Human Capital Investment and the Regional Economic Gap in China 9819949963, 9789819949960

Citation preview

Human Capital Investment and the Regional Economic Gap in China Yaling Li

Human Capital Investment and the Regional Economic Gap in China

Yaling Li

Human Capital Investment and the Regional Economic Gap in China

Yaling Li School of Business and Tourism Management Yunnan University Kunming, China Translated by Fuyu Chen Chongqing Jiaotong University Chongqing, China ISBN 978-981-99-4996-0 ISBN 978-981-99-4997-7 (eBook) https://doi.org/10.1007/978-981-99-4997-7 Jointly published with Social Sciences Academic Press The print edition is not for sale in China (Mainland). Customers from China (Mainland) please order the print book from: Social Sciences Academic Press ISBN of the Chinese edition: 978-7-0101-7960-5 Translation from the Chinese Simplified language edition: “中国人力资本投资与区域经济 差距 李亚玲” by Yaling Li, © People’s Publishing House 2017. Published by People’s Publishing House. All Rights Reserved. Supported by a Grant from the Yunnan University Double First-Class Initiative © Social Sciences Academic Press 2023 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 publishers, 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 publishers 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 publishers remain neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Palgrave Macmillan imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Paper in this product is recyclable.

Preface

Since 1978, over 40 years of reform and opening-up have brought about tremendous changes in the Chinese economy. The opportunities and challenges resulting from this policy have accelerated the economic growth and transformation in China. In 2010, China surpassed Japan for the first time to become the world’s second largest economy. Despite the steady growth in its overall economy, the unbalanced economic development among its regions has become more prominent rather than improved over the past decades. Take for example the statistics of recent years. In 2013, China’s GDP reached RMB 56,613.018 billion. In that year, the Eastern Region, which includes eleven coastal provincial-level regions, created 61.71% of the country’s GDP, with 11% of its land area and 41.26% of its population. In that same year, the Central Region, which consists of eight provinces, created 27.32% of the country’s GDP, with 18% of its land area and 31.7% of its population. By contrast, the vast but sparsely populated Western Region only created 10.97% of China’s GDP, although it is comprised of twelve provincial-level regions and accounts for 71% of the country’s land area and 27% of its population. This reflects an indisputable fact resulting from the unbalanced economic development among the three regions, that is, the flow of talents from the Central and Western Regions to the Eastern Coastal Region. In 2014, China’s GDP reached RMB 63,640 billion, making it the second economy to exceed USD 10 trillion—second only to the US. Yet the uneven economic growth among its regions remained unchanged. v

vi

PREFACE

As the overall economy grew rapidly, the Eastern Region continued to account for over 60% of the national economic growth, the Central Region approximated 30%, and the Western Region lingered around 10%. This lasted in 2015 and 2016. If we make a time series analysis of the proportions of the three regions in the national GDP since the beginning of reform and opening-up, we will find that the situation in recent years is by no means isolated but due to the long-term trend of changes. Since the beginning of reform and opening-up, the Chinese economy has been developing rapidly, but the regional gaps in economic development has been widening. As a result of high-speed economic growth, the Eastern Region has been gradually enlarging this gap with the Central and Western Regions. And the gaps in economic growth are on the decrease from the Eastern to the Central and Western Regions. Such gaps in economic growth have given rise to regional gaps in output and income and exacerbated uneven flow of factors—such as labor and capital—among the three regions. As the first to open to the outside world, the Eastern Region has a solid foundation and a high degree of opening-up and is thus stronger than the Central and Western Regions in the momentum of economic development and in the effect of factor agglomeration. As a result, most physical capital and human capital have been aggregating toward the coastal region. By contrast, the Central and Western Regions are less attractive to factors, a direct consequence of which is their disproportionate allocation of factors. Besides, thanks to the state’s more flexible policy for population mobility since the beginning of reform and opening-up, a great deal of high-level human capital has flown to the Eastern Region, which has further widened the regional gaps in economic growth. As a double-edged sword, regional economic gaps not only help the formation of impetus and competitiveness of regional economic development, and boost competition among the regions, but also promote the free flow of productive factors among the regions and optimize resource allocation nationwide. In the transition period, economic growth that gave priority to the Eastern Region—in particular the coastal areas—was crucial to the rise of China. Nonetheless, if regional development gaps are too large, they will do great harm to the sustained and overall development of the country and society, and to the ethnic unity and political stability in the regions concerned. Judging by regional economic development in China at present, the negative effect of regional gaps in development far outweighs the positive effect.

PREFACE

vii

Scholars at home and abroad have made significant explorations into factors affecting regional economic development. In traditional theory, the “hardware” conditions are the major cause of regional economic gaps. Out of that consideration, many scholars have focused their attention on resource endowments, locations, market economy institutions, investments, and policies. The “hardware” conditions and natural basis did play a non-negligible role in economic development, such as the iron and steel in Panzhihua, the soil and climate in Northeast China, and the transportation hubs in Wuhan. Admittedly, such “hardware” conditions had been the major impetus for regional economic development over a long period of time, and this mechanism of action had been in effect until modern times. However, like many other things, such a mechanism of development is subject to historical limitations—it was only effective in the pre-industrial era and the earlier stage of the post-industrial era. With the arrival of the knowledge economy in the new century, a new system of economic dynamics centered on knowledge, science and technology, and innovation has gradually taken shape, and human capital has already become the core engine of economic growth. As one of the most important factors affecting regional economic growth, human capital is attracting more and more attention. For China to overcome obstacles and realize economic transformation and upgrading, it is of great significance to reexamine the role of human capital in boosting economic development, especially in the context of new normal economic conditions, such as the multiple complex situations facing the country, the pressure of economic structure transformation, the disappearance of demographic dividends, the constraint of the middle-income trap, the trend of population aging, and drastic changes in the international situation. In 1960, the American economist Theodore W. Schultz gave a speech on human capital, which received widespread attention from academia. As Schultz pointed out in his 1961 essay “Investment in Human Capital,” education, training, health, and migration are all part of human capital; investment in them can improve human knowledge, skills, and physical strength, so as to increase individual income and boost economic development; and the rate of return on investment in human capital is higher than

viii

PREFACE

that to other forms of investment.1 Another American economist Gary Becker made additions to and improved the theory of human capital. According to Becker, human capital is “activities that influence future monetary and psychic income by increasing resources in people,” and increase in a society’s individual human capital will bring about socioeconomic increase.2 Professor Schultz regarded investment in education and medical care as human capital investment, highlighting the importance of education and training. He also took the accumulation of market experience as an approach to human capital formation, but merely an auxiliary factor of education and training. American economist Edward F. Denison viewed the input and utilization of productive factors—including human capital—as two major factors for economic growth, thinking that their increase can boost economic growth. Therefore, a country or region can boost its economic growth by increasing its input in human capital and improving the productivity of its human capital. Denison analyzed the data of US economic growth between 1929 and 1957 and found that 23% of that growth came from the development of education and the accumulation of investment in human capital. The American economists Paul Romer and Robert Lucas introduced the human capital theory into the “new growth theory.” In their view, the difference in human capital stocks will influence the longterm economic growth rate by affecting the total factor productivity. With all other conditions equal, a country or region with a larger human capital stock is likely to sustain a higher rate of economic growth in the long run. Human capital is a factor influencing the long-term trend of regional economic gaps. In China, the differences among the Eastern, Central, and Western Regions in human capital investment reflect as gaps in the stocks and structure of human capital, which is bound to influence the uneven economic development among the three regions. Based on a comprehensive study of the existing findings in China and abroad, we have established a theoretical hypothesis in this book—the theory of human capital gaps. We have calculated the indicators reflecting the stocks and structure of human capital of the three regions and estimated their trends

1 Theodore W. Schultz, “Investment in Human Capital,” The American Economic Review, no. 1 (1961): 1–17. 2 Gary Becker, Human Capital (2nd ed.). (New York: NBER, 1975).

PREFACE

ix

of changes. As the result of our comparison suggests, a major problem that coexists with China’s regional economic gaps is the unbalanced distribution of human capital among its regions. Based on this theoretical hypothesis and the reality, we have empirically studied the relationship between China’s regional economic gaps and the imbalance in its human capital investment from the perspective of investment in human capital. This is highly important for further research on approaches to optimizing the regional layout of human capital in China, to narrowing its regional economic gaps by bridging the gaps in human capital investment among the three regions, and to realizing balanced development of regional economy. Meanwhile, it is also necessary for China’s optimization and upgrading of its human capital in the context of structural adjustment and economic transformation and upgrading. The author has annotated literatures and viewpoints referenced during our research and writing, with the information of their authors. As this book goes to press, she would like to express her deepest respect for the academic spirit and contribution of these scholars and welcome different opinions from the readers. Finally, the author would like to extend her heartfelt gratitude to Editor Li Jiaoyuan of the People’s Publishing House for his hard work during the publication of this book. Kunming, China June 2017

Yaling Li

Introduction

According to the neoclassical growth theory and the new growth theory, changes in the stocks of capital and labor will influence the economic growth rate in the short run, and differences in human capital stocks are likely to directly influence the total factor productivity and thus affect the long-term economic growth rate. Hence, human capital is a factor that affects the secular trend of regional economic gaps. In the context of China’s current economic transformation, this book empirically studies the relationship between regional economic gaps and the country’s human capital stocks and structure between 1990 and 2015 and proposes to optimize the regional layout of China’s human capital and narrow regional economic gaps by bridging regional gaps in human capital investment, so as to realize balanced and coordinated development of regional economy.

xi

Contents

1

Human Capital Investment and Regional Economic Catching-Up and Surpassing 1.1 The Uneven Regional Economic Development in China 1.1.1 Characteristics of China’s Economic Growth 1.1.2 Reasons for Regional Gaps in Economic Growth 1.2 Literature Review and Theoretical Hypothesis 1.2.1 Studies on Human Capital and Economic Growth: An Overview 1.2.2 Theoretical Hypothesis of Regional Gaps in Economic Growth: The Theory of Human Capital Gaps 1.3 Human Capital Investment and the Possibility of Regional Economic Catching-Up and Surpassing in China 1.3.1 Regional Factor Structure and Economic Catching-Up and Surpassing 1.3.2 Human Capital and Economic Catching-Up and Surpassing 1.3.3 Conditions for Economic Catching-Up and Surpassing of the Western Region References

1 2 3 10 19 20

27

32 32 35 39 41

xiii

xiv

2

3

4

CONTENTS

Human Capital Investment and Regional Gaps in China: A Comparison 2.1 Stocks of Human Capital Investment in China’s Eastern, Central, and Western Regions and Regional Gaps 2.1.1 Average Level of Education 2.1.2 Input in Public Health and Medical Care 2.1.3 Input in Science, Technology, and Culture 2.1.4 Accumulation of Market Experience 2.2 The Structure of Human Capital Investment and Regional Gaps in China 2.2.1 The Structure of Human Capital Distribution 2.2.2 The Hierarchical Structure of Human Capital 2.3 Conclusions References The Relationship Between Human Capital Stocks and Regional Economic Gaps in China 3.1 The Establishment of the Empirical Model and the Data Source 3.1.1 The Empirical Model 3.1.2 The Data Source 3.2 Human Capital Stocks and Regional Economic Growth: An Empirical Study 3.2.1 Descriptive Statistics Analysis 3.2.2 Unit Root Tests 3.2.3 Cointegration Test 3.2.4 Model Selection 3.3 Discussion on the Results of Metrological Analysis 3.3.1 Regression Results 3.3.2 The Output Elasticity of Factors and Their Relative Contribution to Economic Growth 3.3.3 Regional Differences in the Output Elasticity Coefficients of Factors The Relationship Between Human Capital Structure and Regional Economic Gaps in China 4.1 The Structure of Human Capital Distribution and Its Relationship with Regional Economic Growth in China

43

44 44 48 65 68 71 71 74 78 80 81 82 82 83 84 84 86 87 88 89 89 91 92 93 94

CONTENTS

The Effect Mechanism of Human Capital Distribution Structure on Economic Growth: A Theoretical Framework 4.1.2 The Relationship Between Inequality of Human Capital and Average Stocks of Human Capital 4.1.3 Human Capital Disequilibrium and Regional Economic Imbalance 4.1.4 Quantitative Tests of Annual Cross-Sectional Data 1990–2014 4.2 The Contribution of Different Levels of Education Human Capital Stocks to GDP Growth in China: A Comparison 4.2.1 The Output Elasticity of the Average Level of Human Capital Stocks (H ) and Its Contribution to Output Growth 4.2.2 Contribution Rates of Different Levels of Education Human Capital Stocks to Regional Output Growth References

xv

4.1.1

5

The Relationship Between Factors of Economic Growth and Regional Economic Gaps in China 5.1 Theoretical Framework 5.2 Indicators of the Eastern, Central, and Western Regions: A Comparison 5.2.1 Comparison of Regional Real GDP 5.2.2 Comparison of Regional Fixed Assets Investment 5.2.3 Comparison of Regional Labor Force 5.2.4 Comparison of Regional Education Human Capital 5.2.5 Comparison of Regional Health Human Capital 5.3 The Empirical Model and Description of Variables and Statistics 5.3.1 The Empirical Model 5.3.2 Description of Variables and Statistics 5.4 Metrological Analysis and Result Discussion 5.4.1 Metrological Analysis 5.4.2 Discussion of the Metrological Analysis Results

94 97 97 99

101

104

126 140 143 143 147 147 148 150 150 152 153 153 154 156 156 160

xvi

6

CONTENTS

5.5

Conclusions and Policy Implications 5.5.1 Conclusions References

163 163 166

International Comparison of Human Capital Investment 6.1 Conditions and Policies of Human Capital Investment in Typical Countries 6.1.1 Human Capital Investment in the United States 6.1.2 Human Capital Investment in Germany 6.1.3 Human Capital Investment in East Asia 6.2 Takeaways from Human Capital Investment in Foreign Countries on Regional Human Capital Investment in China 6.2.1 Deepening of Overall Reform in the Education System and Implementation of the Basic Strategy of “Strengthening the Country through Education” 6.2.2 Increasing R&D Investment and Enhancing China’s Scientific Research Capacity 6.2.3 Increasing Human Capital Investment and Optimizing the Human Capital Structure References

167

Bibliography

168 168 178 191

202

204 210 212 216 219

About the Author

Yaling Li was born in December 1969 and is an associate professor and supervisor of Ph.D. students at Yunnan University. She holds a Ph.D. in economics and mainly lectures on microeconomics, macroeconomics, labor economics, personnel economics, and human resource management. Li’s research interests mainly include development economics, labor economics, economic management, and human resource management. She has served as chief researcher for one project sponsored by the National Social Science Fund of China and several provincial-level programs. Li has published over 60 articles in such Chinese academic journals as Management World, The Ideological Front, and Enterprise Economy, as well as international journals indexed by SSCI, SCI, and EI. She has also published four monographs (three by People’s Publishing House and the other one by Science Press) and one translation (by Xinhua Publishing House). Li has served as chief or associate editor of ten textbooks, such as Microeconomics, Macroeconomics, Western Economics, Labor Economics, Marketing, and Consumer Behavior, respectively published by Higher Education Press, Science Press, China Machine Press, Chongqing University Press, Yunnan University Press, etc.

xvii

List of Figures

Fig. 1.1 Fig. 1.2 Fig. 1.3 Fig. 1.4 Fig. 1.5 Fig. 1.6 Fig. 1.7 Fig. 1.8 Fig. 2.1

Fig. 2.2

Provincial economic growth rates in Eastern Region, 1996–2014 Provincial economic growth rates in Central Region, 1996–2014 Provincial economic growth rates in Western Region, 1996–2014 Trends of economic growth in Eastern Region, 1995–2014 Trends of economic growth in Central Region, 1995–2014 Trends of economic growth in Western Region, 1995–2014 Changes in GDP of China’s Eastern, Central, and Western Regions, 1990–2015 Changes in proportions of Eastern, Central, and Western Regions in National GDP, 1990–2015 China’s National Health Expenditure, 1990–2014 (billion RMB) (Source: China Statistical Yearbooks 1991–2015, sorted and recalculated) Per capita health expenditure in China, 1990–2014 (RMB) (Source: China Statistical Yearbooks 1991–2015 and Annual Statistical Bulletins on the Development of Health and Family Planning in China, sorted and recalculated)

4 5 5 7 7 8 8 9

51

51

xix

xx

LIST OF FIGURES

Fig. 2.3

Fig. 2.4

Fig. 2.5 Fig. 2.6

Fig. 2.7

Fig. 2.8

Fig. 2.9

Fig. 2.10

Fig. 2.11

Fig. 2.12

Fig. 2.13

Health expenditures of China’s three regions, 2003–2014 (Source: China Statistical Yearbooks 2004–2015,China Health Statistical Yearbooks 2004–2015, and provincial statistical yearbooks, sorted and recalculated) Health expenditures of China’s three regions in 2011 (billion RMB) (Source: China Statistical Yearbook 2012 and China Health Statistical Yearbook 2012, sorted and recalculated) Health expenditures of China’s three regions in 2012 (billion RMB) Numbers of medical institutions in China, 1990–2014 (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated) Numbers of medical personnel in China, 1990–2014 (million persons) (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated) Numbers of beds in medical and health institutions in China, 1990–2014 (million) (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated) Medical and health institutions in China’s three regions, 1990–2014 (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated) Numbers of medical personnel in China’s three regions, 1990–2014 (million persons) (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated) Numbers of hospital beds in China’s three regions, 1990–2014 (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated) Maternal Mortality in China, 1991–2014 (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated) Mortality Rates of Infants and Children under Five in China, 1991–2014 (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated)

52

55 55

56

57

58

58

59

60

62

63

LIST OF FIGURES

Fig. 4.1

Fig. 4.2 Fig. 4.3 Fig. 4.4 Fig. 4.5 Fig. 4.6 Fig. 4.7 Fig. 4.8 Fig. 4.9 Fig. 4.10 Fig. 4.11 Fig. 4.12 Fig. 4.13 Fig. 4.14

Fig. 4.15 Fig. 4.16

Fig. 4.17 Fig. 4.18

Standard deviation coefficient of per capita GDP, the coefficient of human capital structure (Gh), and the average stocks of human capital (H) of 30 provincial-level regions in China, 1990–2014 Impact of the coefficient of human capital structure on AGDP, 1990–2014 Elasticity coefficients of capital, labor, and education human capital in the Eastern Region, 1990–2014 Elasticity coefficients of capital, labor, and education human capital in the Central Region, 1990–2014 Elasticity coefficients of capital, labor, and education human capital in the Western Region, 1990–2014 Output elasticity coefficient of education human capital in the three regions, 1990–2014 Contribution rates of factors in the Eastern Region, 1990–2014 Contribution rates of factors in the Central Region, 1990–2014 Contribution rates of factors in the Western Region, 1990–2014 Contribution rates of education human capital in the three regions, 1996–2014 Proportions of different levels of education in total human capital stocks of the Eastern Region, 1994–2014 Proportions of different levels of education in total human capital stocks of the Central Region, 1994–2014 Proportions of different levels of education in total human capital stocks of the Western Region, 1994–2014 Contribution rates of different levels of education to growth of human capital stocks (H) in the Eastern Region, 1996–2014 Contribution rates of different levels of education to GDP growth in the Eastern Region, 1996–2014 Contribution rates of different levels of education to growth of human capital stocks (H) in the Central Region, 1996–2014 Contribution rates of different levels of education to GDP growth in the Central Region, 1996–2014 Contribution rates of different levels of education to growth of human capital stocks (H) in the Western Region, 1996–2014

xxi

100 104 120 120 121 121 124 125 125 126 130 131 132

134 135

138 138

139

xxii

LIST OF FIGURES

Fig. 4.19 Fig. 5.1 Fig. 5.2 Fig. 5.3 Fig. 5.4 Fig. 5.5 Fig. 6.1 Fig. 6.2 Fig. 6.3 Fig. 6.4

Fig. 6.5

Fig. 6.6

Contribution rates of different levels of education to GDP growth in the Western Region, 1996–2014 Comparison of Regional Real GDP, 2003–2014 Comparison of Regional Fixed Assets Investment, 2003–2014 Comparison of Regional Labor Force, 2003–2014 Comparison of Regional Education Human Capital, 2003–2014 (Note Based on the statistics in Table 2.1) Comparison of Regional Health Human Capital, 2003–2014 (Note Based on the statistics in Table 2.3) GERD as a Percentage of GDP (Source OECD, “Main Science and Technology Indicators Database,” July 2015) BERD as a Percentage of GDP (Source OECD, “Main Science and Technology Indicators Database,” July 2015) HERD as a Percentage of GDP (Source OECD, “Main Science and Technology Indicators Database,” July 2015) GOVERD as a Percentage of GDP (Source OECD, “Main Science and Technology Indicators Database,” July 2015) College Graduates in China, 2001–2015 (million persons) (Source Education Online, “Numbers of College Graduates in China 2001–2015.” Accessed 5 December 2014) Changes in R&D Input of China, Japan, Germany, and US, 2000–2013 (Source OECD, “Main Science and Technology Indicators Database,” July 2015. Based on data in Tables 6.2, 6.6, 6.9, and 6.13)

140 148 149 151 151 152 174 174 175

175

205

212

List of Tables

Table 1.1 Table 2.1 Table 2.2 Table 2.3 Table Table Table Table

2.4 2.5 2.6 2.7

Table 2.8 Table 2.9 Table 2.10 Table 2.11 Table 3.1 Table 3.2 Table 3.3

A summary of studies on reasons for regional economic gaps Comparison of average years of schooling in China’s three regions (Unit: Year) National input in medical care and public health, 1990–2014 Regional health expenditures in China, 2003–2014 (billion RMB) Health expenditures of China’s three regions, 2011–2014 By-province R&D expenditure in 2014 R&D expenditure of China’s three regions, 1998–2004 Numbers of private entrepreneurs converted from individual businesses, 1990–2014 (Unit: household) Gini coefficients of human capital of China’s three regions, 1990–2014 Proportions of people at different levels of education in the Eastern Region, 1995–2015 (%) Proportions of people at different levels of education in the Central Region, 1995–2015 (%) Proportions of people at different levels of education in the Western Region, 1995–2015 (%) Real GDP of the three regions in China, 2003–2014 (2003 = 100) Descriptive statistical results of regression variables Results of ADF test of the variables

24 47 50 52 54 66 67 70 74 76 77 78 84 85 87

xxiii

xxiv

LIST OF TABLES

Table 3.4 Table 3.5 Table 3.6 Table 4.1 Table 4.2

Table 4.3 Table 4.4 Table 4.5 Table 4.6

Table 4.7

Table 4.8

Table 4.9

Table 4.10

Table 4.11

Table 5.1 Table 5.2 Table Table Table Table Table Table

5.3 5.4 5.5 5.6 5.7 5.8

Results of cointegration test Results of F-test of the panel data Correlation regression results of factors and regional economic growth Correlation coefficients of regional Gini coefficients and average stocks of human capital in China, 1990–2014 Correlation regression equation between per capita GDP and the Gini coefficient of human capital in China, 1990–2014 OLS estimation results, 1990–2014 Average annual growth rates and output elasticity of factors Contribution rates of factors to the output growth (%) Growth rates and proportions of different levels of education human capital stocks in the Eastern Region (%) Growth rates and proportions of different levels of education human capital stocks in the Central Region (%) Growth rates and proportions of different levels of education human capital stocks in the Western Region (%) Contribution rates of different levels of education human capital stocks to output growth of the Eastern Region (%) Contribution rates of different levels of education human capital stocks to output growth of the central region (%) Contribution rates of different levels of education human capital stocks to output growth of the Western Region (%) Comparison of Regional Real GDP (trillion RMB) Comparison of Regional Fixed Assets Investment (trillion RMB) Comparison of Regional Labor Force (million persons) Meanings and Basic Statistics of Variables in the Model Results of the ADF Tests of the Variables Results of the Cointegration Test Results of F-Test of the Panel Data Regression Results of the Correlation Between Factors and Regional Economic Growth

88 89 90 98

102 107 118 122

127

128

129

133

136

137 148 149 150 156 158 159 159 161

LIST OF TABLES

Table 6.1 Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 6.6 Table 6.7 Table 6.8 Table 6.9 Table 6.10 Table 6.11 Table 6.12 Table 6.13 Table 6.14 Table 6.15

R&D Input of US, Japan, Germany, and China, 1981–2000 (billion USD) Relevant Indicators of the US R&D (million USD) Composition of US Human Capital, 1993–2012 Changes in Indicators of German Industrialization, 1870–1913 Elite Universities in Germany Relevant Indicators of German R&D (million USD) Numbers of Apprentices Trained in German Economic Sectors, 2010–2011 The Social Security System in Germany R&D-Related Indicators of Japan (million USD) Numbers of Students in Different Levels of Education Institutions in China (thousand) Growth of Per Student Spending in Public Budget (RMB) Annual Value Added of the Three Industries in China, 2010–2014 (trillion RMB) Relevant Indicators of the Chinese R&D (million USD) Changes in Five Major Capitals and Total Capital of China and US (%) Average Annual Growth in Total Value of Five Major Capitals of China and US, 1990–2003 (%)

xxv

172 173 178 180 185 188 189 190 197 206 207 209 211 213 213

CHAPTER 1

Human Capital Investment and Regional Economic Catching-Up and Surpassing

Economic development is an eternal topic of the human society and always a focus of attention from all walks of life. For a long time, people have pondered over and made extensive explorations of economic development. Economic development is the goal. But what are the driving factors behind this goal? Some people thought that economic development depended on natural resources, which gave rise to a school oriented to natural resources. Others deemed science and technology as fundamental to economic development, which brought into being a school oriented to science and technology. However, with the constant progress of academic explorations, people have found that the orientation to natural resource fails to explain economic development, and that scientific and technological progress is not the fundamental reason for economic development. In further theoretical and practical explorations, people have discovered that human resource is the key to economic development. And therefore, human resource is revered as “the most precious resource.” Such a viewpoint may be referred to as oriented toward human resource. As we think it over, however, we may also find that human resource does not perfectly explain economic development, which has been proven by reality. It is true that human resource is precious as it plays a critical role in boosting economic growth. But this resource may vary greatly—especially in its levels. Only human capital that takes shape through investment is the core of value creation. Based on this, we conclude that human capital comes into being through various forms © Social Sciences Academic Press 2023 Y. Li, Human Capital Investment and the Regional Economic Gap in China, https://doi.org/10.1007/978-981-99-4997-7_1

1

2

Y. LI

of investment and that human capital is the essential factor of economic development—it is the key to revealing the inherent law of regional economic imbalance and economic catching-up and surpassing.

1.1 The Uneven Regional Economic Development in China The imbalance of regional economic development is nothing new. As a matter of fact, regional economic imbalance has taken shape following the growth of the human society. Globally, there are prominent problems of uneven economic development between developed and developing countries and between developed and underdeveloped regions. The same is true of a country or region, e.g., the economic gap and imbalance among China’s Eastern, Central, and Western Regions. Historically, if we review the socioeconomic development of the human society, we can easily find that many factors have caused this problem, such as resource, geography, transportation, education, labor force, and science and technology. For a long time, China has been faced with the problem of uneven economic development, such as the imbalance between its Eastern and Western Regions and that between its urban and rural areas. It is the result of several factors, such as the skewed policy of reform and opening-up, the rural support for the urban areas, and the Central and Western Regions’ support for the Eastern Region. In terms of the economic basis, the Eastern Region is stronger, as it has a longer history of development, while the Central and Western Regions are relatively weak. Despite such gaps in regional economic development, the Chinese economy, in general, has been growing rapidly and its gap with developed countries has been narrowing since the reform and opening-up, which fully confirms China’s economic catching-up and surpassing. In this process, the contribution of human resource is non-negligible. However, to transform and upgrade its industrial structure against the constantly changing global economy in the new era, China is faced with growing pressure for sustained economic catching-up and steady development, especially under the new normal conditions, such as the disappearance of its demographic dividends, which has long been counted on, the middle-income trap, and the downward economic trend. It is thus urgent that China find new driving forces and inject powerful impetus into its economic development. “The conversion of demographic dividends into talent dividends and the transformation of human resource into human capital” have undoubtedly shown us the

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

3

right direction for a new round of economic catching-up and surpassing, and provided the possibility of success. In mainland China, the 31 provincial-level regions are divided into the Eastern, Central, and Western Regions. The Eastern Region consists of eleven provincial-level regions, including Beijing, Tianjin, Hebei, Liaoning, Shandong, Zhejiang, Jiangsu, Shanghai, Fujian, Guangdong, and Hainan. The Central Region is made up of eight provinces, i.e., Heilongjiang, Jilin, Henan, Anhui, Hunan, Hubei, Shanxi, and Jiangxi. The Western Region is comprised of twelve provincial-level regions, namely, Inner Mongolia, Gansu, Shaanxi, Xinjiang, Xizang, Yunnan, Guizhou, Sichuan, Chongqing, Qinghai, Ningxia, and Guangxi. In the following, we will analyze the economic gaps among the three regions. 1.1.1

Characteristics of China’s Economic Growth

The imbalance of regional economic development has long been a problem in China. It mainly manifests as the uneven development among the Eastern, Central, and Western Regions, the uneven development within each region, and the uneven development between the urban and rural areas, the most prominent being the disequilibrium among the three regions and between the urban and rural areas. The main characteristics of China’s regional economic imbalance are the high-speed economic growth and the widening of the regional gaps. “The Chinese speed” stuns the world. The Chinese economy had sustained double-digit growth for some time and has only started to slow down since 2010. Following the high-speed economic growth, regional gaps in economic development keep widening. The widening gaps among the Eastern, Central, and Western Regions, and between the urban and rural areas have become increasingly prominent. 1.1.1.1 High-Speed Growth High-speed growth is a striking characteristic of unbalanced regional economic development in China. For a long time, “the Chinese speed” has caught the eye of the whole world. Since the beginning of the reform and opening-up, especially in the first ten years of the twenty-first century, China had sustained a double-digit economic growth and is now the world’s second largest economy in terms of GDP. The reasons behind such high-speed growth in this period have been a major concern. It is generally believed that the “demographic dividends” was the major reason

4

Y. LI

for its high-speed economic growth in this period. At the same time, some scholars have attributed China’s economic growth in this period to a series of policies for reform and opening-up. Others have ascribed it to the abundant resources in the country. High-speed growth is not only a phenomenon of the Chinese economy as a whole—it is also a characteristic of provincial economic development in the three regions. Using the provincial GDP data between 1996 and 2014, we calculated the growth rates and, using the software EXCEL 2013, got the trends of provincial economic growth rates in China’s Eastern, Central, and Western Regions (See Figs. 1.1, 1.2, and 1.3). Notes The statistics of these three figures (Figs. 1.1, 1.2, and 1.3) are from the National Bureau of Statistics (NBS) and at current prices. Regional GDP data before 2004 are based on Industrial Classification for National Economic Activities (GB/T4754-1994); those between 2004 and 2012 are based on Industrial Classification for National Economic Activities (GB/T4754-2002); and those after 2013 are based on Industrial Classification for National Economic Activities (GB/T4754-2011). As can be seen in Figs. 1.1–1.3, the Eastern, Central, and Western Regions are highly similar in terms of economic growth rates despite their differences in economic base. Generally, the three regions had sustained double-digit economic growth in the first eleven years of the twenty-first century (2000–2011), except in 2009 when economic growth slowed

Fig. 1.1 Provincial economic growth rates in Eastern Region, 1996–2014

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

5

Fig. 1.2 Provincial economic growth rates in Central Region, 1996–2014

Fig. 1.3 Provincial economic growth rates in Western Region, 1996–2014

down due to the delayed effect of the financial crisis. In some years, the highest growth rates even exceeded 20%. There are various reasons for the high-speed growth of the regional economy, and it is quite difficult for us to find out the internal mechanism of such growth. Yet as some scholars have discovered, the demographic dividends had played a big part

6

Y. LI

in it, and the resource and policy advantages had also injected powerful momentum into regional economic development. People are the center of all kinds of activity. Be it policy advantage, resource endowment, or any other reason, we must view them from the perceptive of the people. If we look at the trend of economic development in China, we can find that demographic dividends had added momentum to its economic takeoff. But if we observe the economic growth of the three regions since 2010, we may easily conclude an obvious downward trend of the economy. Although there are many reasons for this trend, the primary factor is none other than people. High-speed growth has long been a characteristic of the Chinese economy, but the three regions are significantly different in their economic growth rates as well as in their momentum for sustained economic growth, such as the range of fluctuation of economic growth, the average growth rate, etc. Every indication shows that there are both similarity and difference among China’s three regions in economic growth. As we can see in Figs. 1.1–1.3, the Eastern Region has a stronger economic foundation, and is smaller than the Central and Western Regions in fluctuation. As Fig. 1.2 shows, the Central Region is marked by evident instability, high growth rates, and high volatility, and is apparently more significant than the other regions in the downward trend of the economy. 1.1.1.2 Widening Regional Economic Gaps Apart from the high-speed growth, the widening of regional economic gaps is another striking characteristic. Not only are regional gaps in economic growth widening, gaps among the provincial-level regions within the three major regions are also expanding. Using relevant statistics and the software EXCEL 2013, we got Figs. 1.4, 1.5, 1.6, 1.7, and 1.8. It is not hard to find that the widening of gaps is not merely a phenomenon among the Eastern, Central, and Western Regions. It is also quite prominent within each region. In terms of economic development, the provincial-level regions in the Eastern Region are clearly higher than those in the Central and Western Regions. Although the Central and Western Regions have been catching up in recent years, there is still an obvious trend of widening gaps. Now let’s look at the gaps within each region. In the Eastern Region, Guangdong, Jiangsu, Shandong, and Zhejiang are obviously higher than

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

7

Fig. 1.4 Trends of economic growth in Eastern Region, 1995–2014

Fig. 1.5 Trends of economic growth in Central Region, 1995–2014

Liaoning, Fujian, Shanghai, Tianjin, and Hainan in GDP and in the trend of economic growth. Such gaps are quite significant and are apparently widening. Compared with the Eastern Region, the internal differences are quite small within the Central Region. Although there are some differences (e.g., Henan, Hunan, Hubei, and Anhui are higher and Jiangxi, Jilin,

8

Y. LI

Fig. 1.6 Trends of economic growth in Western Region, 1995–2014

Fig. 1.7 Changes in GDP of China’s Eastern, Central, and Western Regions, 1990–2015

and Shanxi are lower in economic development), the overall differences are not so distinct. Notes The statistics of Figs. 1.4–1.6 are from the National Bureau of Statistics (NBS) and at current prices. Regional GDP data before 2004 are based on Industrial Classification for National Economic Activities

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

9

Fig. 1.8 Changes in proportions of Eastern, Central, and Western Regions in National GDP, 1990–2015

(GB/T4754-1994); those between 2004 and 2012 are based on Industrial Classification for National Economic Activities (GB/T4754-2002); and those after 2013 are based on Industrial Classification for National Economic Activities (GB/T4754-2011). The economic conditions are quite complicated within the Western Region. A prominent feature is the extreme imbalance of regional economic development and the serious widening of intra-regional gaps. Among the provincial-level regions in the Western Region, Sichuan is particularly outstanding in economic development, followed by Inner Mongolia, Guangxi, Chongqing, Shaanxi, and Yunnan. Those in the northwest are relatively weak. In the following, we will compare the gaps among the three regions in terms of changes in regional GDP and proportions in the national GDP between 1990 and 2015.

10

Y. LI

Notes 1. The time span of Figs. 1.7 and 1.8 is between 1990 and 2015. In Fig. 1.8, “1” indicates the year 1990, “26” indicates the year 2015, and so on. 2. The statistics of 1990 are from http://www.360doc.com/content/ 13/0319/19/1542087_272530211.shtm; those between 1991 and 1994 are from http://www.docin.com/p-250213673.html; and those between 1995 and 2015 are from the National Bureau of Statistics (NBS) and at current prices. Regional GDP data before 2004 are based on Industrial Classification for National Economic Activities (GB/T4754-1994); those between 2004 and 2012 are based on Industrial Classification for National Economic Activities (GB/T4754-2002); and those after 2013 are based on Industrial Classification for National Economic Activities (GB/T4754-2011). As can be seen in Fig. 1.7, the gaps among the three regions were rapidly enlarging. Since 2000 the Eastern Region has been growing much faster than the Central and Western Regions. On the other hand, as Fig. 1.8 shows, the proportions of the three regions in the national GDP have basically been stable over these 26 years. 1.1.2

Reasons for Regional Gaps in Economic Growth

At present, there are huge differences in the path and efficiency of economic growth among different nations. Since the 1990s, countries or regions of the same level of development have even polarized in economic growth. Uneven development is still a serious problem facing the world economy. With the development of the empirical approach in the late twentieth century, the root cause of economic growth has gradually become a topical issue of economic studies. Regional economic gaps are objective as an economic law as well as an economic fact. Yet there are various reasons causing such gaps in regional economic development, such as the size and quality of labor forces, the natural resource endowments, the geographic locations, the national policies, the economic foundation, the degrees of opening, etc. For a long time, all sectors of society—the academic circles in particular—have been

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

11

paying increasing attention to the reasons for such gaps in economic growth. Their views vary and their perspectives differ. There are mainly four types of opinions. 1.1.2.1

Regional Gaps in Economic Growth: An Interpretation Based on Production Factors There are mainly five productive factors: labor, capital, land, information, and management. In this section, we take economic growth as equivalent to “production,” so the key indicators of the production level are the productivity and the harmony of the productive relations. First, labor force. More frequently referred to as “human resources” nowadays, labor force is a core factor of economic development. It boosts and benefits from economic development. Human resources may be further divided by “quantity” and “quality.” In quite a part of human history, the quantity of human resources was regarded as a vital force of economic growth—in the primitive society, the slave society, and the feudal society. Up till now, it still is in the capitalist society and in our country. China boasts a large population and is rich in human resources. For long, it has been relying on its unique demographic advantage. Especially since the beginning of reform and opening-up, its achievements in economic development were to a large extent based on its “demographic dividends.” The cheap but abundant labor force was a new vitality for attracting foreign investment and the construction of major projects. As reform and opening-up was first implemented in the coastal cities, a large labor force flowed from the Central and Western Regions to the eastern cities and participated as workers in the great development of the socialist market economy. Ever since, the Eastern Region has experienced rapid economic growth and some cities in this region have expanded fast, such as Shanghai, Guangzhou, Shenzhen, Zhuhai, and Tianjin. More and more people have noticed that the advantage in the quantity of human resources cannot bring sustained powerful impetus for economic development, and that the “demographic dividends” are bound to disappear—a serious problem confronting China at present. Following its socioeconomic progress and development, China is bound to lose its cost advantage in human resources as its labor cost increases gradually. In the greater East Asian region, China is the first to be affected by such a change. In recent years—especially in the twenty-first century, labor cost keeps rising in China, compared with Vietnam, Myanmar, and Thailand. Consequently, many foreign-funded enterprises are moving out of the

12

Y. LI

country. For such enterprises, production in Southeast Asian countries is more profitable. Under this background, more and more people have realized that it is necessary to transform to talent dividends and improve the quality of laborers to stimulate economic growth. Undeniably, many developed countries—such as the US, Japan, and the UK—have realized the transformation from labor-intensive to talent-oriented economy, followed closely by China, India, South Korea, and Brazil. In fact, much attention has been paid to the quality of labor force, which is referred to as “human capital” in the academic circles. It is worth mentioning that, as the authors of this book, we not only pay close attention to human capital stocks, but also—and even more importantly—to the structure of regional human capital and the internal mechanisms of economic growth and economic catching-up and surpassing. Second, capital. Capital is particularly important for the development of the world economy at present. Undoubtedly, it is the blood of economic growth. Without capital, the economy would be lifeless in the doldrums. If labor force is the first driving force of economic growth, capital is indisputably the second. The Cobb–Douglas production function mainly introduced the two factors of labor (L) and capital (K). Later some scholars separated human capital (H) from capital as an independent factor. The importance of capital to economic growth is self-evident. From the Bretton Woods System centered on the US to the present financial system, capital is becoming increasingly important. The “Belt and Road” strategic layout of China also proves the significance of the capital strategy. For instance, the UK and the US have already transformed into “capital powers” that achieve economic growth by manipulating capital. What needs to be emphasized is that, although “capital-oriented” economic growth influences the whole capital chain, it can lead to enormous crisis. For example, financial crisis is the product of capital manipulation, and there is no way to eradicate it so far. Third, land, information, and management. It goes without saying that land is important as a factor. But we must reexamine the other factors, such as information and management. As we all know, informatization is the dominant trend in the current society. It is also an inevitable product as development of productivity reaches a certain stage. Likewise, management is also branded with economic development, but few scholars have studied regional economic gaps from this perspective. In fact, information and management often manifest as congealed “human capital” on

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

13

“laborers.” They are indeed two basic functions of the administrator— to collect, process, and sort information and to implement management activities. Obviously, information and management should be studied from a microcosmic perspective. That is, we must focus our explorations of human capital on the laborer or the administrator. 1.1.2.2

Regional Gaps in Economic Growth: An Interpretation Based on Human Capital Of the explorations on this issue, the most influential is the human capital theory put forward by Schultz and Becker. Schultz studied the ways and means of human capital formation and made quantitative research on the rate of return on investment in education and the contribution of education to economic growth. Gary S. Becker, who also worked in the University of Chicago, was another advocate of the human capital theory. He focused more on microscopic analysis, which made up for a defect of Schultz who emphasized macroscopic studies only. Becker integrated the theory of human capital investment with income distribution, and his book Human Capital is regarded by the Western academia as the starting point of “a revolution of investment in human capital in economic thought.” He put forward a systematic theoretical framework and extended it from human capital studies to family economics, bringing into being a comprehensive and complete theoretical system. At the same time, American economist Jacob Mincer also contributed to this theory. He was the first to put forth the earning function of human capital. He established a model of return rates of investment in human capital and applied the theory and analytical methods of human capital to labor market behavior and household decision-making. Another American scholar Edward F. Denison proved the role of human capital in economic growth using empirical measurement. His research on the role of education in the American economy is strong evidence of Schultz’s theory. In the mid- and late 1980s, the new growth theory started to take shape, with the publication of two essays as milestones—Romer’s “Increasing Returns and Long-Run Growth”1 in 1986 and Lucas’s “On

1 Paul M. Romer, “Increasing Returns and Long-Run Growth,” The Journal of Political Economy, no. 5 (1986): 1002–1037.

14

Y. LI

the Mechanics of Economic Development”2 in 1988. Romer developed a model of competitive but balanced growth, which considered the spillover effect, the diminishing returns to material output, and the increasing returns to new knowledge. In his theory, there are four factors of production: capital, unskilled labor, human capital, and new thought. Of the four, new thought is the major factor for economic growth. Lucas built an endogenous theoretical framework of growth based on three models, i.e., physical capital accumulation and technical change, human capital, and specialized human capital. He incorporated human capital as an independent factor into his model of economic growth, and combined Schultz’s theory of human capital with Solow’s theory of technical progress using more microscopic individual analysis. He regarded the accumulation of human capital as the decisive factor for long-term economic growth, and internalized and concretized it into the individual and specialized human capital. He thought only the accumulation of such specialized human capital is the real source of growth, which reveals the significant influence of human capital on regional economic development. In recent years, more and more attention has been paid to human capital. The reexamination of human capital has brought a new perspective for the development of the theories of economic growth. Many research results prove that human capital plays an important role in economic growth, but people’s views are divided on how human capital influences economic growth. Moreover, different scholars have adopted different criteria when they make quantitative empirical studies. All this shows the limitation of the existing studies—in explaining the gaps in economic growth in particular. Human capital does play an important role in economic growth. The research findings of various scholars have laid a solid foundation for follow-up studies. Yet human capital is a systematic concept involving many issues, so it is not enough to uncover its very nature merely from one or more dimensions. Human capital is a complex system. It is not only a matter of the total amount, which reflects the quantitative relations, but also a matter of the structure, which reflects the quality. Therefore, it is of significant theoretical and empirical values to interpret the gaps in economic growth in light of the structure of human capital.

2 R. E. Lucas, “On the Mechanics of Economic Development,” Journal of Monetary Economics, no. 22 (1988): 3–42.

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

15

In fact, scholars like Birdsall and Londono (1997),3 Lopez et al. (1998),4 and Castello and Domenech (2002)5 have paid attention to and studied the relationship between human capital structure and economic growth, and demonstrated the realistic relations between them. Noteworthily, their studies mainly make interpretations from the perspective of education, but human capital structure covers a far wider range. In this regard, Chinese scholars Liu Wenge et al. (2006) have made some supplement in their research. Human capital structure includes the investment structure, the internal structure of factors, the structure of industrial distribution, the structure of spatial distribution, the structure of classifications, and the hierarchical structure. It involves not only quantitative studies but also qualitative explorations. In light of the education level, the learning-by-doing effect, and the input in science, education, culture, and health, we will not only analyze the influence of human capital stocks on regional economic gaps and distinguish different levels of human capital, but also make a comparative study of the relationship between the structural difference in human capital distribution across China’s three regions and the regional gaps in economic growth by means of the Gini coefficient of human capital. 1.1.2.3

Regional Gaps in Economic Growth: Technological Determinism According to Adam Smith, the progenitor of the classical economic theory, there are two approaches to boost economic growth. One is to increase the quantity of labor, and the other is to improve labor efficiency. Of these two approaches, the improvement of labor efficiency is more important. It mainly depends on the degree of division of labor and the amount of capital accumulation. Therefore, division and collaboration and capital accumulation are the basic motivating factors of economic growth. According to David Ricardo, the long-term economic growth

3 N. Birdsall and J. L. Londono, “Asset Inequality Matters: An assessment of the World Bank’s Approach to Poverty Reduction,” American Economic Review 87, no. 2 (1997): 32–37. 4 R. Lopez, V. Thomas, and Y. Wang, “Addressing the Education Puzzle: The Distribution of Education and Economic Reforms,” World Band Working Paper, no. 2031 (1998). 5 A. Castello and R. Domenech, “Human Capital Inequality and Economic Growth: Some New Evidence,” The Economic Journal 112, no. 2 (200): 187–200.

16

Y. LI

will stop under the law of diminishing returns. According to Thomas R. Malthus, population growth does not synchronize output growth, and the economic growth expressed as per capita output tends to be limited by population growth which depends on per capita income. Therefore, it is necessary to adopt policies outside the economic system that prevent population growth from surpassing economic growth. Clearly, classical economists have already pointed out the driving forces of economic growth—capital, technology, land, and division of labor—and paid attention to the unique role of natural resources in growth. In the second half of the nineteenth century, the neoclassical theory of economic growth represented by Alfred Marshall started to rise. According to Marshall, the increase in population, wealth (capital), and the level of intelligence, as well as the introduction of industrial organizations (division and collaboration) will improve industrial production and boost economic growth. And the overall influence of these factors on producers is reflected as increasing returns. Therefore, economic growth is correlated with increasing returns. Joseph A. Schumpeter interpreted the economic development of the capitalist society using the concept “innovation” and deemed innovation as the entrepreneur’s new combination of production factors, including new products, new methods of production, new markets, new resources, and new organizations. Based on the theory of “effective demand” of John Maynard Keynes, Roy F. Harod and Evesey D. Domar respectively established their theories of economic growth, namely the Harod-Domar Model. The key hypothesis of this model is that labor and capital are not interchangeable as production factors. With a constant savings rate and population growth rate, and without technological progress and capital depreciation, the economic growth rate G = s/v, where s stands for the savings rate and v for the ratio between capital and output. The conclusion of this model is that the economic growth rate increases with the rise in the savings rate and decreases with the expansion of the ratio between capital and output. Since the 1960s, technological innovation has become a major concern of researchers exploring the reasons for economic growth. Many of them have found that technological innovation and progress is the major factor determining economic growth. Robert M. Solow, Trevor W. Swan, James E. Meade, and Paul A. Samuelson developed the Neoclassical Growth Model, the core of which are three hypotheses on the nature of the aggregate production function, i.e., constant returns to scale, decreasing

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

17

marginal returns to production factors, and the interchangeability of production factors. In the neoclassical growth theory, the process of economic growth is reflected as capital accumulation, which is determined by the rate of return on investment. Under constant returns to scale, per capita income depends solely on the ratio between capital and labor. Only when this ratio keeps rising can per capita income sustain growth. On the other hand, the rate of return on investment equals the marginal return to capital. Like per capita income, the marginal return to capital is exclusively dependent on the ratio between capital and labor. Due to the law of decreasing marginal returns to factors, the marginal return to capital decreases with the increase in the ratio between capital and labor. Solow et al. also pointed out that, in the long run, the determining factor of economic growth is technological progress rather than capital accumulation or labor increase. That is to say, the neoclassical growth theory does not ensure sustained economic growth in the long run. In the mid- to late 1980s, the new growth theory started to take shape. As a representative of this theory, Lucas built an endogenous theoretical framework of growth based on three models, i.e., physical capital accumulation and technical change, human capital, and specialized human capital. He combined Schultz’s theory of human capital with Solow’s theory of technical progress, and endogenized and concretized it into individual and specialized human capital. He thought that only the accumulation of such specialized human capital is the real source of growth. Clearly, such opinions not only state the decisive role of human capital in economic growth, but also expound the important role of technology in it. 1.1.2.4

Regional Gaps in Economic Growth: Institutional Determinism In view of the development of the growth theories, the theories of economic growth prior to the neoclassical growth theory invariably ignored institutions and the influence of institutional changes on economic growth. Even in the neoclassical model of economic growth, we can hardly see the role of institutions and institutional changes. An indisputable fact is the widening rich-poor gap across the world. Then why are poor countries poor? Some people say poor countries were once subjected to serious plunder by colonizers and their poverty today is a continuation of history. Yet many colonies have grown into developed countries while the others remain poor. Some people think that poor countries stay poor owing to a lack of funds and technology. But international

18

Y. LI

institutions and many countries have provided these poor countries with considerable assistance. Meanwhile, a lot of private capital has entered these countries, together with advanced technologies. Why are they still poor? Others think global economic integration has made the rich richer and the poor poorer. In fact, no country has got poor due to reform and opening-up. On the contrary, many countries have benefited much from the open economy and grown from poor to rich. Such claims have invariably attributed the poverty of poor countries to external factors. Yet in fact, the key factor to a country’s poverty is internal instead of external. Then how do we account for the gaps that existed between poor and rich countries in economic development in light of internal reasons? In 1968, Douglass C. North published “Sources of Productivity Change in Ocean Shipping, 1600–1850.” Through a statistical analysis of various aspects of ocean shipping costs, this essay discovered that changes in the shipping and market systems ultimately lowered ocean shipping costs and greatly improved ocean shipping productivity, although there was no significant change in ocean shipping technology in that period. The publication of this essay signified a formal declaration of North’s “determinism of economic growth.” In 1971, North published “Institutional Change and Economic Growth” and pointed out that the development of institutional arrangement is the major historical cause for improvements in productive efficiency and the factor market. He further stated that the factors listed (innovation, economies of scale, education, capital accumulation, etc.) are not the reasons for economic growth— they are growth in themselves. In other words, regional gaps in economic growth are determined by institutions. North et al. pointed out that the key to economic growth is “path dependence,” that is, the establishment of a system conducive to economic growth, without which both capital and technology are futile. Only when a country has established private property rights and a market economy system can it walk onto the road of virtuous cycle of economic growth. Capital accumulation is indeed important, but who will save and invest without a system that protects private property rights? Some people deem industrial revolution and technological innovation as the locomotive of economic development. Yet without a patent system that protects the rights and interests of inventors, who would invent proactively? Others take entrepreneurs as the key to economic development. But without a distribution system based on efficiency, would there be entrepreneurs? Without a modern corporate system, would these

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

19

entrepreneurs put their capacities into full play? Therefore, institutions are the key to a country’s level of affluence. Judging by such historical experience, what today’s poor countries lack is not capital or technology, but a system suitable for economic development. In a word, institutions are the precondition for economic activity and the starting point of economic growth. In light of the development of the above-mentioned theories of economic growth, there are various (rather than single) influencing factors of economic growth, but human capital is undoubtedly an important one—it plays a more and more important role in modern economic growth.

