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Information and Communications Technology (ICT), Productivity and

Economic Growth in China

by

Chee Kong WONG

This thesis is presented for the degree of Doctor of Philosophy

of the University of Western Australia

Business School

December 2007

ABSTRACT

In the current literature on productivity and economic growth, many studies have

explored the relationship between information and communications technology (ICT)

and growth. In these studies, ICT capital stock is treated as an individual input in the

production process that contributes to output growth. In fact, ICT is found to be a key

driver of productivity growth in the developed economies. However, few empirical

studies deal with China which has in recent years become one of the world’s largest ICT

markets and production centres. The lack of empirical work in this field contrasts

sharply with the wealth of literature which presents background and descriptive studies

of China’s high technology sectors that include the telecommunications, the computer

and the Internet sectors.

This dissertation attempts to fill the void in the literature by examining the role of ICT

in China’s economy over the past two decades. It aims to develop a framework which

emphasizes ICT as a production factor and apply it to interpret China’s economic

growth. The dissertation contributes to the empirical literature by focusing on the

following core aspects underlying the linkage between ICT and economic growth. First,

it attempts to estimate the size of China’s ICT capital stock using the perpetual

inventory method. Second, based on such estimates, the dissertation measures the

contribution of ICT to China’s economic growth by means of a production function

model that segregates ICT from all other forms of capital. Third, the dissertation

examines the impact of ICT on technical efficiency in China’s regions by applying a

stochastic frontier model. Lastly, the dissertation looks at the demand aspect of the ICT

industry by estimating and projecting demand for ICT services, namely, the

telecommunications and computer markets in China.

According to this study, ICT capital is found to be a positive driver for the Chinese

economy, and is responsible for about 25% of the country’s economic growth, although

the percentage varies at different periods. ICT capital is also found to have a positive

and significant impact on technical efficiency in the Chinese regions. However, the

disparity between the coastal and inland regions in terms of technical efficiency scores

is found to be very wide, due to the bulk of ICT investment going into the municipal

cities and coastal provinces. It is also found that China may be facing the beginning of a

period of strong productivity growth driven by increased investment in ICT, especially

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innovative investment. Furthermore, projections of demand show that the majority of

Chinese citizens will have access to a fixed-line telephone or the mobile phone in five

years from now, while about half of the Chinese population is expected to use the

computer by 2010.

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TABLE OF CONTENTS

Abstract i

List of tables vii

List of figures x

Acknowledgements xii

1 INTRODUCTION 1

1.1 Background 2

1.2 Objectives and contributions 3

1.3 Outline of the chapters 4

2 DEVELOPMENT OF THE ICT SECTOR 7

2.1 Definitions of ICT 7

2.2 The telecommunications industry 8

2.2.1 The monopoly era (1949-1994) 9

2.2.2 Telecommunications reform and policies 10

2.2.3 Telecommunications developments after WTO 12

2.3 The computer industry 16

2.3.1 Development of the hardware industry 17

2.3.2 Software development 24

2.4 National science and technology programs 32

2.4.1 Brief overview of China’s science and technology (S&T) policy 32

2.4.2 S&T projects 33

2.4.3 High technology development zones 35

2.5 The ICT market in China 40

2.5.1 Rise of the ICT market 41

2.5.2 China’s ICT trade 49

2.6 Conclusion 50

Appendix to Chapter 2 56

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3 ICT, PRODUCTIVITY AND GROWTH: DEBATES AND 59 MEASURES

3.1 Introduction 59

3.2 Debates on the role of ICT 60

3.2.1 ICT and productivity growth: A microeconomic view 60

3.2.2 ICT and productivity growth: A macroeconomic view 62

3.2.3 The ICT productivity paradox 65

3.3 Measuring the contribution of ICT to productivity and economic growth 69

3.3.1 ICT contribution to GDP or output growth 70

3.3.2 ICT contribution to average labour productivity (ALP) growth 71

3.3.3 ICT contribution to TFP growth 73

3.4 Conclusion 75

4 ICT, PRODUCTIVITY AND GROWTH: EMPIRICAL STUDIES 76

4.1 Introduction 76

4.2 ICT contribution to economic growth 76

4.3 ICT and labour productivity growth 89

4.4 ICT and productivity at the industry level 93

4.4.1 ICT-producing vs ICT-using industries 96

4.4.2 ICT contribution to TFP growth 100

4.5 China-related studies 103

4.6 Conclusion 105

5 ESTIMATIONS OF ICT CAPITAL STOCK 107

5.1 Introduction 107

5.2 ICT investment in China 108

5.2.1 Patterns of ICT investment 108

5.2.2 Explaining the growth of ICT investment 114

5.3 Estimation of capital stock 118

5.3.1 A theoretical model 118

5.3.2 Depreciation of ICT capital 119

5.3.3 Measurement of China’s ICT capital stock 120

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5.4 Estimation and sensitivity analysis 124

5.5 Conclusion 126


6 ICT AND ECONOMIC GROWTH: A NATIONWIDE STUDY 138


6.2 ICT, productivity and the Chinese economy 138

6.3 Model specification 145

6.4 Description of data 146

6.5 Estimation results and interpretation 147

6.5.1 Estimation results 147

6.5.2 Decomposition of output growth 148

6.5.3 TFP growth in China 151

6.5.4 Sensitivity analysis 152

6.6 Conclusion 156


7 ICT AND EFFICIENCY IN CHINESE REGIONS 161


7.2 ICT investment in Chinese regions 161

7.3 ICT and technical efficiency: a review 164

7.3.1 Conceptual issues 164

7.3.2 Efficiency measurement 164

7.3.3 ICT and technical efficiency 169

7.3.4 China-related studies 170

7.4 Modelling framework 171

7.5 Description of data 173

7.6 Estimation results and interpretation 175

7.6.1 Estimation results 175

7.6.2 ICT and technical efficiency in China 177

7.7 Conclusion 180


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8 DEMAND FOR ICT SERVICES IN CHINA 184


8.2 Literature review 185

8.2.1 Demand for telecommunications 185

8.2.2 Demand for computers 190

8.3 Modeling demand 191

8.3.1 Modeling demand for fixed-line telecommunications 191

8.3.2 Modeling demand for mobile telecommunications 193

8.3.3 Modeling demand for computers 194

8.4 Data issues 196

8.5 Estimation results 202

8.5.1 Estimation results for fixed-line telecommunications demand 202

8.5.2 Estimation results for mobile telecommunications demand 205

8.5.3 Estimation results for computer demand 209

8.6 Projection of ICT demand 210

8.6.1 Forecasting telecommunications demand 211

8.6.2 Forecasting computer demand 213

8.7 Conclusion and growth prospects 214

9 CONCLUSION 220

9.1 Summary of findings 220

9.2 Future directions of ICT in China 221

9.2.1 Prospects after WTO 221

9.2.2 Moving beyond the Earth: Development of satellite and space 223 technology

9.3 Epilogue 225

BIBLIOGRAPHY 227

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LIST OF TABLES

2.1 Market shares of China’s PCs (%) 19 2.2 China’s CPU market, 2001-2004 23 2.3 Market shares of China’s software (%) 27 2.4 Strategic choices for developing China’s software industry 31 2.5 China’s Golden Projects 36 2.6 Geographic distribution of HTDZs in China 37 2.7 Actual and projected output growth rate of computers and office 43

equipment in China and developed countries, 2004-2009 (% change y-o-y)

2.8 Breakdown of Internet usage by services in China, 2002-2006 49 (million users in June of the year)

A2.1 History of China’s computer history, 1956-2004 56 4.1 Sources of growth in GDP and ALP in the US, 1973-2001 79 4.2 Contribution of ICT to output growth in nine OECD countries, 80 1985-2000 4.3 Contribution of ICT to output growth in ten Asian countries, 82 1990-1999 4.4 Contributions to GDP growth 83 4.5 Sources of growth in non-farm output and ALP in the US, 90

1974-2001 4.6 Contribution of ICT to ALP growth in ten Asian economies, 92 1990-1999 4.7 Sources of growth in Canada, 1972-2001 93 4.8 Sources of growth in Australia, 1965-2000 94 4.9 Contributions of ALP growth: Single country study (Unit: %) 95 4.10 Contributions of ALP growth: Cross-country study, 1995-99 96 (Unit: %) 4.11 Decomposition of US labour productivity growth by industry, 98 1987-2000

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4.12 Decomposition of ALP growth in Canada, EU and US by industry, 98 1995-2000 4.13 Decomposition of TFP growth in the US, 1959-2001 101 4.14 Decomposition of TFP growth in the US, 1959-2003 102 4.15 Decomposition of TFP growth in Japan, 1975-2003 102 4.16 Contribution of ICT production to TFP growth in the EU and US, 104 1995-2001 4.17 Empirical studies of the contribution of ICT to China’s economic 104 and labour productivity growth 5.1 Growth indicators, 1986-2004 (%) 109 5.2 Depreciation rate of ICT equipment 121 A5.1 Real ICT investment in China, 1984-2004 (using CPI) 131 A5.2 ICT capital stock series in China, 1983-2004 (using CPI) 132 A5.3 Real ICT investment in China, 1984-2004 (using hedonic price indices) 134 A5.4 Alternative ICT capital stock series in China, 1983-2004 135 A5.5 Price deflators 137 6.1 Growth rate of labour productivity in China during the Five-year Plan 142

(FYP) periods (%) 6.2 Regression results of China’s sources of economic growth, 1983-2004 148 6.3 Contributions to output growth in China, 1983-2004 149 6.4 Sensitivity tests using various depreciation rates of ICT capital stock 155 in China 6.5 Results of sensitivity analysis 156 A6.1 Regression results of China’s sources of economic growth, 1983-2004 158 A6.2 Contributions to output growth in China, 1983-2004 159 A6.3 Results of sensitivity analysis 160 7.1 MLE estimates of the stochastic frontier models 177 7.2 Average technical efficiency (TE) in China’s regions 178 A7.1 Real ICT investment in China’s regions, 1996-2004 (million yuan) 182

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A7.2 ICT capital stock in China’s regions, 1995-2004 (million yuan) 183 8.1 A comparison of price and income elasticity in the telecommunications 187 market 8.2 A comparison of price and income elasticity in the computer market 191 8.3 Estimation results: fixed-line telephone demand 203 8.4 Estimation results: mobile telephone demand 208 8.5 Estimation results: computer demand 209 8.6 Estimated price and income elasticity for China’s fixed-line telecom 212 demand 8.7 Estimated growth rate of price and income for China’s fixed-line 212 telecom demand 8.8 Estimated price and income elasticity for China’s mobile telecom 213 demand 8.9 Estimated growth rate of price and income for China’s mobile 213 telecom demand 8.10 Estimated price and income elasticity for China’s computer demand 214 8.11 Estimated growth rate of price and income for China’s computer 214

demand

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LIST OF FIGURES

2.1 Production of PCs in China, 1990-2005 18 2.2 Growth and forecasts of Chinese software sales, 2000-2009 26 2.3 Telecommunications network capacity in China, 1978-2006 44 2.4 Number of fixed line and mobile subscribers in China, 1986-2006 45 2.5 Fixed and mobile penetration rates in China, 1988-2006 46 2.6 Number of Internet users in China, 1994-2007 47 2.7 China’s trade in ICT products, 1984-2005 51 2.8 ICT and total trade in China, 1984-2005 52 2.9 Growth rate of ICT and total trade in China, 1985-2005 53 3.1 ICT and firm productivity 61 3.2 ICT, productivity and growth 64 5.1 Real ICT investment in China, 1984-2004 110 5.2 Breakdown of ICT investment in China, 1984-2002 111 5.3 Investment in telecommunications and computer industries, 1984-2004 112 5.4 Ratio of ICT investment to total fixed investment in China, 1984-2004 113 5.5 ICT capital stock in China, 1983-2004 127 5.6 Ratio of ICT to total capital stock and output in China, 1983-2004 128 5.7 Growth rate of ICT capital stock and real GDP in China, 1993-2004 129 6.1 China’s tertiary output, 1978-2005 (in 1978 constant prices) 140 6.2 Labour productivity in China, 1978-2005 141 6.3 ICT investment per worker and labour productivity in China, 144 1985-2004 6.4 Output and input indexes in China, 1984-2004 153 6.5 TFP growth in China, 1984-2004 154 7.1 Correlation between GDP per worker and ICT investment per worker 165 in China’s provinces, 2004

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7.2 Total ICT investment in China’s regions, 1996-2004 166 7.3 Ratio of ICT investment to GDP in China’s regions, 1996-2004 167 7.4 ICT capital stock in China’s regions, 1995-2004 176

7.5 The effect of ICT on technical efficiency in China’s regions, 1995-2004 181 8.1 Growth rate of fixed line, mobile and GDP in China, 1979-2005 198 8.2 Log-linear relationship between fixed line subscribers and ICT price 199 index in China, 1978-2005 8.3 Log-linear relationship between mobile subscribers and ICT price index 200

in China, 1988-2005 8.4 Correlations between fixed-line subscription, mobile subscription, and 201 income per capita in China, 1978-2005 8.5 Forecast of China’s fixed-line telephone demand, 2005-2010 215 8.6 Forecast of China’s mobile telephone demand, 2005-2010 216 8.7 Forecast of China’s computer demand, 2005-2010 217

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ACKNOWLEDGEMENTS

I wish to thank my supervisor, Associate Professor Yanrui Wu, for his patience and

excellent supervision in providing guidance in the course of writing this dissertation. He

has contributed invaluable and constructive comments in all aspects of the dissertation,

covering the econometric exercises, thesis structure and checks for grammar errors.

I am eternally grateful to my parents for the emotional and financial support which

helped me see through periods of anxiety and distress. They have been my greatest

supporters, not only in ensuring that I meet my financial needs, but most importantly,

with the love that they showered unceasingly at all times. It is when I live away from

home that I could feel the strong bonding between us, and truly appreciate the sacrifices

my parents have made in bringing me up.

There are several persons who have become an important part of my life in Perth.

Firstly, I will always remember Mr and Mrs Ng who provided accommodation at

Ballajura during the first six months of my stay upon arrival in Perth, by offering a

room at very low cost and treating me like a part of their family. During these four years

of candidature, I am also fortunate to be acquainted with many local residents, including

Mr and Mrs Jeremy Koh, Paul, Elaine, Jivan, Alvin, Irene, ‘Uncle’ Steven and ‘Aunt’

Cecilia, who have helped me feel at home and adapt to the community life within the

shortest possible time span.

In times of financial difficulties, especially when I had to pay my tuition fees before a

semester, I would not have been able to foot some of the bills without the selfless

assistance from Mr P. C. Wong, Liang Fook, Bernard Peh and David Phan. The

dissertation was also completed with financial support from the C. A. Vargovic Bursary

of UWA Business School and a Completion Scholarship of the Graduate Research

School.

Last but not least, there are many others who have lent their encouragement and moral

support that helped me to persevere. Special thanks go to Ian Li, Martin Lim, Cindy

Chia, Eugene Koo, Kenneth Wu and Dawn Low, who have rendered their assistance

with my research work in one form or another, including data entry and proof-reading.

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Chapter 1

INTRODUCTION

China’s economy has been growing at an average rate of 8.7% annually in the past two

decades (1986-2005).1 Many studies attribute China’s rapid growth to various macro-

economic factors, such as economic reform, including fiscal reform and exchange rate

reform, the huge domestic market and active participation in globalisation, including

increased international trade and inflow of foreign direct investment. However, few

studies have paid attention to the role of information and communications technology

(ICT) as a potential and increasingly significant source of productivity and economic

growth for this economy. 2 This is surprising as there is an increasing amount of

literature which studies China’s high technology sectors, such as telecommunications,

the computer industry and Internet.

The aim of this dissertation is to examine the relationship between ICT and

China’s economic growth over the past two decades, and thus contribute to the literature.

Since the 1990s, China’s economy has been increasingly stimulated through

development in its ICT sector. Most significantly, China has in recent years become one

of the world’s largest ICT markets and production centres. ICT is therefore expected to

be a crucial driving force for China’s economic growth in the 21st century. In fact,

China today is even looked upon as ‘a leader in technology and innovation’, or a

‘telecommunications superpower’ as recognized by the International

Telecommunications Union (Conan, 2005; Low and Johnston, 2005). In a country

where ICT investment is growing at twice the rate of national output, it would be

interesting to look at the impact of ICT on China’s economy over the past two decades.

1 Based on gross domestic product (GDP) data obtained from China Statistical Abstract 2006. 2 The terms, ICT and IT (information technology), have been used interchangeably in the literature. In general, American scholars use IT in their literature. They include, among others, Brynjolfsson (2003), Dedrick, Gurbaxani and Kraemer (2003), Jorgenson, Ho and Stiroh (2003, 2005), Oliner and Sichel (2000, 2003), and Shao and Lin (2001, 2002); whereas European (and OECD) scholars tend to use ICT. Examples include Atzeni and Carboni (2006), Becchetti, Bedoya and Paganetto (2003), Colecchia and Schreyer (2002), Edquist (2005), Fabiani, Schivardi and Trento (2005), Inklaar, O’Mahony and Timmer (2003), Katsuno (2005), OECD (2004), Oulton and Srinivason (2005), Pohjola (2003) and Susiluoto (2003). Chinese scholars have used ‘ICT’ in recent literature, including Jing (2006), and Meng and Li (2002).

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1.1 Background

The general impacts of ICT on the economy can be outlined as follows (OECD, 2004).

First, countries that have a strong ICT-production sector such as the US and Finland

tend to enjoy comparative advantage over those with a weaker ICT sector by generating

technological innovation and creating high demand in their economies. The same goes

for countries that have a strong ICT-using service sector as well. Second, ICT

investment contributes to increased ‘capital deepening’, that is, more capital input per

worker, which leads to improved productive efficiency and therefore increases

productivity. Third, the use of ICT produces networking externalities, meaning that the

resulting greater interaction between firms and their customers or other agents will

improve firm performance and increase total factor productivity (TFP) throughout the

economy. In this respect, ICT capital plays a very significant role in the production

process and should be treated as an individual factor input in growth accounting

methods.

The positive relationship between ICT and productivity in developed economies

is well documented, and this applies to developing countries as well.3 Indeed, there is a

consensus of ICT being a key driver of productivity growth. In particular, the revival of

productivity growth in the US since the mid-1990s has been attributed to the

acceleration in average labour productivity (ALP) and total factor productivity (TFP)

growth, driven by the semiconductor industry (Jorgenson, 2001). However, while the

explosive growth of ICT investment and its rising contribution to GDP and labour

productivity growth in the advanced economies has already been extensively

researched, there has been little research on China, despite the giant developing

economy having one of the world’s largest ICT markets and a rapidly growing ICT

infrastructure. OECD (2004) has also acknowledged that there is a lack of research on a

single developing country which can challenge the findings in the current literature that

‘the contribution of ICT to economic growth in developing countries has been minimal’.

My research therefore aims to develop a framework for interpreting China’s economic

growth by examining the neoclassical growth theories with a special emphasis on ICT

as a factor in economic growth.

3 A review of theoretical literature with respect to this issue is discussed in Chapter 3, while empirical evidence gathered in studies of various countries is discussed in Chapter 4.

2

1.2 Objectives and contributions

The main objective of my dissertation is to examine the role of ICT in China’s

productivity and economic growth. First, it aims to present a review of the existing

literature analysing the effect of ICT on productivity and economic growth. Second, it

applies the conventional growth accounting method to assess the impact of ICT on

China’s economy. Finally, the empirical results provide a comprehensive analysis of the

sources of China’s economic growth during a period of rapid ICT development.

The dissertation will make the following contributions to the existing literature.

Its contribution to academia involves mainly the findings generated from the method

applied. One major contribution of the dissertation is the estimation of an ICT capital

stock series for China from the mid-1980s to the first few years of this century, using

various methods of estimation and assumed rates of depreciation, which is not found

anywhere in the literature. The attempt to estimate the size of ICT capital stock in China

will be an addition to current literature. These estimates are based on a consistent set of

data that can be obtained from the statistical sources available.

Next, based on the ICT capital stock series derived from ICT investment, the

dissertation will adopt a production function that segregates the contribution of ICT

capital to economic growth from that of other forms of capital. The impact of ICT on

China’s economic growth will be analysed at two levels. First, the contribution of ICT

capital to economic growth is examined at the national level. The empirical results

obtained from this exercise are also compared with those found in current literature,

which is still very scant.4 Second, the effect of ICT on technical efficiency is examined

at the provincial/regional level by using a stochastic production frontier model.

Finally, the dissertation attempts to estimate the demand functions for ICT usage

(namely, demand for telecommunications services and computers) and project the

growth of the ICT market in China for the next five years. Just like the literature

examining ICT contributions to growth, very few studies have attempted to estimate the

demand for telecommunications and computer markets in China. The dissertation will

4 As will be noted in chapter 4, there are currently only two empirical papers that examined the contribution of ICT capital to China’s economic growth.

3

estimate the elasticity of demand for ICT with respect to price and income, which can

be compared with findings using data from other parts of the world.

1.3 Outline of the chapters

The dissertation is broadly divided into three major sections, dealing with descriptive,

theoretical and empirical discussions. The descriptive section provides an introductory

background of the concept of ICT and an overview of ICT development in China

(Chapters 1 and 2). The theoretical section embarks on a review of current and recent

literature presenting debates concerning the relationship between ICT and economic

growth, as well as empirical evidence from various countries or regions around the

world (Chapters 3 and 4). The empirical section is focused mainly on analysis of the

role of ICT in China’s economy using data obtained from Chinese statistical sources

(Chapters 5 to 8). Finally, the thesis rounds up with a concluding chapter that

summarises the empirical findings and discusses growth prospects for the Chinese ICT

sector in the near future.

Chapter 2 provides an account of the development of the ICT industry in China

and a review of the science and technology (S&T) programs that have been

implemented to promote the development of this sector. It first defines what the term

‘ICT’ encompasses, and provides an overview of the ICT market in China. Next, the

chapter outlines the development of the telecommunications industry in China, followed

by a background review of the computer industry, which is made up of the hardware

and software sectors. Finally, the chapter provides an account and review of the major

policies that have been implemented to promote the development of science and

technology (S&T) in China.

Chapter 3 presents a review of the conceptual debates and measurement issues

associated with ICT, productivity and economic growth. The chapter will explore how

investment in ICT affects productivity at the firm, industry and country level. It begins

with an outline of evidence that shows how increasing ICT investment, especially in the

US and other developed countries has resulted in the recent revival of productivity

growth in these countries. Besides those supporting the positive link between ICT and

productivity, there are studies that question the real impact of ICT on productivity. This

is followed by a discussion of the theoretical frameworks used to measure the

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contribution of ICT to labour productivity and economic growth, where ICT capital is

distinguished from other factor inputs in the growth accounting exercises.

Chapter 4 continues to review the empirical literature on the contribution of ICT

to economic growth. While current literature focuses mainly on empirical survey in the

developed countries, there is only a handful of research work that examines the

developing countries, including China. The empirical results for various countries are

then presented to illustrate the differing contribution of ICT capital among the

economies.

Chapter 5 is the starting point for empirical exercises in the dissertation. It aims

to estimate the ICT capital stock series in China using the perpetual inventory method.

The estimates are based on data of investment in telecommunications and computer

equipment for the period of 1983 to 2004. This will involve, first, estimating the initial

value of ICT capital stock in 1983, and second, estimating the capital stock series

assuming certain rates of depreciation throughout the entire period. The chapter will

conclude with a sensitivity analysis of capital stock estimation using different rates of

depreciation. The ICT capital stock series generated will be used for empirical exercises

in the subsequent chapters.

Chapter 6 focuses on assessing the contribution of ICT capital as a production

factor to economic growth in China. This chapter will add to the literature by focusing

on China, using the estimates of ICT capital stock series obtained in Chapter 5. This

chapter comprises three main parts. First, it describes the relationship between ICT,

productivity and economic growth in China by comparing the pattern of growth in ICT

capital and labour productivity during the past twenty years or so. Second, the chapter

attempts to specify an appropriate model to examine the contribution of ICT and other

factor inputs to economic growth in China. Finally, the chapter will test the robustness

of the model by comparing empirical results based on different estimates of the ICT

capital stock. Conclusions will then be drawn about the role of ICT in China’s economic

growth over the past two decades.

Chapter 7 seeks to estimate the regional ICT capital stock and to examine the

impact of ICT capital on technical efficiency in China’s regions. It contributes to the

literature by looking at the pattern of disparity in ICT investments in China. It will

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provide a background review of how the pattern of regional disparity in China has

changed as far as ICT investment is concerned. The chapter attempts to look at the

impact of ICT on regional growth and technical efficiency in China. No previous work

in this area has been reported.

Chapter 8 looks at the demand side of the ICT industry by estimating demand

functions for ICT services in China. Specifically, this chapter attempts to estimate a

demand function for the telecommunications and computer markets in China,

respectively. It has two main objectives: first, to estimate the demand elasticity of ICT

services and compare them with those of other countries; second, to project demand for

ICT services in the near future till 2010. It contributes to current literature that has

largely focused on the supply-side growth accounting and hence the contribution of ICT

capital/investment to economic and labour productivity growth.

Finally, the dissertation concludes with a discussion of outlook and growth

prospects for the Chinese ICT industry in the near future. The chapter first summarises

the empirical findings from the previous chapters. It will next discuss the direction of

growth for the ICT sector after the first few years of accession into WTO whereby

China is expected to fulfil its entry commitments, and conclude by highlighting some

important areas where growth will be focused on.

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Chapter 2

DEVELOPMENT OF THE ICT SECTOR

This chapter provides an overview of the development of the information and

communications technology (ICT) sector in China. It outlines the historical

development as well as government policies introduced to encourage and promote

development of this sector. The chapter begins by looking at various definitions of the

term ‘ICT’ used in current literature and provides an assessment of the ICT market in

China. This is followed by an outline of the development of the telecommunications

industry in China and a background review of the computer industry, which is made up

of the hardware and software sectors. Finally, the chapter presents a review of the major

policies that have been implemented to promote the development of science and

technology (S&T) in China. The latter is vital to the development of the ICT sector.

2.1 Definitions of ICT

In most literature, the term ICT is used interchangeably with information technology

(IT) although slight variations exist. ICT is broadly defined in the literature to include

the telecommunications, computer hardware and software sectors. According to the

Information Technology Agreement (ITA) of the World Trade Organization (WTO), IT

includes telecommunications equipment, computers and semiconductors. 1 The ICT

sector was defined by the OECD in 1998 as ‘the combination of manufacturing and

service industries that capture, transmit, and display data and information electronically’

(Jing, 2006). Similarly, ICT is divided into three broad categories according to its use,

namely, computing, communication as well as the transmission of data and

communication via Internet (Quibria et al., 2003).

In the empirical literature, the term ‘IT’ investment generally covers ‘computer

hardware, software and communications equipment’ (Shinjo and Zhang, 2003;

Miyagawa et al., 2004; Timmer and van Ark, 2005). Miyagawa et al. (2004) included a

range of communications and electronic equipment in their definition of ‘IT capital

goods’, such as telecommunications systems, radio, consumer electronic equipment as 1 Following the conclusion of the Ministerial Declaration on Trade in Information Technology Products at the Singapore Ministerial Conference in December 1996, the ITA entered into force with the first stage of reduction in tariffs for IT products that took place on 1 July 1997. See WTO website, http://www.wto.org/English/tratop_e/inftec_e/itaintro_e.htm.

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well as electrical and optical instruments. In another study of the Japanese economy,

Jorgenson and Motohashi (2005) defined ‘IT investment’ in accordance to the Japanese

national accounts, which consists of computer equipment (including computer

peripherals), and communications equipment (including television and radio, video, and

cable and wireless communications devices). However, Timmer and van Ark (2005)

used the term ‘ICT investment’ to include computers, communications equipment

(which comprises radio, TV, telecommunications and photocopiers) and software. In

examining the role of ICT in Australia’s economy, Diewert and Lawrence (2005)

defined ICT capital to consist of computers, software and electrical machinery.

In China, the ICT industries defined above are encompassed in the term

‘electronic industry’, used by the Ministry of Information Industry (MII) and in official

statistical publications, which also includes electronic consumer goods such as

televisions and radio. Time series data that is available from statistical sources

published by MII are investment in the ICT manufacturing sector. As listed in Katsuno

(2005), the ICT sector covers the following category of products – telecommunications

equipment, broadcasting equipment, computer equipment and software, household

electronics, electronic measuring instruments, electronic devices, electronic parts and

equipment, and other materials used for the production of electronics. The computer

sector is further comprised of personal computers (PCs), PC peripherals (such as disk

drives and printer), PC parts (such as motherboard, memory card, power supply units

and other parts), as well as software (consists of operating system, intermediate and

application software). Based on the data available from Chinese statistical sources, the

ICT sector to be discussed in this dissertation covers the telecommunications (excluding

broadcasting equipment and household electronics) and computer sectors (including

software).

2.2 The telecommunications industry

This section provides an overview of development of telecommunications policy in

China by examining how telecommunications policies have changed since the

beginning of economic reform to meet changes in market demand and the need to open

up the telecommunications market to foreign competition. The main focus of this

section is on developments in the Chinese telecommunications industry after entry into

the WTO, as current literature covering developments during the reform period is

8

already substantial (Loo, 2004; Lu, 2000a; Lu and Wong, 2003; Mueller and Tan, 1997;

Wong, 2002). Finally, the section rounds up with an overview of the growth of the

telecommunications market in China, using the most recent statistical data available.

2.2.1 The monopoly era (1949-1994)

The historical development and changing policies of the telecommunications industry in

China have been evaluated by several authors in the recent literature. An account of the

achievements up to the late 1990s is covered in detail by Lu (2000a) and Wong (2002).

These studies began with the formation of the Ministry of Posts and

Telecommunications (MPT) on 27 September 1949, stretching through the Cultural

Revolution (1966-76) and beginning of reform till the end of the 1990s. Other authors

have examined the evolution of China’s telecommunications policy in response to

changing market demands, bureaucratic reform and negotiations for entry into the WTO

(Mueller and Tan, 1997; Lu and Wong, 2003). As such, this dissertation will only

provide a brief outline of changes in China’s telecommunications policy with materials

drawn from the more recent studies.2

Loo (2004) analysed the changing telecommunications policies in China since

the opening by breaking the period of study into four stages – pre-1994, 1994-97, 1998-

99 and 2000 onwards, each period reflecting the change in the way different forces were

influencing telecommunications development. During the 1980s, the primary goal of the

MPT was the provision of universal service of fixed line telephones to the population at

large (Loo, 2004). The MPT enjoyed an almost exclusive monopoly of the public

telecommunications network, with other private and independent networks maintained

by some powerful governmental bodies, such as the Ministry of Railway (MOR), the

Chinese Academy of Sciences (CAS) and the State Education Commission (SEC).

Telecommunications was made a strategic priority during the Seventh Five-Year Plan

(1986-2000) when China placed emphasis on high technology as a means to speed up

telecom development. It was in 1987 that the mobile phone first came into use when

China began its utilization of cellular technology with the analogue TACS (Total

Access Communications System) (Wong, 2002).

2 The main focus will be on the recent developments related to new technologies in the few years after accession into WTO (in section 2.2.3 of this dissertation).

9

China entered the Information Age only in 1994 following a breakthrough in

Sino-American talks concerning the connection of the Chinese network with the

Internet in April of that year (Loo, 2004). Prior to this development, the first computer

networking activities in China took place when CAnet (China Academic Network) was

successfully established on September 20, 1987 between the Institute for Computer

Applications (ICA) in Beijing and Karlsruhe University in Germany.3 Subsequently,

Internet access was extended to the research community from CAS and the universities

in Beijing, Chengdu, Shijiazhuang, Shanghai and Nanjing, following the completion of

the China Research Network (CRN) in May 1989 (Tan, Mueller and Foster, 1997; Loo,

2004). The implementation of the Golden Bridge Project in March 1993 was a further

step initiated by the central government to develop an advanced telecommunications

infrastructure throughout China.4 It can thus be seen that up till the mid-1990s, the

development of the Chinese telecommunications industry was largely led by state

initiatives.

2.2.2 Telecommunications reform and policies

During the second half of the 1990s, the evolution of China’s telecommunications

reform and policies could be seen as a result of the interplay between foreign pressure,

market forces brought about by increased demand and the ‘power tussle’ between

various ministries and government bodies. The Chinese central government hopped

onto the bandwagon of worldwide liberalization of the telecommunications industry by

breaking the monopoly of the MPT and establishing new players in their domestic field.

A major milestone in the history of Chinese telecommunications took place in July 1994

when the MPT was renamed as China Telecommunications Corporation (China

Telecom) and at the same time, a new firm, China United Telecommunications (China

Unicom) was set up to bring in competition to the incumbent monopoly.5 However,

competition did not truly exist as China Telecom still owned the only fixed line network

in China, while China Unicom had a restricted share of the mobile services – less than

5% at the end of 1999 (Wong, 2002). Nevertheless, this was a sign of mounting foreign

3 China Internet Network Information Center (CNNIC), http://www.cnnic.net.cn/en/index/0O/ index.htm. For more details of the origin of China’s Internet connection, refer to “How China was Connected to the International Computer Networks”, Willkommen, http://www-ks.hpi.uni-potsdam.de/ index.php?id=76. 4 The Golden Bridge Project is the first of several “Golden Projects” that have been implemented to modernize the ICT infrastructure in China. Refer to section 2.4 of this chapter for further details. 5 China Unicom was established as a joint venture between the Ministry of Electronics Industry (MEI), the Ministry of Electrical Power (MEP), the Ministry of Railway (MOR) and thirteen autonomous state-owned enterprises (Lu and Wong, 2003).

10

pressure together with surging domestic demand to open up the monopolistic Chinese

telecommunications industry to competition.

Rising market demand has also prompted a rapid expansion of the Internet

infrastructure, which consisted of: the China Science and Technology Network

(CSTNet) which began construction in 1989 and was connected to the global Internet

network in 1994; the ChinaNet which is the primary nationwide commercial network

run by China Telecom and was completed in January 1996; the China Education and

Research Network (CERNet) which provided network connection to academic

institutions in China by October 1994; and the Golden Bridge Network (GBNet) which

was operated by Jitong Communications and completed in 1996.6

The influence of foreign pressure became more visible towards the end of the

1990s as China sought entry into the WTO. As it became apparent that membership into

the world body would not be realised unless the telecommunications industry was

unlocked to foreign investment, the Chinese government took the first step towards

opening up through ministerial restructuring in March 1998 by merging the MPT and

Ministry of Electronic Industry (MEI) to form the Ministry of Information Industry

(MII), which became the ‘super-authority’ overseeing the ICT industry in China.

The following three years (1999-2001) saw an influx of new competitors,

although only in small numbers. First, China Netcom (CNC) was established in April

1999 as the fourth telecommunications operator in China, for the construction of a

broadband Internet Protocol (IP) network (CNCNet). A major restructuring of China

Telecom took place when its code division multiple access (CDMA) Great Wall

Network and Guoxin Paging branch were merged with China Unicom, in which the

incumbent still retained the local fixed line network. The next player to enter the

telecommunications field was China Railway Telecommunications Corporation (China

Railcom) in June 1999. China Railcom had its own exclusive communications network,

6 Other networks include the China Uninet (launched by China Unicom in July 2000), the CNCnet (launched by China Netcom in December 2005), the China International Economy and Trade Net (CIETNet), the CMNet (provided by China Mobile), the China Great Wall Net (Cgwnet), the China Satellite Net (CSNet) and the China Next Generation Internet (CNGI) which is a five-year plan initiated for the implementation of IPv6 (Internet Protocol version 6), scheduled for showcase at the 2008 Olympic Games in Beijing. See China Internet Network Information Center (CNNIC), 10th - 18th Statistical Survey Report on the Internet Development in China (July 2002 - July 2006), http://www.cnnic.net.cn/en/index/0O/index.htm.

11

and was subsequently granted a license to provide the fixed line, Internet and IP

telephony services in 2001 (Lu and Wong, 2003).

