)

0.87 (2) (p = 0.65)

–

0.87 (2) (p = 0.65)

0.62 (2) (p = 0.73)

−1.69 (p = 0.10)

1371

20.53 (9, 639) (p < 0.01)

Yes

-0.0017(0.0085)

−0.093**(0.043)

0.029(0.019)

0.089*(0.049)

2.76*(1.68)

MP (log) (t-1)

MP (log)

0.15***(0.043)

0.16***(0.029)

ADMINUP

3.68 (3) (p = 0.30)

–

3.68 (3) (p = 0.30)

3.18 (3)(p = 0.37)

−2.06 (p = 0.040)

1371

51.55 (11, 639) (p < 0.01)

Yes

0.0018(0.0055)

−0.18***(0.056)

0.014(0.013)

0.086***(0.029)

2.2**(1.08)

0.15***(0.024)

0.042(0.061) 0.54(0.54)

0.92(0.82)

GOVCAP

1.02**(0.51)

−2.32** (1.1)

(1)

GOVCAP (t-1)

Intercept

Dependent variable: log(POP)

Table 6.7 System GMM estimation results for the urban growth model

6.5 Results of Urban Growth Models 153

154

6 Effects of Urban Government Capacity …

models to reflect the influence of the upgrading of administrative levels on urban growth. Year dummies are included in the models to control the time serial trend. The diagnostic tests are the same as those for the urban size models. The results in Table 6.7 have all passed significant tests; hence, the estimated coefficients are valid. Notably, urban growth models are concerned with a short-run equilibrium, and are thus different from urban size models, which are based on a long-run equilibrium. Thus, relative to the urban size models in Table 6.5, the coefficients of the urban growth models differ in magnitude. The coefficient of GOVCAP (t-1) is statistically significant and positive, as reported in columns (2) and (3), whereas the coefficient of GOVGDP is not statistically significant. The results suggest that the base period urban government capacity has a positive effect on urban growth, but that the change in urban government capacity has no significant effect on urban growth when controlling for the base period urban government capacity. This result suggests that cities with high government capacities in the base period experience rapid growth afterwards, but an increase in government capacity may not lead to rapid urban growth. It should be noted that the base period government capacity is inherited from historical conditions, thus reflecting the effects of the accumulation of urban government capacities during a certain period. Cities with high government capacities for the base period may be related to a variety of advantages, such as high quality of urban infrastructure, strong policy capacity and operational efficiency, and larger stock of human capital, all of which are key to urban growth. The estimated results show that the both the MP (t-1) and MP have positive and significant effects on urban growth. The market potential in the base period and increase of market potential in the corresponding period have fairly strong effects on urban growth. The effect of the upgrading of administrative level has no significant association with urban growth. Hence, a relationship between administrative level and urban growth may not exist. The influence of the UAS on urban growth is discussed in next chapter. The base period condition of economic structure (Service/Manu) has a positive effect on urban growth, thus suggesting that cities with a high ratio of service sector experience considerable growth afterwards. The base period urban size has a negative and significant coefficient, which indicates the conditional convergence in urban growth over time. The influence of foreign investments (FAI) may only be important for a few cities rather than for an entire system of cities. This result can explain why the coefficient of FAI is not statistically significant in column (3). Natural growth rate is not an important factor in explaining the differences of urban growth in China. Next, the urban growth model is decomposed to analyze the contributions of each variables. The contribution of each variable is calculated as the product of the estimated coefficient multiplied by the mean value of the variable over the sum of the products of all the variables. Only the variables that are statistically significant are considered. To understand whether the contributions of the variables vary in different groups of cities, we decompose the urban growth model based on various groups. Tables 6.8, 6.9, and 6.10 show the decompositions of urban size growth according to the estimated coefficients in column (3) of Table 6.7. For all the cities, the key positive

Mean

1.11

12.4

0.15

2.2

0.086

−0.18

MP (log) (t-1)

MP (log)

Service/Manu (t-1)

Sum

POP (log) (t-1)

0.29

1.02

GOVCAP (t-1)

1.0

14.42

0.41

1371

All cities

log(POP)

No. of cities

Coefficient

11.46

1.28

0.93

13.37

0.24

0.32

343

Q1

12.06

1.22

1.0

14.51

0.24

0.4

343

Q2

Table 6.8 Decomposition of urban growth by quartiles of urban size

12.52

1.04

1.04

14.83

0.28

0.46

343

Q3

13.58

0.89

1.03

15

0.38

0.46

342

Q4

100

−88.5

3.8

87.2

85.8

11.7

100

−88.0

4.7

87.3

85.5

10.5

Q1

9.6

100

−85.0

4.1

86.1

85.2

Q2

Contribution to urban growth (%) All cities

100

−85.5

3.4

86.8

84.5

10.8

Q3

100

−96.4

3.0

89.4

88.7

15.3

Q4

6.5 Results of Urban Growth Models 155

156

6 Effects of Urban Government Capacity …

Table 6.9 Decomposition of urban growth by different regions Mean

Contribution to urban growth (%)

Coefficient All cities

East

Middle West

1371

547

511

No. of cities log(POP)

All cities

East

Middle West

313

0.41

0.49

0.37

0.34

1.02

0.29

0.31

0.27

0.28

MP (log) (t-1)

0.15

14.42

15.16

14.47

13.06

85.8

83.9

89.0

84.3

MP (log)

2.2

1.0

1.05

0.96

0.98

87.2

85.2

86.6

92.8

Service/Manu (t-1)

0.086

1.11

0.93

1.2

1.28

3.8

3.0

4.2

4.7

12.61

12.35

12.15 −88.5 −83.8 −91.2

−94.1

GOVCAP (t-1)

POP (log) (t-1) Sum

−0.18

12.4

11.7

100

11.7

100

11.4

100

12.3

100

components of urban growth come from the base period market potential and from the increase of such market potential, accounting for 85.8% and 87.2%, respectively. The base period government capacity is also an important contributor of urban growth (11.7%). The Service/Manu ratio plays minimally important role in urban size growth (3.8%). Initial urban size exhibits strong negative effects and accounts for −88.5% of the total effects on urban growth. This result suggests that the congestion effect may limit urban growth rate. Notably, the contribution of government capacity increase is negative. The influence of the state on urban growth has decreased for most Chinese cities because of the marketization since 1978. Accompanied by the development of market forces, the state occupies a less important position in promoting urban growth. At the same time, a decrease in the influence of the state equates to additional opportunities for market actors. Thus, MP has a strong positive association with urban growth, but GOVCAP has no significant effects on urban growth. In general, the findings indicate that the growth of Chinese cities in the post-reform period increasingly relies upon the market forces. The decomposition of urban growth varies in different groups of cities. As shown in Table 6.8, the contribution of the base period government capacity is large for large cities. Meanwhile, market forces are found to have a considerable effect on the growth of large cities. The results show that the state and the market, as well as their interplay, have strong effects on the growth of large cities. With regard to the cities in different regions (Table 6.9), the contribution of base period government capacity is large in western cities, because of the relative lack of resources for urban growth. An increase in market potential has a large negative effect on the growth of cities in the western region. The growth of cities in the eastern region may not depend primarily on government capacity because of the high degree of marketization and the locational advantages. For cities at high administrative levels (Table 6.9), the

Mean

1.11

12.4

0.15

2.2

0.086

−0.18

MP (log) (t-1)

MP (log)

Service/Manu (t-1)

Sum

POP (log) (t-1)

0.29

1.02

GOVCAP (t-1)

1

14.42

0.41

1371

All cities

log(POP)

No. of cities

Coefficient

14.25

0.97

0.94

14.38

0.45

0.44

105

Province capital and higher level

12.67

0.99

0.97

14.5

0.31

0.43

598

Prefecture level

Table 6.10 Decomposition of urban growth by different administrative levels

11.88

1.23

1.03

14.36

0.24

0.39

668

County level

100

−88.5

3.8

87.2

85.8

11.7

100

−116.4

3.8

93.9

97.9

20.8

Province capital and higher level

100

−93.9

3.5

87.9

89.5

13.0

Prefecture level

Contribution to urban growth (%) All cities

100

−81.2

4.0

86.1

81.8

9.3

County level

6.5 Results of Urban Growth Models 157

158

6 Effects of Urban Government Capacity …

contribution of the base period government capacity is much larger than that for other cities. Meanwhile, the contribution of the base period market potential and its increase is larger for cities at high administrative levels. Thus, the effects of urban government capacity are related to administrative levels, and that the interplay of the state and the market has a substantial effect on urban growth in cities at high administrative levels.

6.6 Conclusion By using a series of city-level regression models, we examines how urban government capacity affects urban size and growth and how the interplay of urban government capacity and market forces shapes the growth patterns across cities. In the postreform period, the decentralization of state power from the central government to local governments enables the urban government to be primarily responsible for urban development. Urban government capacity is key to reflect the ability of cities to affect their sizes and growth. A composite index instead of a single variable to measure urban government capacity on the basis of three functional dimensions: fiscal capacity, policy capacity, and SOEs authority. Due to the hierarchical distribution of state power among cities, urban government capacity has a positive relation with urban size. In the city-level regression models, the differences in urban government capacities determine the different sizes and growth rates of cities. To address the problems of endogeneity and the presence of heteroscedasticity, we employ the system GMM estimator in the models. The dependent variables are urban size and urban growth. This chapter presents several findings. First, the results of the urban size models indicate that both urban government capacity and market potential (a measurement of market forces in this book) are dominant factors in determining urban size. Hence, Hypothesis 2a is supported. The interaction term representing the interplay of urban government capacity and market potential is positive and significantly associated with urban size. The findings verify the complementarity between urban government capacity and market potential. It indicates that the urban government capacity can play an enable role in helping to increasing the effect of market forces on urban size. Hence, Hypothesis 2b is supported. The urban growth models show that the base period urban government capacity has a positive effect on urban growth but that the change in urban government capacity has no significant effect on urban growth. However, the market potential in the base period and the increase in market potential in the corresponding period have fairly strong effects on urban growth. This result indicates that market forces have increasingly important effects on urban growth during the post-reform period. When decomposing urban growth in terms of different city categories, the effects of urban government capacity are related to administrative levels, and the interplay of urban government capacity and market potential has a substantial effect on the urban growth of cities at high administrative levels. The findings offer strong support for Hypothesis 2c.