1.2 Literature Review and Theoretical Hypothesis For a long time, human capital and gaps in economic growth have been a hot topic in the academic circles. In a considerable period, the paradigm of economic studies has indeed made great contribution to the world economy, and many fruits and models of classical theoretical studies have been followed by contemporary economic studies in relevant fields. However, as Paul Ormerod said in his book The Death of Economics, the mainstream economic studies are trapped in a mechanical system of worldviews, and their ideal argument goes farther and farther away from the objective reality—they foresaw neither the European debt crisis nor the financial crisis. The economic new normal is undoubtedly the most important reality in China at present, which is followed by the disappearance of the demographic dividends, the middle-income trap, etc. The most prominent in this process are economic gaps and the imbalance of economic development. It is thus important to seek for answers from the perspective of human capital. To this end, in the following, we will be oriented to solving practical problems and draw from the classical theories of our predecessors. On that basis, we will put forward a theoretical hypothesis of gaps in human capital investment and regional economy, and try to reveal the endogenous reasons for economic gaps in light of human capital.

20

Y. LI

1.2.1

Studies on Human Capital and Economic Growth: An Overview

1.2.1.1 The Economic Growth Effect of Human Capital In the 1980s, Lucas and Romer introduced the theory of human resource into the new growth theory. They regarded both knowledge and human capital as production factors like physical resources. According to the neoclassical growth theory and the theory of endogenous growth, changes in capital and labor stocks will influence the economic growth rate in the short run (Solow 19566 ; Swan 19567 ), and the difference in human capital stocks is likely to directly influence the total factor productivity and thus affect the long-term economic growth rate (Romer 1986; Lucas, 1988). Ceteris paribus, countries or regions of larger stocks of human capital are more likely to sustain a higher rate of economic growth in the long run. Starting from this theoretical perspective, it is not hard to find that human capital is an important factor affecting economic gaps. This provides a theoretical foundation and basic idea for our study on the long-term factors for the formation of regional economic gaps in China. On the role of human capital investment, Romer (1986)8 and Lucas (1988)9 introduced the education sector into human capital and maintained that intellectual products and human capital have a spillover effect and therefore have an increasing marginal productivity, and that continuous investment in knowledge and education can constantly improve a country’s long-term growth rate. Romer (1986) argued that research and development (R&D) realizes increasing return to scale, which proves the role of human capital in boosting economic growth.10 According to the connotations and basic views of traditional human capital theories, some scholars made empirical studies on relevant issues, such as the relationship between human capital and economic growth, the contribution of human 6 R. M. Solow, “A Contribution to the Theory of Economic Growth,” Quarterly Journal of Economics 70, no. 1 (1956): 65–94. 7 T. W. Swan, “Economic Growth and Capital Accumulation,” Economic Record 32, no. 2 (1956): 334–61. 8 P. M. Romer, “Increasing Returns and Long-Run Growth,” Journal of Political Economy 94, no. 5 (1986): 1002–37. 9 R. E. Lucas, “On the Mechanics of Economic Development,” Journal of Monetary Economics 22, no. 1 (1988): 3–42. 10 P. M. Romer, “Endogenous Technological Change,” Journal of Political Economy 98, no. 5 (1986): 71–102.

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

21

capital to economic growth, etc. However, their studies and analyses invariably took investment in education as the only explaining variable. It follows that early research findings tended to look at human capital as homogeneous. What they studied was the average level of human capital as a whole, or the relationship between economic growth and the stocks of knowledge in human capital, rather than the different roles of different types of human capital in economic growth. What’s worse, they had ignored the influence of human capital structure on economic growth. In fact, human capital as a concept is not only a quantitative indicator; it also reflects the quality of structural changes. That is why some later models started to analyze the relationship between the structure of human capital distribution and economic growth. Birdsall and Londono (1997) and Lopez et al. (1998) made empirical studies on the relationship between the structure of human capital distribution and economic growth. Using the samples of 43 countries, Birdsall and Londono (1997) measured human capital structure with the standard deviation of the years of schooling in these countries, and concluded that inequality in education has a negative correlation effect on the overall economic growth.11 Nonetheless, using the standard deviation to measure the inequality in human capital can only help study the absolute value of human capital distribution—it cannot control the difference in the average value of human capital distribution. Lopez et al. (1998) used more extensive indicators of human capital and expressed the indicator of the structure of human capital distribution using the coefficient of educational diversity and the logarithmic standard deviation. They studied the human capital accumulation and structure of twelve Asian and Latin American countries, and analyzed their influence on per capita income and economic growth. The results indicate that human capital accumulation has a positive effect on economic growth, which is more obvious under competitive and opening market conditions; human capital structure has a negative correlation effect on per capita income in most countries, but its indicator does not verify that human capital structure has a negative effect

11 N. Birdsall and J. L. Londono, “Asset Inequality Matters: An Assessment of the World Bank’s Approach to Poverty Reduction,” American Economic Review 87, no. 2 (1997): 32–37.

22

Y. LI

on economic growth.12 In order to better study the relationship between human capital structure and economic growth, some scholars introduced the coefficient of human capital structure, which is usually calculated using the equi-section division and Gini coefficient methods. A. Castello and R. Domenech (2002) studied the relationship between human capital and economic growth using a method that measures the coefficient of human capital structure. In the 1980s, India and Indonesia were similar in their average level of human capital, with the average years of schooling of approximately 3.6 years. Yet they were quite different in their human capital structure, which led to great differences in their economic growth. The average years of schooling in East Asian countries were 5.558 years, 0.8 years higher than those in Latin America and the Caribbean Region (4.784 years), but their coefficients of human capital structure were quite close, 0.377 and 0.367, respectively. This indicates that the structure of human capital distribution was relatively unreasonable in East Asian countries. Through their studies of the relationship between the per capita output in the US and a number of variables, such as the coefficient of income distribution inequality, the coefficient of human capital structure, the accumulation of physical capital, and the initial per capita output between 1960 and 1990, A. Castello and R. Domenech (2002) found that the coefficient of human capital structure dropped from 0.41 in 1960 to 0.31 in 1990, whose average rate of changes varied between −0.015 and −0.03. They also found that the coefficient of human capital structure had a negative effect on economic growth, and the influence of its changes on the annual average economic growth rate was between 0.15% and 0.30%. Moreover, its influence on economic growth was more significant than that of the income distribution structure and the average level of education. Besides, A. Castello and R. Domenech (2002) also verified the indirect effect of human capital structure on the accumulation of other factors, and concluded that the coefficient of income distribution structure had a negative effect on physical capital accumulation, and that human capital structure influenced the economic growth rate by affecting the efficiency of resource allocation (mainly the investment rate).13 12 R. Lopez, V. Thomas, and Y. Wang, “Addressing the Education Puzzle: The Distribution of Education and Economic Reforms,” World Band Working Paper, no. 2031 (1998). 13 A. Castello and R. Domenech. “Human Capital Inequality and Economic Growth: Some New Evidence,” The Economic Journal 112, no. 2 (2002): 187–200.

1

1.2.1.2

HUMAN CAPITAL INVESTMENT AND REGIONAL …

23

Reasons for Regional Gaps in Economic Growth: An Overview For a long time, regional economic gaps have been a hot topic in many countries and in all sectors of society. Around the world, economic gaps mainly refer to the gaps between developed and developing countries and between regional integrated economies. Surely, there are also other types of economic gaps. In China, economic gaps mainly refer to the gaps among the Eastern, Central, and Western Regions and between the urban and rural areas. It has long been a focus in the academia. The reasons for such economic gaps include the industrial structure, human capital, labor mobility, institutional factors, factor endowments, comparative advantages, foreign investment, geographical location, technological progress, fiscal decentralization, etc. (see Table 1.1). In the traditional theory, hardware conditions are the major causes of regional economic gaps. Based on this, many scholars have focused their attention on resource endowment, geographical location, market economy, and investment and policy. Admittedly, hardware conditions and natural endowment play a non-negligible role in economic development, such as the iron and steel in Panzhihua of Sichuan Province, the soil and climate in Northeast China, and the transportation hub in Wuhan. It must also be admitted that hardware conditions had been the main driving force of regional economic development for quite a long time, and such a dynamic mechanism had remained in effect until modern times. However, like many other things, such a mechanism of development is subject to historical limitations—it was only effective in the pre-industrial era and the earlier stage of the post-industrial era. With the arrival of the age of the knowledge-based economy in the new century, a new system of economic dynamics centered on knowledge, science and technology, and innovation has gradually taken shape, and human capital has already become the core engine of economic growth. For China to overcome obstacles and realize its economic transformation and upgrading, it is of great significance to reexamine the role of human capital in boosting economic development, especially in the context of new normal economic conditions, such as the multiple complex situations facing the country, the pressure of economic structure transformation, the disappearance of the demographic dividends, the constraint of the middle-income trap, the population aging, and drastic changes in the international situation.

Market and policy factors Production factors (physical capital, human capital, and labor capital), and institutional and structural factors (urbanization, etc.) Unbalanced structure of human capital distribution and education inequality Historical and natural factors, policy and investment factors, the heavy-industry-oriented development strategy, fiscal decentralization and transfer payment, opening theory, FDI, marketization, industrial structure, ownership structure, knowledge and technological progress, etc. Factor input, economic structure, policy and institutional factors, geographical location, and historical factors Institutional differences (in ownership structure, marketization degree, opening degree, etc.)

no. 4 (2005).

17 Xu Xianxiang and Li Huan, “Endogenous Institutional Causes of Provincial Economic Gaps in China,” China Economic Quarterly,

University, no. 1 (2007).

16 Zhou Xiaowei and Zhang Ping, “An Institutional Analysis of the Reasons for Regional Economic Gaps,” Journal of Shangluo

the Gini Coefficients of Regional Human Capital in China,” Management World, no. 12 (2006): 42–49.

15 Li Yaling and Wang Rong, “Structure of Human Capital Distribution and Regional Economic Gaps: An Empirical Study Based on

Development and Growth, no. 9 (2014):79–106.

14 D. Dutta and Yang Y., “Major Factors behind Regional Disparity of Economic Growth in China during 1996–2010,” Economic

Zhou Xiaowei and Zhang Ping16 (2007) Xu Xianxiang and Li Huan17 (2005)

Li Yaling and Wang Rong15 (2006)

D. Dutta and Y. Yang14 (2014)

Reasons for regional economic gaps

A summary of studies on reasons for regional economic gaps

Author/s (Year of publication)

Table 1.1

24 Y. LI

Foreign trade and income distribution Institutions, history and location, state policy, infrastructure, market awareness, etc. Circular relationship among savings growth, investment growth, and economic growth Industrial structure, export-oriented degree of economy, and investment coefficient as the major reasons affecting regional gaps Investment in fixed assets, capital construction of central projects, investment in real estate, foreign investment, and credit rationing Negative correlation of regional space for economic development Foreign trade activity and policy (convenience and degree of opening, FDI, preferential policy, etc.)

Liu Li18 (2005) Zhang Xiusheng and Chen Huinü19 (2008)

Productivity,” Papers in Regional Science, no. 2 (2014): 223–27.

HUMAN CAPITAL INVESTMENT AND REGIONAL …

24 Chen A., P. Nijkamp, and T. Tabuchi, “Regional Science Research in China: Spatial Dynamics, Disparities and Regional

A Perspective of Coordinated Regional Development,” Journal of Guangdong University of Finance and Economics, no. 2 (2016).

23 Sun Yanan, Liu Huajun, and Cui Rong, “Causes of Regional Economic Gaps in China and Their Influence on Spatial Correlation:

no. 5 (2010).

22 Ren Jianjun and Yang Guoliang, “Regional Gaps in Economic Development in China and Their Reasons,” Economic Geography,

11 (2009).

21 Wang Xuanxuan, “Reasons for Gaps in Economic Development Among China’s Four Regions,” Finance and Trade Economics, no.

Economics, no. 9 (2004).

20 Hu Yongping and Zhang Zongyi, “Reasons for Regional Gaps in Economic Growth in China,” Contemporary Finance and

Countermeasures,” Economic Review, no. 2 (2008).

19 Zhang Xiusheng and Chen Huinü, “Regional Gaps in Economic Development in China: Status Quo, Reasons, Influences, and

Nankai Economic Studies, no. 4 (2005).

18 Liu Li, “Foreign Trade, Income Distribution, and Regional Gaps: Reasons in Trade for Regional Economic Gaps in China,”

Source Relevant literatures, sorted

Sun Yanan et al.23 (2016) A. Chen et al.24 (2014)

Ren Jianjun and Yang Guoliang22 (2010)

Wang Xuanxuan21 (2009)

Hu Yongping and Zhang Zongyi20 (2004)

Reasons for regional economic gaps

Author/s (Year of publication)

1

25

26

Y. LI

To reexamine human capital does not mean to ignore other factors. On the contrary, similar to its approaches to industrial structure adjustment and upgrading that are well underway, what China pursues is to fully integrate various factors and diagnose its economic conditions—with human capital as an important entry point—for sustainable, steady, and healthy development of its economy. Therefore, the academic circles are more in favor of comprehensive and systematic studies. For example, Wang Xiaolu and Fan Gang (2004) thought that the influencing factors of regional economic gaps include production factors (physical capital, human capital, and labor capital), institutional factors, and structural factors (the process of urbanization, etc.).25 Sun Haigang (2007) maintained that marketing factors are the major ones causing regional economic gaps, while policy factors have entitled the Eastern Region to a first-mover advantage, and the marketing mechanism has reinforced such an advantage.26 Liu Yanzhuo (2011) pointed out that locational conditions, physical geographic conditions (sunlight, air temperature, hydrology, soil, energy, minerals, creatures, etc.), cultural traditions, resource development, changes in market supply and demand, technological innovation, capital formation, human resource, labor, and technology have significant influences on the formation of regional economic gaps. He also discovered that differences in human capital stocks have a negative correlation with economic gaps in the Northeast Region, the middle reaches of the Yellow River, and the Southwest Region. In the Northern, Eastern, and Southern Coastal Regions, the middle reaches of the Yangtze River, and the Northwest Region, the correlation is positive. In general, human capital stocks have a positive correlation, while the average level of human capital has a negative correlation, with economic gaps.27 As we can see in the above research review, at present, Chinese scholars mainly study regional economic gaps from the perspectives of industrial structure, human capital, labor mobility, institutional factors, factor endowments, comparative advantages, foreign investment, geographic 25 Wang Xiaolu and Fan Gang, Regional Gaps in China: The Trend of Changes and

Influencing Factors Over 20 Years (Beijing: Economic Science Press, 2004). 26 Sun Haigang, “On the Causes of Regional Economic Gaps in China During Marketalization,” Journal of Finance and Economics, no. 9 (2007). 27 Liu Yanzhuo, “On Correlation Between Human Capital Stocks and Regional Economic Gaps in China,” MA Thesis, Capital University of Economics and Business (2011).

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

27

locations, technological progress, and fiscal decentralization. Their studies on the hardware conditions, such as factor endowments, foreign investment, and geographic locations, are quite mature. In this book, we will study the regional economic gaps in China in light of a software condition—human capital investment. 1.2.2

Theoretical Hypothesis of Regional Gaps in Economic Growth: The Theory of Human Capital Gaps

A gap is a difference. In economics, a gap derives from the supply and demand of resource factors. It includes many phenomena. For instance, the supply and demand of resource factors may not have reached a balance but have resulted in a difference. Such a difference is a gap. The existence of a gap reflects an unbalanced state. To reach balance, we must make efforts to fill the gap in certain field. 1.2.2.1 Gaps in Economic Development: A Literature Clue In 1966, American development economist Hollis B. Chenery and Alan M. Strout put forward the famous Two-Gap Model. They studied the modern history of over 50 countries and concluded that the economic development of developing countries is usually subjected to two constraints. One is the savings constraint. When domestic savings are not enough to support investment expansion, it will affect economic development. The other is the foreign exchange constraint. When the export income is smaller than expenditure, the limited foreign exchange is not enough to support the import of capital products necessary to economic development, which will impede domestic production and export development. Therefore, only when developing countries deal well with the relations between investment and savings and between import and export can they increase the growth rates of their national economy. The Two-Gap Model is based on the theory of the equilibrium between the gross demand and gross supply of the national income. In the equation, Y stands for national income, S for national savings, T for tax revenue, M for import, I for domestic investment, G for government expenditures, and X for export. According to Keynes’s theory of national income, we can know: Y =C+S+T +M

(Formula 1.1)

28

Y. LI

Y =C+I +G+X

(Formula 1.2)

C+S+T +M =C+I +G+X

(Formula 1.3)

If tax revenue equals government expenditure, namely T = G, then: S+M = I +X

(Formula 1.4)

M−X =I−S

(Formula 1.5)

That is,

In the Two-Gap Model, M − X is the difference between import and export, which is referred to as the “foreign exchange gap,” and I − S is the difference between domestic investment and national savings, referred to as the “savings gap.” As the model shows, if a country’s total domestic savings cannot meet the need of domestic investment, it will need a difference of the same amount between its import and export to reach balance between both sides of Formula 1.5. That is, it must introduce foreign investment. The foreign exchange gap equals the domestic savings gap. This is the main idea of the Two-Gap Model. Only when the economy is in equilibrium will the two gaps be identical. However, such equilibrium of two identical gaps is usually realized only after the economy is adjusted. Therefore, the Two-Gap Model is a theoretical one for developing countries to reach balance between their foreign exchange and savings gaps, and it provides an option to increase their economic growth. By means of aggregate analysis, the Two-Gap Model explains that developing countries must proactively make use of foreign investment, so as to overcome the constraints of savings and foreign exchange and boost economic growth. Yet what it emphasizes is a short-term equilibrium, but it is helpless in the case of the “constraint of technological gaps” in developing countries, which is a long-term phenomenon in developing countries and is crucial to the technological development of developing countries. If developing countries only focus on filling in the gap in the aggregate by making use of foreign investment, but neglect the “technological gap,” their “savings constraint” and “foreign exchange constraint” will never be fundamentally resolved, and it will be difficult for them to catch up with developed countries in economic development. Since the 1960s, some development economists have improved the Two-Gap Model by adding the technological gap (consisting of such

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

29

elements as technology, management, and entrepreneurs) and the tax gap (the gap in government tax revenue) to the “savings gap” and the “foreign exchange gap,” which have later developed into the Three-Gap and Four-Gap Models. Based on the Two-Gap Model, the Three-Gap Model also considers the constraint of fiscal revenue and expenditure. That is, it studies the constraints of the savings gap, the foreign trade gap, and the fiscal gap on economic development. In the following formulas, AD stands for aggregate demand, I for domestic investment, G for government purchase, X for export, AS for aggregate supply, S for national savings, T for net government revenue, and M for import. Aggregate demand: AD = C + I + G + X

(Formula 1.6)

Aggregate supply: AS = C + S + T + M

(Formula 1.7)

So the gap between social supply and demand can be expressed as: AS − AD = (S − I ) + (T − G) − (X − M)

(Formula 1.8)

On the right of Formula (1.8) are the three gaps in the Three-Gap Model: the gap between investment and savings (S − I ), the gap between fiscal revenue and expenditure (T −G), and the gap between foreign trade income and expenditure (X − M). The Three-Gap Model analyzes the role of government fiscal revenue and expenditure in economic growth, and concludes that a country’s economic development is constrained not only by its savings level and foreign exchange gap, but also by the government’s decision-making. The Four-Gap Model also takes into consideration the technological gap, which involves technology, management, and entrepreneurs, in addition to the three gaps (savings, foreign exchange, and government fiscal revenue and expenditure). According to Albert O. Hirschman, during economic development, the shortage of such resources as technology, management, and entrepreneurs is the most important constraint on the economic development of developing countries, and technological backwardness is another gap facing developing countries in their economic development, which limits their attraction of capital. Using the approaches of structuralism, the gap theory has established a complete and rigorous theoretical system that explains the reasons why

30

Y. LI

developing countries have lagged behind in economic development from the macroscopic perspective of demand. As regards its practical significance, the gap theory believes that there is a series of gaps in the process of economic development of developing countries and regions. Yet some of these gaps can never be filled through their own strength. For such gaps, the only way to go is to introduce, e.g., foreign investment. On the other hand, some gaps (e.g., entrepreneur resources) may be filled through their own accumulation and investment. 1.2.2.2

Theoretical Hypothesis of Regional Gaps in Economic Growth In the traditional economic theory, due to the scarcity of capital in developing countries, their price of capital—namely, their interest rate— is higher than that of developed countries. The nature of capital—the pursuit of profit—determines that it will flow from countries of lower interest rates to those of higher interest rates. In the past, as we have observed, developed countries did export their capital, which had flowed into developing countries. Over the past decades, however, there has been a phenomenon known as the “reverse flow of international capital,” that is, the flow of capital from underdeveloped to developed countries. In today’s world, the most attractive place for capital is no longer developing countries, but developed countries, which is inexplicable in the traditional economic theory. How to account for the “reverse flow of international capital?” The new growth theory has offered an answer. As knowledge and human capital drive modern economic growth, countries of a higher accumulation of both have a higher economic growth rate and income level. People are the most active factor. Developed countries are higher in the accumulation of knowledge and human capital. This means more adequate and more efficient utilization of such factors as capital and technology. Such increasing returns, which derive from the accumulation of knowledge and human capital, attract international capital to flow backward to developed countries with abundant human capital. According to the afore-mentioned literatures on the gap theory and referring to its structuralist approach, we put forward a theoretical hypothesis of regional gaps in economic growth: there is a series of gaps in the process of economic development of developing countries and regions; of these gaps, the human capital gap is a crucial one; the human capital gap of a country or region is inevitably related to its economic gap.

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

31

Development economists regard human resource, natural resource, capital formation, and technology (including that for organization and management) as the four major factors of economic development. In combination with the factors influencing economic growth in the theory of economic growth, we establish the following six-gap model, which includes the human capital gap:   (Formula 1.9) Gap = f gap1 , gap2 , gap3 , gap4 , gap5 , gap6 where Gap stands for the overall gap or the economic growth gap, gap1 for the human capital gap, gap2 for the human resource (labor) gap, gap3 for the physical capital gap, gap4 for the technological gap, gap5 for the institutional gap, and gap6 for the locational gap. In general, the function of a gap is nonlinear. As Formula (1.9) shows, the overall gap of an underdeveloped region is not only related to all the other gaps, but also to their ways of interaction. For an underdeveloped region, there are three ways to fill or narrow the gap. The first is endogenous, which means to gradually narrow the gap with developed regions by means of independent growth, and to catch up with and surpass developed regions relying on its own human capital investment, human resource accumulation and utilization, capital accumulation, technological innovation and progress, and institutional improvements and infrastructure construction (e.g., improvements in traffic and information facilities). The second is exogenous, which means to mainly count on external forces, namely, the introduction of talents, capital, and technology. This is similar to blood transfusion—it does not foster the blood-producing function of the underdeveloped region, so it will only result in an overdependence on external strength and marginalization of its competitiveness. The third is, in face of a moderate gap, to narrow the regional economic gap and catch up with and surpass developed regions by introducing external factors and digesting and absorbing them for effective simulated innovation. Under this pattern, the factors introduced will fill the various gaps of the underdeveloped region by means of interactions among them. For example, the introduction of talents will accelerate the formation of regional human capital and make up for the human capital gap. Meanwhile, as human capital plays its role, it can promote regional technological and institutional innovation, and improve regional locational facilities, so as to narrow the other gaps. As human capital aggregates well with the other factors and the resources

32

Y. LI

in the region, it will realize mutual benefit and coexistence among all the factors and play a big role in narrowing the gaps. Consequently, the economy will grow fast, which will in turn attract the inflow of talents. Thus, the factors and resources will aggregate for a virtuous cycle to take shape.

1.3 Human Capital Investment and the Possibility of Regional Economic Catching-Up and Surpassing in China Human capital takes shape through investment in science, education, culture, and health on the macroscopic level, as well as enterprise training, food, clothing, shelter, and transportation of individuals on the microscopic level. In the traditional theory, the major form of investment in human capital is systematic education, which is even taken as the only variable to measure human capital. Some scholars have studied and analyzed other forms of investment, but they are still unsystematic so far. For the convenience of research, we mainly focus on the macroscopic human capital investment, measure human capital stocks using four indicators (the level of education, the accumulation of market experience, the input in science, technology and culture, and the input in health and medical care), measure the structure of human capital by introducing a coefficient of human capital structure, and on that basis reveal the dynamic mechanism for economic growth and the path for regional economic catching-up and surpassing. 1.3.1

Regional Factor Structure and Economic Catching-Up and Surpassing

The difference in the regional factor structure well explains regional economic gaps, and meanwhile provides a basis and possibility for regional economic catching-up and surpassing. The regional factor structure refers to the constituent relations of various factors affecting regional economic development, including resource, policy, geographical position, population, education, social welfare, etc. The different structural configurations of these factors bring into being different models and levels of economic development. The advantageous factors in China’s Eastern Region include its unique geographical location along the coast, favorable

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

33

skewed policy, sound social welfare, abundant workforce, and, most strikingly, developed education. The economic development in this Region is the result of the integrated effect of multiple factors, but the major driving factors of its sustained economic development are different. At the beginning of the reform and opening-up, its core driving force for economic development was policy advantage, including the setting-up of special economic zones, the rural support for the urban areas, the Central and Western Regions’ support for the Eastern Region, the reform and opening-up policy, the attraction of foreign investment, the preferential fiscal and taxation policies, financial support, etc. It was under the effect of such policies that the Eastern Region experienced a rapid economic growth, which was a policy-dominated model of economic development in our eyes. Nonetheless, such a policy-dominated model of economic development is in essence one of exogenous dynamics, but the sustainable and healthy development of the economy must rely on the endogenous driving force. In fact, through decades of accumulation, the Eastern Region has already realized the modal transformation of its economic development and brought into being a talent-dominated model of economic growth with talent cultivation and education as the core. The core of this model is human capital. Talent cultivation and education has furnished this region a powerful driving force for economic development, which is borne out by its developed education. For a new leap forward in its economy in the new era, the Eastern Region needs not only talents, but also rational allocation of talents, namely, an efficient and scientific structure of human capital. As for talents, it must not only strengthen and improve their cultivation, but also extend their sources and attract excellent talents from elsewhere. In terms of the integration between talent cultivation and industries, it must optimize and upgrade its structure of human capital and follow the development of its industrial structure and the transformation of the industrial structure from processing to financial and service, and transform from the processing-oriented to financial- and service-oriented structure of human capital. At the same time, it should also optimize the development of science, education, culture, and health. On the other hand, the adjustment in the industrial structure does not mean to ignore the development of the primary and secondary industries. Instead, it must match its human capital allocation with industrial development. Moreover, in terms of the structure of human capital investors, it must adhere to and optimize the pluralistic investment structure, and implement the triangle

34

Y. LI

strategy of investment by governments, enterprises and individuals, and social organizations. In the Central and Western Regions, the major model of economic growth is still resource-based, and regional economic development mainly depends on their resource advantages, including the abundant natural and labor resources. In the new era, such an extensive, resource-based model of economic growth is gradually going through changes and turning into a talent-based model of economic growth. The Central and Western Regions are a vast territory rich in natural resources, and seemingly inexhaustible in the supply of labor force. It is worth mentioning that, for quite a long time, the Central and Western Regions have been faced with a contradiction between the abundant human resource and the scarce human capital—their human resource has not converted into human capital that boosts economic growth. Of regional factors other than resource and labor, the skewed policy and the human capital potential are important for the economic catching-up and surpassing of these Regions. For a long time, the Central and Western Regions have managed to sustain regional economic development based on their advantage in natural resources. This is, in essence, a rather extensive and backward model of economic growth, which is followed by the squander and depletion of resources and the destruction of the environment. Such a limited growth of regional economy is realized at the huge cost of the resources and environment. In the new era, economic growth and human capital structure will interact with each other under the background of industrial structure transformation and upgrading in the Central and Western Regions. On the one hand, the economic and industrial structure will transform and upgrade, and the tertiary industry will integrate with the primary and secondary industries. This will promote the development of production services and give rise to multi-dimensional changes in the human capital structure—from labor-intensive to technology- and serviceintensive models of human capital, from exclusive government investment to multiple investment components of governments, enterprises and individuals, and social organizations, from agricultural and industrial human capital to integrated development of financial, service, agricultural, and industrial human capital, and from one-fold talent cultivation to pluralistic and multiple-channel talent cultivation and introduction. In this series of changes, talent cultivation and education are bound to play a major role.

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

35

In the multi-dimensional structure of human capital, particularly crucial is the factor structure, i.e., the internal constituents of human capital, which can only be realized by means of education and policy. In recent years, the Central and Western Regions have been pressing forward with their development of science, education, culture, and health, and significantly improving their education level. Meanwhile, with the steady improvement in the degree of regional opening-up, many foreign enterprises have settled down in these regions, bringing about new opportunities of development and remarkable achievements in talent introduction. All this illustrates that China will elevate its economic development to a new height and is likely to realize regional economic catching-up and surpassing as well as coordinated development in the great process of transformation and upgrading of its industrial structure. 1.3.2

Human Capital and Economic Catching-Up and Surpassing

Economic imbalance is an inevitable product of the market economic system. Currently, China is also faced with a big problem of economic imbalance among its regions and between its urban and rural areas. To some degree, economic imbalance impedes the sustainable and healthy development of the Chinese economy, but driven by the structural optimization and transformation of regional human capital, China also has a big possibility and favorable conditions for regional economic catchingup and surpassing. Since the beginning of the reform and opening-up, the Chinese economy has stunned the world with its high-speed growth. Behind such a high speed is its human capital. For more than 40 years, China’s human capital has been the core factor, either in its elementary level (i.e., labor capital) or its intermediate and advanced levels (i.e., talent capital, which is espoused at present). Therefore, human capital is the core driving force of economic growth, and the enhancement and improvement in human capital investment provides a possibility for economic catching-up and surpassing of backward regions. 1.3.2.1

Human Capital Upgrading amid Industrial Transformation China has long been forging ahead toward a “manufacturing power.” Manufacturing accounts for a large proportion of the Chinese economy and China’s talent structure. In terms of human capital, such a model is,

36

Y. LI

by nature, an extensive way of development, because it pursues socioeconomic development based on cheap labor force and relatively low investment in human capital. It is mainly influenced by the original model of human capital, which is typical of the dated and backward structure of human capital and the mismatch between the structures of human capital and emerging industries. Consequently, it is unable to well boost the development of productive services. For a long time, the human capital structure of Chinese enterprises has been single, fixed, and stereotyped, with an extremely limited coverage of the human capital and a lack of flexibility and adaptability. Once some change arises in the domestic or foreign market environment—for example, an upgrading in the productive technology and an update in the management model, it will be difficult to sustain the original model of human capital. The single, fixed, and stereotyped concept for human capital development has seriously constrained the development of organizations and individuals, which is to the disadvantage of the development of the employees’ creative thinking and lateral thinking. Obviously, the original model of human capital can hardly meet the need of the current adjustment and upgrading in China’s industrial structure, because what the emerging industrial structure requires is a pluralistic talent structure. On the one hand, it requires an overall optimization and upgrading of the original structure of human capital to match the new industrial structure. On the other hand, it has placed greater demands on the internal structure of human capital, which is mainly manifested as the demand for skilled and service- and management-oriented personnel, and a model of innovative development with the introduction of new management concepts (e.g., the concept of happiness management, the concept of EAP development, etc.). Therefore, its interpretation of human capital includes a good variety of factors, such as health, emotions, values, creativity, knowledge, skills, sense of belonging, learning, and team strength. It emphasizes the overall development of individuals and teams, and realizes the goal of a “seamless joint” between organizational development and human capital structure. The original human capital structure is a narrow-minded concept and model. Its development of people’s potential and skills is limited to the simple form of “labor force” and “thinking.” As regards the subdivision of industries and trades, it rests content with the existing industrial forms rather than exploring new industrial fields, which limits the development of human capital. If the original model of human capital

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

37

persists, the emerging industrial structure and the human capital structure can hardly agree with each other and are bound to fall out of line with each other. It is thus impossible to realize the goals of jumping out of the “Lewis turning point,” the conversion of “demographic dividends” into “talent dividends,” avoiding the “middle-income trap,” and boosting economic development. Following the transformation and upgrading of the industrial structure, China’s human capital is also upgrading. In terms of regional distribution, the Eastern Region is gradually shifting its processing industries to the other regions and turning to modern services and financial sectors. In the following decades, it will realize a new leap of its economy through transformation and upgrading. It is worth mentioning that the US largely ignored the roles of its industry and agriculture during its transformation toward services. Therefore, to realize a new leap of its economy in future, China’s Eastern Region must not ignore its processing industries, and, more importantly, it must increase input in human capital transformation and upgrading, so as to match its human capital and industrial structure. The Eastern Region, which boasts an advantage in human capital development, will continue to enjoy a long-term motivation for economic development as a new round of industrial structure adjustment gives birth to the upgrading and optimization of new types of human capital. On the other hand, the Central and Western Regions are undertaking an industrial shift from the Eastern Region. Yet they are not following the traditional resource-based path of development, but are optimizing their human capital structure by accelerating investment in regional human capital construction. Relatively weak in the basis of human capital development and investment, the Central and Western Regions are important potential areas rich in human capital and key areas of economic development. Meanwhile, China’s vigorous promotion of science, education, culture, and health is reforming the structure of its human resource, and its upgrading of human capital will facilitate the conversion from demographic dividends into talent dividends. Overall, the spatial distribution of human capital is being gradually optimized, and the adjustment in the industrial layout is spawning optimal configuration of regional human capital, which highlights the important role of human capital as a core driving force of economic development.

38

Y. LI

1.3.2.2

Remodeling and Optimizing the Human Capital Structure For quite some time, there have been few studies on the relationship between human capital structure and economic growth and no unified concepts of human capital and human capital structure. Mainstream studies mostly take education as the only indicator of human capital and are mainly quantity-oriented, focusing too much on human capital stocks. On the other hand, the very few studies on human capital stocks are usually limited to a single dimension of the structure (e.g., the years of schooling). In fact, there are different dimensions of human capital structure according to different standards. For instance, according to the investors, human capital may be divided into investments by governments, enterprises, and individuals; according to the intrinsic attributes, it may be divided into health, education, science, technology, etc.; according to the industries, it may be divided into human capital of the primary, secondary, and tertiary industries. For example, human capital formed through input in education may be divided according to different majors and levels. Thus, it is quite biased to judge the human capital structure from any dimension alone. In fact, the different dimensions of human capital structure are intertwined in real life. So our concern is with the mechanism of action of the comprehensive structure of human capital on economic gaps and regional economic catching-up and surpassing. Similarly, we may divide China’s human capital structure into the eastern, central, and western dimensions according to the three geographical divisions. Based on such dimensions of division, we may explore an optimized allocation of human capital structure, so as to seek improvement in the overall economic strength. For such division, we may learn as much as possible from the spatial distribution of human capital, and analyze it in combination with the size of regional human capital. Thus, in light of regional division, it seems that we may find the reasons for regional economic imbalance and economic catching-up and surpassing in the human capital structure. In fact, the regional human capital structure is being reshaped and optimized in China, which provides possibilities for regional economic catching-up and surpassing. Thanks to the longterm skewed policy, the Eastern Region has realized high-speed economic development as a result of large-scale investment in its science, education, culture, and health. In the new era, the state is strengthening its support for the Central and Western Regions, which is fundamentally changing their human capital structure and effectively developing and releasing

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

39

their human capital capacity. This will fully guarantee regional economic catching-up and surpassing. 1.3.3

Conditions for Economic Catching-Up and Surpassing of the Western Region

The realization of the potential economic growth rate in a given period relies on certain restrictive conditions, such as the diffusion of knowledge and technology, the rate of structural transformation, the accumulation of physical and human capital, and the expansion of demands. Moses Abramovitz introduced social capacity into the theory of catching-up and surpassing, and pointed out that only those countries with backward technology but advanced social competence have the capacity to catch up and surpass. As Abramovitz (1986–1995) further pointed out, only under three conditions can a country or region realize catching-up and surpassing, namely, the existence of natural resources, moderate social capacity, and appropriate technologies. He also stressed the roles and forms of such capacity in different stages of development. As previously mentioned, human capital investment is the core driving force of economic growth. The enhancement and improvement in human capital investment has made possible the economic catching-up and surpassing of backward regions. It is true that the Western Region is relatively weak in the basis of human capital development and investment, but it has an important potential for human capital development. In order to realize economic catching-up and surpassing, it must accelerate its human capital investment and construction to optimize its human capital structure. On the other hand, the Western Region must make progress in science, education, culture, and health, so as to reform the structure of regional human capital and upgrade its human capital, which will facilitate the conversion of demographic dividends into talent dividends, to further improve the quality and optimize the spatial distribution of its human capital. The ongoing remolding and optimization of the regional human capital structure in China provides a possibility for the Western Region to catch up and surpass. China is implementing the Western Development Strategy. Due to various reasons, however, the Central and Western Regions have lagged behind the Eastern Region in the process of common economic development. The development of the Central and

40

Y. LI

Western Regions—especially the latter—concerns not only the quality and level of life in the western areas, but also the balanced development of the national economy and social stability. It is not only of great economic significance, but also of great political significance. The Western Development Strategy is meant to accelerate the development of the Central and Western Regions by all means, to coordinate the east–west relations, to develop the western areas, and to improve the economic development in the Western Region. Thanks to the state’s constant increase in its support for the Western Region, the human capital structure there has been fundamentally optimized, which has effectively improved its stocks and quality of human capital and provided an adequate guarantee for its economic catching-up and surpassing. In conclusion, in the historical context of increasingly rapid globalization and economic integration, significant changes have taken place to the paths and basic approaches of economic catching-up and surpassing of both developed and underdeveloped countries. At the same time, the acceleration of their economic growth depends more on structural transformation and human capital accumulation under the condition of unbalanced development, as well as on the learning and utilization of advanced institutions and technologies from the outside world. In and outside China, a new round of industrial structure adjustment is well underway. Behind this reformation and adjustment is the division of new economic fields into spheres of influence. Enough evidence suggests that developed countries and regions have already started adjusting their industrial layout and marching into new economic fields, along with upgrading their human capital. It goes without saying that, in face of the new round of economic tide, the key is to grasp the preemptive opportunity of human capital. Unless undeveloped regions implement the catching-up and surpassing strategy as developed regions transform and upgrade their industrial structure based on their economic strength and accumulated advantage in human capital, they are bound to widen their gaps with developed regions. In order to gradually narrow its gap with the Eastern Region, the Western Region must formulate a catching-up and surpassing strategy accordingly and strive for an economic breakthrough, which requires it to adopt new patterns of thinking and new measures, the most important of which will definitely be the talent strategy.

1

HUMAN CAPITAL INVESTMENT AND REGIONAL …

41

References Birdsall, N., and J. Londono. “Asset Inequality Matters: An Assessment of The World Bank’s Approach to Poverty Reduction.” American Economic Review 87, no. 2 (1997): 32–37. Castello, A., and R. Domenech. “Human Capital Inequality and Economic Growth: Some New Evidence.” The Economic Journal 112, no. 2 (2002): 187–200. Chen, A., P. Nijkamp, and T. Tabuchi. “Regional Science Research in China: Spatial Dynamics, Disparities and Regional Productivity.” Papers in Regional Science, no. 2 (2014): 223–27. Dutta, D., and Yang Y. “Major Factors Behind Regional Disparity of Economic Growth in China During 1996–2010.” Economic Development and Growth, no. 9 (2014): 79–106. Hu Yongping and Zhang Zongyi. “Reasons for Regional Gaps in Economic Growth in China.” Contemporary Finance and Economics, no. 9 (2004). Liu Li. “Foreign Trade, Income Distribution, and Regional Gaps: Reasons in Trade for Regional Economic Gaps in China.” Nankai Economic Studies, no. 4 (2005). Liu Wenge, Pan Pengjie, and Zhu Xinglong. “The Extension of Human Capital Theory and the Explanation of Regional Economic Differences.” Economics Information, (2006): 27–30. Liu Yanzhuo. “On Correlation Between Human Capital Stocks and Regional Economic Gaps in China.” MA Thesis, Capital University of Economics and Business (2011). Lopez, R., V. Thomas, and Y. Wang. “Addressing the Education Puzzle: The Distribution of Education and Economic Reforms.” World Band Working Paper 2031 (1998). Lucas, R. “On the Mechanics of Economic Development.” Monetary Economics 22, no. 1 (1988): 3–42. Ren Jianjun and Yang Guoliang. “Regional Gaps in Economic Development in China and Their Reasons.” Economic Geography, no. 5 (2010). Romer, Paul M. “Increasing Returns and Long-Run Growth.” The Journal of Political Economy 94, no. 5 (1986): 1002–37. Solow, R. M. “A Contribution to the Theory of Economic Growth.” Quarterly Journal of Economics 70, no. 1 (1956): 65–94. Sun Haigang, “On the Causes of Regional Economic Gaps in China During Marketalization,” Journal of Finance and Economics, no. 9 (2007). Sun Yanan, Liu Huajun, and Cui Rong. “Causes of Regional Economic Gaps in China and Their Influence on Spatial Correlation: A Perspective of Coordinated Regional Development.” Journal of Guangdong University of Finance and Economics, no. 2 (2016).

42

Y. LI

Swan, T. W. “Economic Growth and Capital Accumulation.” Economic Record 32, no. 2 (1956): 334–61. Wang Xiaolu and Fan Gang. Regional Gaps in China: The Trend of Changes and Influencing Factors Over 20 Years. Beijing: Economic Science Press, 2004. Wang Xuanxuan. “Reasons for Gaps in Economic Development Among China’s Four Regions.” Finance and Trade Economics, no. 11 (2009). Xu Xianxiang and Li Huan. “Endogenous Institutional Causes of Provincial Economic Gaps in China.” China Economic Quarterly, no. 4 (2005). Yaling and Wang Rong. “Structure of Human Capital Distribution and Regional Economic Gaps: An Empirical Study Based on the Gini Coefficients of Regional Human Capital in China.” Management World, no. 12 (2006): 42–49. Zhang Xiusheng and Chen Huinü. “Regional Gaps in Economic Development in China: Status Quo, Reasons, Influences, and Countermeasures.” Economic Review, no. 2 (2008). Zhou Xiaowei and Zhang Ping. “An Institutional Analysis of the Reasons for Regional Economic Gaps.” Journal of Shangluo University, no. 1 (2007).

CHAPTER 2

Human Capital Investment and Regional Gaps in China: A Comparison

In order to realize rational allocation of human capital, to further improve the quality of human capital owners and labor efficiency, to coordinate the human capital structure and economic structure, and to boost balanced development of the regional economy, it is necessary to assess the status quo of human capital investment in China’s Eastern, Central, and Western Regions in light of the human capital stocks and structure, to find out the differences among these three regions, and to formulate human capital policies fit for the local conditions. Most scholars at home and abroad have studied human capital from the perspective of education, but few have taken into consideration the approaches to the formation of human capital, such as investment in health, market experience accumulation, and the input in science and technology. Meanwhile, existing studies on human capital have mainly focused on the quantity of human capital, but very few have discussed the human capital structure in light of the quality of human capital. Using the average years of schooling, the input in public health, the input in science, technology, and culture, and market experience accumulation of China’s Eastern, Central, and Western Regions as the indicators of human capital stocks, this chapter will theoretically and empirically study the human capital structure of these three regions from the perspectives of the Gini coefficient of human capital and education human capital, so as to analyze the stocks and structure of human capital investment in light of the quality

© Social Sciences Academic Press 2023 Y. Li, Human Capital Investment and the Regional Economic Gap in China, https://doi.org/10.1007/978-981-99-4997-7_2

43

44

Y. LI

and quantity and compare the differences and characteristics of human capital investment of these regions.

2.1 Stocks of Human Capital Investment in China’s Eastern, Central, and Western Regions and Regional Gaps The stocks of human capital refer to the quantity and quality of health, knowledge, and capability of laborers at a given time point as a result of various forms of human capital investment. They reflect the intensity of human capital investment. The stocks of human capital can be observed and measured. The more the investment, the more the accumulation, and the higher the stocks. Differences in human capital investment will cause differences in regional human capital stocks, which, through the longterm multiplier effect, will result in regional differences in human capital. Regional stocks in human capital investment not only affect the overall quality and income level of laborers in the region, but also influence the regional capability of resource allocation and the capacity for economic growth. To find out the differences among the three regions in the stocks of human capital investment, we will analyze and compare their stocks of human capital investment from the perspectives of education, health, science and culture, and market experience accumulation. 2.1.1

Average Level of Education

Investment in education—particularly in school education—is the most basic and most important form of human capital investment. It not only facilitates the transmission of knowledge and culture, but also improves the ability, skills, and comprehensive quality of education receivers, and thus raises the labor productivity of the human capital and affects the employment and income of laborers. Both theoretical research and practice have proven that the years of schooling and the education level are directly proportional to the income level. Meanwhile, the ultimate benefit of investment in education further affects the quality of family life of education receivers and even their children’s future development. Therefore, investment in education plays a crucial role and has far-reaching influences. In light of national development, investment in education is of vital importance to a nation’s economy and the people’s livelihood. Only

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

45

by increasing input in education and improving the education level of its citizens can a country fundamentally enhance its comprehensive strength and lay up talents and strength for its economic development. Education human capital can be measured using the output or input approach. The output approach most frequently uses the remuneration of labor, that is, to indirectly reflect the human capital contained in the average remuneration of laborers. Yet this approach is easily affected by salary policies, and the human capital contained in the laborers is not consistent with what is displayed in production. Therefore, it is less adopted. The input approach uses the education level, the technical level (professional titles), and the expenditure on education. Limited by the availability and reliability of the statistics as well as the incomplete labor certification system in China, the technical level (professional titles) approach does not accurately reflect human capital. For reasonable economic men who seek input–output equivalence, the expenditure-oneducation approach undoubtedly better reflects the real human capital. The education-level approach expresses human capital in laborers as the education level or the years of schooling of the laborers. It is a widely used proxy that has been verified by many transnational studies. This is because, on the one hand, its statistics for calculation are accurate and easily available, and on the other hand, it has excluded the influence of the price factor in calculating the costs of human capital investment in terms of money. However, this approach has its deficiencies. For example, it does not adequately reflect the difference in the time value of different stages of education because the marginal accumulation of knowledge is a constant. At the same time, it does not reflect the quality of human capital. In China, the major source of education investment is governmental. Due to its large population, the imbalance in its regional development, and the limited investment capacity of the government, there is a huge difference in education investment among the Eastern, Central, and Western Regions. In order to better measure the human capital stocks of the three regions from the perspective of the education level, and considering that such indicators as “years of schooling,” “education expenditures,” and “future benefits” cannot fully reflect the real level of education, we will measure the stocks of human capital in education using the “average years of schooling,” so as to reflect the overall level of education of the three regions.