Further restructuring of the Chinese telecommunications industry occurred with

the ‘second divestiture’ (a term coined by Lu and Wong, 2003) of China Telecom in

December 2001, almost immediately after the entry of China into the WTO. China

Telecom was restructured geographically when it was to operate only the network of 21

provinces in south China and the western autonomous regions, with the remaining 10

provinces in north China to be taken over by the merger of China Netcom and Jitong

Communications. Finally, the most recent player to join the ‘telecom league’, China

Satellite Communications Corporation (China Satcom), was formed in December 2001,

through the merger of satellite-based telecommunications companies such as China

Telecommunications Broadcast Satellite Corporation, China Orient Telecom, China

Space Mobile Satellite and ChinaSat of China Telecom (Hong Kong) (Lu and Wong,

2003).

Despite rising demand for further deregulation and calls for foreign competition,

the telecommunications field in China still remain almost ‘exclusively Chinese’, over

which the State has majority ownership and control. As of 2006, there are six telecom

operators in China, namely, China Telecom and China Netcom (operating the fixed-line

network), China Mobile and China Unicom (mobile network), China Railcom and

China Satcom.

2.2.3 Telecommunications developments after WTO

This final section examines how further developments in the telecommunications

industry in China after entry into WTO are driven by new technologies. The most recent

developments in the Chinese telecom field are mainly related to the deployment of 3G

(third generation) mobile standards. Currently, among the four leading

telecommunications network operators in China, the mobile carriers, namely China

Mobile and China Unicom, are providing 2G (second generation) and 2.5G mobile

services (i.e. GSM and CDMA) respectively; whereas the fixed-line carriers, China

Telecom and China Netcom, are providing an alternative form of wireless service

known as Xiaolingtong (meaning ‘little smart’ in Mandarin) with limited geographical

12

coverage (Yuan et al., 2006).7 As convergence between the telecommunications and

traditional information technology industries takes shape, the Chinese government and

enterprises alike now recognise the increasingly significant role of 3G technologies in

the race to boost competitiveness.

One of the major breakthroughs in the history of China’s telecommunications

industry occurred when a leading Chinese telecom equipment manufacturer, Datang

Technology8, together with Siemens of Germany, developed the Chinese 3G standard

known as Time Division-Synchronous Code Division Multiple Access (TD-SCDMA),

which was approved by the International Telecommunications Union (ITU) in May

2000 as one of the internationally-accepted 3G mobile communications standards,

rivalling W-CDMA (Wideband CDMA) adopted by Europe and CDMA2000 used in

the US, Japan and Korea. 9 However, it was only in October 2002 that Datang

Technologies obtained support from the Chinese government when the MII announced

an allocation of 155MHz of Time Division Duplex (TDD) resource to TD-SCDMA;

and at the following week, seven telecommunications equipment manufactures –

namely, Datang Technology, Huawei Technology, Huali Group, Southern Hitech,

Shenzhen Zhongxin Technology (ZTE), China Electronics Group and China PuTian

Group, formed the ‘TD-SCDMA Industrial Alliance’ with support from three

government agencies – the State Planning Commission, the MII and the National

Science and Technology Department (Fan, 2006).10

Another significant milestone in Chinese telecommunications development is

exemplified in the achievements of another domestic telecom equipment manufacturer,

Huawei Technology which was established in 1988. After launching its first GSM

7 ‘GSM’ stands for ‘Global System for Mobile Communications’ and ‘CDMA’ stands for ‘Code Division Multiple Access’. Xiaolingtong is based on the Personal Handy Phone System (PHS) technology which originated in Japan in 1995. For more details on a description of the Xiaolingtong technology, refer to Yuan et al. (2006). 8 Datang Telecom Technology Corporation (DTT) was established in the Haidian district of Beijing on September 21, 1998. It is a leading communications equipment manufacturer and provider of a wide range of telecommunications services in China. See DTT website, http://www.datang.com/. 9 “PacificNet Announces 3G Strategy at ITU Telecom World 2006”, TMCnet (December 6, 2006), http://www.tmcnet.com/usubmit/2006/12/06/2148540.htm. 10 The delay in announcement of support from MII was attributed to the fact that TD-SCDMA had less support and R&D investment compared with the other standards, WCDMA and CDMA2000 which are favoured by China Mobile and China Unicom respectively. WCDMA is also supported by major multinational companies such as NTT DoCoMo of Japan, and Ericsson and Nokia of Europe; while CDMA2000 is mainly supported by North American and Korean companies, including Qualcomm, Nortel Networks, Motorola and Samsung. Leading Chinese companies such as Huawei and ZTE have invested in WCDMA and CDMA2000 respectively. For more details, see Fan (2006).

13

equipment in 1997, the company started R&D investment in 3G, i.e. WCDMA in 1998.

In 2002, Huawei established the first 3G Open Lab with NEC in China and introduced

WCDMA core network equipment based on soft switches at ITU (Fan, 2006). Two

years later, Huawei established a joint venture with Siemens to develop the TD-

SCDMA mobile communications technology to serve the Chinese market.11 Adding

further glory to its record of achievements to date, the company obtained three awards

at the 2006 Frost & Sullivan Asia Pacific ICT Awards – namely, ‘2006 Vendor of the

Year’, ‘2006 Optical Vendor of the Year’ and ‘2006 Broadband Equipment Vendor of

the Year’.12

In anticipation of such a trend towards greater application of 3G technology in

the future, nine leading Chinese telecom institutions, namely, the telecom operators –

China Telecom, China Mobile, China Unicom and China Netcom; equipment providers

– Huawei Technologies, ZTE Corporation, Putian Corporation and Vimicro Corporation;

and a research institute – the China Academy of Telecommunication Research of the

MII, formed a mobile multimedia technology alliance (MMTA) in October 2004. The

MMTA alliance will serve to ‘boost technical innovation and development of standards

and applications in the booming mobile multimedia industry, and thereby boosting the

competitiveness of Chinese enterprises in applying upcoming 3G technologies’ (Xiao,

2004).

The key lies in greater co-operation among the Chinese enterprises if they are to

grab a larger market share facing tense competition with international rivals. The mobile

communication multimedia represented by 3G service is expected to attract newcomers

into the industry, with an increasingly wider range of services coming onto the scene,

including the mobile game services, mobile photo services, mobile colour message

services, mobile short message services as well as other existing services (Xiao, 2004).

The latest sign of the ambition by Chinese companies to expand their influence into the

international telecommunications field took place in May 2006, with China Mobile

signing a US$5.3 billion deal to acquire (its first ever overseas acquisition) Millicom

International Cellular SA of Luxembourg, which operates mobile services in 16

countries (Singer and Dean, 2006). 11 Huawei website, http://www.huawei.com/. 12 Frost & Sullivan is a global consulting company for emerging high technology and industrial markets. Huawei had also been awarded “Vendor of the Year” in 2005 based on its strong performance such as revenue growth, new customer wins and innovative strategy. See “Huawei Technologies Bags Three Awards at the 2006 Frost & Sullivan Asia Pacific ICT Awards”, M2 Presswire (Coventry: June 19, 2006).

14

Yet, despite the hype about the competitive advantages that 3G will bring, it

took almost six years since the recognition by ITU in 2000 for an official announcement

from the Chinese authorities concerning the long-awaited issue of 3G licences to the

leading telecommunications operators in the country. On January 20, 2006, the MII

formally announced TD-SCDMA to be the country’s standard of 3G mobile

communications.13 The Chinese government has taken a cautious approach to this issue

as it has invested heavily in developing TD-SCDMA technology, spending about 55

billion yuan (US$6.63 billion) in 2005. It is estimated that more than one trillion yuan

would have to be spent on building the 3G network alone if all the four leading Chinese

telecommunications operators were to be awarded with 3G licenses (Yuan et al., 2006).

Nevertheless, it was a significant development when the nation’s two largest

fixed-line operators, China Telecom and China Netcom, were licensed by the MII in

May 2006 to obtain the number segment prefixed with 188 and 189 respectively in their

test 3G networks in the northern city of Baoding and eastern city of Qingdao. In

addition, China Mobile and China Unicom have already been permitted to use numbers

prefixed with 159 and 153 respectively in the southwestern municipality of Chongqing,

beginning in June 2006.14

The ‘big news’ eventually came when MII Minister, Wang Xudong, seizing the

opportunity at the ITU Telecom World 200615, announced that China would issue 3G

licenses ‘very soon’, and assured that it would be on time for operators to ‘offer 3G

services during the 2008 Olympic Games in Beijing’.16 The 3G licenses are expected to

be issued no later than the first quarter of 2007 to ensure 3G networks to be operational

before the Games begin (Perez, 2006). China Mobile has been reported to be planning

the operation of the TD-SCDMA network in Beijing and Qingdao, before implementing

it in the provincial capitals of the coastal areas and finally in the inland provincial

capitals.17

13 “PacificNet Announces 3G Strategy at ITU Telecom World 2006”, TMCnet (December 6, 2006), http://www.tmcnet.com/usubmit/2006/12/06/2148540.htm. 14 “China Telecom, China Netcom obtain 3G number segment”, SinoCast China Business Daily News (London: May 10, 2006).

15 Held on 4-8 December, 2006, at AsiaWorld-Expo, Hong Kong, China, http://www.itu.int/WORLD2006/ 16 “PacificNet Announces 3G Strategy at ITU Telecom World 2006”, TMCnet (December 6, 2006), http://www.tmcnet.com/usubmit/2006/12/06/2148540.htm. 17 “China Mobile prepares for TD-SCDMA”, SinoCast China Business Daily News (London: January 2, 2007).

15

Finally, the growth of China’s telecommunications can be further boosted by

tapping on the resources of foreign companies. In this respect, the Chinese government

has taken a proactive approach when the National Development and Reform

Commission (NDRC) signed a deal with SK Telecom, the leading provider of mobile

communications services in Korea, in August 2006, to develop the TD-SCDMA for 3G

mobile telecom by setting up a joint centre for research in China. The agreement was

followed up with the joint construction of a TD laboratory between the Korean

company and Datang Technology, with the possibility of ZTE Corporation joining the

partnership to build the first trial TD-SCDMA network in the first quarter of 2007.18

The Chinese telecommunications market received an added boost with news of a

merger between Lucent Technologies of the US and Alcatel of France on November 30,

2006, both of which have strong ties to Datang Technology.19 The former had signed a

deal for TD-SCDMA in November 2005, while the latter had signed a Memorandum of

Understanding (MOU) with the Chinese company in November 2006 (before the

merger) which will reinforce its commitment to invest in the development of TD-

SCDMA in China.20

2.3 The computer industry

This section traces the development of computer hardware and software in China. In

particular, it explores the various government-led policies implemented to foster

development in the computer industry as well as the shift in the focus of policies in

18 “SK Telecom, Chinese government sign 3G services deal”, Asia Pacific Telecom 10 (10), October 2006; “Datang to build 1st TD network in South Korea”, USITO website, http://www.usito.org/news_dl.php?id=76. 19 Based in Paris, Alcatel-Lucent will have the combined revenue of approximately Euro €21.3 billion (US$28 billion) with 79,000 employees in more than 130 countries. With the merger, the company has a global leading position in a wide spectrum of ICT services, such as Internet Protocol (IP) television, broadband access, carrier IP and 3G technologies, including CDMA2000, WCDMA and TD-SCDMA. See Alcatel-Lucent website, http://www.alcatel-lucent.com/. 20 Alcatel and Datang first signed an agreement to invest in TD-SCDMA two years earlier, in November 2004. It is also estimated that the former Lucent had invested a total of US$2.9 billion in China between 1995 and 2005, while the former Alcatel invested more than US$1 billion in the mainland in 2005 alone. See “Lucent Takes IMS (IP Multimedia Subsystem) to China”, Light Reading (November 16, 2005), http://www.lightreading.com/document.asp?doc_id=84416; “Alcatel and Datang Group to advance TD-SCDMA development”, Premium mobile technologies (November 30, 2006), http://premium-mobile.com/content/alcatel-and-datang-group-to-advance-td-scdma-development/. “Alcatel-Lucent to boost mainland investment: Communications giant will concentrate on the enterprise market in China”, South China Morning Post (Hong Kong: December 5, 2006).

16

17

recent years in adaptation to changing consumer demands and the competitive

environment.

2.3.1 Development of the hardware industry

China is now the world’s second largest PC (personal computer) maker and is expected

to become the world’s largest by 2010 (Kshetri, 2005). The production of PC in China

has jumped almost 100 times since 1995, from less than one million units in that year to

80 million in 2005 (Figure 2.1). This is an amazing achievement, considering the fact

that China had only 500,000 PCs for more than 1.2 billion people in 1990 (Kraemer and

Dedrick, 2002b). The sales of Chinese PC (including desktops and laptops) grew

annually by about 15% between 2002 and 2004.21 In 2004, the desktop and laptop PCs

accounted for 88% and 12% of the Chinese PC market respectively. The market was

dominated by the largest domestic firm, Lenovo Group Ltd, holding 25% of the market

share. The largest shares held by foreign companies were Dell Inc. and IBM

Corporation, holding about 7% and 5% respectively (Table 2.1).

There has been less discussion on the history of the computer hardware industry

in China compared with that of the telecoms industry. The development of the computer

industry had been a priority in the agenda of the science and technology development

policies since 1955. An overview of the historical development of computers in China

has been discussed in Witzell and Smith (1989). The history of China’s computer

industry could be said to begin with the founding of its first national computer research

institution, the Institute of Computing Technology within the Chinese Academy of

Sciences (CAS) in 1956.22 With assistance from the Soviet Union, in August 1958, the

CAS Institute built the first generation of computers in China, known as Model 103.23

Such assistance was however ended in 1960 which rendered China with only the ‘self

reliance’ path (Lu, 2000b).

The Cultural Revolution of 1966-76 disrupted the development of the national

economy as well as the computer industry. Yet surprisingly, China seemingly was

closing the gap in computer achievements with the Soviet Union. For instance, by 1977,

the CAS Institute developed the model 013, ‘a third generation computer capable of 2

21 “China PCs 2005”, Snapshots International, March 2005. 22 The Institute of Computing Technology will be referred hereafter as ‘CAS Institute’. 23 Institute of Computing Technology website, http://www.ict.ac.cn/.

0

10

20

30

40

50

60

70

80

90

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

Mill

ion

units

-40

-20

0

20

40

60

80

100

120

140

%

PC (million)Growth rate (%)

Figure 2.1 Production of PCs in China, 1990-2005

18

Source: State Statistical Bureau, China Statistical Abstract 2006, Beijing.

million ops compared with the 1976 RIAD 1060 of the Soviet Union which was only

capable of 1.5 million ops’, the reason being that the Soviet Union ‘chose to concentrate

on producing R&D computers for military use’ (Witzell and Smith, 1989: 37). Yet, in

spite of the disruptions caused by the Cultural Revolution, the computer industry

seemed to overcome the economic turmoil with impressive achievements, as reported

by Witzell and Smith (1989: 41) and listed in Appendix 2.1.

Table 2.1 Market shares of China’s PCs (%) Company 2004 Lenovo Group Ltd Founder Electronics Co. Tsinghua Dongfang Co. Ltd Dell Inc. IBM Corporation Hewlett-Packard Co. Others Total

25.1 9.9 7.8 7.2 5.1 4.8 40.1 100

Source: “China PCs 2005”, Snapshots International, March 2005.

During the pre-reform era, computers in China were not produced for

commercial use until 1973. Computer models developed by various ministries and

universities were used mainly for military and scientific purposes such as developing

the atomic bomb, satellites and weather-forecasting models (Lu, 2000b). As China

sought to revive its economy from the shocks of the Cultural Revolution, the Chinese

government laid out the ‘Four Modernizations’ – Agriculture, Defence, Industry, and

Science and Technology, as the pillars of national economic revival and development.

Consequently, a National Plan for the Development of Science and Technology (1978-

85) emerged at the National Science Conference on March 18, 1978, where Fang Yi (a

member of the Central Committee of the Chinese Communist Party) spoke: ‘The eight-

year outline plan draft gives prominence to the comprehensive science and technology

spheres … agriculture, energy, materials, electronic computers, lasers, space, high

energy physics and genetic engineering.’ Going on further, Fang Yi emphasized that

‘China must make a big new advantage in computer science and technology, and should

lose no time in solving the scientific and technical problems in the production of large

scale integrated circuits (ICs), and make a breakthrough in the technology of ultra-large

scale ICs….. We aim to acquire, by 1985, a comparatively advanced force in research in

computer science and build a fair size modern computer industry… A number of key

19

enterprises will use computers to control the major processes of production and

management’ (Witzell and Smith, 1989: 39).

As economic reform steered the whole country towards a more open and market-

oriented economy, a similar fashion was occurring in the reform of Chinese science and

technology policies that helped further expansion of the computer industry. There was

evident recognition by Chinese leaders from national to local levels of a larger role of

foreign investment and multinational corporations in the development of high

technology in China, thus paving the way for less dependence on government-led

planning initiatives and more on individual innovation and entrepreneurship. China’s

drive to commercialise its computer industry formally began in 1986 with an ambitious

effort to create an electronics industry which was listed as a ‘pillar industry’ for

developing the national economy in accordance with the Seventh Five-Year Plan

(Kraemer and Dedrick, 2002b).

If ‘government involvement’ was the key characteristic defining the

development process during the reform period of the 1980s and early 1990s, then

‘learning’ and ‘innovation’ have been the driving forces behind the rapid growth of the

Chinese computer industry since the 1990s.24 In his study of how indigenous Chinese

computer companies were catching up in the high technology sector, Lu (2000b) noted

that the traditional model of technology transfer which has been used to describe the

export-led technology learning in the East Asian Newly Industrialized Economies

(NIEs) could not explain China’s rapid catch-up in high technology sectors such as

telecommunications equipment and computers. While technology transfer normally

follows a ‘bottom-up’ linear sequence in four stages – cheap labour assembly of

imported kits, original equipment manufacturing (OEM) or the localization of parts and

components, original design manufacturing (ODM) or product redesign, and original

brand manufacturing (OBM) or product design, “learning” assumes ‘top-down’

approach as it could start off at any stage such as product design or redesign.25 Such a

mode of technology learning is also known as “innovation” by definition (Lu, 2000b).

A comparison of China’s experience in developing its computer industry reveals

some similarities as well as differences between them and other developing countries. 24 ‘Learning’ is defined as a process of acquiring the capability to develop technological resources and converting them to commercial uses (Lu, 2000b: 3). 25 The terms ‘OEM’, ‘ODM’ and ‘OBM’ originated from Hobday (1995).

20

China’s policies are said to resemble the developmental approach of Japan and the East

Asian NIEs such as Korea, Taiwan and Singapore with strong support from the

government which combines the promotion of exports to achieve global

competitiveness with strong efforts to develop indigenous technological capabilities

such as building an information infrastructure as well as providing financial and

technical aid to domestic companies (Kraemer and Dedrick, 2002b). In fact, China’s

export-oriented policy turned it into a net computer export for the first time in 1994

when it established export-processing zones and offered tax incentives to attract foreign

investment (Kraemer and Dedrick, 2002b).

However, China’s developmental path differs from other developing countries in

certain ways. First, unlike the East Asian NIEs which rely primarily on export markets,

China enjoys the benefit of having a huge domestic market which ‘provided a stimulus

for indigenous technological innovations for processing Chinese characters in computer

systems’ (Lu, 2000b). China is also different from many developing countries in the fact

that it had established an extensive S&T infrastructure during the central planning era.

The latter enables Chinese companies to tap on both domestic as well as foreign sources

of technology. Finally, and perhaps most importantly, China has been able to attract

foreign investment on terms favourable to the host country – a strategy which is less

successful in many other developing countries. With the lure of their huge market size,

China ‘could exchange market access for foreign technology, by requiring foreign

multinationals to develop joint ventures with domestic companies and allowing

Taiwanese companies to set up production networks in the mainland to support

domestic companies’ (Kraemer and Dedrick, 2002b).

For instance, IBM was unable to penetrate the Chinese PC market until it set up

a joint venture with a domestic company, Great Wall, in 1994 which allowed the latter

access to IBM technology and manufacturing know-how in return for access to local

distribution channels. Other foreign multinationals which have set up joint ventures

include Compaq (with Stone Group), Hewlett-Packard (with Lenovo), Toshiba (with

Tontru) and LG Electronics (with Tontru) (Kraemer and Dedrick, 2002b).

China’s policy to nurture its computer industry is further embodied in the ninth

Five-Year Plan (1996-2000) which emphasized the implementation of several ‘Golden

21

Projects’26 to support the development of ICT and encourage computer use throughout

the country (Kraemer and Dedrick, 2002b). The Plan laid out the following goals:27

• Increase the percentage of domestic components in Chinese-assembled computers and increase

the nation’s capacity to produce peripherals such as monitors, printers, disk drives, add-on cards,

and high-definition displays;

• Achieve a per capita national computer penetration of 1%, and 20% among urban families;

• Develop two to three domestic PC manufacturers into enterprises with an annual production

capacity of more than US$1 billion;

• Apply computer technologies to the renovation of traditional industries;

• Develop uniform PC standards via a production licensing system to answer complaints about

lack of service and intellectual property protection on clone PCs.

Indeed, it was the strategy which focuses on home-grown innovation that

culminated in one of the most astonishing news that rattled across the globe. In

December 2004, for the first time in Chinese and global history of the computer

industry, China’s computer giant Lenovo Group bought over the PC division of IBM for

US$1.25 billion, effectively acquiring the latter’s entire global desktop and laptop

computer R&D and manufacturing business. It was a deal that would turn the Chinese

company into the world’s third largest PC maker with annual revenue exceeding US$10

billion, and accounting for 8% of the world market share.28 On the other hand, IBM will

gradually withdraw from the PC market and focus on the game machine business in

China.29

The turning point for the Chinese computer industry came in September 2002,

when the Institute of Computing Technology (CAS Institute) developed the first ever

Chinese-made CPUs (central processing unit) known as ‘Godson-I’.30 However, the

new developments did not fundamentally alter the structure of the Chinese CPU market

which was still largely dominated by foreign manufacturers. The introduction of

Godson-I did not make any impact in the Chinese market due to limited demand and its

26 See Table 2.5 in this chapter for the list of Golden Projects implemented in China. 27 These points are extracted from Kraemer and Dedrick (2002b). 28 “China’s Lenovo Group acquires IBM’s PC business”, People’s Daily (Beijing: December 8, 2004). 29 “IBM to fade from PC market, quit China PC business”, People’s Daily (Beijing: December 6, 2004). 30 It was designed by Professor Hu Weiwu, a researcher of the CAS Institute who graduated from the University of Science and Technology of China (USTC) in 1991. See “The Chief Designer of the CPU ‘Godson I’ Made his Presentation at His Alma Mater”, USTC website, http://www.ustc.edu.cn/ en/ article/56/42ff2484/.

22

clock speed of 266 MHz failed to meet the minimum requirement of 400 MHz for

procurement by the Beijing government (USITO, 2005).

A new breakthrough occurred a year later with an announcement of Godson-II,

China’s first 64-bit high performance processor which supports the Linux operating

system and X-window system, and it’s the equivalent of Pentium III.31 Compared to the

earlier Godson-I, it has ‘an improved frequency scaling, true 64-bit instruction support,

and significantly reduced power consumption at less than 5 watts for the 500 MHz

model (Richmond, 2003). The introduction of Godson-II increased the market share of

home-grown processors from zero to 1% in 2003 (Table 2.2).

Table 2.2 China’s CPU market, 2001-2004 Market share (%)

Company 2001 2002 2003 2004Intel AMD Via Tech Chinese processors Others

90.05.02.00.03.0

84.0 8.0 3.0 0.0 5.0

83.0 9.5 3.5 1.0 3.0

74.018.0

4.01.03.0

Source: USITO (May 13, 2005).

The story of Chinese processors has not ended though. It was reported in early

2006 that a new type of CPU, the ‘Godson-III’, the equivalent of Pentium IV, was being

developed by a research team at CAS Institute known as the ‘Super Dragon’.32 It did

not take too long for one of the greatest achievements to materialise when the CAS

Institute developed the first Chinese low-cost computer, Longmeng (meaning ‘Dragon

Dream’ in Chinese), having the size of a notebook, which costs only 1,000 yuan

(US$125) which was announced by Zhang Fuxin, a researcher at the institute.33 Using

Red Flag Linux as its operating system, and equipped with a DVD drive and a video

game player, Longmeng is ‘equivalent to a 1G Pentium III desktop’.34 The computer is

marketed for users from low income groups and students in rural areas by the Menglan

Group from Changshu in Jiangsu province. However, it will take a few years to assess

whether there is any impact of the new Chinese processors on their market share

compared to those of major foreign competitors like Intel and AMD.

31 “Chinese-made CPU Chip Equivalent to Pentium III”, China Education and Research Network (Beijing: April 20, 2005), http://www.edu.cn/20050420/3134777.shtml. 32 “Future Super Dragon Super Server”, Zhongguo Wang (China Net) (Beijing: March 4, 2006), http://www.china.org.cn/english/scitech/160125.htm. 33 “China to produce low-cost computers of its own”, China Economic Net (Beijing: March 15, 2006). 34 Ibid.

23

Meanwhile, China has taken great strides in developing its own models of

supercomputers. China had developed the 10-teraflop Dawning 4000A in June 2004 on

its own.35 Later on, when IBM announced its success in developing the 100-teraflop

supercomputer at the end of 2004, the National Research Centre for Intelligent

Computing Systems (NCIC) and the Dawning Company responded with similar

research and had expected to put their 100-teraflop ‘Dawning-5000’ supercomputer

model into use in 2008. 36 Finally, China was also reported to have commenced on a

preliminary research to develop the 1,000-teraflop supercomputers during the 11th Five-

Year Plan (2006-2010), headed by Lenovo Group.37 The key objective of this project,

which started in July 2005, was to develop a supercomputer without dependence from

foreign countries.

2.3.2 Software development

The software market is broadly defined to cover the application and systems software,

as well as the intermediate link software. Presently, it is dominated by the segment of

application software (about 65%), which consists of accounting software, word-

processing packages, anti-virus software and publishing software; followed by platform

software (29%), which includes the operating system (OS) and Linux-based operating

software; and intermediate software (6%).38

In 2005, the Chinese software industry increased its world market share to 3.5%,

exceeding those of India and South Korea.39 Chinese software sales grew annually by

almost 19% between 2000 and 2004. It had increased about six times within a decade

from US$1.1 billion in 1996 to almost US$6 billion in 2004 (Figure 2.2). According to

CCID Consulting, China’s software market is estimated to have reached US$6.8 billion

(56.5 billion yuan) in 2005, climbing to almost US$8 billion (66.1 billion yuan) in mid-

2006, and it’s further forecast to hit more than US$13 billion (110 billion yuan) in 2009,

growing annually at about 18%.40 In 2003, the Chinese software market was dominated

by Microsoft and Ufsoft Co. which had 21% and 17% of the market share respectively.

35 It means the supercomputer system calculates 10 teraflops per second. 36 “China begins preliminary research on 1,000 teraflops supercomputer”, People’s Daily (Beijing: July 22, 2005). 37 Ibid. The US has planned to develop the 1000-teraflop supercomputer in 2010. 38 “China Software 2005”, Snapshots International, March 2005. 39 “China’s Software Industry Sales to reach 1.3 trillion yuan in 2010”, SinoCast China Business Daily News (June 29, 2006). 40 “China Software Industry’s Development Trend and Feature”, China ComputerWorld Research (July 2006), http://www.ccwresearch.com.cn/en/; “China’s Software Industry Sales to reach 1.3 trillion yuan in 2010”, SinoCast China Business Daily News (June 29, 2006).

24

25

The largest shares held by domestic companies were Langchao Group and Beida

Fangzheng Co., at about 15% and 7% respectively (Table 2.3).

Yet, in comparison with its counterpart in India, there are indications that

China's software industry is still at a premature stage in many aspects. Although China

has far outperformed the latter in terms of ICT expenditure and telecommunications

development, its software exports in 2003 of US$2 billion were less than one-sixth of

India’s (US$12.5 billion). The gap in development of the software industry between the

two countries has been mainly attributed to the Chinese policy of ‘emphasizing

hardware while neglecting software’ during the 1980s (Wong and Wong, 2004). As far

as the software standard is concerned, only two Chinese firms have obtained the CMM

Level 5 in 2004, as compared with India’s 60, despite having doubled the number of

software enterprises (Kshetri, 2005). 41 Moreover, China’s software industry is

dominated by small and medium-sized enterprises (SMEs) which account for 97% of

China's total 5,000 software enterprises (Wong and Wong, 2004). Most Chinese

software enterprises hire 25 employees on average, much smaller than India’s average

of 174 (Kshetri, 2005).

China’s primary competitive disadvantage (when compared with India) lies in

the lack of manpower skills and resources. Language and cultural barriers are the main

factors determining their respective software export destinations. While Indian software

is exported mainly to the US (the world’s largest software market) and Europe

(accounting for 80% altogether), about 60% of Chinese exports in 2004 went to Japan,

which has a smaller market size than the US. The lack of English language skills have

also prevented Chinese companies from venturing into the US or other foreign markets,

and therefore miss out on the opportunities to ‘work with clients on the front end of an

IT service contract, such as business process requirements, system architecture and

system design’ (Dedrick, Kraemer and Ren, 2004).

41 CMM (Capability Maturity Model) is the international certification which serves as the primary index for software standardization (Wong and Wong, 2004).

0

20

40

60

80

100

120

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Bill

ion

yuan

0

5

10

15

20

25

30

%

Software sales (billion RMB) Growth rate

Figure 2.2 Growth and forecasts of Chinese software sales, 2000-2009

26

Source: “China Software 2005”, Snapshots International, March 2005.

Table 2.3 Market shares of China’s software (%) Company 2003 Microsoft Ufsoft Co. Langchao Group Beida Fangzheng Co. SAP Sybase Kingdee Software (China) Co. Ltd Newgrand Software Co. Others Total

21.4 16.7 15.4 6.8 6.7 4.2 4.1 3.2 21.5

100.0

Source: “China Software 2005”, Snapshots International, March 2005.

Nevertheless, Chinese policies are now geared towards lesser dependence on

foreign technology via exporting Chinese standards to the world as well as reducing the

dominance of foreign firms in the domestic market. For instance, 30% of the Chinese

software markets have been captured by domestic vendors in 2003, with the aim of

expanding the share to 60% by 2010 (Kshetri, 2005). Marketing research firm Gartner

Group was confident that if China were to continue its fast-track growth from building

software parks, providing incentive packages for programmers, boosting its English

training programs and enforcing protection of intellectual property rights, the country

could catch up with India by 2006.42

Indeed, competition with India need not become an obstacle to China’s advance

in developing its software industry. Chinese President Hu Jintao, in his speech at the

China-India Economic, Trade and Investment Cooperation Summit and CEO Forum

held in Mumbai, India on November 23, 2006, put forward a five-point proposal, in

which he suggested a strengthening of mutual investment involving

telecommunications, software and other emerging industries so as to promote trade and

economic cooperation between the two countries.43 The emphasis is on supplementing

the respective ‘strong points’ in information technology, infrastructure, science and

technology and other areas between them, and thus creating more potential for

cooperation and investment opportunities for their respective domestic companies in

each other’s country.

42 “High Tech in China: Is it a threat to Silicon Valley?”, BusinessWeek Online (October 28, 2002), http://www.businessweek.com/magazine/content/02_43/b3805001.htm. 43 “Chinese president makes proposals at China-India CEO forum – Xinhua”, BBC Monitoring South Asia (London: November 24, 2006).

27

China took a major step to promote the development of its software industry

when on July 24, 2000, the State Council launched a package of policy under the ‘No.

18’ document, known as the ‘Policies for Encouraging the Development of the Software

and Integrated Circuit (IC) Industries’. Among the most favourable to software

businesses were the tax incentives outlined in the Policies as follows (Liu, 2004b):

• A reduction of the value-added tax (VAT) for R&D investment and additional production for

software companies from 17% to 3%.

• A two-year tax holiday for new software companies, and 50% of income tax for the next three

years, starting from the first year of earning profits.

Two years later, the Chinese government launched another policy package under

the ‘No. 47’ document, known as ‘The Principle of Rejuvenating the Software Industry’

which covered financing, exporting, human resources, and government procurement,

etc. (Liu, 2004b). The document stated more explicitly that the government will give

priority to domestic software in its purchasing decision and will use only licensed

software. The document further stated that more than 4 billion yuan will be spent on

R&D in the software industry during the 10th Five-Year Plan (2001-2005).

In a significant move to boost its software industry, China has sought to expand

its software infrastructure and human resource base. First, it has built 19 software parks

in Beijing, Tianjin, Shanghai, Xian, Dalian, Guangzhou, Wuhan, Fuzhou, Xiamen and

Hefei, and established 53 high-tech industrial development zones throughout the

country in 2000 (Wong and Wong, 2004). It currently has six software export bases in

Beijing, Shanghai, Tianjin, Dalian, Shenzhen and Xian cities, and now further aims to

increase its export bases to 15 by 2010 (Zhang, 2006c). Second, where software talent is

concerned, China had 900,000 software industry workers in 2005, and this number is

expected to grow up to 2.5 million in the next five years. The Ministry of Information

Industry (MII) also aims to increase the number of software enterprises with sales

revenue of over 5 billion yuan (US$625 million) in China (Zhang, 2006b).

Finally, the Chinese software industry faces the major problem of piracy. The

MII recognizes the importance of intellectual property rights (IPR) protection, as can be

seen from the crackdown on software piracy in recent years. In a move to address this

issue, the central government allocated almost 150 million yuan (US$18.5 million) for

the purchase of licensed products. Chinese officials were also insistent against claims by

the Business Software Alliance (BSA) that up to 94% of Chinese software could be

28

unlicensed (Zhao, 2006). In April 2005, the MII and the National Copyright

Administration (NCA) required all PCs produced and sold in China to be installed with

authentic operating software systems. The spokesman for the National Copyright

Administration of China, Wang Ziqiang, remarked that such legal requirement proves

the consistent ability of the Chinese government to control the rampant software piracy

(Zhao, 2006). The meeting between Chinese President Hu Jintao and Microsoft founder

Bill Gates in Seattle in April 2006 was also meant to boost the confidence of overseas

investors in China’s determination to crack down on IPR violations.

“During the last ten years, China’s information technology industry has emerged as a global

centre for growth and innovation. We’re encouraged by China’s efforts to strengthen intellectual

property protection, which will provide the foundation for continued expansion of the IT

industry in China. We look forward to working with the Chinese government and partner

companies in China to create new opportunities for growth.”

-- Bill Gates, Chairman and Chief Software Architect of Microsoft, welcome speech to Chinese

President Hu Jintao44

“With the recently announced cooperative engagement agreements with computer manufacturers

to pre-load genuine Windows® operating systems, we see even greater opportunities in China

and the chance to build long-lasting relationships with customers and partners in China.”

-- Tim Chen, Corporate Vice President and Chief Executive Officer of Microsoft Greater China

Region45

In the same year, the Chinese domestic office software producer, Evermore, won

bids for office software products to be used in government sectors in 21 provinces,

municipalities and autonomous regions (Zhao, 2006). One of the major battles over

software piracy was won by Microsoft when the Chinese PC manufacturer, Lenovo,

agreed to load only the legitimate copies of Microsoft Windows operating system onto

their new PCs. Such a move is expected to be followed by another major Chinese firm,

Tsinghua Dongfang Company (Batson, 2006).

44 “President Hu Jintao and Bill Gates to Discuss Microsoft’s Commitment to the Chinese Software Industry”, Microsoft PressPass (April 18, 2006), http://www.microsoft.com/presspass/press/2006/ apr06/04-18ChinesePresidentPR.mspx 45 Ibid.