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This chapter provides empirical evidence on the effects of urban government capacity on urban size and growth. Such evidence lays the micro-foundation of the dynamics of urban system development. As a result of the decentralization of state power since the beginning of market-oriented reforms in 1978, the direct regulations of the central government on urban system development have been relaxed to a large extent. Although the Chinese government still attempts to implement the national urban system policy of controlling the scale of large cities, cities that have large urban government capacities can promote their rapid growth. As shown in this chapter, large cities tend to have larger urban government capacities than that of the medium and small cities. This can explain why large cities are growing rapidly although the central government tries to control their sizes. However, urban government capacities of cities are hierarchically organized by a series of institutional arrangements, such as the fiscal system, preferential policies, and SOEs, which are controlled by the central and provincial governments. Thus, cities can extend their urban government capacity to promote growth but are still controlled by superior governments through institutional arrangements and state policies. In summary, this chapter indicates that the differences in urban government capacities make a difference for urban growth performance. The capacity of an urban government, that is, the ability to take on administrative functions effectively, mobilize fiscal resources, and implement policy instruments as a form of intervention in economic activities, affects urban growth performance. Urban government capacities correspond to the positions of cities in China’s political hierarchy. On the other hand, market forces play increasingly critical roles in urban development, and the interplay of the state and the market is proven to have positive effects on urban size. In other words, the state’s effects on urban development depend not only on urban government capacities of cities but also on the abilities of the urban government to interact with and coordinate market forces, such as stimulate private sector development and attract and cooperate with investors.

Chapter 7

Effects of Urban Administrative System on Urban System Development in China

7.1 Introduction As illustrated conceptually in Chapter 4 and examined empirically in Chapter 6, the UAS lays the foundation for the hierarchical organization of state power among Chinese cities. Thus, cities always have incentives to upgrading their administrative levels, and counties also desire to obtain a city status.1 Upgrading administrative levels equates to considerable political power for the urban government. For the entire urban system, upgrading administrative levels results in changes from two aspects. First, a county-to-city upgrading increases the number of cities, and is thus labeled as “horizontal expansion of urban system” by Fan (1999). Second, a countyto-prefecture upgrading reorganizes the hierarchical structure of the urban system. Upgrading county-level cities to prefecture-level cities can expand their abilities to form ties with their hinterlands, which changes the vertically structured relationships to horizontally interconnected relationships (Solinger 1991). However, an upgrading of administrative level may also generate a negative influence because it brings competition and local protectionism among cities at the same administrative level (Bai et al. 2008; Li and Zhou 2005). As a key institution arrangement for the central government to govern cities in China, the UAS is employed to regulate the development of urban system. By the UAS, the central government can still exert strong impacts on urban growth to achieve its policy goals such as the national urban system policy. The main objective of this chapter is to examine whether an administrative level upgrading, including county-to-prefecture and county-to-city upgrading, can truly promote urban growth. Hypothesis 3 (H3a and H3b), which is developed in Chapter 4, is tested in this chapter. A quasi-experimental approach with propensity 1 In this book, “upgrading” refers to the shift of the administrative level of a city/county from a lower

level to a higher level. A county converted to a county-level city does not change its administrative level, but it can gain state power to extend its abilities after the conversion process. Therefore, a county-to-city upgrading is considered a type of administrative-level upgrading in this book. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J. Wang, The Role of the State in China’s Urban System Development, https://doi.org/10.1007/978-981-33-6362-5_7

161

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score matching (PSM) and difference-in-difference (DID), which are widely used to capture the dynamic effects of policy adoption, is employed to quantitatively assess the effects of administrative level upgrading on urban growth. This chapter is organized as follows. The next section theoretically explains why an administrative level upgrading may affect urban growth. Then, the methodology and model of empirical are introduced. The empirical results are shown in Sects. 7.3 and 7.4, respectively. The final section presents a conclusion with discussion.

7.2 Administrative Level Upgrading and Urban Growth 7.2.1 The Importance of Administrative Level for Chinese Cities In the conceptual framework of this book, the UAS is the most important institutional arrangement that lays the foundation for the political hierarchy of China’s urban system. The UAS directly determines the allocation of various resources among cities, such as fiscal resources, preferential policies, and urban construction land quotas. Within this system, Chinese cities falling under different administrative levels have different levels of political power that affect their capacities to not only provide public services but also attract investment and migration, initiate developmental projects, and acquire land quota for housing and economic development. A close correspondence exists between the administrative level of a city and its governmental competence and management of its territorial space. Moreover, a city at a high administrative level has great state power for its decisionmaking process, fiscal resources allocation, preferential policy competition, and other economic matters. Understanding how the UAS shapes the relations among cities and why administrative level is important for cities is highly significant. There are three basic administrative levels for Chinese cities, namely, provincial level, prefecture level, and county level. In addition, there are 15 cities at the vice-provincial-level, a half level higher than the ordinary prefecture-level cities. There are only 4 provincial-level cities—Beijing, Shanghai, Tianjin, and Chongqing, which are centrally administered cities. The centrally administered cities and viceprovincial-level cities are granted the largest authority to break free from the UAS and are placed at the top of the political hierarchy. For example, the 4 centrally administered cities, 27 provincial capitals, and 18 “relatively large cities (jiaoda de shi)” have been granted a wide range of policy making and local law-enacting power since the 1980s. However, most cities are prefecture-level and county-level cities. Prefecture-level cities, county-level cities, and counties are rigidly organized by the political system into a hierarchical structure. Except for a few special cases, county-level cities and counties are under the administrative “control” of a particular

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prefecture-level city or prefecture.2 The relationships between prefecture-level cities and county-level cities and counties are distinctive in the China’s context and are critical for hierarchical organization of Chinese cities. Their government capacities are heavily conditioned by the administrative levels—prefecture-level cities have larger capacities than county-level cities and counties do. This relationship can be interpreted from different dimensions, such as administrative power, fiscal benefit, and preferential policies. First, the administrative power of prefecture-level cities is much larger than that of subordinate county-level cities and counties. Officially, an administrative unit can only interact directly with the immediate superior administrative unit. A county or county-level city cannot “jump scale” to interact with the provincial or central government without passing through the superior prefecture or prefecture-level city (Ma 2005). As subordinate administrative units, counties or county-level cities are often obligated to follow the interests of superior cities. Superior central cities have high incentives to take advantage of their administrative power to exploit the resources of their subordinate counties in matters such as unequal resource flows toward the former. Thus, the administrative level of a city is significantly related to its capacity to compete for resources related to urban development. The superior administrative status of a prefecture-level city is important in its competition with county-level cities and counties for raw materials, investments, development projects, infrastructures, and other development opportunities. Between a superior central city and a subordinate county within one prefecture-level city, the mayor often favors the former. In a survey conducted by Zhou and Hu (1992), most of the surveyed counties and county-level cities believe that the decision power should be further decentralized from superior central cities. Therefore, the CGC system results in a decreasing administrative power distribution from prefecture-level units to county-level units. Second, a prefecture-level city is a formal “independent fiscal regime (duli hesuan caizheng danwei)” in the Chinese fiscal system, whereas the prefecture is not an official layer in the fiscal system. As an intermediate administrative level between a province and a county, prefecture-level cities are empowered to share a part of the fiscal revenue extracted from subordinate urban districts, counties, and countylevel cities (Zhou 2012).3 In less developed regions, newly established prefecturelevel cities may often face serious financial problems because their weak economic conditions prevent them from leading the entire region. In order to reduce such financial burden, the governments of prefecture-level cities may use their superior administrative status to transfer part of the burden to their subordinate units by extracting tax from these units instead of transferring return to them. Owing to these 2 County-level

cities in Hainan province and Xinjiang autonomous region are directly governed by the provincial governments, and three county-level cities (i.e., Xiantao, Qianjiang, and Tianmen) in Hubei province and one county-level city (i.e., Heyuan) in Henan province are also under direct control by the provincial governments. 3 According to Zhou (2012), the share ratio of prefecture-level cities with subordinate countylevel units varies in provinces, but prefecture-level cities generally enjoy their superior budgetary authorities because they are a formal layer in the fiscal system.

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fiscal benefits, cities at high administrative levels enjoy more fiscal benefits than subordinate units do. Third, administrative level is important in determining the position of a city in the national economic system. Cities at high administrative levels are more likely to enjoy the policy privileges and a wide range of policy-making powers. For example, national-level development zones, which presents an important preferential policy, are granted mostly to cities at high administrative levels (Table 7.1). Hence, these cities have multiple policy instruments to promote their own development. Another example is the distribution of branches of SOEs among cities at different administrative levels. The ownership nature of SOEs determines their hierarchically structured spatial distribution in accordance with urban hierarchy. Central government-owned SOEs usually establish their regional headquarters in provincial capitals, and provincial government-owned SOEs usually establish their headquarters in provincial capitals and branches in prefecture-level cities within the same province. SOEs are less likely to locate their key branches in county-level cities and counties. To a large extent, such a hierarchical structure of SOEs gives rise to the hierarchical economic landscape among cities. In allocating preferential policies, decision powers, and other resources, the central government mainly deals with power distribution among centrally administered cities and parts of vice-provincial-level administrative units, The provincial-level government then deals with power distribution among prefecture-level administrative units. The administrative hierarchy of an urban system is inherently embedded in the country’s political system. In summary, administrative level is important for urban development in the Chinese context, that is, cities at high administrative levels enjoy a wide variety of privileges that favor urban development compared with those at low administrative levels. Although this system has been restructured during the post-reform period, the UAS is still the key to understanding the tensions among the spatial units at different administrative levels. In particular, the relations among prefecture-level Table 7.1 Number of national-level development zones for cities at different administrative levels in 2006 Urban administrative level Province-level cities

Number of cities

Total number of national–level development zones

4

26

15

67

Prefecture-level cities

268

107

County-level cities

369

22

Vice–province-level cities

Note Figures in Column 3 are the sum of all types of development zones at the national level, including Economic and Technological Development Zones (ETDZs), High–Tech Industrial Development Zones (HTIDZs), Bonded Areas (BAs), Export Processing Zones (EPZs), Coastal Open Economic Zones (COEZs), and other special development zones. The data were obtained from the 2006 list of national level development zones published by the National Development and Reform Commission (http://bgt.ndrc.gov.cn/zcfb/200704/t20070406_500240.html)

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cities, county-level cities and counties are fundamental for the hierarchical and tightly linked nature of China’s urban system.