46

Y. LI

According to China’s education system, the National Bureau of Statistics (NBS) takes the duration of schooling as the years of education and calculates the average years of schooling of population aged six and over. We divide the population aged six and over into five groups, namely, population with college education or above (P1), population with high school education (P2), population with secondary school education (P3), population with elementary school education (P4), and illiterate and semiliterate population (P5). We then set the weight of each group according to its years of schooling, namely, H5 = 1 (illiterate and semiliterate population), H4 = 6 (population with elementary school education), H3 = 9 (population with middle school education), H2 = 12 (population with high school education), and H1 = 16 (population with college education or above). Finally, we get the total human capital stocks using the weighed sum method. Total human capital stocks (H) = Stocks of human capital with college education or above (HC) + Stocks of human capital with high school education (HM) + Stocks of human capital with middle school education (HJ) + Stocks of human capital with elementary school education (HP) + Stocks of human capital of illiterate and semiliterate population (HI) = ∑ (population with different levels of education × weights) = P1 H1 + P2 H2 + P3 H3 + P4 H4 + P5 H5 = 16P1 + 12P2 + 9P3 + 6P4 + P5

We divide the total human capital stocks with the total number of people receiving different levels of education, and got the average years of schooling of laborers: __ The average years of schooling H = Total human capital stocks H / Total number of people with different levels of education. ∑n __ i=1 Pi Hi (Formula 2.1) It is expressed as: H = P __

In Formula (2.1), H stands for the average years of schooling of population aged six and over, i for different levels of education, Pi for the number of people with Level i of education, Hi for the years of schooling of people with Level i of education, and P for the total population aged six and over with different levels of education (P = P1 + P2 + P3 + P4 + P5 ). This method of calculation is relatively simple, and the statistics are easy to obtain. Meanwhile, as it is integrated with the length of schooling

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

47

in China, its results also directly and conveniently reflect the sizes and changes of population with different levels of education in different years. According to Formula (2.1) and in combination with statistics of the national population census and relevant statistics in China Statistical Yearbook 2015, we calculate the average years of schooling of the three regions between 1990 and 2012 (see Table 2.1). As is shown in Table 2.1, the education human capital stocks of China’s Eastern, Central, and Western Regions were on the increase between 1990 and 2014. Over this period, the Eastern Region was higher than the Central and Western Regions as well as the national level in terms of the Table 2.1 Comparison of average years of schooling in China’s three regions (Unit: Year) Year

Eastern

Central

Western

National

Eastern-Western gap

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

6.57 6.76 6.84 7.05 6.90 6.98 7.00 7.22 7.32 7.47 7.94 7.91 8.08 8.18 8.26 8.24 8.43 8.56 8.64 8.78 9.17 9.19 9.31 9.41 9.38

6.34 6.45 6.53 6.59 6.76 6.83 6.96 7.22 7.26 7.30 7.72 7.88 7.79 8.08 8.13 7.89 8.10 8.26 8.34 8.42 8.80 8.81 8.90 9.05 9.05

5.75 5.82 5.89 5.64 5.99 6.22 6.33 6.47 6.56 6.65 7.04 7.13 7.20 7.35 7.53 7.17 7.41 7.57 7.65 7.76 8.25 8.36 8.42 8.48 8.50

6.26 6.34 6.42 6.47 6.55 6.68 6.79 7.01 7.09 7.18 7.62 7.68 7.73 7.91 8.01 7.83 8.04 8.19 8.27 8.38 8.80 8.85 8.94 9.05 9.04

0.82 0.94 0.95 1.41 0.90 0.76 0.67 0.75 0.76 0.82 0.90 0.78 0.88 0.83 0.73 1.07 1.02 0.99 0.99 1.02 0.92 0.83 0.89 0.93 0.88

Source: China Statistical Yearbooks 1991–2015, China Demographic Yearbooks 1991–2015, and China Labor Statistics Yearbooks 1991–2015, sorted and recalculated

48

Y. LI

average years of schooling; the Central Region was lower than the Eastern Region but higher than the national level; and the Western Region was far behind the Eastern and Central Regions, although its average years of schooling were on the increase. In light of the average years of schooling, the Eastern Region was richer than the Central and Western Regions in human capital stocks, and there were significant gaps among them. 2.1.2

Input in Public Health and Medical Care

According to Schultz (1981), the economic values of a person include knowledge, skills, and entrepreneurship. They increase with the extension of the effective life span. That is to say, health is an important precondition for people to promote their values—it extends the effective lifetime, so that laborers have more time and energy to work and realize their values, constantly improve their labor productivity, and increase their income. At the same time, health is also a prerequisite for laborers to accept various means of human capital investment, such as education, training, and transfer, which is conducive to improving the quality of their work and extending their working time. Thus, health directly reflects human capital stocks. Health is an important capital of laborers. This capital decreases and depreciates with the increase of the laborers’ age. Therefore, reasonable investment in health is an effective approach to maintenance and appreciation of this capital and thus to increasing the values of human capital. Stocks of health human capital consist of the initial health stock of laborers, which is born, and investment in health, which is made. The initial health stock is difficult to change, but human capital investment in health may be observed and measured. The more the investment and accumulation, the higher the stocks. Different modes and intensities of investment lead to different stocks of health human capital. Investment in health human capital causes differences in regional stocks of health human capital, which results in regional difference in human capital through the long-term multiplier effect. Regional stocks of investment in human capital not only influence the comprehensive quality and the income level of local laborers, but are crucial to the optimal allocation of local resources and regional economic growth. To better understand the differences in regional stocks of health human capital, we will calculate the stocks of health human capital of China’s

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

49

three regions in light of investment and from the perspectives of input in health care, medical and health conditions, development of the health industry, and nutritional and health status of residents. We will analyze the status quo of health human capital in the three regions to illustrate their gaps. 2.1.2.1

Widening Regional Gaps in Input in Medical Care and Public Health Since the beginning of reform and opening-up, economic growth has driven the development of medical care and public health in China. With the constant expansion of the GDP and the increasing attention of the public to health, the state, local governments at all levels, social organizations, and individuals have increased their input in medical care and public health year by year. In light of government expenditure on public health, the national figure was just a bit more than RMB 11 billion (or a per capita of RMB 11.5) in 1978, accounting for 3.02% of the national GDP. The low input in public health was one of the reasons for the low-level national medical care and public health at that time. Thereafter, the national health expenditure has been increasing. In 1992, it exceeded RMB 100 billion. Since then, the figure has been growing rapidly. In 2012, it reached RMB 2,784.684 billion, accounting for 5.36% of the national GDP. This translated into a per capita expenditure of RMB 2,056.6, 178 times the figure in 1978. In 2014, it further increased to RMB 3,531.24 billion, accounting for 5.55% of the national GDP. And there was an upward trend in both the absolute amount of input and its proportion in China’s GDP. As Table 2.2 shows, the national expenditure on public health kept rising from 1990 to 2014, and its proportion in the GDP was basically on the rise, from 4% in 1990 to 5.68% in 2014. Meanwhile, the per capita expenditure on public health increased from RMB 65.4 in 1990 to RMB 2,643.1 in 2014, up by 40.41 times. Figures 2.1 and 2.2 show the trends of changes in China’s national and per capita health expenditures between 1990 and 2014. As is shown in Figs. 2.1 and 2.2, China’s national and per capita health expenditures were on the rise between 1990 and 2014, in particular after 2008. As the country increased its input in medical care and public health, the health expenditures of the three regions rose accordingly. Yet regional gaps in health investment were widening over this period (see Table 2.3).

50

Y. LI

Table 2.2 National input in medical care and public health, 1990–2014 Year

Total expenditure (billion RMB)

Per capita expenditure (RMB)

Proportion in GDP (%)

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

74.739 89.349 109.686 137.778 176.124 215.513 270.942 319.671 367.872 404.750 458.663 502.593 579.003 658.410 759.029 865.991 984.334 1,157.397 1,453.540 1,754.192 1,998.039 2,434.591 2,784.684 3,213.043 3,615.326

65.4 77.1 93.6 116.3 146.9 177.9 221.4 258.6 294.9 321.8 361.9 393.8 450.7 509.5 583.9 662.3 748.8 876.0 1,094.5 1,314.3 1,490.1 1,807.0 2,056.6 2,349.0 2,643.1

4.00 4.10 4.07 3.90 3.65 3.54 3.81 4.06 4.36 4.51 4.62 4.58 4.81 4.85 4.75 4.68 4.55 4.35 4.63 5.15 4.98 5.15 5.36 5.65 5.68

Source: China Statistical Yearbooks 1991–2015 and Annual Statistical Bulletins on the Development of Health and Family Planning in China, sorted and recalculated

Figure 2.3 shows the health expenditures of China’s three regions between 2003 and 2014. As Fig. 2.3 shows, the health expenditures of the three regions were on the rise between 2003 and 2014, but there was an increasingly prominent imbalance in health spending among them. In particular, the Eastern Region was the highest in spending and the fastest in growth. And the gaps have been widening significantly since 2011. Table 2.4 is a summary of the health expenditures of China’s three regions between 2011 and 2014. As we can see through a horizontal comparison, the Eastern, Central, and Western Regions were in

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

51

Fig. 2.1 China’s National Health Expenditure, 1990–2014 (billion RMB) (Source: China Statistical Yearbooks 1991–2015, sorted and recalculated)

Fig. 2.2 Per capita health expenditure in China, 1990–2014 (RMB) (Source: China Statistical Yearbooks 1991–2015 and Annual Statistical Bulletins on the Development of Health and Family Planning in China, sorted and recalculated)

descending order of health expenditures, but were in ascending order in terms of the proportion of health expenditures in GDP. The Western Region was the highest (and higher than the national average level) in the proportion of health expenditures in GDP, followed by the Central Region, which was close to the national average level, and the Eastern

52

Y. LI

Table 2.3 Regional health expenditures in China, 2003–2014 (billion RMB) Year

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Region Eastern

Central

Western

National

Eastern-Western gap

343.888 402.589 447.977 469.217 572.326 701.893 803.853 936.349 1,152.649 1,343.061 1,551.479 1,769.754

173.294 193.249 218.923 218.345 276.33 367.46 459.468 515.543 635.132 759.426 865.024 965.342

141.229 163.191 199.091 194.993 239.457 312.74 367.207 392.152 591.249 647.985 796.54 796.144

658.411 759.029 865.991 882.555 1,088.113 1,382.093 1,630.528 1,844.044 2,379.03 2,750.472 3,213.043 3,531.24

202.659 239.398 248.886 274.224 332.869 389.153 436.646 544.197 561.400 695.076 754.939 973.610

Notes Available statistics of health expenditures in China are mainly at the national level. We did find some statistics of a few provinces, but they were usually limited and mostly available only after 2003. Due to serious data loss, the statistics of most provinces before 2011 were incomplete, and only those between 2011 and 2014 were complete. That being the case, we consulted many theses on the calculation of health expenditures. Considering the authoritativeness of the journals as well as the popularity and levels of the thesis authors, we sorted and recalculated the health expenditures of China’s three regions between 2003 and 2014 based on relevant statistics in China Statistical Yearbooks 2004–2015, the statistical yearbooks of 31 provincial-level regions in China, and China Health Statistical Yearbooks

Fig. 2.3 Health expenditures of China’s three regions, 2003–2014 (Source: China Statistical Yearbooks 2004–2015, China Health Statistical Yearbooks 2004– 2015, and provincial statistical yearbooks, sorted and recalculated)

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

53

Region was the lowest of the three. Yet in terms of health expenditure per capita, the Central and Western Regions were far behind the Eastern Region. The Eastern Region was higher than the other regions as well as the national average level, followed by the Western Region, and the Central Region was the lowest. And both the Central and Western Regions were lower than the national average level in health expenditure per capita. It follows that the Central and Western Regions could not solely shoulder their responsibility for the development of health services as they were behind the Eastern Region in the economic growth rate. What’s worse, they had a large number of impoverished people, most of whom could hardly afford medical care. Consequently, it was difficult to guarantee the health of laborers in these regions. A vertical comparison shows that the health expenditures of the three regions were on the rise between 2011 and 2014, but there was an increasingly prominent imbalance in health spending among them. In particular, the Eastern Region was the highest in health expenditure and the fastest in growth. And the gaps have been widening significantly since 2011. Take the figures of 2011 and 2014 for example. In 2011, the Eastern Region was the highest in health expenditure and was approximate to the sum of the other two regions, accounting for about 50% of the national expenditure. The Central and Western Regions were relatively close in health expenditure, with the former a bit higher than the latter. Besides, these two regions were lower than the national average level in health expenditure per capita. In 2011, the national health expenditure per capita was RMB 1,806.95, but the figures of the Central and Western Regions were RMB 1,498.87 and RMB 1,646.06, respectively. Meanwhile, the health expenditure per capita of the eleven coastal provincial-level regions in the Eastern Region reached RMB 2,078.87, which was higher than the national average level. Clearly, there were significant regional gaps. And we found basically similar cases in other years. Figures 2.4 and 2.5 show the health expenditures of China’s Eastern, Central, and Western Regions in 2011 and 2012. If we compare Figs. 2.4 and 2.5, we can find that there were very small changes in the health expenditures of China’s Eastern, Central, and Western Regions in 2011 and 2012. The Eastern Region accounted for about half of the national expenditure. The same is true of the figures in other years. Clearly, there were significant regional gaps in health expenditures in China.

(%)

(%)

(%)

1,152.649 29,358.145 3.93% 2,078.87 1,343.061 32,073.847 4.19% 2,104.76 1,551.479 34,933.67 4.44% 2,760.20 1,769.754 37,867.91 4.67% 3,128.96

635.132 12,762.470 4.98% 1,498.87 759.426 14,190.857 5.35% 1,786.42 865.024 15,467.01 5.59% 2,027.20 965.342 16,751.49 5.76% 2,252.98

Central

591.249 9,962.913 5.93% 1,646.06 647.985 11,390.480 5.69% 1,778.81 796.54 12,600.29 6.32% 2,174.14 880.23 13,807.353 6.38% 2,389.35

Western

Source: China Statistical Yearbooks 2012–2015 and China Health Statistical Yearbooks 2012–2015, sorted and recalculated

2014

2013

2012

Health expenditures (billion RMB) GDP (billion RMB) Proportion of health expenditures in GDP Health expenditure per capita (RMB) Health expenditure (billion RMB) GDP (billion RMB) Proportion of health expenditures in GDP Health expenditure per capita (RMB) Health expenditures (billion RMB) GDP (billion RMB) Proportion of health expenditures in GDP Health expenditure per capita (RMB) Health expenditures (billion RMB) GDP (billion RMB) Proportion of health expenditures in GDP Health expenditure per capita (RMB)

2011 (%)

Indicator

Year

Eastern

Health expenditures of China’s three regions, 2011–2014

Table 2.4

2,434.591 46,795.655 5.20% 1,806.95 2,784.684 51,894.210 5.37% 2,056.6 3,213.043 56,884.52 5.65% 2,349.02 3,615.326 63,646.3 5.68% 2,643.13

National

54 Y. LI

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

55

Fig. 2.4 Health expenditures of China’s three regions in 2011 (billion RMB) (Source: China Statistical Yearbook 2012 and China Health Statistical Yearbook 2012, sorted and recalculated)

Fig. 2.5 Health expenditures of China’s three regions in 2012 (billion RMB)

2.1.2.2

Widening Regional Gaps in Medical and Health Conditions Thanks to the reform of its medical and healthcare system, China has accelerated reformation of the operating mechanism of medical institutions and the national system for basic drugs. As a result, the medical and health conditions are getting better and medical institutions, medical personnel, and hospital beds are increasing steadily in number. From 1990 to 2014, the number of medical institutions in China increased by

56

Y. LI

Fig. 2.6 Numbers of medical institutions in China, 1990–2014 (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated)

96.91%, up by 3.88% annually. Over that same period, the numbers of hospitals, professional medical institutions, and primary-level clinics were on the rise. Figure 2.6 reflects changes in the number of medical and health institutions in China from 1990 to 2014, which was on a steady upward trend. Primary medical and health institutions—such as community health service stations and village clinics—were the largest in number, accounting for over 90% of all the institutions. Hospitals and professional public health institutions were smaller in number, but were increasing year by year. Such increase in the number of medical and health institutions reflects the gradual improvement and perfection of China’s medical and health system and, on the other hand, makes it more convenient for patients in need of medical service. This plays a great role in improving people’s livelihood and health. Besides, the number of medical personnel is also an important indicator of a country’s medical and health conditions. As we can see in Fig. 2.7, both the total number of medical personnel and that of medical technical personnel, certified physician assistants, and registered nurses were increasing year by year. The increase in the number of health personnel enables patients to receive timely and effective treatment and improves people’s health, which is critical to improving

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

57

Fig. 2.7 Numbers of medical personnel in China, 1990–2014 (million persons) (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated)

the stocks of health human capital. Meanwhile, as Fig. 2.8 shows, the number of hospital beds was also increasing, which has, to a certain extent, alleviated the difficulty of medical service access. To sum up, China’s medical and health conditions are gradually improving, either in light of the number of medical and health institutions, medical personnel, or of hospital beds in the country. However, in the context of the improving national medical and health conditions, there is frequent reporting on difficult and expensive medical treatment, especially in the Central and Western Regions, which shows regional gaps in medical conditions in China. As can be seen in Fig. 2.9, the numbers of medical and health institutions in the Eastern, Central, and Western Regions were on steady growth from 1990 to 2014. In 2009, the state introduced a medical reform plan. Thanks to this plan, the numbers of medical and health institutions have increased sharply in the three regions since 2009. Yet regional gaps in the number of medical and health institutions have remained a problem and have been expanding since 2009. As the major choice for residents to seek medical service and improve their health, medical and health institutions have a direct impact on the level of health and the stocks of health human capital.

58

Y. LI

Fig. 2.8 Numbers of beds in medical and health institutions in China, 1990– 2014 (million) (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated)

Fig. 2.9 Medical and health institutions in China’s three regions, 1990–2014 (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated)

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

59

Figure 2.10 shows the numbers of medical personnel in the Eastern, Central, and Western regions and their changes from 1990 to 2014. It is obvious that the numbers of medical personnel gradually decreased from the Eastern Region to the Central and Western Regions. Although the number of medical personnel in the Western Region was increasing year by year, it was still behind the Eastern and Central Regions. As can be seen in Fig. 2.11, the numbers of hospital beds in medical and health institutions in the three regions were increasing year by year, but the gap between the Eastern Region and the Central and Western Regions was widening. The Eastern Region was far ahead, followed by the Central Region, and the Western Region was the smallest in the number of hospital beds. To sum up, although the numbers of medical and health institutions, medical personnel, and hospital beds in the Eastern, Central, and Western Regions were on the rise in recent years, the growth in the three regions was uneven, and the developed Eastern Coastal Region was still ahead of the Central and Western Regions in terms of medical and health conditions. Regional medical and health conditions are directly related to the health of the local residents and are an essential part of the environment for the formation of health human capital, which will inevitably lead to different stocks of health human capital in different regions.

Fig. 2.10 Numbers of medical personnel in China’s three regions, 1990–2014 (million persons) (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated)

60

Y. LI

Fig. 2.11 Numbers of hospital beds in China’s three regions, 1990–2014 (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated)

2.1.2.3

Widening Regional Gaps in the Development of the Health Industry The increase in health service demand and investment drives the development of the health industry. Yet influenced by social concepts and differences in economic growth, regional development of the health industry is uneven: the Eastern Region has more advantages than the Central and Western Regions. The pharmaceutical industry is closely related to people’s well-being. According to “Top 100 Enterprises in China’s Pharmaceutical Industry 2013” released on http://www.yytj.org.cn/, of the top 20 pharmaceutical enterprises, 75% were in the Eastern Region (represented by Guangzhou Pharmaceutical Holdings Limited, Yangtze River Pharmaceutical Group, and China Resources Pharmaceutical Group Limited), only three (Xiuzheng Pharmaceutical Group, Harbin Pharmaceutical Group Holding Co., etc.) were in the Central Region, and none was in the Western Region. There were obvious regional differences in the distribution of pharmaceutical enterprises. In light of the sales revenue and profit of the pharmaceutical industry in 2013, Shandong, Jiangsu, Guangdong, Henan, and Jilin were the top five provinces in the main business income of the pharmaceutical industry, and Shandong, Jiangsu, Guangdong, Henan, and Beijing were top five in the total profit. Most of

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

61

these provinces are in the Eastern Region. Based on their superior location advantages and infrastructure, the developed coastal areas in the Yangtze River Delta, the Pearl River Delta, and the Bohai Bay have grown into major gathering places of the medical device industry in China, accounting for over 80% of the total output value of the industry. And their medical device products are sold all over the country. The development of the health industry is inseparable from the technical support of biotechnology parks. At present, China boasts hundreds of such parks, which have not only accelerated the development of the health industry, but also become an important way to attract investment, drive consumption, and stimulate exports. In recent years, Wuhan, Chengdu, Nanning, and other central and western cities have made great efforts to build industrial parks. Yet in terms of the distribution, most wellknown biotechnology parks are still located in the economically developed Eastern Region, such as Zhongshan Health Industry Base and Shanghai Zhangjiang Hi-Tech Park. Similar to the spatial layout of biotechnology parks, 60% of the domestic healthcare products are produced in Beijing, Shanghai, and Shandong, showing a certain degree of regional concentration. In addition, health service industries—such as health consulting, leisure and entertainment, and fitness—are directly related to regional economic development. 2.1.2.4

Widening Regional Gaps in Nutrition and Health of Residents Economic development has accelerated the process of urbanization in China. With the increase of income, the nutritional and health status of Chinese residents has also improved. In terms of nutrition, the intake of animal fat and salt has decreased, while that of aquatic products and eggs increased; and the nutritional level of preschoolers has improved significantly, and the anemia rate decreased. In terms of the growth and development of adolescents, the rates of both growth retardation and marasmus in children and adolescents have decreased, their average height increased, and the low birth-weight rate decreased significantly. From the perspective of residents’ health awareness, the proportion of participants in fitness exercises has increased significantly, and more attention has been paid to the rationalization of the dietary structure. With the improvement of nutritional status, however, sub-health problems should not be ignored. The problems of overweight and obesity caused by the unbalanced dietary structure and unhealthy lifestyle have become increasingly

62

Y. LI

prominent, and the number of patients with high blood pressure, hyperlipidemia, and diabetes has increased rapidly. In sharp contrast to the economically developed areas, residents in the vast backward areas still suffer from significant malnutrition, and the anemia rate of preschoolers and pregnant women is still high. Life expectancy and the mortality rate can effectively reflect the health status of residents. In the early years of the People’s Republic of China, when the Chinese economy was in recovery, the development of medical and health services was seriously lagging behind. Consequently, China’s average life expectancy was only 35 years. In 2010, the figure was 74.8 years. As Fig. 2.12 shows, China’s maternal mortality decreased from 80 per 100,000 persons in 1991 to 21.7 per 100,000 persons in 2014. Over this period, the maternal mortality rate in both urban and rural areas had decreased in varying degrees, and the decline in rural areas was more significant, dropping from 100 to 22.2 per 100,000 persons. Figure 2.13 shows a downward trend of the infant and child mortality in China. In 1991, the infant mortality rate in China was over 50%, but it decreased to 8.7% in 2014. In particular, the mortality rate of children under five fell to 11.7%.

Fig. 2.12 Maternal Mortality in China, 1991–2014 (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated)

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

63

Fig. 2.13 Mortality Rates of Infants and Children under Five in China, 1991– 2014 (Source: China Statistical Yearbook 2015 and China Health Statistical Yearbook 2015, sorted and recalculated)

According to the above data, the health condition of Chinese residents has been greatly improved, and China is in the forefront among the developing countries in terms of the overall health level. Yet compared with developed countries, China is still low in the health status of its residents whose physique is declining. This is mainly due to sub-health problems caused by occupational factors that affect health, such as high mental pressure and overwork, as well as adverse life factors such as smoking, alcoholism, and irregular work and rest. Judging by residents’ nutrition and health, China’s Eastern, Central, and Western Regions have invariably made considerable progress, despite the regional gaps. From the perspective of nutritional intake, different levels of economic development determine different degrees of nutritional intake: there are great differences in nutritional intake between developed and poor areas and between urban and rural areas. As far as the whole society is concerned, over-nutrition and under-nutrition coexist. In terms of health status, there are also differences among the three regions in the maternal mortality rate, the infant mortality rate, and the mortality rate of children under five. Thanks to its perfect medical system, advanced medical equipment, and the concentration of many well-known physicians at home and from abroad, the developed coastal region is significantly lower than the Central and Western Regions in the

64

Y. LI

maternal mortality rate and the mortality rate of infants and children. In contrast, the Central and Western Regions, where more than 90% of China’s national poverty-stricken counties are located, have lagged behind the coastal Eastern Region in economic development. The restriction of the backward economy on the construction of medical infrastructure is one of the main reasons for the relatively high mortality rate. Nationally, the government, society, and individuals have increased their health expenditures year by year, and accelerated the improvement and development of medical and health infrastructure in China, which is conducive to the creation of a good national medical and health environment. With the improvement in national medical and health conditions, the numbers of medical and health institutions and health personnel are increasing year by year, and the difficulty in access to medical service has been eased to some extent. The health industry is booming nationwide. Although its current contribution to China’s GDP is only about 4%, it is playing an increasingly important role in stimulating domestic demand, promoting employment, and driving economic growth. On the other hand, Chinese residents’ nutritional intake has improved considerably, their life expectancy extended, and various mortality rates decreased year by year. Of course, there are still defects in China’s health care and its residents’ health conditions, which requires that the central and local governments at all levels increase their support for health services, in addition to changing the residents’ health concepts. In general, the health status of Chinese citizens is gradually improving, and the stocks of health human capital are increasing. In contrast to the increasing health human capital across the country, regional gaps still exist in the stocks of health human capital among China’s Eastern, Central, and Western Regions. Historically, the Central and Western Regions have lagged far behind the coastal Eastern Region in economic development, and are relatively weak in medical spending. In addition, their living conditions are relatively poor, which has some impact on the local stocks of health human capital. In light of the development trend of regional stocks in health human capital, the accumulation of regional stocks in health human capital is affected, to varying degrees, by the inequality in health expenditures, the huge difference in the number of medical and health institutions and personnel, the imbalance in the development of regional health industry, and the difference in the nutritional and health status of residents in different regions.

2

2.1.3

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

65

Input in Science, Technology, and Culture

Human capital investment in science, technology, and culture can help laborers obtain or improve their knowledge and skills as participants in scientific research activities, so as to maximize their utility of human capital and improve the stocks of human capital. At the same time, when new scientific and technological achievements and knowledge resulting from scientific and cultural investment are put on the market, they can not only bring about improvement in productivity, but also drive the improvement of other forms of human capital, and therefore have a strong spillover effect. According to “Statistical Bulletin on National Science and Technology Investment 2014” jointly released by National Bureau of Statistics, Ministry of Science and Technology, and Ministry of Finance, the national fiscal expenditure on science and technology reached RMB 645.45 billion in 2014, an increase of RMB 26.96 billion or 4.4% over the previous year, accounting for 4.25% of the national fiscal expenditure in that year. National research and development (R&D) expenditure reached RMB 1,301.56 billion, an increase of RMB 116.9 billion or 9.9% over the previous year. In terms of regional distribution, seven provincial-level regions input an R&D expenditure of over RMB 50 billion, accounting for 62.32% of the total national expenditure. They are Beijing, Jiangsu, Guangdong, Shandong, Zhejiang, Shanghai, and Hubei, all in the Eastern Region except Hubei. Ten provincial-level regions reached or exceeded the national average level in R&D expenditure. They are Beijing, Jiangsu, Guangdong, Shandong, Zhejiang, Shanghai, Hubei, Tianjin, Liaoning, and Sichuan, which are all located in the Eastern Region except Hubei and Sichuan (see Table 2.5). The “R&D expenditure” in Table 2.5 refers to the expenditure used by the whole society for basic research, applied research, and experimental development in the statistical year, including the fees for personnel service, raw materials, fixed asset purchase and construction, management, and other expenses. Our analysis of Table 2.5 and the provincial R&D expenditure in previous years shows that the Eastern Region has always been higher than the Central and Western Regions in R&D expenditure. Table 2.6 shows the R&D expenditure of China’s three regions from 1998 to 2014. As we can see, the R&D expenditure of the Eastern, Central, and Western Regions were on the rise in this period. The figure

66

Y. LI

Table 2.5 By-province R&D expenditure in 2014 Provincial-level region National Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Xizang Shaanxi Gansu Qinghai Ningxia Xinjiang

R&D expenditure (billion RMB)

R&D input intensity (%)

1,301.56 126.88 46.47 31.31 15.22 12.21 43.52 13.07 16.13 86.2 165.28 90.79 39.36 35.5 15.31 130.41 40.0 51.09 36.79 160.54 11.19 16.9 20.19 44.93 5.55 8.59 0.4 36.68 7.69 1.43 2.39 4.92

2.05 5.95 2.96 1.06 1.19 0.69 1.52 0.95 1.07 3.66 2.54 2.26 1.89 1.48 0.97 2.19 1.14 1.87 1.36 2.37 0.71 0.48 1.42 1.57 0.60 0.67 0.26 2.07 1.12 0.62 0.87 0.53

Source National Bureau of Statistics, Ministry of Science and Technology, and Ministry of Finance, Statistical Bulletin on National Science and Technology Investment 2014

of the Eastern Region was RMB 31.49 billion in 1998 and increased to RMB 918.59 billion in 2014, 29.17 times that of 1998, with an average annual increase of more than 180%. The figure of the Central Region was less than RMB 10 billion in 1998 and reached RMB 226.97 billion in

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

67

2014, an increase of 26.30 times, with an average annual increase of more than 160%. The figure of the Western Region was only RMB 8.49 billion in 1998, equivalent to 26.96% of that of the Eastern Region in 1998. In 2014, it reached RMB 156.01 billion, an increase of 18.38 times, with an average annual growth of more than 110%. Clearly, the R&D expenditure of the three regions increased rapidly from 1998 to 2014. Even the slowest growth, which was found in the Western Region, was more than 110%, annually. As can be seen in Table 2.6, the R&D expenditure of the Eastern Region has always been the largest part of the national expenditure. From 1998 to 2014, the R&D expenditure of the eleven provincial-level regions in the Eastern Region accounted for between 60 and 70% of the national expenditure, while the total input of the 20 provincial-level regions in the Table 2.6 R&D expenditure of China’s three regions, 1998–2004 Year

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Eastern

Central

Western

R&D expenditure (bn RMB)

Proportion in national expenditure

R&D expenditure (bn RMB)

Proportion in national expenditure

R&D expenditure (bn RMB)

Proportion in national expenditure

31.49 38.85 60.98 71.96 90.8 109.47 142.37 177.39 218.62 269.62 332.76 405.22 498.67 618.26 729.18 837.06 918.59

64.78% 66.59% 68.07% 68.95% 70.01% 70.06% 72.41% 72.40% 72.80% 72.67% 72.09% 69.84% 70.61% 71.17% 70.81% 70.66% 70.57%

8.63 10.0 14.52 16.54 20.49 24.48 28.97 36.4 45.91 57.28 74.75 102.49 120.15 146.34 176.66 205.56 226.97

17.75% 17.14% 16.21% 15.85% 15.80% 15.67% 14.73% 14.86% 15.29% 15.44% 16.19% 17.66% 17.01% 16.85% 17.15% 17.35% 17.44%

8.49 9.5 14.09 15.86 18.4 22.3 25.3 31.21 35.75 44.13 54.08 72.49 87.44 104.11 124.03 142.05 156.01

17.47% 16.28% 15.73% 15.20% 14.19% 14.27% 12.87% 12.74% 11.90% 11.89% 11.72% 12.49% 12.38% 11.98% 12.04% 11.99% 11.99%

Source National Bureau of Statistics, Ministry of Science and Technology, and Ministry of Finance, Statistical Bulletins on National Science and Technology Investment 1993–2012, sorted and recalculated

68

Y. LI

Central and Western Regions was between 30 and 40% only. Of this 30– 40%, the proportion of the Central Region (consisting of eight provinces) was higher than that of the Western Region (consisting of 12 provinciallevel regions). From 1998 to 2014, as we can see, the absolute amount of scientific, technological, and cultural investment in these three regions kept growing, but its growth was uneven. In the meantime, the input of different provincial-level regions within the three regions was also very different. Take for example the investment of Hainan Province in the Eastern Region in 2014. In 2014, Hainan’s R&D expenditure reached a maximum over the past years, but compared with the figures of Beijing and Shanghai in the same region, its investment (RMB 1.69 billion) was only 1.33% that of Beijing and 1.96% that of Shanghai. Moreover, it was only 5.40% of Hebei, which is in the same region and just one place ahead of it. Therefore, the imbalance in investment among and within the three regions is extremely prominent. Moreover, judging by the proportions of regional R&D expenditure in the national figure, such an imbalance remained quite stable and did not change significantly from 1998 to 2014. Such gaps in input will inevitably have an impact on the different stocks of human capital among the regions. 2.1.4

Accumulation of Market Experience

In addition to the investment in education, health care, and science, technology, and culture, the formation of human capital also includes what Romer calls “learning by doing,” that is, the accumulation of market experience. This accumulation can be expressed using the indicator of “entrepreneurship.” By “entrepreneurship,” we mean the internal quality of entrepreneurs, both innate and acquired. Similar to the innate factors (e.g., talent), entrepreneurs’ innovative spirit and organizational and managerial abilities cannot be obtained simply relying on investment in education and health care, but must be formulated through constant accumulation of market experience. Therefore, entrepreneurs’ human capital is another important indicator for studying the stocks of human capital. Because entrepreneurs’ innovative spirit and managerial ability are abstract concepts difficult for quantitative analysis, we have selected and referred to relevant literature to measure entrepreneurs’ human capital. We use the total number of state-owned, foreign-funded and private enterprises in the three regions to replace the number of entrepreneurs,

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

69

and take the number of entrepreneurs as an indicator to measure their human capital. According to this indicator, the number of entrepreneurs in the three regions decreases from east to west, with obvious regional gaps. Considering the large number of individual businesses in China, which is an indispensable part of its socioeconomic development, we also include in our study the number of entrepreneurs in individual businesses. However, due to their small size and registered capital, individual businesses must not be compared with state-owned, foreign-funded, or private enterprises. So, we divide the average registered capital of private enterprises nationwide over the past years with the average registered capital of individual businesses nationwide, and get a ratio θ. Then we divide the number of individual businesses in each region with θ, and reduce it to a number of private entrepreneurs in each region. In this way, we convert the number of individual businesses into that of private entrepreneurs. Finally, we sum it up with the numbers of state-owned, private, and foreign entrepreneurs in the three regions, and measure the entrepreneurs’ human capital with the sum. As we can see in Table 2.7, from 1990 to 2014, the Eastern Region was always higher than the Central and Western Regions in the number of private entrepreneurs converted from individual businesses. In fact, the number of the Eastern Region was greater than the sum of the Central and Western Regions. Although the number of private entrepreneurs in the Central and Western Regions was increasing year by year, there was still a big gap with the Eastern Region. It is true individual businesses do not reflect the total number of enterprises, but as the most active part of the private economy, they reflect the number of entrepreneurs in a region. Thus, from the perspective of entrepreneurs’ human capital, the human capital stocks in the Eastern Region are richer than those in the Central and Western Regions, and the gaps among the three regions are significant. Due to its superior geographical location and rapid economic growth, the Eastern Region is significantly higher than the Central and Western Regions in the number of state-owned, foreign-funded, and private enterprises, and is relatively abundant in entrepreneurs’ human capital. Compared with it, the Central and Western Regions are relatively low in the stocks of entrepreneurs’ human capital. The Western Region, in particular, is the most backward in this regard. To sum up, judging by the four main ways of human capital formation (including education investment, health investment, R&D expenditure,

70

Y. LI

Table 2.7 Numbers of private entrepreneurs converted from individual businesses, 1990–2014 (Unit: household)

Year

Eastern

Central

Western

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

66,976 73,598 95,328 158,213 285,590 432,621 530,000 610,522 759,009 993,412 1,185,527 1,364,656 1,649,991 2,050,415 2,477,000 2,892,988 3,349,726 3,659,341 4,289,718 4,765,201 5,431,235 6,143,976 6,787,000 7,797,653 9,476,000

17,213 18,915 24,500 44,600 91,000 138,000 178,000 211,000 250,000 276,396 292,754 330,105 384,133 480,000 606,010 723,998 858,464 993,974 1,208,046 1,371,023 1,596,000 1,833,461 2,080,000 2,390,000 3,011,000

13,911 15,287 19,800 34,200 56,000 84,000 112,000 140,208 193,142 240,097 281,869 332,540 398,317 477,576 570,000 682,947 777,212 860,270 1,077,791 1,270,864 1,422,181 1,699,339 1,990,000 2,351,000 2,977,000

Source China Statistical Yearbooks 1991–2015 and China Industrial and Commercial Administration Yearbooks 1991–2015, sorted and recalculated

and market experience accumulation), there are large gaps in human capital stocks among the three regions. Based on the data analysis of the four measurement indicators, we can see that the Eastern Region has unparalleled advantages over the Central and Western Regions in education, health, scientific research, and the number of enterprises.

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

71

2.2 The Structure of Human Capital Investment and Regional Gaps in China Only when the human capital structure of a country or region adapts to its economic structure can it realize effective allocation and utilization of its human capital resources and give full play to the role of human capital in boosting economic growth. Before the 1990s, academic research on the mechanism of human capital on economic growth mainly focused on the stocks of human capital, and only a few studies focused on the structure of human capital. Since the 1990s, human capital structure, as an important factor affecting the quality of human capital, has received close attention of scholars at home and abroad, and become another important variable in the study of economic growth. From the perspective of human capital formation, human capital investment includes not only input in education, but also input in health and medical care, science, technology, and culture, and the accumulation of market experience. According to the current theoretical and empirical studies on human capital at home and abroad, education investment, as the main form of human capital, is still the major variable to measure human capital. Of course, in addition to the above aspects, there are more ways of human capital formation, such as population migration and child rearing. In terms of the structure of human capital investment, due to the limitation of data availability, this section will take education as the main variable to measure human capital structure, including its distribution, hierarchy, and category. In the following, we will study the differences among the Eastern, Central, and Western Regions in the distribution structure and the hierarchical structure of human capital. We look forward to further research and explorations on the impact of other ways of human capital formation on the structure of human capital investment. 2.2.1 2.2.1.1

The Structure of Human Capital Distribution

The Structure of Human Capital Distribution and Regional Economic Gaps The structure of human capital distribution refers to the distribution of total human capital among different groups of a society. It reveals the inequality of human capital distribution. Take education for example. The

72

Y. LI

distribution structure of education human capital reflects the inequality of education, which is generally measured by means of the coefficient of human capital structure. The larger the coefficient, the greater the education inequality. Research at home and abroad shows that the coefficient of human capital structure is negatively correlated with economic growth. Castello and Domenech (2002) studied the statistics of 108 countries over 40 years and found that the coefficients of human capital structure of most countries were on a downward trend and were negatively correlated with their economic growth. The impact of the coefficient of human capital structure on economic growth was more obvious than that of income distribution structure and the average level of education. Li Yaling and Wang Rong (2006) measured and compared the Gini coefficients of human capital of 29 provincial-level regions in China from 1993 to 2004, and tested the correlation between the sectional data and the per capita GDP each year. They proved that the regional gaps in human capital in China are mainly reflected in the structure of human capital distribution, and there is a strong negative correlation between the Gini coefficient of human capital and regional economic development. Therefore, the structure of human capital distribution is one of the key factors affecting regional economic growth. This lays a theoretical foundation for the study of the structure of human capital investment in China’s Eastern, Central, and Western Regions. 2.2.1.2

The Gini Coefficient of Human Capital: Calculation of the Gini Coefficient of Education The coefficient of human capital structure, namely the Gini coefficient of education, is calculated using the years of schooling, which reflects the inequality of human capital distribution. The formula is as follows: Gh =

n m 1 ∑∑ |xi − x j |n i n j . 2H m i=0

(Formula 2.2)

j=0

In Formula (2.2), G h stands for the Gini coefficient of human capital of population of a certain age and over, and h for the average years of schooling of this group of people; i and j for different levels of education; X i and X j for the total years of schooling of different levels of education;

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

73

and n i and n j for the proportions of population of different levels of education. The Gini coefficient of human capital falls somewhere between 0 and 1, and the greater G h , the more unbalanced the human capital distribution. Using the research method of Li Yaling and Wang Rong (2006), we divide education into four levels: illiteracy (0), primary education (1), secondary education (2), and higher education (3). We set n = 3 and m = 3, so T X 0 = 0, T X 1 = X 1 , T X 2 = X 1 + X 2 , and T X 3 = X 1 + X 2 + X 3 . We put these identical equations into Formula (2.2) and get the formula for calculating the Gini coefficient of human capital as Formula (2.3). G h = n0 +

n 1 X 2 (n 2 + n 3 ) + n 3 X 3 (n 1 + n 2 ) n 1 X 1 + n 2 (X 1 + X 2 ) + n 3 (X 1 + X 2 + X 3 )

(Formula 2.3)

Of the variables in Formula (2.3), X 0 = 0 (Illiteracy = no schooling), X 1 = 6 (Six years of primary education), X 2 = 6 (Six years of secondary education), and X 3 = 4 (Since the statistics in the Statistical Yearbooks do not distinguish between college education and above, we assume that the length of higher education is four years). According to the NBS calculation of the average years of schooling, the population in Formula (2.3) refers to people aged six and over (n 1 = proportion of population with primary education, n 2 = proportion of population with secondary education, and n 3 = proportion of population with higher education). As Table 2.8 shows, the Gini coefficients of human capital in the Eastern, Central, and Western Regions were on a downward trend from 1990 to 2014. They declined rather quickly from 1990 to 2000, but slowed down after 2000. Over these 25 years, the Gini coefficients of human capital of the three regions decreased without exception. The figures in the Eastern and Central Regions were relatively close, while that in the Western Region was the largest. This shows that the unbalanced distribution of human capital in the three regions was somehow relieved in this period, but regional gaps were still significant, and education inequality was more serious in the Western Region than in the Eastern and Central Regions.

74

Y. LI

Table 2.8 Gini coefficients of human capital of China’s three regions, 1990– 2014 Year

Gini coefficients of human capital

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

Eastern

Central

Western

Eastern-Western gap

0.3254 0.3192 0.3133 0.3027 0.3020 0.2960 0.2927 0.2842 0.2779 0.2685 0.2310 0.2390 0.2329 0.2344 0.2281 0.2274 0.2171 0.2090 0.2034 0.1977 0.1792 0.1830 0.1808 0.1777 0.1820

0.3387 0.3260 0.3202 0.3147 0.3129 0.2982 0.2840 0.2687 0.2654 0.2661 0.2339 0.2306 0.2360 0.2204 0.2166 0.2358 0.2237 0.2146 0.2080 0.2024 0.1866 0.1870 0.1851 0.1806 0.1850

0.3819 0.3782 0.3723 0.3946 0.3766 0.3458 0.3396 0.3241 0.3178 0.3142 0.2840 0.2887 0.2865 0.2788 0.2680 0.2932 0.2732 0.2601 0.2532 0.2495 0.2290 0.2324 0.2269 0.2260 0.2286

−0.0565 −0.0590 −0.0590 −0.0919 −0.0746 0.0498 −0.0469 −0.0399 −0.0399 −0.0457 −0.0530 −0.0497 −0.0536 −0.0444 −0.0399 −0.0658 −0.0561 −0.0511 −0.0498 −0.0518 −0.0498 −0.0494 −0.0461 −0.0483 −0.0466

Source: China Statistical Yearbooks 1991–2015, China Population Statistics Yearbooks 1991–2015, and China Labor Statistical Yearbooks 1991–2015, sorted and recalculated

2.2.2

The Hierarchical Structure of Human Capital

The hierarchical structure of human capital refers to the proportions of talents with different levels of education who constitute the total amount of human capital. The reasonable hierarchical structure determines the effective allocation and utilization of human capital resources. Some domestic and foreign scholars have studied the relationship between human capital hierarchy and economic growth. For poor countries, there is a positive correlation between human capital accumulation

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

75

in the initial stage and the economic growth rate. For middle-income countries, there is a positive correlation between human capital accumulation in the middle stage and the economic growth rate. For developed countries, there is a positive correlation between human capital accumulation in the advanced stage and the economic growth rate. Chinese scholar Hu Yongyuan (2004) made meaningful tentative research in this respect. He defined the achievers of middle school and primary education as general human capital, those of adult higher education and secondary education (including technical secondary schools, high schools, and vocational high schools) as skilled human capital, and those of general higher education as innovative human capital. He carried out a linear regression of the data from 1978 to 1998. The results show that the contribution of general and skilled human capital to economic growth tends to weaken, while that of innovative human capital increases constantly. These empirical studies show that the human capital hierarchy of a country or region is not the higher the better, but should adapt to the economic structure and the stage of its economic development. What is suitable is the best. The structure, if mismatched with economic development, will lead to a waste of human capital and low allocation efficiency. What on earth are the gaps among the Eastern, Central, and Western Regions of China in the human capital hierarchy? What are the impacts of different levels of human capital on China’s economic growth? The above-mentioned literatures offer no answer. For the first question, we will analyze in the following the hierarchical structure of human capital in the three regions, and we will discuss the second question in Sect. 5.2, Chapter 5. According to the above-mentioned formula for calculating the total stocks of human capital: The total stocks of human capital (H) = Human capital stocks of higher education (HC) + Human capital stocks of high school education (HM) + Human capital stocks of primary education ∑ (HP) + Illiterate and semi-illiterate human capital stocks (HI) = (Numbers of receivers of different levels of education × weights) = P1 H1 + P2 H2 + P3 H3 + P4 H4 + P5 H5 = 16P1 + 12P2 + 9P3 + 6P4 + P5 . We divide human capital into five levels: illiterate and semi-illiterate, primary education, middle school education, high school education, and college education and above.

76

Y. LI

Tables 2.9, 2.10, and 2.11 respectively reflect the proportions of people aged six and over at different levels of education in the Eastern, Central, and Western Regions of China from 1995 to 2015. As we can see in Tables 2.9–2.11, the three regions are quite similar in the proportions of population at different levels of education from 1993 to 2015. In general, the population aged six and over nationwide is divided into five groups according to their levels of education. The proportion of people with middle school education is the largest, followed by those with primary school education; the proportion of those with high school education is the third largest; the proportion of those with Table 2.9 Proportions of people at different levels of education in the Eastern Region, 1995–2015 (%) Year

Illiterate and semi-illiterate

Primary school

Middle school

High school

College and above

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

15.64 17.09 14.72 14.69 13.77 13.26 12.34 8.37 9.31 8.93 9.23 8.83 8.66 7.30 6.72 6.32 5.84 4.04 4.69 4.47 4.20 4.64 4.64

37.69 26.10 40.35 38.90 37.48 36.37 35.33 34.46 33.65 31.71 30.35 29.28 29.71 29.81 28.62 27.96 26.94 25.13 24.06 23.43 22.91 22.92 22.92

31.75 35.73 32.13 33.64 33.75 34.78 36.09 38.97 38.55 39.24 38.68 40.02 40.36 40.45 41.57 41.99 42.59 42.96 42.65 41.95 41.80 41.04 41.04

11.32 15.89 9.93 10.22 11.69 12.08 12.28 13.59 13.52 9.31 14.97 14.87 14.41 14.64 14.87 15.36 15.39 16.53 16.76 17.31 17.59 17.87 17.87

3.61 5.20 2.87 2.55 3.30 3.51 3.95 4.62 4.97 5.89 6.77 7.00 6.86 7.81 8.21 8.38 9.25 11.34 11.84 12.83 13.49 13.54 13.54

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

77

Table 2.10 Proportions of people at different levels of education in the Central Region, 1995–2015 (%) Year

Illiterate and semi-illiterate

Primary school

Middle school

High school

College and above

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

17.10 18.54 14.63 13.40 15.64 11.55 11.90 8.26 8.35 8.97 7.88 7.69 9.49 8.12 7.56 6.32 6.78 4.80 5.07 4.92 4.49 4.69 4.69

41.45 33.62 42.64 42.02 37.69 39.81 38.03 37.70 34.87 34.83 31.92 31.01 31.99 31.58 29.83 27.96 27.97 27.28 26.64 26.06 25.31 25.66 25.66

31.24 35.24 31.75 33.11 31.75 35.40 36.80 38.77 39.73 40.13 41.75 42.75 41.38 41.77 42.77 41.99 44.53 44.22 43.88 43.49 42.70 42.06 42.06

8.65 10.82 9.02 9.44 11.32 10.72 10.55 11.82 12.87 7.74 13.62 13.54 12.38 13.03 13.90 15.36 14.44 15.32 16.09 16.62 17.70 17.24 17.24

1.56 1.78 1.95 2.03 3.61 2.52 2.72 3.46 4.18 4.08 4.84 5.01 4.76 5.50 5.95 8.38 6.29 8.38 8.33 8.92 9.80 10.35 10.35

college education and above is the fourth largest; and the proportion of illiterate and semi-illiterate people is the smallest. It follows that the overall education level of Chinese citizens is relatively low: the proportions of people with primary and middle school education are too large, while the proportion of people with college and above education is much too low. In the future, we need to further improve the education level of Chinese citizens.

78

Y. LI

Table 2.11 Proportions of people at different levels of education in the Western Region, 1995–2015 (%) Year

Illiterate and semi-illiterate

Primary school

Middle school

High school

College and above

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

26.38 21.29 18.64 19.51 17.10 16.90 16.50 12.74 13.35 13.43 12.41 11.33 13.83 11.69 10.39 9.70 9.35 6.73 7.26 7.00 6.80 7.31 7.31

42.35 24.53 45.03 43.58 41.45 44.27 43.25 44.02 41.47 39.46 39.27 38.10 39.92 39.44 38.62 38.13 37.21 36.04 34.08 33.19 32.96 32.09 32.09

23.47 45.98 25.69 26.57 31.24 27.92 29.05 30.36 31.12 32.61 32.83 34.29 31.92 33.70 35.34 36.59 37.07 36.81 36.59 37.03 37.05 36.57 36.57

6.60 6.84 8.91 8.30 8.65 8.75 8.81 9.80 10.22 10.62 10.94 11.26 9.69 10.38 10.74 10.51 10.77 12.35 12.74 13.71 13.48 14.26 14.26

1.19 1.37 1.73 2.04 1.56 2.16 2.39 3.08 3.84 3.88 4.55 5.02 4.63 4.79 4.90 5.07 5.60 8.07 9.33 9.07 9.72 9.79 9.79

Source: China Statistical Yearbooks 1994–2016, China Demographic Yearbooks 1994–2016, and China Labor Statistics Yearbooks 1994–2016, sorted and recalculated

2.3

Conclusions

This chapter analyzes the stocks and structure of human capital in the three regions of China from 1990 to 2014. As we can see, there are significant gaps among China’s Eastern, Central, and Western Regions in the stocks of human capital, and the structure of human capital is unreasonable. On the one hand, from the perspective of human capital stocks, the Eastern and Central Regions are relatively close, the Western Region is the shortest in terms of the average years of schooling, and there are large

2

HUMAN CAPITAL INVESTMENT AND REGIONAL GAPS …

79

regional gaps in the stocks of human capital. In terms of health expenditure, the Eastern Region still leads the country in the total amount of investment and per capita health expenditure, but it is lower than the Central and Western Regions as well as the national average level in the proportion of total health expenditure in GDP. The Western Region is lower than the Eastern and Central Regions in the total health expenditure, but its proportion in GDP is the highest of the three regions. The Central Region is between the Eastern and Western Regions in the total health expenditure, but its per capita spending is the lowest of the three and is lower than the national average level. In terms of science and technology input, there is also a decreasing trend from east to west. The input of the Eastern Region (basically between 60 and 70%) is more than twice the sum of the Central and Western Regions, which shows the huge gap in science and technology development. Through our measurement of entrepreneurs’ human capital, we can see that the Eastern Region also has advantages in the number of entrepreneurs, while the Central and Western Regions are relatively backward in this regard. To sum up, judging by the four main ways of human capital formation, there are large gaps in the stocks of human capital among the three regions of China. Meanwhile, the unreasonable structure of human capital stocks is quite prominent, and the regional gaps in human capital are quite large. On the other hand, from the perspective of human capital structure, according to our calculation of the Gini coefficients of human capital, the distribution of human capital is unbalanced among the three regions, which reflects their inequality of education. In addition, the hierarchical structure of education human capital in the three regions is also unbalanced. The proportion of primary and middle school education receivers is too large, and that of people with college or above education is too low. This reflects that the overall education level of Chinese citizens is low, and it is necessary to further improve the national education level in future. Human capital, as the aggregation of laborers’ knowledge, skills, and abilities, is one of the key factors for promoting regional economic development and accelerating the optimization and upgrading of regional industrial structure. Due to the influence of natural conditions, history and culture, economic concepts, and other factors in different regions, the stocks and structural distribution of human capital are not balanced among the three regions, which seriously restricts the coordinated development of regional economy in China. The stocks and structure of human

80

Y. LI

capital are affected by human capital investment. Human capital investment can improve the production efficiency of human capital itself and other production factors, and bring the synchronous growth of individual and social benefits. According to the new growth theory, the differences of human capital stocks in education, health, science and technology, culture, and market experience accumulation among the three regions of China and the resulting structural differences are important reasons for the differences in regional economic growth.