29

China’s software industry has further taken on a structural shift, by increasing its

emphasis on the development of open source software (OSS), which offers an

alternative to foreign software and an opportunity to break the domination of foreign

companies in the industry (Dedrick, Kraemer and Ren, 2004). It started when the

Chinese government established Red Flag Linux with the Chinese Academy of Science

and the Beijing Software Industry Production Centre in 1999. In 2002, Yangfan Linux

was launched by the centre, based on versions of Linux developed by Red Flag and the

China Computer Software Corporation. In fact, the Dawning 4000 supercomputer is

based on the locally-designed Linux operating system (Kshetri, 2005).46

With this new strategy that has shifted from the traditional reliance on domestic

consumption to exporting to major overseas markets such as the US and Europe, the

MII recognizes software outsourcing as ‘a shortcut that will allow the Chinese software

industry to catch up with the developed countries’. The MII aims to triple its software

exports from 2005 to US$ 12.5 billion by 2010.47 As an example of a joint development

between Chinese and foreign companies, Datang Mobile, a core member of Datang

Technologies has joined a global consortium of Linux-based operating systems, Open

Source Development Labs (OSDL) in November 2006, to accelerate the deployment of

Linux on its mobile handsets.48

At its current position, there is a strong case for China to focus on its domestic

market while at the same time build alliances that foster greater cooperation with

foreign investors to spur the growth of its software industry. To understand the

strategies that China could adopt to strengthen its software industry, it would be useful

to look at the framework of Li and Gao (2003). The software industry is categorized

into a 2 x 2 contingency table representing two dimensions – the target market served

(domestic vs. export) and the type of business (service vs. packages) (Table 2.4).

Positions A and B, which represent export-oriented strategies, have been

successfully used by India and Israel respectively – the former having captured 16% of

the global market in customized software while the latter has emerged as a source of

46 OSS has enabled the Chinese military to use domestically produced supercomputers. The Red Flag Linux applications are also deployed in Chinese aircraft, weapons systems, vehicles, industrial equipment and other consumer devices besides the PCs (Kshetri, 2005). 47 Ibid. 48 “OSDL Mobile Linux Initiative Gains Another Heavy Hitter”, PR Newswire (New York: November 28, 2006).

30

enterprises developing packaged products such as Internet security and antivirus

software (Li and Gao, 2003). Although China has the potential to develop its software

industry for exporting to overseas markets, such an export-oriented approach may result

in a ‘brain drain’ as it diminishes the flow of skills and technology into the domestic

market, since the net benefits would be passed on to its overseas customers. As a

relatively new player in the software export market, China does not have the adequate

legal establishment, infrastructure and track records as those countries that have

successfully built up their software export bases such as India, Ireland, Israel and

Singapore (Li and Gao, 2003).

Table 2.4 Strategic choices for developing China’s software industry

Software Business

Service Packages

Export A B Market served

Domestic D C E

Source: Li and Gao (2003).

Position C represents a strategy whereby the country will face tough competition

from international rivals, as seen in the case of the price battle between Chinese

software producer Kingsoft and international giant Microsoft over their respective

word-processing software, WPS Office and MS Office, whereby the former sold its

software for only US$157 (1,300 yuan) compared to the latter’s US$846 (7,000 yuan)

(Wong and Wong, 2004). Such a strategy however easily invites piracy which is already

widely prevalent in the country.

In the opinion of Li and Gao (2003), the most appropriate starting point for

China to develop its software industry is Position D, as there is a huge and growing

domestic demand for software services in the country, stimulated by high economic

growth. Li and Gao (2003) further introduced a Position E, situated at the cross-junction

of all other positions in Table 2.6, which represents a form of specialization for a variety

of niche markets that can be categorized by sector (banking, insurance, health

administration, hotel management, mining, forestry, etc), application (Web browser or

utility programs) or linguistics (regional languages). Therefore, China need not follow

the same path of India, as it should focus on the domestic market (Position D) with

specialized services that cater to specific markets (Position E).

31

A drawback of the above-mentioned strategy is its neglect of the interaction

between domestic and foreign software companies. Despite its shortcomings in terms of

software development and availability of human capital, China could make up for it

through fostering greater collaboration and cooperation by building alliances with

software enterprises from India or other software superpowers. Eventually, players from

all sides will gain as the burgeoning Chinese market and favourable government

policies attracts a greater inflow of foreign investment into the country, while at the

same time, Chinese firms will benefit from tapping the expertise and management skills

of the foreign partners that they work with (Li and Gao, 2003).

2.4 National science and technology programs

2.4.1 Brief overview of China’s science and technology (S&T) policy

The development of ICT industry in China has gone hand in hand with the nation’s

economic growth since the beginning of reform. Driven by the belief that developing

science and technology (S&T) was the key to ‘catching up’ with the developed nations

of the West, the Chinese government has developed S&T policies right from the

beginning of the founding of the People’s Republic in 1949, when at that time all

research and development activities were put under the control of the State

Development Planning Commission and the State Science and Technology

Commission.

As mentioned in the earlier sections of this chapter, resources for S&T (in

telecommunications as well as computer development) were mainly channelled to

military and non-commercial uses by government-linked administrative bodies during

the pre-reform period. It was only in the mid-1980s when the Chinese government

started to initiate various projects or programmes that have heavily supported the

development of high technology in line with market-oriented reforms. These projects

included the 863 Program (named after the year and month that the project was

implemented), the Torch Program, the High Technology Development Zones or Parks

and the 973 Program.

The development of S&T in China entered a period of rapid take-off in the early

1990s with new policy changes that further opened up the Chinese economy to foreign

investment. First, the Chinese government indicated its concrete support for non-

32

governmental corporations with the 1993 ‘Decision on Several Problems Facing the

Enthusiastic Promotion of Non-governmental Technology Enterprises’ which

recognised the role of non-state-owned enterprises in ‘developing a new innovation

system based on market-oriented technology firms as well as changing an S&T system

dominated by public institutions to one that embraced organizations of various

ownership structures’ (Naughton and Segal, 2001). This was followed up with a 1995

‘Decision on Accelerating S&T Development’ which further lent encouragement to

non-state companies as ‘an important force in the high-tech field’ and accepted the role

of market in the development of applied technologies (Naughton and Segal, 2001).

Second, the year 1993 witnessed the implementation of a series of projects that

sought to emulate the US’ information superhighway, known as the “Golden Projects”,

initiated by the former Ministry of Electronics Industry (MEI). It began with the

establishment of Jitong Communications Corporation that oversaw the Golden Bridge

Network in March 1993, which aimed to build a nationwide telecommunications

network. Subsequently, a series of Golden Projects was implemented, each serving

different aspects of ICT infrastructure in China (Table 2.5).

2.4.2 S&T projects

In March 1986, four Chinese scientists gathered together to propose a project that would

accelerate China’s high technology development, in order to meet the global challenges

of high-tech competition and revolution.49 The plan, known as 863 (which stands for

the year, 1986, and the month of March), aimed to ‘pool together the best technological

resources in China over fifteen years to keep up with international high-technology

development, bridge the gap between China and other countries in several high

technologies and strive for breakthroughs’ (Segal, 2003: 30). It was approved by former

Chinese leader Deng Xiaoping. The 863 Program targeted industries in the area of

biotechnology, new materials, lasers, energy, information, robotics, and space.

In spite of the numerous breakthroughs made since its implementation, the 863

Program was not without problems. As various institutions competed for resources and

equipment for R&D and production, this made coordination among them difficult.

Furthermore, the program participants had very few connections with private 49 They were Wang Daheng, Wang Ganchang, Yang Jiachi and Chen Fangyun. See the website of Ministry of Science and Technology of the People’s Republic of China, http://www.most.gov.cn.

33

enterprises, and therefore provided little incentives for innovative activities. The central

policymakers sought to refocus on the potential role of small non-governmental

enterprises in technological innovation. As a solution, the central government passed

numerous regulations that would provide greater support for R&D and

commercialization in the state owned enterprises (SOEs), and also encourage

individuals to start their own non-governmental enterprises.

One such plan is the Torch Plan, officially initiated by the Chinese government

in May 1988, the main objective of which was to promote the development of science

and education, and also to transform laboratory projects into commercial products,

thereby improving China’s competitiveness internationally (Lin, 2003). The plan would

expand the sources of funds available to nongovernmental enterprises and link them to

the development of high technology development zones (HTDZ) (Segal, 2003: 31-2).

The Torch Plan has also provided financial support to the software industry. For

instance, it has funded more than 600 software projects since 1995, and built 19

software parks in Beijing, Tianjin, Shanghai, Xi’an, Dalian, Guangzhou, Wuhan,

Fuzhou, Xiamen and Hefei (Kharbanda and Suman, 2002).

In an initiative to spur further breakthroughs from research in order to meet the

fast changing socio-economic demands, the State Science and Education Steering

Committee formulated the ‘National Plan on Key Basic Research and Development’

and implemented the ‘National Program on Key Basic Research Project’ on June 4,

1997, known as the ‘973 Program’. The main objectives of the program were to

mobilize Chinese scientific talents in conducting innovative research on major scientific

issues in agriculture, energy, information, resources and environment, population and

health, as well as materials and related areas, in accordance with the objectives of

China’s economic, social and S&T development goals up to 2010 and the mid-21st

century.50

50 Ministry of Science and Technology website, http://www.most.gov.cn

34

2.4.3 High technology development zones

Besides expanding the source of funding for technology enterprises, the Torch Plan

further promoted the creation of new high technology development zones (HTDZs) to

support these industries. Also known as the ‘high-tech parks’, the HTDZs have

contributed significantly to the Chinese economy through the development of ICT and

more recently, Internet-related products and services (Lin, 2003).

The establishment of HTDZs was accomplished in three stages.51 First, the State

Council officially approved the establishment of the first batch of 26 state-level HTDZs

in 1991.52 Subsequently, another 25 new and high-tech zones were approved in 1992,

when the State Basic Policy for High-Tech Industrial Development Zones was issued

which covered five areas of concern pertaining to high-tech industries, namely, taxation,

finance, trade, pricing and personnel policy (Segal, 2003). These policies were

favourable to new technology enterprises. In 1997, the construction of Yangling New

and High Agrotechnology Development Zone was approved. 53 Together with the

Beijing New Technology Experimental Zone, there are 53 HTDZs established

throughout the country by 2000 (Table 2.6).

The HTDZs in China have become important bases for the development of new

and high technology industry and promoting development of the regional economy.

During the late 1990s, the HTDZs have seen rapid annual growth. For instance, from

1997 to 1999 alone, the number of high-tech enterprises grew by 28% while the number

of employees increased by more than 50%; and the value of total output grew annually

by about 40%.54 By the end of 1998, more than 70,000 small and medium-sized high-

tech businesses have sprouted all over China.55

51 High-tech parks in fact started as early as 1980 when a group of researchers from the Chinese Academy of Sciences (CAS) set up a small shop in Zhongguancun, about twelve miles to the northwest of Beijing city, engaging mainly in the repair and servicing of equipment used by CAS institutes (Lin, 2003: 31). 52 “China’s New and High-Tech Development Zones”, China Internet Information Centre (Beijing: September 16, 2001). 53 Ibid. 54 “General Characteristics of the Development of New and High Technology Industry Development Zones in China”, World Economy & China (Beijing: November/December 2000). 55 “The Next Tech Superpower”, Asiaweek (Hong Kong: July 27, 2001).

35

Table 2.5 China’s Golden Projects

Project title Project design and goals Golden Bridge Golden Customs Golden Card Golden Enterprises Golden Health Golden Intelligence Golden Macro Golden Medical Golden Real Estate Golden Tax

The national public telecommunications network and the foundation of China’s entire ICT infrastructure. It provides the country with satellite and optical fiber cable networks for the financial, customs, foreign trade, tourism, meteorological, traffic service, State security, and other scientific and technological sectors. Develops applied information system services to track quota permits, bank sales of foreign currency, and trade statistics for China’s General Administration of Customs. It also aims to link all foreign trade departments and firms, and realize electronic data exchange and paperless trading. To replace cash transactions with an electronic service system for savings, withdrawals, and payments through credit and debit cards, through the use of the Golden Bridge telecommunications networks. A production and marketing information system supported by the State Economic and Trade Commission to link the state-owned enterprises and other industrial and commercial firms with Chinese government offices. It offers online services and assists commercial and industrial firms in the efficient use of personnel, capital and natural resources. Provides all Chinese citizens with an optical memory card containing personal health care data by the year 2002. A national science and technology information network for academic use. To build macroeconomic tools for use by ministries and provincial-level information centres. To connect large hospitals and research institutions, facilitating transmission of critical medical information and images among health organizations, sponsored by the Ministry of Health and Jitong Corporation. To facilitate the exchange of real estate information across different regions in China, jointly developed by the Ministry of Construction and local agencies. A computerized tax collection system sponsored by the Ministry of Finance, the People’s Bank of China, the State Tax Administration and the Ministry of Electronics Industry.

Source: Simon and Ashton (1996: 10).

36

Table 2.6 Geographic distribution of HTDZs in China Geographic location

HTDZs Number %

Northeast North East Coastal Area Central Northwest Southwest Total

Harbin, Changchun, Jilin, Daqing, Shenyang, Anshan, Dalian Beijing Zhongguancun, Tianjin, Zhengzhou, Shijiazhuang, Taiyuan, Jinan, Baoding, Luoyang, Qingdao, Weihai, Zibo, Weifang Shanghai, Nanjing, Suzhou, Wuxi, Changzhou, Hefei, Hangzhou, Nanchang Guangzhou, Shenzhen, Fuzhou, Xiamen, Zhongshan, Huizhou, Foshan, Zhuhai, Haikou Wuhan, Changsha, Xiangfan, Zhuzhou Lanzhou, Baoji, Xi’an, Yangling, Baotou, Wulumugi Chengdu, Chongqing, Kunming, Mianyang, Guiyang, Guilin, Nanning

7 12 8 9 4 6 7 53

13.2 22.6 15.1 17.0 7.5 11.4 13.2 100.0

Source: Ma and Goo (2005: 332).

In 1999, the establishment of the Beijing Zhongguancun Science and

Technology Park was approved by the State Council, as part of the Chinese

government’s move to speed up development of new and high-tech industry. Beijing

has developed a massive linkage of high-tech industries covering electronic

information, bio-engineering and new medicine, integration of photoelectron,

machinery and electronics, new materials, new energy, environmental protection,

aerospace, and earth and space technology.56

Beijing has promulgated and implemented more than 70 policies for the

development of high-tech industries. It has established preferential policies guided by

Certain Suggestions of Beijing Municipality on Promoting Development of New and

High-Tech Industry (33 articles) and an industrial policy system led by Guidelines for

Major Areas in New and High-Tech Industrialization to be Given Priority in 56 “The Development Trend of New and High-Tech Zones”, World Economy & China (Beijing: November/December 2000).

37

Development.57 In 1999, the capital city registered an industrial value-added of 16.5

billion yuan in the high-tech industry, accounting for 65% of industrial growth. The

Zhongguancun Science and Technology Park alone churned out 52.7 billion yuan value

of industrial output, and exporting US$820 million.58

In 2000, the HTDZs produced a total output value of 794.2 billion yuan,

equivalent to about 9% of GDP. About 50-80% of ICT products, such as optical fibre

cable, computer and related devices, and software and network product, are produced by

enterprises based in the HTDZs. By end of 2000, the HTDZs have registered more than

1,250 companies whose annual output exceeded 100 million yuan, compared with only

seven in 1991; 143 with an annual output exceeding 1 billion yuan, and six with 10

billion yuan. Some famous domestic enterprises have developed rapidly in the HTDZs,

including Lenovo, Stone, Founder, Huawei, Zhongxing and Diao.59 In 2003, the total

industrial output generated by the 53 HTDZs increased 34% over the previous year to

hit more than 1,730 billion RMB (raising the contribution to 15% of GDP).60

The HTDZs are also an important ground for attracting and fostering new and

high-tech talents. They have formulated special policies to attract talents and strengthen

the protection of intellectual property rights. For instance, there are more than 2,000

enterprises in the HTDZs established by scientific and technological personnel from

universities and scientific research institutes, with 2.5 million employees in 2000.

Among them, one-third have a college education or above, about 350,000 hold

professional titles, more than 30,000 hold masters’ degrees and over 4,000 have

doctoral degrees. The HTDZs have also attracted almost 5,000 students returned from

overseas.61 By 2002, the number of employees had risen by 40% to 3.5 million.62

As China moved into the early years of the 21st century, they have continued to

press on with policies designed to adapt to new and changing demands in the

international as well as domestic economic and technological environment. During the

10th Five Year Plan (2001-2005), the Chinese government aimed to focus on innovation

57 Ibid. 58 Ibid. 59 “China’s New and High-Tech Development Zones”, China Internet Information Centre (Beijing: September 16, 2001). 60 “High-tech zones to spur local economies”, China Daily (New York: February 12, 2004). 61 Ibid. 62 “Plugging into high-tech”, China Daily (New York: September 20, 2003).

38

and creations by implementing the strategies of ‘developing China through science and

education’ and ‘sustainable development’, as well as strive for greater breakthrough in

areas such as electronic information, software, bioengineering, optical-

electromechanical, new materials, new energy and environmental protection

industries.63

At the National Conference on Industrialization of New and High Technologies

in Beijing that marked the 15th anniversary of the Torch Programme, Science and

Technology Minister Xu Guanhua emphasized ‘the need to develop internationally

competitive high-tech industries and development zones, by urging domestic firms to

compete overseas in the global market and stepping up innovations to earn advantages

of intellectual property, while at the same time integrating themselves with advanced

technologies and overseas capital.’64

The Beijing Zhongguancun Science and Technology Park has also become an

attractive venue for multinational enterprises. In 2002, 39 out of the world’s top 500

enterprises have set up research and development centers in Beijing, including

Microsoft, IBM, Nokia, Nortel, Motorola, Intel and GE. 65 Multinationals such as

Microsoft, Hewlett-Packard, Oracle and IBM have set up software research centers in

Shandong Province, Shanghai, Beijing and Xiamen respectively to train and hire

software professionals over the next few years (Wong and Wong, 2004).

Another sign of increasing international co-operation came about in February

2003, when one of the largest semiconductor projects was launched in Nanjing New and

High-tech Industrial Development Zone, in which US$360 million (almost three billion

yuan) was invested by domestic as well as foreign firms from Singapore, France, US,

Japan, Germany and Korea in the Nanjing Semiconductor Manufacturing Corporation.

This project was regarded to be in line with China’s investment priorities. For instance,

during the 10th Five Year Plan, a total of US$10 billion (more than 82 billion yuan)

would be expected to be invested in integrated circuit manufacturing in order to boost

production to 700,000 pieces a month.66

63 “China’s New and High-Tech Development Zones”, China Internet Information Centre (Beijing: September 16, 2001). 64 “Plugging into high-tech”, China Daily (New York: September 20, 2003). 65 “Tech park strives for more giants to come”, China Daily (New York: June 18, 2002). 66 “Semiconductor production to expand”, China Daily (New York: February 25, 2003).

39

To further boost the contribution of the HTDZs to economic growth of the

country, the Ministry of Science and Technology (MOST) has planned to widen the

scope of the 53 development zones. First, the High-Tech R&D Center of the Ministry of

Science and Technology and Guangzhou Development Zone jointly set up the National

863 Program (Guangzhou) Industrialization Promotion Office in Guangzhou in

December 2003, aimed at carrying on the National 863 Program to improve China’s

overall capability of R&D in high technology.67

Besides providing continued support to the Zhongguancun in Beijing, the MOST

established seven high-tech industrial zones in the provinces of Heilongjiang, Jilin and

Liaoning in Northeast China as part of the “Northeast Revitalization Programme” in

2004. The measures include ‘commercialization of the latest high-tech advancement

among related industrial sectors as well as encouraging private high-tech firms to jointly

develop new technologies with State-owned firms.’68 To date, the most recent project

involving a foreign firm took place when Applied Materials, an US semiconductor

equipment manufacturer, signed a contract to construct its Global Development

Capability Centre in Xi’an HTDZ, in April 2006.69

2.5 The ICT market in China

The development of individual sectors related to ICT in China, i.e. the

telecommunications and computer sectors have been reviewed in the preceding sections.

In this section, the state of the overall ICT market as well as the telecommunications

market in the country will be discussed. China has emerged as a ‘star’ performer in the

global ICT market in recent years. According to the Italian Information Industry

Association, China has become a ‘global leader’ in the ICT market, topping the growth

rate at 19.7% in 2005, well above the world average of 6.1%.70 China Telecom existed

as a monopoly for almost five decades since the founding of the People’s Republic in

1949. With the courtesy of a deregulation policy that began with ministerial reform in

67 “Guangzhou Development Zone carries on 863 Project”, SinoCast China Business Daily News (London: January 2, 2004). 68 “High-tech zones to spur local economies”, China Daily (New York: February 12, 2004). 69 “Semiconductor firm plans new development centre in Xi’an”, China Daily (New York: April 11, 2006). 70 This is followed by 6.0% in Spain, 5.0% in the US, 3.3% in France, 3.1% in the UK, 2.9% in Japan, 2.5% in Germany and 0.9% in Italy, indicating that the growth rate of ICT market in China more than doubles that of any other single country. See “China Recording Highest ICT Market Growth in 2005”, Info-Prod Research (Ramat-Gan: April 11, 2006).

40

1998, the monopoly disintegrated into seven licensed competitors within a span of three

years till the nation’s entry into WTO in 2001. With the backing of a massive domestic

market for building high technology industries, China has also become the world’s

second largest personal computers (PCs) market – producing 81 million computers in

2005, and the world’s third largest producer of semiconductors after the US and Japan

(OECD, 2006). The rapid rise of the Chinese ICT industry is mainly attributed to the

rapid increase in ICT investment (especially after 1992 following the ‘southern tour’ by

former leader Deng Xiaoping) and the establishment of high technology development

zones (HTDZs). The rapid adoption of ICT by millions of Chinese consumers and

businesses is becoming an ‘antidote’ to the slowing sales growth of ICT markets in the

West.71

2.5.1 Rise of the ICT market in China

Government support for investment in the ICT industry has proven to be crucial since

the early stages of development. For instance, in the 1980s, the Chinese government

created special funds for the development of integrated circuits (ICs), computers,

software and communications switches (Dong, 2004). In 1992, the ICT industry was

listed as a pillar industry, and subsequently ICT products were emphasized as the new

drivers for China’s economic growth (Dong, 2004). The driving force of growth in

China’s ICT sector is perhaps best summed up in the words of Jamie Popkin, Vice-

President of Gartner:72

China’s growth in information and communication technology sectors is fuelled by strong

government involvement through state-owned enterprises, agency programs and policies.

China’s ICT market survived the downturn in the global ICT industry in 2001

and the negative impact of the SARS outbreak in early 2003. This was reflected in the

increase in sales volume of desktop computers due to factors such as the cut in desktop

prices, the increase in Internet users, upgrading of hardware, as well as the integration

of consumer electronics such as the digital camera, digital video and MP3 player with

computer hardware for family use.73 In rise in computer sales volume was also partly

71 “The Next Tech Superpower”, Asiaweek (Hong Kong: July 27, 2001). 72 “China sets its sights on technology superpower status”, Manufacturing Business Technology, November 2005. 73 “6 Million Computers Shipped in China”, SinoCast China Business Daily News (London: September 1, 2004).

41

attributed to an increasing number of domestic ICT enterprises expanding their sales in

the overseas market.74

In comparison with the rest of the world, China has attained a relatively higher

growth rate of the output of ICT equipment. For instance, the output of computers and

office equipment in China grew by 82% in 2005, which doubled that of Germany (36%)

and was much higher than the US growth rate of 8%, at the time when most countries of

Western Europe were facing negative growth of their ICT production (Table 2.7). A

forecast by International Data Corporation (IDC) estimates China’s ICT market to reach

430 billion yuan (US$51.74 billion) by 2008, and may become the world’s third largest

ICT market after the US and Japan in 2010, and assume the first position in 2015.75 In

another forecast, China is projected to have the sixth-highest growth rate in ICT

investment from 2005 to 2009 among 55 countries (Zhang, 2006a).76 However, the

combined ICT spending of the top five countries is less than that of China. Moreover,

China is the only country expected to enjoy accelerated growth in ICT spending over

the next five years.77 As a matter of fact, China’s prominent position in the global ICT

market is expected to continue as the country expands its infrastructure investment in

the six mainland cities designated for the 2008 Summer Olympics, i.e. Beijing, Tianjin,

Shanghai, Qingdao, Qinhuangdao and Shenyang, together with Hong Kong (Perez,

2006).

The booming Chinese economy and changing telecommunications policies

associated with rising market demand are the major factors which have led to China

becoming the world’s largest telecommunications market. The country had a network

capacity of 503 and 611 million lines for the fixed line and mobile phone segments,

serving up to 368 and 461 million subscribers in 2006 respectively. In 2006, the total

revenue for the Chinese telecommunications industry reached 648 billion yuan, an

increase by 11.7% from 2005, which was about 4% of GDP. 78 The development of

China’s telecommunications industry has often been described as a success story

initiated by the state at the beginning of economic reform in 1979. In fact, the expansion

74 “China IT Market is Robust”, Asiainfo Daily China News (Dallas: August 6, 2002). 75 “China Forecast to be Third Biggest IT Market Worldwide”, SinoCast China Business Daily News (London: May 12, 2005). 76 The only countries projected to have a faster growth in ICT spending are Russia, India, Turkey, Indonesia and Vietnam (Zhang, 2006a). 77 Ibid. 78 Ministry of Information Industry website, http://www.mii.gov.cn/.

42

43

of the Chinese telecommunications network is attributed to ongoing reforms which have

been determined by ‘the forces of state consideration, foreign influence and market

forces, subject to changing domestic politics and technological advancement’ (Loo,

2004).

Table 2.7 Actual and projected output growth rates of computers and office equipment in China and developed countries, 2004-2009 (% change y-o-y)

2004 2005 2006 2007 2008 2009Germany

France Italy

United Kingdom Spain

Netherlands Sweden

Belgium Switzerland

Western Europe

United States Japan

China

9.4 -5.3 7.9-23.3-23.1 0.6 25.3-11.2 -8.5 -6.3

1.6 3.9

37.4

35.9-45.3-48.6 -3.8 0.0 -8.1 -5.0 19.9 -2.2 1.8

8.1-2.1

82.1

26.0-19.1-23.1 -5.8 -9.1 -4.3 2.4 11.5 1.9 9.3

9.1 1.1

41.7

8.7 2.3 1.8 1.0 1.8 2.3 2.2 3.8 3.1 5.8

9.7 1.8

30.2

6.7 1.1 0.5 3.3 0.5 2.2 0.6 2.8 2.7 5.1

12.2 1.5

28.3

6.0 1.0 1.1 2.2 0.4 2.0 0.3 2.2 2.6 4.4

11.1 1.9

20.0Source: “Electronics and Computers”, International Industrial Prospects Quarterly, Spring 2006, 20-22.

As China enters the era of information technology, the growth of its

telecommunications market can be seen from the rate at which its number of telecom

and Internet users has increased over the past decade. For instance, its local switchboard

capacity and fixed-line subscription has grown by about 30% on average over the past

decade (Figures 2.3 and 2.4). The Chinese telecom market is also characterised by the

rapid expansion of its mobile network since the mid-1990s (Figure 2.4). It can be

noticed that mobile phone subscribers exceeded that of the fixed-line phone in 2003.

This pattern is also noticed within regional boundaries, where mobile phone

subscription has exceeded that of fixed-line phone in all provinces except Liaoning,

Jiangsu, Anhui, Hainan, Gansu, Xinjiang and Tibet in 2006. The market for mobile

phone is even almost twice as big as the size of fixed-line market in Beijing and

Guangdong Province.

Figure 2.3 Telecommunications network capacity in China (1978-2006)

0

100

200

300

400

500

600

700

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

Mill

ion

lines

Fixed network Mobile network

Source: Lu and Wong (2003), Ministry of Information Industry (MII) website, http://www.mii.gov.cn/.

44

Figure 2.4 Number of fixed line and mobile subscribers in China (1988-2006)

0

50

100

150

200

250

300

350

400

450

500

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Mill

ion

subs

crib

ers

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Fixed-line subscribers Mobile subscribers Ratio of mobile to fixed-line subscribers

Source: Lu and Wong (2003), MII website, http://www.mii.gov.cn/.

45

Figure 2.5 Fixed and mobile penetration rates in China (1988-2006)

0

5

10

15

20

25

30

35

40

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

%

Fixed penetration Mobile penetration

Note: The penetration rate is defined as the number of phone subscribers per hundred residents.

Source: Estimated from Figure 2.4.

46

Source: Lu and Wong (2003); China Internet Network Information Center (CNNIC), 15th, 17th, 18th and 19th Statistical Survey Report on the Internet Development in China (Jan 2005, Jan 2006, July 2006 and January 2007), http://www.cnnic.net.cn/en/index/0O/index.htm.

0

20

40

60

80

100

120

140

160

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Mill

ion

subs

crib

ers

Figure 2.6 Number of Internet users in China (1994-2007*)

47

Note: * As of January 2007.

Figure 2.5 shows that the mobile penetration rate in China has been increasing

rapidly since 1999, and it has exceeded the fixed line penetration rate since 2003, at

slightly above 20% and rising to 35% in 2006. Yet this rate was still low when

compared to other transition economies such as the Czech Republic (96.5% in 2003) as

well as Hungary, Estonia and Slovenia which had penetration rates of over 70%

(Vagliasindi et al., 2006). Another phenomenon is that the mobile penetration rate has

been growing by almost 70% annually on average, much higher than the 18% annual

growth rate of the fixed penetration rate. Whether there is any evidence of a fixed-

mobile substitution effect will be explored in Chapter eight of the dissertation.79

The dawn of the information age in China can also be shown by the explosive

growth of its Internet user population since the late 1990s (Figure 2.6). Internet

subscribers have exceeded 120 million in 2006, eight times the size in 2000. A

fundamental change in the Chinese Internet market is also reflected in the surge in

broadband access in recent years. Coined as the ‘broadband revolution’ by Morris

(2006), China has seen the number of broadband users jump almost forty times, from 2

million in June 2002 to 53 million four years later, growing at 90% annually on average

– in sharp contrast with that of dial-up service which grew by only 40% over the four

years and an average of 9% annually (Table 2.8). The number of China’s broadband

users has overtaken that of the US it reached 77 million in June 2006, which was 20

million more than the 57 million predicted for end of 2007 by market analysts, and

much higher than 54 million predicted for the US. 80

79 Fixed-mobile substitution is defined as “the use of mobile instead of fixed phone for calls or access to telecom services” (Vagliasindi et al., 2006: 350). 80 In terms of the total number of broadband subscribers, China was in the world’s third position in 2004 and rose to second in 2005. See “China will pass US in Broadband Lines by late 2006”, WebSiteOptimization.Com, http://www.websiteoptimization.com/bw/0601/, and “China to trump US in broadband subscribers”, CNET News.com, http://www.websiteoptimization.com/bw/0601/.

48

Table 2.8 Breakdown of Internet usage by services in China, 2002-06 (million users in June of the year)

Leased linea Dial-upb Broadbandc Mobile and

other forms of accessd

Totale

2002 16.1 33.4 2.0 - 45.8 2003 23.4 45.0 9.8 1.8 68.0 2004 28.7 51.6 31.1 2.6 87.0 2005 29.7 49.5 53.0 4.5 103.0 2006 26.8 47.5 77.0 19.1 123.0 Note: a. Leased line refers to users connected through the Local Area Network (LAN) via Ethernet. b. Dial-up users include ISDN (Integrated Services Digital Network) users. c. Broadband users refer to those who connect through the xDSL (Digital Subscriber Line) or cable

modem. d. Users who have access to Internet through the mobile telephone or other types of accessing

facilities such as mobile terminals or information appliances. e. The total does not add up to 100% as Internet users who adopt multiple methods of access have

been recounted. Source: China Internet Network Information Center (CNNIC), 10th - 18th Statistical Survey Report on the Internet Development in China (July 2002 - July 2006), http://www.cnnic.net.cn/en/index/0O/ index.htm.

2.5.2 China’s ICT trade

The rising importance of ICT to the Chinese economy is also indicated by the expansion

of trade in ICT products. The export of ICT products has grown 650 times since the

mid-1980s, from US$343 million in 1984 to US$226 billion in 2005 (Figure 2.7). As a

further demonstration of China’s rapid rise in the arena of ICT trade, the nation has

emerged as the world’s top exporter of ICT products (which include laptops, mobile

phones, digital cameras and other communications equipment) in 2004 – its export of

more than US$170 billion exceeded that of the US’ $149 billion.81 During the first three

quarters of 2006, China’s export of ICT products was estimated at US$520 billion, ten

times greater than that of 2001, the year when the nation joined the WTO (Zi, 2006).

China’s import of ICT has grown eighty times from US$2 billion in 1984 to

almost US$160 billion in 2005. From being an importer of ICT since the 1980s when it

relied primarily on technology transfer and import for the development of its ICT

industry, China became a net exporter for the first time in 1995. A recent study by

OECD also showed that China’s ICT exports have exceeded those of imports since

1995 (Katsuno, 2005). In the beginning of this century, the only category of ICT goods

81 One year earlier, in 2003, the US was the world’s top exporter of ICT products, at US$ 137 billion compared to China’s US$120 billion. See “OECD names China as top ICT goods exporter”, Telecomworldwire (Coventry: December 13, 2005); “China’s Export of ICT Products Topped USD180 billion in 2004”, SinoCast China Business Daily Nws (London: December 13, 2005); and “China beats US to top ICT exporter spot”, Office Products International (London: February 2006).

49

50

which China imported more than it exported are the integrated circuits (ICs) and some

basic components such as memory chips and CPUs (Katsuno, 2005). Since the mid-

1990s, China’s export share out of total ICT trade has gradually rose to almost 60% in

2005, except for two years – 1999 and 2000 – when it fell slightly below import share.

Furthermore, China’s trade balance in ICT products has been widening since 2001,

increasing by 25 times from US$2.7 billion in that year to US$65 billion in 2005.

China’s total trade volume of ICT has expanded by almost 170 times since 1984.

Its trade in ICT products has also steadily increased as a proportion of total trade, from a

mere 4% in 1984 to more than 27% in 2005 to reach more than US$386 billion (Figure

2.8).82 This ratio has been constantly rising since the mid-1980s, and such a trend is

therefore projected to continue in the next decade. The average annual growth rate of

trade in ICT products has also been consistently above that of total trade, except in 1986

when it dipped to a negative 34% (Figure 2.9). In fact, the growth of ICT trade has been

at least twice as fast as that of the total trade through most years of the past two decade.

2.6 Conclusion

Regarded as a relatively ‘latecomer’ to the ICT sector, China has risen very rapidly to a

prominent place in the world in terms of telecommunications infrastructure, market size,

ICT investment and high technology development. Convergence holds the key to the

future development of China’s ICT industry, at least in the next five years. A continued

rapid growth will be driven mainly by new products, new services and new demand

created by convergence between the traditional information technology sector (i.e.

computer) and the telecommunications sector, spurred by the Internet and 3G mobile

communications network applications.83

82 ICT products are referred to as ‘office and telecom equipment’ in WTO’s statistical source. 83 “Convergence assists ICT market growth”, China Daily (New York: March 23, 2005).

Figure 2.7 China’s trade in ICT products, 1984-2005

0

50000

100000

150000

200000

250000

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

US$

mill

ion

Exports Imports

Source: WTO Trade Statistics, http://www.wto.org/english/res_e/statis_e/statis_e.htm.

51

Figure 2.8 ICT and total trade in China, 1984-2005

0

200

400

600

800

1000

1200

1400

1600

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

US$

bill

ion

0

5

10

15

20

25

30

%

ICT trade Total trade Proportion of ICT out of total trade

Source: WTO Trade Statistics, http://www.wto.org/english/res_e/statis_e/statis_e.htm; State Statistical Bureau, China Statistical Yearbook, various issues.

52

-40

-20

0

20

40

60

80

100

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

%

Growth of ICT trade Growth of total trade

Figure 2.9 Growth rates of ICT and total trade in China, 1985-2005

53

Source: Estimated from Figure 2.8.

A clear departure from the 1980s, Chinese policy on promoting the production

and use of ICT is now fixated on developing home-grown technologies and standards,

and providing continued support to domestic companies (Dedrick, Kraemer and Ren,

2004). To achieve these objectives, China has laid out the following high technology

projects to be undertaken for the 11th Five-Year Plan (2006-2010)84:

• Integrated circuits (IC) and software: establishing IC research and development

centres, industrializing the technology for 90-nanometer and smaller ICs, and

developing basic software, middleware, large key applied software and integrated

systems.

• New-generation network: building next-generation Internet demonstration projects,

a nationwide digital TV network and mobile communication demonstration

networks with independent property rights.