7.2.2 Effect of Administrative Level Upgrading on Cities Owing to the importance of administrative level for cities, upgrading of administrative level has become a common strategy adopted by many Chinese cities to extend their state power. This section attempts to examine the relationship between administrative level upgrading and urban size and growth from an institution analysis perspective, providing a conceptualization for the empirical studies in next sections. As argued by Ma (2005), administrative level is tightly associated with geographical scale, and this relationship, which is referred to as “scale as level,” implies that administrative level can be seen as a special type of scale. According to Lin (2009), the concept of scale refers to the political and spatial arena in which the relations between cities at different levels and their hinterlands are constituted. Thus, upgrading of the administrative level could be considered as a type of scaling-up strategy. Two types of scaling-up strategies are widely used by cities—administrative annexation and administrative level upgrading. The first strategy is usually used by cities at the prefecture-level and above to annex suburban counties or county-level cities and incorporate them into an expanding urban district. The second strategy has more important effects on the urban system development, which is the focus of this chapter. Because the number of centrally administered cities and vice-provinciallevel cities is constant and small, few cities could be upgraded to the top administrative level. Therefore, this chapter attempts to emphasize the upgrading from county-level cities to prefecture-level cities and the upgrading from counties to county-level cities. Emphasis is particularly placed on the problem of how upgrading change the state power of cities (counties) in terms of their growth. Figure 7.1 presents the change in the number of cities from 1978 to 2013. The number of prefecture-level cities increased steadily, whereas the number of county-level cities increased quickly from the late 1980s to the mid-1990s and then decreased after 1997. Upgrading from the county-level to the prefecture-level has no official rule, but this is likely to be used as a strategy to restructure the regional economic system. Prefecture-level cities are created in three ways, namely, by merging prefecture-level cities with prefectures, by upgrading from county-level cities, or by upgrading directly from counties. The first way is carried out by merging an existing prefecture-level city with the prefecture in which the city is located (dishi hebing). Prior to such merging, the prefecture has a prefecture-level city within its borders, and the prefecture administers the entire area except for the city. After merging, the pre-existing prefecture government is eliminated, and the entire geographical area is governed by a newly established prefecture-level government. Simultaneously, the previous area of the prefecture-level city is usually converted to an urban districts in which the new prefecture-level city government is located. A typical example for the first method is the merging of Suzhou city with Suzhou

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Fig. 7.1 Changes in number of Chinese cities from 1978 to 2013 (Source NBS 2010, 2011)

prefecture in 1983 (Fig. 7.2). Before merging, Suzhou city was a prefecture-level city that only governed the central urban area, and the Suzhou prefecture was another prefecture-level administrative unit that governed six counties. After merging, the

Fig. 7.2 Merging a prefecture-level city with a prefecture: Suzhou’s case

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former Suzhou city has been turned into four urban districts, and the six counties were placed under the administrative control of the new prefecture-level Suzhou city. The second method of creating a prefecture-level city is involving abolishing prefectures and establishing prefecture-level cities (chedi sheshi or di gai shi). This method eliminates a prefecture and upgrades a county-level city located in the prefecture to a prefecture-level city. The method places the rest of the prefecture’s counties or county-level cities are placed under the newly upgraded prefecture-level city. The previous area of the upgraded county-level cities is converted into the urban districts in which the new prefecture-level city government is located. This method has been widely implemented in many areas since 1978 and has increased the number of prefecture-level cities. For example, the Dezhou prefecture comprised two countylevel cities (Dezhou and Leling) and nine counties before 1994 (Fig. 7.3). During this period, the Dezhou prefecture was abolished, and the county-level Dezhou city was upgraded to a prefecture-level city, and the other ten county-level units were placed under this newly established city. The previous area of the county-level Dezhou city was converted to the Decheng district in which the new prefecture-level city government was located. The third way is implemented in a similar method of the second way, but the difference is that a county within the prefecture is upgraded directly to prefecturelevel city to enable it to administer its surrounding counties. An example of the third way is Qingyuan which was previously a county in Guangdong province. In 1988, the county of Qingyuan was promoted as prefecture-level city, and a county of Guangzhou and five counties from Shaoguan were placed under the new prefecturelevel city Qingyuan (Fig. 7.4). The previous area of Qingyuan county was converted

Fig. 7.3 Upgrading of a county-level city to a prefecture-level city: Dezhou’s case

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Fig. 7.4 Upgrading of a county to a prefecture-level city: Qingyuan’s case

to two urban districts where the prefecture-level government located. The first method does not change the administrative level and number of prefecture-level cities but extends their administering geographical areas. By contrast, the second and third methods upgrade the administrative level of cities/counties and increase the number of prefecture-level cities. It should be noted that the third method is not common in practice because it typically upgrades a relatively developed city instead of a county. As discussed in the above section, prefecture-level cities are granted higher state power compared with counties or county-level cities. Such upgrading actually establishes a development center in the region. Therefore, the process should exert a significant influence on urban size and urban growth. Different from the upgrading of a county-level city to prefecture-level city, the upgrading of a county to a county-level city follows official criteria, which were first issued in 1983 by the Ministry of Civil Affairs. Owing to the low minimum requirements, approximately 100 counties obtained city status from 1983 to 1986. After 1986, the central government raised the minimum requirements for the upgrading of a county to a county-level city. Approximately 180 counties upgraded to countylevel cities during the period of 1986–1993. In 1993, the newly issued requirement set different standards for counties with different population densities. After 1993, approximately 70 counties obtained city status. However, the criteria are not rigidly applied in practice. According to Li (2011), the upgrading process is not automatic for those who satisfy the criteria but is taken as a policy instrument to give local governments the right incentive to promote economic development. After 1997, the central government suspended the massive upgrading of counties to county-level cities because the newly upgraded cities led to considerable fiscal burden. Thus, the

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number of county-level cities increased slowly during the late 1990s, and declined after 2000 because a few of county-level cities have been converted to urban districts. No evidence in the existing literature demonstrates whether upgrading from a county to a county-level city can promote urban growth. County-level cities and counties are at the same administrative level, although the former may have larger state power than the latter because of the benefits of being a city. Table 7.2 lists the benefits of county-level cities over counties. Considering that these benefits vary from province to province, the table only shows the general conditions. For example, compared with counties, county-level cities can secure more quotas to convert farm land to construction land, can approve large investment projects independently, and are likely to establish branches of large SOEs. Moreover, county-level cities enjoy a higher urban construction tax ratio (7%) than that of the counties (5%), and are allowed to collect the surcharges levied on the issuance of motorcycle registration. County-level cities also enjoy greater administrative power to facilitate foreign trade, attract investors, and extend business services, among others. However, these benefits may not lead to significant differences in government capacities between county-level cities and counties. Both county-level cities and counties are under the administrative control of prefecture-level units. Hence, upgrading from counties to county-level cities may not be able to provide endogenous driven forces for county-level cities. In the next section, the effects of administrative-level upgrading on urban size and urban growth are explored. Table 7.2 Incomplete lists of benefits of being a county-level city over counties Dimensions

Benefits

Tax and fiscal

County-level cities enjoy a higher urban construction tax (7% compared to 5% for counties); could collect the surcharges levied on the issuing of motorcycle registration

Land development

Cities are more likely to get more urban construction land quotas from the superior government than counties

Preferential policy

After achieving the status of “line item under province” (Shengji Jihua Danlie), the county-level cities can report directly to the province government to ask for investment projects

Administrative power

County-level cities have more authority on foreign trade and exchange management; gains authority over police recruitment and vehicle administration; could establish the branch of custom and large State-Owned banks; could approve projects with higher cap of investment

Government size

County-level cities could establish more branches of government and have a larger size of government employees

Rank and salary

At times, the bureaucratic rank and salary of officials are raised after upgrading to county-level cities

Reputation

County-level cities generally carry greater prestige and rare more attractive to investors

Source Li (2011)

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7.3 Methodology and Model 7.3.1 A Quasi-Experiment with PSM and DID This chapter employs the PSM method and the DID approach, which collectively create a quasi-experimental setting that controls for endogeneity and precisely captures the dynamic effects of administrative-level upgrading on urban size and urban growth. By virtue of a quasi-experimental approach, we can capture the causal relationship between the adoption of administrative level upgrading and urban growth, which is difficult to address using regression models. In the regression models presented in Chapter 6, the variable that measures the effect of administrative level upgrading on urban growth indicates an insignificant relationship between the two (see Table 6.7). In these regression models, the urban growth of newly upgraded cities is virtually compared with that of other cities that have been upgraded for a certain period. The issue explored in this chapter is whether a city grows faster if its administrative level is upgraded than that if its administrative level remains the same. The ideal method for capturing the causal effect of administrative level upgrading is the conduct of a controlled experiment, in which administrative level upgrading is randomly assigned to one group of cities (treatment) whose urban growth is compared with that of another group of cities not receiving the same treatment (control). However, unlike in natural sciences and engineering, such a controlled experiment cannot be performed in reality. The essence of the quasi-experiment approach is to create a control group of twinlike counties/cities for the treatment group of counties/cities that have upgraded their administrative levels, and subsequently compare the differences in the urban growth patterns of the two groups in a certain period (Fig. 7.5). The PSM method developed by Rosenbaum and Rubin (1983) is used to match the control group. The PSM method matches each city that upgraded its administrative level ex post (treatment) to a city that was not upgraded ex post but is found to have an approximately equal likelihood of upgrading its administrative level ex ante (control). The assumption

Fig. 7.5 Illustration of the basic concept of the PSM and DID approaches

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171

of the PSM estimator is called the “selection on observables” (Abadie and Imbens 2006; Heckman et al. 1998). Specifically, the propensity score is estimated using logit regression based on ex ante observable urban characteristics that may affect the likelihood of administrative level upgrading, including economic structure, size, location, and so on. Put it simply, the PSM method selects a county/city whose administrative level has not been upgraded and whose characteristics are sufficiently similar (this implies that it has the largest probability to be upgraded) to those of another city whose administrative level has already been upgraded. In this chapter, the PSM method, in conjunction with the DID approach, is used to control for endogeneity in the decision pertaining to the upgrading of urban administrative levels. The DID estimator is a popular tool for estimating causal relationships resulting from specific interventions or treatment (Angrist and Pischke 2008). In this chapter, the DID method is used to estimate the effect of administrative level upgrading on urban size based on the control group constructed using the PSM method. The effect is assessed by comparing the pre-upgrading and post-upgrading difference in the urban sizes of the treatment and control groups. By comparing the differences in the urban sizes of the treatment group and control group before and after the administrativelevel upgrading, we can use the DID estimator to evaluate the so called “treatment effect” (the administrative effects in this chapter). The regression DID model takes the form of Pit = α0 + α1 (AF T E R)t + α2 (T R E AT )i + α3 (AF T E Rt × T R E ATi ) + β  X it + εit