References Castello, A., and R. Domenech. “Human Capital Inequality and Economic Growth: Some New Evidence.” The Economic Journal 112, no. 2 (2002): 187–200. Hu Yongyuan and Liu Zhiyong. “Effects of Different Types of Human Capitals on Economic Growth.” Population & Economics, no. 2 (2004): 55–58. Li Yaling and Wang Rong. “Structure of Human Capital Distribution and Regional Economic Gaps: An Empirical Study Based on the Gini Coefficients of Regional Human Capital in China.” Management World, no. 12 (2006): 42–49. Schultz, T. W. Investing in People—The Economics of Population Quality. Berkeley: University of California Press, 1981, 124–148.

CHAPTER 3

The Relationship Between Human Capital Stocks and Regional Economic Gaps in China

According to American economists Paul Romer and Robert Lucas, the difference in human capital stocks affects the long-term economic growth rate by affecting the total factor productivity; ceteris paribus, countries or regions with larger human capital stocks may maintain a higher economic growth rate in the long run. Therefore, human capital is a factor affecting the long-term trend of regional disparity. Human capital stocks reflect the quantity and quality of laborers’ health, knowledge, and ability at a certain time point, mainly including the human capital stocks formed through education investment, health investment, R&D expenditure, and “learning by doing” (market experience accumulation). In Chapter 2, we have made a clear comparison of the human capital stocks of the three regions by analyzing the main ways of human capital formation, such as human capital investment in education, health investment, and R&D expenditure. On that basis, this chapter will empirically study the relationship between the four indicators of human capital stocks (i.e., the education, health and R&D levels, and market experience accumulation) and regional economic growth, and analyze their relative contributions to regional economic growth using the elastic values of the four indicators.

© Social Sciences Academic Press 2023 Y. Li, Human Capital Investment and the Regional Economic Gap in China, https://doi.org/10.1007/978-981-99-4997-7_3

81

82

Y. LI

3.1

The Establishment of the Empirical Model and the Data Source 3.1.1

The Empirical Model

According to the theory of endogenous economic growth, we have constructed an empirical model of human capital stocks and economic growth. The Cobb–Douglas production function assumes a decreasing marginal return of factors, but the particularity of human capital determines that it will not change systematically with the change in the years of schooling and GDP. Therefore, we adopt the exponential form when introducing human capital into the model. At the same time, according to the patterns of human capital formation, we divide human capital into four categories: education human capital, health human capital, entrepreneurs’ human capital, and human capital formed by R&D input, and express them in the form of an exponential function. Using this method, we introduce into the function education, health, entrepreneurs’ human capital, and human capital formed by R&D input. β

φ

Yt = AE tα Ht Bt Rtδ eμ

(Formula 3.1)

In Formula (3.1), Yt stands for GDP; E for education human capital; H for health human capital; B and R for entrepreneurs’ human capital and human capital formed by R&D input, respectively; α, β, φ, and δ for the elastic coefficients of education human capital, health human capital, entrepreneurs’ human capital, and human capital formed by R&D input, respectively; A for the residual value of other factors that human capital cannot explain; and μ is the random error. We take logarithms on both sides of Formula (3.1) and get: LnYt = Ln At + α Ln E t + β Ln Ht + φ Ln Bt + δ Ln Rt + μt (Formula 3.2) where Ln E t , Ln Ht , Ln Bt , and Ln Rt , respectively, stand for the logarithmic value of education human capital, health human capital, entrepreneurs’ human capital, and human capital formed by R&D input in Year t; and α, β, φ, and δ are parameters to be estimated and stand for the output elasticity of the four independent variables in the equation. According to our previous analysis, we add the regional factor i and sort out Formula (3.2), and get the empirical model as follows: LnYit = C + αLn E it + β Ln Hit + φ Ln Bit + δLn Rit + μt (Formula 3.3)

3

THE RELATIONSHIP BETWEEN HUMAN CAPITAL STOCKS …

83

In Formula (3.3), the meanings of Ln E t , Ln Ht , Ln Bt , and Ln Rt are the same as in Formula (3.2); α, β, φ, and δ are the coefficients of elasticity of education human capital, health human capital, entrepreneurs’ human capital, and human capital formed by R&D input in Formula (3.2); C is Ln A; εit is the random disturbance; the subscripted i stands for the region; and t for the year. 3.1.2

The Data Source

3.1.2.1 The Stocks of Education Human Capital The human capital stocks formed through education investment are measured by the average years of schooling (see Table 2.1 for the statistics). 3.1.2.2 The Stocks of Health Human Capital As an important part of human capital investment, health investment is crucial to the formation of human capital stocks. We choose health expenditure as the measurement standard of health human capital (see Table 2.3 for the statistics). 3.1.2.3 The Stocks of Entrepreneurs’ Human Capital The formation of entrepreneurs’ human capital is influenced by both innate and acquired factors. In addition to congenital factors (such as talent), entrepreneurs’ human capital is mainly formed through continuous accumulation of market experience in work. Yet it is difficult to quantify the accumulation of market experience, so we select the number of private entrepreneurs converted from individual businesses as a proxy to measure entrepreneurs’ human capital (see Table 2.7 for the statistics). 3.1.2.4 The Human Capital Stocks Formed Through R&D Input By participating in scientific research activities, laborers can improve their knowledge and skills, give full play to their human capital utility, and improve the stocks of human capital. We select the research and development (R&D) expenditure of the three regions in China as the indicator to measure human capital stocks formed by input in science, technology, and culture (see Table 2.6 for the statistics). 3.1.2.5 GDP See Table 3.1 for real GDP statistics.

84

Y. LI

Table 3.1 Real GDP of the three regions in China, 2003–2014 (2003 = 100) Year

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Eastern

Central

Western

GDP (billion RMB)

Proportion in national GDP (%)

GDP (billion RMB)

Proportion in national GDP (%)

GDP (billion RMB)

Proportion in national GDP (%)

8,297.161 8,669.256 8,939.652 10,215.48 11,710.82 13,046.4 14,487.25 16,307.51 18,044.19 19,724.55 21,512.49 23,363.04

59.46 58.59 57.92 58.12 58.14 57.83 57.49 57.18 56.59 56.08 55.98 55.96

3,259.037 3,551.865 3,737.806 4,232.247 4,840.68 5,456.795 6,111.056 6,951.272 7,838.118 8,694.52 9,449.057 10,170.76

23.36 24.01 24.22 24.08 24.03 24.19 24.25 24.38 24.58 24.72 24.59 24.36

2,397.521 2,574.716 2,756.569 3,127.516 3,591.416 4,058.024 4,603.477 5,259.175 6,000.995 6,752.618 7,470.499 8,218.41

17.18 17.40 17.86 17.80 17.83 17.99 18.27 18.44 18.82 19.20 19.44 19.68

Source Provincial GDP statistics in China Statistical Yearbook 2015, converted into real GDP using regional GDP indexes to eliminate the influence of price factors, with 2003 as the base year

3.2 Human Capital Stocks and Regional Economic Growth: An Empirical Study 3.2.1

Descriptive Statistics Analysis

In order to better explore the relationship between human capital stocks and economic growth, and understand the impact of different types of human capital on economic growth, we select the annual statistics from 2003 to 2014—an important and special period in China’s economic development, and sort the statistics of 31 provincial-level regions (according to China’s administrative divisions) into the panel data of the Eastern, Central, and Western Regions. Panel data has both temporal and spatial dimensions, which increases the variability among variables, reduces collinearity, and improves effectiveness. Therefore, it is more suitable for studying dynamic economy. It can be used to analyze not only the phased changes of the main driving factors of economic growth and the characteristics of economic transformation from the changes of time series, but also the impact of the changes of the main factors of economic growth on regional economic gaps by comparing the

3

THE RELATIONSHIP BETWEEN HUMAN CAPITAL STOCKS …

85

Table 3.2 Descriptive statistical results of regression variables Variable Meaning Y

E

H

B

R

Maximum

Minimum

Mean

Economic 23,363.035 2,397.521 8,483.943 growth (billion RMB) Education 9.41 7.17 8.38 human capital (years) Health 1,769.754 141.229 585.2399 human capital (billion RMB) Entrepreneurs’ 9.476 0.477576 2.567456 human capital (million entrepreneurs) Human 918.59 22.3 205.6131 capital formed by R&D input (billion RMB)

Sample Cross-section 36

3

36

3

36

3

36

3

36

3

three regional cross-sections. Moreover, it can also be used to comprehensively analyze China’s economic transformation under the background of regional economic gaps from two dimensions. The descriptive statistical results of each regression variable are shown in Table 3.2. In Table 3.2, the maximum values of Variables Y, H, B, and R are the data of the Eastern Region in 2014, and the minimum values are the data of the Western Region in 2003. The maximum value of Variable E is the data of the Eastern Region in 2013, and the minimum value is the data of the Western Region in 2005. Judging by the absolute data, we conclude some characteristics of China’s economic development from 2003 to 2014. On the one hand, the economy developed rapidly, with an average annual growth of 10.47% in these 12 years. On the other hand, regional economic gaps were large and had not been significantly improved in this period. In 2003, for example, the Eastern, Central, and Western Regions, respectively, accounted for 60.00%, 22.93%, and 17.06% in the national GDP. In 2014, they, respectively, accounted for

86

Y. LI

55.96%, 24.36%, and 19.68% in the national GDP. Besides, there were also significant regional gaps in factor input. 3.2.2

Unit Root Tests

Our research shows that most of the economic variables are nonstationary. If we carry out regression analysis on the variables at this time, “pseudo regression” may occur, and the empirical results obtained at this time will be of no economic significance. Therefore, we must test the stationarity of the series before an empirical analysis. Compared with the graphical method, the unit root test is more accurate in judging the stationarity of the series, and the ADF test is used most frequently. We conduct the ADF test using the following three models. yt = δyt−1 +

P 

λ j yt− j + μt

(Model 3.1)

j=1

yt = α + δyt−1 +

P 

λ j yt− j + μt

(Model 3.2)

j=1

yt = α + βt + δyt−1 +

P 

λ j yt− j + μt

(Model 3.3)

j=1

Model (3.1) contains neither the constant term nor the trend term; Model (3.2) contains the constant term; and Model (3.3) contains both the constant and terms. The null hypothesis of the three models is H0 : δ = 0. That is, there is one unit root. Our ADF test starts with Model (3.3), followed by Model (3.2) and Model (3.1). Whenever the null hypothesis is rejected (that is, the t value obtained by the test is smaller than the given critical value under the significance level), we will stop the test and consider the original sequence stable. Otherwise, we will continue to test Model (3.2) and Model (3.1). We to perform the ADF test on the time series, and get the results which are shown in Table 3.3. In Table 3.3, we add D in front of the variables to mean the firstorder difference sequence. Take the stationary test of lnY, for example. The t values of ADF-Fisher Chi-square and ADF-ChoiZ-stat are 0.00134 and 6.61339, respectively, both greater than the significant level at 10%.

3

THE RELATIONSHIP BETWEEN HUMAN CAPITAL STOCKS …

87

Table 3.3 Results of ADF test of the variables Variable

Testing method

lnY

ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat

DlnY lnE DlnE lnH DlnH lnR DlnR lnB DlnB

t value

P value

Conclusion

0.00134 6.61339 14.1827 −2.07508 0.12390 3.82855 24.6533 −3.64796 0.21490 3.15399 18.9472 −2.84192 2.71314 0.73744 10.7877 −1.40808 1.08119 2.17385 15.8823 −2.53701

1.0000 1.0000 0.0277 0.0190 1.0000 0.9999 0.0004 0.0001 0.9998 0.9992 0.0043 0.0022 0.8439 0.7696 0.0952 0.0796 0.9823 0.9851 0.0144 0.0056

Unstable Unstable Stable Stable Unstable Unstable Stable Stable Unstable Unstable Stable Stable Unstable Unstable Stable Stable Unstable Unstable Stable Stable

Note The conclusion “Stable” means that the series has passed the stationary test at the significance level of 10%

Meanwhile, the P values of tests are greater than 0.1, which means that there are unit roots in the original sequence and that the lnY sequence is unstable, so we must conduct a stationary test on the first-order difference sequence. At this time, the t values of ADF-Fisher Chi-square and ADFChoiZ-stat are both smaller than the significance level at 10% and their P values both smaller than 0.1, which means that lnY is a stable sequence after the first-order difference. Similarly, we can find that the sequences lnE, lnH, lnR, and lnB are unstable, but they become stable after the first-order difference. 3.2.3

Cointegration Test

Although the unit root test ensures the stability of the sequence, it may cause a loss in the long-term useful information that the variables may contain. Therefore, there may still be the phenomenon of

88

Y. LI

Table 3.4 Results of cointegration test ADF Residual variance HAC variance

t-statistic

Prob.

−3.576011 0.000317 0.000441

0.0002

“pseudo regression” after the economic variables pass the unit root test. At this time, it is necessary to carry out a cointegration test to eliminate the “pseudo regression” and enable the regression results to be of explanatory significance. According to the cointegration theory, if a stable linear combination (i.e., the cointegration equation) exists between non-stationary economic variables, there must be a long-term equilibrium between various economic variables, and regression analysis can be carried out for each variable at this time. According to the requirements of the cointegration test, there should be at least two variables, and the single integration order of the explanatory variable should be greater than or equal to that of the explained variable. When there are at least two explanatory variables, the single integration order should be consistent. The unit root test verifies that all economic variables are first-order single integration, so the cointegration test can be carried out. We use the software EViews 6.0 and get the results which are shown in Table 3.4. The results of the cointegration test show that the P value corresponding to ADF is obviously lower than 0.05, so the null hypothesis is rejected. That is, there is a long-term stable equilibrium relationship between the variables, so we may proceed to the next step of the regression analysis. 3.2.4

Model Selection

Before a regression analysis is done, it is necessary to determine which model (the mixed estimation model or the individual fixed-effect model) is suitable for the panel data. The mixed estimation model means that there is no significant difference between the individuals, or between the different sections, in different time series, and the panel data can be directly estimated by the OLS least square method. The individual fixedeffect model means that there are different intercepts for different time

3

THE RELATIONSHIP BETWEEN HUMAN CAPITAL STOCKS …

Table 3.5 Results of F-test of the panel data

Effects test Cross-section F Cross-section Chi-square

Statistic 32.391489 42.252752

d.f. (2,29) 2

89

Prob. 0.0000 0.0000

series and different sections. At this time, it is necessary to add dummyvariable estimation parameters to the model. Before selecting the model, we must carry out an F-test of the panel data. We first establish the following hypothesis: H 0: H 1:

ai = a The intercepts of different individuals in the model are the same (i.e., the mixed estimation model). The intercepts of different individuals in the model are different (i.e., the individual fixed-effect model).

The test results are shown in Table 3.5. According to the data in Table 3.5, we find F = 32.391489 > F0.05 (2,29) = 3.328, which accepts the null hypothesis H 0 , so we must construct an individual fixed-effect model which is different in the intercepts corresponding to different time sections. At this time, we must add dummy-variable estimation parameters to the model. So the model is finalized as LnYit = C1 + C + αLn E it + β Ln Hit + φ Ln Bit + δLn Rit + μt (Formula 3.7)

3.3

Discussion on the Results of Metrological Analysis 3.3.1

Regression Results

We use the software EViews 6.0 and conduct the OLS least square regression of the regional panel data, where i stands for the regional variable, EAST for the Eastern Region, MIDD for the Central Region, and WEST for the Western Region. The regression results are shown in Table 3.6. According to the results of the parameter estimation, R2 = 0.999249 2 (R = 0.998749 after adjustment), which shows a good degree of model

90

Y. LI

Table 3.6 Correlation regression results of factors and regional economic growth Dependent variable: LNY? Method: Pooled least squares Sample: 2003 2014 Included observations: 12 Cross-sections included: 3 Total pool (balanced) observations: 36 Variable Coefficient C 6.407146 EAST–E 1.989119 MIDD–E 1.491555 WEST–E 0.907558 EAST–H 0.318323 MIDD–H 0.120794 WEST–H 0.043295 EAST–R 0.048865 MIDD–R 0.177330 WEST–R 0.254924 EAST–B 0.105251 MIDD–B 0.204110 EAST–B 0.298015 Fixed effects (Cross) EAST–C1 −1.820737 MIDD–C1 0.075420 WEST–C1 1.745317 R-squared Adjusted R-squared S.E. of regression Sum squared reside Log likelihood F-statistic Prob (F-statistic)

0.999249 0.998749 0.022192 0.010342 95.70867 1996.427 0.000000

Std. error 0.751830 0.817479 0.532492 0.354593 0.120341 0.102486 0.096121 0.125791 0.106702 0.100035 0.181115 0.110361 0.125309

t-statistic 8.522068 2.433235 2.801087 2.559435 2.645173 1.178641 0.450417 0.388460 1.661916 2.548353 0.581130 1.849470 2.378238

Mean dependent var. S.D. dependent var. Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat.

Prob. 0.0000 0.0240 0.0107 0.0183 0.0151 0.2517 0.6570 0.7016 0.1114 0.0187 0.5673 0.0785 0.0270

11.15640 0.627365 −4.483815 −3.824015 −4.253527 1.545272

Note The t values of the parameter estimation have passed the test at the 5% significance level

fitting. The F-statistic of the model is 1,996.427, and the P value is 0, indicating that all the independent variables have a high degree of explanation for the dependent variables, and the model has passed the significance test. The regression equations are Eastern Region: lnY = −1.82 + 6.41 + 1.99lnE + 0.32lnH

3

THE RELATIONSHIP BETWEEN HUMAN CAPITAL STOCKS …

91

+ 0.05lnR + 0.11lnB Central Region: lnY = 0.08 + 6.41 + 1.49lnE + 0.12lnH + 0.18lnR + 0.21lnB Western Region: lnY = 1.75 + 6.41 + 0.91lnE + 0.04lnH + 0.25lnR + 0.30lnB The sum of the output elasticity coefficients of the four independent variables is greater than one, indicating that China’s economic growth is in the stage of increasing returns to scale. According to the regression results, the correlation coefficients of the four independent variables E, H, R, and B of the Eastern Region (i.e., the output elasticity of each factor) are (in descending order): education human capital, health human capital, entrepreneurs’ human capital, and human capital formed by scientific, technological, and cultural input. The correlation coefficients of the four independent variables E, H, R, and B of the Central and Western Regions (i.e., the output elasticity of each factor) are (in descending order): education human capital, entrepreneurs’ human capital, human capital formed by scientific, technological, and cultural input, and health human capital. This indicates that the relative contribution of various factors to China’s economic growth has changed significantly since 2003, and that there is a significant regional difference in the output elasticity coefficient of each factor. The following is a detailed analysis. 3.3.2

The Output Elasticity of Factors and Their Relative Contribution to Economic Growth

3.3.2.1 The Output Elasticity of Education Human Capital Judging by its output elasticity, education human capital makes the largest relative contribution to economic growth. The relative contribution of education human capital to the economic growth of the Eastern, Central, and Western Regions is 1.99, 1.49, and 0.91, respectively. That is to say, a 1% increase in the average years of schooling boosts an economic growth of 1.99%, 1.49%, and 0.91%, respectively, of the Eastern, Central, and Western Regions. Education human capital makes greater contribution to the economic growth than the other factors.

92

Y. LI

3.3.2.2 The Output Elasticity of Health Human Capital Compared with education human capital, the contribution of health human capital to economic growth is much smaller but is the second largest of the four indicators. The correlation coefficients of the three regions are 0.32, 0.12, and 0.04, indicating that a 1% increase in health human capital boosts an economic growth of 0.32%, 0.12%, and 0.041% of the Eastern, Central, and Western Regions, respectively. 3.3.2.3 The Output Elasticity of Entrepreneurs’ Human Capital The output elasticity coefficients of entrepreneurs’ human capital in the three regions reach 0.11, 0.21, and 0.30, respectively, but they are much smaller than education human capital in terms of contribution to economic growth. Compared with the Eastern Region, the Central and Western Regions—especially the former—depend more on investment in entrepreneurs’ human capital for economic growth. 3.3.2.4

The Output Elasticity of Human Capital Formed by Scientific, Technological, and Cultural Input The output elasticity coefficients of human capital formed by scientific, technological, and cultural input of the three regions are 0.32, 0.12, and 0.04, respectively. Clearly, the Eastern Region is far greater than the Western Region in terms of the contribution of scientific, technological, and cultural input to economic growth. 3.3.3

Regional Differences in the Output Elasticity Coefficients of Factors

Judging by the absolute data of various indicators and the regression equation, the three regions are consistent in the overall trend of changes, but there is serious regional disparity. The Eastern Region is much higher than the Central and Western Regions in the contribution rate of education human capital and health human capital, which are the main factors of regional economic gaps. Compared with the developed Eastern Region, the Central and Western Regions have fallen behind in all factors except the accumulation of market experience, which indicates that the allocation of these factors is unbalanced and there are gaps in development among these three regions.

CHAPTER 4

The Relationship Between Human Capital Structure and Regional Economic Gaps in China

Of the existing studies on human capital in China, very few have touched on the role and mechanism of the difference in human capital structure on economic growth from the perspective of human capital structure. As proven by research abroad, human capital structure is another variable for the study of economic growth, which can better describe the relationship between human capital and economic growth in terms of quality. In view of these issues, this chapter will empirically study the human capital structure and the relationship between the distribution structure of human capital and regional economic growth in the three regions of China using the Gini coefficient of human capital. At the same time, it will break the hypothesis of homogeneous human capital, distinguish the contribution of different levels of education to economic growth and, on that basis, study the role of different levels of education structure in regional economic growth. Through a comparison of the influence of each variable, we can make targeted adjustments when choosing policies on economic growth, not only based on the levels of human capital stocks, but also in line with regional differences in the human capital structure.

© Social Sciences Academic Press 2023 Y. Li, Human Capital Investment and the Regional Economic Gap in China, https://doi.org/10.1007/978-981-99-4997-7_4

93

94

Y. LI

4.1 The Structure of Human Capital Distribution and Its Relationship with Regional Economic Growth in China 4.1.1

The Effect Mechanism of Human Capital Distribution Structure on Economic Growth: A Theoretical Framework

Solow’s Neoclassical Growth Model does not discuss the role of human capital; the endogenous growth model of the new growth theory puts human capital at the core; the Lucas model believes that long-term economic growth benefits from the accumulation of human capital; and the Romer model attributes sustained growth to the existing human capital stocks, that is, to realize sustained growth by stimulating innovation or improving the ability to introduce or imitate new technology so as to promote technological progress. However, the impact of human capital on economic growth is not only a matter of the total amount. The human capital structure plays a decisive role in the process of economic growth. The mechanism is shown in the following aspects. 4.1.1.1

The Distribution Structure of Human Capital Influences Economic Growth by Affecting the Structure of Distribution Income Research on the mechanism of human capital investment, which regards education as a human capital investment, studies the effect mechanism of education on income inequality within the framework of human capital theory. Most of the early human capital theories (e.g., Schultz 19611 ; Becker 19752 ) believed that, as the most important accumulation of human capital, education is an important reason for the promotion of equality in income distribution. Becker and Chiswick (1966) proved that income inequality among different regions in the United States is positively related to education inequality and negatively to the average education level.3 Psacharopoulos (1977) used the data of 49 countries (including 37 less developed ones) to study the impact of education

1 T. W. Schultz, “Investment in Human Capital,” The American Economic Review 51, no. 1 (1961): 1–17. 2 G. S. Becker, Human Capital, 2nd ed. (New York: NBER, 1975). 3 G. S. Becker and B. R. Chiswick, “Education and the Distribution of Earnings,” The

American Economic Review 56, no. 1/2 (1966): 358–69.

4

THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

95

inequality on income distribution. The result shows that the variable of education inequality, expressed as the difference coefficient of school enrollment at different levels, has always had a negative impact on income distribution, and that education inequality explains about 23% of the Gini coefficient of income. Later, in some models that analyze the relationship between income inequality and economic growth, the role of human capital is very important, if not critical, because income inequality mainly derives from the inequality of human capital distribution (Glomm and Ravikumar 19924 ; Saint-Paul and Verdier 19935 ; Galor and Tsiddon 19976 ). Theoretically, income distribution inequality is manifested in the expansion of the income gap, which may slow down economic growth through four mechanisms. First, due to the imperfection of the credit market, the expansion of the income gap will force more poor people to face credit constraints, and thus reduce their investment in physical capital and human capital accumulation. And these impacts are interactive (Galor and Zeira 19937 ; Fishman and Simhon 20028 ). Second, in a democratic society, the widening income gap will make more people support tax increases to promote redistribution, but higher taxes will have a negative incentive effect on economic growth (Alesina and Rodrik 19949 ; Persson and Tabellini 199410 ; Benabou 199611 ). Third, the widening income gap will cause social and political turbulences, worsen the social investment

4 G. Glomm and B. Ravikumar, “Public Versus Private Investment in Human Capital: Endogenous Growth and Income Inequality,” Journal of Political Economy 100, no. 4 (1992): 818–34. 5 G. Saint-Paul and T. Verdier, “Education, Democracy and Growth,” Journal of Development Economics 42, no. 2 (1993): 399–407. 6 O. Galor and K. Tsiddon, “The Distribution of Human Capital and Economic Growth,” Journal of Economic Growth 2, no. 1 (1997): 14–93. 7 Oded Galor and Joseph Zeira, “Income Distribution and Macroeconomics,” Review of Economic Studies 60, no. 1 (1993): 35–52. 8 A. Fishman and A. Simhon, “The Division of Labor, Inequality and Growth,” Journal of Economic Growth, no. 7 (2002): 117–36. 9 Alberto Alesina and Dani Rodrik, “Distributive Politics and Economic Growth,” Quarterly Journal of Economics 109, no. 2 (1994): 465–90. 10 Torsten Persson and Guido Tabellini, “Is Inequality Harmful for Growth? Theory and Evidence,” American Economic Review 84, no. 3 (1994): 600–21. 11 R. Benabou, “Inequality and Growth,” NBER Macroeconomics Annual 11 (1996): 11–76.

96

Y. LI

environment, and cause more resources to be used in protecting property rights, thus reducing the accumulation of productive material capital (Benhabib and Rustichini 199612 ). And fourth, low-income families are higher in the fertility rate but lower in the investment in human capital, so when the income gap widens, the proportion of low-income families will increase, thus reducing the education level and economic growth of the whole society (De la Croix and Doepke 200413 ). Most literatures believe that the widening income gap is detrimental to economic growth because it reduces the accumulation of physical capital and human capital. As we can see, the inequality of human capital distribution affects economic growth by affecting the inequality of income distribution. 4.1.1.2

The Distribution Structure of Human Capital Affects the Efficiency of Resource Allocation and the Economic Growth Rate The unreasonable Gini coefficient of human capital has a negative effect on the investment rate and affects the changes of other variables, such as population growth, public consumption, and the initial stocks of human capital, thus weakening the efficiency of resource allocation and the investment rate. Castello and Domenech (2002) verified the indirect effect of the distribution structure of human capital on the accumulation of other factors. The Gini coefficient of human capital has a negative effect on material accumulation. Data in the 1960s showed that countries with unequal distribution structure of human capital often have lower investment rates, resulting in lower economic growth rates.14 Therefore, theoretically speaking, the unequal distribution of human capital in the three regions of China can explain the gaps in regional economic development. Yet what we need to do is verify the extent of this impact through empirical studies.

12 J. Benhabib and A. Rustichini, “Social Conflict and Growth,” Journal of Economic Growth 1, no. 1 (1996): 129–46. 13 David De La Croix and Matthias Doepke. “Inequality and Growth: Why Differential Fertility Matters,” American Economic Review 93, no. 4 (2004): 1091–113. 14 A. Castello and R. Domenech, “Human Capital Inequality and Economic Growth: Some New Evidence,” The Economic Journal 112, no. 2 (2002): 187–200.

4

4.1.2

THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

97

The Relationship Between Inequality of Human Capital and Average Stocks of Human Capital

According to the Gini coefficients and the average stocks of human capital (i.e., the average years of schooling) of China’s three regions from 1990 to 2014 in Tables 2.8 and 2.1, we calculate the correlation coefficient between the Gini coefficient of human capital and the average stocks of human capital (see Table 4.1). Our result shows that there exists a negative correlation between the two. Specifically, the correlation coefficient of the Eastern Region is −0.54038, that of the Central Region is − 0.56727, and that of the Western Region is −0.53909. This indicates that increasing the average stocks of human capital helps reduce the inequality of human capital distribution. Meanwhile, however, we have also noticed that the correlation coefficients of the two are smaller than −0.5, yet the difference in the average stocks of human capital is not large, though there is significant inequality in human capital distribution in the three regions. This shows that the difference in the average stocks of human capital is only one of the reasons that affect the inequality in the distribution of human capital. There must be more important reasons for the formation of regional gaps in the unequal regional distribution of human capital. What are the reasons for the regional gaps in the distribution of human capital? What is their formation mechanism? What kind of mechanism exists between human capital stocks and the inequality of human capital distribution? How do they affect each other? These questions deserve further study in the future. 4.1.3

Human Capital Disequilibrium and Regional Economic Imbalance

Per capita GDP represents the level of economic development of a country or region. Since 1990, there have been significant regional differences in per capita GDP in China. According to data of China Statistical Yearbooks, in 1990 the provincial-level region with the highest per capita GDP (Shanghai) was 7.30 times the lowest (Guizhou). The figure expanded to 10.24 times in 1995 and 12.98 times in 2000. In 2005, it was 10.26 times. Although the gap has narrowed in recent years, it was still 5.8 times in 2010. In 2014, the provincial-level region with the highest per capita GDP (Tianjin) was 3.98 times the lowest (Gansu). In light of the standard deviation reflecting the dispersion of

98

Y. LI

Table 4.1 Correlation coefficients of regional Gini coefficients and average stocks of human capital in China, 1990–2014 Eastern H Eastern H Central H Western H Eastern Gh Central Gh Western Gh

Central H

Western H

Eastern Gh

Central Gh

Western Gh

1 0.991107

1

0.987269

0.995243

1

−0.54038

−0.56735

−0.53912

1

−0.54023

−0.56727

−0.53929

0.999944

1

−0.53989

−0.56652

−0.53909

0.99964

0.99985

1

per capita GDP among Chinese provinces, the standard deviations were 2,123.38 in 1990, 3,885.23 in 1995, 6,563.73 in 2000, 11,313.02 in 2005, 17,466.34 in 2010, and 22,990.07 in 2014. The standard deviation in 2014 was 10.83 times higher than that in 1990, which indicates that the regional gaps in economic growth have been widening over the past 25 years. At the same time, there has been a serious imbalance in human capital input in different regions. In 1990, the provincial-level region with the highest Gini coefficient of human capital (Xizang) was 3.13 times the lowest region (Beijing), and the region with the highest average stocks of human capital (Beijing) was 4.05 times the lowest region (Xizang). In 1996, the figures were 3.19 times and 3.27 times, respectively. In 2000, the ratio between the region with the highest Gini coefficient of human capital (Xizang) and the lowest region (Beijing) increased to 3.54 times, and the region with the highest average stocks of human capital (Beijing) was 2.91 times the lowest region (Xizang). In 2005, the ratio between the region with the highest Gini coefficient of human capital (Xizang) and the lowest region (Beijing) decreased to 3.35 times, and the region with the highest average stocks of human capital (Beijing) was 2.8 times the lowest region (Xizang). In 2010, the ratio between the region with the highest Gini coefficient of human capital (Xizang) and the lowest region (Beijing) increased again to 3.60 times, and the region with the highest

4

THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

99

average stocks of human capital (Beijing) was 2.17 times the lowest region (Xizang). In 2010, the ratio between the region with the highest Gini coefficient of human capital (Xizang) and the lowest region (Beijing) reached 4.10 times, and the region with the highest average stocks of human capital (Beijing) was 2.81 times the lowest region (Xizang). In 2014, the ratio between the region with the highest Gini coefficient of human capital (Xizang) and the lowest region (Beijing) remained at 4.10 times, and the region with the highest average stocks of human capital (Beijing) was 2.54 times the lowest region (Xizang). Since the mean of each group is very different, we can eliminate the influence of the mean using the standard deviation coefficient. To compare their dispersion, we use the standard deviation coefficients to investigate the imbalance of regional economy and human capital from 1990 to 2014, as shown in Fig. 4.1. The standard deviation coefficient of per capita GDP in each year is higher than the Gini coefficient of human capital and the average stocks of human capital, indicating that the imbalance of per capita GDP distribution among regions is getting more serious. One possible explanation is that human capital is only one of the reasons for the formation of regional disparity, and the other reason is that the speed of changes in per capita GDP may be faster than that of human capital. Since 1999, the standard deviation coefficient of per capita GDP has declined in general, indicating that the regional economic gap has narrowed considerably; the standard deviation coefficient of the average stocks of human capital has decreased slightly, indicating that the gap in the average education level has gradually narrowed in these regions; the standard deviation coefficient of the Gini coefficient of human capital has risen in slight fluctuations, indicating that the regional gap in the distribution of human capital has widened. The standard deviation coefficient of the Gini coefficient of human capital is larger than that of the average stocks of human capital, indicating that China’s regional gaps in human capital are mainly manifested in the disparity of the distribution structure of human capital, rather than the disparity of the average stocks of human capital. 4.1.4

Quantitative Tests of Annual Cross-Sectional Data 1990–2014

To study the relationship between human capital structure and the differences in economic growth in China’s three regions, we assume that there is a negative correlation between the base coefficient of regional human

100

Y. LI

Fig. 4.1 Standard deviation coefficient of per capita GDP, the coefficient of human capital structure (Gh), and the average stocks of human capital (H) of 30 provincial-level regions in China, 1990–2014

capital and economic development. Based on the good linearity between Ln AG D Pi and LnG ih , we establish the following model: Ln AG D Pi = a + bLnG ih + u i

(Formula 4.1)

In Formula (4.1), the value of i is between 1 and 30, indicating 30 provincial-level regions in China; the Gini coefficients of regional human capital are explanatory variables; the per capita GDP (AGDP) of corresponding regions is explained variables; and all the data are from China Statistical Yearbooks. To compare the differences among the regions and find out the rules of change in different years, we use the cross-sectional data of 30 provinciallevel regions in China to calculate the regression formula of each year from 1990 to 2014. We have adopted neither the time series data nor the panel data, because too short a time series will affect the regression effect, and, in addition, the panel data cannot be used for a comparison of the impact effect across the years. Considering that the cross-sectional data are used and that the differences among the regions are too large, which will bring heteroscedasticity and thus nullify the parameter estimation (the results of our ordinary least-square regression and the White

4

THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

101

test show there exists heteroscedasticity), we choose the weighted leastsquare method (WLS) for estimation. The regression results are shown in Table 4.2. The empirical tests show that the goodness of fit of the regression equation is excellent from 1990 to 2014 and has passed the t-test, the F test, the autocorrelation test, and the White test. According to the regression results, there is a negative correlation between the Gini coefficient of human capital and regional economic development in China, which is consistent with our theoretical analysis. The coefficient of lnGh represents the impact of the Gini coefficient of human capital on per capita GDP. We observe the coefficient over these years and come to the following conclusions. On the one hand, the value of this coefficient was greater than 1 before 2013, which indicates that every decline in human capital inequality led to a greater increase in per capita GDP. To a certain extent, it can explain the unbalanced distribution of per capita GDP among the regions, which is greater than that of the Gini coefficient of human capital among the regions. On the other hand, human capital inequality had the greatest impact on per capita GDP in 2001 and 2004, when every 1% decrease in education inequality caused a 2.20% increase in per capita GDP. The smallest impact was in 2013, when every 1% decrease in education inequality resulted in a 0.80% increase in per capita GDP (see Fig. 4.2). As we find through a horizontal comparison among the regions, the Gini coefficient of human capital in the Western Region is far greater than that in the Eastern Region, which is one of the most important reasons for the widening growth and economic gaps between the two regions. Such influence is further strengthened as the Eastern Region is higher than the Western Region in the initial level of development and human capital.

4.2

The Contribution of Different Levels of Education Human Capital Stocks to GDP Growth in China: A Comparison

The Gini coefficient of human capital integrates the information of different levels of education and reflects the degree of inequality in education. However, it does not reflect the difference in the structure of education. As we can see clearly from the Lorentz Curve, behind the same Gini coefficient may be the distribution of different levels of education,

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

Year

lnAGDP = 5.76 − (10.07*) lnAGDP = 5.94 − (108.16*) lnAGDP = 6.01 − (46.72*) lnAGDP = 5.71 − (1415.41*) lnAGDP = 6.04 − (135.28*) lnAGDP = 5.65 − (84.89*) lnAGDP = 5.97 − (236.21*) lnAGDP = 5.66 − (172.86*) lnAGDP = 5.65 − (427.14*) lnAGDP = 5.57 − (25.10*) lnAGDP = 5.80 − (24.45*) lnAGDP = 5.65 − (107.51*) lnAGDP = 6.13 − (50.04*) 1.57lnGh (−2.96*) 2.14lnGh (−31.13*) 1.89lnGh (−11.96*) 1.88lnGh (−405.20*) 1.70lnGh (−32.81*) 2.04lnGh (−29.34*) 1.72lnGh (−83.53*) 1.98lnGh (−77.09*) 2.04lnGh (−193.16*) 2.14lnGh (−12.02*) 1.87lnGh (−10.98*) 2.02lnGh (−53.01*) 1.76lnGh (−15.18*)

Regression result and t values

0.89

0.99

0.81

0.84

1.00

1.00

1.00

0.97

0.97

1.00

0.84

0.97

0.24

R2

0.89

0.99

0.80

0.83

1.00

1.00

1.00

0.97

0.97

1.00

0.83

0.97

0.21

R2

230.34*

2810.43*

120.66*

144.37*

37,310.83*

5942.73*

6976.92*

860.88*

1076.72*

1,641.86*

142.93*

969.15*

8.75*

F

2.02

2.09

2.25

2.29

2.22

2.20

1.97

1.93

2.03

2.03

2.09

2.20

1.87

D.W

6.12

1.00

6.88

5.42

0.58

0.16

0.10

0.36

0.14

0.46

2.78

6.32

0.41

nR2

Table 4.2 Correlation regression equation between per capita GDP and the Gini coefficient of human capital in China, 1990–2014

102 Y. LI

lnAGDP = 6.54 − (5760.96*) lnAGDP = 5.60 − (38.21*) lnAGDP = 6.16 − (65.14*) lnAGDP = 6.25 − (58.09*) lnAGDP = 6.13 − (59.94*) lnAGDP = 6.23 − (70.53*) lnAGDP = 6.39 − (38.08*) lnAGDP = 6.07 − (6.22*) lnAGDP = 6.82 − (16.51*) lnAGDP = 7.18 − (16.45*) lnAGDP = 8.50 − (51.22*) lnAGDP = 8.33 − (42.14*) 1.46lnGh (−1356.16*) 2.20lnGh (−19.00*) 1.93lnGh (−29.82*) 1.93lnGh (−19.82*) 2.03lnGh (−23.73*) 1.98lnGh (−28.82*) 1.92lnGh (−17.20*) 2.04lnGh (−3.56*) 1.65lnGh (−6.66*) 1.47lnGh (−5.69*) 0.80lnGh (−9.44*) 0.91lnGh (−8.94*)

Regression result and t values

0.74

0.76

0.54

0.61

0.31

0.91

0.97

0.95

0.93

0.97

0.93

1.00

R2

0.73

0.75

0.52

0.60

0.29

0.91

0.97

0.95

0.93

0.97

0.93

1.00

R2

79.89*

89.07*

32.36*

44.30*

12.70*

295.91*

830.81*

563.17*

392.70*

889.27*

360.96*

1,8391.77*

F

2.13

1.80

2.47

2.48

2.23

2.55

2.51

2.50

2.31

2.52

2.18

2.10

D.W

7.53

3.33

0.85

2.90

1.83

2.06

0.77

1.49

0.96

2.25

4.38

0.12

nR2

Notes 1. The figures in parentheses are t values; nR 2 is the result of the White test (which obeys χ 2 distribution); * means passing the significance test at the 0.01 level 2. The White test is used again to determine whether there is heteroscedasticity. At the significance level α = 0.01, the auxiliary regression model of corresponding residual square nR 2 < χ 2 0.01 (2) = 9.21, so the regression equations have eliminated the heteroscedasticity

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

Year

4 THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

103

104

Y. LI

Fig. 4.2 Impact of the coefficient of human capital structure on AGDP, 1990– 2014

and there may be ambiguity in the process of comparison. Therefore, it is necessary to specifically compare and analyze the contribution of education human capital stocks at different levels to the output growth in the three regions of China. 4.2.1

The Output Elasticity of the Average Level of Human Capital Stocks (H ) and Its Contribution to Output Growth

4.2.1.1 Model Selection and Explanation We use the production function method to analyze the input and output of production factors, and make specific estimations using C-D production functions. These production functions incorporate human capital into the production function model as an independent variable that affects economic growth. The expression is: Y = AK a L β H γ eμ

(Formula 4.2)

where K stands for physical capital input; L for simple labor input; H for human capital input; A for technological progress; α, β, and γ for the output elasticity of different factors; and e μ for the error. We assume that this production function is free from production scale constraint, so (α + β) does not necessarily equal 1.

4

THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

105

We take the logarithm of the production function and get: LnY = Ln A + αLn K + β Ln L + γ Ln H + μ

(Formula 4.3)

4.2.1.2 Indicator Selection and Data Processing of the Variables We measure the total output Y using regional GDP in billion RMB. The data are from China Statistical Yearbooks. The physical capital input may be measured in three ways, namely the capital stocks, the total capital formation, and the fixed asset investment. The capital stocks include the capital formation of previous years. At present, the most influential measurement methods at home and abroad are the perpetual inventory method developed by Goldsmith in 1951 and the capital lease price method of Jorgensen. Due to a lack of statistics on the stocks and use of fixed assets in China, Chinese scholars mostly estimate them by making a series of assumptions on the depreciation rate, the added value of inventories, and the formation of initial fixed capital. Due to its strong subjectivity and lack of provincial data, this indicator will not be used here. The total capital formation refers to the net amount of fixed assets and inventories obtained by a resident unit minus disposal within a certain period, including the total fixed capital formation and the inventory increase. This indicator calculates a part of the GDP of the current year using the expenditure method. Yet it is difficult to obtain the statistics of the provincial-level regions, so we will not use this indicator, either. The annual current-value fixed assets investment is the sum of the workload of the whole society in building and purchasing fixed assets and the related costs in a certain period expressed in currency. The data of this indicator are easy to obtain. According to the empirical studies of Xu Yingchun et al. using the national statistics, the fixed assets investment model is better than the capital stocks model and is not significantly different from the total capital formation model. Therefore, we decide to adopt the fixed assets investment in billion RMB. Our data are from China Statistical Yearbooks. Strictly speaking, the simple labor force input should be measured by the labor time of standard labor intensity. However, due to the lack of statistics in this regard, we use the number of social employees (in billion persons) at the end of each year to express it. Our data are based on the statistics of the fourth and fifth national population censuses as well as the

106

Y. LI

adjusted number of working population of the 1% population sampling survey in 1995. Education is the main way of human capital formation. In this chapter, we will express human capital input as education human capital. To match the afore-mentioned Gini coefficient of human capital and reflect the contribution of different levels of education to economic growth, we will express the human capital stocks as the average years of schooling of laborers. We calculate relevant data in China Demographic Yearbooks using Formula (2.1) and get the results as in Table 2.1. 4.2.1.3 Measurement Methods and Results To analyze and compare the differences in the contribution of human capital of regions to different levels of economic development, we use the clustering method and divide all the provincial-level regions into three groups, namely the Eastern, Central, and Western Regions. We then regress the production function in Formula (5.3) using the crosssectional data of the corresponding provincial-level regions in the three major regions of China from 1996 to 2014. As there is not much difference between the values of the groups, we use the ordinary least-square method (OLS) and the EViews software package for estimation (see Table 4.3 for the regression results). According to the regression results, the coefficient of determination R 2 , the adjusted coefficient of determination R 2 , and the F statistic of each regression equation are quite high, indicating a significant effect of test and an excellent fitting effect of the regression equation. All the equations have passed the autocorrelation and heteroscedasticity tests. The t statistic of the elasticity coefficient of capital is high in the Eastern Region from 1996 to 2006, in the Central Region from 1990 to 1994 and from 2001 to 2002, and in the Western Region from 1991 to 2012, and the t-test of parameter estimation passed the test at the 10% significance level. The t statistic of the elasticity coefficient of capital is ordinary in the Eastern Region in 1993, 1995, 2007, 2008, and 2014, in the Central Region from 1997 to 1999 and from 2010 to 2013, and in the Western Region in 2013, and the t-test of parameter estimation passed the test at the 20% significance level. In the other years of this period, the t statistic of the elasticity coefficient of capital is low, and the t-test of parameter estimation passed the test at the significance level of over 20%. Besides, the t statistic of the elasticity coefficient of labor is high in the Eastern Region from 1995 to 1998, in 2001, and from 2007 to 2014, in the

0.69lnK (1.13) (0.30) 0.29lnK (0.38) (0.72) 0.25lnK (0.41) (0.69) 0.76lnK (1.77) (0.12) 0.05lnK (0.15) (0.88) 0.40lnK (1.75) (0.12) 0.59lnK (5.45) (0.01) 0.63lnK (5.39) (0.01)

+ 0.28lnL (0.53) (0.61) + 0.02lnL (0.03) (0.98) + 0.09lnL (0.18) (0.86) + 0.19lnL (0.65) (0.53) + 0.32lnL (1.25) (0.25) + 0.69lnL (3.45) (0.01) + 0.49lnL (4.86) (0.01) + 0.46lnL (3.79) (0.01)

+ 0.03lnH (0.01) (0.99) + 1.84lnH (0.66) (0.53) + 1.65lnH (0.90) (0.40) + 0.19lnH (0.20) (0.85) + 0.42lnH (1.82) (0.11) + 4.62lnH (3.45) (0.01) + 0.51lnH (1.08) (0.32) + 0.69lnH (1.12) (0.30)

Regression result and t values

Year

Eastern Region 1990 lnY = 2.17 + (1.86) (0.10) 1991 lnY = 1.37 + (1.02) (0.33) 1992 lnY = 2.14 + (1.10) (0.31) 1993 lnY = 1.31 + (0.51) (0.63) 1994 lnY = 2.70 + (1.48) (0.18) 1995 lnY = 2.40 + (1.29) (0.24) 1996 lnY = 3.68 + (5.47) (0.01) 1997 lnY = 3.06 + (3.79) (0.01)

OLS estimation results, 1990–2014

Table 4.3

0.91

0.83

0.64

0.80

0.66

0.87

0.98

0.98

0.88

0.75

0.86

0.76

0.91

0.99

0.99

R2

0.94

R2

157.91

163.27

22.82

7.43

14.31

7.01

17.72

35.74

F

2.81

2.84

1.67

1.37

1.20

0.97

0.99

0.98

D.W

(continued)

10.98 (0.01)

10.19 (0.01)

10.85 (0.01)

9.96 (0.01)

10.93 (0.01)

8.50 (0.01)

10.43 (0.01)

9.94 (0.01)

nR2

4 THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

107

2005

2004

2003

2002

2001

2000

1999

lnY = 2.68 + (3.58) (0.01) lnY = 2.37 + (2.91) (0.02) lnY = 1.75 + (1.77) (0.12) lnY = 1.93 + (2.15) (0.07) lnY = 2.35 + (2.22) (0.06) lnY = 1.35 + (0.99) (0.36) lnY = 1.86 + (1.40) (0.20) lnY = 1.01 + (0.75) (0.48)

1998

+ 0.31lnL (2.85) (0.02) + 0.23lnL (1.62) (0.15) + 0.22lnL (1.60) (0.15) + 0.24lnL (1.87) (0.10) + 0.19lnL (1.25) (0.25) + 0.22lnL (1.17) (0.28) + 0.14lnL (0.61) (0.56) + 0.29lnL (1.19) (0.27)

Regression result and t values

Year

0.77lnK (6.79) (0.01) 0.82lnK (5.65) (0.01) 0.82lnK (6.04) (0.01) 0.79lnK (6.02) (0.01) 0.83lnK (5.82) (0.01) 0.79lnK (4.69) (0.01) 0.84lnK (4.36) (0.01) 0.72lnK (3.39) (0.01)

(continued)

Table 4.3

+ 0.26lnH (0.46) (0.66) + 0.18lnH (0.27) (0.80) + 0.49lnH (0.60) (0.57) + 0.54lnH (0.73) (0.49) + 0.15lnH (0.18) (0.86) + 0.77lnH (0.79) (0.45) + 0.26lnH (0.25) (0.81) + 1.23lnH (1.27) (0.24) 0.97

0.97

0.97

0. 98

0.99

0.99

0.98

0.99

R2

0.96

0.96

0.96

0. 98

0.98

0.98

0.98

0.98

R2

81.21

87.09

81.21

135.46

160.91

167.35

142.79

179.22

F

3.08

3.42

3.26

3.01

2.56

2.60

2.59

2.95

D.W

5.59 (0.01)

9.48 (0.01)

10.60 (0.01)

10.64 (0.01)

10.13 (0.01)

10.98 (0.01)

10.98 (0.01)

10.82 (0.01)

nR2

108 Y. LI

2014

2013

2012

2011

2010

2009

2008

2007

+ 0.40lnL (1.24) (0.25) + 0.60lnL (2.07) (0.08) + 0.74lnL (3.01) (0.02) + 0.93lnL (4.16) (0.01) + 0.88lnL (3.10) (0.02) + 0.91lnL (4.67) (0.01) + 0.96lnL (4.45) (0.01) + 0.99lnL (3.89) (0.01) + 0.81lnL (3.93) (0.01)

lnY = 1.17 + (0.83) (0.44) lnY = 1.43 + (0.96) (0.37) lnY = 1.44 + (0.87) (0.41) lnY = 1.45 + (0.87) (0.41) lnY = 0.44 + (0.16) (0.88) lnY = 0.82 + (0.39) (0.71) lnY = 1.68 + (0.67) (0.52) lnY = 2.39 + (0.79) (0.46) lnY = 0.21 + (0.08) (0.94)

2006

0.62lnK (2.19) (0.06) 0.46lnK (1.79) (0.12) 0.34lnK (1.51) (0.18) 0.19lnK (0.90) (0.40) 0.20lnK (0.73) (0.49) 0.20lnK (1.02) (0.34) 0.15lnK (0.72) (0.50) 0.11lnK (0.46) (0.66) 0.35lnK (1.70) (0.14)

Regression result and t values

Year + 1.63lnH (1.35) (0.22) + 2.27lnH (2.23) (0.06) + 2.85lnH (3.40) (0.01) + 3.57lnH (4.58) (0.01) + 3.95lnH (3.37) (0.01) + 3.87lnH (5.06) (0.01) + 3.70lnH (4.64) (0.01) + 3.56lnH (3.78) (0.01) + 3.41lnH (4.48) (0.01) 0.97