• Advanced computing: making breakthrough in technology for petaflop computer

systems, building grid-based advanced computing platforms, and commercializing

the production of teraflop computers.

• Satellite application: developing new meteorological, oceanographic, resource and

telecommunication satellites, and pollution-free thrust augmented carrier rockets;

building earth observation and navigation positioning satellite systems and facilities

and application demonstration projects for civil satellite ground systems.

• New materials: building demonstration projects for commercial production of high-

performance new materials badly needed in the information, biological and

aerospace industries.

As a result of the state policy focusing on computer hardware development for

domestic consumption, China will need to overcome various institutional and cultural

barriers if it were to catch up with the leading players (India at least) in the software

market. To overcome its competitive disadvantage posed by the language, it has been

suggested that China could focus on ‘niche markets’ in the ICT sector. For instance, the

Chinese could utilise the programming synergies between China, Japan and South

Korea due to the common requirements for developing software using the double-byte

84 Extracted from “Major High-tech Projects Planned for 2006-2010”, Zhongguo Wang (China Net), http://www.china.org.cn/english/2006lh/160294.htm.

54

programming.85 To overcome its shortage of skilled talents, China could establish more

joint ventures with foreign operators by setting up R&D and other forms of

development centres.

A few points should be noted about the structure of China’s ICT industry in the

years to come. Firstly, although the telecommunications industry, especially in

equipment manufacturing, is becoming more intensely competitive with more domestic

and foreign businesses appearing onto the scene, the provision of fixed line and mobile

phone services is still dominated by the ‘Big Four’ national carriers. For instance, in

2005, China’s fixed line telephone services were dominated by China Telecom and

China Netcom with market shares of about 60% and 33% respectively; while the mobile

phone services were dominated by China Mobile and China Unicom with shares of

about 63% and 33% respectively. 86 Secondly, China has moved away from the

traditional approach of relying on technological import and transfer from foreign firms.

Instead, with a vision to steer the country towards becoming a world power in science

and technology, as pointed out in the newly drawn 11th Five-Year Plan, China’s

competitive edge in ICT will more likely come from investment in innovation and the

application of new technologies, in which the country seems well-positioned to

‘increase its innovative capacity with strong political and financial support from the

government and a huge pool of scientists, engineers and researchers’.87

85 “China industry: Focusing on niche markets in the IT sector”, EIU ViewsWire (New York: January 21, 2004). 86 Snapshots International, China Fixed Telephone Services, March 2006; Snapshots International, China Mobile Phone Services, March 2006. 87 “Powerhouse that’s reinventing itself: A surge of innovation is fuelling growth in the mainland through an ambitious science and technology agenda”, South China Morning Post (Hong Kong: September 11, 2006).

55

APPENDIX TO CHAPTER 2

Table A2.1 History of China’s computer industry (1956-2004) 1956 1958 1959 1964 1965 1968 1973 1975 1976 1977 1979 1983

The preparatory committee of Institute of Computing Technology, Chinese Academy of Sciences (CAS) was set up on August 25. Model 103 (“August 1” in Chinese), China’s first computer was produced in August. The Institute of Computing Technology of CAS was formally founded on May 17. Model 104, China’s first large-scale Vacuum tube computer was developed in September. Model 119, a large-scale general computer was put into use in April. Model 109C, a large-scale general transistor digital computer passed the test conducted by the State Science and Technology Commission in June. The first 717 transistor computer was developed in July. Model 109B Computer was developed in December. Szuprowicz reported China making its own integrated circuits (ICs). Nippon Electric reported that China was producing high capacity LSIs (10K transistor elements compared to 12k US and Japanese elements) at the Beijing Semi-Conductor Plant in October. Model 013, a large-scale Vacuum tube computer was developed in November. Szuprowicz reported rapid strides in micro-processor work as a result of use of LSI work being done at Qinghua University. East China Research Institute of Computer Technology built the first large IC computer capable of five million ops. China developed a small electronic computer capable of responding to 100 voice commands. The Chinese Academy of Sciences (CAS) reported that the Shanghai Institute of Metallurgy developed an ECL 1024 bit random access memory of world standards. Model 757, a large-scale vector computer was developed in November.

1985

The Software Institute of CAS, which comprised three laboratories that split from the Institute of Computing Technology, was formed in February to accelerate the development of software technology in China.

56

1987 1990 1991 1993 1995 1998 1999 2000

CAS was expected to have a super computer capable of 20 million ops by that year. The National Research Centre for Intelligent Computing Systems (NCIC) was founded in March. Model KJ8920, a large-scale data processing system for Petroleum and Mineral Exploration was developed, which won the first prize of CAS Science and Technology Progress Award. Dawning Computer Company was founded in Beijing. Dawning-I, the first of a series of Dawning high-performance computers, was developed in October. The Computer Network Centre of CAS was founded in March when a part of lab 10 split from the Institute of Computing Technology. Dawning-1000, a massive parallel processor (MPP) system was developed in May. The Institute of Computing Technology commenced its “knowledge innovation project” initiated by the CAS in September. The Dawning 2000-II Superservers were exhibited at the Achievements Exhibition in September for the celebration of the 50th Anniversary of the People’s Republic of China. On January 7, a key project of the National ‘863’ Program – the combination of computer communication with house appliance II, passed the scientific and technological achievements appraisal organized by the CAS. On January 28, the Dawning 2000-II Superservers passed the scientific and technological achievements appraisal organized by the Ministry of Science and Technology, and passed the acceptance acknowledgement by CAS. From May 14-17, at the 4th Asia-Pacific High-Performance Computing International Conference and Exhibition held in Beijing, an Information Technology International Panel (ITIP) highly praised the Dawning 2000-II Superserver and considered that Chinese high-performance computers were close to the standards of advanced Western countries. On June 15, the Dawning 2000-II Superserver was formally situated at the Network Centre of CAS. On August 18, the Institute of Computing Technology signed a cooperation agreement with Nokia on the testing of IPV6 protocol. On November 27, the Dawning Series of Scalable Parallel computer

2001

system passed the scientific and technological achievements appraisal organized by the CAS. The Dawning-3000 Superserver was verified and accepted by the Ministry of Science and Technology in January.

57

2002 2004

The grid-oriented Linux Superserver was introduced in September. Dawning-4000A was developed in June.

Sources: Institute of Computing Technology, http://www.ict.ac.cn/; National Research Centre for Intelligent Computing Systems, http://www.ncic.ac.cn/; Witzell and Smith (1989).

58

Chapter 3

ICT, PRODUCTIVITY AND GROWTH: DEBATES AND MEASURES

3.1 Introduction

With the invention of the computer in the 1960s and emergence of the Internet since the

early 1990s, the age of information and communications technology (ICT) has dawned

upon the world. The widespread use of the Internet has brought about fundamental

changes to the livelihood of people around the world. Also hailed as the “third industrial

revolution”, the ICT revolution affects the economy on all fronts by improving

production methods and changing consumer behaviour. While businesses and

governments are investing in ICT and electronic commerce (or e-commerce) to reduce

costs and improve productivity, consumers now enjoy the benefit of time and cost savings

as they can shop on the Internet with access to more complete information and better

quality of goods and services that are available (Chaker, 2005). In macroeconomic

theory, such phenomenon has been brought about by increased production and use of ICT

in the production process, which in turn generates more output as a result of increased

labour productivity.

This phenomenon has been observed in most developed countries, especially the

US. However, even among the developed countries, it is found that productivity growth

attributed to ICT was higher in the US than in other countries. As my research focuses on

the emergence of ICT as well as its rising importance to China’s economy, it is necessary

to conduct a review of the main debates and measurement issues. The chapter begins with

a general discussion of debates on issues underlying the role of ICT in the ‘new

economy’. This is followed by a discussion of the debate concerning the “Solow

paradox” which originated from observations that productivity growth did not seem to

materialise from increased ICT investment. Finally, the chapter examines the theoretical

frameworks used to measure the contribution of ICT to economic and labour productivity

growth, where ICT capital is distinguished from other factor inputs in the growth

accounting framework.

59

3.2 Debates on the role of ICT

The explosive growth in ICT investment, especially during the 1990s in the US and other

developed economies, has led to a call for new economic theories and models, which can

be employed to assess the impact of ICT investment on economic growth. In the literature,

ICT is often associated with the term ‘New Economy’, which generally refers to an

economy that is characterized by increased investment in and use of ICT. Kraemer and

Dedrick (2002a) defined the ‘New Economy’ as ‘the association of non-inflationary,

sustained economic growth with high investment in ICT and a restructuring of the

economy due to the use of ICT-led innovations such as enterprise systems, supply chain

management, customer relationship management, the Internet and e-commerce.’

Salvatore (2003) referred the New Economy to “the rapid improvements and spread in the

use of ICT, based on computers, software, and communications systems.” Lipsey (2004)

used the term ‘New Economy’ to refer to ‘the social, economic and political changes

brought about by the current revolution in ICTs – the revolution that is driven by

computers, lasers, satellites, fibre-optics, the Internet and other communications-related

technologies’. It can thus be seen that the investment in and use of ICT in generating

output is the heart of the issue.

3.2.1 ICT and productivity growth: A microeconomic view

The relationship between ICT and output growth can be explained from the

microeconomic as well as macroeconomic perspectives. The microeconomic perspective

normally explains how firms derive productivity gains from investment in ICT. It is based

on the assumption that ICT can be treated as an input in the production function of a firm

and a substitution effect exists between ICT and non-ICT factors. Firms generally can

benefit from ICT investments through adaptation and innovation in their work processes

(OECD, 2004). ICT investments therefore create competitive advantages by improving

the operational efficiency of business processes which in turn will lead to better

firm-level performance (Hu and Quan, 2005).

The following explanation is drawn from Oz (2005), as illustrated in Figure 3.1.

When a firm adopts a new ICT (which may be an innovative hardware equipment or

software application), its productivity as well as income and profit increase as the new

technology enables the firm to produce the same goods or services more efficiently, or to

produce new goods or services. Gradually, as all firms competing in an industry

60

successfully adopt the technology, the ICT becomes standard over time. 1 With the

increased productivity, firms are now able to offer their goods or services at lower prices.

In the final phase, firms may experience a decrease in cash value of their goods and

services due to the decrease in prices in spite of the productivity gains. However,

productivity has increased simply because the firm can now produce more units of its

goods or services at lower costs, and subsequently, is able to invest more in ICT in the

future.

Figure 3.1 ICT and firm productivity

Adoption of new ICT

Cash value decreases due to productivity gains

Increased income and profit

New ICT becomes standard

Competition pushes prices down

Source: Oz (2005: 794).

One of the earliest researches at the firm-level found that spending on information

technology capital has had a substantial contribution to output, providing an answer to

the ongoing debate on the ‘productivity paradox’ which will be discussed in a later

section (Brynjolfsson and Hitt, 1996). In that study which used data on information

systems spending in more than 300 firms for the period of 1987-1991, the marginal

product for computers was found to be higher than investment returns on other types of

capital. More specifically, the marginal product for computer capital was approximately

0.81 per year, meaning that an additional dollar of spending on computer capital yields an

increase in output by 81 cents.

A few hypotheses have been constructed pertaining to the relationship between

ICT investments and firm/industry productivity, according to Hu and Quan (2005). First,

industries with high value of ICT intensity such as manufacturing, transportation,

1 For instance, all banks adopted ATM technology which became standard by the late 1970s in the US (Oz, 2005).

61

banking and the retail sector would benefit more from ICT investments as efficiency is

significantly enhanced by the use of computer applications which further reduces errors

and time taken for the completion of business processes.

Second, ICT investments can also improve the efficiency of ‘value-chain’

activities such as human resource management, procurement and technology

development. However the contribution of ICT to productivity will be limited in

industries with low value-chain information intensity, such as the construction and

building materials industries which is mainly dependent on the production technology.

Therefore the impact of ICT investments on a firm’s productivity will depend on whether

it invests in ICT assets to enhance its strategic and operational capabilities in times of

favourable conditions, such as in anticipation of higher productivity.

A few studies of Italian manufacturing firms have found a strong impact of ICT

investment on firms’ productivity growth. In the work of Atzeni and Carboni (2006), ICT

productivity was found to be about eight times greater than that of non-ICT investment,

given the former’s proportionately low share of total investment. Firms which invested in

technology appeared to be more efficient than those which invested mainly for

replacement purpose. In addition, firms which invested in standardised technology were

found to be more productive than those which invested in new technology due to the costs

and risk involved in learning and adopting the new skills. In summary, realising the

benefits of ICT adoption requires some reorganisation of the ‘complete cluster of tangible

and intangible components that make up the firm such as skill, infrastructure,

organisation structure, diffusion and adaptation, etc.’ (Atzeni and Carboni, 2006). In

another study of about 1,500 Italian manufacturing firms, ICT is found to have a stronger

impact on productivity in information-intensive industries than that of traditional

low-technology industries, especially those firms that adopt ICT and ‘change their

internal organization by reducing the number of hierarchical levels and making more

intensive use of teamwork and worker participation’ (Fabiani, Schivardi and Trento,

2005).

3.2.2 ICT and productivity growth: A macroeconomic view

In the preceding section, the ‘microeconomics view’ which looks at how ICT investments

create values to output at the firm and industry level, mainly through generating excess

returns over other forms of capital investments, was discussed. This section goes on to

62

examine the ‘macroeconomic’ perspective, which looks at how the national economy

benefits from ICT investment.

A typical macroeconomic framework pertaining to the relationship between ICT,

productivity and economic growth is illustrated in Figure 3.2. Traditionally, this is based

on the production function model where output is a function of factor inputs, namely,

capital and labour. In the ICT literature, capital is divided into ICT capital and non-ICT

capital. The theoretical basis with which to distinguish ICT capital from other forms of

capital was established in Yorukoglu (1998). First, compared with other capital, ICT

capital has a much higher pace of technological improvement.2 Second, the rapid pace of

technological improvements in ICT equipment gives rise to the problem of poor

compatibility between ICT and non-ICT capital. Lastly, the efficient use of ICT is

relatively more dependent on skills and experience, which requires more investment in

human capital. In the production function model, the factor inputs are transformed into

outputs through the processes of capital deepening, improvement in labour quality and

technical progress (also known as total factor productivity, or TFP). 3 During the

transformation process, the production methods can be improved or enhanced by

complementary factors such as investment in human capital or a more efficient

organizational practice. Output can be measured at three levels, namely, country (i.e.

national), industry and firm level.

As the chapter will show later, there has been an acceleration of productivity

growth in many developed countries since the mid-1990s. To explain how the late 1990s

is different from earlier periods, Pohjola (2002) identified three main trends, i.e.

technological breakthrough in the semiconductor industry occurring in the mid-1990s,

increase in network computing due to the rapid diffusion of the Internet, and acceleration

in labour productivity growth in the US non-farming business sector. Pohjola also

summed up three major ways of measuring the size of ICT in the New Economy: (i) the

shares of production, employment and export of ICT in the economy; (ii) the use of ICT,

i.e., average share of ICT spending in GDP; and (iii) the size of the Internet which

provides a proxy measure of a country’s degree of global integration through digital

means.

2 For instance, within a span of less than a decade, IBM introduced its Pentium PCs that were 20 times more powerful in terms of speed and memory capacities at about the same costs (Yorukoglu, 1998). 3 In the traditional growth accounting literature, TFP and technical progress are used interchangeably. In the stochastic frontier literature, technical progress is only a part of TFP.

63

Figure 3.2 ICT, productivity and growth

InputsCapital ICT capital Non-ICT capital Labour

Process Capital deepening Labour quality Technical progress (Total factor productivity)

Outputs Economic growth/ productivity growth measured at 3 levels: Country level Industry level Firm level

Complementary factors Organization and management practices Industry organization and regulation Economic structure Government policy Investment in human capital

Source: Dedrick, Gurbaxani and Kraemer (2003: 3).

Therefore, how is ICT related to productivity growth and why is it important? In

the analysis, there are two distinct concepts of ‘productivity’. The first is average labour

productivity (ALP) which usually means output per hour worked; and second is the total

factor productivity (TFP), also known as multifactor productivity (MFP), which refers to

the growth in total output that is not accounted for by the growth in factor inputs. 4 TFP

growth is often analogously used to mean technological progress, the sources of which

include new technology, organizational skills and economies of scale (McGuckin, Stiroh

and van Ark, 1997). Conceptually, TFP growth allows additional output to be produced

from the same inputs, as a result of improved production methods. TFP is usually

measured by estimating a production function such as:

)( tttt LKfAY ⋅= (3.1)

where At represents TFP which is also equivalent to:

)( tt

t

LKfYTFP = (3.2)

64

However, it should be noted that although TFP is often used to represent

technological change caused by more efficient production methods or better skills of the

labour force, it could also be a function of exogenous factors such as government policy,

monetary shocks and military spending, or even crime and disease which have negative

effects on output for any given amount of inputs as they cause workers to become less

productive.5

3.2.3 The ICT productivity paradox

Generally, the national economy gains when there is an overall improvement in

productivity across all firms. Networking among firms investing in ICT will also reduce

transaction costs and speed up the innovation process in the economy (OECD, 2004).

Such network externalities have been generated with the widespread use of the Internet

and e-commerce which leads to further diffusion of ICT within the economy. The channel

through which ICT affects economic growth is explained by Timmer and van Ark (2005).

First, the production of ICT goods increases TFP growth in ICT-producing industries.

Next, ICT investment is induced by rapidly falling prices of ICT goods. Jorgenson

(2001), in accounting for the sharp acceleration in the level of economic activity since

1995, demonstrated that the decline in ICT prices will continue for some time, which will

in turn provide incentives for the ongoing substitution of ICT for other productive inputs.

He concludes that the accelerated ICT price decline signals faster productivity growth in

ICT-producing industries, which have been the main source of aggregate productivity

growth throughout the 1990s. Parham (2004) however questioned the effect of decline in

prices on productivity, as it merely leads to input substitution and movements along the

production frontier, but not any shifts of the frontier. Finally, ICT is viewed to function as

a form of General Purpose Technology (GPT) which ‘facilitates and induces firms to

introduce more efficient organizational forms, subsequently boosting productivity

growth throughout the economy’ (Timmer and van Ark, 2005). 6 Three major

technological developments have been identified that helped establish ICT as a form of

GPT: technological advances that combined computing power with portable sizes (such

as desktops and laptops) at affordable prices; the convergence of information

technologies and communications technologies to form what we now refer to as ICT such

4 MFP is used in conjunction with TFP in the literature, and the dissertation will use TFP throughout as the standard term that defines productivity growth that is attributed to variables other than factor accumulation.

5 “Dictionary Definition of Total Factor Productivity”, Economics at About.com, http://economics.about.com/.

65

as the Internet and other networking devices; and new software applications which

improve the user-friendliness of computers to a larger segment of the workforce (Parham,

2004).

The story behind the relationship between ICT and economic growth has largely

been that of input substitution between ICT capital (mainly computers) and non-ICT

capital. First, there has been a dramatic decline in the price for computers in the US since

the 1970s. According to McGuckin et al. (1997), the price of computers in the US

decreased at more than 17% annually between 1975 and 1996. This in turn led to a

massive investment in computers, resulting in the share of computers in the producers’

durable equipment increasing from zero to more than 27% during the same period.

However, it is also found that the most computer-intensive sectors have produced the

slowest productivity growth in the US during the 1970s and 1980s, which led Robert M.

Solow to remark in 1987 that ‘you can see the computer age everywhere but in the

productivity statistics’, which came to be known as the ‘Solow paradox’ or ‘ICT

productivity paradox’ (Pohjola, 2002). It was generally concluded that ICT investment

was a too insignificant share of the national capital stock to produce substantial economic

effects (Dedrick et al., 2003). Nevertheless, there has been a rapid increase in the share of

ICT investment. For example, Dedrick et al. (2003) reported that ICT capital as a share of

the total capital investment in the US increased from a mere 3.5% in 1980 to 9% in 1990.

Another study found that the average share of ICT expenditure in the country’s GDP

during the period of 1992-99 was 8.1% for Australia and the US, 7.6% for Canada and

Singapore, 6.5% for Japan and Hong Kong, 5.3% for Korea and 4.1% for Taiwan in the

same period (Pohjola, 2002).7

There have been efforts to examine the Solow paradox. McGuckin et al. (1997)

found that computers were actually highly concentrated in the service sectors and in only

a few manufacturing sectors during the 1970s and 1980s, although they might “appear

everywhere”. They also reported a similar pattern occurring in the OECD countries where

computers were highly concentrated in specific sectors. The authors further revealed that

computer-using sectors showed faster ALP growth than other sectors in the 1980s. When

they restricted their sample to the manufacturing sector alone, it was found that the

6 General purpose technologies (GPTs) can be defined as ‘changes which transform both household life and the ways in which firms conduct business. They include the steam engine, electricity and information technology’ (Jovanovic and Rousseau, 2003). 7 The share of ICT investment in China’s GDP will be estimated in Chapter five of the dissertation.

66

computer-using sectors showed dramatic increases in ALP growth which was almost

three times faster than that of the non-computer-using sectors for the period of 1979-91.

Despite the productivity slowdown in the past two or three decades, there is

compelling evidence to support the argument that ICT is still a significant factor in

economic and productivity growth. For instance, Jorgenson and Stiroh (1999) found that

while TFP growth for 1973-90 was only 0.3% on average and it lowered further to 0.2%

in 1990-96, the TFP decline was due to the decline in the growth of capital inputs. They

then provided evidence that computer substitution for other forms of capital and labour

inputs had indeed taken place in the US which contributed to economic growth. They

found that computer inputs contributed 0.16% to the annual output growth of 2.4% for

1990-96, which is a direct consequence of substitution toward relatively cheaper

computers.

Even with evidence in support of a strong productivity revival that is associated

with the increase in ICT investment, there is also some debate that questions whether

such revival has been due to other factors such as the effects of the business cycle or

structural changes in the economy. Robert J. Gordon is an ardent skeptic of the ‘ICT

productivity’ debate, even though he reports that Robert M. Solow had declared his 1987

paradox statement as obsolete (Gordon, 2000). His argument comes in twofold: (i) the

major revival in TFP productivity growth in 1995-99 occurred mainly within the durable

manufacturing sector, including the manufacturing of computers and semiconductors,

which accounted for only 12% of the private business economy. The spread of the New

Economy into the remaining 88% of the economy is therefore questionable; (ii) the period

of 1995-99 is considerably shorter than the earlier time periods under study, and he

therefore questions the sustainability of long-term growth of the New Economy.

Nevertheless, the Solow paradox has been ‘put to rest’ (Dedrick et al., 2003: 22).

As the ongoing debate will further prove, ICT investment is and will continue to be a

significant force to improving firm, industry as well as national productivity, especially

due to innovation and invention of more advanced softwares as a result of increased

expenditure on R&D and human capital. This can be explained by the fact that ‘while it

was difficult for companies to adopt automation processes such as phone calls and

personal face-to-face services during the period before the mid-1990s, ICT gradually

provided the ability to improve efficiency and productivity in services as it developed

67

rapidly’ (Atkinson, 2006). Examples include electronic banking payments, the design of

cars using computer programmes and delivery of machine parts in the automobile

industry (Atkinson, 2006).

There were recent signs of slowing productivity growth of the US workers. The

latter dropped to 1.8% in 2005 from 3% in the previous year. In response to that, the

former Federal Reserve Chairman Alan Greenspan suggested that ‘the economy is

unlikely to maintain rapid advances from productivity gains unless information

technology can be obtained from new sources, which could be the greater and more

efficient collaboration between people and companies’ (Chabrow and McGee, 2006).

Conference Board economist Catherine Guillemineau however noted that US

productivity growth was still higher than that of the European Union, due to ‘greater

flexibility and adaptability among US companies and managers, particularly their ability

to adapt very quickly to far-reaching innovation’ (Chabrow and McGee, 2006).

Future productivity gains will depend on the following factors, as explained by

Atkinson (2006): technology that is easier to use and more reliable, i.e. less complicated

digital technologies; linking together a variety of services, such as the interconnection

between televisions, phones, laptops, printers and MP3 players at home, or the integration

of new devices such as smart cards, e-book readers and sensors in information systems;

improved technologies are needed in areas such as monitoring of large networks,

distributed database and systems integration, or other technologies such as better voice,

handwriting and optical recognition features which facilitate easier interaction between

humans and computers; and lastly, a more ‘ubiquitous or simultaneous adoption of the

Internet across homes and industries such as broadband connections and use of electronic

bill payment in the government, health care, transportation and many other business

service sectors’ (Atkinson, 2006). In fact, it has often been speculated that robots would

play the key role in the economy, especially after 2015 when the rapid rate of progress in

computer chip technology known as Moore’s Law is predicted to reach its end (Atkinson,

2006).

The debate over the paradox between ICT and productivity growth could perhaps

be wrapped up from a microeconomic or organisational perspective, as summed up in

Hughes and Morton (2005). Based on a case study of Schneider National Inc., the second

largest full truckload transportation company in the US, two key lessons were drawn

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about the impact of ICT on overall productivity growth at the national level. First, ICT

users are more significant than ICT producers in accounting for labour productivity

growth. The demand for, as well as the capacity to absorb the output of ICT-producing

sectors by high-tech users are the crucial drivers of recent performance in the US

economy, which further depend on ‘the technical competence of management in

implementing appropriate investments in complementary assets’. The second factor

relates to the time taken for the process to work through the productivity transformation

of the late 1990s. Despite the rapid fall in ICT prices (such as the price of computers), it

had to take decades for cost savings to materialise when the link with ICT through

adoption of communications standards and implementation of a software system

produced the productivity acceleration of the 1990s (Hughes and Morton, 2005). In other

words, ICT will only become significant when it is used effectively to enhance business

competitiveness.

3.3 Measuring the contribution of ICT to productivity and economic growth

In the computation of ICT capital contribution to growth, authors generally define ICT

capital to be comprised of computer hardware, software and communications (or

electronic) equipment (Dedrick et al., 2003). Literature on productivity gains of ICT

capital goods looks at the potential for increasing returns to investment in non-ICT capital

goods as well as labour, as ICT products have ‘high up-front development costs and low

marginal costs’ and at the same time, ICT innovations are easily ‘captured in replicated

sets of instructions such as semiconductors and software code’ (Kraemer and Dedrick,

1999).

ICT capital contributes to growth in a different manner from other forms of

capital. By comparing the intrinsic differences between ICT and non-ICT capital, it was

found that ICT capital differs from other forms of capital in terms of the rate of

technological progress, the compatibility between old and new capital, as well as the

extent of learning by doing (Atzeni and Carboni, 2006). First, technological progress

would bring uncertainty to usage of new ICT capital goods due to the effects of learning,

compatibility and organizational structure, as capital goods of different vintages may not

be perfectly compatible with each other owing to changes in technological standards.

Second, firms do not simply invest in new machines and equipment although more

efficient ICT (as well as non-ICT) capital becomes available. Rather they would also

69

invest in capital with technological standards that are equivalent to those already owned.

This section discusses three main areas where the contribution of ICT to growth is

concerned, namely, the contribution to output growth, average labour productivity (ALP)

growth, and total factor productivity (TFP) growth.

3.3.1 ICT contribution to GDP or output growth

Most studies that explore the contribution of ICT to GDP or output growth employ the

standard neo-classical growth accounting framework pioneered by Solow (1957). The

most commonly used method to measure the contributions of ICT to (US) economic

growth and labour productivity growth is the production function approach, notably

employed by Jorgenson and Stiroh (2000), Jorgenson (2001), and Jorgenson, Ho and

Stiroh (2003a).

Based on Equation (3.1), an extended form of the production function that

explicitly distinguishes ICT capital from non-ICT capital is given by Jorgenson, Ho and

Stiroh (2003a) as:

Yt (Yn, IICT ) = At · X(ICTt , KNt , Lt) (3.3)

where total output, Yt, comprises ICT investment goods (IICT) and non-ICT output (Yn),

and capital services are decomposed into ICT capital (ICTt) and non-ICT capital services

(KNt). Total factor productivity (TFP) is represented by (A) which augments the input

function (X). Therefore, an advantage of employing this production function is having a

clearly defined contribution of ICT capital to economic growth. We can measure the

contribution of individual inputs to output growth by re-expressing the relationship

between inputs and output from Equation (3.3) as follows:

Growth of GDP = ICT capital’s share times growth of ICT capital input + Non-ICT capital’s share

times growth of non-ICT capital input + Labour’s share times growth of labour input + Aggregate

TFP growth

The technical detail which is based on Oulton and Srinivasan (2005) will be

explained in Chapter 6 of the dissertation. A positive correlation between ICT investment

and GDP growth has been found by Kraemer and Dedrick (1999) in a study of 43

countries including China, from 1985 to 1995. They show that ICT investment makes a

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significant contribution to economic growth of the developed countries due to the

existence of a ‘complementary system of ICT infrastructure’ but not to the developing

countries which are obviously lacking in such investments. In that study, China is

regarded as an ‘outlier’ case with its exceptionally high correlation between growth in

ICT investment and GDP growth, as well as on a per worker basis. Therefore, one

question is to what extent a cross-country analysis of developing countries is applicable to

China, which has a low proportion of ICT investment to output but has one of the world’s

largest ICT market and a rapidly-growing ICT infrastructure such as the telecom network

and Internet.

In a recent study of the contribution of ICT to economic growth in Australia,

using a database that covers 10 capital stocks, 5 inventory stocks and labour as inputs in

the production function, it is shown that the returns from ICT investment more than

compensates its cost to producers (Diewert and Lawrence, 2005). This is attributed to

several factors, such as rapidly falling ICT prices, innovation-related externalities

associated with investment in ICT technologies, and investment in human capital

associated with the acquisition and operation of ICT technologies in the business sector

(i.e. the process of ‘learning-by-doing’). For the reasons above, investment in ICT

equipment tends to contribute more to GDP growth than an equal investment in other

forms of capital such as structures and transport equipment in developed countries

(Diewert and Lawrence, 2005).

3.3.2 ICT contribution to average labour productivity (ALP) growth

The contribution of ICT investment on the labour productivity has also attracted attention

from researchers. Labour productivity growth, or the growth in output per worker, is a

measure of the efficient use of resources in creating value to goods and services, since it

‘allows the economy to provide lower-cost goods and services relative to the income of

domestic consumers, and to compete for customers in international markets’ (Dedrick et

al., 2003: 4).

The calculation of ALP is derived from the production function approach outlined

in Equation (3.1) above. Jorgenson et al. (2003a) defined ALP as the ratio of output to

hours worked such that ALP = y = Y/H, where y denotes output (Y) per hour (H). The

ALP function can be rewritten in the double-log form as

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Δln yt = αICT ΔlnICTt +βKNΔlnKNt + γL (Δln Lt – Δln Ht) + Δln At (3.4)

where αICT, βKN and γL denote the respective factor income shares of output, whereas

ΔlnICTt and ΔlnKNt (denoted in lowercase) reflect changes in the respective use of ICT

and non-ICT capital per worker, or capital deepening, as explained below. Equation (3.4)

indicates that ALP growth comprises three components: (i) capital deepening, which

Jorgenson et al. (2003) defined as “the contribution of capital services per hour and

allocated between ICT and non-ICT components” (=αICTΔlnICTt + βKNΔlnKNt). It

enhances the efficiency of labour by increasing capital per worker in proportion to the

capital share (that is, increase in the capital-labour ratio); (ii) labour quality, which is

defined as ‘the contribution of increases in labour input per hour worked’ [γL (Δln Lt – Δln

Ht)]. It reflects changes in the composition of the workforce and raises labour

productivity in proportion to the labour share; and (iii) TFP, which augments factor

accumulation (Δln At) (Jorgenson et al., 2003a).

The relationship between ICT and productivity has generated numerous debates.

In particular, the debates have focused on how ICT contributes to productivity growth.

Indeed, the general consensus is ICT being a key (or rather, the key) driver of factor

productivity and growth resurgence, particularly with reference to the acceleration of the

US’ ALP and TFP growth since the mid-1990s, albeit differences in the magnitude of

contribution to growth. All studies show that the contribution of ICT capital to the growth

of output, labour productivity and TFP in developed as well as developing countries has

increased over the past decade. Most literature also shows that ICT capital contributes to

ALP growth through capital deepening, that is, an increase in the capital-labour ratio.

These studies are examined in the remaining sections of the chapter.

A distinctively different approach to the conventional growth accounting method

that examines the association between ICT investment and economic growth was

explored in Lee, Gholami and Tan (2005). The authors used the Solow residual approach

on 20 countries to examine evidence of the contribution of ICT investments to economic

growth. The authors found that developed countries and NIEs experienced growth in ICT

investment, while developing countries (including China) did not gain productivity

improvements from their ICT investments. They further suggested ICT-complementary

factors to rectify possible flaws in ICT policies as a contribution towards improvement in

global productivity.

72

On top of the discussion about the contribution of ICT, questions have also been

raised about the effect of intangible factors on labour productivity. Brynjolfsson (2003)

asked what the real drivers of productivity growth are: ICT capital, technology

complements or intangible assets such as human capital and business culture. Sharpe

(2003) pointed out the following factors as the main drivers of productivity growth -

capital intensity, technological innovation and human capital. He further elaborated on

the environment which may influence the productivity drivers - economies of scale and

scope, taxes, social policies, unionization, regulation, capacity utilization, minimum

wages and competition. Some of these factors may have implications for China, such as

the impact of deregulation and competition on output, trade and employment following

its entry into the World Trade Organization (WTO). Atzeni and Carboni (2006) have also

pointed out that the benefits from ICT may be lost and give rise to higher costs rather than

improving output if ICT does not improve together with the other components of a

‘complex set of causalities’ which include both tangible and intangible assets such as

skill, infrastructure, organisation, diffusion, adoption and adaptation.

3.3.3 ICT contribution to TFP growth

Most of the literature reviewed in this chapter uses the conventional growth accounting

method to measure the contribution of ICT capital to output growth or labour productivity

growth. In the production function, TFP is derived as a residual. However, one question is:

what drives TFP growth? There are a few studies that further examine the role of

ICT-producing sector to the growth of TFP. The production possibility frontier model

developed by Jorgenson et al. (2003a) is a typical representation of the methodology

employed to measure the contribution of ICT to TFP growth.8

Jorgenson et al. (2003a) used the ‘price dual approach’ to estimate TFP growth in

the ICT-producing industries, based on the assumption that TFP growth is reflected by

the decline in relative prices of ICT investment goods. The price declines are weighted by

the share of ICT investment in total output in order to estimate the contribution of ICT

production to the national TFP growth. TFP growth is then decomposed as follows:

NNICTICT AuAuA lnlnln Δ+Δ=Δ (3.5)

73

where aggregate TFP growth is represented by AlnΔ , ICTu represents ICT’s average

share of output, is TFP growth related to ICT production, and therefore ICTAlnΔ

ICTICT Au lnΔ is the contribution of ICT production to TFP while NN Au lnΔ is the

contribution of non-ICT production to TFP growth derived as a residual of Equation (3.5).

The authors estimated the contribution of ICT production to TFP growth by estimating

the output shares and growth rates of TFP for computer hardware, software and

communications equipment.

Earlier studies had emphasized the role of semiconductors as a key driver of the

acceleration in TFP growth in the ICT-producing sector, and attributed productivity (i.e.

TFP) gains to the sharp decline in semiconductor prices, especially for the years after

1995 (Jorgenson, 2001; Jorgenson et al., 2003a; Oliner and Sichel, 2003). The reduction

in the product cycle of semiconductors from three to two years in 1995 was cited as a key

source of TFP growth acceleration, owing to increasing competition in the semiconductor

industry. The average TFP growth for ICT production in the US was 7.35% for

1990-1995, and increased to 9.31% for 1995-2000 (Jorgenson et al., 2003). Oliner and

Sichel (2003) constructed a five-sector model that specifically focused on the

contribution of semiconductors to TFP growth, and presented the equation for TFP as

follows:

sii

i PFTPFTPFT +=∑=

4

1μ

(3.6)

where i represents the four final-output sectors, s denotes the semiconductor sector, and

the µ term for each sector represents its output expressed as a share of total non-farm

business output in current dollars. By isolating from the other ICT sectors, they found

semiconductors as the greatest contributor to TFP growth, and even exceeding that of the

four aggregate non-ICT sectors for the period of 1995-2001. Similarly, Jorgenson (2001)

highlighted the significance of the development and deployment of semiconductors as the

foundation for US growth resurgence, owing to the decline in the prices of ICT

equipment. He further queried the implications of the lack of ‘constant quality price

indexes for semiconductors and ICT in national accounting systems outside the US’ for

8 In Jorgenson et al. (2003), the term ‘productivity growth’ is used to refer to TFP growth.

74

developing countries such as China and India.