(7.1)

where Pit indicates urban size of city i at time t. The regression DID model comprises the following four elements: (1) A dummy variable for the post-upgrading period, which is denoted as AFTERt , varies over time. This dummy controls for the fact that conditions change over time regardless of whether the city belongs to the treatment or control group. This dummy variable is equal to 1 if time t is in the post-upgrading period; otherwise, it is equal to zero. (2) A dummy variable for the treatment group, which is denoted as TREAT i , varies across cities. This variable controls for the fixed differences in the cities being compared. It is equal to 1 if a city belongs to the treatment group; otherwise, it is equal to 0. (3) An interaction dummy variable, which is denoted AFTERt × TREAT i , is generated by multiplying the above two dummies. The coefficient of this term, α3 , is the DID causal effect. It is equal to 1 if a city belongs to the treatment group in the post-upgrading period, otherwise, it is equal to 0. (4) A vector of socioeconomic variables denoted as X it , which controls for other factors, may affect the urban population size of cities under both groups. Based on a panel data set, the DID estimator can evaluate whether the administrative level upgrading has a causal effect on urban size and urban growth rate for a

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certain period after the upgrading. The coefficient of this term AFTERt × TREAT i , α3 , indicates the causal effect of interest.

7.3.2 Estimation Issues Several key issues regarding the estimation need to be clarified before performing the analysis. The first issue is the period of analysis. Massive urban administrative upgrading occurred in late 1980s and early 1990s. In 1997, the China State Council issued a notice to suspend the massive upgrading. The urban data set used in this book covers four periods—1984, 1990, 2000 and 2010. Considering that the causal effect of administrative level upgrading requires sufficient time to influence urban size and growth, we select the year 1990 as treatment time (t). As such, the year 1984 is considered as the pre-upgrading period, and 2000 and 2010 are taken as the post-upgrading periods. Using these four periods, we can measure the changes in urban size and urban growth rate from t-1 (1984) through t + 2 (2010). The DID estimation can track the changes in urban sizes and urban growth rates in the span of over 10 years, which is an adequately long period to capture the effect of administrative level upgrading. The second issue is the selection of cities in the treatment and control groups. Cities whose administrative levels were upgraded during 1990 and 2000 are selected to comprise the treatment group. Most of these cities were upgraded during the early 1990s (approximately 1990–1994). The two types of administrative upgrading, namely, the upgrading form a county-level city to a prefecture-level city and the upgrading form a county to a county-level city, are analyzed separately. The treatment cities were county-level cities/counties in 1990, later became prefecturelevel cities/county-level cities before 2000, and finally remained as prefecture-level cities/county-level cities in 2010. The control group cities were matched from the cities that were county-level cities/counties from 1990 to 2010. The numbers of treatment cities of the two types of upgrading are 64 and 179, respectively. The numbers of cities used to match the control groups of these two treatment groups are 203 and 1732, respectively. The third issue is the matching of the control group using the PSM method. Several matching methods include one-to-one match, k-nearest neighbor matching, radius matching, kernel matching, and stratification or interval matching. Given the nature of the data set in this chapter, the one-to-one match method is used. In matching the control group for the city-level to prefecture-level upgrading, no additional constraints are imposed because the number of potential control group cities is only 203. In matching the control group for the county to city-level city upgrading, the following conditions is imposed—cities must be located in the same sub-region. This chapter adopts the division method that introduced in Sect. 3.2.4, which divides the Chinese territory into seven sub-regions—Northeast China, North China, Northwest China, East China, Central China, South China, and Southwest China (Fig. 7.6). Cities within the same sub-region are assumed to face similar

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Fig. 7.6 Seven sub-regions of China

socioeconomic contexts. Such constraint tighten the match because it controls for unobservable factors of cities, such as policies, geographical factors, and exogenous shocks. As present in the next two sections, the PSM and DID approaches are employed for the two types of upgrading. In matching control group, we run the probit regressions to predict the propensity scores based on the urban characteristics of 1990. Using the matched city pairs, the DID estimation can measure the changes in urban sizes and urban growth rates before and after administrative level upgrading.

7.4 Upgrading of a County-Level City to a Prefecture-Level City 7.4.1 PSM Results A total of 64 county-level cities which were upgraded to prefecture-level cities and were still prefecture-level cities during 1990–2000. These 64 cities are taken as treatment group cities in this analysis. The 203 remaining county-level cities from 1990 to 2010 are used to match the control group. The first step is to run a probit regression using the entire sample (267 cities) and calculate the propensity scores of being upgraded based on the probit regression. The probit regression is run using 15 variables that measure the socioeconomic characteristics of cities: urban population, urbanization ratio, GDP, proportion of population aged between 16 and 64 years,

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Fig. 7.7 Geographical distribution of treatment and control cities for the county-level to prefecturelevel upgrading

proportion of migration population, second and tertiary employment share, market potential,4 proportion of population with college degree, per capita budgetary fiscal revenue and expenditure, per capita foreign direct investment, savings from total residence accounting, and proportion of employment of foreign firms. With the propensity score calculated from the probit regression, we match the treatment group cities with the control group cities using STATA’s psmatch2 (Leuven and Sianesi 2015). A total of 64 control group cities are matched using the one-to-one match method. The distribution of both treatment and control group cities are shown in Fig. 7.7. Before comparing the relative urban sizes and growth rates of the treatment and control group cities, we must ensure that the matching results create comparable groups. Table 7.3 shows the balancing tests for the treatment and control groups. The tests first compare the sample means of the variables included in the matching procedures of the treatment and control groups using individual t-tests. The test results indicate that these variables have no significant differences in the treatment and control groups,5 thereby confirming the balancing hypothesis that the cities of the two groups share the same likelihood of being upgraded in 1990. Hotelling’s T2 test is performed to test the joint null hypothesis that the means of all the variables included in the propensity score calculation are equal. The F-statistic of Hotelling T2 4 The

variable of market potential is derived from the calculation in Chapter 5. significance of the variable measuring the proportion of the population with a college degree is 0.053, which indicates that that this variable may differ in the treatment and control groups if the threshold for significance is 0.1. Nonetheless, the result is acceptable, considering that the number of cities used to match the control group is only 203.

5 The

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175

Table 7.3 Balancing tests for the treatment and control groups Variables Urban population

t-test on the mean of each matching variable Treatment

Control

159,318

142,164

Urbanization ratio GDP

36.4 89,942

31.9 87,072

t-stat

p-value

1.58

0.117

1.16

0.248

0.32

0.753

−0.39

0.694 0.402

% of population between age 16–64

0.017

0.018

% of migration population

6.3

5.5

0.84

% of Second sector

27.0

25.4

0.82

0.415

% of Tertiary sector

25.8

23.8

1.07

0.289

Market potential

1.8E + 06

1.6E + 06

1.33

0.184

% of population with college degree

0.78

0.54

1.95

0.053

Per capita budgetary fiscal revenue

199.6

154.6

1.58

0.117

Per capita budgetary fiscal expenditure

152.6

143.3

0.58

0.563

0.12

0.908

0.56

0.576

0.06

0.952

0.10

0.920

Per capita foreign direct investment Total residence accounting savings

2.17 36,809

Per capita fixed assets investment

316.3

% of employment of foreign firms

35,056 312.3

0.99

# of Matches Hotelling test

1.98

64 T-squared 16.6

0.92 64 F-stat

P>F 0.98

0.478

N 128

test is 0.98 with a p-value of 0.478, which indicates that the null hypothesis cannot be rejected. In other words, the matching results satisfy the balancing condition and the cities of the two resulting groups are comparable.

7.4.2 DID Estimation Table 7.4 shows the tests for the changes in the urban sizes and growth rates of the treatment and control groups. This table displays the test results that indicate the differences in the urban sizes and urban growth rates of the 64 upgraded cities relative to the urban sizes and urban growth rates of their county-level counterparts.

176

7 Effects of Urban Administrative System …

Table 7.4 Changes in the urban sizes and growth rates of the treatment and control groups for the county-level to prefecture-level upgrading Urban size Year

1984

1990

2000

2010

Treatment

120,774

159,318

387,814

522,976

Control

115,990

142,164

267,262

337,424

ATT

103,398***

168,398***

S.E.

3.0E + 04

3.9E + 04

t-statistic

3.42

4.28

64

64

#Match Hotelling test

64 F = 28.4*** (p-value = 0.000)

Annual urban growth rate (%) Year

1986–1990

1990–2000

2000–2010

Treatment

6.22

9.96

3.48

Control

6.31

7.74

2.52

ATT

2.31**

1.05

S.E.

1.18

1.1

t-statistic

1.96

0.95

129

129

#Match Hotelling test

F = 4.10** (p-value = 0.044)

The average treatment effect on the treated (ATT)6 measures the difference between the two groups of cumulative changes in urban size and growth rate since the year prior to upgrading (1990). The results demonstrate that during the first period of administrative upgrading (1990–2000), the upgraded cities experienced an average increase in urban size that was 103,398 higher than that of the county-level cities in the control group (Table 7.4). During the second decade (2000–2010), ATT increased to 168,398. These estimates are all significant at the 1% level, thus implying a significance in statistics. The change in the annual urban growth rate is also examined through the same test. The results show that during the first period of administrative upgrading (1990–2000), the average increase in the annual growth rate of the treatment cities was 2.31% higher than that of the control group cities. However, the ATT decreased to 1.05, which is not statistically significant. To further examine the effect of administrative upgrading, we perform a regression using Eq. 7.1. Three years’ worth of data (1990, 2000, and 2010) are used in the regression models. The variable After is coded as 1 if the observations cover the year 2000 or 2010, and is coded as 0 if the observations cover the year 1990. Table 7.5 6 The

ATT is calculated using the equation below, where subscript k represents the one and two decades (2000 adoption of upgrading and 2010) since the   in year t (1990): k = n1 (xt+k − xt )T r eat − (xt+k − xt )Contr ol The corresponding standard errors are calculated, and a t-test is then employed to determine whether the accumulated difference between these two groups is statistically significant.