0.94

0.96

0.97

0.94

0.98

0.97

0.97

0.97

R2

0.96

0.92

0.94

0.96

0.92

0.96

0.96

0.96

0.96

R2

70.59

38.70

56.16

74.41

39.89

91.66

80.39

80.77

76.84

F

1.78

2.21

2.11

1.73

1.83

1.74

1.97

2.34

2.58

D.W

(continued)

2.28 (0.01)

10.84 (0.01)

11.00 (0.01)

10.71 (0.01)

10.27 (0.01)

6.31 (0.01)

10.93 (0.01)

9.03 (0.01)

10.15 (0.01)

nR2

4 THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

109

0.84lnK (3.10) (0.04) 1.05lnK (4.13) (0.01) 0.89lnK (2.22) (0.09) 0.51lnK (2.43) (0.07) 0.52lnK (2.17) (0.10) 0.24lnK (0.92) (0.41) 0.49lnK (1.36) (0.24) 0.54lnK (2.03) (0.11)

+ 0.38lnL (2.68) (0.06) + 0.45lnL (2.93) (0.04) + 0.40lnL (1.58) (0.19) + 0.12lnL (0.64) (0.56) + 0.04lnL (0.21) (0.84) + 0.74lnL (2.07) (0.11) + 0.44lnL (1.01) (0.37) + 0.37lnL (1.06) (0.35)

Regression result and t values

Year

Central Region 1990 lnY = 5.94 + (3.92) (0.02) 1991 lnY = 5.90 + (4.73) (0.01) 1992 lnY = 6.17 + (2.81) (0.05) 1993 lnY = 3.56 + (3.08) (0.04) 1994 lnY = 4.14 + (4.06) (0.02) 1995 lnY = 2.96 + (1.51) (0.21) 1996 lnY = 1.91 + (0.88) (0.43) 1997 lnY = 1.38 + (0.59) (0.59)

(continued)

Table 4.3

+ 2.80lnH (21.96) (0.01) + 3.55lnH (18.27) (0.01) + 3.19lnH (7.99) (0.01) + 3.53lnH (6.37) (0.01) + 3.26lnH (4.55) (0.01) + 1.08lnH (1.03) (0.36) + 1.65lnH (0.95) (0.39) + 1.70lnH (1.01) (0.37) 0.99

0.98

0.97

0.97

0.96

0.85

0.82

0.88

0.99

0.98

0.98

0.98

0.91

0.90

0.93

R2

0.99

R2

17.57

11.96

14.09

52.23

71.55

70.81

139.83

219.73

F

2.16

2.24

2.32

2.25

1.91

1.70

1.86

1.77

D.W

4.65 (0.01)

5.06 (0.01)

1.45 (0.01)

6.16 (0.01)

6.23 (0.01)

5.90 (0.01)

4.63 (0.01)

1.63 (0.01)

nR2

110 Y. LI

2006

2005

2004

2003

2002

2001

2000

1999

+ 0.39lnL (1.48) (0.21) + 0.34lnL (0.98) (0.38) + 0.57lnL (1.64) (0.18) + 0.18lnL (0.67) (0.54) + 0.16lnL (0.68) (0.54) + 0.34lnL (0.49) (0.65) + 0.56lnL (1.44) (0.22) + 0.76lnL (2.61) (0.06) + 0.65lnL (2.83) (0.05)

lnY = 2.12 + (1.23) (0.29) lnY = 3.35 + (1.41) (0.23) lnY = 1.62 + (0.59) (0.69) lnY = 2.38 + (1.05) (0.35) lnY = 0.38 + (0.15) (0.89) lnY = 0.53 + (0.06) (0.95) lnY = 1.62 + (0.30) (0.78) lnY = 3.18 + (0.95) (0.40) lnY = 1.99 + (0.60) (0.58)

1998

0.51lnK (1.92) (0.13) 0.53lnK (1.54) (0.20) 0.23lnK (0.57) (0.60) 0.76lnK (2.14) (0.10) 0.87lnK (2.29) (0.08) 0.60lnK (0.57) (0.60) 0.22lnK (0.34) (0.75) 0.03lnK (0.06) (0.96) 0.16lnK (0.47) (0.66)

Regression result and t values

Year + 1.45lnH (1.10) (0.33) + 0.74lnH (0.44) (0.69) + 2.71lnH (1.40) (0.23) + 0.34lnH (0.21) (0.85) + 0.89lnH (1.03) (0.36) + 1.84lnH (0.43) (0.69) + 2.82lnH (1.71) (0.16) + 3.21lnH (3.06) (0.04) + 3.00lnH (2.38) (0.08) 0.91

0.94

0.89

0.81

0.94

0.94

0.91

0.91

0.95

R2

0.84

0.89

0.80

0.66

0. 90

0.89

0.83

0.84

0.91

R2

13.15

19.51

10.43

5.58

21.35

20.08

12.80

13.29

24.98

F

2.28

1.76

2.47

2.22

1.56

1.95

1.43

1.81

1.49

D.W

(continued)

1.71 (0.01)

3.99 (0.01)

1.33 (0.01)

1.06 (0.01)

1.82 (0.01)

4.73 (0.01)

1.28 (0.01)

3.28 (0.01)

5.01 (0.01)

nR2

4 THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

111

Regression result and t values

lnY = 0.83 + 0.36lnK + 0.53lnL + 2.74lnH (0.26) (1.33) (2.88) (2.65) (0.81) (0.26) (0.04) (0.06) lnY = 1.22 + 0.20lnK + 0.64lnL + 3.31lnH (0.43) (0.87) (3.92) (3.16) (0.69) (0.43) (0.02) (0.03) lnY = 0.38 + 0.28lnK + 0.63lnL + 3.33lnH (0.10) (0.90) (2.92) (2.35) (0.93) (0.42) (0.04) (0.08) lnY = −1.56 + 0.28lnK + 0.65lnL + 4.24lnH (−0.74) (1.99) (7.07) (6.36) (0.50) (0.12) (0.01) (0.01) lnY = −4.52 + 0.64lnK + 0.35lnL + 3.97lnH (−1.09) (1.60) (1.34) (3.43) (0.34) (0.18) (0.25) (0.03) lnY = −5.14 + 0.94lnK + 0.20lnL + 2.86lnH (−0.90) (1.90) (0.61) (1.47) (0.42) (0.13) (0.57) (0.22) lnY = −7.68 + 1.47lnK + 0.23lnL + 1.36lnH (−1.27) (2.05) (0.44) (0.66) (0.27) (0.11) (0.68) (0.54) lnY = −5.09 + 0.25lnK + 0.67lnL + 1.06lnH (−0.70) (0.45) (1.48) (2.11) (0.52) (0.68) (0.21) (0.10)

Year

2007

2014

2013

2012

2011

2010

2009

2008

(continued)

Table 4.3

0.94

0.96

0.94

0.97

0.98

0.93

0.94

0.92

R2

0.89

0.92

0.90

0.95

0.97

0.87

0.89

0.87

R2

20.28

28.38

21.51

43.51

85.31

16.95

19.73

15.99

F

1.52

1.62

1.90

2.26

2.59

2.07

2.06

2.61

D.W

1.76 (0.01)

1.36 (0.01)

3.20 (0.01)

2.69 (0.01)

0.18 (0.01)

1.24 (0.01)

1.79 (0.01)

1.91 (0.01)

nR2

112 Y. LI

0.62lnK (1.25) (0.25) 0.77lnK (2.02) (0.08) 0.90lnK (2.53) (0.04) 0.83lnK (2.19) (0.06) 0.92lnK (2.27) (0.06) 0.85lnK (2.10) (0.07) 0.75lnK (8.54) (0.01) 0.77lnK (6.25) (0.01) 0.73lnK (5.16) (0.01)

+ 0.40lnL (1.37) (0.21) + 0.32lnL (1.41) (0.20) + 0.24lnL (1.11) (0.30) + 0.25lnL (0.97) (0.36) + 0.14lnL (0.48) (0.65) + 0.18lnL (0.61) (0.56) + 0.31lnL (4.84) (0.01) + 0.30lnL (3.30) (0.01) + 0.30lnL (2.91) (0.02)

Regression result and t values

Western Region 1990 lnY = 3.59 + (1.79) (0.12) 1991 lnY = 2.91 + (1.71) (0.13) 1992 lnY = 2.22 + (1.33) (0.22) 1993 lnY = 2.07 + (1.07) (0.32) 1994 lnY = 1.41 + (0.62) (0.55) 1995 lnY = 1.17 + (0.45) (0.67) 1996 lnY = 2.51 + (4.88) (0.01) 1997 lnY = 2.29 + (3.14) (0.02) 1998 lnY = 2.41 + (2.78) (0.03)

Year + 0.26lnH (0.37) (0.72) + 0.15lnH (0.26) (0.80) + 0.06lnH (0.12) (0.91) + 0.26lnH (0.40) (0.70) + 0.29lnH (0.48) (0.64) + 0.72lnH (1.33) (0.22) + 0.37lnH (1.96) (0.09) + 0.41lnH (1.38) (0.21) + 0.44lnH (1.34) (0.22) 0.88

0.91

0.92

0.91

0.91

0.90

1.00

0.99

0.99

0.94

0.94

0.94

0.93

0.93

1.00

0.99

0.99

R2

0.92

R2

253.26

371.41

686.34

30.63

33.38

35.12

39.47

33.59

25.90

F

1.22

1.05

1.60

2.14

2.17

2.30

2.10

2.01

1.97

D.W

THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

(continued)

9.34 (0.01)

5.02 (0.01)

6.26 (0.01)

10.83 (0.01)

9.81 (0.01)

10.92 (0.01)

6.33 (0.01)

4.68 (0.01)

7.39 (0.01)

nR2

4

113

2006

2005

2004

2003

2002

2001

2000

lnY = 1.93 + (1.61) (0.15) lnY = 1.69 + (1.22) (0.26) lnY = 1.60 + (0.94) (0.38) lnY = 1.47 + (0.98) (0.35) lnY = 2.40 + (1.84) (0.11) lnY = 1.72 + (1.94) (0.09) lnY = 2.17 + (3.10) (0.02) lnY = 1.89 + (2.75) (0.03)

1999

+ 0.24lnL (1.76) (0.12) + 0.24lnL (1.57) (0.16) + 0.26lnL (1.46) (0.19) + 0.24lnL (1.54) (0.17) + 0.25lnL (2.58) (0.04) + 0.26lnL (3.17) (0.02) + 0.29lnL (4.17) (0.01) + 0.27lnL (3.95) (0.01)

Regression result and t values

Year

0.85lnK (4.70) (0.01) 0.85lnK (4.12) (0.01) 0.82lnK (3.11) (0.02) 0.88lnK (3.80) (0.01) 0.89lnK (6.84) (0.01) 0.84lnK (7.08) (0.01) 0.75lnK (8.24) (0.01) 0.76lnK (8.70) (0.01)

(continued)

Table 4.3

+ 0.21lnH (0.69) (0.51) + 0.31lnH (0.77) (0.47) + 0.47lnH (0.82) (0.44) + 0.29lnH (0.57) (0.58) + 0.23lnH (0.90) (0.39) + 0.30lnH (0.89) (0.40) + 0.40lnH (1.71) (0.13) + 0.49lnH (1.88) (0.10) 0.99

0.99

0.99

0.99

0. 98

0.97

0.98

0.99

R2

0.99

0.99

0.99

0.98

0. 97

0.96

0.97

0.98

R2

353.95

329.72

232.80

189.12

113.14

88.56

121.22

172.29

F

2.34

2.23

1.92

1.39

1.54

1.64

1.63

1.40

D.W

8.70 (0.01)

10.65 (0.01)

10.84 (0.01)

10.63 (0.01)

2.41 (0.01)

5.79 (0.01)

8.37 (0.01)

7.64 (0.01)

nR2

114 Y. LI

lnY = 1.77 + 0.73lnK + 0.27lnL + 0.66lnH (3.05) (9.96) (4.39) (2.89) (0.01) (0.01) (0.01) (0.02) lnY = 1.43 + 0.77lnK + 0.22lnL + 0.64lnH (2.32) (9.62) (3.32) (2.45) (0.05) (0.01) (0.01) (0.04) lnY = 1.26 + 0.81lnK + 0.17lnL + 0.47lnH (2.04) (10.52) (2.58) (2.02) (0.08) (0.01) (0.04) (0.08) lnY = 1.34 + 0.75lnK + 0.23lnL + 0.72lnH (1.59) (6.71) (2.52) (1.72) (0.16) (0.01) (0.04) (0.13) lnY = 0.02 + 1.01lnK + 0.05lnL + 0.17lnH (0.06) (18.63) (1.04) (0.75) (0.96) (0.01) (0.33) (0.48) lnY = −0.45 + 1.13lnK + 0.02lnL + 0.26lnH (−0.79) (12.45) (0.23) (0.96) (0.45) (0.01) (0.82) (0.37) lnY = 6.42 + 0.17lnK + 0.75lnL + 1.22lnH (4.00) (1.54) (6.07) (1.99) (0.01) (0.17) (0.01) (0.09) lnY = −2.41 + 1.34lnK + 0.14lnL + 0.44lnH (−0.87) (3.90) (0.53) (0.78) (0.42) (0.01) (0.62) (0.47)

2007

0.99

0.95

1.00

1.00

0.99

1.00

1.00

1.00

R2

0.98

0.93

1.00

1.00

0.99

0.99

0.99

0.99

R2

130.69

47.43

1194.31

1620.01

305.57

554.33

515.24

581.86

F

1.99

1.32

1.26

0.97

2.53

2.64

2.65

2.63

D.W

1.48 (0.01)

10.37 (0.01)

10.76 (0.01)

5.99 (0.01)

10.08 (0.01)

10.99 (0.01)

10.67 (0.01)

10.90 (0.01)

nR2

Notes: 1. The figures in parentheses in the first row of each regression equation are t values; those in parentheses in the second row are the significance levels of the coefficients. nR 2 is the result of the White test (which obeys χ 2 distribution); the figures in parentheses in this column are the significance levels of the heteroscedasticity test 2. The White test is used again to determine whether there is heteroscedasticity. At the significance level α = 0.01, the auxiliary regression model of corresponding residual square nR 2 < χ 2 0.01 (20) = 37.566, so the regression equations have eliminated the heteroscedasticity

2014

2013

2012

2011

2010

2009

2008

Regression result and t values

Year

4 THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

115

116

Y. LI

Central Region from 1990 to 1991 and from 2005 to 2010, and in the Western Region from 1996 to 1998 and from 2003 to 2010, and the t-test of parameter estimation passed the test at the 10% significance level. The t statistic of the elasticity coefficient of labor is ordinary in the Eastern Region in 1999 and 2000, in the Central Region in 1992, 1995, and 2000, and in the Western Region in 1991 and from 1999 to 2002, and the t-test of parameter estimation has passed the test at the 20% significance level. In the other years of this period, the t statistic of the elasticity coefficient of labor is low, and the t-test of parameter estimation has passed the test at the significance level of over 20%. Finally, the t statistic of the elasticity coefficient of education human capital is high in the Eastern Region in 1995 and from 2007 to 2014, in the Central Region from 1990 to 1994, from 2005 to 2011, and in 2014, and in the Western Region in 1996, from 2006 to 2009, and in 2013, and the t-test of parameter estimation has passed the test at the 10% significance level. The t statistic of the elasticity coefficient of education human capital is ordinary in the Eastern Region in 1994, in the Central Region in 2004, and in the Western Region in 2005 and 2010, and the t-test of parameter estimation has passed the test at the 20% significance level. In the other years of this period, the t statistic of the elasticity coefficient of education human capital is low, and the t-test of parameter estimation has passed the test at the significance level of over 20%. In general, the t-statistics of the elasticity coefficients of capital, labor, and education human capital are low in a few years only. The t-test of parameter estimation has passed the test at the significance level of over 20%. It is thus clear that the coefficient significance effect of the regression equation is generally good. 4.2.1.4 Contribution Rates of Different Factors to GDP Growth We analyze the factors using the growth rate equation (i.e., the Solow Function) and seek the total derivative on both sides of Formula (4.2), and get dA dK dL dH dY = +α +β +γ Y A K L H and Growth rate of factor × Output elasticity of factor Contribution = rate of factor Growth rate of per capita GDP × 100 (Formula 4.4)

4

THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

117

We calculate the average annual growth rate of each factor according to relevant annual data and get the output elasticity of each factor according to the regression results in Table 4.3 (see Table 4.4). In the Eastern Region, as Fig. 4.3 shows, the elasticity coefficient of education human capital fluctuated greatly before 2004—it increased significantly from 2004 to 2010 and decreased to some extent after 2010. The elasticity coefficient of labor was on an upward trend from 1991 to 1995 and then on a downward trend until 2004; it increased drastically after 2004 and decreased to some extent in 2014. The elasticity coefficient of capital fluctuated greatly before 1994; it was on the rise from 1994 to 2004 but on a downward trend after 2004, and rose again in 2014. Before 1996 (except in 1993) and after 2005, the elasticity coefficient of education human capital was greater than that of capital and labor. From 1996 to 2005, the elasticity coefficient of capital was the largest, followed by that of education human capital, and that of labor was the smallest. In the Central Region, as Fig. 4.4 shows, the elasticity coefficient of education human capital decreased with fluctuation before 2001, increased with fluctuation from 2001 to 2010, and decreased significantly after 2010. Over this same period, there was no obvious upward or downward trend of the elasticity coefficients of labor and capital. From 1990 to 2014 (except in 2001, 2002, 2013, and 2014), the elasticity coefficient of education human capital was significantly greater than that of capital and labor. In the Western Region, as Fig. 4.5 shows, the elasticity coefficient of education human capital was generally on an upward trend, and there was no obvious upward or downward trend of the elasticity coefficients of labor and capital. From 1990 to 2014 (except in a few years), the elasticity coefficient of capital was the largest, followed by that of education human capital, and that of labor was the smallest. As Fig. 4.6 shows, the output elasticity coefficients of education human capital in the three regions increased with fluctuation from 1990 to 2010, but decreased considerably after 2010. In most of these years, the Central Region was the greatest in the output elasticity coefficient of education human capital, followed by the Eastern Region, and the Western Region was the smallest. We put the figures in Table 4.4 into Formula (4.4) and calculate the contribution rate of each factor to the output growth (see Table 4.5 for the results).

118

Y. LI

Table 4.4 Average annual growth rates and output elasticity of factors Year

GY

Eastern Region 1990 1991 10.94 1992 18.46 1993 16.59 1994 8.28 1995 10.16 1996 10.67 1997 11.14 1998 11.17 1999 10.98 2000 14.56 2001 12.43 2002 12.64 2003 15.23 2004 18.52 2005 17.29 2006 16.83 2007 16.07 2008 14.16 2009 12.42 2010 16.29 2011 14.53 2012 8.91 2013 7.66 2014 7.43 Central Region 1990 1991 10.63 1992 15.26 1993 15.59 1994 8.88 1995 9.92 1996 11.90 1997 12.37 1998 10.69 1999 8.92 2000 12.24 2001 10.09 2002 12.00

GK

GL

16.60 29.88 28.83 21.21 13.73 14.59 6.36 11.67 5.57 6.90 10.69 13.51 27.16 19.71 20.10 18.08 17.27 12.48 28.03 17.06 5.35 15.97 18.64 11.89

2.64 2.23 1.13 1.88 1.17 4.58 0.31 −0.02 0.22 2.31 1.10 1.37 2.26 3.34 2.96 2.18 3.27 1.96 2.55 1.52 2.64 1.50 1.51 0.99

9.48 17.27 16.35 16.36 14.33 19.04 8.31 15.89 8.96 8.53 14.95 15.69

2.14 2.18 1.60 2.79 1.85 3.13 2.04 0.49 0.84 1.77 0.04 0.81

GH

α

β

γ

−21.24 8.76 1.96 3.18 1.73 −0.35 13.38 −5.21 6.01 1.54 0.61 40.55 −28.07 1.37 0.58 0.43 22.76 −13.80 −0.36 0.53 0.19

0.69 0.29 0.25 0.76 0.05 0.40 0.59 0.63 0.77 0.82 0.82 0.79 0.83 0.79 0.84 0.72 0.62 0.46 0.34 0.19 0.20 0.20 0.15 0.11 0.35

0.28 0.02 0.09 0.19 0.32 0.69 0.49 0.46 0.31 0.23 0.22 0.24 0.19 0.22 0.14 0.29 0.40 0.60 0.74 0.93 0.88 0.91 0.96 0.99 0.81

0.03 1.84 1.65 0.19 0.42 4.62 0.51 0.69 0.26 0.18 0.49 0.54 0.15 0.77 0.26 1.23 1.63 2.27 2.85 3.57 3.95 3.87 3.70 3.56 3.41

−13.69 5.29 4.44 3.86 1.11 −1.73 5.29 2.50 1.83

0.84 1.05 0.89 0.51 0.52 0.24 0.49 0.54 0.51 0.53 0.23 0.76 0.87

0.38 0.45 0.40 0.12 0.04 0.74 0.44 0.37 0.39 0.34 0.57 0.18 0.16

2.80 3.55 3.19 3.53 3.26 1.08 1.65 1.70 1.45 0.74 2.71 0.34 0.89

(continued)

4

THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

119

Table 4.4 (continued) Year

GY

2003 15.73 2004 16.18 2005 16.04 2006 14.97 2007 15.76 2008 12.61 2009 9.78 2010 18.01 2011 14.76 2012 8.06 2013 6.89 2014 6.20 Western Region 1990 1991 10.98 1992 11.99 1993 10.37 1994 5.78 1995 8.00 1996 10.48 1997 11.31 1998 12.06 1999 10.68 2000 11.52 2001 11.49 2002 13.69 2003 13.20 2004 16.15 2005 17.11 2006 16.17 2007 16.55 2008 14.52 2009 10.36 2010 17.36 2011 16.08 2012 11.25 2013 10.02 2014 9.26

GK

GL

23.04 21.37 28.05 31.10 25.80 19.94 40.47 22.09 3.84 22.67 19.18 12.39

0.94 1.08 2.12 1.34 1.38 1.20 1.31 1.65 1.35 1.19 1.37 0.77

14.76 15.89 18.58 9.48 15.95 13.81 14.19 20.66 13.95 13.65 18.32 19.66 24.82 16.08 21.41 21.14 21.00 15.59 37.69 19.41 15.16 25.57 26.50 15.02

3.46 1.96 −3.47 1.54 1.52 2.96 1.10 0.33 0.26 0.66 0.17 0.67 0.14 1.33 0.61 1.21 −1.05 1.13 1.32 1.05 1.69 0.39 1.77 1.69

GH

α

β

γ

3.68 0.09 27.44 −28.01 1.32 −0.46 −0.66 16.74 −14.06 −1.02 0.93 0.43

0.60 0.22 0.03 0.16 0.36 0.20 0.28 0.28 0.64 0.94 1.47 0.25

0.34 0.56 0.76 0.65 0.53 0.64 0.63 0.65 0.35 0.20 0.23 0.67

1.84 2.82 3.21 3.00 2.74 3.31 3.33 4.24 3.97 2.86 1.36 1.06

21.23 −24.06 1.38 2.27 2.14 −1.22 5.60 1.92 5.18 2.32 1.94 25.42 −27.16 1.70 −0.03 −0.10 16.63 −12.94 −1.18 0.14 0.83

0.62 0.77 0.90 0.83 0.92 0.85 0.75 0.77 0.73 0.85 0.85 0.82 0.88 0.89 0.84 0.75 0.76 0.73 0.77 0.81 0.75 1.01 1.13 0.17 1.34

0.40 0.32 0.24 0.25 0.14 0.18 0.31 0.30 0.30 0.24 0.24 0.26 0.24 0.25 0.26 0.29 0.27 0.27 0.22 0.17 0.23 0.05 0.02 0.75 0.14

0.26 0.15 0.06 0.26 0.29 0.72 0.37 0.41 0.44 0.21 0.31 0.47 0.29 0.23 0.30 0.40 0.49 0.66 0.64 0.47 0.72 0.17 0.26 1.22 0.44

Note GY, GK, GL, and GH respectively stand for the average annual growth rates of the total output (GDP), physical capital, simple labor, and human capital

120

Y. LI

Fig. 4.3 Elasticity coefficients of capital, labor, and education human capital in the Eastern Region, 1990–2014

Fig. 4.4 Elasticity coefficients of capital, labor, and education human capital in the Central Region, 1990–2014

4

THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

121

Fig. 4.5 Elasticity coefficients of capital, labor, and education human capital in the Western Region, 1990–2014

Fig. 4.6 Output elasticity coefficient of education human capital in the three regions, 1990–2014

It should be noted that in Table 4.5 the contribution rate of human capital experienced substantial fluctuation in 1994, 1995, 2000, 2005, 2006, 2010, and 2011. This is obviously abnormal. It is probably because the statistics in 1995, 2000, 2005, and 2010 were census figures, while

122

Y. LI

Table 4.5 Contribution rates of factors to the output growth (%) Year

Contribution of physical capital

Eastern Region 1991 4.40 1992 6.83 1993 20.03 1994 0.97 1995 5.02 1996 7.87 1997 3.66 1998 8.21 1999 4.18 2000 5.17 2001 7.72 2002 10.25 2003 19.61 2004 15.13 2005 13.23 2006 10.25 2007 7.26 2008 3.88 2009 4.87 2010 3.12 2011 0.98 2012 2.19 2013 1.87 2014 3.81 Central Region 1991 9.37 1992 14.46 1993 7.85 1994 8.00 1995 3.24 1996 8.77 1997 4.22 1998 7.62 1999 4.47 2000 1.85 2001 10.69 2002 12.84

Contribution of labor

Contribution of human capital

Contribution of comprehensive factors

0.05 0.18 0.20 0.55 0.74 2.05 0.13 −0.01 0.05 0.46 0.24 0.24 0.45 0.43 0.78 0.80 1.79 1.33 2.17 1.22 2.20 1.32 1.37 0.73

−8.15 36.99 0.91 2.00 0.41 −0.06 5.99 −2.57 0.82 1.09 0.14 45.59 −41.83 2.85 1.50 1.41 82.19 −48.80 −1.21 1.72 0.60

106.64 57.25 89.17 94.20 91.38 95.84 88.38 94.61 88.69 78.85 84.29 40.40 130.79 88.09 93.29 91.56 13.47 145.63 97.70 95.04 94.86

0.91 0.82 0.18 0.10 1.29 1.30 0.71 0.18 0.27 0.95 0.01 0.12

−41.98 5.37 6.89 6.17 1.51 −1.20 13.49 0.80 1.53

133.88 90.10 83.04 88.90 90.68 96.47 83.72 88.50 85.50

(continued)

4

THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

123

Table 4.5 (continued) Year

Contribution of physical capital

2003 13.00 2004 4.42 2005 0.79 2006 4.68 2007 8.74 2008 3.75 2009 10.66 2010 5.82 2011 2.31 2012 20.05 2013 26.52 2014 2.91 Western Region 1991 10.35 1992 13.02 1993 14.04 1994 7.94 1995 12.34 1996 9.43 1997 9.95 1998 13.73 1999 10.80 2000 10.57 2001 13.68 2002 15.76 2003 20.12 2004 12.30 2005 14.62 2006 14.63 2007 13.96 2008 10.94 2009 27.80 2010 13.26 2011 13.95 2012 26.32 2013 4.10 2014 18.33

Contribution of labor

Contribution of human capital

Contribution of comprehensive factors

0.30 0.57 1.52 0.82 0.69 0.72 0.78 1.01 0.44 0.22 0.30 0.49

6.37 0.24 82.86 −79.05 3.40 −1.43 −2.07 66.77 −52.51 −2.74 1.19 0.43

80.33 94.77 14.83 173.55 87.17 96.96 90.63 26.40 149.76 82.47 72.00 96.17

1.01 0.43 −0.79 0.20 0.25 0.84 0.30 0.09 0.06 0.14 0.04 0.15 0.03 0.31 0.16 0.30 −0.26 0.23 0.20 0.22 0.08 0.01 1.21 0.22

5.61 −15.78 0.47 0.85 0.86 −0.23 1.58 0.82 1.37 0.49 0.53 9.26 −12.12 1.02 −0.02 −0.04 10.90 −2.00 −0.28 0.16 0.33

86.25 103.18 89.26 88.90 85.32 89.37 87.70 85.46 82.73 79.36 86.85 75.96 97.19 85.27 88.86 72.03 75.62 87.98 73.96 94.53 81.13

124

Y. LI

those in the other years were sampled data. Therefore, the contribution rate of human capital changed significantly in these years and in the previous year and the following year. Accordingly, we will delete the data of these special years in Figs. 4.7 through 4.10. As Fig. 4.7 reflects, the contribution rate of physical capital was quite great in the Eastern Region before 2010, much greater than that of labor and human capital; and the contribution rate of labor was stable before 2007 (except in 1996), but it increased slightly thereafter. As Fig. 4.8 reflects, the contribution rate of physical capital was quite great in the Central Region, much greater than that of labor and human capital. If we compare Figs. 4.7 and 4.8, we can find that the Central Region was lower than the Eastern Region in this regard. Meanwhile, as shown in Fig. 4.8, the contribution rate of labor was stable in the Central Region, and that of human capital went through some fluctuation in this period. As Fig. 4.9 reflects, the contribution rate of physical capital was quite great in the Western Region, much greater than that of labor and human capital. On the other hand, the contribution rates of labor and human capital were relatively stable.

Fig. 4.7 Contribution rates of factors in the Eastern Region, 1990–2014

4

THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

125

Fig. 4.8 Contribution rates of factors in the Central Region, 1990–2014

Fig. 4.9 Contribution rates of factors in the Western Region, 1990–2014

As Fig. 4.10 reflects, the Central Region was higher than the Eastern and Western Regions in the contribution rate of education human capital before 2007 (except in 1999 and 2001). After 2007 (except in 2012), the Eastern Region was higher than the Central and Western Regions in this regard. There were substantial fluctuations in the contribution rate

126

Y. LI

Fig. 4.10 Contribution rates of education human capital in the three regions, 1996–2014

of education human capital of the Eastern and Central Regions, while that of the Western Region was relatively stable. Generally speaking, the Western Region was lower than the Eastern and Central Regions in this contribution rate and must take measures to improve it. 4.2.2

Contribution Rates of Different Levels of Education Human Capital Stocks to Regional Output Growth

We mark the growth rates of higher education human capital stocks (HC), high school education human capital stocks (HM), middle school education human capital stocks (HJ), primary education human capital stocks (HP), and illiterate and semi-illiterate human capital stocks (HI) as GHC, GHM, GHJ, GHP, and GHI, respectively, and their proportions in the total human capital stocks as RHC, RHM, RHJ, RHP, and RHI, respectively. Based on relevant statistics, we calculate the figures of the Eastern, Central, and Western Regions and get the results as in Tables 4.6, 4.7 and 4.8. To ensure the comparability of the data, we have deleted the data of 1994 and 1995 when drawing the figures, as their sources are different from those of 1996–2014. As shown in Fig. 4.11, the proportion of illiterate and semi-illiterate human capital stocks in the total human capital stocks of the Eastern Region was small (below 2.5%) and decreased between 1996 and 2014.

−16.90 5.78 6.28 0.52 3.41 1.49 15.86 −5.91 5.60 −1.10 3.12 42.13 −29.43 2.78 0.67 0.35 18.75 −14.62 −3.27 −0.81 −1.40 3.61 −26.50 4.54 14.60 3.63 −0.52 18.69 −5.31 9.25 5.46 −0.99 36.52 −28.47 1.62 2.93 −0.89 26.49 −12.83 1.56 1.20 2.02

6.46 −35.05 −10.09 29.97 6.84 9.95 25.36 2.36 22.96 15.28 3.09 38.22 −19.90 5.18 1.67 9.29 44.26 −10.20 6.61 4.66 0.77

−21.24 8.76 1.96 3.18 1.73 −0.35 13.38 −5.21 6.01 1.54 0.61 40.55 −28.07 1.37 0.58 0.43 22.76 −13.80 −0.36 0.53 0.19 2.22 2.07 2.06 1.87 1.78 1.62 1.04 1.16 1.09 1.12 1.06 1.04 0.86 0.78 0.73 0.66 0.44 0.51 0.48 0.44 0.49

RHI 20.36 34.05 32.68 30.57 29.27 27.90 25.76 25.24 23.27 22.01 21.04 21.40 21.03 19.91 19.27 18.30 16.37 15.63 15.03 14.55 14.59

RHP 41.81 40.67 42.39 41.30 41.98 42.75 43.69 43.37 43.20 42.08 43.13 43.61 42.79 43.38 43.42 43.39 41.97 41.57 40.35 39.82 39.19

RHJ

24.79 16.75 17.18 19.08 19.43 19.40 20.31 20.29 20.91 21.72 21.37 20.76 20.64 20.69 21.18 20.90 21.53 21.78 22.20 22.34 22.75

RHM

10.83 6.46 5.70 7.18 7.54 8.32 9.20 9.94 11.52 13.08 13.41 13.18 14.68 15.23 15.40 16.76 19.69 20.51 21.95 22.85 22.98

RHC

−48.87 81.89 −2.15 −3.46 −2.62 −5.00 4.67 −7.11 −2.25 −3.98 −3.82 42.98 −29.34 −4.01 −2.63 −4.66 9.81 −17.66 −4.22 −2.70 0.51

GH

−19.32 1.36 1.26 −6.07 −3.36 −9.00 −27.21 5.79 −0.49 3.70 −4.61 38.24 −40.67 −7.90 −6.32 −8.63 −18.56 0.00 −6.29 −6.45 10.76

GHC

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

GHM

GHP

GHI

Year

GHJ

Growth rates and proportions of different levels of education human capital stocks in the Eastern Region

Table 4.6 (%)

4 THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

127

−5.45 37.81 6.83 4.47 3.28 1.67 5.38 2.93 3.93 4.22 1.84 26.83 −29.05 1.84 0.03 0.99 11.32 −14.88 −2.85 −2.54 −1.07 4.88 27.46 7.22 11.83 2.49 −3.74 12.10 9.38 −4.17 13.74 −1.08 19.75 −25.98 6.06 1.92 −1.08 18.89 −9.90 1.26 5.69 −2.14

−4.40 68.04 6.64 29.16 −3.12 5.71 26.86 21.49 0.46 18.77 2.92 24.73 −18.85 7.49 0.04 2.55 49.51 −14.81 5.00 9.07 6.08

−13.69 52.95 4.44 3.86 1.11 −1.73 5.29 2.50 1.83 3.68 0.09 27.44 −28.01 1.32 −0.46 −0.66 16.74 −14.06 −1.02 0.93 0.43 2.66 2.10 1.89 1.60 1.56 1.60 1.06 1.05 1.14 0.97 0.94 1.19 0.99 0.91 0.84 0.80 0.54 0.57 0.55 0.49 0.52

RHI 29.00 36.78 35.54 33.24 32.37 30.78 28.98 26.27 26.51 23.47 22.66 24.03 23.17 21.47 20.78 19.76 18.51 18.04 17.47 16.69 16.92

RHP 45.59 41.07 42.01 42.26 43.17 44.67 44.70 44.89 45.82 46.05 46.86 46.63 45.96 46.19 46.42 47.19 45.00 44.57 43.74 42.24 41.61

RHJ

18.67 15.56 15.97 17.20 17.43 17.07 18.18 19.40 18.25 20.03 19.79 18.60 19.12 20.01 20.49 20.41 20.78 21.79 22.29 23.34 22.74

RHM

4.09 4.49 4.59 5.70 5.46 5.88 7.08 8.40 8.28 9.49 9.75 9.55 10.76 11.42 11.47 11.84 15.17 15.04 15.95 17.24 18.21

RHC

−32.00 93.99 0.94 −2.86 −1.55 −6.56 −0.86 −7.10 2.78 −8.20 −3.37 35.18 −30.60 −6.08 −3.68 −5.54 9.36 −16.25 −4.13 −3.59 1.84

GH

−9.10 20.75 −6.16 −12.35 −0.81 0.78 −30.58 1.62 10.43 −12.02 −2.89 61.80 −39.89 −7.46 −7.79 −5.50 −20.72 −9.36 −4.88 −9.42 4.99

GHC

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

GHM

GHP

GHI

Year

GHJ

Growth rates and proportions of different levels of education human capital stocks in the Central Region

Table 4.7 (%)

128 Y. LI

104.32 −55.54 5.25 1.72 4.54 1.58 4.90 3.09 9.20 1.02 4.18 22.03 −25.32 4.61 2.49 −0.09 9.36 −14.67 −0.68 −0.52 −0.65

7.99 3.77 −5.29 2.44 4.19 −1.69 11.61 4.87 8.26 3.44 2.63 12.85 −24.29 3.30 −3.17 1.03 26.27 −11.41 5.61 −2.27 6.49

19.92 0.92 19.98 4.57 2.30 7.87 29.37 25.70 5.29 17.48 10.23 20.85 −26.85 2.05 2.47 8.98 58.52 −0.71 −4.57 6.48 1.37

21.30 −24.06 1.38 2.27 2.14 −1.22 5.60 1.92 5.18 2.32 1.94 25.42 −27.16 1.70 −0.03 −0.10 16.63 −12.94 −1.18 0.14 0.83

3.10 2.85 2.99 2.65 2.51 2.42 1.78 1.84 1.83 1.66 1.48 1.89 1.55 1.35 1.25 1.19 0.81 0.86 0.82 0.80 0.85

RHI 21.45 41.28 40.08 40.49 39.45 38.08 36.84 34.26 32.29 31.52 29.92 32.76 31.44 30.19 29.52 28.44 26.01 24.25 23.45 23.12 22.48

RHP 60.31 35.31 36.65 36.45 37.31 38.37 38.12 38.56 40.03 39.52 40.39 39.30 40.29 41.44 42.49 42.49 39.84 39.05 39.25 38.99 38.42

RHJ

11.96 16.34 15.26 15.29 15.60 15.52 16.41 16.88 17.37 17.57 17.68 15.91 16.54 16.80 16.27 16.46 17.82 18.13 19.38 18.91 19.97

RHM

3.19 4.23 5.01 5.12 5.13 5.60 6.86 8.47 8.47 9.73 10.52 10.14 10.18 10.22 10.47 11.42 15.53 17.71 17.10 18.18 18.28

RHC

−39.60 46.16 −1.55 3.30 −0.49 −4.64 2.14 −5.22 −0.86 −0.11 −3.23 37.31 −30.10 −2.33 −2.25 −3.77 6.67 −18.82 −4.44 −1.26 −1.99

GH

−15.84 −30.27 6.46 −9.54 −3.14 −4.69 −22.47 5.43 4.83 −7.26 −9.00 60.08 −40.22 −11.31 −7.59 −4.93 −20.81 −7.32 −5.44 −3.34 8.14

GHC

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

GHM

GHP

GHI

Year

GHJ

Growth rates and proportions of different levels of education human capital stocks in the Western Region

Table 4.8 (%)

4 THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

129

130

Y. LI

Over this period, the proportion of the human capital stocks of primary education in the total human capital stocks was on the decline, dropping from nearly 35% to about 15%. From 1996 to 2009, the proportion of the human capital stocks of middle school education in the total human capital stocks increased with fluctuation, but decreased between 2009 and 2014 and stabilized around 40%. From 1996 to 2014, the proportions of the human capital stocks of high school education and higher education in the total human capital stocks were both on the rise, but the latter increased faster than the former—the former rose from 17.18% in 1996 to 22.75% in 2014, while the latter jumped from 5.70% to 22.98% over that same period. As shown in Fig. 4.12, the proportion of illiterate and semi-illiterate human capital stocks in the total human capital stocks of the Central Region was small (below 2.7%) and decreased from 1996 to 2014. Over this period, the proportion of the human capital stocks of primary education in the total human capital stocks was on the decline, dropping from 35.54% in 1996 to 16.92% in 2014. From 1996 to 2009, the proportion of the human capital stocks of middle school education in the total human capital stocks increased with fluctuation, but decreased from 2009 to 2014 and stabilized around 40%. From 1996 to 2014, the proportions of the human capital stocks of high school education and higher education in the total human capital stocks were both on the rise, but the latter

Fig. 4.11 Proportions of different levels of education in total human capital stocks of the Eastern Region, 1994–2014

4

THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

131

increased faster than the former—the former rose from 15.97% in 1996 to 22.74% in 2014, while the latter increased from 4.59% to 18.21% over that same period. As shown in Fig. 4.13, the proportion of illiterate and semi-illiterate human capital stocks in the total human capital stocks of the Western Region was small (below 3.00%) and decreased between 1996 and 2014. Over this period, the proportion of the human capital stocks of primary education in the total human capital stocks was on the decline, dropping from 40.08% in 1996 to 22.48% in 2014. From 1996 to 2009, the proportion of the human capital stocks of middle school education in the total human capital stocks increased with fluctuation, but decreased from 2009 to 2014 and stabilized around 40%. From 1996 to 2014, the proportions of the human capital stocks of high school education and higher education in the total human capital stocks were both on the rise, but the latter increased faster than the former—the former rose from 15.26% in 1996 to 19.97% in 2014, while the latter increased from 5.01% to 18.28% over that same period. Hence, we come to the following conclusions. The contribution rate of a certain level of education to the growth of human capital stocks (H) = (the average annual growth rate of a certain level of human capital stocks × the proportion of a certain level of human

Fig. 4.12 Proportions of different levels of education in total human capital stocks of the Central Region, 1994–2014

132

Y. LI

Fig. 4.13 Proportions of different levels of education in total human capital stocks of the Western Region, 1994–2014

capital stocks in the total human capital stocks)/the growth rate of the total human capital stocks (GH). The contribution rate of a certain level of education to GDP growth = (the contribution rate of a certain level of education to the growth of H/the sum of the contribution rate of a certain level of education to the growth of H) × the contribution rate of human capital to output growth. Using the statistics in Tables 4.6 through 4.8, we calculate the contribution rates of different levels of education human capital stocks to the total human capital stocks and to the output of the Eastern, Central, and Western Regions (see Tables 4.9 through 4.11 for the results). In Table 4.9, the contribution rate of human capital experienced substantial fluctuation in 1994, 1995, 2000, 2005, 2006, 2010, and 2011, which is obviously abnormal. It was probably because the statistics in 1995, 2000, 2005, and 2010 were census figures, while those in the other years were sampled data. Therefore, the contribution rate of human capital changed significantly in these years and in the previous and following years. Accordingly, we will delete the data of these special years in Figs. 4.14 through 4.19. As shown in Fig. 4.14, from 1996 to 2014, the contribution rate of illiteracy and semi-illiteracy to the growth of human capital stocks (H) in the Eastern Region was basically 0 and did not change much. The contribution rate of primary education to the growth of human capital stocks

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Year

0.39 0.07 0.27 −0.79 −0.73 −0.97 −0.66 −0.23 −0.02 0.52 −1.69 0.20 0.30 −0.99 −1.71 −2.91 −0.11 0.00 1.79 −1.17 4.95

HI

14.43 38.08 −7.48 −7.12 −9.24 −9.79 1.95 7.02 −1.89 −12.01 −27.62 4.46 4.47 −12.28 −18.16 −41.68 1.58 4.19 36.96 −15.38 7.74

HP 6.30 5.52 26.11 1.38 16.29 15.76 10.14 9.91 8.08 −6.18 43.08 8.96 9.14 17.31 10.06 6.97 7.15 8.89 76.23 −12.34 −57.6

HJ

HC −0.49 −8.67 −6.67 10.75 5.68 7.10 3.15 −0.83 7.59 22.85 13.25 2.53 1.87 11.07 8.82 66.4 6.52 2.91 −75.93 38.78 18.22

HM −0.64 −15.01 7.77 15.78 8.00 7.90 5.42 4.14 6.24 14.82 −7.03 3.85 4.21 4.88 21.00 −8.78 4.86 4.00 −19.05 10.11 46.69

Contribution rates of different levels of education to H growth

−0.16 0.13 0.01 −0.08 −0.01 0.00 −0.20 0.03 0.00 0.03 −0.01 0.45 −0.63 −0.14 −0.13 −0.21 −0.44 0.00 −0.11 −0.10 0.15

HI −5.88 70.43 −0.34 −0.71 −0.19 0.03 0.58 −0.90 −0.08 −0.65 −0.19 10.17 −9.36 −1.75 −1.36 −2.94 6.48 −10.23 −2.24 −1.32 0.23

HP −2.57 10.22 1.19 0.14 0.33 −0.05 3.04 −1.27 0.33 −0.34 0.30 20.43 −19.12 2.47 0.75 0.49 29.38 −21.7 −4.61 −1.06 −1.73

HJ

0.26 −27.75 0.35 1.58 0.16 −0.02 1.62 −0.53 0.26 0.81 −0.05 8.78 −8.81 0.70 1.57 −0.62 19.99 −9.77 1.15 0.87 1.40

HM

HC 0.20 −16.03 −0.3 1.08 0.12 −0.02 0.94 0.11 0.31 1.25 0.09 5.76 −3.91 1.58 0.66 4.68 26.78 −7.11 4.59 3.33 0.55

Contribution rates of different levels of education to GDP growth

Table 4.9 Contribution rates of different levels of education human capital stocks to output growth of the Eastern Region (%)

4 THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

133

134

Y. LI

Fig. 4.14 Contribution rates of different levels of education to growth of human capital stocks (H) in the Eastern Region, 1996–2014

(H) was negative in most years, but was positive in 2001, 2012, and 2014. The contribution rate of middle school education to the growth of human capital stocks (H) was positive and stabilized at around 10% in most years and, but was negative in 2003, 2013, and 2014. The contribution rate of high school education to the growth of human capital stocks (H) was positive in most years, but was negative in 2004, 2009, and 2012. The contribution rate of higher education to the growth of human capital stocks (H) was positive in most years, but was negative in 1996, 2001, and 2012. As Fig. 4.15 reflects, from 1996 to 2014, the contribution rate of illiteracy and semi-illiteracy to GDP growth in the Eastern Region was basically 0 and did not change much. The contribution rate of primary education to GDP growth was negative in most years and on a downward trend over this period, but was positive in 1999, 2000, and 2014. The contribution rate of middle school education to GDP growth was positive in most years and on a downward trend over this period, but was negative in 1999, 2001, 2003, 2012, 2013, and 2014. The contribution rate of high school education to GDP growth was positive in most years, but was negative in 1999, 2001, 2004, and 2009. The contribution rate of higher education to GDP growth was positive in most years, but was negative in 1999. In general, higher education and high school education made greater contribution to GDP growth (Table 4.10).

4

THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

135

Fig. 4.15 Contribution rates of different levels of education to GDP growth in the Eastern Region, 1996–2014

As shown in Fig. 4.16, from 1996 to 2014, the contribution rate of illiteracy and semi-illiteracy to the growth of human capital stocks (H) in the Central Region fluctuated around 0 and did not change much. The contribution rate of primary education to the growth of human capital stocks (H) was positive in most years, but was negative in 1997, 1998, 2001, 2003, 2004, 2007, and 2013. The contribution rate of middle school education to the growth of human capital stocks (H) was positive in most years and on a downward trend, but was negative in 1999, 2008, 2009, 2013, and 2014. The contribution rate of high school education to the growth of human capital stocks (H) was positive in most years, but was negative in 2002, 2008, 2012, and 2014. The contribution rate of higher education to the growth of human capital stocks (H) was positive in most years and on an upward trend, but was negative in 1998, 1999, 2008, 2009, and 2012. As Fig. 4.17 reflects, from 1996 to 2014, the contribution rate of illiteracy and semi-illiteracy to GDP growth in the Central Region was basically 0 and did not change much. The contribution rate of primary education to GDP growth was negative in most years and on a downward trend over this period, but was positive in 2002. The contribution rate of middle school education to GDP growth was positive in most years and was on a downward trend over this period, but was negative in 2012, 2013, and 2014. The contribution rate of high school education to GDP

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Year

0.34 0.21 −0.6 −1.2 −0.2 −0.1 −1.9 0.14 1.19 −0.7 −0.6 0.42 0.34 −1.1 3.10 1.39 −0.2 0.07 0.55 −1.1 1.15

HI

17.21 10.29 1.56 −5.27 −9.31 24.59 −1.00 −16.49 7.96 −11.80 −7.20 5.81 5.25 −21.28 34.63 34.60 2.21 4.28 14.67 −13.56 14.31

HP 3.31 6.51 12.62 9.75 25.04 −8.37 9.08 10.49 19.26 10.49 15.20 9.16 9.67 12.74 −0.70 −13.82 6.38 9.53 24.97 −24.00 −21.01

HJ

HC 0.24 1.05 1.34 6.94 −3.21 −3.62 5.97 12.20 0.42 8.44 6.00 1.76 1.29 12.17 −0.19 −8.81 7.00 3.20 −14.80 31.26 48.76

HM −1.10 1.94 5.06 9.80 7.72 7.54 7.81 13.66 −8.82 13.62 6.60 2.85 3.45 17.49 −16.84 6.64 4.60 2.93 −5.39 27.41 −23.20

−0.71 0.06 −0.20 −0.37 −0.02 0.01 −1.25 0.01 0.09 −0.24 −0.01 1.75 −1.34 −0.19 −0.22 −0.14 −0.66 −0.19 −0.08 −0.07 0.02

HI −36.12 2.76 0.54 −1.63 −0.70 −1.48 −0.68 −0.66 0.61 −3.76 −0.09 24.07 −20.76 −3.62 −2.48 −3.58 7.37 −11.23 −2.01 −0.81 0.31

HP −6.95 1.75 4.35 3.01 1.89 0.50 6.12 0.42 1.47 3.34 0.18 37.96 −38.23 2.17 0.05 1.43 21.30 −25.01 −3.42 −1.43 −0.45

HJ

2.30 0.52 1.74 3.02 0.58 −0.45 5.27 0.55 −0.68 4.34 0.08 11.80 −13.64 2.97 1.20 −0.69 15.37 −7.68 0.74 1.63 −0.50

HM

Contribution rates of different levels of education to GDP growth

−0.50 0.28 0.46 2.14 −0.24 0.22 4.03 0.49 0.03 2.69 0.07 7.28 −5.08 2.07 0.01 0.91 23.39 −8.39 2.03 1.86 1.05

HC

Contribution rates of different levels of education human capital stocks to output growth of the central

Contribution rates of different levels of education to H growth

Table 4.10 region (%)

136 Y. LI

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Year

HP

−16.01 −8.23 −9.29 11.67 −1.84 30.01 2.91 −20.07 −1.14 −0.30 −10.47 8.78 7.26 −8.58 −4.80 −5.19 2.28 7.56 18.24 −4.16 −11.15

HI

−0.66 0.78 2.67 −2.51 −0.78 1.93 −1.94 1.01 0.34 −1.15 −1.54 0.70 0.56 −2.06 −3.40 −3.18 −0.30 0.09 0.79 −3.88 1.57 35.07 27.85 26.90 5.54 15.52 −9.65 6.72 12.30 13.69 3.53 17.01 7.00 7.33 21.79 9.45 7.15 4.78 9.03 4.47 3.44 −6.19

HJ 1.01 −0.37 −12.56 3.29 6.00 4.33 6.44 8.34 5.38 5.16 4.76 1.79 2.85 6.40 6.40 3.22 5.20 3.14 −17.20 9.00 29.73

HM 0.60 −0.02 12.28 2.02 1.10 −6.62 5.88 18.42 1.73 12.76 10.24 1.73 2.00 2.45 12.35 18.00 8.04 0.17 13.71 15.60 6.03

HC −0.19 −0.62 0.06 −0.11 −0.03 −0.02 −0.15 0.04 0.02 −0.03 −0.04 0.32 −0.34 −0.11 0.00 0.01 −0.16 −0.01 −0.01 −0.03 0.03

HI −4.49 6.49 −0.22 0.50 −0.08 −0.35 0.23 −0.82 −0.08 −0.01 −0.28 4.07 −4.40 −0.44 0.00 0.01 1.24 −0.76 −0.26 −0.03 −0.18

HP 9.84 −21.97 0.63 0.24 0.67 0.11 0.53 0.50 0.94 0.09 0.45 3.24 −4.44 1.11 −0.01 −0.01 2.61 −0.90 −0.06 0.03 −0.10

HJ

0.28 0.30 −0.30 0.14 0.26 −0.05 0.51 0.34 0.37 0.13 0.13 0.83 −1.72 0.33 −0.01 −0.01 2.83 −0.31 0.24 0.07 0.49

HM

Contribution rates of different levels of education to GDP growth

0.17 0.02 0.29 0.09 0.05 0.08 0.46 0.76 0.12 0.31 0.27 0.80 −1.21 0.13 −0.01 −0.04 4.38 −0.02 −0.19 0.12 0.10

HC

Contribution rates of different levels of education human capital stocks to output growth of the Western

Contribution rates of different levels of education to H growth

Table 4.11 Region (%)

4 THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

137

138

Y. LI

Fig. 4.16 Contribution rates of different levels of education to growth of human capital stocks (H) in the Central Region, 1996–2014

growth was positive in most years, but was negative in 1999, 2002, 2009, and 2014. The contribution rate of higher education to GDP growth was positive in most years, but was negative in 1998. In general, higher education and high school education made greater contribution to GDP growth.