3.4 Conclusion

This chapter serves mainly to draw the theoretical link between ICT investments or

capital and productivity growth, based on the microeconomic (or firm-level) as well as

macroeconomic perspectives. The former explains how ICT investment benefits firms by

improving their competitiveness and efficiency, and thereby increasing productivity. The

latter approach normally explains how ICT capital contributes to the national productivity

and output growth through the improvement of production methods, using the production

function model. This chapter further introduces how authors have recently modified the

conventional production function model by distinguishing the effect of ICT capital from

other forms of capital. Essentially, there are three major approaches in examining the

issue of ICT and growth: the contribution of ICT to (i) economic growth, (ii) average

labour productivity (ALP) growth, and (iii) total factor productivity (TFP) growth.

Empirical evidence related to these issues will be presented in Chapter 4 of the

dissertation.

75

Chapter 4

ICT, PRODUCTIVITY AND GROWTH: EMPIRICAL STUDIES

4.1 Introduction

This chapter aims to review the most recent empirical studies on the contribution of ICT

to productivity and economic growth of various countries, covering the past five years

or so. This chapter carries on from the previous one by examining the empirical

evidence related to the contribution of ICT to economic growth. It presents a survey of

empirical evidence concerning productivity revival in the US and other countries, which

tells whether ICT capital is becoming a major driving force of growth. The empirical

results for various countries, which are mainly developed and a few developing

countries, are then presented to illustrate the differing contribution of ICT capital. The

chapter begins with the following section which presents a set of empirical studies that

look at the contribution of ICT capital to economic growth of the country/countries

involved. This is followed by another section which surveys empirical evidence dealing

with the contribution of ICT capital to average labour productivity (ALP) growth. The

subsequent section further examines the contribution of ICT to total factor productivity

(TFP) growth at both country and industry level. Finally, literature that has included

China in the studies is highlighted.

4.2 ICT contribution to economic growth

Being the world’s largest investor in ICT capital, the US is not surprisingly the most

extensively researched for an individual country in this area on the relationship between

ICT capital and growth/productivity. Other developed countries have been found to lag

behind the US in terms of the size of ICT investment and the contribution of ICT to

productivity growth. Salvatore (2003) attributed the US’ superior performance to other

G7 countries to the greater efficiency of the US’ labour market, a more sophisticated

financial market, and the highest price competitiveness as well as growth potential

among the G7 countries.

The empirical evidence for the resurgence of US economic and productivity

growth during the 1990s is well established in Jorgenson (2001), Jorgenson et al.

(2003a), and Oliner and Sichel (2000, 2003). Empirical works on countries other than

76

the US include Colecchia and Schreyer (2002), Diewert and Lawrence (2005),

Harchaoui et al. (2003), Javala and Pohjola (2001), Jorgenson and Motohashi (2005),

Kim (2002), Lee and Khatri (2003), Oulton and Srinivasan (2005), Parham et al. (2001),

Robidoux and Wong (2003), Schreyer (2000), and Simon and Wardrop (2002). Among

the studies pertaining to countries outside the G7 countries, Parham et al. (2001), Simon

and Wardrop (2002) and Diewert and Lawrence (2005) look at Australia specifically,

Javala and Pohjola (2001) study Finland, Kim (2002) investigates Korea, and Lee and

Khatri (2003) include several Asian developing countries, including China, in their

model.1

Current studies show that the GDP growth rate has generally accelerated in the

US and other OECD countries between the pre-1995 and post-1995 periods. The US,

for instance, has witnessed a jump in the GDP growth rate from about 2.7% to more

than 4% between the periods before and after 1995 (Jorgenson et al., 2003a; Oliner and

Sichel, 2003). Output growth in most of the OECD countries has increased between the

first and second halves of the 1990s, with the exception of Germany, Japan and Korea.

For the years between 1995 and 2000, output growth ranged from 2.8% in France to

over 4% in Australia, Canada and US, and 6% in Finland (Colecchia and Schreyer,

2002; Harchaoui et al., 2003; Jalava and Pohjola, 2001; Simon and Wardrop, 2002).

At the same time, there has been an increase in the contribution of ICT capital to

the economic growth of these countries. During the first half of the 1990s, the share of

contribution from ICT capital ranged from 8% in the former West Germany to more

than 18% in Canada and France (Schreyer, 2000). The contribution from ICT capital to

GDP growth in the US increased from 0.42 percentage point in 1973-95 to 0.98

percentage points in 1995-2000, with the share rising from 15% to 24% respectively

(Table 4.1).

Among the OECD countries under study, only Japan and Korea have a

contribution of ICT capital of more than 25% which exceeds that of the US (Colecchia

and Schreyer, 2002; Kim, 2002). Colecchia and Schreyer (2002) extended the analysis

of Schreyer (2000) to compare the impact of ICT capital accumulation on output growth

in the G7 countries with the inclusion of Australia and Finland for the period of 1980-

1 Literature in this field of research is exhaustive, and it would suffice for the dissertation to focus on a sample which will provide a summary of evidence showing the relationship between ICT and growth.

77

2000. They found that over the past two decades, ICT contributed between 0.2 - 0.5%

per year to economic growth, depending on the country. During the period of 1995-

2000, this contribution rose to 0.3-0.9% per year. Table 4.2 shows that the contribution

of ICT to output growth in 1995-2000 was highest in the US (with 0.87 percentage

point), followed by Australia and Canada (with 0.79 and 0.51 percentage points

respectively). The authors further showed that concurrent with the rise in demand for

ICT investment, prices for ICT capital goods fell in relative and absolute terms, which

led to substitution effects toward ICT capital goods and away from other factors of

production. In another study that includes only the computer hardware and software in

the measurement of ICT capital stock, it is found that the contribution from ICT capital

to output growth in Australia during 1995-2001 reached as high as 33% (Simon and

Wardrop, 2002).

Atzeni and Carboni (2006) found the returns from ICT investment to be about

eight times higher than that arising from non-ICT investment in a survey of Italian

manufacturing firms. First, this finding was based on the fact that ICT investment

accounted for only 12% of total investment. Yet it accounted for 34% of total

investment growth. Using their methodology of Partial Price Changes, the rate of return

on ICT capital is 0.814, compared with 0.1 for non-ICT capital. ICT capital generally

contributed about 0.5 percentage point to output growth, smaller than those found in the

US, due to having a lower share of ICT to the total capital stock (for example, 2.1%

compared to 7.4% in the US in 1996) (Schreyer, 2000). One main reason was that ICT

investment in Italy is concentrated in the service industries which occupy a relatively

small share in the economy.

The Asian developing countries, however, have a scenario different from that of

the developed countries. Many Asian countries experienced declines in their GDP

growth rates between the two halves of the 1990s, except for the Philippines and India

(Lee and Khatri, 2003). This is due mainly to the Asian financial crisis which started

with devaluation of the Thai baht in mid-1997 and contagiously spilled over to other

countries towards the end of the decade. China’s economic growth decreased slightly

from 10.6% in 1990-94 to 8.8% in 1995-99, but it still remained the strongest in the

Asia-Pacific region. Yet, the contribution from ICT capital to economic growth has

largely increased in spite of the economic downturn. The share of ICT capital

contribution to economic growth ranges from a mere 2-3% in China and India to more

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than 50% in Hong Kong during the period 1995-99. However, the increase in ICT

capital share can be attributed to the decline in GDP growth rate since ICT capital now

occupies a relatively greater portion of economic growth than that during the early

1990s (Table 4.3).

Table 4.1 Sources of growth in GDP and ALP in the US, 1973-2001

Contributions Period

GDP ICT KN L TFP

1973-95 2.78 (100)

0.42 (15.1)

0.98 (35.3)

1.12 (40.3)

0.26 (9.4)

1995-00 4.07 (100)

0.98 (24.1)

1.10 (27.0)

1.37 (33.7)

0.62 (15.2)

1995-01 3.55 (100)

0.93 (26.2)

1.10 (31.0)

1.12 (31.5)

0.40 (11.3)

ALP ICT KN L TFP 1973-95 1.33

(100) 0.37 (27.8)

0.43 (32.3)

0.27 (20.3)

0.26 (19.5)

1995-00 2.07 (100)

0.87 (42.0)

0.37 (17.9)

0.21 (10.1)

0.62 (30.0)

1995-01 2.02 (100)

0.85 (42.1)

0.54 (26.7)

0.22 (10.9)

0.40 (19.8)

Note: Figures in italic parentheses are the shares of each factor growth. ICT = ICT capital KN = Non-ICT capital L = Labour TFP = Total factor productivity Source: Jorgenson, Ho and Stiroh (2003a).

OECD (2004) highlighted some intrinsic characteristics of developing countries

which distinguish the effects of ICT on their productivity and economic growth from

those of the developed countries. First, most developing countries do not have a large

ICT-production sector, except those with huge markets such as China and India.

Investment in ICT is obtained primarily through foreign capital rather than the domestic

market. Second, the size of ICT investment in developing countries as a proportion of

their total output is much lower compared with developed countries as they are largely

dominated by primary industries such as raw materials or agriculture. As such, the

OECD found that ‘the contribution of new technologies to growth in developing

economies has been minimal from a macroeconomic perspective’. However, China has

features which distinguish it from many other developing countries. As shown in

Chapter two, the Chinese economy is no longer dominated by primary industries. As a

matter of fact, it has already emerged as one of the world’s largest ICT market and

production centre. Therefore, any study of developing countries in general should not be

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extrapolated to the case of China.

Table 4.2 Contribution of ICT to output growth in nine OECD countries, 1985-2000

1985-90 1990-95 1995-00

Output growtha

ICT KN Output growth

ICT KN Output growth

ICT KN

Australia Canada Finland France Germany Italy Japan UK US

3.79 (100) 2.90 (100) 3.42 (100) 3.46 (100) 3.59 (100) 3.04 (100) 5.14 (100) 3.90 (100) 3.31 (100)

0.51 (13.5) 0.36 (12.4) 0.25 (7.3) 0.21 (6.1) 0.16 (4.5) 0.20 (6.6) 0.18 (3.5) 0.23 (5.9) 0.43 (13.0)

1.46 (38.5) 0.89 (30.7) 0.58 (17.0) 0.70 (20.2) 0.64 (17.8) 0.66 (21.7) 1.2 (23.3) 0.87 (22.3) 0.67 (20.2)

3.37 (100) 1.79 (100) -0.70 (100) 0.97 (100) 2.22 (100) 1.44 (100) 1.33 (100) 2.12 (100) 2.64 (100)

0.47 (13.9) 0.28 (15.6) 0.01 (-1.4) 0.13 (13.4) 0.22 (9.9) 0.10 (6.9) 0.14 (10.5) 0.15 (7.1) 0.43 (16.3)

0.88 (26.1) 0.44 (24.6) 0.02 (-2.9) 0.60 (61.9) 0.77 (34.7) 0.52 (36.1) 1.19 (89.5) 0.59 (27.8) 0.54 (20.5)

4.62 (100) 4.20 (100) 5.62b

(100) 2.81 (100) 2.06 (100) 1.93b

(100) 1.10b

(100) 3.55 (100) 4.40 (100)

0.79 (17.1) 0.51 (12.1) 0.20 (3.6) 0.27 (9.6) 0.22 (10.7) 0.16 (8.3) 0.29 (26.4) 0.27 (7.6) 0.87 (19.8)

0.94 (20.3) 0.58 (13.8) -0.05 (-0.9) 0.78 (27.8) 0.61 (29.6) 0.66 (34.2) 0.68 (61.8) 0.77 (21.7) 0.84 (19.1)

Note: Figures in italic parentheses are the shares of each factor growth. For denotations of factor inputs and TFP in this and subsequent tables, refer to Table 4.1.

a. Business sector only. b. Data for the period of 1995-99. Source: Colecchia and Schreyer (2002).

The empirical findings pertaining to the contribution of ICT to economic growth

is summarized in Tables 4.3 and 4.4. The growth accounting framework employed by

most authors is more or less similar. One slightly different model is found in Kim

(2002) who examines the sources of Korea’s economic growth and productivity during

the period of 1971-2000 using the ‘extended growth model’, by adding the business

cycle to the model. Overall, while one would expect the US to lead the world in ICT

investment, yet the highest contribution of ICT capital to growth during the period of

1995-2003 occurred in Germany and Japan, with shares exceeding 46% and 40%

respectively (Jorgenson and Vu, 2005 – Table 4.4).

80

81

Another group of empirical research work focused on cross-country studies,

including Schreyer (2000) covering G7, Colecchia and Schreyer (2002) on G7 plus

Australia and Finland, Lee and Khatri (2003) on Asian developing economies, and

Jorgenson and Vu (2005) on the world economies. Schreyer (2000) measured the

contribution of ICT capital to output growth in the G7 countries for the first half of the

1990s. They found that in all of these countries except the former West Germany, ICT

contributed more than 10% to economic growth (Table 4.4). In a later work that

examined the sources of growth in nine OECD countries (i.e. the G7, Australia and

Finland) for the second half of the 1990s, the highest contributions from ICT investment

to output growth were found in Japan (26%), the US (20%) and Australia (17%)

(Colecchia and Schreyer, 2002). Among the Asian developing economies, the highest

contributions from ICT to economic growth in the second half of the 1990s came from

Hong Kong (56%), Singapore (25%) and Korea (19%) (Lee and Khatri, 2003).

Jorgenson and Vu (2005) analysed the impact of ICT investment (including

equipment and software) on the growth of the world economy, as well as growth for

each of the seven regions and 14 major economies (i.e. seven countries of the G7 and

seven major developing economies, including China) (Table 4.4). The US data was

revised and updated since the earlier work of Jorgenson (2001). The contribution of ICT

to world economic growth increased from 10.8% in 1989-1995 to 15.4% in 1995-2003;

for the US, it rose from 20.2% to 24.6%; for the G7 as a whole, the contribution jumped

by 10 percentage points, from 17.4% to 27%. Empirical findings for the developing

economies also showed a general increase in the contribution of ICT to economic

growth between 1989-1995 and 1995-2003, except for Sub-Sahara Africa. For instance,

the contribution of ICT to economic growth increased from 2% to 7.7% in the 16

countries of developing Asia; from 15.8% to 16.3% in the 15 countries of non-G7; from

5.2% to 18.5% in Latin America; from -1.4% to 8% in Eastern Europe; from 10.7% to

10.1% in Sub-Sahara Africa; and from 3.4% to 9.8% in North Africa and Middle East.

Each of the seven major developing economies (excluding China, which will be

discussed in the next section) except Mexico has also experienced a rising trend in the

share of ICT to economic growth. The findings are as follows: from 4.6% to 23.7% in

Brazil; from 1.8% to 4.2% in India; from 1.5% to 3.7% in Indonesia; from 11% to 6.5%

in Mexico; from -0.8% to 3.1% in Russia; and 3.9% to 11.2% in South Korea. The

increasing trend in the contribution of ICT to economic growth in developing Asia was

Table 4.3 Contribution of ICT to output growth in ten Asian economies, 1990-99 1990-94 1995-99

GDP ICT KN L TFP GDP ICT KN L TFP China Hong Kong India Indonesia Korea Malaysia Philippines Singapore Taiwan Thailand

10.63 (100) 5.02 (100) 5.22 (100) 8.51 (100) 7.96 (100) 9.40 (100) 2.73 (100) 8.76 (100) 6.92 (100) 9.59 (100)

0.14 (1.3) 0.86 (17.1) 0.06 (1.1) 0.15 (1.8) 0.90 (11.3) 0.43 (4.6) 0.18 (6.6) 0.94 (10.7) 0.40 (5.8) 0.22 (2.3)

3.23 (30.4) 0.98 (19.5) 1.62 (31.0) 3.51 (41.2) 1.10 (13.8) 3.78 (40.2) 0.78 (28.6) 0.96 (11.0) 1.87 (27.0) 2.80 (29.2)

3.78 (35.6) 2.70 (53.8) 2.78 (53.3) 2.70 (31.7) 3.82 (48.0) 6.91 (73.5) 2.84 (104.0) 8.42 (96.1) 2.43 (35.1) 2.35 (24.5)

3.49 (32.8) 0.48 (9.6) 0.77 (14.8) -4.30 (-50.5) 2.13 (26.8) -1.73 (-18.4) -1.08 (-39.6) -1.56 (-17.8) 2.22 (32.1) 4.21 (43.9)

8.76 (100) 2.10 (100) 6.56 (100) 1.26 (100) 5.90 (100) 5.12 (100) 4.96 (100) 5.55 (100) 6.46 (100) 1.89 (100)

0.27 (3.1) 1.17 (55.7) 0.11 (1.7) 0.19 (15.1) 1.10 (18.6) 0.57 (11.1) 0.31 (6.3) 1.36 (24.5) 0.58 (9.0) 0.23 (12.2)

3.39 (38.7) 0.70 (33.3) 1.50 (22.9) 1.88 (149.2) 0.63 (10.7) 2.37 (46.3) 0.73 (14.7) 0.66 (11.9) 1.67 (25.9) 1.11 (58.7)

1.34 (15.3) 2.60 (123.8) 2.79 (42.5) 3.50 (277.8) 1.16 (19.7) 3.30 (64.5) 2.81 (56.7) 3.82 (68.8) 1.92 (29.7) 1.38 (73.0)

3.76 (42.9) -2.36 (-112.4) 2.16 (32.9) -4.30 (-341.3) 3.00 (50.8) -1.12 (-21.9) 1.11 (22.4) -0.28 (-5.0) 2.29 (35.4) -0.83 (-43.9)

Note: See the note to Table 4.1. Source: Lee and Khatri (2003).

82

Table 4.4 Contributions to GDP growth (Unit: %) Author Country Period GDP ICT KN L TFP Other

variable Jorgenson and Stiroh (1999) US 1990-96 2.36

(100) 0.12 (5.1)

0.51 (21.6)

1.22 (51.7)

0.23 (9.7)

0.28a

(11.9) Jorgenson and Stiroh (2000) US 1995-98 4.73

(100) 0.76 (16.1)

0.86 (18.2)

1.57 (33.2)

0.99 (20.9)

0.56a

(11.8) Oliner and Sichel (2000) US 1996-99 4.82

(100) 1.10 (22.8)

0.75 (15.6)

1.81 (37.6)

1.16 (24.1)

Jorgenson (2001) US 1995-99 4.08 (100)

0.99 (24.3)

1.07 (26.2)

1.27 (31.1)

0.75 (18.4)

Jorgenson, Ho and Stiroh (2003a) US 1995-2001 3.55 (100)

0.93 (26.2)

1.10 (31.0)

1.12 (31.5)

0.40 (11.3)

Parham et al (2001) Australia 1995-2000 4.9 (100)

1.3 (26.5)

0.8 (16.3)

0.7 (14.3)

2.0 (40.8)

Simon and Wardrop (2002) Australia 1996-2001 3.86 (100)

1.26 (32.6)

0.60 (15.5)

0.57 (14.8)

1.43 (37.0)

Harchaoui et al (2003) Canada 1995-2000 4.9 (100)

0.7 (14.3)

1.0 (20.4)

2.2 (44.9)

1.0 (20.4)

Jalava and Pohjola (2001) Finland 1995-1999 6.0 (100)

0.7 (11.7)

-0.4 (-6.7)

1.6 (26.7)

4.2 (70.0)

Jorgenson and Motohashi (2005) Japan 1995-2003 0.83 (100)

0.54 (65.1)

0.62 (74.5)

-0.32 (-38.6)

0.45 (54.2)

Kim (2002) Korea 1996-2000 4.96 (100)

1.22 (24.6)

2.62 (52.8)

0.21 (4.2)

1.40 (28.2)

-0.49b

(-9.9) Schreyer (2000) Canada

France W. Germany Italy Japan

1990-96

1.7 (100) 0.9 (100) 1.8 (100) 1.2 (100) 1.8 (100)

0.31 (18.2) 0.17 (18.9) 0.15 (8.3) 0.21 (17.5) 0.20 (11.1)

0.39 (22.9) 0.83 (92.2) 0.85 (47.2) 0.49 (40.8) 0.80 (44.4)

83

UK US

2.1 (100) 3.0 (100)

0.31 (14.8) 0.41 (13.7)

0.59 (28.1) 0.49 (16.3)

Colecchia and Schreyer (2002) Australia Canada Finland (1995-99) France Germany Italy (1995-99) Japan (1995-99) UK US

1995-2000

4.62 (100) 4.20 (100) 5.62 (100) 2.81 (100) 2.06 (100) 1.93 (100) 1.10 (100) 3.55 (100) 4.40 (100)

0.79 (17.1) 0.51 (12.1) 0.20 (3.6) 0.27 (9.6) 0.22 (10.7) 0.16 (8.3) 0.29 (26.4) 0.27 (7.6) 0.87 (19.8)

0.94 (20.3) 0.58 (13.8) -0.05 (-0.9) 0.51 (18.1) 0.61 (29.6) 0.66 (34.2) 0.68 (61.8) 0.77 (21.7) 0.84 (19.1)

Lee and Khatri (2003)

US Hong Kong Indonesia Korea Malaysia Philippines

1995-99

3.64 (100) 2.10 (100) 1.26 (100) 5.90 (100) 5.12 (100) 4.96 (100)

0.79 (21.7) 1.17 (55.7) 0.19 (15.1) 1.10 (18.6) 0.57 (11.1) 0.31 (6.3)

0.29 (8.0) 0.70 (33.3) 1.88 (149.2) 0.63 (10.7) 2.37 (46.3) 0.73 (14.7)

1.30 (35.7) 2.60 (123.8) 3.50 (277.8) 1.16 (19.7) 3.30 (64.5) 2.81 (56.7)

1.26 (34.6) -2.36 (-112.4) -4.30 (-341.3) 3.00 (50.8) -1.12 (-21.9) 1.11 (22.4)

84

Singapore Taiwan Thailand India China (1995-99) (1990-94)

5.55 (100) 6.46 (100) 1.89 (100) 6.56 (100) 8.76 (100) 10.63 (100)

1.36 (24.5) 0.58 (9.0) 0.23 (12.2) 0.11 (1.7) 0.27 (3.1) 0.14 (1.3)

0.66 (11.9) 1.67 (25.9) 1.11 (58.7) 1.50 (22.9) 3.39 (38.7) 3.23 (30.4)

3.82 (68.8) 1.92 (29.7) 1.38 (73.0) 2.79 (42.5) 1.34 (15.3) 3.78 (35.6)

-0.28 (-5.0) 2.29 (35.4) -0.83 (-43.9) 2.16 (32.9) 3.76 (42.9) 3.49 (32.8)

Jorgenson and Vu (2005) World (110 economies) G7 Developing Asia Non-G7 Latin America Eastern Europe Sub-Sahara Africa North Africa & Middle East G7 economies Canada France

1989-95

2.50 (100) 2.18 (100) 7.35 (100) 2.03 (100) 3.06 (100) -7.05 (100) 1.21 (100) 4.36 (100) 1.39 (100) 1.30 (100)

0.27 (10.8) 0.38 (17.4) 0.15 (2.0) 0.32 (15.8) 0.16 (5.2) 0.10 (-1.4) 0.13 (10.7) 0.15 (3.4) 0.49 (35.2) 0.19 (14.6)

0.91 (36.4) 0.90 (41.3) 1.73 (23.5) 0.68 (33.5) 0.58 (18.9) -0.15 (2.1) 0.24 (19.8) 0.72 (16.5) 0.27 (19.4) 0.93 (71.5)

0.79 (31.6) 0.49 (22.5) 1.61 (21.9) 0.42 (20.7) 1.57 (51.3) -0.50 (7.0) 2.22 (183.5) 1.99 (45.6) 0.62 (44.6) 0.44 (33.8)

0.53 (21.2) 0.42 (19.3) 3.86 (52.5) 0.61 (30.0) 0.75 (24.5) -6.50 (92.2) -1.39 (-114.9) 1.50 (34.4) 0.01 (0.7) -0.26 (-20.0)

85

Germany Italy Japan UK US Major developing economies (D7) Brazil China India Indonesia Mexico Russia South Korea All D7

2.34 (100) 1.52 (100) 2.56 (100) 1.62 (100) 2.43 (100) 1.97 (100) 9.94 (100) 5.03 (100) 6.82 (100) 2.19 (100) -8.44 (100) 7.48 (100) 3.45 (100)

0.26 (11.1) 0.26 (17.1) 0.31 (12.1) 0.27 (16.7) 0.49 (20.2) 0.09 (4.6) 0.17 (1.7) 0.09 (1.8) 0.10 (1.5) 0.24 (11.0) 0.07 (-0.8) 0.29 (3.9) 0.13 (3.8)

1.05 (44.9) 0.86 (56.6) 1.16 (45.3) 1.69 (104.3) 0.71 (29.2) 0.29 (14.7) 2.12 (21.3) 1.18 (23.5) 1.62 (23.8) 0.95 (42.4) -0.07 (0.8) 2.31 (30.9) 1.17 (33.9)

-0.09 (-3.8) 0.03 (2.0) 0.15 (5.9) -0.24 (-14.8) 0.93 (38.3) 1.38 (70.1) 1.32 (13.3) 1.70 (33.8) 2.07 (30.4) 1.86 (1.3) -0.65 (1.3) 1.76 (23.5) 1.13 (32.8)

1.12 (47.9) 0.37 (24.3) 0.94 (36.7) -0.10 (-6.2) 0.31 (12.8) 0.20 (10.2) 6.33 (62.7) 2.06 (41.0) 3.04 (44.6) -0.87 (-39.7) -7.79 (92.3) 3.13 (41.8) 1.03 (29.9)

86

World (110 economies) G7 Developing Asia Non-G7 Latin America Eastern Europe Sub-Sahara Africa North Africa & Middle East G7 economies Canada France Germany

1995-2003

3.45 (100) 2.56 (100) 5.62 (100) 3.01 (100) 2.11 (100) 2.87 (100) 2.88 (100) 4.08 (100) 2.51 (100) 1.92 (100) 0.86 (100)

0.53 (15.4) 0.69 (27.0) 0.43 (7.7) 0.49 (16.3) 0.39 (18.5) 0.23 (8.6) 0.29 (10.1) 0.40 (9.8) 0.65 (25.9) 0.36 (18.8) 0.40 (46.5)

1.03 (29.9) 0.74 (28.9) 2.27 (40.4) 0.77 (25.6) 0.61 (28.9) -0.81 (28.2) 0.68 (23.6) 0.88 (21.6) 0.61 (24.3) 0.75 (39.1) 0.50 (58.1)

0.89 (25.8) 0.46 (18.0) 1.19 (21.2) 1.26 (41.9) 1.44 (68.2) 0.40 (13.9) 1.60 (55.6) 2.51 (61.5) 0.84 (33.5) 0.29 (15.1) -0.15 (-17.4)

0.99 (28.7) 0.67 (26.2) 1.72 (30.6) 0.49 (16.3) -0.32 (-15.2) 3.06 (106.6) 0.32 (11.1) 0.30 (7.4) 0.42 (16.7) 0.52 (27.1) 0.11 (12.8)

Italy Japan UK US

1.48 (100) 1.39 (100) 2.55 (100) 3.56 (100)

0.46 (31.1) 0.56 (40.3) 0.65 (25.5) 0.88 (24.7)

0.96 (64.9) 0.26 (18.7) 0.19 (7.5) 1.01 (28.4)

0.88 (59.5) -0.10 (-7.2) 0.64 (25.1) 0.67 (18.8)

-0.82 (-55.4) 0.67 (48.2) 1.07 (42.0) 0.99 (27.8)

87

Major developing economies (D7) Brazil China India Indonesia Mexico Russia South Korea All D7

1.94 (100) 7.13 (100) 6.15 (100) 2.41 (100) 3.56 (100) 3.18 (100) 4.09 (100) 5.18 (100)

0.46 (23.7) 0.63 (8.8) 0.26 (4.2) 0.09 (3.7) 0.23 (6.5) 0.10 (3.1) 0.46 (11.2) 0.40 (7.7)

0.24 (12.4) 3.17 (44.5) 1.77 (28.8) 1.47 (61.0) 1.11 (31.2) -1.30 (-40.9) 1.67 (40.8) 1.70 (32.8)

1.04 (53.6) 0.84 (11.8) 1.63 (26.5) 1.32 (54.8) 2.07 (58.1) 0.65 (26.4) 1.12 (27.4) 1.13 (21.8)

0.21 (10.8) 2.49 (34.9) 2.49 (40.5) -0.47 (-19.5) 0.14 (3.9) 3.73 (117.3) 0.85 (20.8) 1.96 (37.8)

88

Note: See the note to Table 4.1. a. Consumer durables services b. Business cycle

attributed to the surge in investment in ICT equipment and softwares after 1995, with the

highest investment recorded in China and India. This is not the end of the story, however,

as the next section will show whether ICT is contributing more towards labour productivity

growth.

4.3 ICT and labour productivity growth

In assessing the US productivity growth, there is an unanimous conclusion about 1995 as

the specific breaking point of acceleration in productivity growth attributed to a surge in

ICT investments (Jorgenson, 2001; Jorgenson et al., 2003a; Oliner and Sichel, 2000, 2003).

There is comparatively less empirical work on ICT contribution to ALP growth than that on

ICT contribution to GDP growth. The countries examined are the US (Jorgenson et al.,

2003a; Oliner and Sichel, 2003), Canada (Harchaoui et al., 2003; Robidoux and Wong,

2003), Australia (Parham et al., 2001), Finland (Jalava and Pohjola, 2001) and ten Asia-

Pacific countries (Lee and Khatri, 2003). Most findings reveal an increase in ALP growth

in major economies around the world between the periods before and after the mid-1990s

(Jorgenson and Stiroh, 2000; Jorgenson, 2001; Jorgenson et al., 2003a; Oliner and Sichel,

2000, 2003; Parham et al., 2001; Harchaoui et al. 2003; Robidoux and Wong, 2003; Lee

and Khatri, 2003). An exception is Finland which experienced a decline of 0.4 percentage

points in ALP growth rate between 1990-95 and 1995-99 (Jalava and Pohjola, 2001).

Among the developing countries in Asia which experienced economic decline during the

late 1990s due to the financial crisis, only China, India and Korea are found to have an

increase in ALP growth between 1990-94 and 1995-99 (Lee and Khatri,2003).

Corresponding to the increase in productivity growth rate in the developed countries

is a rise in the contribution of ICT capital deepening. In terms of the share of ICT capital

contribution to ALP growth, there is a clear lag between the US and other developed

countries. For the US, the share of ICT capital contribution to ALP growth amounted to

42% during the period of 1996-2001 (Jorgenson et al., 2003a; Oliner and Sichel, 2003)

(Table 4.5). This is followed by Singapore (with 38% in 1995-99), Australia (35% in 1995-

2000), Canada (24% in 1995-2000) and Korea (18% in 1995-99) (Parham et al., 2001;

Harchaoui et al., 2003; Lee and Khatri, 2003). Ironically, Hong Kong which had the largest

share of ICT contribution to GDP growth in 1995-99 (56%), experienced a negative -184%

89

share of the corresponding contribution to ALP growth during the same period. Other Asian

NIEs and developing countries have less than 15% share of the contribution from ICT

capital deepening to ALP growth. Besides Hong Kong, the only country that had a negative

contribution from ICT capital is Indonesia (Table 4.6).

Table 4.5 Sources of growth in non-farm output and ALP in the US, 1974-2001 Contributions

Period Non-farm GDP ICT KN L TFP 1974-90 3.06

(100) 0.49 (16.0)

0.86 (28.1)

1.38 (45.1)

0.33 (10.8)

1991-95 2.75 (100)

0.57 (20.7)

0.44 (16.0)

1.26 (45.8)

0.48 (17.5)

1996-99 4.82 (100)

1.10 (22.8)

0.75 (15.6)

1.81 (37.6)

1.16 (24.1)


(100) 0.41 (30.1)

0.37 (27.2)

0.22 (16.2)

0.37 (27.2)

1991-95 1.54 (100)

0.46 (29.9)

0.06 (3.9)

0.45 (29.2)

0.58 (37.7)

1996-01 2.43 (100)

1.02 (42.0)

0.17 (7.0)

0.25 (10.3)

0.99 (40.7)

Note: See the note to Table 4.1. Source: Oliner and Sichel (2000, 2003).

The increase in ICT contribution relative to other factor inputs lends weight to the

argument about ICT being the predominant source of the productivity revival in the US.

This is due to (i) an increase in TFP growth in the ICT-producing sectors (computer

hardware, software and telecom) and (ii) induced capital deepening in ICT equipment

(Jorgenson et al., 2003). These two contributions account for a majority of the acceleration

in labour productivity growth after 1995. The contribution of ICT to ALP growth gained by

0.50 percentage points between 1973-95 and 1995-2000, with the corresponding share

rising from 28% to 42% (Table 4.1). Jorgenson et al. (2003) further found that when

compared with 1995-2000, the growth rates of GDP and ALP in the US as well as the

contributions from ICT capital and TFP to output and productivity growth was lower in

1995-2001, suggesting the dampening effect of the recession in 2001 on the US’ ICT

productivity. Their findings are further supported by those of Oliner and Sichel (2003) who

showed that the share of ICT capital contribution has risen from 30% in 1991-95 to 42% in

1996-2001 (Table 4.5). Oliner and Sichel (2003) also generated a steady-state growth

framework which projects growth in labour productivity of about 2% per year. They

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observed that future increases in labour productivity will depend significantly on the pace

of technological advance in the semiconductor industry and on the extent to which products

embodying these advances diffuse through the economy. This observation is consistent

with the emphasis on semiconductor technology in Jorgenson (2001), as discussed earlier in

Chapter three.

The US’ closest neighbour, Canada, however, has a different story. Two different

empirical works have produced contrasting results. Harchaoui et al. (2003) show that while

Canada has had an improvement in economic performance between the periods before and

after 1995 just as in the US, the share of contribution from ICT capital deepening to ALP

growth actually declined, even though it remained more than 20% in 1995-2000. In another

finding, the corresponding share of ICT contribution to ALP growth increased from 27% in

1988-96 to 33% in 1996-2001 (Table 4.7).

The difference may lie in the methods employed. Robidoux and Wong (2003) do

not include labour quality as a factor in their analysis, thereby possibly overestimating the

contribution of ICT capital deepening. Harchaoui et al. (2003) include a ‘structures’

variable as one of the non-ICT capital inputs, which refers to all forms of fixed assets such

as land, dwellings and construction of infrastructure. Nevertheless, in both cases, ICT is

found to be the largest contributor to growth within the capital services, although its

contribution is lower than that in the US. TFP growth is also found to be a key source of

ALP growth in Canada, constituting more than 50% (Table 4.7). The improvement in ALP

growth therefore reflects not only increased capital deepening in ICT, but also higher TFP

growth.