7.4 Upgrading of a County-Level City to a Prefecture-Level City

177

Table 7.5 DID estimation of the effect of county-level to prefecture-level upgrading on urban size DV: Urban size (1) Intercept Treat After

(3)

10.10***

(0.059)

(0.26)

(0.0081)

(0.030)

0.12

0.065

−0.00095

0.0063

(0.075)

(0.053)

(0.0097)

(0.010)

0.88*** 0.26*** (0.069)

GOVCAP

0.54*** (0.053) 0.23*** (0.055)

0.063***

(4)

11.65***

(0.053) Treat × After

DV: Annual urban growth rate (2)

−0.012 (0.0079) 0.17*

0.021** (0.0092) −0.16***

(0.25)

(0.042) −0.0044**

0.12***

Service/Manu FAI (log)

0.024** (0.010)

(0.0093)

0.64***

MP (log)

0.18***

(0.019)

(0.0022)

−0.11***

−0.017***

(0.032)

(0.0034)

0.0041

−0.0031*

(0.011)

(0.0017)

Observations

384

384

384

384

Number of cities

128

128

128

128

R2 Wald χ2

0.79 977.2

0.85 130.6

0.01 14.5

0.23 77.2

Note *** Significant at 0.01 level; ** Significant at 0.05 level; * Significant at 0.1 level

shows the results of the DID estimations. In column (1) and (2), the dependent variable is urban size measured by urban population; in column (3) and (4), the dependent variable is annual urban growth rate. Column (1) presents the baseline model, and column (2) includes four variables in order to control for urban characteristics that may influence urban size. The results of these two columns show that the coefficient of the interaction term Treat × After is positive and significant, which suggests that upgraded cities significantly become larger than control group cities after controlling for other urban characteristics. Similarly, column (3) represents the baseline model, and column (4) includes control variables. The results also present that the urban growth rates of treatment cities are significantly higher than those of control group cities. The results suggest that county-level to prefecture-level upgrading has a positive effect on urban size and urban growth. Figure 7.8 provides evidence of the common trends of the urban size growth of the treatment and control groups. The newly founded prefecture-level cities experienced significantly rapid growth during the first decade after the upgrading (1990–2000). During the second decade (2000–2010), the

178

7 Effects of Urban Administrative System …

Fig. 7.8 Trends in the urban growth of the treatment and control groups for the county-level to prefecture-level upgrading

growth rate of these treatment cities became slowed down but still remained higher than that of the control group cities. The ATT of the annual urban growth rate in the second decade is 1.05%, implying the effect of administrative upgrading on urban growth continued in the second decade after they are upgraded. The regression result also verifies the urban growth enhancing effects of administrative level upgrading. However, such effect decreases over time, as evidenced by the ATT of the annual urban growth rate, which was positive but not significant in the period of 2000–2010 (1.05%).

7.5 County to County-Level City Upgrading 7.5.1 PSM Results A total of 179 counties were upgraded to county-level cities during the period of 1990–2000 and remained county-level cities in 2010. The 170 cities are taken as the treatment group. A total of 1732 counties remained their county status from 1990 to 2010. Owing to the large number of counties for matching, this chapter matches each treatment city to the counties within the same sub-region. The other PSM procedures are the same as those presented in the previous section. However, given

7.5 County to County-Level City Upgrading

179

Fig. 7.9 Geographical distribution of treatment and control cities/counties for the county to countylevel upgrading

the limited availability of county data, the variables used to run the probit regression are fewer than those used in the above section. Ten variables are included to measure the socioeconomic characteristics of counties: urban population, urbanization ratio, GDP, proportion of population aged between 16 and 64 years, proportion of migration population, second and tertiary employment share, proportion of population with college degree, share of employment over total population, and market potential. On the basis of the propensity scores obtained from the probit regression, 179 control counties are matched to 1732 counties. Figure 7.9 shows the geographical distribution of both the treatment and control group cities. The balancing tests for the treatment and control groups are also conducted to examine the validity of the matching. Both the individual t-tests and Hotelling’s T2 test are used to test whether significant differences exist in the 10 variables of the two groups (Table 7.6). The means of the 10 variables have no significant differences for both groups, and the F-statistic of Hotelling’s T2 test is 0.81 with a p-value of 0.617, which indicates that the 10 sets of means are equal between the two groups. Thus, the results confirm that the balancing conditions are satisfied and that the two groups are comparable. The matching result is essentially much tighter than that in the previous section.

180

7 Effects of Urban Administrative System …

Table 7.6 Balancing tests for the whole sample Variables Urban population Urbanization ratio GDP % of population between age 16–65

t-test on the mean of each matching variable Treatment

Control

83,671

78,227

15.06 11,541 66.3

14.82 11,123 65.8

t-stat

p-value

1.29

0.195

0.19

0.846

0.60

0.548

1.20

0.228

0.95

0.340

−0.29

0.767

% of population with college degree

0.14

0.13

% of migration population

2.3

2.4

% of Second sector

15.3

13.8

1.29

0.197

% of Tertiary sector

10.9

11.0

−0.24

0.811

% of employment in total population

56.7

56.2

0.65

0.514

1.97E + 06 −0.18

0.859

Market potential

1.95E + 06

# of Matches

179

Hotelling test

T-squared 8.33

179 F-stat 0.81

P>F

N

0.617 358

7.5.2 DID Estimation According to the result of the PSM, this section examines the effect of the county to county-level city upgrading on urban size and growth rate. Table 7.7 presents the estimations of the ATT for urban size and urban growth rate since the year prior to the upgrading (1990). In terms of the changes in urban size, the results demonstrate that during the first period of administrative upgrading (1990–2000), the newly founded county-level cities experienced an average increase in size that was 65,680 higher than that of the remaining counties in the control group. However, the ATT decreased to 51,352, which implied the small differences in the sizes of the treatment and control groups. This result can be verified with the ATT tests for annual urban growth rate. The ATT of the first decade after the upgrading (1990–2000) was 3.70%, which was positively significant; by contrast, the ATT of the second decade (2000–2010) was negative and significant (−3.30%). This difference suggests that the growth rates of these upgraded cities were significantly higher than those of the counties during the first decades after the upgrading but that such rates became significantly smaller than those of the counties in the second decade. In other words, the effect of the county to county-level city upgrading was not likely to continue in the second decade after upgrading. DID estimation by panel data regression is performed to assess the effect of the upgrading. In Table 7.8, column (1) and (2) present the regressions on urban size, and columns (3) and (4) present the results of the regression on annual urban growth rate. The estimations indicate that the county to county-level city upgrading exerts a positive effect on urban size even after controlling for several important factors because

7.5 County to County-Level City Upgrading

181

Table 7.7 Changes in urban size and growth rate of upgraded cites and control group cities Urban size Year

1986

1990

2000

2010

Treatment

74,339

83,671

223,209

299,010

Control

74,524

78,227

152,085

242,214

65,680***

51,352***

S.E.

1.1E + 04

1.6E + 04

t-statistic

5.76

3.12

#Match

179

179

ATT

Hotelling test

F = 25.8*** (p-value = 0.000)

Annual urban growth rate (%) Year

1986–1990

1990–2000

2000–2010

Treatment

5.52

11.7

3.13

Control

4.78

7.26

5.69

3.70**

−3.30**

ATT S.E.

1.60

1.58

t-statistic

2.27

−2.05

179

179

#Match

179

Hotelling test

F = 2.18 (p-value = 0.14)

the coefficients of the interaction term Treat × After are positive and significant in columns (1) and (2). By contrast, the coefficients are not statistically significant in columns (3) and (4), suggesting no significant differences in the treatment and control groups in the pre-upgrading and post-upgrading periods. Synthesizing the results of the DID estimations and ATT tests reveals that county to county-level city upgrading results in a wave of rapid growth for such cities in a short period (1990– 2000) following the upgrading. However, this effect cannot continue for a long period (may not be longer than 10 years). Figure 7.10 clearly presents the development trends of treatment and control group cities. After adopting administrative level upgrading after 1990, the county-level cities experienced a rather faster growth in urban size compared with the control group counties. However, the average growth rate of the former was smaller than that of the counties in the second decade after the upgrading (2000–2010). The DID estimations of the two types of administrative upgrading suggest that both have positive effects on city sizes. After administrative level upgrading, these upgraded cities became significantly larger than the cities/counties that remained at a lower administrative level. However, differences are observed in the effects of the two types of upgrading on urban growth rate. The county-level to prefecture-level upgrading has a longer influence on urban growth than the county to county-level upgrading has. This finding can be explained by the fact that the ATT of the annual growth rate was negative and significant one decade after the county to county-level

182

7 Effects of Urban Administrative System …

Table 7.8 DID estimation of the effect of the county to county-level city upgrading on urban size and urban growth

Intercept

DV: Urban size (log)

DV: Annual urban growth rate

(1)

(3)

(2)

11.07*** (0.042)

Treat After

0.048*** (0.013)

0.062

0.038

0.0073

0.0050

(0.061)

(0.015)

(0.016)

(0.035) 0.28*** (0.053) GOVCAP MP (log)

0.38*** (0.038) 0.33*** (0.049)

0.017 (0.013)

FAI (log)

R2

0.018 (0.017)

0.022

−0.018***

(0.025)

(0.0033) 0.00037

(0.019)

(0.0024)

−0.14***

−0.016***

(0.029)

(0.0041)

0.052

0.0037**

Wald

(0.0017)

1432

1074

1074

1074

358

358

358

358

0.71 χ2

(0.014)

(0.016)

(0.011) Number of city groups

0.030**

0.0022

0.14***

Service/Manu

Observations

0.14*** (0.035)

(0.058) 0.92***

Treat × After

9.20*** (0.24)

(4)

1642.7

0.79 2582.3

0.01 12.5

0.05 83.4

Note *** Significant at 0.01 level; ** Significant at 0.05 level; * Significant at 0.1 level

city upgrading. However, the corresponding ATT was positive for the county-level to prefecture-level upgrading. The prefecture-level cities that upgraded from the county level experienced a significantly faster growing period than the remaining countylevel cities did and then experienced a roughly parallel growth with the remaining county-level cities. The average growth rate of these county-level cities that upgraded from counties was significantly smaller than that of the remaining counties in the second decade after the upgrading. Several theoretical arguments can be drawn from these empirical findings. First, administrative level upgrading as a policy instrument has significant effects on the size of treated cities and has thus shaped the structure of China’s urban system. Moreover, upgrading of a county-level city to a prefecture-level city has “longterm” effects on urban growth, mainly because such type of upgrading provides the newly founded prefecture-level cities with great administrative authority to become regional centers. The county-level to prefecture-level upgrading can exert a type of “cumulative effects” on the upgraded cities. As a prefecture-level city, it governs a

7.5 County to County-Level City Upgrading

183

Fig. 7.10 Trends in the urban growth of the treatment and control groups for the county to countylevel city upgrading

set of count-level spatial units and benefits from the central role in this region. By contrast, the county to county-level city upgrading only provides “one-time” stimulation to these cities that could not continue in the long term. Many of these newly upgraded county-level cities only receive a short period of exogenous stimulation for urban growth instead of endogenous development abilities. For most newly upgraded county-level cities, the fast growth in the short period after the upgrading is obtained at the expense of a slow growth and even stagnation in the next period.