Fig. 4.17 Contribution rates of different levels of education to GDP growth in the Central Region, 1996–2014

4

THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

139

As shown in Fig. 4.18, the contribution rate of illiteracy and semiilliteracy to the growth of human capital stocks (H) in the Western Region fluctuated around 0 and did not change much from 1996 to 2014. The contribution rate of primary education to the growth of human capital stocks (H) was negative in most years and on a downward trend over this period, but was positive in 1997, 1999, and 2012. The contribution rate of middle school education to the growth of human capital stocks (H) was positive in most years and on a downward trend, but was negative in 1999 and 2014. The contribution rate of high school education to the growth of human capital stocks (H) was positive in most years and on an upward trend, but was negative in 1996 and 2012. The contribution rate of higher education to the growth of human capital stocks (H) was positive in most years and on an upward trend, but was negative in 1999. As Fig. 4.19 reflects, from 1996 to 2014, the contribution rate of illiteracy and semi-illiteracy to GDP growth in the Western Region was basically 0 and did not change much. The contribution rate of primary education to GDP growth was negative in most years, but was positive in 1997 and 2009. The contribution rate of middle school education to GDP growth was positive in most years and on a downward trend over this period, but was negative in 1996, 2008, 2009, and 2014. The contribution rate of high school education to GDP growth was positive in most years, but was negative in 1996, 1999, 2008, and 2009. The contribution

Fig. 4.18 Contribution rates of different levels of education to growth of human capital stocks (H) in the Western Region, 1996–2014

140

Y. LI

Fig. 4.19 Contribution rates of different levels of education to GDP growth in the Western Region, 1996–2014

rate of higher education to GDP growth was positive in most years, but was negative in 2008 and 2009. In general, higher education and high school education made greater contribution to GDP growth. The above results of empirical studies suggest that the three regions were consistent in terms of the contribution of different levels of education to GDP growth, that is, higher education and high school education contribute more to GDP growth.

References Alesina, Alberto and Dani Rodrik. “Distributive Politics and Economic Growth.” Quarterly Journal of Economics 109, no. 2 (1994): 465–90. Becker, G. S. Human Capital. 2nd ed. New York: NBER, 1975. Becker, G. S., and B. R. Chiswick. “Education and the Distribution of Earnings.” The American Economic Review 56, no. 1/2 (1966): 358–69. Benabou, R. “Inequality and Growth.” NBER Macroeconomics Annual 11 (1996): 11–74. Benhabib, J., and A. Rustichini. “Social Conflict and Growth.” Journal of Economic Growth 1, no. 1 (1996): 129–46. Castello, A., and Domenech R. “Human Capital Inequality and Economic Growth: Some New Evidence.” The Economic Journal 112, no. 2 (2002): 187–200.

4

THE RELATIONSHIP BETWEEN HUMAN CAPITAL …

141

De La Croix, David, and Matthias Doepke. “Inequality and Growth: Why Differential Fertility Matters.” American Economic Review 93, no. 4 (2004): 1091–113. Fishman, A., and A. Simhon. “The Division of Labor, Inequality, and Growth.” Journal of Economic Growth, no. 7 (2002): 117–36. Galor, O., and K. Tsiddon. “The Distribution of Human Capital and Economic Growth.” Journal of Economic Growth 2, no. 1 (1997): 14–93. Galor, Oded, and Joseph Zeira. “Income Distribution and Macroeconomics.” Review of Economic Studies 60, no. 1 (1993): 35–52. Glomm, G., and B. Ravikumar. “Public Versus Private Investment in Human Capital: Endogenous Growth and Income Inequality.” Journal of Political Economy 100, no. 4 (1992): 818–34. Psacharopoulos, G. “Unequal Access to Education and Income Distribution: An International Comparison.” De Economist, no. 125 (1977): 383–392. Persson, Torsten, and Guido Tabellini. “Is Inequality Harmful for Growth? Theory and Evidence.” American Economic Review 84, no. 3 (1994): 600–21. Saint-Paul, G., and T. Verdier. “Education, Democracy and Growth.” Journal of Development Economics 42, no. 2 (1993): 399–407. Schultz, T. W. “Investment in Human Capital.” The American Economic Review 51, no. 1 (1961): 1–17.

CHAPTER 5

The Relationship Between Factors of Economic Growth and Regional Economic Gaps in China

5.1

Theoretical Framework

Economic transformation is inevitable in the long-term economic development of a country. According to international experience, both developed and emerging industrialized countries have achieved sustained and rapid development in the process of economic transformation and upgrading. The theory and growth practice of these countries have proven that, in different stages of economic growth, different factors of economic growth have different effects on and make different contributions to growth. Many research results (Tang Jijun et al. 20071 ; Hu Wenguo and Wu Dong, 20042 ; Guo Hongzhen, 20043 ; Liu Wei et al. 20014 ;

1 Tang Jijun, Zhang Zongyi, and Fu Yunying, “China’s Economic Transformation and Growth,” Management World, no. 1 (2007). 2 Hu Wenguo and Wu Dong, “Theoretical and Empirical Analysis of the Factors of China’s Economic Growth,” Journal of Tsinghua University (Social Sciences Edition), no. 4 (2004). 3 Guo Hongzhen, “Contemplation on the Factors Contributing to China’s Economic Growth,” China Statistics, no. 4 (2004). 4 Liu Wei and Li Shaorong, “Changes in Ownership and the Promotion of Economic Growth and Factor Efficiency,” Economic Research Journal, no. 1 (2001).

© Social Sciences Academic Press 2023 Y. Li, Human Capital Investment and the Regional Economic Gap in China, https://doi.org/10.1007/978-981-99-4997-7_5

143

144

Y. LI

Liang Zhao, 20005 ; Cai Fang and Wang Dewen 19996 ; Shen Kunrong, 19997 ) have shown that, in light of the driving effect of growth factors on economic growth, China’s rapid economic growth between 1978 and 2003 benefited from large-scale investment and abundant low-cost labor, but the contribution rate of labor was on a downward trend. Now, with the decline of investment efficiency, the gradual disappearance of the demographic dividends, and the impact of the decline in export growth, China’s economic growth has slowed down and the Chinese economy has entered a new normal condition, under which the traditional growth model is no longer sustainable. How to carry out economic transformation and upgrading is a major strategic issue relating to the long-term sustainable growth of China’s economy and, as a hot topic, has attracted much attention. In recent years, many scholars (Lin Justin Yifu, 20148 ; He Chengying et al. 20129 ; Liu Shijin, 201110 ; Wei Jie, 200911 ; Zhang Yong et al. 201412 ) have studied China’s economic transformation from the perspectives of the economic structure adjustment and the modal transformation of economic growth, and made a good many valuable achievements. From the perspective of China’s economic restructuring, a popular view in the study of macroeconomic growth is that the economic growth rate depends on the demand intensity of consumption, investment, and exports—the three major drivers of the Chinese economy. What follows is the policy that stimulates growth—to 5 Liang Zhao, “Analysis of Major Factors of Sustained Growth of the National Economy,” Journal of World Economy, no. 7 (2000). 6 Cai Fang and Wang Dewen, “The Sustainability and Labor Contribution of China’s Economic Growth,” Economic Research Journal, no. 10 (1999). 7 Shen Kunrong, “Empirical Analysis of the Factors of China’s Economic Growth 1978– 1997,” Economic Science, no. 4 (1999). 8 Zhang Yong, Wang Xi, and Gu Mingming, “A Comparative of the Development Potentials of China and India,” Economic Research Journal, no. 5 (2009). 9 He Chengying, Xu Xiangyang, and Weng Yuanyuan, “The Internationalization of the Capital Market and the Modal Transformation of China’s Economic Growth,” Economic Perspectives, no. 9 (2012). 10 Liu Shijin, “The Slowdown of Growth Rate and the Transformation of Development Models,” Economic Perspectives, no. 5 (2011). 11 Wei Jie, “Several Controversial Questions on Sustaining China’s Economic Growth,” Economic Perspectives, no. 5 (2009). 12 Lin Justin Yifu, “On the Transformation of the Models of Economic Growth,” Scientific Development, no. 2 (2014).

5

THE RELATIONSHIP BETWEEN FACTORS OF ECONOMIC …

145

stimulate investment and exports in the aggregate demand. The longterm implementation of this policy has brought about many problems, such as overcapacity, the rising local debt, the excessive reliance on exports for economic growth, and excess liquidity, which have made the macro economy vulnerable and caused systemic risks to the economy. At the same time, due to the law of diminishing returns on investment, the effects of macro-fiscal and monetary policies are decreasing in recent years. Therefore, the final direction of the demand structure adjustment is to expand domestic demand, especially its consumption, to reverse the unbalanced demand structure. However, as the expansion of consumption is constrained by such problems as the lagging reform of the income distribution structure and the low proportion of residents’ income, it is difficult to achieve satisfactory results in the short term. In the 1970s, the theory of the “three major drivers” was challenged by “stagflation” in the west. At present, when economic transformation is underway, there have emerged some problems that are difficult for China to address. The reason is that the “three major drivers” theory is derived from Keynes’s short-term analysis framework and is suitable for discussing short-term economic problems, but not for analyzing the long-term trend of economic growth. In light of the modal transformation of China’s economic growth, existing literature mainly deals with the transformation from an extensive to an intensive model of economic growth based on the neoclassical growth theory and the endogenous growth theory, and provides us with many valuable research results for exploring the path of modal transformation for China’s economic growth and for really valuable and meaningful sustainable growth. Nonetheless, no matter the “three major drivers” theory, the neoclassical growth theory, or the endogenous growth theory, it can only explain the short-term growth in a certain stage of development in human history. It cannot answer within a unified theoretical framework how human history gradually took off from the Malthusian Trap and entered the long-term sustained economic growth. Yet the change in economic growth stages is a common phenomenon in the process of economic development of all countries, and a major concern is how to achieve long-term sustainable economic growth in this process. In order to apply economic growth theories to explain the endogenous transformation of economy in different stages of human social development, to better explain the growing income gap among different countries or regions, and to reasonably explain the population

146

Y. LI

transformation and large diversion following the endogenous transformation of economy, Galor and Weil (2000) combined—first ever—the Malthusian theory with modern economic growth theories and attempted to establish a unified theoretical framework of growth.13 According to Galor’s definition (2008), the word “unified” has two meanings: the unification between the macroeconomic model and the microscopic foundation, and the unification of different growth stages into one model in the process of long-term economic development.14 In view of these two criteria, the various theories of economic growth developed before the unified growth theory had different degrees of defects. According to the unified growth theory (Aoki, 2015), the influencing factors of economic growth are not the “three major drivers” in the Keynesian theory, but five important variables: the flow of labor force from less productive industries to highly productive ones, demographic dividends, capital investment, human capital investment, and the proportion of the working population in the total population.15 The modernization of a country goes through three growth stages. From low to high, they are the stages respectively dominated by agriculture, modern manufacturing and service industry, and human capital. In different growth stages, the main driving factors of economic growth are different, and they boost the leap of long-term economic growth from the lower to the higher stage. Of course, due to various historical reasons, the time and length of economic transformation in different countries and regions are different, and different stages may overlap with each other. Different from the demand theory centered on the “three major drivers,” the unified growth theory studies the impact of changes in the driving factors of economic growth on changes in economic growth stages from the perspective of supply, so as to achieve sustained economic growth. What it refers to as economic transformation is the changes in the economic growth stages. This provides a new way of thinking and perspective for the study of China’s economic transformation aimed at 13 O. Galor and D. Weil, “Population, Technology, and Growth: From Malthusian

Stagnation to the Demographic Transition and Beyond,” American Economic Review 90, no. 4 (2000): 806–28. 14 O. Galor, “Towards a Unified Theory of Economic Growth,” World Economics, no. 9 (2008): 97–151. 15 Masahiko Aoki, “Law-Based Rule of a Country: Rule of Law or Rule by Law?” http://yuanchuang.caijing.com.cn/2015/0717/3928144.shtml. Accessed 17 July 2015.

5

THE RELATIONSHIP BETWEEN FACTORS OF ECONOMIC …

147

achieving long-term sustainable growth. It is also the perspective of our study on economic transformation in this chapter. According to the unified growth theory, the five factors influencing economic growth are going through great changes in China. These changes seem to indicate that the Chinese economy is going through an important transition period—from an old to a new stage. However, there is so far no research—especially empirical evidence—on the mechanism of transformation of China’s economic growth stages in light of growth factors. Current research abroad shows that valuing the mechanism of the changes in the driving factors of economic growth on the transformation of economic growth stages is conducive to studying the long-term sustainable growth of a country’s economy from the perspective of supply. Based on an analysis of the effect of the changes in the driving factors of economic growth on the transformation of economic growth stages, this chapter will empirically test whether the conclusion that such changes affect the transformation of economic growth stages is tenable, and then put forward corresponding policy suggestions in combination with the characteristics of significant regional economic gaps in China’s economic transformation. This is of great practical significance for China’s economic transformation aimed at achieving long-term sustainable growth.

5.2 Indicators of the Eastern, Central, and Western Regions: A Comparison 5.2.1

Comparison of Regional Real GDP

In order to measure economic growth using GDP, we collect the provincial statistics in China Statistical Yearbook 2015 and convert them into real GDP (in trillion RMB) by eliminating the influence of price factors using the regional GDP index, with 2003 as the base year. (see Table 5.1 for the data). Figure 5.1 shows that the real GDP increased significantly in the three regions from 2003 to 2014; the Eastern Region was far greater than the other two in the real GDP and its growth rate; the gap between the Central and Western Regions in the real GDP was small, and their growth was relatively slow. This indicates that the gap between the rich and poor regions was widening.

148

Y. LI

Table 5.1 Comparison of Regional Real GDP (trillion RMB)

Year

Eastern

Central

Western

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

8.297161 8.669256 8.939652 10.215476 11.710819 13.046402 14.487252 16.307506 18.044185 19.724545 21.51249352 23.3630353

3.259037 3.551865 3.737806 4.232247 4.84068 5.456795 6.111056 6.951272 7.838118 8.69452 9.449057393 10.17076005

2.397521 2.574716 2.756569 3.127516 3.591416 4.058024 4.603477 5.259175 6.000995 6.752618 7.470499475 8.218410309

Source Provincial statistics in China Statistical Yearbook 2015, converted into real GDP with 2003 as the base year, with the influence of price factors eliminated using the regional GDP index

Fig. 5.1 Comparison of Regional Real GDP, 2003–2014

5.2.2

Comparison of Regional Fixed Assets Investment

We collect the provincial statistics in China Statistical Yearbook 2015 and convert them into real fixed assets investment (in trillion RMB) by eliminating the influence of price factors using the regional index of real fixed assets investment, with 2003 as the base year. (see Table 5.2 for the data).

5

THE RELATIONSHIP BETWEEN FACTORS OF ECONOMIC …

Table 5.2 Comparison of Regional Fixed Assets Investment (trillion RMB)

149

Year

Eastern

Central

Western

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

3.276711746 3.978637973 4.789935069 5.582759643 6.428046359 7.178649115 9.062073286 10.61685879 11.14642897 12.94480994 15.25449806 17.17124959

1.181073001 1.44333414 1.843395739 2.41806986 3.062269469 3.690741098 5.139239386 6.267015399 6.455131127 7.879742812 9.431984748 10.7622353

1.12556263 1.324114646 1.66911986 2.055342703 2.5495128 2.961106774 4.256972951 5.007579703 5.428962599 6.628465264 7.558249078 9.31939411

Source Provincial statistics in China Statistical Yearbook 2015, sorted and recalculated with 2003 as the base year

Figure 5.2 shows that the fixed assets investment increased significantly in the three regions from 2003 to 2014; the Eastern Region was far greater than the Central and Western Regions, and the Central Region was greater than the Western Region, in fixed assets investment and its growth rate.

Fig. 5.2 Comparison of Regional Fixed Assets Investment, 2003–2014

150

Y. LI

Table 5.3 Comparison of Regional Labor Force (million persons)

Year

Eastern

Central

Western

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

249.5333 257.4868 267.8869 294.5625 288.3646 297.211 308.3599 315.71 328.4383 335.5987 340.6576 343.9243

211.1356 214.8214 217.9074 237.8303 223.8451 227.3703 232.8669 239.759 254.9797 258.824 259.4968 261.4959

187.9601 190.7832 194.4802 198.6859 201.2979 204.9318 208.3851 212.876 205.2571 201.0744 209.982 212.7056

Source Employee numbers from China Statistical Yearbooks 2004– 2015 and regional statistical yearbooks, sorted and recalculated

5.2.3

Comparison of Regional Labor Force

We assume that the intensity and efficiency of labor are homogeneous across the three regions, and approximately express the labor input using the number of employees. We collect the employee numbers of 31 provincial-level regions between 2003 and 2014 from China Statistical Yearbooks and regional statistical yearbooks, and get the labor force (in million persons) of the three regions. (see Table 5.3 for the data). Figure 5.3 shows that the labor force increased significantly in the three regions from 2003 to 2014; the Eastern Region was greater than the Central and Western Regions, and the Central Region was greater than the Western Region, in labor force and its growth rate. 5.2.4

Comparison of Regional Education Human Capital

There are several commonly used approaches to measure education human capital, such as the “education cost approach,” the “future income approach,” and the “years of schooling approach.” The “average years of schooling” is deemed by the academia as the most reasonable indicator to measure education human capital at present. Using this indictor, we can calculate the average years of schooling of employees aged six and over nationwide, so as to reflect the human capital stocks in China (see Table 2.1 in Chapter 2).

5

THE RELATIONSHIP BETWEEN FACTORS OF ECONOMIC …

151

Fig. 5.3 Comparison of Regional Labor Force, 2003–2014

Figure 5.4 shows that education human capital was on the rise in the three regions from 2003 to 2014; the Eastern Region was greater than the other two in education human capital, and was faster than the Central Region in the growth rate of education human capital; the regional gaps in education human capital widened slightly in this period.

Fig. 5.4 Comparison of Regional Education Human Capital, 2003–2014 (Note Based on the statistics in Table 2.1)

152

Y. LI

5.2.5

Comparison of Regional Health Human Capital

Health is influenced by both innate and acquired factors. Considering the particularity and complexity of the formation of health human capital as well as the availability of statistics, existing literatures usually take mortality, life expectancy, nutritional intake, and public health input as indicators of health human capital. Some literatures use multiple indicators. It is not comprehensive to measure health human capital with a single indicator, but multi-indicator measurement will lead to difficulty in quantifying. After comparing and weighing the various indicators and considering the availability of statistics, we take public health input as the entry point and choose the total health expenditure as the indicator of health human capital. Our statistics come from Table 2.3. Figure 5.5 shows that the health human capital increased significantly in the three regions from 2003 to 2014; the Eastern Region was far greater than the other two in health human capital and its growth rate; and the regional gaps were widening in this period.

Fig. 5.5 Comparison of Regional Health Human Capital, 2003–2014 (Note Based on the statistics in Table 2.3)

5

5.3

THE RELATIONSHIP BETWEEN FACTORS OF ECONOMIC …

153

The Empirical Model and Description of Variables and Statistics 5.3.1

The Empirical Model

The Cobb–Douglas production function is widely used owing to its sound economic connotation and attractive mathematical and statistic features. Based on the theoretical logic described in the above literature review and the C-D aggregate production function, we establish a dynamic model including the total factor productivity, labor, physical capital, and the relationship between economic growth and human capital input. Considering the difficulty in quantifying, we take the total factor productivity as the parameter, which is a comprehensive indicator characterized by institutional innovation and technological progress. Compared with Solow’s neoclassical growth model, this model has taken into consideration the factors for long-term economic growth by adding the time variable t, which represents different years. On the other hand, it has separated human capital from the Solow residual as an endogenous variable. In terms of formation, human capital mainly takes shape through education and training, health investment, “learning by doing,” and market experience accumulation. Of these factors, education human capital has a significant impact on economic growth and interacts with health human capital. However, health human capital, different from education human capital in many ways, is also different from other forms of human capital in its role in driving economic growth (Strauss and Thomas, 1998). Meanwhile, it is difficult to quantify “learning by doing” and market experience, so we divide human capital into education human capital and health human capital, and construct the following function model: βt

φt

Yit = Ait K itαt L it E it Hitδt eu it

(Formula 5.1)

In Formula (5.1), Y stands for GDP, A for the total factor productivity, K for physical capital, L for labor, and E and H for education human capital and health human capital, respectively. The superscript α, β, φ, and δ are parameters to be estimated, representing the output elasticity coefficients of the four independent variables of physical capital, labor, education human capital, and health human capital (namely the economic growth rates when these factors increase by 1%), which reflect the relative contribution of these factors to economic growth or their

154

Y. LI

roles in driving economic growth. The output elasticity of a factor multiplied by its growth rate is the absolute contribution of the factor. μ is the random error, and i is the regional variable representing the three regions of China. We take the logarithm of both sides of Formula (5.5–5.1) and get the following dynamic linear equation: LnYit = Cit + αt Ln K it + βt Ln L it + φ Ln E it + δt Ln Hit + μit (Formula 5.2) In Formula (5.2), Cit is the intercept (Ln Ait ) we get by taking the logarithm of A in Formula (5.1), and μit is the random disturbance. The advantage of the logarithmic model is that when we take the logarithm of an indicator, it becomes the growth rate of the indicator, so LnYit is the growth rate of Y, and α Ln K it is the absolute contribution of capital K to economic growth. In an economic sense, the contribution of a factor to economic growth depends not only on the elasticity coefficient of the factor output, but also on the growth rate of the factor. Therefore, compared with the absolute contribution, the relative contribution of a factor can better reflect the dynamic effect of the factor on economic growth. For this reason, we will in this chapter use relative contribution, that is, the output elasticity coefficient of the factors, to analyze the dynamic effects of the factors on economic growth. 5.3.2

Description of Variables and Statistics

We choose the data of 31 provincial-level regions from 2003 to 2014, sort them according to China’s administrative divisions, and collate them into the panel data of the Eastern, Central, and Western Regions. Panel data has the dimensions of both time and space, which increases the variability between variables, reduces collinearity, and improves effectiveness. Therefore, it is suitable for the study of dynamic economy. Using the panel data, we can analyze not only the phased changes of the main driving factors of economic growth and the prominent characteristics of economic transformation in light of the changes of time series, but also the impact of the changes in the main factors of economic growth on regional economic gaps by comparing the cross-sections of the three regions. Finally, we will analyze, from the dimensions of time and space, China’s economic transformation under the background of regional economic gaps. The sources of the variables and data are as follows.

5

THE RELATIONSHIP BETWEEN FACTORS OF ECONOMIC …

155

5.3.2.1 The Explained Variables The explained variable of the model is economic growth. We measure economic growth with GDP 5.3.2.2 The Explanatory Variables (1) Physical capital. We measure physical capital stocks with fixed investment. (2) Labor. We assume that the intensity and efficiency of labor are homogeneous and use the number of employees to approximate labor input. (3) Education human capital. There are several commonly used methods to measure education human capital, such as the “education cost method,” the “future income method,” and the “years of schooling method.” The “average years of schooling” is the most reasonable indicator to measure education human capital. Therefore, we calculate the average years of schooling of China’s working population aged six and over, so as to reflect the country’s stocks of human capital (see Table 2.1 for the data). (4) Health human capital. Health is influenced by both innate and acquired factors. Considering the particularity and complexity of the formation of health human capital as well as the availability of statistics, existing literatures usually take mortality, life expectancy, nutritional intake, and public health input as common indicators to measure health human capital. Some literatures use multiple indicators. It is not comprehensive to measure health human capital with a single indicator, but multi-indicator measurement will lead to difficulty in quantifying. After comparing and weighing the various indicators and considering the availability of statistics, we take public health input as the entry point and choose the total health expenditure as the measurement indicator of health human capital (see Table 2.3 for the data). Besides, due to the difficulty in quantifying, we take as the parameter the comprehensive indicator of total factor productivity, which is characterized by institutional innovation and technological progress.

156

Y. LI

Table 5.4 Meanings and Basic Statistics of Variables in the Model Variable

Meaning

Y

Economic growth (trillion RMB) Physical capital (trillion RMB) Labor force (million persons) Education human capital (years) Health human capital (trillion RMB)

K L E H

5.4

Maximum

Minimum

Mean

Sample

Section

23.36

2.40

8.48

36

3

17.17

1.13

6.02

36

3

343.92

187.96

247.12

36

3

9.41

7.17

8.38

36

3

1.77

0.14

0.59

36

3

Metrological Analysis and Result Discussion 5.4.1

Metrological Analysis

5.4.1.1 Descriptive Statistical Analysis of Variables Table 5.4 shows the descriptive statistical results of the regression variables in the model. In Table 5.4, the maximums of Y, K, L, and H are the statistics of the Eastern Region in 2014, and the minimums are those of the Western Region in 2003; the maximum of E is the statistic of the Eastern Region in 2013, and the minimum of E is that of the Western Region in 2005. In view of the absolute data, China’s economic development between 2003 and 2014 had the following characteristics. One is the rapid development of the Chinese economy, which grew by 10.47% annually over these twelve years. The other is large regional economic gaps that were not significantly improved in this period. For instance, in 2003, the Eastern, Central, and Western Regions, respectively, accounted for 60.00%, 22.93%, and 17.06% in China’s GDP; in 2014, the figures were 55.96%, 24.36%, and 19.68%. Moreover, there were significant regional gaps in factor input. 5.4.1.2 Unit Root Tests If a time series is unstable, it will cause a correlation to be tested between two independent variables, giving rise to the phenomenon of “pseudo regression.” In this case, the empirical research results are of no economic significance. Therefore, before regression, the panel data needs to go

5

THE RELATIONSHIP BETWEEN FACTORS OF ECONOMIC …

157

through a unit root ADF test to judge the stability of the data. The ADF test is completed using the following three models. yt = δyt−1 +

p 

λ j yt− j + μt

(Model 5.1)

j=1

yt = α + δyt−1 +

p 

λ j yt− j + μt

(Model 5.2)

j=1

yt = α + βt + δyt−1 +

p 

λ j yt− j + μt

(Model 5.3)

j=1

Model 4 does not include constant or trend terms, Model 5 includes constant terms, and Model 6 includes both constant and trend terms. The null hypothesis of the three models is H0 :δ = 0, that is, there is one unit root. The ADF test starts with Model 6, followed by Model 5 and Model 4. Whenever the null hypothesis is rejected (i.e., the t value of the test is smaller than the critical value at a given significance level), the test will be stopped, and the original series is deemed stable. Otherwise, the test will continue until Models 5 and 4 are completed. We carry out the ADF test of the time series using the software EViews 6.0. The results are shown in Table 5.5. In Table 5.5, the prefix D before the variables means the first-order difference sequence. Take the stability test of lnY, for example. The t statistics of ADF-Fisher Chi-square and ADF-Choi Z-stat are 1.07750 and 2.14564, respectively, and both are greater than the significance level of 10%. Meanwhile, the P values of the test are greater than 0.1, so the original sequence has unit roots, but the LNY sequence is unstable. It is, therefore, necessary to test the stability of the first-order difference sequence DlnY. At this time, the t statistics of ADF-Fisher Chi-square and ADF-Choi Z-stat are both smaller than the significance level of 10%, and their P values are both smaller than 0.1, indicating that lnY is a stable sequence after the first-order difference. Likewise, we learn that lnK, lnL, lnE, and lnH are unstable sequences, but they turn stable after the firstorder difference.

158

Y. LI

Table 5.5 Results of the ADF Tests of the Variables Variable

Test method

t statistic

P value

Conclusion

lnY

ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat ADF-Fisher Chi-square ADF-Choi Z-stat

1.07750 2.14564 23.9309 −3.22477 3.60723 0.36487 18.1830 −2.81508 0.84472 1.96613 19.9375 −3.11046 3.35845 0.38442 21.6898 −3.29170 0.21490 3.15399 18.9472 −2.84192

0.9825 0.9840 0.0005 0.0006 0.7297 0.6424 0.0058 0.0024 0.9908 0.9754 0.0028 0.0009 0.7627 0.6497 0.0014 0.0005 0.9998 0.9992 0.0043 0.0022

Unstable Unstable Stable Stable Unstable Unstable Stable Stable Unstable Unstable Stable Stable Unstable Unstable Stable Stable Unstable Unstable Stable Stable

DlnY lnK DlnK lnE DlnE lnL DlnL lnH DlnH

Note The conclusion “stable” indicates that the series has passed the stability test at the significance level of 10%

5.4.1.3 Cointegration Test Although lnK, lnL, lnE, and lnH are stable sequences after the first-order difference, there may still exist the problem of “pseudo regression” in the regression analysis, because some long-term useful information that the variables may contain will get lost in the unit root test. At this time, we must make a cointegration test to eliminate “pseudo regression,” so that the regression results are of explanatory significance. According to the requirements of the cointegration test, there should be at least two variables, and the single integer order of the explanatory variable should be greater than or equal to that of the explained variable. When there are at least two explanatory variables, the single integer order should be the same. The above unit root test proves that all the economic variables are first-order single integration elements, so the cointegration test can be carried out. We establish the null hypothesis: there is no cointegration relationship among the variables. We get the residual statistics from

5

THE RELATIONSHIP BETWEEN FACTORS OF ECONOMIC …

Table 5.6 Results of the Cointegration Test ADF Residual Variance HAC Variance

159

t-Statistic

Prob

−5.962675 0.000607 0.000538

0.0000

the panel data for testing and conduct the cointegration test using the software EViews 6.0. The results are as follows (Table 5.6). Judging by the results of the cointegration test, the P value corresponding to ADF is significantly smaller than 0.05, so the null hypothesis is rejected. There is a long-term stable equilibrium relationship among the variables, so we can proceed to the next step of the regression analysis. 5.4.1.4 Model Selection Before the regression analysis, we must make an F-test of the panel data to determine which model is to be built. In a mixed estimation model, there is no significant difference among the individuals or among different cross-sections on different time series, so the OLS least square method can be directly used to regress and estimate the parameters of the panel data. In an individual fixed-effect model, there are different intercepts of different time series and different cross-sections, and therefore, it is necessary to add estimation parameters of dummy variables to the model. We first make the following assumptions: H0 : For different cross-sections, the intercepts of the model are the same, namely, Ai = A (Construction of a mixed estimation model). H1 : For different cross-sections, the intercepts of the model are different (Construction of an individual fixed-effect model).

Table 5.7 shows the test results. Table 5.7 Results of F-Test of the Panel Data

Effects Test Cross-section F Cross-section Chi-square

Statistic 0.672826 2.235934

d.f. (2,21) 2

Prob 0.5209 0.3269

160

Y. LI

According to the statistics in Table 5.7, we know that F = 0.672826 < F0.05 (2,21) = 3.467, which means to accept the null hypothesis H0 , so we must build a mixed estimation model. That is, for different sections of time, the parameters Ct , at , βt , φt , and δt are the same. So the model is LnYit = C + αLn K it + β Ln L it + φ Ln E it + δLn Hit + μit

5.4.2

Discussion of the Metrological Analysis Results

Using the software EViews 6.0, we conduct the OLS least square regression of the regional panel data and get the following results (for the variable of regions i, we use EAST to stand for the Eastern Region, MIDD for the Central Region, and WEST for the Western Region) (Table 5.8). According to the results of the parameter estimation, R2 = 0.999170 (R2 = 0.998617 after adjustment), which shows a good degree of fitting of the model. The F-statistic of the model is 1,806.108 and the P value is zero, indicating that all the independent variables well explain the dependent variables and that the model has passed the significance test. The regression equations are as follows: Eastern Region: LnY = -4.50 + 0.03lnK + 0.33lnL + 1.71lnE + 0.40lnH. Central Region: LnY = -5.43 + 0.19lnK + 0.50lnL + 1.02lnE + 0.28lnH. Western Region: LnY = 1.41 + 0.33lnK-0.20lnL + 0.91lnE + 0.24lnH. The sums of the output elasticity coefficients of the four independent variables are greater than one, which indicates that China’s economic growth is in the stage of increasing returns to scale. According to the regression results, the correlation coefficients of the four independent variables K, L, E, and H (i.e., the output elasticity of each factor) of the Eastern Region are (in descending order): education human capital, health human capital, labor, and physical capital. Those of the Central Region are (in descending order): education human capital, labor, health human capital, and physical capital. And those of the Western Region are (in descending order): education human capital,

5

THE RELATIONSHIP BETWEEN FACTORS OF ECONOMIC …

161

Table 5.8 Regression Results of the Correlation Between Factors and Regional Economic Growth Dependent Variable: LNY? Method: Pooled Least Squares Sample: 2003 2014 Included observations: 12 Total pool (balanced) observations: 36 Variable Coefficient Std. Error t-Statistic EAST–C −4.501738 3.844226 −1.171039 MIDD–C −5.430898 2.641775 −2.055776 WEST–C 1.405133 5.290586 0.265591 EAST–K 0.031704 0.148779 0.213094 MIDD–K 0.190916 0.055067 3.467009 WEST–K 0.330491 0.096629 3.420217 EAST–L 0.333603 0.395894 0.842658 MIDD–L 0.496527 0.256431 1.936297 WEST–L −0.196576 0.524274 −0.374948 EAST–E 1.712600 0.723348 2.367602 MIDD–E 1.019793 0.554347 1.839627 WEST–E 0.906165 0.344708 2.628789 EAST–H 0.402229 0.111755 3.599216 MIDD–H 0.280390 0.081143 3.455513 WEST–H 0.236142 0.082629 2.857868 R-squared 0.999170 Mean dependent var Adjusted R-squared 0.998617 S.D. dependent var S.E. of regression 0.023331 Akaike info criterion Sum squared reside 0.011431 Schwarz criterion Log likelihood 93.90676 Hannan-Quinn criter F-statistic 1806.108 Durbin-Watson stat Prob (F-statistic) 0.000000

Prob 0.2547 0.0524 0.7931 0.8333 0.0023 0.0026 0.4089 0.0664 0.7115 0.0276 0.0800 0.0157 0.0017 0.0024 0.0094 1.946064 0.627365 −4.383709 −3.723909 −4.153421 1.527211

Note The t-test of the parameter estimation means that it has passed the test at the significance level of 5%

physical capital, health human capital, and labor. This shows that significant changes have taken place to the driving effect of these factors on China’s economic growth since 2003; total factor productivity has an important influence on the economic growth of the three regions in China; and there are significant regional gaps in the coefficients of the output elasticity and total factor productivity. The following are the details of our analysis.

162

Y. LI

5.4.2.1

The Output Elasticity of Factors and Its Role in Driving Economic Growth (1) The Output Elasticity of Education and Health Human Capital

In light of the output elasticity, education human capital has become the biggest driving force of economic growth. The correlation coefficients of the Eastern, Central, and Western Regions are 1.71, 1.02, and 0.91, respectively. That is to say, a 1% growth in the average years of schooling equals an economic growth of 1.71%, 1.02%, and 0.91%, respectively. Clearly, education human capital has a much bigger role than the other factors in boosting economic growth. Compared with education human capital, health human capital plays a much smaller role in boosting economic growth, and its correlation coefficients are 0.40, 0.28, and 0.24, respectively. (2) The Output Elasticity of Physical Capital Compared with the years before 2003, the output elasticity of physical capital has declined thereafter, but its long-term influence on the economy should not be ignored, especially for the Western Region. The coefficients of the output elasticity of physical capital of the three regions are 0.03, 0.19, and 0.33, respectively, which are much smaller than the influence of the average years of schooling on economic growth. In particular, its role is quite insignificant in boosting economic growth in the Eastern Region. Compared with the Eastern Region, the Central and Western Regions—especially the latter—rely much more on physical capital input for economic growth. (3) The Output Elasticity of Labor In terms of the influence of labor input on economic growth, the three regions are quite different—the correlation coefficients of labor are positively correlated (positive contribution) in the Eastern and Central Regions (0.33 and 0.50, respectively), but negatively correlated (negative contribution) in the Western Region (-0.20).

5

THE RELATIONSHIP BETWEEN FACTORS OF ECONOMIC …

163

5.4.2.2 The Total Factor Productivity Parameter The values of the intercept term parameter (C) are quite large in the Eastern, Central, and Western Regions (4.50, 5.43, and 1.41, respectively). This indicates that total factor productivity is an important factor affecting regional economic growth in China. It plays the biggest role in boosting the growth of the Central Region, followed by the Eastern and Western Regions, which reflects considerable regional gaps in institutional environment and technological levels in China. 5.4.2.3 Regional Economic Gaps Through a comparison of the absolute data and the regression equations of various indicators, we can see that, although the three regions are consistent in the overall trend of changes, there is serious regional differentiation. Compared with the developed Eastern Region, the Central and Western Regions have fallen behind in terms of all the factors except capital, which indicates that the macro-allocation of factors is unbalanced among the three regions and that there are regional gaps in development in China. Regional gaps in education human capital investment and total factor productivity are the main factors leading to regional economic gaps. On the other hand, the influences of health human capital, physical capital investment, and the size of labor force on regional differences in economic growth are insignificant.

5.5

Conclusions and Policy Implications 5.5.1

Conclusions

The unified growth theory analyzes the endogenous economic transformation with the changes of the economic growth stages, and the transformation mechanism of economic growth stages is the changes in the main driving factors of economic growth. When a country’s demographic dividends decrease, the main driving force of its economic growth turns to the increase in the per capita output rate. The ways to improve the per capita output rate include investment in human capital and physical capital and improvement in the total factor productivity. In this chapter, we use the empirical evidence of China from 2003 to 2013 to confirm the idea of Galor and Weil (2000) on the relationship between the changes in major driving factors of economic growth and economic transformation. Our empirical research results show that

164

Y. LI

the main driving factors of China’s economic growth have undergone structural changes, and the Chinese economy is in the transition from the post-Malthusian growth stage of rapid population growth to the modern economic growth stage based on human capital, in which population transformation plays an important role. Meanwhile, there are significant regional economic gaps. Seen from the impact of the structural changes in the production factors, China’s economic growth rate is slowing down due to the decline in the growth rate of labor input caused by the changes in the population structure, the decline in capital input caused by the aging of the population as the traditional industrialization comes to its end, and the slowdown of technological progress. This is the inevitable price of structural slowdown in the process of economic transformation. Such changes in the stages of economic growth indicate that the original economic system can no longer adapt to the new changes. Therefore, the important task of economic transformation is to restructure a new economic system. Increasing human capital investment and improving total factor productivity are inevitable choices to optimize the supply structure, promote China’s economic transformation, and achieve the goal of sustainable economic growth. Due to the existence of regional economic gaps, we should take them into account when pushing forward the gradient advancement in the process of economic transformation. 5.5.1.1 Policy Implications The above conclusions have the following policy implications. Firstly, we must strengthen the investment in human capital and implement the talent strategy as the primary, long-term strategy for the country’s economic transformation. Judging by the international comparison of the absolute data, China is still at a very low level of human capital investment, and there is huge room to boost its economic growth relying on human capital investment. On the one hand, we must strengthen school education, including training and vocational education, which will be conducive to industrial upgrading; on the other hand, we should increase investment in public health. In the long run, with the accumulation of human capital and the improvement of labor quality, the productivity will be improved in the future. Only in this way can we realize the “second demographic transformation,” turn the demographic dividends into talent dividends, and realize the transformation of the Chinese economy from quantity-based growth to quality-based development.

5

THE RELATIONSHIP BETWEEN FACTORS OF ECONOMIC …

165

Secondly, we must improve total factor productivity through institutional reform and technological innovation. From an institutional perspective, we must carry out a series of reforms to break the dual economic structure between the urban and rural areas as China enters a new stage of economic growth. In addition, we must continue to promote and deepen enterprise reform, eliminate backward enterprises, and promote industrial restructuring and upgrading, while establishing a mechanism to encourage technological innovation. Thirdly, we must attach importance to the accumulation of physical capital and steady investment. As a large developing country with a per capita GDP of only 1/5 of that of Europe and the United States, China still has a strong effective demand for domestic capital accumulation and investment capacity. Of course, we must take into full account regional economic gaps and treat them differently, and focus on increasing investment in social infrastructure in the Central and Western Regions. This can not only improve the development environment and facilitate the catch-up growth of these two regions, but also help attract talents. Fourthly, we must reasonably guide the transfer of labor among the regions. The existence of regional economic gaps has provided great space for the gradient advancement of China’s economic transformation. While upgrading the industrial structure in the developed areas in the Eastern and Central Regions, we must gradually transfer the mid—and lowerend labor-intensive industries and guide the flow of labor resources to the Western Region. In this way, the Western Region can get resources and power to accelerate its development. On the other hand, the Eastern Region can speed up its industrial transformation and upgrading. This will bring into being a gradient transformation and a smooth transition among the three regions. Finally, we must establish a long-term mechanism to narrow regional economic gaps. We must establish a mechanism that favors investment in education in the Central and Western Regions, equalize the allocation of educational resources, and achieve educational equity. We must construct a supportive platform to increase technology transfer and introduce and encourage technological innovation. At the same time, we must improve the institutional environment, establish a fair and orderly competitive environment and an effective incentive mechanism, and cultivate the awareness of the market economy.

166

Y. LI

References Aoki, Masahiko. “Law-Based Rule of a Country: Rule of Law or Rule by Law?” http://yuanchuang.caijing.com.cn/2015/0717/3928144.shtml. Cai Fang and Wang Dewen. “The Sustainability and Labor Contribution of China’s Economic Growth.” Economic Research Journal, no. 10 (1999). Galor, O., and D. Weil. “Population, Technology, and Growth: From Malthusian Stagnation to the Demographic Transition and Beyond.” American Economic Review 90, no. 4 (2000): 806–28. Galor, O. “Towards a Unified Theory of Economic Growth.” World Economics, no. 9 (2008): 97–151. Guo Hongzhen. “Contemplation on the Factors Contributing to China’s Economic Growth,” China Statistics, no. 4 (2004). He Chengying, Xu Xiangyang, and Weng Yuanyuan. “The Internationalization of the Capital Market and the Modal Transformation of China’s Economic Growth.” Economic Perspectives, no. 9 (2012). Hu Wenguo and Wu Dong. “Theoretical and Empirical Analysis of the Factors of China’s Economic Growth.” Journal of Tsinghua University (Social Sciences Edition), no. 4 (2004). Liang Zhao. “Analysis of Major Factors of Sustained Growth of the National Economy.” Journal of World Economy, no. 7 (2000). Liu Shijin. “The Slowdown of Growth Rate and the Transformation of Development Models.” Economic Perspectives, no. 5 (2011). Liu Wei and Li Shaorong. “Changes in Ownership and the Promotion of Economic Growth and Factor Efficiency.” Economic Research Journal, no. 1 (2001). Lin Justin Yifu. “On the Transformation of the Models of Economic Growth.” Scientific Development, no. 2 (2014). Shen Kunrong. “Empirical Analysis of the Factors of China’s Economic Growth 1978–1997.” Economic Science, no. 4 (1999). Strauss J., and D. Thomas. “Health, Nualth, Nutrition and Economic Development.” Journal of Economic Literature, no. 36 (1998): 766–817. Tang Jijun, Zhang Zongyi, and Fu Yunying. “China’s Economic Transformation and Growth,” Management World, no. 1 (2007). Wei Jie. “Several Controversial Questions on Sustaining China’s Economic Growth.” Economic Perspectives, no. 5 (2009). Zhang Yong, Wang Xi, and Gu Mingming. “A Comparative of the Development Potentials of China and India.” Economic Research Journal, no. 5 (2009).

CHAPTER 6

International Comparison of Human Capital Investment

In terms of investment, China seems to have attached importance to physical capital investment but neglected human capital investment. In view of human capital investment, it still lags far behind developed countries in the proportion of education expenditure, the average education level, the education investment structure, the medical and healthcare input, the value-added capacity of enterprise human capital, and investment in science, technology, and culture. China is still at a relatively low level of human capital investment, so there is a significant “spillover effect” of people with knowledge and skills to accept new technologies and help the whole society. As pointed out by James J. Heckman, a 2000 Nobel Laureate in Economics, a recent study suggests that if we consider the contribution to social output, rather than personal income, the return on human capital in China is as high as 30 to 40%, which is higher than the return on physical capital investment (estimated to be 20%), and also higher than the return on human capital in developed countries, such as the United States. Human capital has become the key to international competition, and human capital investment is becoming more and more important for each country. At present, to gain advantages in economic development, all countries in the world—especially developed countries—have regarded the development of human capital investment (e.g., national education) as their most important policy and measure. In this chapter, we will study the conditions and policies of human capital investment in some typical countries. On that basis, we will © Social Sciences Academic Press 2023 Y. Li, Human Capital Investment and the Regional Economic Gap in China, https://doi.org/10.1007/978-981-99-4997-7_6

167

168

Y. LI

make an international comparison of China’s human capital investment. Through such analysis and comparison, we will sum up the overall characteristics of China’s human capital investment and its successful experience for reference. Finally, according to the problems in our government’s human capital investment, starting from the reality of China’s social and economic development, we will formulate human capital investment strategies in connection with social needs, increase education investment and improve the education structure, balance human capital investment in different regions, and establish and improve incentive mechanisms of human capital.