The question of whether using the dating of the US productivity revival, that is,

1995 as a universal benchmark is appropriate for all countries is questioned by Robidoux

and Wong (2003). They point out that labour productivity growth in Australia improved

earlier than in the US. This comment is supported by Parham et al. (2001) and Parham

(2004) who show that labour productivity growth in Australia has been steadily increasing

since the late 1980s (Table 4.8). Furthermore, ICT capital has consistently contributed more

than 30% to output and ALP growth in Australia since 1995. However, a drawback in the

analysis of Parham et al. (2001) is that labour quality was omitted in the accounting

1990-94 1995-99 ALP ICT KN L TFP ALP ICT KN L TFP

China Hong Kong India Indonesia Korea Malaysia Philippines Singapore Taiwan Thailand

6.63 (100) 3.81 (100) 4.09 (100) 6.70 (100) 5.13 (100) 5.31 (100) -0.12 (100) 5.04 (100) 5.17 (100) 7.80 (100)

0.10 (1.5) 0.73 (19.2) 0.05 (1.2) 0.12 (1.8) 0.69 (13.5) 0.29 (5.5) 0.13 (-108.3) 0.66 (13.1) 0.33 (6.4) 0.19 (2.4)

2.16 (32.6) 0.80 (21.0) 1.35 (33.0) 2.78 (41.5) 0.80 (15.6) 2.58 (48.6) 0.15 (-125.0) 0.58 (11.5) 1.50 (29.0) 2.38 (30.5)

1.10 (16.6) 1.79 (47.0) 2.01 (49.1) 1.82 (27.2) 1.64 (32.0) 4.23 (79.7) 0.66 (-550.0) 5.58 (110.7) 1.22 (23.6) 1.20 (15.4)

3.27 (49.3) 0.50 (13.1) 0.68 (16.6) 1.98 (29.6) 2.01 (39.2) -1.79 (-33.7) -1.07 (891.7) -1.78 (-35.3) 2.12 (41.0) 4.04 (51.8)

7.41 (100) -0.49 (100) 5.82 (100) -0.69 (100) 5.21 (100) 1.78 (100) 2.12 (100) 2.68 (100) 5.04 (100) 1.21 (100)

0.21 (2.8) 0.90 (-183.7) 0.09 (1.5) 0.15 (-21.7) 0.94 (18.0) 0.41 (23.0) 0.23 (10.8) 1.02 (38.1) 0.47 (9.3) 0.19 (15.7)

2.92 (39.4) 0.33 (-67.3) 1.34 (23.0) 1.30 (-188.4) 0.61 (11.7) 1.39 (78.1) 0.19 (9.0) 0.35 (13.1) 1.35 (26.8) 0.99 (81.8)

0.65 (8.8) 0.54 (-110.2) 2.34 (40.2) 2.38 (-344.9) 0.76 (14.6) 1.16 (65.2) 0.61 (28.8) 1.56 (58.2) 0.99 (19.6) 0.95 (78.5)

3.63 (49.0) -2.26 (461.2) 2.05 (35.2) -4.52 (655.1) 2.91 (55.9) -1.18 (-66.3) 1.09 (51.4) -0.24 (-9.0) 2.23 (44.2) -0.93 (-76.9)

Table 4.6 Contribution of ICT to ALP growth in ten Asian economies, 1990-99

92


framework of ALP growth. Nevertheless, just as in the US, the increasing share of ICT

in Australian productivity growth is a result of falling prices in ICT capital since the

1970s (Parham et al., 2001).

Table 4.7 Sources of growth in Canada, 1972-2001 Author Period Output ICT KN L TFP

1981-88 3.3 (100)

0.4 (12.1)

1.0 (30.3)

1.7 (51.5)

0.2 (6.1)

1988-95 1.5 (100)

0.4 (26.7)

0.6 (40.0)

0.8 (53.3)

-0.3 (-20.0)

1995-00 4.9 (100)

0.7 (14.3)

1.0 (20.4)

2.2 (44.9)

1.0 (20.4)


(100) 0.3 (23.1)

0.3 (23.1)

0.5 (38.5)

0.2 (15.4)

1988-95 1.2 (100)

0.4 (33.3)

0.5 (41.7)

0.6 (50.0)

-0.3 (-25.0)

Harchaoui et al. (2003)

1995-00 1.7 (100)

0.4 (23.5)

0.0 -

0.3 (17.6)

1.0 (58.8)

ALP ICT KN TFP 1972-88 1.2

(100) 0.3 (25.0)

0.9 (75.0)

0.0

1988-96 1.1 (100)

0.3 (27.3)

0.3 (27.3)

0.5 (45.5)

Robidoux and Wong (2003)

1996-01 1.8 (100)

0.6 (33.3)

0.2 (11.1)

0.9 (50.0)

Note: See the note to Table 4.1. Source: Harchaoui et al. (2003), Robidoux and Wong (2003).

The empirical findings pertaining to the contribution of ICT to ALP growth is

summarized in Tables 4.9 and 4.10. It can be observed that ICT capital deepening has

played an important role in improving labour productivity, be it in the developed

countries or the developing countries in Asia, especially during the second half of the

1990s. The contribution of ICT capital stock to labour productivity rose from less than

10% to almost 30% during the 1990s. The contribution of ICT to growth in Asia during

the 1990s comes mainly from capital deepening (i.e. increase in labour productivity

through a larger capital-labour ratio).

4.4 ICT and productivity at the industry level

The evidence that ICT has become the main driving force behind productivity growth,

especially in the late 1990s, can be further reinforced by looking at research at the

industry level. Although the main emphasis of this thesis is the study at country level, a

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review of industry studies is also helpful, which will further prove whether ICT

producers and users have contributed the most to productivity growth. Dubbed the

‘bottom-up’ approach (in contrast with the ‘top-down’ approach of analysing data at the

national-level), industry-level studies enable researchers to assess the contribution from

industries that produce and use ICT to productivity and output growth (Jorgenson, Ho

and Stiroh, 2003b).

Table 4.8 Sources of growth in Australia, 1965-2000 Author Period Output

growth ICT KN L TFP

1965-74 4.9 (100)

0 2.3 (46.9)

1.3 (26.5)

1.3 (26.5)

1974-82 2.2 (100)

0.3 (13.6)

1.1 (50.0)

-0.2 (-9.1)

1.0 (45.5)

1982-90 3.2 (100)

0.6 (18.7)

1.0 (31.3)

1.2 (37.5)

0.4 (12.5)

1990-00 3.5 (100)

1.1 (31.4)

0.7 (20.0)

0.3 (8.6)

1.4 (40.0)

Period ALP growth ICT KN TFP 1965-74 2.7

(100) - 1.4

(51.9) 1.3

(48.1) 1974-85 2.4

(100) 0.4 (16.7)

1.1 (45.8)

0.9 (37.5)

1985-89 0.9 (100)

0.7 (77.8)

-0.2 (-22.2)

0.4 (44.4)

1989-94 2.1 (100)

0.8 (38.1)

0.7 (33.3)

0.6 (28.6)

Parham et al. (2001)

1994-00 3.0 (100)

1.1 (36.7)

0.2 (6.7)

1.7 (56.6)

Period ALP growth ICT KN TFP 1990-95 2.2

(100) 0.6 (27.3)

0.5 (22.7)

1.1 (50.0)

Parham (2004)

1995-00 3.2 (100)

1.2 (37.5)

0.4 (12.5)

1.6 (50.0)

Note: See the note to Table 4.1.

Table 4.9 Contributions to ALP growth: Single country study (Unit: %) Author Country Period ALP ICT KN L TFP Other

variable Jorgenson and Stiroh (2000) US 1995-98 2.37

(100) 1.13

(47.7) 0.25 (10.5)

0.99 (41.8)

Oliner and Sichel (2000) US 1996-99 2.57 (100)

0.96 (37.4)

0.14 (5.4)

0.31 (12.1)

1.16 (45.1)

Jorgenson (2001) US 1995-99 2.11 (100)

0.89 (42.2)

0.35 (16.6)

0.12 (5.7)

0.75 (35.5)

Jorgenson, Ho and Stiroh (2003a)

US 1995-2000 1995-2001

2.07 (100) 2.02 (100)

0.87 (42.0) 0.85 (42.1)

0.37 (17.9) 0.54 (26.7)

0.21 (10.1) 0.22 (10.9)

0.62 (30.0) 0.40 (19.8)

Oliner and Sichel (2003) US 1996-2001 2.43 (100)

1.02 (42.0)

0.17 (7.0)

0.25 (10.3)

0.99 (40.7)

0.42a

(17.3) Parham et al (2001) Australia 1995-2000 3.7

(100) 1.3 (35.1)

0.4 (10.8)

2.0 (54.1)

Jalava and Pohjola (2001) Finland 1995-99 3.5 (100)

0.6 (17.1)

-1.4 (-40.0)

0.2 (5.7)

4.1 (117.1)

Harchaoui et al (2003) Canada 1995-2000 1.7 (100)

0.4 (23.5)

0.0 -

0.3 (17.6)

1.0 (58.8)

Robidoux and Wong (2003) Canada 1996-2001 1.8 (100)

0.6 (33.3)

0.3 (16.7)

-

1.0 (55.6)

Oulton and Srinivasan (2005) UK 1995-2000 2.93 (100)

1.37 (46.8)

0.79 (27.0)

0.45 (15.4)

0.32 (10.9)

95

Note: See the note to Table 4.1. a. Semiconductors

Table 4.10 Contributions to ALP growth: Cross-country study, 1995-99 (Unit: %) Country ALP ICT KN L TFP Hong Kong Indonesia Korea Malaysia Philippines Singapore Taiwan Thailand India China

-0.49 (100) -0.69 (100) 5.21 (100) 1.78 (100) 2.12 (100) 2.68 (100) 5.04 (100) 1.21 (100) 5.82 (100) 7.41 (100)

0.90 (-183.7) 0.15 (-21.7) 0.94 (18.0) 0.41 (23.0) 0.23 (10.8) 1.02 (38.1) 0.47 (9.3) 0.19 (15.7) 0.09 (1.5) 0.21 (2.8)

0.33 (-67.3) 1.30 (-188.4) 0.61 (11.7) 1.39 (78.1) 0.19 (9.0) 0.35 (13.1) 1.35 (26.8) 0.99 (81.8) 1.34 (23.0) 2.92 (39.4)

0.54 (-110.2) 2.38 (-344.9) 0.76 (14.6) 1.16 (65.2) 0.61 (28.8) 1.56 (58.2) 0.99 (19.6) 0.95 (78.5) 2.34 (40.2) 0.65 (8.8)

-2.26 (461.2) -4.52 (655.1) 2.91 (55.9) -1.18 (-66.3) 1.09 (51.4) -0.24 (-8.96) 2.23 (44.2) -0.93 (-76.9) 2.05 (35.2) 3.63 (49.0)


4.4.1 ICT-producing vs ICT-using industries

Research at the industry level distinguishes between contributions from ICT-producing

(or IT-producing) and ICT-using (or IT-using) industries to productivity growth.

Dedrick et al. (2003) defined ICT-producing industries as ‘those which manufacture

semiconductor, computer, or telecommunications hardware or provide software and

services that enable these technologies to be used effectively in organizations’, whereas

ICT-using industries are ‘all the other sectors of the economy that apply ICT as part of

their operations in order to achieve greater efficiency and effectiveness, and they

include manufacturing (durable and nondurable), wholesale and retail trade, finance,

insurance and real estate, business and professional services, etc.’

96

The study of industry productivity is exemplified in Stiroh (2001, 2002a) who

explains that productivity revival can be proven by evidence that ICT producers and the

most intensive users experience the largest productivity acceleration in the late 1990s.

As he commented: ‘If productivity increases have been widespread, then the

productivity revival is likely to be more enduring. In contrast, if the increases have been

concentrated in a single sector, then the revival may be vulnerable to a slowdown in that

sector’ (Stiroh, 2001). In this exercise, he finds productivity to have accelerated in eight

of ten broad sectors in the US during the period of 1987-95; only mining and agriculture

were the exceptions. Among the eight sectors, the durable manufacturing sector which

produces ICT equipment achieved the most impressive productivity gains after 1995,

and this is attributable to the ‘rapid technological advances driving the ICT revolution’

(Stiroh, 2001). Overall, he finds that ICT-producing industries have shown the largest

productivity gains when compared with ICT-using and other industries.

Stiroh (2002a) addressed the issue of the contribution of ICT production and use

to the US aggregate productivity revival in the late 1990s by examining the variation in

productivity growth over time and across industries and by exploring the link with ICT

capital. He finds further evidence that suggests the US productivity revival is not

confined to only a few ICT-producing industries, with the mean productivity

acceleration from 1987-1995 to 1995-2000 for 61 industries being 0.87% (Stiroh,

2002a). His findings also show that ICT-producing and ICT-using industries accounted

for all of the direct industry contributions to the US productivity revival (Table 4.11).

On the whole, ICT-using industries, which made up 51% of total industry, have a

greater share of contribution to ALP growth than ICT-producing industries, which

comprise only 4% of the total industry. It should also be noted that while the ICT-

producing industries contributed 0.54 percentage points to ALP growth in 1995-2000,

the non-ICT industries which made up 44% of total industry contributed only 0.53

percentage points during the same period (Stiroh, 2002a).

Van Ark, Inklaar and McGuckin (2003) compared the contributions of ICT-

producing, ICT-using and non-ICT industries in Canada, Europe and the US to labour

productivity growth, with a detailed categorization of ICT and non-ICT industries. They

found that ICT-using industries contribute more than ICT-producing industries to ALP

growth in Canada and the US, whereas the opposite case occurred in Europe (Table

4.12). Another study found that the ICT-producing industries contributed more to ALP

97

growth than ICT-using industries in Sweden (Edquist, 2005).

Table 4.11 Decomposition of US labour productivity growth by industry, 1987-2000 Period ALP2 ICT-producing ICT-using Other

industries Material reallocation

Hours reallocation

1987-95 0.98 (100)

0.37 (37.8)

0.75 (76.5)

0.74 (75.5)

-0.40 (-40.8)

-0.47 (-48.0)

1995-00 2.29 (100)

0.54 (23.6)

1.58 (69.0)

0.53 (23.1)

-0.02 (-0.9)

-0.34 (-14.8)

Note: The numbers in parentheses are percentage shares.

Source: Stiroh (2002a).

Evidence of a stronger contribution from ICT capital deepening, i.e. ICT-using

industries, than that of ICT-producing industries to labour productivity growth can also

be found in Australia (Parham et al., 2001; Colecchia and Schreyer, 2002; and Parham,

2004). In a detailed examination of industrial productivity in Australia, Parham et al.

(2001) found a strong contribution from ICT capital deepening among ICT-using

industries, especially in the service sectors such as restaurants, finance and insurance,

utilities, transport and communications, and the retail sector, as well as in the

manufacturing and construction sectors. Similarly, Parham (2004) reinforced the

conclusion that the increase in ICT use has raised TFP growth in Australia.

Table 4.12 Decomposition of ALP growth in Canada, EU and US by industry, 1995-2000

Country/ Region

ALP ICT KN

Production Use Canada EU US

1.76 (100) 1.40 (100) 2.49 (100)

0.42 (23.9) 0.46 (32.9) 0.74 (29.7)

0.83 (47.2) 0.41 (29.3) 1.40 (56.2)

0.52 (29.5) 0.47 (33.6) 0.36 (14.5)

Note: See the note to Table 4.1. Source: Van Ark, Inklaar and McGuckin (2003).

2 Note that the share of industrial contribution to ALP growth exceeds 100%. The two other factor inputs which contributed negatively to ALP growth in both periods are: ‘material reallocation’ (RM), which ‘reflects variation in intermediate input intensity across industries’; and ‘hours reallocation’ (RH), which “weights industries by their (lagged) share of aggregate hours” (Stiroh, 2002a).

98

Other studies have produced mixed results about the effects of ICT-producing

and ICT-using industries on economic growth are concerned. Colecchia and Schreyer

(2002) demonstrated that Australia, which has a relatively small ICT-producing sector

in comparison with the G7 countries, has experienced the highest GDP growth rate

during 1995-2000. Its share of contribution from ICT capital deepening to economic

growth is exceeded only by Japan and the US. By contrast, Japan, which has the largest

ICT-producing sector among the G7 countries, experienced the lowest GDP growth rate

during 1995-1999, which coincided with the Asian financial crisis that occurred within

that period. The analysis of Colecchia and Schreyer (2002) suggests that the existence

of a large ICT-producing industry is neither a necessary nor a sufficient condition to

successfully experience the growth effects of ICT. Finally, a study that compares the

effects of ICT on labour productivity growth between the European Union (EU) and the

US found that Europe’s lagging growth performance behind the US was caused by

‘having a smaller ICT-producing sector, lower ICT investment rates and/or a failure to

renew business processes in ICT-using industries’ (Timmer and van Ark, 2005).

The proposition of ICT-using industries having a stronger impact on

productivity growth is also supported by Oulton and Srinivasan (2005) who examined

the role of ICT in explaining productivity growth in the UK, using data for 34 industries

for the period of 1970-2000. Oulton and Srinivasan found that ICT capital deepening

was concentrated in only a small number of industries during the 1990s, namely,

business services, finance, communications, wholesaling and retailing – all of which are

in the service sector. Furthermore, among the manufacturing industries (which

accounted for only 14% of ICT capital deepening), only the ICT-producing industries,

namely electrical and electronics, had a significant role in productivity growth.

Another group of research concentrated on examining the relationship between

information intensity and labour productivity growth. Hu and Quan (2005) used the

Granger causality model to test the correlation between ICT investments and industry-

level productivity for eight industries in the US from 1970 to 1999. Their findings

suggested a causal relationship between ICT investments and productivity in six out of

eight industries, namely mining, service, retail, wholesale, transportation and

manufacturing, most of which have high value-chain information intensity.

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In examining the link between ICT intensity and labour productivity growth of

29 industries in New Zealand over the period of 1988-2003, evidence was found to

support the view that labour productivity growth of more ICT intensive industries has

improved over time relative to that of other industries (Engelbrecht and Xayavong,

2006). The authors have found ICT intensity to be higher in sectors such as

publishing/media, machinery and equipment manufacturing, wholesale trade, retail

trade, transport and storage, communication services, finance and insurance, business

services, government, education, health and community services, cultural and

recreational services as well as other personal and home ownership services – a finding

similar to those of Stiroh (2002a).

4.4.2 ICT contribution to TFP growth

In applying the production function to measure factor contribution to growth, some

literature finds that ICT-producing industries contribute to ALP growth through

increased TFP, while ICT-using industries contribute to ALP growth through increased

capital deepening (i.e., an increased capital-labour ratio) (Stiroh, 2002b; Van Ark,

Inklaar and McGuckin, 2003). Studies that measure the contribution of ICT-producing

industries mainly attempt to determine the contribution of ICT industries to TFP

growth. For example, Jorgenson and Stiroh (2000), using the “price dual approach” (by

estimating for moderate and rapid price decline in high-tech investment goods) to

measure productivity at the industry level, found a marked increase in non-ICT TFP

growth in the late 1990s compared to the early 1990s, and that its contribution was

higher than that of ICT capital in the base case.

In earlier works, McGuckin, Stiroh and van Ark (1997) showed that the

computer sector alone accounted for one-third of the TFP growth for the entire US

economy in the 1980s, despite having only less than 3% share of the GDP. Stiroh

(1998), in examining the relationship between computers and economic growth using

US sectoral data from 1947 to 1991, found that the computer-producing (CP) sector

showed strong TFP growth that reflects the fundamental technological progress behind

the computer revolution, and resulting in a large substitution towards computers as an

input for computer-using (CU) sectors. In other words, computers have made a clear

and increasing contribution to economic growth. The contribution of computer capital to

aggregate output growth increased from 0.03% per year for 1947-73 to 0.19% for 1981-

91. The contribution of CP sector to aggregate TFP growth increased from 0.01% to

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0.16% for the same periods (Stiroh, 1998). Many sectors are taking advantage of the

lower price of computer services and substituting towards computers as a production

input.

Further empirical support for the contribution of falling semiconductor prices to

TFP growth, as discussed in Chapter 3, is found in Jorgenson et al. (2003a) (Table 4.13).

In conjunction with the rapid decline of computer prices since the 1970s, as reported by

McGuckin et al. (1997), ICT had become the major contributor to TFP growth in the US

since 1973 (more than 80%), in contrast with earlier periods of the 1950s and 1960s

when it accounted for about 30% or less to TFP growth. In fact, when 2001 was

included in the analysis, Jorgenson et al. (2003) found an overwhelming contribution

from ICT exceeding 100%, as that from other factor inputs was found to be negative.

The large contribution from ICT to TFP growth was attributed to high rates of

technological progress in ICT production, driven by falling prices and the high marginal

product of ICT capital.

Table 4.13 Decomposition of TFP growth in the US, 1959-2001

1959-2001 1959-1973 1973-1995 1995-2000 1995-2001 TFP growth ICT Non-ICT

0.59 0.19 (32.2) 0.40 (67.8)

1.16 0.09 (7.8) 1.07 (92.2)

0.26 0.21 (80.8) 0.05 (19.2)

0.62 0.45 (72.6) 0.17 (27.4)

0.40 0.41 (102.5) -0.01 (-2.5)

Note: Figures in italic parentheses are the shares of TFP growth. Source: Jorgenson, Ho and Stiroh (2003a).

When Jorgenson et al. extended the time period of their analysis to 2003, a

different conclusion was drawn about the decomposition of TFP growth during the early

years of this decade. While the contribution from ICT to TFP growth had exceeded that

of non-ICT since the 1970s, the period of 1995-2003 saw a larger contribution from

non-ICT capital (Table 4.14). The authors however treated this result with caution as the

increase was regarded as ‘transitory and cyclical in nature due to firms expanding their

output, but it was unclear how much of such increase was due to permanent technology

and efficiency gains’ (Jorgenson, Ho and Stiroh, 2004).

101

Table 4.14 Decomposition of TFP growth in the US, 1959-2003

1959-2003 1959-1973 1973-1995 1995-2003 TFP ICT Non-ICT

0.74 0.25 (33.8) 0.49 (66.2)

1.12 0.09 (8.0) 1.03 (92.0)

0.34 0.24 (70.6) 0.10 (29.4)

1.14 0.53 (46.5) 0.61 (53.5)

Note: Figures in italic parentheses are the shares of TFP growth. Source: Jorgenson, Ho and Stiroh (2004).

A few empirical works examined the contribution of ICT to TFP growth in

countries outside the US. The case for acceleration in TFP growth owing to ICT after

1995 was also found in Japan (Table 4.15). For instance, the share of ICT contribution

to TFP growth doubled from 40% during the early 1990s to 80% after 1995. Such

increase was attributed to the more rapid fall in the relative price of computers

compared with the prices of communications equipment and software in Japan

(Jorgenson and Motohashi, 2005).

Table 4.15 Decomposition of TFP growth in Japan, 1975-2003

1975-1990 1990-1995 1995-2003 TFP ICT Non-ICT

1.57 0.23 (14.6) 1.35 (86.0)

0.80 0.32 (40.0) 0.48 (60.0)

0.45 0.36 (80.0) 0.10 (22.2)

Note: Figures in italic parentheses are the shares of TFP growth. Source: Jorgenson and Motohashi (2005).

One of the recent works has compared the contribution of ICT to TFP growth

between US and the European countries (Timmer and van Ark, 2005). The authors used

the ‘Domar weighting system’ which estimates the contribution of ICT industry to

aggregate TFP growth by weighting TFP growth for the industry based on the ratio of

the ICT industry’s output to aggregate GDP in each country. It was found that the

contribution of ICT industry to TFP growth was larger in the US than most countries in

the European Union (EU), except Ireland, Finland and Sweden (Table 4.16), since the

former has a relatively larger ICT-producing industry. For instance, the ICT-producing

industries in the US contributed 0.17 percentage points more than the EU (0.44 vs 0.27)

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to TFP growth. This was attributed to the US having a much larger electronic

components manufacturing industry (which manufactures semiconductors) than the

latter (Timmer and van Ark, 2005).

Within the EU, the three afore-mentioned countries (Ireland – producing mainly

computers; Finland and Sweden – communications equipment) have relatively larger

ICT-producing industries than the rest (Timmer and van Ark, 2005). The evidence for

the contribution of declining semiconductor prices to the acceleration of TFP growth

was further found in Finland (Daveri and Silva, 2004). In this study (which examined

technological spillover between Nokia and other industries in the Finnish economy),

ICT-producing industries contributed about 75% to TFP growth in 1992-1994, against

25% of non-ICT producers. The share of ICT producers increased to 103% in 1995-

2000, as non-ICT producers had a negative contribution to TFP growth. The fact that

productivity gains were concentrated in Nokia as well as few other firms belonging to

the ‘ICT cluster’ led the authors to conclude that the acceleration in TFP growth in

Finland was not due to the spillovers from Nokia, but to ‘the world decline in the price

of semiconductors’ (Daveri and Silva, 2004). Based on the collection of empirical

results gathered so far, it can therefore be deduced that the fall in semiconductor prices

has been highly significant in accelerating TFP growth in the developed countries.

4.5 China-related studies

Among the current literature reviewed to date, the contribution of ICT to Chinese

economic growth is examined only in two studies, namely Lee and Khatri (2003) and

Jorgenson and Vu (2005) (Table 4.17). The former measures the contribution of ICT

capital stock to the growth of GDP and labour productivity in major Asian economies

for the period 1990-1999, while the latter addresses the impact of ICT investment on the

growth of world economy, seven regions and 14 major economies during the period

1989-2003.

103

Table 4.16 Contribution of ICT production to TFP growth in the EU and US, 1995-2001

Contribution of ICT production to aggregate TFP (%) Country

Office, accounting and

computing equipment

Electronic components

Communication equipment

Total

Ireland Finland Sweden

United Kingdom Portugal

France Austria

Germany Italy

Belgium Spain

Netherlands Denmark

Greece

European Union United States

2.310.100.040.260.160.120.220.120.060.190.080.080.030.00

0.130.15

1.130.050.070.070.110.120.020.080.090.010.040.010.040.01

0.080.23

0.18 0.53 0.46 0.05 0.04 0.07 0.01 0.04 0.07 0.00 0.03 0.02 0.03 0.00

0.06 0.06

3.620.690.570.380.310.310.250.240.210.210.150.110.100.01

0.270.44

Source: Timmer and van Ark (2005). Table 4.17 Empirical studies of the contribution of ICT to China’s economic and

labour productivity growth

Contributions Period GDP ICT KN Labour TFP

10.63

0.14 (1.3)

3.23 (30.4)

3.78 (35.6)

3.49 (32.8)

8.76 0.27 (3.1)

3.39 (38.7)

1.34 (15.3)

3.76 (42.9)

Lee and Khatri (2003): 1990-94 1995-99

9.94 0.17

(1.7) 2.12 (21.3)

1.32 (13.3)

6.33 (63.7)

Jorgenson and Vu (2005): 1989-95 1995-03 7.13 0.63

(8.8) 3.17 (44.5)

0.84 (11.8)

2.49 (34.9)

ALP ICT KN Labour TFP 1990-94 6.63 0.10

(1.5) 2.16 (32.6)

1.10 (16.6)

3.27 (49.3)

1995-99

7.41

0.21 (2.8)

2.92 (39.4)

0.65 (8.8)

3.63 (49.0)

Note: See the note to Table 4.1.

Lee and Khatri (2003) showed that the contribution of ICT capital to GDP

growth in China has increased steadily from 1% in 1990-94 to 3% in 1995-99; while the

corresponding contribution to ALP growth increased from 1.5% to almost 3% during

104

the same periods. One of the deficiencies in Lee and Khatri (2003) is that the time frame

under study covered only the 1990s. However, the more recent study of Jorgenson and

Vu (2005) found the contribution of ICT capital to China’s economic growth increasing

from 1.7% during 1989-1995 to 8.8% during 1995-2003. China, being the world’s

largest recipient of foreign investment, is also the most attractive market for ICT

investment in the developing world. The factors contributing to the sharp rise in ICT

investment in the most recent years will be discussed in Chapter 5 of the dissertation.

There is a handful of studies which provide a descriptive account of ICT

development in China. The earliest work could be found in Meng and Li (2002) who

provided some empirical evidence on the development of China’s ICT industry during

the 1990s. Meng and Li argued that in order to boost the development of ICT, China

will have to address the problems of financing its ICT industry (through venture

capital), overcome the problem of brain drain by encouraging the return of overseas

talents and expatriates (through the building of high-tech parks), and deregulate to

increase competition in the sector. More recent accounts that analyse the development

of ICT in China are found in Katsuno (2005) and Jing (2006). In a similar fashion to

Meng and Li (2002), these authors compile a wide array of statistical evidence to

present an overview of the growth of ICT market in China. Katsuno (2005) represents

the first attempt by OECD to compile a complete set of indicators which could be used

for comparing China’s ICT development with other countries. The author concludes that

China is not only developing as an ICT hardware production centre, but also rapidly

emerging as a software development centre. Therefore, while China is turning into one

of the world’s major ICT producer on one hand (but its role is more like a major

assembly line for foreign manufacturers), it is also seen to pursue a balanced

development between both the hardware and software sectors on the other hand. Finally,

in the most recent study of ICT in China, Jing (2006) introduced various definitions of

ICT and used market indicators to compare the level of ICT development in China with

other countries. The latter also looked at the issue of regional disparity in China where

the use of ICT is concerned.

4.6 Conclusion

This chapter reinforces the previous one with findings dealing with the contribution of

ICT capital to economic and labour productivity growth in major economies throughout

105

the world. Based on a review of current literature, this chapter finds that ICT has a

positive impact on productivity and economic growth in developed countries. Such

evidence can be found in studies that look at the firm, industry and national levels.

However, differences occur between the US, Europe and other developed countries with

regards to the contributions from ICT production and use to economic growth. On the

whole, a stronger contribution is found to come from ICT use in the US, Canada,

Australia and New Zealand, whereas ICT production is found to be the primary cause of

productivity growth in Europe, with the exception of UK.

A survey of recent literature finds the contribution of ICT to economic growth to

have increased between the two periods before and after 1995, be it the developed or

developing world. Differences occur between developed and developing countries, and

between individual countries within the developed world as to the magnitude of the

contribution. For instance, in developed countries as well as the Asian NIEs, the

contribution rate of ICT to economic growth tends to exceed 20% in the period after

1995; whereas in other parts of the world, the contribution rate usually falls below 15%.

The literature review of both the previous and this chapter found there is very

little empirical studies on China where the contribution and impact of ICT on

productivity and growth is concerned. A survey of the literature finds that the

contribution of ICT capital to Chinese economic growth has increased from less than

2% before 1995 to almost 9% in the period after. In the subsequent chapters 5 and 6, the

dissertation will focus on empirical exercises which estimate the size of ICT capital

stock and the contribution of ICT capital to economic growth in China. This will allow

us to draw further conclusions about the role of ICT in the economic growth of China.

The main contribution of this chapter to current literature is the attempt to piece

together the empirical findings of recent academic works related to sources of growth

that distinguishes ICT capital from other factor inputs. This would allow readers to

compare the similarities and differences between the findings of different authors and

make some inferences about the actual contribution of ICT as well as other factors to

output and productivity growth.

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Chapter 5

ESTIMATIONS OF ICT CAPITAL STOCK

5.1 Introduction

The literature review in the previous chapters has provided an overview of the role and

contribution of information and communications technology (ICT) capital in the

production process. It is obvious that capital as a factor of production is one of the most

important inputs in growth accounting analysis. In economic theory, the process of

investing in the real productive stock of equipment in the economy is known as capital

formation (Black, 1997). This can be achieved either through construction or purchase

from suppliers. The process of increasing the capital stock as a source of economic

growth is known as capital accumulation. To examine the role of ICT in economic

growth, the starting point is invariably the estimation of ICT capital stock which is not

available in statistical sources.

This chapter serves as the beginning of the empirical exercises in the

dissertation. It aims to employ the perpetual inventory method to estimate the ICT

capital stock series in China for the period of 1983 to 2004. This will involve, first,

estimating the initial value of ICT capital stock in 1983, and second, estimating the

capital stock series assuming certain rates of depreciation throughout the entire period.

The chapter begins with a background overview of the pattern of ICT investment that

has taken place in China since the mid-1980s. The time series for ICT investment in

China is drawn from Chinese statistical sources. By comparing the growth rates of ICT

investment and overall economic growth in China, some inferences can be drawn about

the relationship between ICT and economic growth in the country. The chapter begins

by examining the factors that account for the increase in ICT investment over the past

two decades. This is followed by a discussion of methods used to estimate the ICT

capital stock and a review of empirical studies pertaining to determination of the

depreciation rate of ICT capital. Next, a series of ICT capital stock in China is derived

using the conventional perpetual inventory method. The dissertation attempts to apply

three approaches to estimate alternative sets of ICT capital stock series. The chapter

will finally conclude with a sensitivity analysis of capital stock estimates using different

rates of depreciation.

107

5.2 ICT investment in China

5.2.1 Patterns of ICT investment

Since statistical data for ICT capital stock is not readily available in Chinese statistical

sources, it is derived from investments in ‘telecommunications equipment’ and

‘computer equipment’. However, we can only find data on gross ICT investment in the

Yearbook of China’s Electronics Industry from the year 1984 onwards. Time series data

for ICT investment from 1996-2004 is also available from China Statistical Yearbook of

High Technology Industry. In both sources, the data appear in the form of ‘investment

in capital construction’ and ‘investment in innovation’ for various sectors in the

electronics and high technology industry respectively. 1

For the period of 1984-1992, data for ICT investment appear only in the

category of investment in capital construction. However, data for capital investment

from 1993 onwards includes investment in innovation, thus creating a picture of a surge

in investment after 1992 (Figure 5.1). It is noted that investment in capital construction

has been consistently twice the size of investment in innovation since the early 1990s

(Figure 5.2). 2 However, the ratio of investment in innovation over that in capital

construction has been rising steadily since 1998, from 33% to over 60% in 2002. This

statistical evidence supports empirical findings that China has moved away from

dependence on technology transfer or import during the 1980s (Lu, 2000b). Instead,

domestic companies in China have gradually emphasized on technology acquisition by

building up their ‘innovation capability’ to enhance competitiveness since the mid-

1990s, be it telecommunications or computer (PC manufacturing and software

development) enterprises (Fan, 2006). Another important reason for the surge in

investment may be related to China’s new phase of high-speed economic growth since

the ‘southern tour’ (nanxun) by Deng Xiaoping in the spring of 1992. As shown in

Figure 5.1, the total real investment in ICT has increased by more than 300 times since

1992.

1 Investment in innovation refers to ‘the renewal of fixed assets and technological innovation of the original facilities by the enterprises and institutions as well as the corresponding supplementary projects and related activities covering only projects each with a total investment of 500,000 RMB or more.’ Investment in capital construction refers to ‘the new construction projects and related activities of enterprises, institutions or administrative units for the purpose of expanding production capacity or improving project efficiency covering only projects each with a total investment of 500,000 RMB or more.’ See State Statistical Bureau (2002), China Statistical Yearbook, pp. 243. 2 From 2003 onwards, no data on the breakdown of ICT investment into ‘capital construction’ and ‘innovation’ in the statistical source, i.e. China Statistics Yearbook on High Technology Industry is available.

108

109

Figure 5.3 illustrates the growth of investment in the telecommunications and

computer industries. It indicates a huge jump in telecommunications investment in

2001. Although no direct explanations can be found in official sources so far, several

phenomena may be attributed to the surge. First, there were signs of burgeoning demand

for telecommunications services in that year, when China overtook the US as the

world’s largest mobile phone market, exceeding 120 million subscribers, and its local

telephone switchboard capacity reached 200 million lines. Second, the number of

Internet users doubled from 15 million in 2000 to more than 32 million the following

year (Lu and Wong, 2003). This further contributed to the increased demand for

telecommunications services since access to the Internet requires the use of telephone

lines.

The ratio of ICT investment to total investment has shown an increasing trend

over the past two decades, although it still remained below 1% in 2004 (Figure 5.4). It is

also shown that ICT investment has grown at a higher rate compared with total

investment as well as real output. In addition to the Sixth Five-year Plan Period (1986-

1990) when ICT investment declined by more than 24% on average, and the Seventh

FYP (1991-95) when its growth rate shot over 100%, the growth of ICT investment has

been three to four times that of real GDP during the past ten years (Table 5.1). Such a

trend suggests that the importance of ICT investment to China’s economic growth can

only increase in the future.

Table 5.1 Growth indicators, 1986-2004 (%) Five-year Plan Period (FYP)

ICT investment Non-ICT investment

Total investment

GDP

7th (1986-90) 8th (1991-95) 9th (1996-00) 10th (2001-04)

-46.3

88.5

16.8

16.1

1.3

17.0

8.7

15.3

1.3

17.0

8.7

15.3

4.5

11.5

8.0

10.5

Sources: State Statistical Bureau, Yearbook of China’s Electronics Industry, China Statistics Yearbook on High Technology Industry and China Statistical Yearbook (various issues).

Figure 5.1 Real ICT investment in China, 1984-2004

0

2000

4000

6000

8000

10000

12000

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Inve

stm

ent (

mill

ion

yuan

)

Source: State Statistical Bureau, Yearbook of China’s Electronics Industry and China Statistics Yearbook on High Technology Industry (various issues).