7.6 Conclusion This chapter examines the relationship between the UAS and urban system development in China. As a distinctive institutional arrangement, the UAS imposes a hierarchical power structure into China’s urban system. Cities are administratively governed by the UAS and are organized into a typical hierarchical structure. From the institutional perspective, administrative level is key to urban development. Cities at high administrative levels enjoy a greater variety of privileges that favor urban development compared with cities at low administrative levels. Thus, Administrative level upgrading is a widely implemented strategy adopted by many Chinese cities to extend their state power. A wide range of benefits could be obtained by cities after

184

7 Effects of Urban Administrative System …

upgrading their administrative levels, and in this sense, such upgrading is likely to promote urban growth. Therefore, this chapter examines the effects of administrative level upgrading on urban size and growth using the PSM technique in conjunction with the DID approach to estimate causal effects. Two types of administrative-level upgrading, namely, county-level to prefecture-level upgrading and county to countylevel upgrading, are analyzed through this quasi-experiment method. The basic idea of this method is to create a control group of twin-like counties/cities for the treatment group of counties/cities that have upgraded their administrative levels and subsequently compare the differences in the urban growth patterns of the two groups in the long term. The empirical results indicate that both types of administrative level upgrading have positive effects on the size of the treated cities. The population size of the upgraded cities is significantly larger than that of their remaining counterparts. In particular, the county-level city to prefecture-level city upgrading has “long-term” effects on urban growth. This upgrading places the newly founded prefecture-level city in the center of the regional economy, which essentially changes the political power of this city. This increase in political power as a result of the prefecture-level city upgrading has “cumulative effects” on the growth of these newly established regional central cities. By contrast, the effect of county to county-level city upgrading only generates “one-time” stimulation to the growth of these newly upgraded cities. During a short period after the upgrading, these cities became significantly larger than their remaining counterparts. However, the abnormally fast growth in the short period after the upgrading is achieved at the cost of slow growth and even stagnation in the next period. The reason is as follows: county-level cities and counties are at the same administrative levels, and they are all under the control of prefecture-level administrative units. Moreover, the benefits of being county-level cities could not result in fundamental changes in their political power. In sum, this chapter verifies hypothesis 3 (H3a and H3b) developed in Chapter 4. This chapter shows that although restructured, the UAS continues to be a rigid and hierarchical system in the post-reform period. With the UAS, Chinese cities are still governed administratively, resulting in their hierarchically linkages based on their administrative levels. The UAS differentiates and reorders the urban growth processes of Chinese cities. As a result, China’s urban system is strongly shaped by the omnipresent state through the administrative system, which lays the foundation for the political hierarchy of China’s urban system. The central government can use the UAS as a tool to achieve their goal of regulating urban system development. By controlling the administrative level upgrading process, it can strictly control the number of cities in order to prevent cities from growing too fast. The central government stopped the massive county to county-level city upgrading in 1997. If the trend of county to county-level city upgrading continued to the 2000s, China should have more cities and a larger urban population than it has now. In addition, the number of cities at high administrative level (i.e., provincial level or vice-provincial level) is small and constant (only Chongqing was upgraded to the provincial level centrally administered city in 1997). Some prefecture-level cities may be able to grow faster if they can be upgraded to vice-provincial or provincial level cities.

7.6 Conclusion

185

Therefore, the Chinese government can regulate urban system development and control the scale and number of large cities through the UAS. Meanwhile, the UAS is the institutional barrier for the further growth of some county-level cities or counties that have locational advantages. The city status of some counties, particularly those in coastal regions, could not be officially approved despite meeting the criteria. In this sense, the UAS cannot achieve the goal of the national urban system policy which attempts to facilitate the development of small cities.

Chapter 8

Conclusion

8.1 Major Findings China’s urban system has witnessed a rapid development and dramatic restructuring in the past three decades. Despite the implementation of economic reforms and opening-up policies since 1978, the Chinese economy is inherently political and the continuously powerful role of the state should be emphasized in studies of Chinese cities (Lin 1999; Ma 2002; Pred 1980; Wu et al. 2007; Yeh et al. 2015). A critical literature review suggests that the existing theories derived from experiences of Western advanced counties seldom consider the state as a major force in affecting urban system development. China is one of the few countries in the world that has a national urban system policy that “strictly control the scale of large cities, rationally develop medium and small cities” in regulating China’s urban system development to avoid the urban problems plaguing the primate cities in the urbanization process of the developing countries. A series of policies and institutional arrangements, such as the hukou system and the promotion of TVEs, are used to achieve the goal of controlling the scale of large cities and facilitating the development of medium and small cities. With the market-oriented reforms, globalization, and decentralization of state power, the national urban system policy may become less strong as before and the ways in which the policy are implemented are changing. Most scholars agree that the state plays an important role in affecting the development of China’s urban system, although economic reforms and opening-policies have been implemented since 1978. However, few studies empirically investigate how the state intervenes in the China’s urban system development process. In addition, the existing research considers the country as a whole or focuses on the nationwide policies, such as the hukou system, the promotion of TVEs, and globalization (Wu 2010; He et al. 2008; Naughton 1996; Sit and Yang 1997; Zhao and Zhang 1999; Chan and Zhang 1999; Shen 2006a), but shed relatively little light on the power relations and institutional changes or fix in the post-reform period which have profoundly changed the ways in which the state intervenes in urban development. Therefore, the significance and the salient inconsistencies between theory and reality have entailed © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J. Wang, The Role of the State in China’s Urban System Development, https://doi.org/10.1007/978-981-33-6362-5_8

187

188

8 Conclusion

a new conceptual framework and further empirical analysis on the role of the state in China’s urban system development. The central concern of this book is to understand how the state intervenes in the urban system in China in the post-reform period. To investigate this general question, this book constructs a conceptual framework based on the perspective of political hierarchy, suggesting that the state power is hierarchically organized in China’s urban system that leads to variations in urban government capacities among cities. A series of quantitative analyses are performed to investigate four sets of specified questions. Accordingly, three sets of testable hypotheses are formulated in Chapter 4, which have been supported by the empirical investigations. In conclusion, the major findings are summarized question by question in the following parts. What are the development patterns and distinctive features of China’s urban system in the post-reform period? China’s urban system development exhibits a development trajectory that is different from that in the pre-reform period owing to the economic reforms and opening-up policies. The transformation of ideology has led the urban development policies from “anti-urbanism” to “urban-biased”. In the post-reform period, the development and restructuring of China’s urban system have occurred in three dimensions—temporal, hierarchical, and spatial. There were a surge of new cities and rapid growth of existing cities in China, resulting in both the horizontal and vertical expansions of urban system. The development focus has gradually shifted from the interior to the coastal region in the post-reform period, giving rise to the concentration of newly designated cities and high growth cities in the eastern region. In addition, the development focus has gradually shifted from small and medium cities to large cities especially for the large cities located in the eastern region. Another critical feature is the emergence of the mega-city regions such as the PRD and YRD. The economic reforms and resulting changes of policies and institutions—introducing the market mechanism and redefining of the role of the state, shift of the focus of state policies, reforms on the hukou system, and restructuring of the UAS—have triggered the rapid development and dramatic restructuring of China’s urban system in the post-reform period. However, a close examination reveals that China’s urban system development is distinguished by several distinctive features. For example, the growth of Chinese cities do not follow the Gibrat’s law, which is an empirical regularity derived from Western advanced counties. Moreover, although China’s city-size distribution exhibits some properties of Zipf distribution, the empirical evidence also indicates that the city-size distribution of China deviates from the standard Zipf distribution. The distinctive features suggest that China’s urban system development is driven by the forces different from that of Western advanced countries. The state is a major driving force of the development of China’s urban system that may largely contribute to the distinctive features, but the influence of the state in Western advanced countries is relatively low. Has China’s national urban system policy achieved its goal of regulating the development of urban system in the post-reform period? What would China’s

8.1 Major Findings

189

urban system be if state regulation was reduced or largely eliminated since the beginning of economic reforms? This book suggests that the national urban system policy has achieved its goal of “strictly control the scale of large cities, and rationally develop medium and small cities”. The direct evidence is that the urban growth rates of Chinese cities have a negative relationship with urban sizes throughout the post-reform period, and the Zipf coefficient of the city-size distribution is significantly small. Based on the comparison between the simulated urban systems and the actual urban system in 2010, this book finds that the total urban population of all simulated urban systems with no or less state interventions are larger than that of the actual urban system in 2010. The actual sizes of large cities are significantly smaller than that of the simulated urban systems. Meanwhile, the actual sizes of medium and small cities are larger than that of the simulated urban systems. This suggests that China’s urban system is under-developed because of the national urban system policy. Especially, the growth of large cities is strictly controlled by using the hukou system and other related institutional arrangements. However, the medium and small cities have experienced rapid growth that promoted by the state policies such as the TVEs and relaxation of the hukou system in the post-reform period. More specifically, the effects of the national urban system policy on the development of China’s urban system are twofold. On the one hand, the state tries to control the development of large cities to avoid the urban problems plaguing the primate cities in the urbanization process of the developing countries. Thus, state policies are implemented to facilitate the development of the medium and small cities, and strict hukou control has not relaxed for these large cities. On the other hand, the state needs the large cities to play as engine of economic development. Development priorities, such as different types of preferential policies, have given to the cities with better locational advantages (e.g., cities in the PRD and YRD), and market force are also conducted by the state’s preferential policies to favor these cities. The twofold effects of the state should be altogether attributed to the national urban system policy of “strictly control the scale of large cities, rationally develop medium and small cities”. In order to achieve the goal of this policy, the state also promotes the development of medium and small cities at the cost of economies of scale and locational disadvantages. As a result, China has successfully avoided the urban problems of the primate cities in other developing countries, but this success is achieved at the expense of the economic efficiency. In addition, this book also shows that the national urban system policy that attempts to control the scale of large cities became less strong since the late 1990s. The urban growth rate has a significant negative relationship with urban size in 1982– 1990 and 1990–2000, but does not have significant relationship in 2000–2010 which suggests a stochastic urban growth process in this period. Moreover, the Zipf coefficient increased from 0.805 in 2000 to 0.850 in 2010, indicating the relative faster growth of large cities in this period. Although the Chinese state continues to control the scale of large cities and develop medium and small cities based on the national urban system policy, it has increasingly realized that this strategy cannot be effectively implemented since the late 1990s.