6.1 Conditions and Policies of Human Capital Investment in Typical Countries The theoretical research and practical exploration of human capital started in the Western developed countries. It was first seen in Adam Smith’s The Wealth of Nations (1776), followed by Fisher’s The Nature of Capital and Income (1906), and Schultz’s Investment in Human Capital (1960). In the field of human capital research, the most influential scholars include (by the frequency of citation): G. S. Becker (155), R. E. Lucas (147), R. J. Barro (115), J. Mincer (88), P. Romer (77), Acemoglu (70), J. Heckman (61), and T. Schultz (57). The frequency of citation shows that Schultz is eighth on the list. In contrast, Becker has established the basic framework of modern human capital theory and is undoubtedly the founder of Western human capital theory. It is not difficult to find that the United States and Germany are typical in human capital research. Meanwhile, Japan, an Asian island country that used to be an economic miracle, is also writing the myth of economic takeoff with human capital. In order to thoroughly and carefully study and analyze the human capital theory and practice, we will in this chapter refer to relevant measures of human capital investment in the United States, Germany, and Japan and, through comparative research, summarize scientific countermeasures and suggestions suitable for the construction and development of human capital in China. 6.1.1

Human Capital Investment in the United States

As is known to all, the United States is the leader of the Western developed countries. It not only has long held a dominant position in economic

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

169

development as the world’s economic leader, but also has a unique position in theoretical studies. Many modern classic theories and almost all the outstanding research achievements have originated from Western developed countries spearheaded by the United States. The same is true of human capital theory. It was born in the United States, constantly enriched by many famous scholars, and finally developed into the system of human capital theories that has been used to this day. Ever since its birth, it has injected a strong vitality into the sustainable development of the American economy, enabling it to last up till now. In this aspect alone, its experience of human capital investment is worth learning for China. 6.1.1.1 Strategic Management of Human Capital in the US Different from other countries, the United States has long raised its emphasis on human capital to a strategic height. When the other countries (including China) still use “human resource” to name their organizations or institutions and define their human resource management, the United States has already stood at the forefront of human capital. The strategic management of human capital in the United States began in the early twenty-first century, and its landmark event was the President’s Management Agenda issued by the Bush Administration in August 2001. The Agenda mainly includes five important management reforms, namely, human capital, competitive procurement, financial performance improvement, e-government, and budget performance integration. It is worth mentioning that human capital reform ranks first among the five important reform agendas, which fully reveals its importance to the government reform. The agenda on human capital reform points out that all departments of the federal government will select, develop, and train public sector human capital according to the philosophy of human capital, and conduct effective management to improve and enhance government performance. Although in the operation of enterprises in all walks of life, “human resource” is still the mainstream, but it has changed in its content and nature, and such change is established first in the form of administrative reform in the government public sectors. Such reform represents a trend, namely, the trend of human capital reform, which is undoubtedly leading the era of the knowledge economy in the twenty-first century. To facilitate the strategic management of human capital, the US government has issued a series of administrative bills, such as the Chief Human Capital Officers Act of 2002, the Act of Homeland Security of 2002, the Federal Work Force Flexibility Act of 2004, and the GAO

170

Y. LI

Human Capital Reform Act. At the same time, it has also established functional departments specifically responsible for the strategic management of human capital, including the General Administration of Personnel, the Bureau of Management and Budget, the General Accountability Office of the Government, and the Chief Human Capital Officers’ Council. In addition, to facilitate the evaluation of the effectiveness of human capital strategic management, they have also developed the Human Capital Assessment and Accountability Framework, which summarizes five important aspects to evaluate human capital strategic management: strategic alliance, leadership and knowledge management, result-oriented performance culture, talent management, and accountability. The implementation of strategic management of human capital is nothing new to the United States, but it is far ahead of China in this regard. In terms of the philosophy of human capital development, China is still far behind the United States. For example, some departments in China are still named in the old ways, such as the Ministry of Human Resources and Social Security, the Ministry of Personnel, and the Human Resources Department, and the design of some textbooks still follows traditional concepts and templates. All this shows that human capital has not yet been raised to a strategic height in China, and the concept of human capital development is still quite backward in the country. 6.1.1.2 The R&D Indicator R&D, namely research and development, is an important indicator to measure the comprehensive strength of a country (or region). It reflects the level and strength of science and technology, culture, and knowledge. R&D aims at increasing the total amount of knowledge and meanwhile using knowledge to create new knowledge fields (innovation activities). It includes three basic activities: basic research, applied research, and experimental development. R&D not only reflects the scientific and technological strength of a country (or region), but also is regarded as the life of an enterprise. In essence, R&D is a form of human capital investment. R&D input improves the quantity and quality of human capital, and thus enhances the core competitiveness. The twenty-first century is an era of the knowledge economy. Knowledge has an incomparable and irreplaceable position in society, politics, economy, culture, science, and technology. It is not only a top priority for a country or region, but also an extremely important factor for enterprises and other social organizations. It is not difficult to see this from

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

171

the evolution of the production function. The early production function, namely the Douglas production function, only considered two production factors: labor (L) and capital (C). Later, scholars gradually introduced human capital and other factors separately into the production model. What human capital emphasizes are exactly the internal factors, such as knowledge and health. To acquire and increase its total amount of knowledge, an enterprise may take a series of activities, which may be collectively referred to as knowledge development, and R&D is the most critical link of it. R&D includes three basic activities: basic research, applied research, and experimental development. Specifically, it includes many aspects of an enterprise’s knowledge development, such as its conventional scientific and technological development and human resource development. For a country or region, R&D investment mainly refers to investment in scientific research. Table 6.1 shows the basic information of R&D input of China, the United States, Japan, and Germany from 1981 to 2000. As the calculation standard of Chinese R&D indicator is not consistent, we will only compare the R&D input of the United States, Japan, and Germany. The statistics of these three countries are from OECD. From the above data, it is not difficult to find that the R&D of the United States has been at a high level and on a growth trend, which is critical to the sustainable development of its economy. The OECD’s statistical description of R&D includes many indicators, such as GERD (gross domestic expenditure on R&D, i.e., the total R&D input), BE (percentage of gross domestic expenditure on R&D performed by the business enterprise sector, i.e., the proportion of R&D input of an enterprise or institution), HE (percentage of gross domestic expenditure on R&D performed by the higher education sector, i.e., the proportion of R&D input of higher education), GOV (percentage of gross domestic expenditure on R&D performed by the government sector, i.e., the proportion of R&D input of the government), BERD (business enterprise expenditure on R&D, i.e., the R&D input of an enterprise or institution), HERD (higher education expenditure on R&D, i.e., the R&D input of higher education), and GOVERD (government intramural expenditure on R&D, i.e., the R&D input of the government). According to the statistical classifications of OECD, we can find that GERD (gross domestic expenditure on R&D) consists of BERD (business enterprise expenditure), HERD (higher education expenditure), and

172

Y. LI

Table 6.1 R&D Input of US, Japan, Germany, and China, 1981–2000 (billion USD) Year

US

Japan

Germany

China

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

11.59 122 13.08 14.33 15.58 15.98 16.28 16.7 17.04 17.58 17.95 18.01 17.62 17.62 18.72 19.73 20.81 21.93 23.27 24.76

4.04 4.33 4.68 5.02 5.56 5.66 6.06 6.53 7.14 7.72 7.9 7.81 7.61 7.53 8.01 8.55 8.9 9.13 9.18 9.54

2.86 2.93 2.98 3.06 3.36 3.47 3.64 3.77 3.91 3.96 4.22 4.1 3.96 3.89 3.96 3.99 4.11 4.23 4.55 4.77

6.158 6.529 7.903 9.472 10.259 11.257 11.379 12.112 12.787 13.912 16.069 18.926 22.561 26.825 30.236 34.863 40.886 43.86 54.39 57.56

Notes The statistics of US, Japan, and Germany are from OECD, “Main Science and Technology Indicators Database” (Paris, May 2002) and National Science Foundation Division of Science Resource Statistics of US (Quoted from Lai Mingyong and Yuan Yuan, “R&D, International Technology Spillover and Human Capital: An Empirical Research,” Science Research Management, no. 4 (2005). The statistics of China are based on the research findings of Lai Mingyong et al. (2005), and the calculation formula is: lnTFPt = lnYt –αlnKt—βlnLt

GOVERD (government intramural expenditure). Since 2000, the R&D input of the US has been on the rise (see Table 6.2). According to the data in Table 6.2, it is not hard to find that the US R&D has been on the rise since 2000. It mainly depends on enterprises, which account for around 70% of the overall input. What follows are higher education input and government input, which are of a considerable size and on an upward trend. Figures 6.1 through 6.4 show the changes in the proportions of GERD, BERD, HERD, and GOVERD in the US GDP between 1995 and 2013. As the figures reflect, GERD, BERD, and HERD of the US were on the rise, with slight fluctuations, between 1995 and 2013. By contrast, GOVERD experienced significant

407,238.0 2.77 71.4 13.2 11.3 290,681.0 53,917.0 46,220.0

2008 406,405.0 2.82 69.5 14.0 12.0 282,393.0 56,972.0 48,860.0

2009 410,093.0 2.74 68.0 14.7 12.7 278,977.0 60,374.0 52,121.0

2010 428,745.0 2.76 68.6 14.6 12.6 294,092.0 62,446.0 53,917.0

2011

436,745.0 2.70 69.3 14.5 12.0 302,251.0 63,284.0 52,320.0

2012

456,977.0 2.73 70.6 14.2 11.2 322,528.0 64,680.0 51,022.0

2013

Notes GERD stands for gross domestic expenditure on R&D; BE proportion for percentage of gross domestic expenditure on R&D performed by the business enterprise sector; HE proportion for percentage of gross domestic expenditure on R&D performed by the higher education sector; GOV proportion for percentage of gross domestic expenditure on R&D performed by the government sector; BERD for business enterprise expenditure on R&D; HERD for higher education expenditure on R&D; and GOVERD for government intramural expenditure on R&D Source OECD, “Main Science and Technology Indicators Database,” July 2015

269,513.0 2.62 74.2 11.4 10.8 199,961.0 30,693.0 29,076.0

2000

Relevant Indicators of the US R&D (million USD)

GERD GERD proportion (%) BE proportion (%) HE proportion (%) GOV proportion (%) BERD HERD GOVERD

Indicator

Table 6.2

6 INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

173

174

Y. LI

fluctuations in this period—it was on the decline between 1995 and 1999, between 2003 and 2006, and between 2010 and 2013, but on the rise in the other years. Note worthily, it was on an obvious downward trend in recent years (from 2010 to 2013), whereas GERD, BERD, and HERD were on the rise over the same period. This reflects the structural contradiction and problem of the US R&D. Despite all this, the US remains the world’s strongest country in terms of R&D, which still forcefully supports the economic and social development of the country (Fig. 6.2–6.4).

Fig. 6.1 GERD as a Percentage of GDP (Source OECD, “Main Science and Technology Indicators Database,” July 2015)

Fig. 6.2 BERD as a Percentage of GDP (Source OECD, “Main Science and Technology Indicators Database,” July 2015)

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

175

Fig. 6.3 HERD as a Percentage of GDP (Source OECD, “Main Science and Technology Indicators Database,” July 2015)

Fig. 6.4 GOVERD as a Percentage of GDP (Source OECD, “Main Science and Technology Indicators Database,” July 2015)

6.1.1.3 The Structure of Human Capital Human capital is a complex system rather than a single topic. In light of the value chain of human capital, to realize the value of human capital, we must rely on a value chain system of human capital that focuses on the development of human capital value and involves a wide range of value chain links. According to the Diamond Model of Michael Porter, it is not hard to see that the value chain of human capital can be decomposed into four aspects: the demand for human capital value, the human capital resource endowment, the support from relevant industries and stakeholders, and the structure and strategy of human capital organization.

176

Y. LI

Meanwhile, it also requires some other factors, such as state policy intervention. All our citations hereby are meant to illustrate the importance of the human capital system or the human capital structure. The structure of human capital investment is the basis of the human capital structure. Although Table 6.1 and Figs. 6.1 through 6.4 reflect the structure of human capital investment in the United States, which is dominated by enterprise investment, it does not mean that higher education investment and government investment do not play an important role. On the contrary, enterprise investment in human capital is the core stage of the development of human capital value, while the higher education investment and government investment previously made are basic input that serves as a foundation. China Education Daily once reported that the US Federal Government’s fiscal budget for education in 2015 totaled USD 68.6 billion, up by USD 1.3 billion (or 1.9%) over 2014 and by USD 3 billion over 2013.1 Meanwhile, data shows that the average years of schooling in the United States are 15 years, and the enrollment rate of higher education is 81%.2 In his 2013 article “How Does Education Adapt to the Future: A Discussion Under the Background of American Education,” Prof. Henry M. Levin with the Teachers College of Columbia University explored how education improves workers’ capacity—a topic never before discussed specifically—and believed that increasing the years of schooling can bring economic benefits.3 Feng Xiaoling and Zhang Jian (2013) empirically analyzed the impact of human capital, R&D investment, per capita disposable income, the urbanization level, the intermediate demand of manufacturing, and the impact of FDI in producer services on the US producer services, using such methods as

1 Zhou Hongxia, “US Education Funding: The Largest Share Goes to Preschool and Basic Education,” China Education Daily, (2015, September 30). Quoted from China Education and Research Network, http://www.edu.cn/zhong_guo_jiao_yu/yiwujiaoyu/ 201509/t20150930_1322971.shtml. 2 Zeng Xiangquan, Labor Economics (2nd Edition), (Shanghai: Fudan University Press, 2010), 176. 3 Henry M. Levin, “How Does Education Adapt to the Future: A Discussion Under the Background of American Education,” Peking University Education Review 11, no. 2 (2013).

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

177

cointegration analysis, the VAR model, the impulse response, and the variance decomposition methods (see Table 6.3).4 Zhang Yan, Xu Yunxiao, and Wang Zhiqiang (2010) pointed out through empirical analysis that the average savings rate of human capital in the United States is 2.64 times that in China, and that this difference of more than twice in the savings rate of human capital explains the gap of nearly 10 times in per capita output between the two countries.5 As we all know, the United States is second to none in the world in education, which is a true reflection of the third transfer of the world center. After World War II, with the decline of other war-affected countries, the United States rose rapidly and attracted many scientific and technological talents. In addition, its favorable conditions and treatment have made true the flow of scientific and technological talents for nearly a century. This trend has continued to this day. By contrast, China is no exception in terms of talent outflow. The number of high-level talents flowing to the United States is increasing every year. Under the influence of such advantages, the United States has gradually formed its high-quality human capital structure dominated by talents with higher education qualifications. This is undoubtedly the core competitive advantage of the United States, which is sufficient to support the cross-century development of its economy and society. To sum up, the three major advantages of human capital investment in the US are the human capital strategy, R&D (research and development), and the human capital structure. In addition, its social welfare and social security also constitute an important part of its human capital investment. As is known to all, the United States is a country with sound social welfare and security, and this expenditure accounts for a considerable proportion in the entire national economy. It is precisely because of the above measures that the United States has a unique model of economic growth

4 Feng Xiaoling and Zhang Jian, “Technical Analysis of the Influencing Factors of Producer Services in the US: An Empirical Test Based on the VAR Model,” International Economics and Trade Research, no. 10 (2013). 5 Zhang Yan, Xu Yunxiao, and Wang Zhiqiang, “Human Capital, Physical Capital, and Output Difference Between China and the US—Empirical Studies of the Panel Data of 52 Countries Between 1981 and 2005 Using the Human Capital Model,” Finance and Trade Economics, no. 9 (2010).

178

Y. LI

Table 6.3 Composition of US Human Capital, 1993–2012 Year

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Bachelor’s or associate degree

Master’s degree or above

Total

Persons (thousand)

Proportion in total employment (%)

Persons (thousand)

Proportion in total employment (%)

Persons (thousand)

Proportion in total employment (%)

28,905 29,119 29,130 29,545 29,866 31,936 31,288 31,995 32,209 31,713 32,429 32,968 33,616 34,159 34,569 35,076 33,915 33,724 33,901 34,699

27.1 26.9 27.7 27.7 27.3 27.46 26.67 27.11 26.92 26.37 25.89 26.15 26.24 26.27 26.16 26.39 25.47 25.35 25.49 25.91

19,946 19,918 19,770 20,692 21,223 33,719 34,918 35,501 35,967 36,867 38,575 39,291 40,211 41,612 43,193 44,009 43,564 43,839 44,821 46,216

18.7 18.4 18.8 19.4 19.4 28.99 29.77 30.08 30.06 30.66 30.80 31.16 31.38 32.01 32.69 33.12 32.72 32.96 33.70 34.51

48,851 49,037 48,900 50,237 51,089 65,655 66,146 67,496 68,276 68,580 71,004 72,259 73,827 75,771 77,762 79,085 77,479 77,563 78,722 80,915

45.8 45.3 46.5 47.1 46.7 56.45 56.44 57.19 56.98 57.03 56.69 57.31 57.62 58.28 58.85 59.51 58.19 58.31 59.19 60.42

Source US Department of Commerce, 2013

based on the “talent dividends” with human capital investment and development as the core, which is quite different from the Chinese model of economic growth based on “demographic dividends.” 6.1.2

Human Capital Investment in Germany

The rise of Germany also relies on human capital. In the process of world economic development, Germany has risen three times (the first being its unification in the nineteenth century, the second being its rise after World War I, and the third being its rise since World War II), all of which are legendary. German and foreign scholars generally

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

179

believe that the rise of Germany is mainly due to its education. It is true that education plays an extremely important role in the economic development of Germany, and the country was once the world center of education. The Enlightenment, the Renaissance, the New Culture Movement, and so on have profound influence on Germany’s internal spirit. At the same time, there have emerged in Germany a large number of most outstanding scientific workers in human history, such as Albert Einstein (1879–1955), Carl Friedrich Gauss (1777–1855), Heinrich Rudolf Hertz (1857–1894), Georg Simon Ohm (1787–1854), Otto Hahn (1879– 1968), Wernher von Braun (1912–1977), Carl Friedrich von Weizsäcker (1912–2007), and so on. Germany is aware of the importance of education for national development and strength, especially in the era of the knowledge economy at present. Of course, in addition to education, Germany has other conditions to facilitate its economic development, including social welfare, national culture and spirit, social environment, etc. However, if we explore the essence through the various factors, the core and root of the rise of Germany several times are actually “human capital investment.” 6.1.2.1

Promotion of the National Human Capital Strength Through Constant Optimization and Upgrading of the Human Capital Structure Human capital, especially its structure, has played an irreplaceable role in the rise of Germany. After the reunification of Germany, driven by the two industrial revolutions, the country soon realized overall prosperity of the economy and society. From 1870 to 1913, according to data, Germany’s key indicators (including steel, coal, and railway) were invariably on an unprecedented growth trend (see Table 6.4). The advanced education has brought into shape a unique human capital structure of Germany dominated by higher education receivers. At the stage of industrial development, Germany’s human capital structure reflected distinct characteristics of industrialization. (1) The industrial employment increased significantly, and the industrial population accounted for a large proportion of the total population, while the employment in non-industrial fields, such as agriculture, and its proportion in the total population, decreased rapidly. (2) The trend of urbanization was constantly reinforced. The urban population expanded rapidly and became the main part of the entire population, while the rural population declined significantly. (3) The outflow of population decreased

180

Y. LI

Table 6.4 Changes in Indicators of German Industrialization, 1870–1913 Indicator

1870

1913

Steel (thousand tons) Coal (thousand tons) Railway (thousand kilometers) Government and enterprise powered ships (billion tons)

126 5,100 18.8 0.82

13,000 191,500 60.5 43.8

Increase (times) 102.17 36.55 2.22 52.42

Source Research Group of “Human Capital in the Rise and Fall of Countries,” Development Research Center of the State Council, “The Decisive Role of Human Capital Quality in the Rise of Germany,” Oriental Morning Post (April 12, 2016), sorted

while the inflow of population increased, thus gathering a large number of outstanding foreign talents. It is worth mentioning that the German process is not a simple-sense “pure industrialization” that focused on industrial fields only but ignored or even sacrificed non-industrial fields. On the contrary, in the process of German industrialization, with the rapid increase in the industrial output value, the output value of nonindustrial fields, such as agriculture, was also on the rise. As pointed out by the Long Guoqiang-led Research Group of “The Decisive Role of Human Capital Quality in the Rise of Germany,” from 1882 to 1907, German population in agriculture and other fields decreased by 1.5433 million, but the employment in these fields increased by 1.6468 million. In the twenty-first century, to adapt to the economic development, many countries have carried out a new round of industrial restructuring. Against this background, their human capital structure is bound to transform and upgrade accordingly. Germany and other developed countries have taken the lead in industrial restructuring, and the service economy centered on the tertiary industry has become the focus of this industrial adjustment. In fact, as early as the middle and late nineteenth century, the Western developed countries had begun their industrial structure adjustment as well as the corresponding adjustment of their human capital structure. With the increasing attention to and investment in science, education, culture, health, and social welfare, people’s basic human capital (such as health) has been effectively guaranteed, and their physical quality constantly improved. Meanwhile, the reform and progress in education and the prosperity of cultural undertakings have made true unprecedented development in people’s spiritual world. As a result, the improvement in the quality and level of people’s comprehensive human capital, as well

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

181

as the optimization and upgrading of the human capital structure, will undoubtedly become a powerful tool and an important driving force for a new round of industrial restructuring and the ensuing new round of global industrial competition. Clearly, Germany and other Western developed countries are speeding up the formation of a new generation of human capital structure, and developing countries will follow suit. And this is bound to be the core of a new global race. 6.1.2.2 Human Capital Giving Priority to Education As a saying goes, “It takes ten years to grow trees, but a hundred to rear people.” The real potential for the development of a country (or region) lies in the quality and level of education. Education is the foundation for the formation of a country’s comprehensive strength and a fundamental way to realize human capital. Compared with other countries, Germany is unique in national education. On the one hand, Germany is strong in economic strength and able to make huge investment in education. On the other hand, Germany has long formed an advanced educational philosophy, under which teachers are good at teaching and students are good at learning. Besides, Germany is also one of the countries that have been most affected by the historical and cultural development of Europe. Human capital gives priority to education. This is a perfect interpretation of Germany’s strategy of revitalizing the country through education. (1) The dual-system vocational education lays a foundation for human capital German education is second to none in the world. Many scholars hold the view that German economic and social development has benefited from its progress in education. Then, which education model does Germany implement so as to win such a high praise from the academic circles and all sectors of society? The dual-system vocational education is a highly efficient model of education implemented in Germany. The dual-system model refers to the participation of the government and enterprises in vocational education, which is different from the traditional school education model in China. This model extends the scope of student training and education to society and enterprises and introduces them into school teaching, so as to form

182

Y. LI

a basic model of dual-subject participation of the government and enterprises in higher education and education investment. Considered as “a secret engine” for Germany’s economic takeoff, it has attracted much attention from the academic circles and has been widely followed by the international community. In recent years, the innovative education models in China’s vocational education reform, such as the “school-enterprise integration” and “dual-qualification teachers,” are largely influenced by the German model of education. The dual-system model of vocational education in Germany is quite special, and its particularity is determined by the characteristics of the country. Germany is a federal country. Unlike the situation in the United States, the German Basic Law clearly stipulates the functions and powers between the federal and local states, which has in fact determined the authority of the federal system. In contrast, the federal power of the United States comes more from local states. Therefore, in many aspects, the power of local states is greater than that of the federal government, including school education. Germany is evidently different. The German Federation has the right to directly take charge of vocational education (including legislation and other aspects) and is also take charge of vocational education in enterprises. This means that the dual-system model of vocational education in Germany is dominated by the government and assisted by the enterprises, and the enterprises are subject to the federal government in this regard. In their article “Comparison and Takeaways from Vocational Education Funding Models in Developed Countries,” Yang Lin and Guo Yang (2015) made a detailed research and analysis of education legislation in developed countries.6 In order to fully guarantee the implementation of vocational education, Germany promulgated and implemented the Federal Vocational Education Law as early as 1969, and after amendments and corrections, it has become the highest law in the German vocational education sector, laying a solid legal foundation for the dual-system model of vocational education.

6 Yang Lin and Guo Yang, “Comparison and Takeaways from Vocational Education Funding Models in Developed Countries,” Vocational and Technical Education Forum, no. 4 (2015).

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

183

(2) Advanced education philosophy promotes human capital accumulation and value realization According to data, Germany is the first in the world to propose and implement the strategy of “rejuvenating the country through science and education.” Education has provided great support for German human capital investment. In addition, the various educational policies, laws, and regulations have brought about a unique human capital advantage of the country. According to the Human Development Index (HDI) Report 2015 released by the United Nations Development Program (UNDP), the average length of schooling in Germany is 13.1 years.7 At the same time, other data shows that its average length of education is 15 years, and its enrollment rate of higher education is 45%.8 (1) Forerunner of compulsory education Historical data shows that Germany was the first to implement compulsory education. As early as 1763, Prussia Germany promulgated and implemented the first education law in the world—the General Compulsory Education Law, which clearly stipulated that minors receive compulsory education. Different from China’s nine-year compulsory education, compulsory education in Germany starts at the age of seven and ends at the age of 18, a total of 12 years. The German education system includes four stages: primary, secondary, advanced, and higher education. Primary education is a basic compulsory stage. It is stipulated that all children over seven must accept it, which lasts for four years (or six years in some areas). It is worth mentioning that Germany’s primary education (four-year primary school) has no such measures as the score orientation or repetition of a grade. On the contrary, it is featured by teacher evaluation, learning enhancement,

7 Human Development Index Report 2015 released by the United Nations Development Program, reported by Daily Mail on 16 December 2015. Quoted from http://lx. huanqiu.com/lxnews/2015-12/8225834.html. 8 Zeng Xiangquan, Labor Economics (2nd Edition), (Shanghai: Fudan University Press, 2010), 176.

184

Y. LI

free and independent learning, thematic teaching, open teaching, practical teaching, half-day teaching, cross-grade teaching, and other teaching modes. Data shows that the primary school enrollment rate in Prussia reached 60% in 1816 and 93% in 1864. In 1890, the illiteracy rate was 0.51% in Germany. In contrast, it was 0.8% in Switzerland, 9.5% in France, 7.3% in the Netherlands, 13.6% in Belgium, 30.8% in Austria, and 41.4% in Italy. We can see the huge gap between Germany and other European countries in education. Secondary education, which is also called “primary-stage education” in Germany, mainly includes four types of schools: vocational preparatory schools, practical middle schools, liberal arts and science middle schools, and comprehensive middle schools. Of these four types, vocational preparatory schools mainly engage in vocational education, similar to the case in China at present. Practical middle schools are also characterized by vocational education, but their teaching mode is far higher than that of vocational preparatory schools. Liberal arts and science middle schools, similar to regular middle schools in China, pave the way for students to go to high schools. Therefore, they mainly teach theoretical knowledge, rather than vocational education. Comprehensive middle schools, as a social welfare, provide education for disadvantaged students or those with lower grades. Advanced education in Germany is similar to high school education in China. It includes the dual system of vocational education (vocational schools) and the higher grades of liberal arts and science middle schools (Gymnasien). The difference is that, in liberal arts and science middle schools, the curriculum system is the main basis for teaching and learning, and students can choose their own courses according to their preferences or development plans. Germany is highly developed in higher education. To date, it has about 340 colleges and universities and some 2.7 million students. It also conducts a rolling selection of elite universities (see Table 6.5) through the Excellence Initiative. This dynamic selection, carried out every five years, aims to encourage universities to develop themselves and improve teaching. The selection of elite universities, the well-staged education system, and the distinctive and innovative education model have endowed Germany with unique advantages.

National National National National National National National National National National National National National

University of Munich Technical University of Munich RWTH Aachen University Free University of Berlin University of Heidelberg University of Konstanz Dresden University of Technology Humboldt University of Berlin University of Tubingen University of Cologne University of Bremen Karlsruhe Institute of Technology University of Freiburg

Source 360 Encyclopedia (https://baike.so.com/)

Category

Elite Universities in Germany

University

Table 6.5

1472 1868 1870 1948 1386 1966 1828 1810 1477 1388 1971 1825 1457

Year of establishment 44,000 32,000 37,917 28,500 26,500 10,076 36,592 34,072 23,500 44,282 18,000 23,905 20,500

Number of students

2006, 2006, 2007, 2007, 2007, 2007, 2012 2012 2012 2012 2012 2006 2007

2012 2012 2012 2012 2012 2012

Year/s of selection

6 INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

185

186

Y. LI

(2) Pioneer of the dual-track education model What we refer to as the dual-track education model is the development of vocational education and general education in parallel in Germany. In terms of vocational education, Germany’s dual-system vocational education model can be regarded as a role model in the world. As a developed industrial country, it is only too natural that Germany attaches great importance to vocational education, but it is different from vocational education in a simple sense. In the industrialization stage, German vocational education focused on training industrial and technological talents. Therefore, over the long period of industrialization, the human capital of Germany was featured with typical orientation of industrialization. This feature continues in Germany even today. It is noteworthy that vocational education in Germany is also dynamic and adaptive—it keeps pace with the times. At present, a new round of industrial structure adjustment is underway all over the world. As a traditional industrial country, Germany is taking the lead in advancing into the post-industrialization era, and actively adjusting and optimizing its industrial structure according to its national conditions. The new round of industrial adjustment centers on the tertiary industry with the service economy as the core. In order to adapt to this change, Germany has made adjustment to its education system. In fact, such dynamic adjustment has been in the German education system all along. Be it primary, secondary, advanced, or higher education, the German education serves the whole national economy and society, and constantly makes adaptive changes. During the current optimization and upgrading of the tertiary industry, Germany is making adjustment to its vocational education system, including the teaching content, the teaching modes, and many other aspects. To adapt to this background, certain changes are brought into some areas of the German general education system. In the selection of elite universities, the alliance of TU9 (German Universities of Technology), and the implementation of other dividend policies, changes are taking place in the German education. Such dynamic changes in the education model are meant to meet the needs of the education system to optimize and adjust itself and, on the other hand, to meet the needs of economic and social development. It is worth noting that such subtle changes in education do not directly affect the economy and society—their core effect is on the change of the structure of human capital. The education system, the teaching models, and

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

187

the teaching contents will directly change the structure of the national human capital, and thus change the structure of the social human capital of the whole country. The adjustment, optimization, and upgrading of the human capital structure will eventually affect all aspects of the society and economy, so as to realize economic and social development. For example, during the industrialization era, Germany attached great importance to vocational education. If we look at the development of Germany, we can find that in its industrialization stage, almost all its scientific research results were featured with the characteristics of industrialization. This is particularly true of its vocational education. In the context of the current global adjustment of the service economy in the tertiary industry, it is not difficult to understand that Germany has carried out a reform in both its vocational and general education centering on the tertiary industry. What is worth learning for China is that developed countries have successively carried out industrial structure adjustment with the service economy as the core, and many developing countries have followed suit. As the largest developing country, China is bound to adjust its industrial structure, and such adjustment is bound to cause changes in its national education, or transformation of its human capital structure, and boost the optimization and upgrading of its human capital structure. However, this adjustment is the first ever—there is no experience for any country to learn from. Yet Germany has long accumulated advantages and experience in industrial structure adjustment, education model transformation, and human capital structure optimization, which are well worth learning for China. 6.1.2.3 The R&D Indicator As one of the founders of OECD, Germany became a member state of the organization as early as 1961. It also leads the way in R&D input. According to the official statistics of the OECD, Germany’s GERD input totaled about USD 100,991.4 million in 2013. On the other hand, it had also witnessed substantial input in BERD, HERD, and GOVERD (see Table 6.6). If we compare Tables 6.1 and 6.6, it is not difficult to find that although Germany is smaller than the United States and Japan in R&D input, it is on an upward trend in terms of the annual input and is dominated by enterprise investment. In particular, we must point out the apprenticeship model in Germany. In essence, the German apprenticeship is the embodiment of the “dual-system vocational education

81,970.7 2.60 69.2 16.7 14.0 56,764.6 13,690.8 11,515.3

2008 82,822.2 2.73 67.6 17.6 14.8 55,954.3 14,593.4 12,274.4

2009 87,822.0 2.72 67.1 18.1 14.8 58,921.1 15,901.5 12,999.5

2010 96,282.4 2.80 67.7 17.8 14.5 65,136.3 17,151.1 13,995.0

2011

100,699.1 2.88 68.0 17.7 14.3 68,469.1 17,794.7 14,435.3

2012

100,991.4 2.85 66.9 18.0 15.1 67,569.5 18,178.6 15,243.3

2013

Notes GERD stands for gross domestic expenditure on R&D; BE proportion for percentage of gross domestic expenditure on R&D performed by the business enterprise sector; HE proportion for percentage of gross domestic expenditure on R&D performed by the higher education sector; GOV proportion for percentage of gross domestic expenditure on R&D performed by the government sector; BERD for business enterprise expenditure on R&D; HERD for higher education expenditure on R&D; and GOVERD for government intramural expenditure on R&D Source OECD, “Main Science and Technology Indicators Database,” July 2015

52,375.4 2.40 70.3 16.1 13.6 36,835.3 8,428.8 7,111.4

2000

Relevant Indicators of German R&D (million USD)

GERD GERD proportion (%) BE proportion (%) HE proportion (%) GOV proportion (%) BERD HERD GOVERD

Indicator

Table 6.6

188 Y. LI

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

Table 6.7 Numbers of Apprentices Trained in German Economic Sectors, 2010–20119

Training sector Industry and commerce Handicraft Agricultural Civil servants Freelancers Housekeeping Total

189

2010

2011

873,402 434,907 38,667 37,587 113,682 10,086 1,508,328

850,689 414,207 26,624 37,998 111,861 9,276 1,460,658

Source Statistics from German Federal Statistical Office

model.” Apprentices are not only students or employees, but also laborers who independently sign labor contracts with enterprises, yet they are “apprentices” in identity. This is how Germany presses forward with its dual-system vocational education (see Table 6.7). The apprenticeship model is a major innovation in German human capital investment. As a talent cultivation model, it well combines college education with enterprise training. However, the German apprenticeship system is not simply a “combination of schools and enterprises,” but a new type of labor and employment relations. 6.1.2.4 The German National Spirit and Social Security System In addition to the above-mentioned aspects, the German national spirit and the developed and complete social security system are also important parts of human capital investment in Germany. Whether in academia or other sectors of society, the German national spirit is a topical issue. Undoubtedly, this spirit has played an irreplaceable role in the times of its rise. The German national spirit has taken shape through constant collision and condensation in the long history of Germany’s development. The unique historical origin, geoculture, social structure, and mode of thinking have brought into shape this national spirit and culture. The combination of compulsory education and military

9 Zhou Hongli and Zhang Wanxing, “On the Modern Apprenticeship Model in Germany in Light of the Human Capital Theory,” Higher Education Exploration, no. 4 (2014).

190

Y. LI

service systems has given birth to a unique national cultivation mechanism in Germany. The combination of college education and militarized training has forged a unique national spirit of the country. In general, the German national spirit is a set of spiritual systems including innovation, meticulousness, speculativeness, diligence, and the enterprising spirit. Under the influence of the traditional spirit and culture, the German people are generally well educated. At the same time, enterprises born in Germany always devote themselves to their duties, innovation, and refinement. Therefore, German workers are highly efficient and enthusiastic for innovation. The shaping of such a spirit constitutes another important link in the structure of the German human capital investment. In particular, the “spiritual capital” put forward by the German scholar Friedrich List was the early form of modern human capital theory. On the other hand, the gradually improving social security system has contributed to the accumulation of human capital in Germany. In her book Social Security System of Germany, Yao Lingzhen (2011) elaborated on the German social security system.10 As early as the 1980s, Germany had established a relatively complete social security system (see Table 6.8). Table 6.8 The Social Security System in Germany Law/Act

Year

Keywords

Workers’ Medical Insurance Act Accident Insurance Law Endowment Insurance Law Staff Insurance Act Imperial Miners Union Act Unemployment Insurance Law Law of Assistance to the Aged Farmers Social Care Insurance Act …

1983 1984 1989 1911 1923 1927 1957 1995 …

Medical Accident Endowment Staff Miners Unemployment Social assistance Social care …

Source 360 Encyclopedia (https://baike.so.com/)

10 Yao Lingzhen, Social Security System of Germany, (Shanghai: Shanghai People’s Publishing House, 2011).

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

191

Meanwhile, data shows that Germany’s social security expenditure reached 721 billion euros in 2008, accounting for 29% of its GDP. The figure in 2009 was 31.9% and rose to 32.4% in 2010.11 6.1.3

Human Capital Investment in East Asia

The East Asian miracle has given birth to the “East Asian model.” One of the most important reasons for this is the human capital factor. Human capital investment in the East Asian model is highly representative in the world. 6.1.3.1 Human Capital Investment in Japan Undoubtedly, human capital investment is one of the key elements of Japan’s rapid rise in modern times. From the Meiji Restoration to Japan’s rapid development into the most powerful capitalist country in Asia, from the economic decline after its defeat in World War I and World War II to its economic takeoff and growth into the world’s second largest economy since World War II, fundamentally speaking, Japan’s rise has been deeply branded with human capital investment. As Zhang Kangsi (2009) pointed out, as the most important economic resource, human capital has brought into being Japan’s economic miracle and made it a world economic power.12 Using the data of Japan’s innovation and transformation from 1965 to 2009 and the Granger causality test analysis, Gao Xirong, Zhang Wei, and Chen Liuting (2014) concluded that human capital is a long-term driving force of Japan’s independent innovation.13 (1) Optimization and improvement of Japan’s human capital structure to forge its overall strength of human capital Japan attaches great importance to human capital development and investment. Due to the limitation of its geographical conditions, the Japanese people have generally realized that their natural resources are 11 Business Group of Munich Office, “The German Social Security Expenditure to Account for Record-High 32.4% of GDP,” Bne IntelliNews (http://info.taiwantrade.com/ CH/bizsearchdetail/1493369/C/). 12 Zhang Kangsi, “The Cultivation of Sustainable Competitiveness of Enterprises in Macroeconomic Cataclysms,” Macroeconomics, no. 5 (2009). 13 Gao Xirong, Zhang Wei, and Chen Liuting, “Human Capital: A Long-Term Driving Force of National Independent Innovation—Based on an Empirical Analysis of Japan’s Innovation and Transformation,” Science and Technology Progress and Policy, no. 3 (2014).

192

Y. LI

very limited, and they have to rely on human capital investment, fully develop their human resources, and give play to the initiative of laborers to make up for the limited natural conditions. On the one hand, Japanese government departments have identified human capital development and investment as one of the national strategies, and mobilized resources from all aspects to create conditions and facilities to fully facilitate its human capital development and introduction of foreign human capital. This strategy has become an important means for Japan to develop its economy, and its government has made efforts in education, law, and social security. Under the integrated effect of various policy measures, Japan has actually brought into being a gradually complete structure of social human capital. It includes many aspects, such as domestic human capital and human capital introduced from abroad. Japan has a relatively small population, and in recent years, it has entered a stage of accelerated population aging, which is marked by an increasingly prominent trend of counter growth of its population. Therefore, in addition to vigorously strengthening domestic human capital development and investment, it also attaches great importance to the introduction of foreign talents by means of immigration, attraction of overseas students, company dispatch, etc. According to data, on an annual basis, the number of immigrants to Japan from other parts of the world is on the rise, with the largest part being international students. The large-scale influx of foreign students into Japan has become an important part of its human capital reserve. On the other hand, in light of its domestic human capital development, Japan has made great efforts to promote education, social security, scientific research, culture, and medical care, especially in its national education (including primary, secondary and college education, university education and above, as well as national continuing education). Similar to what is happening in most countries (especially developed ones), education has long been Japan’s basic and most important national policy. The highly valued national education and the advanced education concept ahead of the times have become a significant part of Japan’s human capital investment and development. It is generally believed by scholars that the advanced education has laid the foundation for Japan’s human capital and economic development. Research shows that Japan attaches great importance to high school education—its entrance rate of middle school graduates reached 80% in 1970, 90% in 1973, and 98% in 2009. In addition, data also shows that the average years of schooling of Japanese working-age population in

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

193

2005 were 12.9 years, nearly seven years higher than that of China in the same year.14 Education is fundamental to human capital. It is through its developed education that Japan keeps accumulating human capital and has brought into being a highly competitive human capital structure with excellent social welfare and security. For the same reason, Japan’s human capital structure is a modern one characterized by higher education and high quality. In terms of its composition of national human capital, Japan had finished the transformation from a general primary human capital structure to a special high-level human capital structure as early as the middle and late twentieth century. The general, primary human capital structure includes only some general and basic human capital. At present, it is still a major part of the human capital structure of some developing countries. For example, it still accounts for a considerable part of the human capital structure of China. In contrast, the transformation and upgrading of Japan’s advanced human capital structure are in line with the needs of its ongoing industrial structure transformation and upgrading. The accumulation of human capital and the optimization and upgrading of its structure have become an important driving force for Japan’s rapid economic rise. This driving role is not only reflected in its economic recovery and catching-up after World War II, but also in its current modernization process. The current catch-up of Japan centered on human capital is undoubtedly the most prominent feature of its modernization. As Li Hui and Yu Qinkai (2004) pointed out, by deepening the reform and upgrading of its education system, Japan has vigorously expanded and popularized its basic education, deeply promoted its “talent training plan,” and constantly increased its investment in human capital. This has greatly improved the overall quality of Japanese laborers on the domestic labor market, optimized and upgraded the structure of Japanese human capital, and created an institutional environment for giving full play to the economic efficiency of human capital.15

14 Zhang Kangsi, “The Cultivation of Sustainable Competitiveness of Enterprises in Macroeconomic Cataclysms,” Macroeconomics, no. 5 (2009). 15 Li Hui and Yu Qinkai, “The Driving Role of Human Capital Accumulation in Japan’s Economic Catch-up After WWII and Its Enlightenment,” Contemporary Economy of Japan, no. 6 (2004).

194

Y. LI

(2) Three engines for cultivation of enterprise human capital There are three magic tools for cultivating human capital on the domestic labor market of Japanese enterprises: lifelong employment, senioritybased wage, and internal promotion. In his article “Japan’s Labor Market System and Its Challenges and New Changes,” Zhao Zengyao (2000) made a detailed analysis of the three engines for human capital cultivation in Japanese enterprises.16 First, lifelong employment. The lifelong employment system was first proposed by Panasonic Corporation. It began in Japan’s economic recovery after World War II. Yet it is still widely used and plays a huge role in enterprises and institutions of many countries or regions today. From the perspective of human capital, the starting point of the lifelong employment system is human capital investment. The original intention of the lifelong employment system is to provide a guarantee for employees so that they do not have to worry about losing their jobs, thus giving them a sense of psychological security, and effectively reducing the rate of employee turnover and job hopping. Then, there is no doubt that lifelong employment, as a form of human capital investment, focuses on the psychological capital investment of employees. By creating a sense of security, it can improve employees’ loyalty to the enterprise, thus encouraging them to work actively. At the same time, the starting point of the enterprise is also to reduce the employee turnover rate, so as to effectively reduce the risk cost brought to the enterprise by employee turnover, which is also part of human capital. In fact, at a more in-depth level, the benefit of the lifelong employment system also lies in that the workers who are employed for life have established an extremely solid relationship with the enterprise, which is an important part of modern corporate governance theory and an ideal state pursued by modern enterprises. This psychological contractual relationship is highly conducive to the development and accumulation of human capital within the enterprise, and such human capital will never drain away. Second, the seniority-based wage system. Similar to the lifelong employment system, seniority-based payment also originated in Japan’s economic recovery after World War II. This system centers on the wage.

16 Zhao Zengyao, “Japan’s Labor Market System and Its Challenges and New Changes,” World Economics and Politics, no. 12 (2000).

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

195

Its basic principle is that the wage income of employees increases gradually with the number of years of service. Generally speaking, the senioritybased wage system follows the rule that, in the early years when the employees enter the enterprise (that is, when they are younger) they enjoy relatively low wages, which are lower than their performance output; and when they become senior members of the company (that is, when they are older) they will enjoy a salary higher than their performance output. It may as well be understood as a “bitter-before-sweet” system. This remuneration system has indeed had an important impact in a certain period. On the one hand, it effectively cultivates the sense of belonging of employees and strengthens their relationship with enterprises; on the other hand, it improves employee loyalty and reduces employee turnover. In fact, it is also greatly beneficial for the cultivation of enterprise human capital. At the initial stage of employee growth, the major form of enterprise human capital is often general human capital. In the process of continuous growth, such general human capital will gradually transform into special human capital with the deepening of learning and training. Such special human capital is exactly what the enterprise requires, which is an important part and source of the core competitiveness of the enterprise. From this perspective, it is easy to understand the wage system based on seniority. The general human capital of employees at the initial stage does not require large-scale investment, so the strategy of low return is adopted. However, as time goes by, the knowledge and skills acquired by employees are constantly improving, and the special human capital is gradually accumulated to form the core competitiveness of the enterprise. Even if this special human capital may not be directly reflected in the actual output, the enterprise must pay more than its actual output. Third, internal promotion. Internal promotion provides an upward channel and guarantee for the career development of employees in Japanese enterprises, which greatly encourages their enthusiasm. In fact, internal promotion is highly consistent with lifelong employment and seniority-based payment, which are mutually beneficial and complementary in function. Internal promotion provides employees with a signal of career development opportunities, which means that as long as they work hard, they will be able to obtain the opportunity for promotion. It is easy to understand that internal promotion, as an important means of enterprise human capital investment, stimulates employees to constantly learn, innovate, and improve themselves through internal incentives, rather than

196

Y. LI

rely solely on the enterprise’s unilateral investment to achieve increase and promotion of human capital. Apart from the above, Japan has many successful models of enterprise human capital investment. They are well worth learning in terms of corporate governance theory and practice. For example, the internal trade union system in Japanese enterprises is different from the trade unions in European and American enterprises. It represents the interests of employees and is not a subsidiary representing the interests of the company. By contrast, the trade union in China is indeed and to a large extent a subsidiary of the company and does not really fulfill its roles. (3) The R&D indicator In terms of R&D input, Japan is on an upward trend in all the indicators. According to official statistics of the OECD, Japan’s R&D investment increased from USD 98,758 million in 2000 to USD 160,246.8 million in 2013 (see Table 6.9). The increasing R&D investment has not only laid a solid foundation for the rapid development of the Japanese economy, but is also an important way for Japan to accumulate human capital. Its R&D investment has not only generated direct economic benefits, but also cultivated a large number of outstanding R&D talents. Besides, studies show that Japan attaches great importance to R&D activities, and its proportion of R&D personnel in the total population has been growing rapidly and steadily. As of 2009, the proportion of senior R&D personnel in the total population had reached 7% in Japan, which means that there are seven senior R&D personnel in every 1,000 persons. In today’s international community, R&D investment has long been an important indicator of the scientific research capacity and comprehensive strength of a country or region. 6.1.3.2 Human Capital Investment in Singapore Unlike Japan’s model of human capital investment which is typically dominated by enterprises, Singapore’s human capital investment is unique in its “government-led model.” Its success in government-led human capital investment provides an important reference for China. Over the past three decades, the rapid economic development of Singapore has created another myth of economic takeoff in Asia and made the country one of the “Four Asian Tigers.” Compared with the takeoff

148,719.2 3.47 78.5 11.6 8.3 116,687.8 173,06.1 12,387.0

2008 136,954.0 3.36 75.8 13.4 9.2 103,759.2 18,366.0 12,619.7

2009 140,607.4 3.25 76.5 12.9 9.0 107,584.6 18,099.5 12,689.0

2010 148,389.2 3.38 77.0 13.2 8.4 114,204.6 19,603.5 12,428.3

2011

151,810.0 3.34 76.7 13.4 8.6 116,321.3 20,276.0 13,086.3

2012

160,246.8 3.47 76.1 13.5 9.2 121,932.7 21,578.5 14,692.1

2013

Notes GERD stands for gross domestic expenditure on R&D; BE proportion for percentage of gross domestic expenditure on R&D performed by the business enterprise sector; HE proportion for percentage of Gross domestic expenditure on R&D performed by the higher education sector; GOV proportion for percentage of gross domestic expenditure on R&D performed by the government sector; BERD for business enterprise expenditure on R&D; HERD for higher education expenditure on R&D; and GOVERD for government intramural expenditure on R&D Source OECD, “Main Science and Technology Indicators Database,” July 2015

98,758.0 3.00 71.0 14.5 9.9 70,079.9 14,348.1 9,767.3

2000

R&D-Related Indicators of Japan (million USD)

GERD GERD proportion (%) BE proportion (%) HE proportion (%) GOV proportion (%) BERD HERD GOVERD

Indicator

Table 6.9

6 INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

197

198

Y. LI

of some developed countries, the rise of Singapore has attracted much more attention. Since 1780, Britain had doubled its per capita income in more than 60 years. Japan achieved the same goal in over 30 years since 1885. Yet it only took Singapore over one decade to achieve the goal of doubling its per capita income, nearly five times shorter than the UK and nearly twice shorter than Japan. Since the 1990s, Singapore has learned from the experience of industrial adjustment in Western developed countries, proactively adjusted its industrial structure, and realized the reshaping, optimization, and upgrading of its industrial structure. The adjusted industrial structure directly targets the high-tech industry and financial service industry. Meanwhile, electronics is also the lifeblood of the domestic manufacturing industry, accounting for over 40% of Singapore’s industrial output value. A 1994 report from the World Economic Forum in Davos, Switzerland, pointed out that Singapore is second only to the United States in international competitiveness in terms of eight indicators, namely, economic strength, internationalization, government influence, financial strength, public infrastructure construction, enterprise management, science and technology, and human resources. Undoubtedly, Singapore has created a myth of economic development, and behind this is its close attention to and investment in human resources. Singaporeans have always been proud of this, but it is also a concern of scholars who have long been committed to Singapore studies. Singapore is an urban country with a small land area, a small population, and extremely scarce resources. The only thing that can be used is its human resources, the only asset of the country. Therefore, it has always been Singapore’s basic policy to spare no effort to increase investment in human resources and to pursue and maximize the value of human capital. Even though Singapore’s export-oriented strategy is easy to succeed because of its unique port advantages, what flows behind it is still the blood of human capital. Its prerequisite is technology, which requires both the development of powerful scientific research staff and the technical operation of high-tech talents. Singapore is different from other countries in the way to take off, but there is something very similar: education is fundamental to its rise. This is widely recognized by the vast academic community. For this sole asset, the Singaporean government has always given top priority to its national education as a long-term basic national policy. From the perspective of financial support, Singapore’s annual investment in education is very large, second only to its defense expenditure in terms of the proportion in

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

199

the national fiscal expenditure. What’s more, its investment in education is still growing at an annual rate of 30%. Data shows that since the founding of Singapore, its education expenditure has increased from about SGD 61.4 million to about SGD 3.2 billion, a record high in recent years. As early as 1995, the average education allowance given by the Singaporean government to students accounted for nearly 90% of the average education cost of students, which means that the compulsory education model has been fully popularized in Singapore, and the students’ education cost burden is almost zero. In addition, as an important supplement to education support, the Singaporean government stipulates that all employees of enterprises and institutions pay 21.5% of their salaries as the social welfare and education training fund, and employees of other units pay 18.5% of their total salaries as the source of this fund. Such sources fully guarantee the needs of social welfare and education, and are mainly used for education and training. For example, all employees can apply for this fund and receive as high as 90% of their education and training costs from the fund. At the same time, a vocational training bureau has been set up, with a number of education and training institutions under it, which are mainly responsible for in-service education and training of employees in enterprises and institutions. In terms of the education model, the Singaporean government holds that the education system must fully develop children’s abilities and build a responsible, cohesive, healthy, and prosperous society. To this end, Singapore keeps exploring and improving its national education system, emphasizing the cultivation of creativity in kindergartens and primary and secondary schools. For example, in Singapore, children begin to learn about computers at the age of four, and primary school students learn English, mathematics, computers, ethics, and history. Similar to the German education model, Singapore’s secondary education is combined with practical skills training, which serves as an important indicator of evaluation. Singapore’s secondary education lasts four years. After that, students must pass the primary examination of the national general education and obtain the education certificate before they can enter higher education institutions. In terms of higher education, the Singaporean government has always regarded it as a focus of education. For this reason, it has been constantly enriching and improving the teaching and training model of the National University of Singapore, and has established specialized institutions, such as Nanyang Technological University.