110

0

1000

2000

3000

4000

5000

6000

Inve

stm

ent (

mill

ion

yuan

)

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Figure 5.2 Breakdown of ICT investment in China, 1984-2002

Capital construction Innovation

Note: The breakdown of ICT investment by ‘capital construction’ and ‘innovation’ is not available in statistical sources beyond 2002.


111

0

1000

2000

3000

4000

5000

6000

7000

Inve

stm

ent (

mill

ion

yuan

)

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Figure 5.3 Investment in telecommunications and computer industries, 1984-2004

Telecommunications Computer


112

Figure 5.4 Ratio of ICT investment to total fixed investment in China, 1984-2004

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

%


113

5.2.2 Explaining the growth of ICT investment

This section seeks to account for the factors explaining the rapid growth of ICT

investment in China over the past two decades. The answer could be found in the

channels through which investment in ICT flowed into China. During the early stages

of ICT development in China, this could be largely attributed to the transfer of foreign

technology which led to inflow of ICT capital. After the founding of the PRC in 1949,

China had relied primarily on technology transfer and imports for acquisition of foreign

technology as a major channel of introducing ICT capital and expertise into the country

(Zhao, 1995). Between 1978 and 1980, China reached training agreements with

multinational companies from the US, Britain, France, Germany, Italy and Japan with

the aim of expanding its skilled labour pool of computer personnel involved in R&D,

maintenance and manufacture. On the other hand, China also sought to increase

technology import by entering into foreign joint venture and technology cooperation

agreements with foreign governments of Australia, Belgium, Britain, Denmark, Finland,

France, Greece, Italy, Japan, Luxembourg, Norway, Sweden, US and West Germany

since 1978 (Zhao, 1995).

Japan had been the leading country in technology transfer to China up to the

early 1990s. It was shown that Japanese contractual investments dropped sharply from

1988 to 1991, before rising again by five times in 1992, which accounted for the

negative growth rate of ICT investment during the late 1980s (Tang, 1997). Xu (1997)

identified three phases in China’s import of technologies since the 1980s. There was a

stagnation of technology import during the period 1988-91, followed by a surge in

imports from 1992 onwards. The period of stagnation in the late 1980s is illustrated in

this chapter’s findings on ICT investment which decreased by 7% annually on average

during 1987-1991. This was due to “the implementation of a contractionary

macroeconomic policy which saw a drop in the number of technology import contracts

from 437 in 1988 to 232 in 1990” (Xu, 1997: 86).

China’s investment in ICT increased twenty times in 1992. This phenomenon

occurred in parallel to China’s new phase of high-speed economic growth since the

“southern tour” (nanxun) by Deng Xiaoping in the spring of 1992. In 1992, the ICT

industry was listed as a pillar industry, and ICT products recognized as the new drivers

for economic growth (Dong, 2004). The establishment of high technology parks and

R&D centers in the early 1990s is also a major factor that explains the huge increase in

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ICT investment. For instance, total investment in science and technology for the Torch

Plan, in which a key task was to establish high-technology development zones,

increased by more than 40 times from 100 million yuan at its inception in 1988 to 4.4

billion yuan in 1992 (Segal, 2003).

By 1993, there were fifty-two designated high technology development zones

(HTDZs) which could enjoy preferential treatment granted by the Provisional

Stipulations about Some Policy Measures on State High-tech Industrial Development

Zones and the Stipulations about Tax Policies on State High-tech Industrial

Development Zones, which were approved by the State Council on 6 March 1991, and

subsequently promulgated as the State Basic Policy for High-Tech Industrial

Development Zones in 1992 which covered taxation, finance, imports and exports,

pricing and personnel policy (Wall and Yin, 1997; Segal, 2003). Those preferential

policies that favored investment include the reduction in the income tax rate for high-

tech enterprises by 15%, and 10% for those enterprises whose share of exports in total

output exceeds 70%; a two-year tax holiday for newly established high-tech enterprises

in the HTDZs; provision of bank loans to high-tech enterprises located within the zones;

exemption from export taxes or import tariffs and license, etc. (Segal, 2003).

During 1993-2001, China’s ICT investment grew by an average of almost 33%

annually. Xu (1997) cited two main reasons for the enormous rise in technology imports

since 1992. The first is attributed to inward FDI which became ‘a strong force in the

development of China’s economy which created a new channel for the import of

technology’. The second factor is the increasing role given to private enterprises which

now had greater autonomy to import technology, whereas the main task of government

was simply ‘to set macro targets for technology imports but not to seek micro-control of

the structure and content of such imports’ (Xu, 1997: 88-9).

As explained earlier, the share of innovation out of the ICT investment in China

has been steadily increasing since the late 1990s. The innovative achievements of

China’s indigenous enterprises such as Lenovo and Stone Group Corporation have been

examined in Chapter 2. The effect of innovation on a firm’s output is explained by

Oulton and Srinivasan (2005). Investment in innovation (which may include consultant

fees, management salary or expenditure on the retraining of workers) often incurs total

costs that could be several times the amount spent on equipment and software.

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However, incurring such costs would actually enable firms to acquire the capability to

absorb new technology in the future, therefore creating a stock that ‘yields future

benefits’ (Oulton and Srinivasan, 2005). However, it has been cautioned that putting

more investment in innovation should be complemented with a corresponding

improvement in the quality of human capital owing to the complexity of the learning

process, as the country would have to face the problem of standardization following the

technological improvements in ICT capital (Atzeni and Carboni, 2006). The

effectiveness of such investment also depends on the pace of firms’ adaptation to the

new technology. Therefore, with the current structure of ICT investment in China,

whereby investment in capital construction and innovation make up approximately 62%

and 38% of total ICT investment in 2002 respectively, it is important that China

maintains a balanced approach to investment over the next few years, although a rising

share of innovation is expected.

Lastly, with the blessings of geography and culture, China has become the

biggest beneficiary of being the closest neighbour to one of the world’s major producers

of ICT – i.e. Taiwan.3 Taiwan has become the largest source of FDI in ICT investment

for China.4 According to study carried out by Rand Corporation which examines the

political and security impact of cross-strait transfer of ICT production, the rapid growth

of the ICT industry in China is attributed mainly to the transfer of technology and

capital from Taiwan. 5 In fact, more than 70% of the computer hardware produced in

China came from factories funded by Taiwanese companies (Hu and Chan, 2004). In a

survey of the top 100 Taiwanese ICT companies located in China, over 85% have set up

R&D centres in China between 1999 and 2004 (Hsu, 2006).

Taiwanese ICT investment, consisting of desktop PCs, monitors, motherboards,

keyboards, cables and other components, began flowing into mainland China in the

early 1990s due to rising costs and labour shortage in Taiwan, concentrating mainly in

the Yangtze River Delta regions of Shanghai and Suzhou (Dedrick, Kraemer and Ren,

2004).6 After the mid-1990s, with further opening up and rapid growth of the Chinese

3 Taiwanese companies accounted for 60% of the world’s notebook PC production in 2003 (Dedrick, Kraemer and Ren, 2004). 4 According to the Ministry of Foreign Trade and Economic Cooperation, between 1979 and 1998, over 70% of the total FDI in China came from Hong Kong, Taiwan and Singapore (Hsu, 2006). 5 “Taiwan’s IT exports to China not hurting US”, The Straits Times (6 August 2004, Singapore). 6 Taiwanese investment in China took the following pattern: First, it started in the late 1980s with investment mainly in labour-intensive, traditional sectors such as garments, food-processing and downstream production chain of the petrochemical industry. Second, in the mid-1990s, investment was

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economy, the mainland became the ‘green pasture’ for Taiwanese investors seeking

greater wealth and prosperity. While low cost was still the main pull factor, the

availability of skilled labour in mainland China had become one of the most critical

factors since the late 1990s (Hsu, 2006). One survey of 56 Taiwanese ICT companies

revealed that accessing the local human resources in China has become the main motive

of extending R&D activities over there, which is ‘abundant and cost-effective with

benefits of linguistic and geographical proximities’ (Lu and Liu, 2004).

In the meantime, the mainland Chinese government implemented policies to

attract more ICT investment from Taiwan.7 Taiwanese manufacturers have also been

lured to shift their production to mainland China by the latter’s high technology

industrial parks, despite the ‘go slow, no haste’ policy (Jieji Yongren in Chinese)

adopted by former Taiwanese President Lee Teng-Hui in August 1996 which restricted

the production of high value-added products such as notebook PCs in China (Hu and

Chan, 2004). However, Taiwan’s notebook manufacturers were able to circumvent the

ruling by ‘making components and base units in China, and then shipping them abroad

for final assembly’ (Kraemer and Dedrick, 2002b). In fact, they are now investing in

mainland facilities to produce complete notebooks (Kraemer and Dedrick, 2002b).

In a move to attract more investment from Taiwan, the Xi’an HTDZ (which

currently houses 66 Taiwanese companies) planned to construct a technology industrial

base for Taiwanese enterprises, covering telecommunications, software and

bioengineering in 2005. It will include a central plant, ICT and integrated circuit

manufacturing center as well as an international exhibition, information and logistics

center. In addition, it will also build community facilities for the Taiwanese that include

five-star hotels, a hospital, a kindergarten and an international school.8

Taiwan has also played an important role in China’s transition from being

merely a ‘production factory’ to an ‘innovation centre’. Taiwanese investors have

spearheaded by upstream firms trying to skirt round the restrictions imposed by their government’s ‘go slow, no haste’ policy. The third round of investment, which began in the late 1990s, were led by firms in the ICT sector producing computer peripherals, assembly parts, programming and finally the semiconductor (Chang and Cheng, 2002). 7 For instance, local governments established closer relations with Taiwanese investors by allowing the latter to set up the ‘Taiwanese investor associations’ (TIAs) in major host cities such as Shanghai, Suzhou, Kunshan and Dongguan (Hsu, 2006). 8 “Xi’an to build technology industrial base for Taiwan firms”, SinoCast China Business News (London: July 19, 2005).

117

brought to mainland China not only their capital and new standards in technology, but

also the entrepreneurial spirit that is embedded in Taiwanese companies and that is

beneficial to young and highly-educated Chinese engineers (Lu and Liu, 2004). Indeed,

it was such continuous and massive inflow of investments that resulted in mainland

China replacing Taiwan as the world’s third largest producer of computer hardware in

2000, and eventually surpassing Japan to assume second place in 2002 (Dedrick,

Kraemer and Ren, 2004).

5.3 Estimation of capital stock

Capital, being a factor of production of the economy as well as a measure of the wealth

of a nation, has attracted a great deal of attention in economics literature. Capital stock

is defined to consist of ‘all assets which are durable (with a service life longer than a

year), reproducible and tangible’ (Bohm et al., 2002). For instance, the average service

life of computer equipment is determined to be five years in France (Brilhault, 2000).

Capital stock excludes assets such as land and natural resources which are not

reproducible and inventory stocks which are not durable (Pyo, 1988). The estimation of

aggregate (or gross) capital stock is useful for determining its contribution to economic

growth. Data on capital stock, however, are seldom available in official statistical

sources, and usually estimated using data of gross investment. Investment figures are

normally first deflated using a price index.

5.3.1 A theoretical model

One of the most commonly used methods of estimating the capital stock is the perpetual

inventory method, which ‘requires a time series of deflated values of capital investment

data, obtained by dividing the current value of investment by a capital goods price index

in order to adjust for changes in the purchasing power of investment dollars’ (Anderson

and Rigby, 1989). The perpetual inventory method has been applied in various studies

of estimating capital stock using investment data from the national accounts in the US

(Jorgenson, 1989), Australia (Levtchenkova and Petchey, 2000; Diewert and Lawrence,

2005), European Union (Timmer and van Ark, 2005), France (Brilhault, 2000), Japan

(Shinjo and Zhang, 2003; Miyagawa, Ito and Harada, 2004; Jorgenson and Motohashi,

2005), Russia (Hall and Basdevant, 2003), Spain (Mas, Perez and Uriel, 2000), and

other selected developed economies (Abadir and Talmain, 2001).

118

In estimating the capital stock, the perpetual inventory method takes into

account the depreciation rate, δ, which is defined as ‘the rate of decline in value of

capital asset due to wear and tear, obsolescence, accidental damage and aging’

(Fraumeni, 1997). Assuming a constant rate of depreciation, i.e. ‘an equal proportion of

the services of capital is withdrawn in each of the λ periods following its installation’

(Anderson and Rigby, 1989), the capital stock is estimated according to the following

equation, which is commonly used by various authors (for example, Anderson and

Rigby, 1989; Nadiri and Prucha, 1996; Shinjo and Zhang, 2003; Wu, 2004; Diewert and

Lawrence, 2005; Timmer and van Ark, 2005):

Kt = It + (1–δ) Kt-1 (5.1)

where the capital stock, Kt, at year t is dependent on the level of capital investment, It in

the same year and capital stock level in the preceding year which is deflated by the rate

of depreciation, δ. Similarly, Nadiri and Prucha (1996) used the same method to

measure the R&D capital stock in the US manufacturing sector.

5.3.2 Depreciation of ICT capital

As pointed out by Nadiri and Prucha (1996), and based on a review of recent empirical

literature, few studies have been carried out to measure the depreciation rate of ICT

capital stock. Most studies covering the contribution of ICT capital to economic or

productivity growth do not provide an estimate of the depreciation rate for the

respective country or countries. Following Pyo (1998), Kim (2002) used 13.1% as the

depreciation rate of Korean IT capital for the period of 1970-77, and 14.2% for 1977-

2000.

The measurement of depreciation requires data on the price and quantity of

investment goods, with the price of acquiring capital goods being given by the unit cost

of acquisition which depends on its characteristics as well as its age (Jorgenson, 2000).

Essentially, depreciation depends on the cost as well as usage life of the asset. For

example, the unit cost of using a computer for a specified period of time determines its

rental price (Jorgenson, 2000). The measurement of depreciation for ICT capital was

explored in Fraumeni (1997) for US computing equipment during the periods before

and after 1978, and in Doms et al. (2004) for US personal computers. Fraumeni (1997)

used the following formula to derive the depreciation rate, δ,

119

TR

=δ (5.2)

where T is the average asset service life and R is the estimated declining-balance rate.

Doms et al (2004) attempted to derive the depreciation rate of personal computers based

on the price of computers specified by a set of embodied characteristics including the

speed of the processor, the size of the hard drive and the amount of memory, etc. One

problem of estimating δ with the perpetual inventory method, however, is that the

specification of the service lifetime of the capital good is usually not empirically

observed (Anderson and Rigby, 1989), as in the case of China where the time series

data is not available.

Nadiri and Prucha (1996) estimated the depreciation rates of both physical and

R&D capital stocks for the US manufacturing sector. Their results show that physical

capital and R&D capital depreciate at the rates of 6% and 12%, respectively. The

authors employed the method of Pakes and Schankerman (1986) who stated that ‘the

conceptually appropriate rate of depreciation of knowledge’ (i.e. the proxy definition of

R&D capital) is ‘the rate at which the appropriate revenues decline, which arises not

from any decay in productivity of knowledge, but due to inability to appropriate the

benefits from the innovations and the obsolescence of original innovations by new

ones’. In other words, the depreciation rate of ICT capital depends very much on

‘investments in innovation’ and the obsolescence of ICT equipment. Pakes and

Schankerman (1986) reported a depreciation rate of 26% for R&D capital in the UK in

the 1950s and 17% in the 1960s and 1970s, and about 12% for France and Germany.

Therefore, it can be assumed that the depreciation rate of ICT (or R&D) capital stock

falls within the range of 10-30% (Table 5.2).

5.3.3 Measurement of China’s ICT capital stock

The estimation of China’s capital stock during the pre-reform era was reported in Chow

(1993) for the period of 1952-85. He estimated China’s capital stock series for five

sectors, namely, agriculture, industry, construction, transportation and commerce. Wu

(2004) also derived a comprehensive database for China’s total capital stock series for

1953-2000. Wu (2004) adopted three approaches to the estimation of China’s capital

stock, namely, the initial value, backcasting and integral approaches. As no study has

120

ever estimated the ICT capital stock of China, the dissertation attempts to do so using

these three approaches.

Table 5.2 Depreciation rate of ICT equipment Author(s) Category of equipment Depreciation rate (%) Jorgenson (1989) Nadiri and Prucha (1996) Fraumeni (1997) Kim (2002) Miyagawa, Ito and Harada (2004) Oulton and Srinivasan (2005)

Office, computing and accounting machinery - 1977 Communications equipment - 1977 R&D capital stock Office, computing and accounting machinery - Before 1978 - 1978 and later Communications equipment - Business services - Other industries ICT capital (Korea) - 1970-77 - 1977-00 ICT capital (Japan) Office, computing and accounting machinery - Before 1978 - 1978 and later Communications equipment - Business services - Other industries ICT capital (UK) - 1992-2000 Computers Software Communications equipment

27 12 12 27 31 15 11 13 14 27 31 15 11 31.5 40 11

The first approach, initial value approach, assumes that the only unknown

variable is the initial value of capital stock. Wu (2004) adopted the initial value

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approach by estimating the value of China’s total capital stock in 1952, which is the

unknown variable. To calculate the initial value, one needs to look at the relationship

between investment and capital stock since the function of investment is to replace

depreciating capital, and create new capital to maintain economic growth (Harberger,

1978). Therefore the relationship between investment and capital stock can be expressed

in the following form:

It = (δ + γ) Kt-1 (5.3)

where investment in the current year, It, is dependent on the value of depreciated capital

stock in the previous year, Kt-1, given an assumed depreciation rate, δ. Assuming that

capital stock has been growing at the same rate as output (i.e. GDP), γ is therefore taken

to be the average growth rate of GDP over a period of time.

By re-expressing Equation (5.3), the value of capital stock in the initial year can

thus be written as follows:

γδ += 1

0IK (5.4)

where K0 is the value of ICT capital stock in the initial year, which is determined by I1, the level of ICT investment in the first year of the series that is available from the

statistical source; δ, the depreciation rate for ICT capital, and γ, the average growth rate

of real GDP. This method has been applied for calculating the initial value of ICT

capital stock in the US (Shinjo and Zhang, 2003), Japan (Miyagawa et al., 2004) and the

Central American countries (Reinsdorf and Cover, 2005). Nadiri and Prucha (1996) also

applied the same formula for calculating the initial value of the US’ R&D capital stock

by using the growth rate of total capital stock reported in Musgrave (1992) and an

arbitrary depreciation rate of 10%.

Owing to the time series data available for this exercise, the initial year is taken

to be 1983. In the absence of data on the price of acquisition of capital goods, the choice

of the depreciation rate for China’s ICT capital is based on those used by other authors.

The depreciation rate used for the period after 1983 is based on Kim (2002) who

assumed a rate of 14% for Korean ICT capital for the period of 1977-2000. The

122

dissertation thus adopts a depreciation rate of 15% for China’s ICT capital stock from

1983 to 2004. To determine the value of γ, the growth rate of real GDP during the three

years before the initial year, i.e. 1981-1983 is taken from Wu (2004), which is

equivalent to about 10%. Thus, to calculate the initial value of ICT capital stock in

1983, it is assumed that δ = 0.15 and γ = 0.10.

As no data on an ICT price index is available in Chinese statistical sources,

unlike that of the US in which the hedonic price index is available from the website of

Bureau of Economic Analysis (BEA) of the U.S. Department of Commerce, ICT

investment is deflated by the fixed asset investment price index at constant prices

(1991=1) obtained from China Statistical Yearbook. The real ICT investment and

capital stock data derived using this approach is presented in the Appendix to this

chapter.9 Using the formula in equation (5.4), the initial value of ICT capital stock in

1983 is estimated to be 887 million yuan.

The second approach attempts to estimate the data series for incremental value

of capital stock by backcasting to a much earlier period, 1950, assuming ICT investment

increases at a constant rate. The equation (5.1) is then expanded to the following form,

used in Wu (2004):

1950,19501951

0, )1()1( it

ktt k

ti KIrK −−

−−+−= ∑ δ (5.5)

where a capital stock series can be derived given the value of capital stock in 1950 and a

given depreciation rate. ICT investment is first backcast to 1951, assuming that it had

been growing at a constant rate (r) of 19% since that year till the early 1980s. Next, in

order to derive the value of ICT capital stock in 1950, a few assumptions are made. t is

assumed that the ICT equipment used in the 1980s is similar to those of the US and

Japan in the 1970s. It was also noted that in 1987, China was still ‘ten to fifteen years

behind the world leaders in almost all aspects of the computer spectrum except for the

area of Chinese I/O’ (Witzell and Smith, 1989). Based on the empirical information

given in Table 5.2, by comparing the depreciation rates for office, computing and

accounting machinery in the US and Japan corresponding to the period before and after

1978, used by Fraumeni (1997) and Miyagawa et al (2004), it can be assumed that the

9 An alternative set of ICT investment and capital stock data are derived from deflating total investment by the US’ ICT hedonic price index, and presented in the Appendix to this chapter.

123

depreciation rate before the 1980s is lower than that of the period after. Therefore, since

the depreciation rate for the period after 1983 is already assumed to be δ = 0.15, we

assume the depreciation rate for the period of 1950-1983 to be lower, at δ = 0.10. Using

this approach, the initial value of capital is estimated to be 5 million yuan in 1950, and

734 million yuan in 1983.

The third approach, integral approach, assumes capital stock in the first period to

be the sum of all past investments, as used in Wu (2004). By using the investment data

of 1951, and assuming a constant growth rate of r, the value of ICT capital stock in the

initial year (1983) can be expressed as follows:

(5.6) ∑=

+⋅=31

019511983 )1(

t

trIK

where the value of ICT investment in 1951 was estimated to be 0.75 million yuan, and r

is assumed to be 19%. This approach yields the highest initial value of ICT capital stock

at 984 million yuan in 1983.

A comparison of the ICT capital stock series obtained from the three approaches

is illustrated in Figure 5.5. To establish some relationships between ICT capital and the

economy, the capital stock series based on the initial value approach is used as the

benchmark. One reason is that the initial value estimated from this approach lies in

between those obtained from the other two approaches. Furthermore, this approach has

been used by many authors for deriving the initial value of capital in their empirical

work. One aspect of the relationship is illustrated by the ratio of ICT capital stock to the

total capital stock and real GDP (at constant prices) in China, which is still immensely

low even in the recent years, averaging below one per cent. Yet it has been steadily

increasing over the past two decades, the respective ratios rising from 0.03% during the

early 1980s to 0.25% twenty years later, and 0.04% to almost 0.9% during the same

period (Figure 5.6).

5.4 Estimation and sensitivity analysis

Figure 5.5 illustrates the phenomenal increase in the ICT capital stock estimated using

the three approaches outlined earlier. Although the initial values of capital are different,

124

the values of capital stock have gradually converged since the 1990s. ICT capital stock

has grown by almost thirty times since 1992, for instance, from about 1.5 billion yuan in

1992 to almost 40 billion yuan in 2004 – using the initial value approach (Table A5.2 in

the appendix). The ratio of ICT capital stock to total capital stock and GDP has also

consistently increased over the past two decades (Figure 5.6). The ratio of ICT capital

stock to GDP has risen from 0.3% in 1995 to 1.2% within a span of ten years in 2004.

However, this figure is still below that of the OECD countries which had a ratio of 4.7%

for ICT capital to GDP in 1999 (OECD, 2001). The relationship between capital stock

and output will be examined in the next chapter which calculates the contribution of

ICT capital and other factor inputs to output growth of China. Another phenomenon to

look at is that the growth of ICT capital has been consistently above that of real output

in China over the past two decades (Figure 5.7). The growth rate of ICT capital stock

shot up by over 200% and 100% in 1992 and 1993 respectively. This is a direct

consequence of a fifty-five fold increase in ICT investment over 1991, as shown in the

statistical sources (Table A5.1 in the appendix). Other explanations were discussed in

the preceding section.

The robustness of the estimation results can be examined with a sensitivity

analysis by assuming different rates of depreciation for the ICT capital stock. From

Table 5.2, it is shown that current literature estimated the depreciation rate of ICT

capital ranging between 10% and 30%. The dissertation conducts a sensitivity analysis

for all three approaches by adopting depreciation rates of δ = 0.1, 0.15, 0.2, 0.25 and 0.3

for the period after 1983.

The various scenarios for estimation of ICT capital stock series using different

rates of depreciation are illustrated in Table A5.2 of the appendix to this chapter, where

the following observations are noted. First, the size of capital stock is inversely related

to the depreciation rate regardless of the estimation method used. Next, although the

initial value of the capital stock in 1983 is different for each estimation method, the

value of capital stock gradually converges towards the 21st century. For instance, the

value of ICT capital stock is estimated to reach about 28 billion yuan in 2004 at δ =

0.30; 31 billion yuan at δ = 0.25; 35 billion yuan at δ = 0.20; 40 billion yuan at δ = 0.15;

and 46 billion yuan at δ = 0.10.

125

126

An alternative set of ICT investment and capital stock series is also derived

using the ICT hedonic price index reported by the Bureau of Economic Analysis (BEA),

as no data is available for an ICT price index in Chinese statistical sources (Table A5.4

of the appendix to this chapter).10 The hedonic price index for ICT is used in US and

Japanese statistical sources (Shinjo and Zhang, 2003). The use of US hedonic price

index for ICT as an appropriate proxy measure of price changes of ICT assets in other

countries is supported by Timmer and van Ark (2005). As the ICT hedonic price index

uses 2000 as the base year, all other variables, i.e. GDP and non-ICT investment are

deflated by the consumer price index and fixed asset investment price index respectively

converted to the same base year (Table A5.5 of the appendix to this chapter). In this

case, since investment at the initial years was deflated by a higher price index,

considerably lower initial values of ICT capital stock in 1983 are obtained, i.e. 9, 12 and

22 million yuan respectively, according to the three approaches. Whichever set of ICT

database is more appropriate for the empirical exercises of the dissertation will be

determined in the following chapter which examines the contribution of factor inputs

(including ICT) on output growth.

5.5 Conclusion

China’s ICT capital stock intensity (i.e. capital-output ratio) today is comparatively

much lower than OECD countries. The size of China’s ICT capital stock remains

miniscule – less than 1% of the total capital stock.11 Nevertheless, its increasing ratio to

total capital stock as well as GDP reflects the role of ICT becoming a significant driver

of growth in China’s new economy. China has experienced a dramatic transformation as

far as the accumulation of ICT capital is concerned. From being a nation that relied

primarily on technology transfer and import from technologically advanced countries

since the founding of the PRC, China is now building up ICT capital through attracting

FDI by investing in high-tech infrastructure as well as cultivating homegrown

innovative enterprises.

10 Bureau of Economic Analysis (BEA), “National Economic Accounts”, in

http://bea.gov/bea/dn/nipaweb/index.asp. 11 By comparison, the ratio of ICT capital to total capital stock is much higher in Japan, ranging from 10% to 25% in several industries (Miyagawa et al., 2004).

0

5000

10000

15000

20000

25000

30000

35000

40000

Mill

ion

yuan

1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Figure 5.5 ICT capital stock in China, 1983-2004

Initial value approach Backcasting approach Integral approach

Source: Estimates of this study.

127

Figure 5.6 Ratio of ICT capital stock to total capital stock and output in China, 1983-2004

0

0.2

0.4

0.6

0.8

1

1.2

1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

%

ICT-total capital ICT-GDP


128

Figure 5.7 Growth rate of ICT capital stock and real GDP in China, 1993-2004

0

10

20

30

40

50

60

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

%

ICT GDP

129


This chapter provides the basis for subsequent empirical exercises by estimating

the value of ICT capital stock in China. Using the perpetual inventory method, the ICT

capital stock is derived from deflating the ICT investment figures with the fixed assets

investment price index and using an arbitrary depreciation rate for ICT capital

determined from the literature reviewed, based on three distinct approaches. The initial

values of China’s ICT capital stock – set at 1983, are estimated to be 887, 734 and 984

billion yuan respectively. Some limitations of this exercise lie in the lack of official data

on the price index of ICT capital and other characteristics that determine the

depreciation rate of ICT equipment, such as the unit price of ICT goods and service life

of assets. However, the robustness of the exercise here is proven from the fact that ICT

capital stock converges to similar values using different approaches with different initial

values.

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APPENDIX TO CHAPTER 5 Table A5.1 Real ICT investment in China, 1984-2004 (using CPI) Year Telecommunications Computer Total1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

122.86130.39102.05100.5057.4613.1710.878.60

712.30523.87615.46

1441.082227.73956.76

2591.672813.163053.506230.774303.184503.224611.79

97.95 104.88 85.75 26.46 56.70 20.38 12.34 8.00

215.22 723.52 356.23 495.64 475.76 983.92 761.67

2199.11 1443.46 2378.57 3605.26 5880.36 6596.04

220.81235.27187.81129.96114.1533.5523.2116.60

927.521247.40971.69

1936.722703.491940.694353.335012.274496.978609.347908.44

10383.5811207.83

Unit: million yuan.

131

Table A5.2 ICT capital stock series in China, 1983-2004 (using CPI)

Rates of depreciation Year 0.10 0.15 0.20 0.25 0.30 Initial value approach 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

886.80 1018.93 1152.31 1224.89 1232.35 1223.27 1134.50 1004.26

956.43 1788.31 2856.88 3542.88 5125.31 7316.27 8525.33

12026.13 15835.79 18749.18 25483.60 30843.68 38142.90 45536.43

886.80 974.59

1063.68 1091.93 1058.10 1013.54

895.06 784.01 683.01

1508.08 2529.26 3121.56 4590.04 6605.03 7554.97

10775.05 14171.07 16542.37 22670.36 27178.25 33485.09 39670.15

886.80 930.25 979.48 971.39 907.06 839.81 705.40 587.53 486.62

1316.82 2300.85 2812.37 4186.61 6052.79 6782.92 9779.67

12836.00 14765.77 20421.96 24246.01 29780.39 35032.14

886.80 885.91 899.71 862.59 776.90 696.83 556.17 440.34 346.86

1187.66 2138.14 2575.29 3868.19 5604.64 6144.16 8961.46

11733.36 13296.99 18582.08 21845.00 26767.34 31283.33

886.80 841.57 824.37 764.87 665.36 579.91 439.49 330.85 248.20

1101.26 2018.28 2384.48 3605.86 5227.59 5600.00 8273.34

10803.60 12059.49 17050.98 19844.13 24274.48 28199.96

Backcasting approach 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

734.42 881.80

1028.89 1113.81 1132.38 1133.30 1053.52

971.38 890.84

1729.28 2803.75 3495.06 5082.27 7277.54 8490.47

11994.76 15807.55 18723.76 25460.73 30823.10 38124.37 45519.76

734.42 845.07 953.59 998.35 978.56 945.93 837.59 735.16 641.49

1472.79 2499.26 3096.06 4568.37 6586.61 7539.31

10761.74 14159.75 16532.76 22662.18 27171.30 33479.19 39665.13

734.42 808.35 881.95 893.37 844.65 789.88 665.45 555.57 461.06

1296.37 2284.49 2799.28 4176.14 6044.41 6776.21 9774.30

12831.71 14762.34 20419.21 24243.81 29778.63 35030.73

734.42 771.63 814.00 798.30 728.68 660.67 529.05 420.00 331.60

1176.22 2129.56 2568.86 3863.36 5601.02 6141.45 8959.42

11731.84 13295.84 18581.22 21844.36 26766.85 31282.97

734.42 734.91 749.71 712.60 628.78 554.30 421.56 318.31 239.41

1095.11 2013.97 2381.47 3603.75 5226.12 5598.97 8272.61

10803.10 12059.14 17050.74 19843.96 24274.35 28199.87

132

Integral approach 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

984.47 1106.84 1231.42 1296.09 1296.43 1280.95 1186.40 1090.98

998.48 1826.15 2890.93 3573.53 5152.89 7341.10 8547.67

12046.24 15853.89 18765.46 25498.26 30856.88 38154.77 45547.12

984.47 1057.61 1134.24 1151.91 1109.08 1056.87

931.89 815.32 709.62

1530.70 2548.49 3137.91 4603.94 6616.84 7565.00

10783.59 14178.32 16548.54 22675.60 27182.70 33488.88 39673.37

984.47 1008.39 1041.98 1021.39

947.07 871.81 731.00 608.01 503.01

1329.93 2311.34 2820.76 4193.32 6058.15 6787.21 9783.10

12838.75 14767.97 20423.71 24247.41 29781.52 35033.04

984.47 959.17 954.65 903.79 807.80 720.00 573.55 453.38 356.63

1195.00 2143.64 2579.42 3871.28 5606.96 6145.91 8962.76

11734.34 13297.72 18582.63 21845.42 26767.65 31283.56

984.47 909.94 872.23 798.37 688.81 596.32 450.98 338.90 253.83

1105.20 2021.04 2386.41 3607.21 5228.54 5600.67 8273.80

10803.93 12059.72 17051.14 19844.24 24274.55 28200.01

Unit: million yuan.

133

Table A5.3 Real ICT investment in China, 1984-2004 (using hedonic price indices)

Telecommunications Computer Total

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

2.803.713.584.413.280.960.930.92

105.13119.52177.56552.34

1239.26746.54

2811.934151.445536.00

14372.6212302.5115544.4418298.39

2.23 2.98 3.01 1.29 3.23 1.49 1.06 0.85

31.77 165.07 102.77 189.97 264.66 767.73

1911.39 3245.27 2617.00 5486.69

10307.21 20298.15 26171.37

5.036.696.585.706.512.452.001.77

136.90284.59280.33742.31

1503.921514.274723.337396.718153.00

19859.3222609.7235842.5944469.76

Unit: million yuan.

134

Table A5.4 Alternative ICT capital stock series in China, 1983-2004 Rates of depreciation Year 0.10 0.15 0.20 0.25 0.30 Initial value approach 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

9.08 13.21 18.58 23.30 26.68 30.52 29.92 28.92 27.80

161.91 430.31 667.62

1343.17 2712.77 3955.77 8283.52

14851.87 21519.69 39227.03 57914.05 87965.24

123638.50

9.08 12.75 17.53 21.49 23.97 26.88 25.30 23.50 21.74

155.38 416.66 634.50

1281.64 2593.31 3718.59 7884.13

14098.22 20136.48 36975.33 54038.74 81775.53

113979.00

9.08 12.30 16.53 19.81 21.55 23.75 21.45 19.16 17.09

150.57 405.05 604.37

1225.81 2484.57 3501.93 7524.87

13416.60 18886.28 34968.34 50584.39 76310.11

105517.80

9.08 11.84 15.58 18.27 19.40 21.06 18.25 15.68 13.53

147.04 394.87 576.49

1174.68 2384.93 3302.97 7200.56

12797.12 17750.84 33172.45 47489.05 71459.38 98064.30

9.08 11.39 14.67 16.85 17.50 18.76 15.58 12.90 10.80

144.46 385.71 550.33

1127.55 2293.20 3119.52 6906.99

12231.60 16715.12 31559.90 44701.65 67133.75 91463.38

Backcasting approach 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

12.10 15.93 21.03 25.51 28.66 32.30 31.52 30.37 29.10

163.08 431.37 668.56

1344.02 2713.54 3956.46 8284.14

14852.43 21520.19 39227.49 57914.46 87965.60

123638.80

12.10 15.32 19.71 23.34 25.55 28.22 26.44 24.47 22.57

156.08 417.26 635.00

1282.07 2593.68 3718.90 7884.39

14098.44 20136.67 36975.49 54038.88 81775.64

113979.10

12.10 14.72 18.46 21.36 22.79 24.74 22.24 19.79 17.60

150.98 405.37 604.63

1226.02 2484.74 3502.06 7524.98

13416.69 18886.35 34968.40 50584.44 76310.14

105517.90

12.10 14.11 17.28 19.54 20.36 21.78 18.78 16.08 13.83

147.27 395.04 576.62

1174.78 2385.00 3303.03 7200.60

12797.15 17750.87 33172.47 47489.07 71459.39 98064.30

12.10 13.51 16.15 17.89 18.23 19.27 15.94 13.15 10.97

144.58 385.79 550.39

1127.59 2293.23 3119.54 6907.00

12231.61 16715.13 31559.90 44701.65 67133.75 91463.38

135

Integral approach 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

22.45 25.24 29.40 33.05 35.45 38.41 37.02 35.31 33.55

167.09 434.97 671.81

1346.94 2716.17 3958.83 8286.27

14854.35 21521.92 39229.04 57915.85 87966.86

123639.90

22.45 24.11 27.19 29.69 30.95 32.81 30.34 27.78 25.39

158.47 419.29 636.73

1283.54 2594.93 3719.96 7885.29

14099.21 20137.33 36976.04 54039.35 81776.04

113979.40

22.45 22.99 25.09 26.65 27.03 28.13 24.95 21.96 19.34

152.36 406.48 605.52

1226.73 2485.30 3502.52 7525.34

13416.98 18886.58 34968.58 50584.58 76310.26

105518.00

22.45 21.87 23.09 23.90 23.63 24.23 20.63 17.46 14.87

148.05 395.62 577.05

1175.10 2385.25 3303.21 7200.73

12797.26 17750.94 33172.52 47489.11 71459.43 98064.33

22.45 20.75 21.21 21.43 20.71 21.00 17.15 14.00 11.57

145.00 386.09 550.60

1127.73 2293.33 3119.61 6907.05

12231.64 16715.15 31559.92 44701.66 67133.76 91463.39

Unit: million yuan.