190

8 Conclusion

If the state regulations are completely eliminated in the Chinese context, the gap between the large and small cities as well as between coastal and interior cities will be further enlarged. Spatially, if the state regulations are completely eliminated, the regional differences will be enlarged and population will increasingly be concentrated in the coastal cities with locational advantages. Hierarchically, the urban development process without state regulations will enhance the growth of larger cities and suppress the growth of small cities, confirming the phenomenon of “rich get richer”, repeating the urbanization process and concomitant urban problems in the developing countries which China would like to avoid. The state, as a regulation force, has therefore reduced the gaps between cities at the top and bottom of the urban hierarchy and between cities located in the coastal and interior regions. Thus, regarding the objective of economic efficiency, the effects of the state may be negative. But, in terms of the objective of equality or balanced development, the effects of the state should be positive. How can we understand the role of the state in urban system development in China? In this study, urban government capacity, institution and policy are three important factors for understanding how the state affects China’s urban system development in the post-reform period. Specifically, urban government capacity directly determines the abilities of cities to affect urban development, but several institutions (e.g., fiscal system, the UAS, hukou system) and policies (e.g., national urban system policy, preferential policies) are powerful instruments for the central government and provincial governments to influence the distribution of urban government capacities among cities. In this sense, the central government has indirect impacts on urban system that are exercised through the institutions and policies. Owing to the profound reorganization of state power and institutional changes or fix during the post-reform period, China’s state has changed from a single unitary power to a power matrix in which Chinese cities are hierarchically organized by the administrative system. Thus, the ways in which the state affect urban system development are rather different from that in the pre-reform period. During the prereform period, the state can be seen as a unitary power under which all productions activities were regulated by a centrally planned economy, and local governments at different levels merely implemented plans of the central government. After “grow out of the plan”, the central government has on longer directly intervened in local affairs. State regulation on urban system development cannot be implemented through direct commands and plans but in a way that is close to that in a market economy. The major responsibility for urban development has gradually devolved from the central government to urban governments. As the de facto actors responsible for urban development, the urban governments have been empowered to extend their capacities to promote urban development. Therefore, urban government capacity has become a critical factor for urban development. The capacity of an urban government, that is, the ability to take on administrative functions effectively, mobilize fiscal resources, and implement policy instruments as a form

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191

of intervention in economic activities, primarily affects urban size and growth. Urban government capacities differ across cities at different administrative levels because of the hierarchical distribution of state power among cities. Moreover, China’s urban system is still strongly shaped by the omnipresent state through the UAS, which lays the foundation for the political hierarchy of China’s urban system. In addition to emphasize the importance of the national urban system policy and the hukou system, the hierarchical power relations amongst cities and resulting variations in urban government capacities are the keys for unfolding the “black box” of the state in understanding the role of the state in China’s urban system development in the post-reform period. The UAS is employed as a key institutional arrangement to regulate the development of the urban system. The central government can exert strong impacts on urban growth to achieve its policy goals, such as the national urban system policy, through the UAS. By controlling the administrative level upgrading process, it can strictly control the number of cities in order to prevent cities from growing too fast. Chinese cities bear a resemblance to the “marionettes”, that is, their state power is controlled from superior governments using the UAS. To what extent are urban government capacity responsible for the sizes of cities and the uneven growth of cities within the urban system? How does the state interact with market forces in affecting urban growth? Variations in urban government capacity across cities at different administrative levels make a difference for urban growth performance. By a series of regression models at the city-level in Chapter 6, this book reveals that both urban government capacity and market potential (a measurement of market forces in this book) are dominant factors in determining urban size. The interaction representing the interplay of urban government capacity and market potential is positive and significantly associated with urban size. The findings verify the complementarity between urban government capacity and market potential. It indicates that the urban government capacity can play an enable role in helping to increasing the effect of market forces on urban size. The urban growth models show that the base period urban government capacity has a positive effect on urban growth but that the change in urban government capacity has no significant effect on urban growth. However, the market potential in the base period and the increase in market potential in the corresponding period have fairly strong effects on urban growth. This result indicates that market forces have increasingly important effects on urban growth during the post-reform period. When decomposing urban growth in terms of different city categories, the effects of urban government capacity are related to administrative levels, and the interplay of urban government capacity and market potential has a substantial effect on the urban growth of cities at high administrative levels. As a result of the decentralization of state power since the beginning of marketoriented reforms in 1978, the direct regulations of the central government on urban system development have been relaxed to a large extent. Although the Chinese government still attempts to implement the national urban system policy of controlling the scale of large cities, cities that have large urban government

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capacities can promote their rapid growth. As shown in Chapter 6, large cities tend to have larger urban government capacities than that of the medium and small cities. This can explain why large cities are growing rapidly although the central government tries to control their sizes. However, urban government capacities of cities are hierarchically organized by a series of institutional arrangements, such as the fiscal system, preferential policies, and SOEs, which are controlled by the central and provincial governments. Thus, cities can extend their urban government capacity to promote growth but are still controlled by superior governments through institutional arrangements and state policies. How does the Urban Administrative System (UAS) influence the urban system development? As a distinctive institutional arrangement, the UAS imposes a hierarchical power structure into China’s urban system. Cities at high administrative levels enjoy a greater variety of privileges that favor urban development compared with cities at low administrative levels. Thus, administrative level upgrading is a widely implemented strategy adopted by many Chinese cities to extend their state power. However, the urban administrative level upgrading is rigidly controlled by the central government. The empirical analysis in Chapter 7 reveals that both types of administrative level upgrading have positive effects on the size and urban growth. During a short period after the upgrading, the population size of the upgraded cities is significantly larger than that of their remaining counterparts. In particular, the county-level city to prefecture-level city upgrading has “longterm” effects on urban growth. This upgrading actually places the newly founded prefecture-level city in the center of the regional economy, which essentially changes the political power of the city. This increase in political power as a result of the prefecture-level city upgrading has “cumulative effects” on the growth of these newly established regional central cities. By contrast, the effect of county to county-level city upgrading only generates “one-time” stimulation to the growth of these newly upgraded cities. During a short period after the upgrading, these cities became significantly larger than their remaining counterparts. However, the abnormally fast growth in the short period after the upgrading is achieved at the cost of slow growth and even stagnation in the next period. The reason is that county-level cities and counties are at the same administrative levels, and they are all under the control of prefecture-level administrative units. Although restructured, the UAS continues to be rigid and hierarchical in the postreform period. With the UAS, Chinese cities are still governed administratively, resulting in their hierarchically linkages based on the administrative levels. The UAS differentiates and reorders the urban growth processes of Chinese cities. As a result, China’s urban system is strongly shaped by the omnipresent state through the administrative system, which lays the foundation for the political hierarchy of China’s urban system. The central government can use the UAS as a tool to achieve their goal of regulating urban system development. By controlling the administrative level upgrading process, it can strictly control the number of cities in order to prevent cities from growing too fast. The central government stopped the massive county to county-level city upgrading in 1997. If the trend of county

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to county-level city upgrading continued to the 2000s, China should have more cities and a larger urban population than it has now. In addition, the number of cities at high administrative level (i.e., provincial level or vice-provincial level) is small and constant (only Chongqing was upgraded to the provincial level centrally administered city in 1997). Some prefecture-level cities may be able to grow faster if they can be upgraded to vice-provincial or provincial level cities. Therefore, the Chinese government can regulate urban system development and control the scale and number of large cities through the UAS. Meanwhile, the UAS is the institutional barrier for the further growth of some county-level cities or counties that have locational advantages. The city status of some counties, particularly those in coastal regions, could not be officially upgraded despite meeting the criteria. In this sense, the UAS cannot achieve the goal of the national urban system policy which attempts to facilitate the development of small cities.