200

Y. LI

These institutions are responsible for student education and the cultivation of high-quality and high-level talents needed by the country and for providing in-service training for employees of enterprises and institutions. In addition to formal school education and training, the Singaporean government also attaches importance to the strength of non-governmental training institutions (organizations). In fact, these non-governmental forces (e.g., the Singapore Chinese Chamber of Commerce) have played an important role in Singapore’s talent cultivation. The Singaporean government also attaches importance to human capital investment. As pointed out by Prof. Huang Chaohan, a famous economist, although capital investment has played an important role in Singapore’s success, the key lies in its great attention to the investment in and appreciation of human capital. If we look at the decades of economic development of Singapore and its human capital investment with education and training as the core, it is not difficult to find that the leading role of its government departments has a profound impact on its human capital investment. Singapore’s “government dominated” human capital investment and construction model is indeed the key to its success. On the one hand, Singapore is an urban country with a small population and limited resources. Due to such special national conditions, it has to rely on the macro-control of a strong government in order to avoid various limitations. On the other hand, it is precisely because of its government’s leadership that Singapore has effectively made up for the unbalanced human capital under the market economy model. As the public authority and resources benefit all citizens, it has comprehensively improved Singapore’s human capital level. 6.1.3.3 Human Capital Investment in South Korea Also as one of the “Four Asian Tigers,” South Korea was comparable to China in human capital in the 1940s and 1950s. However, amid today’s rapid economic development and change, the gap is quite substantial between them, although both have significantly improved their human capital. According to estimates of James J. Heckman, winner of the 2000 Nobel Prize in Economics, the proportions of human capital and physical capital investment are 5.4% and 17% for the US, 3.6% and 30% for South Korea and 2.5% and 30%, respectively for China. This means China invests less toward human capital as a proportion of its GDP than the world average and also than South Korea. Their difference in human capital

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

201

investment well explains why South Korea has risen more rapidly than China. According to data, in 2009, the total population of South Korea was about 48.6 million, with a density of about 500 people per square kilometer, but its per capita GDP reached RMB 149,033, one of the highest in the world. Similar to Singapore in national conditions, South Korea has a small land area, scarce resources, and a high population density, and its only asset is human resources. On the reasons for the rapid development of South Korea’s economy, scholars basically agree that human resources are the fundamental driving force—its constant increase and optimization of human capital investment has made it one of the world’s economic leaders as well as one of the “Four Asian Tigers.” Similar to the cases of Singapore, Japan, and Germany, human capital investment has played a major role in the rise of South Korea, as education and healthcare investment accounts for the largest part of its human capital investment. Under such a structure of human capital investment, the adult literacy rate of South Korea is 99%, one of the highest in the world, and the average years of schooling of its people over 25 are 11.48 years. This has resulted from the rapid development of its national education. When the Republic of Korea was established, there were only 19 colleges and universities across the country, with no more than 7,000 students. Through decades of investment and construction, its colleges and universities have increased by nearly ten times. At present, there are 208 four-year universities, including 42 public ones. In the composition of Korean residents’ education, the proportions of high school and college (and above) graduates are the highest, reaching 38.41% and 36.51%, respectively. To promote the development of its national education and cultivate outstanding talents for national development, South Korea spends huge amounts of money on education every year. In 2009, its per capita education expenditure was up to RMB 39,014.83. Such huge education expenditure mainly comes from government departments and universities, including funds by the central and local governments and independent funds of private schools, of which the former accounts for the largest part. The budget of the central government’s financial funds for education expenditure mainly comes from national taxes and goes to the department of primary and secondary education, national universities, private universities, educational administrations, and related research institutions. Of the education expenditure funds of local governments 85% come from the

202

Y. LI

central finance, and the rest is borne by students and local governments, which greatly reduces the cost of students’ education. Moreover, South Korea’s annual investment in education is still increasing, and this can be seen from the proportion of education expenditure in GDP, which was 4.62% in 2000, 4.63% in 2005, and 4.75% in 2009. Massive investment in education has greatly promoted and improved South Korea’s education level, which has also brought about a high level of human capital. For example, in 2009, the enrollment rates of primary schools, middle schools, high schools, and universities and above in South Korea were 99.1%, 94.4%, 92.0%, and 71.1%, respectively. In terms of scientific research, South Korea’s advantages in the knowledge economy are becoming more and more prominent. For example, its number of patents per capita is second only to Japan, with 160,000 patent application filed annually. Besides, statistics from the World Bank show that, there were 3,780, 4,187 and 4,627 researchers per million people in South Korea in 2005, 2006, and 2007, and its R&D indicator reached a high level. In addition to spending generously on education, South Korea also invests significant amounts of money toward social welfare and health care. As we can see, South Korea, Singapore, and Japan (especially the first two) are highly similar in how they achieved the economic miracle. Both Singapore and South Korea have followed a government-led path of human capital. Japan is slightly different: with the strong support of the government, its enterprises have also played an important role. Although these countries may be different in their national conditions, their success and experience are worth learning for China.

6.2 Takeaways from Human Capital Investment in Foreign Countries on Regional Human Capital Investment in China It is nothing new that human capital is a key to promoting sustainable and healthy economic development. Over the past few centuries, Western developed countries have accumulated rich human capital under the comprehensive effect of economy, education, social security and welfare, and scientific research. Such human capital has produced prominent positive effects on and made great contributions to all aspects of their economic and social life. At this stage, China has lagged behind.

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

203

At present, a new round of industrial restructuring and transformation is well underway around the world, and is bound to give birth to a new type of human capital structure in the new period. This will undoubtedly lead to the fourth generation of global economic waves. Whereas the core element of the previous three generations of global economic waves was resources, the core winning factor of the fourth generation will undoubtedly be human capital. The first generation of global economic waves took place in the feudal natural economy; the second took shape in the early period of capitalist industrialization; the third started in the middle of the post-industrial capitalism and has lasted up till now; and the fourth will be the upcoming new economic era. The previous three generations of economic waves were without exception based on natural resources, whereas the fourth will center on human capital, which represents the soft power. The difference is that the post-industrial era and the previous economic eras relied on simple human labor (i.e., simple human capital), while the new economic era will focus on special high-level human capital, which is the development of the subjective initiative and creativity of individuals. Without doubt, in the new economic era, whoever can transform and upgrade human capital and take the lead in human capital reform will gain the first-mover advantage and hold the competitive advantage. In the new historical period and the new era of industrial economy and under the background of new normal conditions of the Chinese economy, promoting the diversified transformation of human capital is the only way to realize the transformation and upgrading of China’s industrial structure and the steady and healthy development of its economy, and is also the inevitable choice to solve China’s current difficulties, such as the “Lewis turning point,” the disappearance of the “demographic dividends,” and the “middle-income trap.” China is still a “rising star” and has many shortcomings, such as the shortage of theoretical research, the lack of practical exploration experience, the weak coordination between theory and practice, and so on, which are bottlenecks restricting the sustainable and healthy development of the Chinese economy. In the new era, the transformation and upgrading of the industrial structure is a general trend. This is particularly true of China. Whether its transformation is successful or not depends on many aspects, but human capital is undoubtedly the most critical one. In China, studies on human capital started late, and most of its research results are borrowed from Western developed countries. Therefore, in practice and exploration, Chinese scholars

204

Y. LI

must “cross the river by feeling the stones” and promote and implement their research results relying on “pilot experience.” Under the background of globalization, regional economic integration, and the new development pattern of the Belt and Road Initiative (BRI), we have extensively absorbed and introduced foreign advanced experience, always followed the basic idea of “introduction—absorption—combination—reintroduction,” always taken China’s specific and real conditions as the starting point, blazed a path of socialist human capital with Chinese characteristics in line with China’s specific conditions, and opened up a new road of human capital that suits China’s economic and industrial development in the new era and under the new pattern. To this end, it is important that China learn from the successful experience of foreign countries in human capital measures. Specifically, we recommend that China explore new paths of human capital investment in the following aspects. 6.2.1

Deepening of Overall Reform in the Education System and Implementation of the Basic Strategy of “Strengthening the Country through Education”

Education is the foundation of all undertakings and the long-term development of a country. China has already established the basic strategy of “rejuvenating the country through science and education” and extensively carried out compulsory education, so as to improve the ideological, moral, and cultural quality of its people. Since the implementation of the reform and opening-up policy, China has constantly developed its education and made a series of gratifying achievements. In light of the trend of development, China saw the number of college graduates constantly increasing between 2001 and 2015. For example, college graduates reached 7.49 million in 2015. In 2016, it even rocketed to a record high of 7.65 million (see Fig. 6.5). Although the large size of college graduates has brought some difficulty in employment, the rising figure largely reflects the level and quality of education of the Chinese people and the continuous development of education in China. Given the above, compared with developed countries and against its specific conditions and actual needs, China must further develop its education and deepen the reform of its education system and mechanism. Currently, China must learn from the advantages of foreign countries in education investment and make improvements in the following aspects.

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

205

Fig. 6.5 College Graduates in China, 2001–2015 (million persons) (Source Education Online, “Numbers of College Graduates in China 2001–2015.” Accessed 5 December 2014)

6.2.1.1

Enlarging Education Investment and Improving Its Efficiency The small size of education investment and the huge demand for education is a prominent contradiction between supply and demand, which is the crux of China’s education investment at present. The size of education investment is mainly reflected in the overall level of education received by the labor force and the amount and scale of education investment. In terms of the overall level of national education, data shows that the average years of schooling in moderately developed countries is 12.26 years, while it is only 8.5 years in China; in 2008, China’s higher education enrollment rate (the gross enrollment rate) was 23.3%, while the average enrollment rate in developed countries had reached 54.75% as early as 1996. Since the expansion of college enrollment in China, its proportion of college students has been rising, but still a large part of its population has not received higher education or has only received education for a short time (see Table 6.10). It is not hard to find in Table 6.10 that, as the numbers of students in colleges and high schools increased year by year, those in middle schools, primary schools, and kindergartens were on a downward trend. Besides, in terms of education investment, the proportion of total education investment in the national economy was still small. Since its reform and opening-up, China has been expanding its investment in education.

206

Y. LI

Table 6.10 Numbers of Students in Different Levels of Education Institutions in China (thousand) Year

Higher education

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

326 304 313 376 433 457 470 482 519 594 723 931 1,146 1,298 1,420 1,613 1,816 1,924 2,042

High school

Middle school

Primary school

Kindergarten

1,337 1,355 1,365 1,448 1,293 1,610 1,780 1,905 1,978 2,032 2,000 2,021 2,283 2,523 2,824 3,070 3,321 3,409 3,440

3,426 3,465 3,518 3,599 3,681 3,945 4,180 4,289 4,408 4,656 4,969 5,161 5,240 5,209 5,058 4,781 4,557 4,364 4,227

10,707 10,502 10,413 10,656 10,819 11,010 11,273 11,435 11,287 10,855 10,335 9,937 9,525 9,100 8,725 8,358 8,192 8,037 7,819

1,725 1,907 2,072 2,190 2,219 2,262 2,208 2,058 1,944 1,864 1,782 1,602 1,595 1,560 1,617 1,676 1,731 1,787 1,873

Note Higher education institutions include regular and adult higher education institutions; high schools include regular and vocational high schools, regular specialized, technical, and adult technical secondary schools, and adult high schools; and middle schools include regular and vocational middle schools Source Statistical Bulletin on the Development of National Education from 1990 to 2008

For example, the financial expenditure on education increased from RMB 8.124 billion in 1978 to RMB 828.021 billion in 2007, an increase of over 100 times, with an annual growth rate of over 10%.17 However, its proportion of financial education investment in GDP has always lingered around 0.2–0.3, which is far smaller than the world average of 0.52 over the same period. In view of this, expanding the total amount of education investment has become an important way for China to implement the strategy of strengthening itself with human capital in the new era.

17 Ministry of Education, “Statistical Bulletin on the Development of National Education,” http://www.moe.edu.cn/edoas/website/8/54/in-fol209972965475254.htm.

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

207

In recent years, China’s total investment in education has been expanding and its support for education has been increasing (see Table 6.11). On the one hand, China has strengthened the development of general education, including the construction of regular middle schools, regular high schools, and regular colleges and universities. In particular, the construction of academic institutions of higher education has become a focus of higher education at present. On the other hand, it attaches great importance to vocational education and, as a result, the national investment in vocational education is growing steadily. There is no doubt that vocational education has become an important item on China’s agenda of education development, and has risen to a strategic height. According to “The Announcement of the Statistics on National Education Expenditures in 2014” jointly released by the Ministry of Education, the National Bureau of Statistics, and the Ministry of Finance, China’s financial input in education in 2014 reached RMB 2,642.058 billion, up by 7.89 percentage points year on year, and its proportion in GDP rose to 4.15%; the national public expenditure on education was RMB 2,257.601 billion, up 5.47 percentage points year on year (including RMB 410.159 billion by the central government, which was up 8.20%); at the same time, the total investment in education in that year reached RMB 3,280.646 billion, an increase of 8.04 percentage points year on year.18 Table 6.11 Growth of Per Student Spending in Public Budget (RMB) Level Regular primary school Regular middle school Regular high school Secondary vocational school Regular higher education

2013

2014

Growth rate (%)

6,901.77 9,258.37 8,448.14 8,784.64 15,591.72

7,681.02 10,359.33 9,024.96 9,128.83 16,102.72

11.29 11.89 6.83 3.92 3.28

Source Ministry of Education, National Bureau of Statistics, and Ministry of Finance, “The Announcement of the Statistics on National Education Expenditures in 2014,” Education Expenditures No. 9 (2015), October 2015

18 People.cn, “The Proportion of National Financial Education Funds in GDP Has Exceeded 4% for Three Consecutive Years,” http://edu.ifeng.com/a/20151014/414 90053_0.shtml.

208

Y. LI

6.2.1.2

Optimizing and Upgrading the Education System Reform and Mechanisms and Innovating and Introducing Advanced Philosophy and Models For a long time, China has inherited a traditional educational system as well as traditional educational models and philosophy, namely a rigid educational system, outdated educational models, and backward educational philosophy. In the era of the knowledge economy, this relatively backward educational mechanism can hardly meet the needs of economic and social development, and a profound educational reform is urgently needed. If we say that increasing investment in education is to inject blood and power into education, the fundamental measure is to optimize and upgrade the education system and innovate its concepts and models. In order to cultivate innovative human capital in the new era, the inevitable choice is to promote the transformation and upgrading of the educational system and mechanisms and to accelerate the optimization and innovation of educational concepts and models, which is the key to promoting the “quantity-to-quality” transformation of human capital and to the “quantity-to-quality” transformation of education itself. As is known to all, in addition to insufficiencies in the “quantity” of investment, there are also serious limitations on the “quality” of talent training in China. Under the traditional “teaching” model, teachers are the center of the one-way teaching and students are passive receivers of knowledge. On the one hand, the teachers’ ability has a profound impact on the efficiency of knowledge transfer, but they are usually monotonous and tedious in teaching methods and limited in teaching content. On the other hand, the students’ learning initiative and innovativeness are constrained, and their thought can hardly be unleashed. Consequently, the human capital accumulated is mostly general human capital, which is relatively low in quality and level. Yet it is exactly this kind of human capital, or “demographic dividends,” that China has been relying on in its economic development over the past decades. However, with the development of the economy and society, the demographic dividends that China has relied on is gradually fading away, and the advantage of cheap and rich labor for economic development has already disappeared. What follows is the transformation and upgrading of its industrial structure. In order to realize this goal, we must rely on the corresponding human capital structure. In a deeper sense, we must transform our education system and innovate our education philosophy.

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

209

As a matter of fact, quality-oriented education and vocational education have attracted the attention of the Party, the country, and all sectors of society. At present, China is entering a critical period of industrial structure transformation and upgrading, and the structure of its emerging industries with the service industry as the core has become the focus of this industrial transformation. Table 6.12 shows the value added of China’s three industries between 2010 and 2014. As we can see, its tertiary industry grew fastest, increasing from RMB 17,100.5 billion in 2010 to RMB 30,673.9 billion in 2014, up 79.37% over these five years. It must be stressed that the emerging industrial structure does not emphasize the proportion and status of the tertiary industry alone—it integrates the primary, secondary, and tertiary industries to boost the coordinated development of productive and life services. The integration of the three industries and the structural change of the tertiary industry have raised its requirements on the corresponding human capital and talent capital. In fact, as early as the late twentieth century, China has been advocating explorations of education system reform. Up to now, this reform has entered a critical stage and a deep-water zone, and education departments at all levels are promoting the implementation of the reform plan. A series of reform measures are being carried out in full swing, such as middle school vocational education, adult vocational education, retraining of unemployed migrant workers, the construction of academic universities. At the same time, new talent training models are gradually implemented in schools across the country: (1) The popularization of electronic devices in teaching has changed the traditional blackboardbased teaching mode. Relying on computer network technology, it has Table 6.12 Annual Value Added of the Three Industries in China, 2010–2014 (trillion RMB) Year

Primary industry

Secondary industry

Tertiary industry

Annual GDP

GDP growth rate (%)

2010 2011 2012 2013 2014

4.0497 4.7112 5.2377 5.6957 5.8332

18.6481 22.0592 23.5319 24.9684 27.1392

17.1005 20.3260 23.1626 26.2204 30.6739

39.7983 47.1564 51.9322 56.8845 63.6463

10.6 9.5 7.7 7.7 7.4

Source China Statistical Yearbook 2015, recalculated

210

Y. LI

made the class more colorful and flexible. (2) The construction of the teaching staff has achieved initial results (e.g., training of dually qualified teachers), which encourages teachers to keep learning and improving their knowledge and comprehensive abilities. 3) Breakthroughs and innovations are being made in the talent training mode, vocational and general education runs on a dual track, and vocational skills and quality education is being carried out in an all-round way. 4) Flexible talent training models, such as MOOC, the flipped classroom, and practice- and case-based teaching, are favored by teachers and students. Issued by the General Office of the State Council, the Notice on Carrying out the Pilot Reform of the National Education System marks the full launch of the reform pilot of the national educational system. This special reform involves basic education (to accelerate the development of preschool education, to promote balanced development of compulsory education, and to explore ways to reduce the academic burden of primary and secondary school students) and higher education (to reform the model of talent training and the running mode of colleges and universities, and to build a modern university system). In addition, it also aims to reform the running mode of vocational education, and to improve the development environment of private education, the teacher management system, and the mechanism of education investment. From its content and outline, it is not difficult to find that this educational system reform in China is systematic, comprehensive, and profound. 6.2.2

Increasing R&D Investment and Enhancing China’s Scientific Research Capacity

R&D represents the scientific research strength and reflects the human capital level of a country or region, so expanding its investment will effectively promote the quantity and quality of regional human capital. According to the official statistics of OECD, China’s R&D investment was USD 32,646.6 million in 2000 and reached USD 336,495.4 million in 2013, an increase of more than 10 times (see Table 6.13). Nonetheless, compared with developed countries, China still lags far behind (see Fig. 6.6). On the one hand, China has a large population, but its proportion of R&D personnel is very small. On the other hand, its R&D investment keeps rising year by year. In 2008, China surpassed Germany in R&D investment, ranking third in the world; in 2009, it surpassed Japan and ranks second ever since. Yet it still lags far behind

144,684.9 1.47 73.3 8.5 18.3 105,995.8 12,229.4 26,459.7

2008 184,379.2 1.70 73.2 8.1 18.7 135,012.0 14,877.6 34,489.5

2009 213,009.9 1.76 73.4 8.5 18.1 156,395.5 18,014.7 38,599.7

2010 247,808.3 1.84 75.7 7.9 16.3 187,684.1 19,650.4 40,473.8

2011

293,064.5 1.98 76.2 7.6 16.3 223,168.7 22,212.5 47,683.3

2012

336,495.4 2.08 76.6 7.2 16.2 257,793.9 24,334.4 54,367.1

2013

Notes GERD stands for gross domestic expenditure on R&D; BE proportion for percentage of gross domestic expenditure on R&D performed by the business enterprise sector; HE proportion for percentage of gross domestic expenditure on R&D performed by the higher education sector; GOV proportion for percentage of gross domestic expenditure on R&D performed by the government sector; BERD for business enterprise expenditure on R&D; HERD for higher education expenditure on R&D; and GOVERD for government intramural expenditure on R&D Source OECD, “Main Science and Technology Indicators Database,” July 2015

326,46.6 0.90 60.0 8.6 31.5 19,575.1 2,797.0 10,274.5

2000

Relevant Indicators of the Chinese R&D (million USD)

GERD GERD proportion (%) BE proportion (%) HE proportion (%) GOV proportion (%) BERD HERD GOVERD

Indicator

Table 6.13

6 INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

211

212

Y. LI

Fig. 6.6 Changes in R&D Input of China, Japan, Germany, and US, 2000– 2013 (Source OECD, “Main Science and Technology Indicators Database,” July 2015. Based on data in Tables 6.2, 6.6, 6.9, and 6.13)

the United States in this regard. Moreover, it is quite small in per capita terms, and its proportion in GDP is relatively small, too. In recent years, China has constantly increased its R&D investment. In 2013, its R&D investment reached 336,495.4, second only to the United States. 6.2.3

Increasing Human Capital Investment and Optimizing the Human Capital Structure

As pointed out by some scholars, China is much lower than the United States in human capital, and is also slower than the latter in terms of the growth rate. From 1990 to 2003, the proportion of China’s human capital only increased from 5.7 to 7.8%, while that of the United States increased from 16.7 to 19.4% in 2003 (see Table 6.14). Meanwhile, as Wang Yahua and Hu Angang (2005) pointed out through their statistical analysis, of the five major capitals, China was 2.4 times the US in terms of the ratio of physical capital to GDP; the US was 4.2 times China in terms of the annual human capital investment, and 2.4 times and 3.3 times China in terms of the ratio of public education expenditure to GDP and the ratio of public health investment to GDP (see Table 6.15).

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

213

Table 6.14 Changes in Five Major Capitals and Total Capital of China and US (%) China 1990

US 2003

Physical 35.0 44.0 capital International 15.2 36.4 capital Human 5.7 7.8 capital Knowledge 3.3 13.7 capital Natural −16.7 −2.2 capital Total capital 40.0 81.5

Change Average 1990 2003 Change Average (1990–2003) annual (1990–2003) annual growth growth 9.0

0.69

18.0 18.0

0.0

0.00

21.3

1.64

13.8 17.0

3.2

0.25

2.1

0.116

16.7 19.4

2.7

0.21

10.4

0.80

7.3

7.8

0.5

0.04

14.5

1.12

-0.2

1.2

1.4

0.11

41.5

3.19

47.8 54.1

6.3

0.48

Source Word Bank (2005). Quoted from Wang Yahua and Hu Angang, “China’s Economic Catch-up with the United States: A Comparison of the Five Major Capitals,” Comparative Economic and Social Systems, no. 1 (2007)

Table 6.15 Average Annual Growth in Total Value of Five Major Capitals of China and US, 1990–2003 (%)

China Annual growth US Annual growth

GDP

Physical capital

International capital

Human capital

Knowledge capital

9.72

11.67

17.38

12.38

22.47

2.95

2.94

4.61

4.16

3.48

Natural Total capital capital

6.47

—

15.89

3.93

Note The Chinese GDP in 1990 = 100; all the indicators are at comparable prices (at constant US dollar prices in 2000) Source Wang Yahua and Hu Angang, “China’s Economic Catch-up with the United States: A Comparison of the Five Major Capitals,” Comparative Economic and Social Systems, no. 1 (2007)

It is not hard to find that there is a huge gap between China and the US in human capital. Meanwhile, there is also a big gap in other auxiliary

214

Y. LI

capitals, such as public education expenditure, social welfare, and public health investment. It is worth mentioning that this huge gap in human capital is not only reflected in the “quantity” and “quality,” but also in the structure of human capital. There are currently four basic conditions of human capital in China: a small proportion of highly intelligent and high-tech labor force, seriously deficient human capital in rural areas, a huge gap with developed countries in the development of human capital, and a low utilization rate of human capital. Accordingly, China has put forward some countermeasures, such as changing the concepts, adjusting the investment structure, improving the efficiency of human capital investment, and strengthening the institutional construction for human capital investment. If we look at the human capital investment of Western developed countries, it is not difficult to find that the key to their success in human capital investment lies in two aspects. On the one hand, they have strengthened human capital investment. Developed countries, such as Japan, Germany, and the United States, have all invested huge amounts in human capital, in such fields as public education, health care, and social welfare and security. On the other hand, they have attached great importance to the structure of human capital investment. From the afore-mentioned human capital investment in the United States, Germany, and Japan, it is not hard to find that a diversified structure of human capital investment has basically taken shape in these countries, with government investment as the major part and enterprises and other social organizations as the auxiliary. Besides, in terms of the structure of social human capital, through diversified human capital investment using a multi-pronged approach, a high-level human capital structure is brought into being characterized by high level, high quality, and high technology. In contrast, there are three pairs of basic contradictions in China’s structure of human capital investment. First, the unbalanced structure of human capital investment. Basically, government investment plays the main part, while investment from other social organizations is quite rare. In fact, the participation of social organizations is an important part of human capital investment. Human capital investment institutions, including enterprises, institutions, and social organizations, are directly related to the owners of human capital. They are both demanders and users of human capital. Therefore, more and more Western developed countries have turned to social human

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

215

capital investment which constitutes an important part of the structure of national human capital investment. Similarly, China must implement a diversified human capital investment strategy, vigorously introduce private capital investment, encourage enterprises and institutions to invest independently, constantly improve the human capital cultivation system in enterprises, and improve the performance of human capital investment. Meanwhile, the government must play a leading role in human capital investment and a great driving role in promoting social human capital investment. The government’s macro-regulation and guidance of major project investment, such as public education, social welfare, and security, are important channels to human capital. Therefore, it is of great significance to always maintain the dominant position of government investment in human capital investment. Second, the significant regional gaps in human capital investment. This is mainly reflected in the differences among the Eastern, Central, and Western Regions. The Eastern Region is large in human capital investment, relatively high in the quality of human capital investment, and outstanding in investment performance, whereas the Central and Western Regions, constrained by their geographical and economic conditions, have relatively low levels of human capital investment and investment performance. Under the influence of regional differences in human capital investment, the gap between rural and urban residents in human capital investment is becoming increasingly prominent. To a certain extent, the differences in regional human capital investment are one of the important reasons of regional gaps in economic development. Since the implementation of the Great Western Development Strategy, the Western Region has gained considerable development in economy, which has also benefited from the increased investment in human capital, including investment in education, health care, and other aspects. Finally, the lack of competitiveness in the internal human capital structure of laborers. A large proportion of Chinese laborers are still general human capital, while highly intelligent and high-quality human capital is quite scarce. The low level of human capital structure of the laborers is mainly due to the limitations of China’s current education system and its low level of education. Therefore, in order to optimize the internal human capital structure of laborers, it is necessary to strengthen the reform of the educational system and mechanisms and to innovate the education and cultivation models and concepts. In the meantime, the real problem lies in laborers themselves. While government departments and

216

Y. LI

social organizations keep increasing investment in human capital, laborers should proactively acquire new knowledge and new skills, give full play to their subjective initiative, and actively improve their comprehensive human capital. They should not only increase their human capital stocks, but also improve the quality of human capital, so as to meet the needs of the transformation of a new industrial structure.

References Feng Xiaoling and Zhang Jian. “Technical Analysis of the Influencing Factors of Producer Services in the US: An Empirical Test Based on the VAR Model.” International Economics and Trade Research, no. 10 (2013). Gao Xirong, Zhang Wei, and Chen Liuting, “Human Capital: A Long-Term Driving Force of National Independent Innovation—Based on an Empirical Analysis of Japan’s Innovation and Transformation,” Science and Technology Progress and Policy, no. 3 (2014). Human Development Index Report 2015 released by the United Nations Development Program, reported by Daily Mail on 16 December 2015. Quoted from http://lx.huanqiu.com/lxnews/2015-12/8225834.html. Lai Mingyong, Zhang Xin, Peng Shuijun, and Bao Qun. “The Source of Economic Growth: Human Capital, Research and Development, and Technology Spillover.” Social Sciences in China, no. 2 (2005). Li Hui and Yu Qinkai, “The Driving Role of Human Capital Accumulation in Japan’s Economic Catch-up After WWII and Its Enlightenment,” Contemporary Economy of Japan, no. 6 (2004). Yang Lin and Guo Yang. “Comparison and Takeaways from Vocational Education Funding Models in Developed Countries.” Vocational and Technical Education Forum, no. 4 (2015). Yao Lingzhen. Social Security System of Germany. Shanghai: Shanghai People’s Publishing House, 2011. Zeng Xiangquan, Labor Economics, 2nd ed. (Shanghai: Fudan University Press, 2010), 176. Zhang Yan, Xu Yunxiao, and Wang Zhiqiang. “Human Capital, Physical Capital, and Output Difference Between China and the US—Empirical Studies of the Panel Data of 52 Countries Between 1981 and 2005 Using the Human Capital Model.” Finance and Trade Economics, no. 9 (2010). Zhang Kangsi. “The Cultivation of Sustainable Competitiveness of Enterprises in Macroeconomic Cataclysms.” Macroeconomics, no. 5 (2009). Zhou Hongxia. “US Education Funding: The Largest Share Goes to Preschool and Basic Education.” China Education Daily (2015, September 30). Quoted

6

INTERNATIONAL COMPARISON OF HUMAN CAPITAL …

217

from China Education and Research Network, http://www.edu.cn/zhong_ guo_jiao_yu/yiwujiaoyu/201509/t20150930_1322971.shtml. Zhao Zengyao. “Japan’s Labor Market System and Its Challenges and New Changes.” World Economics and Politics, no. 12 (2000). Zhou Hongli and Zhang Wanxing. “On the Modern Apprenticeship Model in Germany in Light of the Human Capital Theory.” Higher Education Exploration, no. 4 (2014).

Bibliography

Acemoglu, D. “Patterns of Skill Premia.” Review of Economic Studies, no. 4 (2003): 231–51. Alesina, Alberto and Dani Rodrik. “Distributive Politics and Economic Growth.” Quarterly Journal of Economics 109, no. 2 (1994): 465–90. Aoki, Masahiko. “Law-Based Rule of a Country: Rule of Law or Rule by Law?” http://yuanchuang.caijing.com.cn/2015/0717/3928144.shtml. Arnold, J., A. Bassanini, and S. Scarpetta. “Solow or Lucas? Testing Speed of Convergence on a Panel of OECD Countries.” Research in Economics 65, no. 2 (2011): 110–23. Balasubramanyam, V. N., M. Salisu, and D. Sapsford. “Foreign Direct Investment as an Engine of Growth.” The Journal of International Trade and Economic Development 8, no. 1 (1999): 27–40. Barro, R. J. “Human Capital and Growth in Cross-Country Regressions.” Swedish Economic Policy Review 6, no. 2 (1999): 237–77. Barro, R. J., and X. Martin. Economic Growth. Cambridge, MA: MIT Press, 2004. Becker, G. S. Human Capital. 2nd ed. New York: NBER, 1975. Becker, G. S., and B. R. Chiswick. “Education and the Distribution of Earnings.” The American Economic Review 56, no. 1/2 (1966): 358–69. Benabou, R. “Inequality and Growth.” NBER Macroeconomics Annual 11, (1996): 11–74. Benhabib, J., and M. Spiegel. “The Role of Human Capital in Economic Development.” Monetary Economics 34, no. 2 (1994): 143–73. Benhabib, J., and A. Rustichini. “Social Conflict and Growth.” Journal of Economic Growth 1, no. 1 (1996): 129–46.

© Social Sciences Academic Press 2023 Y. Li, Human Capital Investment and the Regional Economic Gap in China, https://doi.org/10.1007/978-981-99-4997-7

219

220

BIBLIOGRAPHY

Bernanke, B. S., and S. Refet. “Is Growth Exogenous? Taking Mankiw, Romer and Weil Seriously.” NBER Working Paper 8365 (2001). Bian Zhijing and Shen Lisheng. “Empirical Analysis of the Influence of Human Capital on Economic Growth of China’s Eastern and Western Regions.” The Journal of Quantitative and Technical Economics, no. 12 (2004). Birdsall, N., and J. Londono. “Asset Inequality Matters: An Assessment of The World Bank’s Approach to Poverty Reduction.” American Economic Review 87, no. 2 (1997): 32–37. Bleakley, Hoyt. “Health, Human Capital and Development.” Annual Review of Economics, no. 2 (2010). Blomström, Magnus, and Fredrik Sjoholm. “Technology Transfer and Spillovers: Does Local Participation with Multinationals Matter?” European Economic Review 43, nos. 4–6 (1999): 915–23. Business Group of Munich Office. “The German Social Security Expenditure to Account for Record-High 32.4% of GDP.” Bne IntelliNews. http://info.tai wantrade.com/CH/bizsearchdetail/1493369/C/. Cai Fang and Wang Dewen. “The Sustainability and Labor Contribution of China’s Economic Growth.” Economic Research Journal, no. 10 (1999). Cao Fang. “Measurement and Decomposition of the Spatial Distribution of China’s Human Capital.” Northwest Population Journal, no. 4 (2008). Carnoy, M., P. Loyalka, and M. Dobryakova. University Expansion in a Changing Global Economy: Triumph of the BRICs? Stanford: Stanford University Press, 2013. Castagna, Alina, and Furia Donatella. “Demographics and Health in the EU27 Development Process.” Procedia—Social and Behavioral Sciences 9, no. 3 (2013). Castello, A., and R. Domenech. “Human Capital Inequality and Economic Growth: Some New Evidence.” The Economic Journal 112, no. 2 (2002): 187–200. Chen, A., P. Nijkamp, and T. Tabuchi. “Regional Science Research in China: Spatial Dynamics, Disparities and Regional Productivity.” Papers in Regional Science, no. 2 (2014): 223–27. Chen Binkai, Zhang Pengfei, and Yang Rudai. “Government Education Input, Human Capital Investment, and Urban-Rural Income Gap in China.” Management World, no. 1 (2010). Chen, D., and C. J. Dahlman. “Knowledge and Development: A Cross-Section Approach.” USA: World Bank Policy Research Working Paper 3366 (2004). Chen Hao. “Analysis of the Structure of the Influence of Human Capital on Economic Growth.” The Journal of Quantitative and Technical Economics, no. 8 (2007). Ciccone, A., and E. Papaioannou. “Human Capital, the Structure of Production, and Growth.” The Review of Economics and Statistics 91, no. 1 (2009): 66–82.

BIBLIOGRAPHY

221

De La Croix, David, and Matthias Doepke. “Inequality and Growth: Why Differential Fertility Matters.” American Economic Review 93, no. 4 (2004): 1091–1113. Dutta, D., and Yang Y. “Major Factors Behind Regional Disparity of Economic Growth in China During 1996–2010.” Economic Development and Growth, no. 9 (2014): 79–106. Edmonds, Eric, Nina Pavcnik, and Petia Topalova. “Trade Liberalization and the Allocation of Labor Between Households and Markets in a Poor Country.” Journal of International Economics, no. 7 (2006): 257–95. Fang Hongjun and Liu Fengxia. “A Visual Analysis of International Human Capital Research.” Science and Technology Management Research, no. 18 (2010). Feng Xiaoling and Zhang Jian. “Technical Analysis of the Influencing Factors of Producer Services in the US: An Empirical Test Based on the VAR Model.” International Economics and Trade Research, no. 10 (2013). Feng Yan and Chai Zhihong. “The Influence of Health Human Capital on Economic Growth.” Research on Economics and Management, no. 2 (2016). Fishman, A., and A. Simhon. “The Division of Labor, Inequality, and Growth.” Journal of Economic Growth, no. 7 (2002): 117–36. Fuente, A., and R. Domenech. “Human Capital in Growth Regressions: How Much Difference Does Data Quality Make?” Journal of the European Economic Association, no. 4 (2006): 1–36. Galor, O. “Towards a Unified Theory of Economic Growth.” World Economics, no. 9 (2008): 97–151. Galor, O., and D. Weil. “Population, Technology, and Growth: From Malthusian Stagnation to the Demographic Transition and Beyond.” American Economic Review 90, no. 4 (2000): 806–28. Galor, O., and K. Tsiddon. “The Distribution of Human Capital and Economic Growth.” Journal of Economic Growth 2, no. 1 (1997): 14–93. Galor, O., and Joseph Zeira. “Income Distribution and Macroeconomics.” Review of Economic Studies 60, no. 1 (1993): 35–52. Gao Suying, Zhao Shuming, and Wang Yajie. “On the Dynamic Correlation Between Human Capital and Regional Economic Growth.” Labor Economics and Human Capital, no. 1 (2010). Gao Xirong, Zhang Wei, and Chen Liuting. “Human Capital: A Long-Term Driving Force of National Independent Innovation—Based on an Empirical Analysis of Japan’s Innovation and Transformation.” Science and Technology Progress and Policy, no. 3 (2014). Gaudu, R. S. “Health as a Component of Human Capital Formation: Does It Matter for the Growth of the Nigerian Economy?” Canadian Social Science 7, no. 4 (2011): 207–18.

222

BIBLIOGRAPHY

Gittens, D. “The Effects of Foreign Direct Investment on the Accumulation of Human Capital in Developing Countries.” A Dissertation for the Degree of Doctor of Philosophy, University of the West Indies, 2006. Glomm, G., and B. Ravikumar. “Public Versus Private Investment in Human Capital: Endogenous Growth and Income Inequality.” Journal of Political Economy 100, no. 4 (1992): 818–34. Glover, Saundra H., Xirasagar Sudha, Jeon Yunho, Keith T. Elder, Crystal N. Piper, Pastides Harris. “A Human Capital Approach to Reduce Health Disparity.” Journal of Health Disparities Research and Practice 3, no. 1 (2009): 1–14. Grossman, M. “On the Concept of Health Capital and the Demand for Health.” Journal of Political Economics 80, no. 2 (1972): 223–40. Grossman, G. M., and E. Helpman. “Trade, Knowledge Spillovers, and Growth.” NBER Working Paper (1991). Guo Hongzhen. “Contemplation on the Factors Contributing to China’s Economic Growth,” China Statistics, no. 4 (2004). Guo Zhiyi and Cao Jianyun. “The Influence of Human Capital on Regional Economic Growth in China: The Application of Ridge Estimation in the Multicollinearity Data Model.” Chinese Journal of Population Science, no. 4 (2007). Gyimah-Brempong, Kwabena, and Mark Wilson. “Health Human Capital and Economic Growth in Sub-Saharan African and OECD Countries.” Quarterly Review of Economics and Finance 44, no. 2 (2003): 296–320. He Chengying, Xu Xiangyang, and Weng Yuanyuan. “The Internationalization of the Capital Market and the Modal Transformation of China’s Economic Growth.” Economic Perspectives, no. 9 (2012). Human Development Index Report 2015 released by the United Nations Development Program, reported by Daily Mail on 16 December 2015. Quoted from http://lx.huanqiu.com/lxnews/2015-12/8225834.html. Hu Wenguo and Wu Dong. “Theoretical and Empirical Analysis of the Factors of China’s Economic Growth.” Journal of Tsinghua University (Social Sciences Edition), no. 4 (2004). Hu Yongping and Zhang Zongyi. “Reasons for Regional Gaps in Economic Growth in China.” Contemporary Finance and Economics, no. 9 (2004). Hu Yongyuan and Liu Zhiyong. “Effects of Different Types of Human Capitals on Economic Growth.” Population & Economics, no. 2 (2004): 55–58. Jin Weidong. “The Dynamic Matching Effect of Human Capital and Industrial Structure Transformation.” Economic Review, no. 6 (2010). Jonathan, M. “Foreign Direct Investment and Human Capital Enhancement in Developing Countries.” Competition and Change, no. 4 (2002): 363–72. Krueger, A. B., and M. Lindahl. “Education for Growth in Sweden and the World.” Cambridge, MA: NBER Working Paper 7190 (1999).

BIBLIOGRAPHY

223

Lai, C. W. “Essays on International Trade and Human Capital.” A Dissertation for the Degree of Doctor of Philosophy, University of Virginia, 2008. Lai Mingyong, Zhang Xin, Peng Shuijun, and Bao Qun. “The Source of Economic Growth: Human Capital, Research and Development, and Technology Spillover.” Social Sciences in China, no. 2 (2005). Levin, Henry M. “How Does Education Adapt to the Future: A Discussion with American Education as the Background.” Peking University Education Review, no. 4 (2013). Liang Zhao. “Analysis of Major Factors of Sustained Growth of the National Economy.” Journal of World Economy, no. 7 (2000). Liao Chuhui. “The Structure of China’s Human Capital and Physical Capital and Government Input in Education.” Social Sciences in China, no. 1 (2006). Li Hui and Yu Qinkai. “The Driving Role of Human Capital Accumulation in Japan’s Economic Catch-up After WWII and Its Enlightenment.” Contemporary Economy of Japan, no. 6 (2004). Li Xiumin. “Human Capital, Human Capital Structure, and Coordinated Regional Development: Evidence from Provincial-Level Regions in China.” Management World, no. 11 (2004). Li Yaling and Wang Rong. “Structure of Human Capital Distribution and Regional Economic Gaps: An Empirical Study Based on the Gini Coefficients of Regional Human Capital in China.” Management World, no. 12 (2006): 42–49. Lin Justin Yifu. “On the Transformation of the Models of Economic Growth.” Scientific Development, no. 2 (2014). Liu Guoen, William H. Dow, Fu Zhenghong, and John Akin. “Health Human Capital and Income Growth in China.” China Economic Quarterly, no. 1 (2004). Liu Haiying, Zhao Yingcai, and Zhang Chunhong. “On the Relationship Between Human Capital ‘Homogenization’ and the Quality of China’s Economic Growth.” Management World, no. 11 (2004). Liu Li. “Foreign Trade, Income Distribution, and Regional Gaps: Reasons in Trade for Regional Economic Gaps in China.” Nankai Economic Studies, no. 4 (2005). Liu Shijin. “The Slowdown of Growth Rate and the Transformation of Development Models.” Economic Perspectives, no. 5 (2011). Liu Tieming, Yuan Jianchang, and Wang Xiuwen. “Interpretations of the Elements of Human Capital.” Commercial Times, no. 28 (2007). Liu Wei and Li Shaorong. “Changes in Ownership and the Promotion of Economic Growth and Factor Efficiency.” Economic Research Journal, no. 1 (2001).

224

BIBLIOGRAPHY

Liu Wenge, Pan Pengjie, and Zhu Xinglong. “The Extension of Human Capital Theory and the Explanation of Regional Economic Differences.” Economics Information, (2006): 27–30. Liu Yanzhuo. “On Correlation Between Human Capital Stocks and Regional Economic Gaps in China.” MA Thesis, Capital University of Economics and Business (2011). Lopez, R., V. Thomas, and Y. Wang. “Addressing the Education Puzzle: The Distribution of Education and Economic Reforms.” World Band Working Paper 2031 (1998). Luo Kai. “Health Human Capital and Economic Growth: Evidence from Provincial Data in China.” Economic Science, no. 4 (2006). Luo Yongmin. “Public Health Expenditure, Health Human Capital, and Economic Growth.” South China Journal of Economics, no. 4 (2011). Lucas, R. “On the Mechanics of Economic Development.” Monetary Economics 22, no. 1 (1988): 3–42. Ma Lei. “The Influence of Human Capital Structure and Total Factor Productivity on Urban-Rural Income Gap.” East China Economic Management, no. 2 (2016). Ma Lei. “Human Capital Structure, Technological Progress, and Urban-Rural Income Gap.” Research on Economics and Management, no. 4 (2016). Manuelli, R., and A. Seshadri. “Human Capital and the Wealth of Nations.” USA: Society for Economic Dynamics Working Paper (2007). Marcin, K. “How Does FDI Inflow Affect Productivity of Domestic Firms.” The Journal of International Trade and Economic Development, no. 3 (2008): 155–73. Masahiko Aoki. “Law-Based Rule of a Country: Rule of Law or Rule by Law?” http://yuanchuang.caijing.com.cn/2015/0717/3928144.shtml. Accessed 17 July 2015. Mushkin, S. J. “Health as an Investment.” Journal of Political Economy 70, no. 2 (1962): 129–57. Nelson, R., and E. S. Phelps. “Investment in Humans, Technological Diffusion, and Economic Growth.” American Economic Review 51, no. 2 (1966): 69– 75. Psacharopoulos, G. “Unequal Access to Education and Income Distribution: An International Comparison.” De Economist, no. 125 (1977): 383–392. Persson, Torsten, and Guido Tabellini. “Is Inequality Harmful for Growth? Theory and Evidence.” American Economic Review 84, no. 3(1994): 600–21. Ren Jianjun and Yang Guoliang. “Regional Gaps in Economic Development in China and Their Reasons.” Economic Geography, no. 5 (2010). Romer, Paul M. “Increasing Returns and Long-Run Growth.” The Journal of Political Economy 94, no. 5 (1986): 1002–37.

BIBLIOGRAPHY

225

Sahn, D. E. “Nutrition, Health and Economic Performance.” Encyclopedia of Health Economics, 2014. Saint-Paul, G., and T. Verdier. “Education, Democracy and Growth.” Journal of Development Economics 42, no. 2 (1993): 399–407. Schultz, T. W. “Investment in Human Capital.” The American Economic Review 51, no. 1 (1961): 1–17. Schultz, T. W. Investing in People—The Economics of Population Quality. Berkeley: University of California Press, 1981, 124–148. Schultz. T. W. “Human Capital, Schooling and Health.” Economics and Human Biology 1, no. 2 (2004): 207–11. Shen Kunrong. “Empirical Analysis of the Factors of China’s Economic Growth 1978–1997.” Economic Science, no. 4 (1999). Slaughter, M. J. “Does Inward Foreign Direct Investment Contribute to Skill Upgrading in Developing Countries.” Prepared for New School University’s CEPA Conference (2002). Solow, R. M. “A Contribution to the Theory of Economic Growth.” Quarterly Journal of Economics 70，no. 1 (1956): 65–94. Strauss J., and D.Thomas. “Health, Nualth, Nutrition and Economic Development.” Journal of Economic Literature, no. 36 (1998): 766–817. Sun Haigang. “On the Causes of Regional Economic Gaps in China During Marketalization.” Journal of Finance and Economics, no. 9 (2007). Sun Yanan, Liu Huajun, and Cui Rong. “Causes of Regional Economic Gaps in China and Their Influence on Spatial Correlation: A Perspective of Coordinated Regional Development.” Journal of Guangdong University of Finance and Economics, no. 2 (2016). Swan, T. W. “Economic Growth and Capital Accumulation.” Economic Record 32, no. 2 (1956): 334–61. Tang Jijun, Zhang Zongyi, and Fu Yunying. “China’s Economic Transformation and Growth,” Management World, no. 1 (2007). Tang Xiangjun. “Capital Deepening, Human Capital Accumulation, and Sustainable Economic Growth in China.” The Journal of World Economy, no. 8 (2006). Vogl, T. S. “Education and Health in Developing Economies.” Encyclopedia of Health Economics, 2014. van Zon, A., and J. Muysken. “Health and Endogenous Growth.” Journal of Health Economics 20, no. 2 (2001): 169–85. Wang Jinying and Zheng Shupeng. “Comparison of the Role of Human Capital in Economic Growth Between the Eastern and Western Regions.” Population and Economics, no. 4 (2010). Wang Xiaolu and Fan Gang. Regional Gaps in China: The Trend of Changes and Influencing Factors Over 20 Years. Beijing: Economic Science Press, 2004.

226

BIBLIOGRAPHY

Wang Xuanxuan. “Reasons for Gaps in Economic Development Among China’s Four Regions.” Finance and Trade Economics, no. 11 (2009). Wei Jie. “Several Controversial Questions on Sustaining China’s Economic Growth.” Economic Perspectives, no. 5 (2009). Woode, Maame Esi, Carine Nourry, and Bruno Ventelou. “Children Preventive Care, Adult Healthcare and Economic Growth: The Role of Healthcare Financing.” Economics Letters 124, no. 1 (2014). Xu Xianxiang and Li Huan. “Endogenous Institutional Causes of Provincial Economic Gaps in China.” China Economic Quarterly, no. 4 (2005). Yang Jianfang, Gong Liutang, and Zhang Qinghua. “The Formation of Human Capital and Its Influence on Economic Growth.” Management World, no. 5 (2006). Yang Jun and Li Xuesong. “Education Inequality, Human Capital Accumulation, and Economic Growth: An Empirical Study Based on China.” The Journal of Quantitative and Technical Economics, no. 2 (2007). Yang Lin and Guo Yang. “Comparison and Takeaways from Vocational Education Funding Models in Developed Countries.” Vocational and Technical Education Forum, no. 4 (2015). Yang Liyan and Pan Huifeng. “Human Capital, Basic Research, and Economic Growth.” Economic Research Journal, no. 4 (2003). Yao Lingzhen. Social Security System of Germany. Shanghai: Shanghai People’s Publishing House, 2011. Yu Changlin. “The Structure of Human Capital Investment and Economic Growth: Research on the Theory of Endogenous Growth Model Including Educational Capital and Health Capital.” Journal of Finance and Economics, no. 10 (2006). Yu Dongping and Duan Wanchun. “Health Human Capital, Education Human Capital, and Economic Growth: An Empirical Study Based on Provincial Panel Data in China.” Journal of Wuhan University of Technology (Social Sciences Edition), no. 3 (2011). Zeng Xiangquan. Labor Economics. 2nd ed. Shanghai: Fudan University Press, 2010, 176. Zhang Bin and Sang Baichuan. “The Contradiction Between Human Capital Structure and the Demands of Reindustrialization in the US.” Research on Economics and Management, no. 5 (2015). Zhang Hejie and Ruan Jie. “A Literature Review of the Theory and Application of the ‘Three Gaps’ Model: A Chinese Perspective.” Economic Forum, no. 9 (2013). Zhang Juwei. “Changes in the Human Capital Return Rate and the Income Gap: The ‘Matthew Effect’ and Its Policy Implications.” Economic Research Journal, no. 12 (2006).

BIBLIOGRAPHY

227

Zhang Kangsi. “The Cultivation of Sustainable Competitiveness of Enterprises in Macroeconomic Cataclysms.” Macroeconomics, no. 5 (2009). Zhang Xiusheng and Chen Huinü. “Regional Gaps in Economic Development in China: Status Quo, Reasons, Influences, and Countermeasures.” Economic Review, no. 2 (2008). Zhang Yan, Xu Yunxiao, and Wang Zhiqiang. “Human Capital, Physical Capital, and Output Difference Between China and the US—Empirical Studies of the Panel Data of 52 Countries Between 1981 and 2005 Using the Human Capital Model.” Finance and Trade Economics, no. 9 (2010). Zhang Yong, Wang Xi, and Gu Mingming. “A Comparative of the Development Potentials of China and India.” Economic Research Journal, no. 5 (2009). Zhao Jianglin. “Foreign Capital and Human Capital Development: A Summary of the Chinese Experience.” Economic Research Journal, no. 2 (2004). Zhao Pu. “Economic Growth and Stages of Human Capital Accumulation: Rethinking the Formation of Human Capital.” Academic Forum, no. 2 (2009). Zhou Hongli and Zhang Wanxing. “On the Modern Apprenticeship Model in Germany in Light of the Human Capital Theory.” Higher Education Exploration, no. 4 (2014). Zhou Hongxia. “US Education Funding: The Largest Share Goes to Preschool and Basic Education.” China Education Daily (2015, September 30). Quoted from China Education and Research Network, http://www.edu.cn/zhong_ guo_jiao_yu/yiwujiaoyu/201509/t20150930_1322971.shtml. Zhou Xiao and Zhu Nong. “On the Role of Human Capital on Economic Growth in Rural China.” Chinese Journal of Population Science, no. 6 (2003). Zhou Xiaowei and Zhang Ping. “An Institutional Analysis of the Reasons for Regional Economic Gaps.” Journal of Shangluo University, no. 1 (2007). Zhao Zengyao. “Japan’s Labor Market System and Its Challenges and New Changes.” World Economics and Politics, no. 12 (2000). Zhu Changcun and Ma Jingzhi. “The Broad-Sense Spillover of Rural Human Capital and the Income Gap Between Urban and Rural Areas.” China Rural Survey, no. 4 (2009). Zhu Chengliang, Shi Ping, Yue HongZhi, and Han Xianfeng. “Human Capital, Human Capital Structure, and Regional Economic Growth Efficiency.” China Soft Science, no. 2 (2011).