8.2 Theoretical and Policy Implications The contribution of this book lies in the two aspects: theoretical and policy. This book has four contributions to the existing literature and Chinese urban studies. First, this book contributes to the literature on urban system development by examining the role of the state. As stated previously, there are mainly two strands of literature to explain the urban system development which emphasize the geographical factors and market forces in affecting urban system development, respectively. The role of the state has received less academic attention in these two strands of literature which mainly derived from experiences of Western advanced countries. Placing the urban system development in China’s political economy, this book argues that the state is a critical factor for understanding the development of urban system. Moreover, the power relations, institutions and policies have occupied central positions in explaining the role of the state in affecting urban system development. In effects, as shown by Henderson and Wang (2007), political institution plays a key role in urban system development process across countries. The differences of the state’s role may be attributed to different power relations in different counties. Moreover, countries with similar institution are likely to have different strategies for regulating their urban systems (Bourne 1975). Therefore, the role of the state as well as related institutions and policies deserves more emphases when one investigates urban system development. Second, this book also contributes to the literature on urban studies in China, by empirically investigating the ways in which the state intervenes in urban system development. Although the majority of scholars have a consensus that the state plays an important role, but few studies have explained how the state intervenes in China’s urban system development processes. Thus, this book investigates the question of “how” rather than “whether”. By constructing a conceptual framework based on the political hierarchy and by a series of empirical models, it attempts to explain the ways in which the state intervenes in the urban system development. Moreover,

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this book has also empirically examined the interaction effects between the state and market in affecting urban development. The empirical findings in this book are helpful to enhance the understanding the dynamics and mechanisms of urban system development processes in post-reform China. Third, this book enriches the theories on China’s state by unfolding the “black box” of the state by conceptualizing the power relations between governments at different administrative levels. A conceptual framework based on the perspective of political hierarchy is formulated to understand the role of the state in China’s urban system development. Decentralization of state power from the central government to local governments, representing a change of China’s state from a single unitary power to a power matrix in geographical space. The post-reform Chinese state is therefore conceptualized as an active and dynamic institutional ensemble which is spatially and temporally contingent on the structure of cross-scalar power relations. Within this power relations, Chinese cities are organized into a hierarchically structured power relations, and the positions of cities in the hierarchy have primarily determined their urban government capacities which are critical factor for urban development. The urban governments are de facto actors responsible for urban development, and the central government has indirect impacts on urban system that are exercised through the institutions and policies. Among these institutions, the UAS lays the foundation of the hierarchical organization of cities. Fourth, this book has established the micro-foundation of the dynamics of China’s urban system development. The city-level regression models performed in in Chapter 5 and the evaluation of the effects of administrative upgrading provide empirical evidence for better understanding the relationship between the state and urban system development. Moreover, the analyses treat the state endogenously, as an endogenous factor that in the urban size and urban growth models. The variable of government capacity is used to measure the effect of the state on urban growth. Based on the city-level evidence, this book is able to investigate the macro-level development of the urban system. In addition to these theoretical implications, this book also has some implications for policy making and institutional arrangements. In general, the empirical analyses suggest that if the state continues to play dominated role, state regulation may place certain limitations on the future development of urban system. The first evidence is shown in Chapter 5, in which the total urban population of all simulated urban systems are larger than that of the actual urban system in 2010, suggesting China’s urban system is significantly under-developed. Because the simulated urban systems are under scenarios with no or less state interventions, it is therefore argued that the national urban system policy has achieve its goal of controlling large cities and regulating the urban system development. In terms of the objective of avoiding the urban problems plaguing the primate cities in the urbanization process of the developing countries, the effects of the state should be positive and successful. However, this success is achieved at the expense of the economic efficiency. Therefore, regarding the objective of economic efficiency, the effects of the state may be negative. Another important evidence is the results of the urban growth models in Chapter 6, showing the increase of urban government capacity does not bring about faster urban

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growth. On the one hand, central government still influence urban development through state policies and institutional arrangements such as the UAS. Although cities have large urban government capacities, they are not able to promote urban growth as what they expect. On the other hand, market forces play an increasingly important and effective role in affecting urban growth. Urban government need to coordinate market forces in promoting urban growth. In terms of the future reforms on the policies and institutions that related to the development of China’s urban system, several important messages emerge from the analyses of this book. Specifically, policy implications are provided for the national urban system policy, hukou system, UAS and preferential policies in light of the empirical analyses in the preceding chapters. In Chapter 5, this book directly assesses the effects of the national urban system policy on urban system development in the past three decades. Also, hukou system is the most important tool of the state used to achieve its goal of regulation. Therefore, the policy implications for national urban system policy and hukou system are based on the findings in Chapter 5. Similarly, this book examines the effect of the UAS on urban system development in Chapter 7. The preferential policies are distributed to cities through the UAS. Thus, the policy implications for the UAS and preferential policies are mainly based on the empirical results of Chapter 7. Next, the policy implications will summarized one by one. 1. National urban system policy The national urban system policy has been deleted in the new issued Law of Urban and Rural Planning in 2008. The new urban system development tends to stress the coordinated development of metropolises, medium and small cities, and small town. With the national urban system, China has been quite successful in controlling the scale of large cities in the past. Deletion of the national urban system policy does not implies that the Chinese government will facilitate the development of large cities. It mainly because that the Chinese government has increasingly realized that this strategy cannot be effectively implemented. Although this book indicates the large cities in China are under-developed, we also argue that the large cities should not grow too fast. Urbanization at this scale and speed has overwhelmed the ability of Chinese governments at various levels to manage urban areas. The flood of rural-urban migration in large cities has exacerbated the infrastructure burden of cities and led to serious urban problems, such as the environment pollution, urban poverty, widespread misuse of land, urban sprawl, traffic congestion, and so on. Therefore, we argue that there should be a national urban system plan for China’s urban system development in the future. In a country that has such a strong role of the state and without a mature market mechanism, a national urban system plan is needed to avoid the urban problems in other developing countries. The urban system plan should emphasize the structural growth of cities at different scales. Regulating or facilitating development of cities at particular scale may lead to the unbalanced development of the urban system. 2. Hukou system

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China may need to accelerate phased reforms of the hukou system to ensure that population could migrate in response to the market demands. With deepening of market-oriented reforms and evolution of the market economy, the market forces are proven to be more efficient to determine urban growth. Further reforms of hukou system, which can reduce ineffective state controls on population mobility, could exert long-term influence on urban system development. As shown in this book, Chinese large cities are under-developed partly due to the hukou system control. The recent reforms of hukou has largely relaxed the hukou migration control for the small and medium cities, but the hukou control in the large cities remain strict. The key problem of hukou status is the access to public services— health care, education, and housing. The critical constraint is the urban governments have neither fiscal resources nor incentives to extend the public services to cover the migration population. Therefore, two possible approaches may be able to promote the third phase of the hukou reform. First, separating urban public services from the hukou system, and constructing the nationwide Residential Permit System. Second, the central government should encourage cities at different administrative levels and regions to make the residential permit criteria that are tailored to their specific situations. These two changes can benefit the development and restructuring of China’s urban system because labors could move in response to market signals. Moreover, according to the empirical studies in this book, population may become increasingly concentrated in cities in the upper tail of the urban hierarchy and cities in the eastern region if the suggested changes of hukou system could be implemented. Attracting labor forces may become a serious problem for the small and medium cities, especially for the small and medium cities in the western and middle regions. As a result, the gap between large and small cities as well as the coastal and interior cities will be enlarged. 3. Urban Administrative System Adjustments of the UAS are necessary to change the hierarchically structured state power relations among cities. Although the state power has been decentralized from the central government to local governments during the post-reform period, Chinese cities are still hierarchically organized by the administrative system. As shown in Chapter 6, the UAS has restricted the growth of some low administrative cities which have advantageous factors, while it also results in over-development of some prefecture-level cities which may not be able to sustain long-term growth. For the entire urban system, the UAS has enhanced the vertical linkages among cities, but has limited their horizontal linkages. As a result, such a hierarchical structure is likely to produce negative effect on urban system development. Thus, further reforms should be put forward to free cities the rigid hierarchical relations. First, the CGC system may need to be reformed. The CGC system places an institutional obstacle for these subordinate counties or county-level cities which have comparative advantages and large market potential. In effects, the “provincegoverning-county” (PGC) system has been experimented in a few provinces. I

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argue that the PGC system could be applied to the developed provinces first and this may be helpful for the development of counties and county-level cities. Second, the county-to-city upgrading may also need further adjustment. The county-to-city upgrading has been suspended since 1997 because of the great fiscal burdens. As a result, a large number of counties which have already met the criteria of city could not obtain the city status. The number of city decreased from 666 to 651 because some county-level cities have been changed to urban districts. Thus, it is reasonable to suggest that the county-to-city upgrading should restart again after setting appropriate criteria and adjusting the CGC system. Adjustments of the two institutional arrangements are likely to reduce the negative effects of the hierarchical structured state power relations and benefit the development of the entire urban system. 4. Preferential policies Some balanced urban development policies need to be re-evaluated based on the development of the entire urban system. As examined in this book, the effect of the state on urban system development is characterized as the balance between efficiency and equality, which have reduced the gaps between large and small cities as well as between coastal and interior cities. However, they also bring about negative effects on urban system development. For example, the urban development guideline, which is labeled as “strictly control the size of large cities, rationally develop medium–sized cities, and actively develop small cities and towns”, aims to achieve the balance development between large and small cities. The negative effect is the significant under-development of the large cities, and the small cities have also not benefited from this policy. Another example is the regional development policies with the objective to obtain regional balance development, such as the policies of “Revitalize the Old Northeast Industrial Bases” and “Great Western Development Strategy”. According our simulations and comparisons with the actual urban system in Chapter 5, the results of these policies are not as expected, that is, the policy of “Revitalize the Old Northeast Industrial Bases” is difficult to prevent the decline of the Northeast cities, and the “Great Western Development Strategy” has limited effects in promoting development of western cities. Thus, I suggest the emphases of the state may be better to be placed on adjustment of institutions instead of these balance development policies. These institutions such as the hukou system, UAS, fiscal system are more important for the urban system development than these balanced development policies.

8.3 Limitations and Future Research Although this book provides a conceptual framework and performs a series of quantitative analyses to better understand China’s urban system development during the post-reform period, it also has several limitations that need to be addressed in future studies.

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First, this book pays inadequate attention to the effect of city types on urban system development. City types is an important determinant of urban growth as argued by Berry (1971). However, this book only considers a variable of Service/Manu to indicate the economic structure in Chapter 6. Owing to the data limitation, we are difficult to employ other variables. Future studies need to consider the effects of city type. Second, the empirical analyses of this book only are performed only based on the datasets of four years dataset (1984, 1990, 2000 and 2010). The data limitation is always the bottleneck for urban studies in China. There is a lack of the reliable consecutive data especially the data of the urban population. Therefore, we use the data of the Population Census which is available for every ten years. Nevertheless, the population census data only reflect the decennial evolution of the urban system. Moreover, urban system development is a long-term process so the decennial data can capture this process. Future studies can attempt to construct a reliable consecutive dataset to examine the short-term changes of China’s urban system. Third, this book focuses on the state interventions, institutions, and policies without paying adequate attention to the roles of urban residents and migrant population. This is largely this book is to evaluate the role of the state in urban system development. However, urban residents and migrant population can also exert important impacts on urban system development. Urban residents and migrant population may be influenced by different factors. Thus, future studies on urban system development should also examine the growth of urban residents and migrant population, respectively. Finally, there are a range of institutional arrangements and state policies which contribute to hierarchically organized power relation among cities, but this book only evaluate the effect of the UAS, which lays the foundation of the hierarchical organization of Chinese cities, on urban size and growth. Other institutions and policies that are also important to urban system development, such as hukou system, preferential policies and fiscal system, should be examined in future studies.

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