136

Table A5.5 Price deflators Year Consumer price

index (2000=1) Fixed asset investment price index (2000=1)

ICT hedonic price index (2000=1)

1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

0.265 0.270 0.277 0.303 0.323 0.346 0.412 0.486 0.501 0.518 0.551 0.632 0.784 0.918 0.994 1.022 1.014 1.000 1.004 1.011 1.003 1.015

0.306 0.284 0.298 0.314 0.339 0.407 0.484 0.497 0.552 0.636 0.805 0.889 0.941 0.978 0.995 0.993 0.989 1.000 1.004 1.006 1.028 1.085

27.884 22.603 19.021 16.255 14.011 12.945 12.006 10.485 9.387 7.812 6.395 5.584 4.451 3.189 2.312 1.659 1.215 1.000 0.789 0.638 0.540 0.496

137

Chapter 6

ICT AND ECONOMIC GROWTH: A NATIONWIDE STUDY

6.1 Introduction

The previous chapters of the dissertation have provided a review of debates and

empirical studies pertaining to the relationship between information and

communications technology (ICT) and economic growth. Various studies have shown

ICT to be instrumental in propelling productivity and economic growth in the developed

countries. This chapter will add to the literature by focusing on China. It examines the

relationship between ICT and China’s economic growth using the estimates of ICT

capital stock series obtained in Chapter 5. Given China’s current stage of economic

development, it will be interesting to find out how this country is similar to or different

from the advanced economies with regard to the contribution of ICT to economic

growth.

This chapter comprises three main parts. First, it provides a preliminary analysis

of the relationship between ICT and China’s productivity growth, based on data

obtained from Chinese statistical sources. Second, the chapter attempts to specify an

appropriate model drawn from the literature to analyze the contribution of ICT and

other factor inputs to economic growth in China. Finally, the chapter will test the

robustness of the model by comparing empirical results based on different estimations

of the ICT capital stock. Conclusions will then be drawn about the role of ICT in

China’s economic growth over the past decades.

6.2 ICT, productivity and the Chinese economy

Current discussions on China’s economic development tend to focus on its transition

from an agriculture-based to an industrial economy that relies more on the

manufacturing sector. An ‘information economy’ or a ‘knowledge economy’ is

normally associated with the tertiary or service sector which relies heavily on the use of

communications and computer services. As the tertiary sector has not attained a

significant share of total output, there is a debate over whether it is too early to even

discuss the relevance of the information or knowledge economy to China (Lan and

Sheehan, 2002).

138

139

However, the share of tertiary industry output over total output in China has

been gradually increasing since the beginning of economic reform. The tertiary sector

has maintained a share of more than 30% of real GDP since the late 1980s, peaking at

42% in 2002 before dropping to about 40% in 2005 (Figure 6.1). The share of China’s

primary industry, on the other hand, has declined from less than 20% since 1997 to

about 12% in 2005, while the secondary industry (comprising mainly manufacturing

and construction) made up about 47% of the GDP in 2005.1 At the same time, China’s

economy is increasingly spurred on through development in the ICT sector. As China is

now the world’s largest telecommunications (fixed line and mobile phone) market, and

achieving rapid growth in its computer industry as well (see Chapter 2 for an overview

of its development history), the implications of the growth in ICT for the rise of the

service sector in the Chinese economy cannot be ignored.

Figure 6.2 illustrates the trend of labour productivity growth in China from the

beginning of reform in 1979 till the most recent year, 2005. Labour productivity

(measured as GDP per worker) declined in 1989 and 1990 when economic sanction was

imposed by US and other Western countries following the occurrence of the Tiananmen

incident in June 1989. However, it did not last long, as labour productivity shot up

almost immediately soon after, reaching its peak growth rate of 14% in 1992, the year

that Deng Xiaoping went on his southern tour which sparked off an investment boom.

From then on, Chinese labour productivity has been rising steadily into the 21st century,

achieving a peak growth rate of 11.5% in 2004. While there has been much discussion

in the literature (as examined in Chapters 3 and 4) of a productivity revival in the US

and other developed countries after 1995, in China the breaking point appears to be

2000, after which labour productivity has been growing annually at more than 9%

(Table 6.1). This point can be reinforced when this chapter examines the growth rate of

TFP during that period in a later section.

1 China Statistical Abstract 2006, pp. 20-21.

Figure 6.1 China's tertiary output, 1978-2005 (in 1978 constant prices)

0

200

400

600

800

1000

1200

1400

1600

1800

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Bill

ion

yuan

0

5

10

15

20

25

30

35

40

45

%

Tertiary output % of GDP

Source: State Statistical Bureau, China Statistical Yearbook 2005; China Statistical Abstract 2006.

140

Figure 6.2 Labour productivity in China, 1978-2005

0

1000

2000

3000

4000

5000

6000

1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Yua

n pe

r wor

ker

-15

-10

-5

0

5

10

15

20

%

Labour productivity Growth rate

Source: State Statistical Bureau, China Statistical Yearbook 2005; China Statistical Abstract 2006.

141

Table 6.1 Growth rate of labour productivity in China during the Five-year Plan (FYP) periods (%)

Five-year Plan Period (FYP) Labour productivity growth (%) 6th (1981-85) 7th (1986-90) 8th (1991-95) 9th (1996-00) 10th (2001-05)

6.83

-0.69

10.48

6.86

9.82 Sources: State Statistical Bureau, China Statistical Yearbook 2005; China Statistical Abstract 2006.

As discussed earlier in the literature review in Chapter 3, there had been some

debates on the ‘ICT productivity paradox’ which casts doubts on productivity gains

from ICT investment, as productivity growth was shown to slow down during the 1970s

and 1980s when the US invested heavily in ICT equipment. While recent studies have

dispelled such controversy by showing that the returns on ICT investments have a

positive payoff for the developed countries, the case has not been the same for

developing economies.

In a recent study that examined the differences between Asian and non-Asian

countries in terms of ICT usage and the resulting productivity gains, it was found that

ICT investment has a negative correlation with labour productivity for Asian countries –

the only positive correlation being found with non-ICT investment (Kraemer and

Dedrick, 2002a). Kraemer and Dedrick (2002a) attributed such a negative correlation to

factors such as high prices of computers, a highly regulated telecommunications market

with little competition, the problem of coding in the English language, a low level of

ICT adoption and usage due to language barriers and trade barriers, industry structure

(Asian countries rely more heavily on manufacturing rather than the service sectors, and

therefore are more likely to reap gains in productivity from investments in non-ICT

capital), and management style of the companies.

By plotting the correlation between the growth rate of ICT investment per

worker and real GDP per worker, a different pattern has been observed for China in this

dissertation (Figures 6.3). The trend line is positive for ICT investment in China,

suggesting that the country and its companies are using ICT effectively to improve

productivity, in contrast to the findings for Asian countries in general. Some of those

142

143

factors mentioned above that account for the negative correlation in Asian countries are

no longer applicable to China, especially since the 1990s. The rapid expansion of the

telecom and computer markets in China, which is followed by the drop in prices of ICT

equipment, has benefited residential as well as industrial users of ICT. This was evident

in the telecommunications market when the break-up of monopoly since 1993 and

bureaucratic reform during the late 1990s initiated pricing competition between

different telecommunications service providers. Furthermore, as discussed earlier, the

tertiary or service sectors have become more important contributors to the national

output, thus fuelling greater demand for ICT services, such as the broadband and

wireless Internet access, mobile telephony and other channels of communications.

However, as the manufacturing sector still occupies a major share of GDP, we expect

the payoff from investments in non-ICT capital such as factory plant and equipment to

be positive as well.

Another way of examining the role of ICT in the national economy is to

compare the size and growth rate of ICT capital stock relative to the total capital stock.

The share of ICT out of the total capital stock in China still miniscule – still slightly less

than 1% in 2004 (Figure 5.5 in Chapter 5), yet the former has grown at a faster rate than

the latter. For instance, ICT capital grew by about 22% on average annually from 2000

to 2004, which was almost double the growth rate of total capital stock at 12%. The

growth rate of China’s ICT capital stock is comparable to that of OECD countries,

which grew between 15% and 35% in 1999 (OECD, 2001). As shown in Figure 5.6 of

Chapter 5, the rapid rise in the share of ICT capital out of GDP – almost 3% in 2004 –

highlights its role as an increasingly significant driver of growth in China’s new

economy.

This section has shown the pace at which ICT has grown in importance relative

to the entire Chinese economy. In the next section, the dissertation shall seek to find the

empirical evidence, by measuring the contribution of ICT capital to output growth in

China, in comparison with those from other factor inputs. This would round up the

whole picture about the relationship between ICT and China’s economic growth.

Figure 6.3 ICT investment per worker and labour productivity in China, 1985-2004

y = 0.1538x + 9.1232R2 = 0.7443

8.4

8.6

8.8

9

9.2

9.4

9.6

9.8

10

10.2

-4 -3 -2 -1 0 1 2 3 4

Log (ICT per worker)

Log

(GD

P pe

r wor

ker)

GDP per worker Linear (GDP per worker)

144

Source: Figures 5.1 and 6.2 in this study.

6.3 Model Specification

The core objective of this chapter is to examine the sources of economic growth in

China that takes into consideration the role of ICT capital. To account for the

contribution from factor accumulation, the chapter employs the production model which

segregates ICT capital from other forms of physical capital inputs that produce output in

the form of real GDP. Technological progress, or TFP, is derived as the residual of a

production function. The production function in its simplest form is expressed in

Equation (3.1) of Chapter 3. An extended form of the production function that explicitly

distinguishes ICT capital from non-ICT capital is developed by Jorgenson, Ho and

Stiroh (2003a, 2005) as:

Yt = A ·(ICTtα, KNt

β, Ltθ) (6.1)

where Yt represents real GDP in constant prices, ICTt and KNt stand for the stock of ICT

and non-ICT capital respectively, and Lt is employment. The method of estimating the

sources of growth is based on the work of Jorgenson, Ho and Stiroh (2003a, 2005). The

growth of GDP is the aggregate sum of the share-weighted growth of inputs and growth

in TFP, expressed as follows:

(6.2) ttttt ALKNICTY⋅⋅⋅⋅⋅

+++= θβα

where Yt, ICTt, KNt and Lt are the respective growth rates of real output, ICT capital,

non-ICT capital and labour, while At measures TFP growth. The coefficients, i.e. α, β

and θ represent the weighted share of the respective inputs in real GDP, and they

determine the elasticity of output with respect to each of the factor input. The

contribution of an input is therefore dependent on the size of its coefficient, its average

growth rate during the entire period of study as well as the growth rate of real GDP.

Under the assumption of constant returns to scale, the shares of all inputs add to one, i.e.

α + β + θ = 1.

145

6.4 Description of Data

The variables in the model are defined as follows:

Yt = real GDP

ICTt = real value of ICT capital stock

KNt = real value of non-ICT capital stock

Lt = total employment

Real GDP data is derived from nominal GDP deflated by the constant price index. GDP,

consumer price index and total employment is obtained from China Statistical Abstract

2006, for which the period of 1978-2005 is available. As for the variable representing

labour, unlike those of the US and Australian sources, data on the number of hours

worked is not available in Chinese statistical sources. The dissertation chooses

employment as the proxy variable for labour, after some trial and error with various

proxies.2

ICT and non-ICT capital stock

ICT capital stock is estimated in Chapter 5 and available for the period 1983-2004.

Non-ICT capital stock is derived from investment in non-ICT capital, which is

estimated by first taking the difference between total fixed asset investment and

investment in the ICT sector. The real value of non-ICT capital stock is then derived

using the same perpetual inventory method. Data for total fixed asset investment is

available from 1980 to 2004. As explained in Chapter 5, the ICT capital stock series is

based on an assumed depreciation of 15% over the period 1983-2004, with the initial

value in 1983 determined by the three approaches of estimation, i.e. initial value,

backcasting and integral approach.

Figures for ICT and non-ICT capital investment are deflated by the fixed asset

investment price index, which are obtained from China Statistical Abstract 2006. As

for the choice of the capital depreciation rate, δ, it is assumed that ICT equipment turns

obsolete faster than other forms of capital. Thus, for non-ICT capital stock, the

dissertation adopts a depreciation rate of 4% for the period from beginning of reform till 2 I have tried using other data such as number of staff and workers, as well as labour compensation as proxies for labour input, both of which did not work well in the regressions. Labour compensation, as defined in China Statistical Yearbook, includes ‘wages, bonuses and allowances the labourers earn in monetary form and in kind, as well as the free medical services and expenses provided to the labourers, traffic subsidies, social insurance and housing fund paid by employers’.

146

1992, and 5% for the period from 1993 onwards, which were used by Islam and Dai

(2005).3

6.5 Estimation Results and Interpretation

6.5.1 Estimation results

To examine the role of ICT in Chinese economic growth, the first step is to measure the

contribution of ICT to economic growth in China. This is accomplished by a regression

of output (real GDP) against factor accumulation, expressed in the following equation:

lnYt = β0 + β1lnICTt + β2lnKNt + β3lnLt + ui (6.3)

where total GDP in constant prices, Yt, is a function of ICT capital, non-ICT capital and

labour, represented by ICTt, KNt and Lt respectively. However, the chapter will also

apply the translog production function which is an unrestricted form of the production

function, expressed as follows:

lnYit = β0 + β1lnICTt + β2lnKNt + β3lnLt + γ1(lnICTt) 2 + γ2 (lnKNt) 2 + γ3 (lnLt) 2 +

η1 (lnICTt lnKNt) + η2 (lnICTt lnLt) + η3 (lnKNt lnLt) + ut (6.4)

where β, γ and η are the parameters to be estimated. A test of linear restriction on the

translog function is carried out using the Wald test in the MicroFit programme, based

on the null hypothesis of H0: γ1 = γ2 = γ3 = η1 = η2 = η3 = 0. The test statistic of χ2(6) =

0.0026 is obtained, and therefore the model specified by the Cobb-Douglas function

cannot be rejected at all levels of significance. The sample has 22 observations for the

period of 1983-2004. The initial estimates of the parameters in Equation (6.3) are

presented in Table 6.2. All regressions in this chapter are run using MicroFit 4.0.

All coefficients of the parameters have the correct sign at different levels of

significance. ICT and non-ICT capital are statistically significant at the 1% level, but

labour at the 10% level. The results show that the growths of ICT capital as well as

physical capital are positively related to China’s economic growth from the mid-1980s

3 In another study, the depreciation rate of China’s capital stock for the period of 1990 to 2000 is assumed to be 5% (Qian and Smyth, 2006).

147

till the beginning of the 21st century. The 2R is shown to be very high, at 0.99. This is

not unusual as empirical results for other countries have proven to be similar.4

Table 6.2 Regression results of China’s sources of economic growth, 1983-2004 Explanatory variables Model specification Initial value Backcasting Integral Intercept lnICT lnKN lnL R2

Adjusted R2

Standard Error Observations Durbin-Watson statistic

-4.5390 (-8.330)*** 0.1180 (4.652)*** 0.5256 (5.635)*** 0.3850 (1.722)* 0.9955 0.9947 0.0411 22 0.9371

-4.2741 (-7.802)*** 0.1210 (4.856)*** 0.5146 (5.612)*** 0.3675 (1.717)* 0.9957 0.9950 0.0402 22 0.9574

-4.6831 (-8.593)*** 0.1164 (4.541)*** 0.5314 (5.641)*** 0.3946 (1.721)* 0.9954 0.9946 0.0416 22 0.9259

Note: Figures in parentheses are the t-ratios. *, ** and *** indicate significance at 10%; 5% and1%.

Based on Durbin’s d statistic obtained from the three estimations, with the

values ranging from 0.9259 (integral approach) to 0.9574 (backcasting approach), there

is no conclusive evidence of the presence of positive first-order serial correlation as

these values lie between dL = 0.831 and dU = 1.407. Therefore, another test, the

Breusch-Godfrey (BG) test is conducted using EView 5.0. The test statistic of χ2(1) =

4.2468 (with p-value of 0.039) is obtained, suggesting that the null hypothesis of no

serial correlation is rejected at 5%, but not rejected at 1% level of significance. A

sensitivity test (to be discussed in section 6.5.4) reveals that a higher d statistic is

obtained at a higher depreciation rate of ICT capital.

As for non-ICT capital stock, this chapter has also attempted to estimate a series

using the backcasting and integral approach. However, the regression results based on

these estimations resulted in the labour variable being statistically insignificant at all

levels, and therefore the non-ICT capital stock series estimated from the initial value

approach is used in the dissertation.5

6.5.2 Decomposition of output growth

Using the estimates shown in Table 6.2, the sources of economic growth can be derived.

The backcasting approach is chosen for computation as it has the largest t-ratio for ICT

4 For instance, in a study of 69 countries all over the world, 48 have a 2R of 0.99 or higher (Dadkhah and Zahedi, 1990). Similarly, Diewert and Lawrence (2005) obtained an R2 of 0.9986 for the Australian production function. 5 Note that tests for unit root and stationarity are not considered in this exercise due to the fact that the results are potentially sensitive to the small sample size which is a limitation of this study. Owing to the limited number of observations, unit root tests would be unreliable with small sample sizes.

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capital and the smallest standard error of the regression among the three estimations.

The contributions of the factor accumulations and technical change (or technological

progress) to real output growth in China for the period of 1983-2004 are shown in Table

6.3, based on the assumed depreciation rate of 15% for ICT capital. The results differ

from those of previous studies carried out by the IMF. For example, Lee and Khatri

(2003) show the contribution from ICT capital and TFP in China to be 3% and 43%

respectively during the 1990s, while in Wang and Yao (2003), TFP contributed 25% to

economic growth. Total capital accumulation (ICT and non-ICT capital) contributes to

half of economic growth. In another study that included human capital as a factor input,

TFP and human capital contributed to 22% and 13% of total GDP growth respectively

(Qian and Smyth, 2006).

Table 6.3 Contributions to output growth in China, 1983-2004 (unit: %)

Period

1983-2004 1983-1991 1992-2000 2001-2004

ICT Capital Other Capital Labour TFP Output

2.30 (25.1) 5.51 (60.1) 0.84 (9.2) 0.52 (5.6) 9.17 (100.0)

-0.21 (-2.5) 5.58 (69.3) 1.58 (19.6) 1.10 (13.6) 8.05 (100.0)

4.37 (46.1) 5.23 (55.1) 0.39 (4.1) -0.51 (-5.3) 9.49 (100.0)

2.65 (24.7) 6.01 (56.1) 0.39 (3.6) 1.66 (15.5) 10.70 (100.0)

Note: Figures in parentheses are the weights of each factor growth.

In this study, the contributions from ICT and non-ICT capital sum up to more

than 80% of total output growth in China. It also finds that the contribution from ICT

capital to growth has fluctuated substantially between the 1980s and the recent years,

whereas that from TFP ranges between negative and positive values during the same

period. Finally, Table 6.3 also shows remarkably contrasting results at different periods

during the 1980s and 1990s. During the 1980s, the contribution from ICT to economic

growth was a negative 2.5%, but the share increased to over 45% in the 1990s. Such a

huge jump in the share of contribution to growth is explained by the sharp increase and

the high growth rate of ICT investment during the early 1990s. During the first three

years of the 21st century, ICT capital contributed about 25% to economic growth. The

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low share of TFP contribution to economic growth may be attributed to an extremely

low share of the ICT-producing industry in GDP, a result that is similar to that found in

Europe by Timmer and van Ark (2005).

It can be noted that the contribution rates of this finding are different from those

of other studies that analyse the contribution of ICT to Chinese economic growth,

namely, Lee and Khatri (2003) and Jorgenson and Vu (2005). Lee and Khatri (2003)

measure the contribution of ICT capital stock to the growth of GDP and labour

productivity in key Asian economies for the period 1990-1999, while Jorgenson and Vu

(2005) address the impact of ICT investment on the growth of world economy, seven

regions and 14 major economies during the period 1989-2003. This dissertation finds

the contribution from ICT capital to be considerably higher, and conversely, that from

labour to be much lower compared to the findings of other authors. For instance, the

contribution from labour has declined from 20% in the 1980s to about 3% in the most

recent years. The contribution from non-ICT capital has also declined from 70% during

1983-91 to 56% in 2001-04. The only findings in this chapter that are close to those of

the other authors are the contribution rates from non-ICT capital and TFP during the

years 2001-2004.

It may also be observed that this chapter has found a much lower proportion of

ICT capital to real GDP – less than 1% during the 1990s (refer to Chapter 5), compared

to 2% found in Lee and Khatri (2003). However, it should be noted that the latter

measured the ratio of ICT capital stock to nonfarm business GDP, whereas this

dissertation looks at the ratio to total GDP in China. As for non-ICT capital stock, the

proportion out of GDP found in this chapter is comparatively higher – 530% in this

chapter vs 172% in Lee and Khatri (2003) for the period of 1992-99. This could be

attributed to the fact that the non-ICT capital used in this dissertation is derived from

investment figures deflated by the fixed asset investment price index, instead of the

consumer price index, thus resulting in relatively higher values.

Another difference stems from the definition of ICT investment or spending. Lee

and Khatri (2003) used data on total ICT spending which comprises a wide range of

components such as spending on hardware, software, IT services (including IT

consulting, operations management, IT training and education, processing and IT

support), internal ICT spending (covering IT operating budget, internally customized

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software, and other expenses related to IT ‘that cannot be tied to a vendor’), and other

office equipment and telecommunication.6 However, this dissertation uses only data on

investment in communications equipment, computer hardware and software. What can

be concluded is the surge in ICT production has been a major contributor to TFP growth

since the late 1990s.

In another study, Jorgenson and Vu (2005) used the same but a more recent

source of data as that of Lee and Khatri (2003). Although Jorgenson and Vu found the

contribution of ICT to economic growth in China to be considerably low compared to

that of this chapter – 9% for 1995-2003 vs 25% for 2001-2004, the authors attribute the

increase in its contribution rate over the last ten years to the surge in ICT investment

and software, especially after 1995, a conclusion supported by the dissertation. In

addition, Jorgenson and Vu (2005) further remarked that the next most important

increase in the investment in ICT equipment and software after the G7 economies was

in developing Asia, led by China. Thus there is a general consensus that the rising ICT

investment will lead to further increases in its contribution to China’s economic growth.

6.5.3 TFP growth in China

One of the best indicators of economic performance is total factor productivity (TFP),

which is brought about by technological progress and a more efficient management

practices. Figure 6.4 compares the total output (GDP) index with the input indices in

China, using 1984 as the base year. 7 The output and input indices illustrate the pace at

which each of the variables has grown over the past two decades. The growth rate of

TFP, illustrated in Figure 6.5, is derived from the difference between output growth and

sum of the share-weighted growth of inputs given in Equation (6.2):

∆ln At = Δln Yt – Σ{ αICT ΔlnICTt +βKNΔlnKNt + γL Δln Lt)} (6.5)

where TFP growth (ΔlnAt) is the difference between the growth of real GDP, Yt, and the

growth of factor inputs, i.e. ICT capital, non-ICT capital and labour, represented by

ICTt, KNt and Lt respectively; whereas α, β and γ are the respective weights of the three

factors.

6 Data was obtained from World Information Technology and Services Alliance (WITSA) and International Data Corporation (IDC). However, data on hardware spending was reported to be biased upwards as they include household spending. 7 Adopted from Diewert and Lawrence (2005) who estimated productivity growth for Australia.

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152

Figure 6.4 shows that ICT capital has been growing much faster than output and

all other inputs. This could explain the difference between China and other Asian

countries, in which the latter have been reported to experience a negative correlation

between ICT and productivity growth in Kraemer and Dedrick (2002a)). As observed in

Chapters 2 and 5, China’s massive investment in ICT since 1992, made favourable by

the building of high technology industrial parks, is a major contributor to the rapid

growth of ICT capital. By contrast, TFP growth has been observed to be volatile over

the last two decades, facing spells of negative growth during the periods of 1988-91 and

1996-98. It grew at an annual rate of 0.1%, but negative growths were experienced in

1986-90 and 1995-99 (Figure 6.5). However, it can be observed that China has had a

positive and increasing trend in the growth rate of TFP since 2001.

6.5.4 Sensitivity analysis

The robustness of the estimation results can be examined with a sensitivity analysis by

assuming different rates of depreciation for the ICT capital stock. A series of

regressions and tests are run using ICT capital stock determined by depreciation rates

that vary from 10% to 30% (Table 6.4). The estimated growth rates of ICT capital stock

show a similar trend of contribution under different depreciation rates (Table 6.5). It can

be noted that the contribution from ICT capital gets lower as δ increases, whereas that

from non-ICT capital gets higher and that from labour becomes lower. This can be

explained from the fact that the estimation of non-ICT capital stock is derived from the

difference total fixed asset investment and ICT investment. Therefore, a higher

depreciation of ICT capital stock results in an increased share of non-ICT in total capital

stock, and thereby increasing its contribution to output growth. In this analysis, the

contribution from TFP to output growth is relatively unresponsive to changes in the

depreciation rate of ICT capital although it increased by a miniscule magnitude, as

shown in Table 6.5.

Figure 6.4 Output and input indexes in China, 1984-2004

0

5

10

15

20

25

30

35

40

45

50

1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Output index ICT index Non-ICT Capital index Labour index

Source: State Statistical Bureau, China Statistical Yearbook 2005; China Statistical Abstract 2006.Yearbook of China’s Electronics Industry and China Statistics Yearbook on High Technology Industry (various issues).

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Figure 6.5 TFP growth in China, 1984-2004

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

%

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As the depreciation rate of ICT capital increases, the regression results become

more robust as standard errors are reduced and the t-ratios of all variables increase.

Using the Breusch-Godfrey test, the null hypothesis of no serial correlation is rejected at

5% but not rejected at 1% level of significance when the depreciation rate, δ = 0.10,

given the test statistic, χ2(1) = 4.8926 (p-value = 0.027). However, for depreciation rates

of δ = 0.20, 0.25 and 0.30, the respective test statistics, χ2(1) = 3.6408 (p-value = 0.056),

3.0977 (p-value = 0.078) and 2.636 (p-value = 0.104) are produced, which show that the

null hypothesis of no serial correlation is rejected at 10% but not rejected at 5% level of

significance. The results of the sensitivity tests therefore suggest that China may have a

depreciation rate of ICT capital stock higher than that of developed countries such as

Japan and Korea even today. However, the regression results show that labour becomes

statistically insignificant when the assumed depreciation rate of ICT capital stock

reaches 20%. Therefore the dissertation concludes with the assumption of 15% as the

current depreciation rate of ICT capital stock in China.

Table 6.4 Sensitivity tests using various depreciation rates of ICT capital stock in China

Explanatory variables

Depreciation rates

0.10 0.20 0.25 0.30 Intercept lnICT lnKN lnL R2

Adjusted R2

Standard Error Observations d-statistic

-3.852(-6.427)*** 0.143 (4.666)*** 0.461 (4.359)*** 0.403 (1.785)* 0.9955 0.9948 0.0410 22 0.9140

-4.555 (-8.764)*** 0.104 ( 5.023)*** 0.559 ( 6.895)*** 0.327 ( 1.609) 0.9959 0.9942 0.0393 22 1.0054

-4.745 (-9.415)*** 0.091 (5.152)*** 0.595 (8.152)*** 0.286 (1.472) 0.9960 0.9953 0.0388 22 1.0561

-4.875 (-9.845)*** 0.080 (5.240)*** 0.625 (9.332)*** 0.248 (1.319) 0.9961 0.9954 0.0384 22 1.1061

Note: Figures in parenthesis ( ) are the t-ratios. *, ** and *** indicate significance at 10%; 5% and1%.

In addition, an alternative set of analysis are also produced using GDP and

capital stock. The latter is derived from investment deflated by the hedonic price index,

which showed a much lower initial value of ICT capital stock (see Table A5.4 of

Chapter 5 for the alternative capital stock series). The estimation results are presented

and discussed in the Appendix to this chapter. These findings, which are presented in

Tables A6.1-A6.3, show that the changes in contribution from ICT to output growth as δ

changes are lower than that presented in Table 6.4 earlier. The contribution from TFP to

output growth is also relatively lower – for instance, 1.6% in Table A6.3 compared with

5.6% in Table 6.4 for δ = 0.15.

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Table 6.5 Results of sensitivity analysis

Contribution to output growth (%) Depreciation rate of ICT capital (%) ICT

KN L TFP

10 15 20 25 30

2.80 (30.5) 2.30 (25.1) 1.92 (20.9) 1.62 (17.7) 1.39 (15.2)

4.94 (53.8) 5.51 (60.1) 5.98 (65.2) 6.37 (69.5) 6.70 (73.0)

0.92 (10.1) 0.84 (9.2) 0.75 (8.2) 0.66 (7.2) 0.57 (6.2)

0.51 (5.6) 0.52 (5.6) 0.52 (5.7) 0.52 (5.7) 0.52 (5.7)

Note: Figures in italic parenthesis are the shares of each factor growth.

6.6 Conclusion

The empirical results indicate that China’s economic growth largely comes from factor

accumulation, which shows that the neo-classical approach to growth accounting is still

very much relevant today. China’s economic growth is largely driven by the expansion

of capital formation. Even though ICT capital has only a miniscule share out of total

capital stock and GDP, it has grown at a faster rate than any other form of capital. From

the fact that ICT investment as a proportion of GDP is much lower than that of other

forms of investment, and yet its contribution to economic growth is almost half of the

latter, it can be ascertained that ICT has become an important contributor to the growth

of China’s economy. The latter will in turn ensure a continued high demand for ICT

products and services.

This chapter highlighted findings on the sources of China’s economic growth in

the late 1990s and the early 21st century that are different from existing literature, even

though the actual depreciation rate of China’s ICT capital stock is still unknown.

Current literature has found the contribution of ICT to China’s economic growth to be

less than 10% even during the late 1990s and the early years of this century, much lower

than this dissertation’s findings of about 25%. The contribution from TFP to economic

growth in China is much lower compared with those from other literature – for instance,

about 6% in this chapter compared with 35% in Jorgenson and Vu (2005) for the period

after 1995.

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There would be a need for further empirical research on the sources of China’s

productivity growth that investigates whether there has been a capital reallocation

between the ICT sector (i.e., ‘ICT-producing’ and ‘ICT-using’ industries) and the non-

ICT sectors. It should also be acknowledged that China’s regional disparity in ICT

investment will be an issue for examination. This could bring up a debate over whether

China should focus its ICT investment in the more developed eastern or coastal regions,

or to invest more in the inward or western regions. This issue will be examined in the

next chapter, which looks at the impact of ICT on technical efficiency in the Chinese

regions. Finally, one limitation of this study concerns the decomposition of ICT

contribution to TFP growth. There is currently no statistical source available that

provides data on ICT investment in individual industries in China that would enable any

formal analysis of the contribution of ICT to TFP growth.

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This appendix presents the estimation results based on the hedonic price index. The

sample has 22 observations for the period of 1983-2004. The initial estimates of the

parameters in equation (6.6) are presented in Table A6.1. All coefficients of the

parameters are statistically significant with the correct sign at all levels of significance.

Based on Durbin’s d statistic obtained from the three different model specifications,

with the values ranging from 0.9282 to 0.9305, there is no conclusive evidence of the

presence of positive first-order serial correlation as these values lie between dL = 0.831

and dU = 1.407. The Breusch-Godfrey (BG) test suggests that the null hypothesis of no

serial correlation is rejected at 5%, but not rejected at 1% level of significance.

Table A6.1 Regression results of China’s sources of economic growth, 1983-2004 Explanatory variables Model specification Initial value Backcasting Integral Intercept lnICT lnKN lnL R2

Adjusted R2


-3.0330 (-3.110)*** 0.1337 (2.860)*** 0.4513 (5.712)*** 0.4541 (2.207)*** 0.9764 0.9724 0.0855 22 0.9283

-2.4662 (-2.414)*** 0.1342 (2.895)*** 0.4299 (5.051)*** 0.4077 (2.102)*** 0.9765 0.9726 0.0851 22 0.9305

-3.1539 (-3.253)*** 0.1340 (2.855)*** 0.4554 (5.856)*** 0.4647 (2.229)*** 0.9763 0.9724 0.0855 22 0.9282


Decomposition of output growth

Using the estimates shown in Table A6.1, the sources of economic growth can be

derived. The backcasting is chosen for computation as it has the largest t-ratio for ICT

capital and the smallest standard error of the regression among the three models. The

contributions of the factor accumulations and technical change (or technological

progress) to real output growth in China for the period of 1983-2004 are shown in Table

A6.2.

The estimation results do not differ much from those reported in Table 6.2. The

contributions from ICT and non-ICT capital sum up to more than 80%. Similarly, this

study also finds that the contribution of ICT capital to growth has largely fluctuated

between the 1980s and the recent years, whereas that of TFP has increased to more than

40% during the most recent years. Finally, Table A6.2 also shows remarkably

contrasting results at different periods during the 1980s and 1990s. During the 1980s,

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ICT had a contribution of only 3% to economic growth, but it increased to more than

60% in the 1990s. During the first three years of the 21st century, ICT capital

contributed 21% to economic growth.

Table A6.2 Contributions to output growth in China, 1983-2004 (unit: %) Period 1983-2004 1983-1991 1992-2000 2000-2004 ICT Capital Other Capital Labour TFP Output

2.79 (30.1) 5.41 (58.2) 0.94 (10.1) 0.15 (1.6) 9.28 (100.0)

0.29 (2.6) 6.26 (76.5) 1.75 (21.4) -0.04 (-0.4) 8.19 (100.0)

5.01 (61.8) 4.76 (58.8) 0.43 (5.4) -2.10 (-26.0) 8.10 (100.0)

2.97 (21.0) 5.16 (36.5) 0.43 (3.1) 5.58 (39.5) 14.14 (100.0)

Note: Figures in parentheses are the shares of each factor growth.

Sensitivity analysis

The robustness of the estimation results can be examined with a sensitivity analysis by

assuming different rates of depreciation for the ICT capital stock. The estimated growth

rates of ICT capital stock show a similar trend of contribution under different

depreciation rates (Table A6.3). Similar to the results shown in Table 6.5 earlier, the

contribution from ICT capital gets lower as δ increases, whereas that from TFP gets

higher. As the depreciation rate of ICT capital increases, the regression results become

more robust as standard errors are reduced and the t-ratios of all variables increase.

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Table A6.3 Results of sensitivity analysis

Contribution to output growth (%) Depreciation rate of ICT capital (%)

ICT

KN L TFP

10 15 20 25 30

2.88 (31.0) 2.79 (30.1) 2.71 (29.2) 2.64 (28.5) 2.58 (27.8)

5.41 (58.2) 5.41 (58.2) 5.41 (58.2) 5.41 (58.2) 5.41 (58.2)

0.94 (10.1) 0.94 (10.1) 0.94 (10.1) 0.94 (10.1) 0.94 (10.1)

0.06 (0.7) 0.15 (1.6) 0.23 (2.5) 0.30 (3.2) 0.36 (3.9)

Note: Figures in italic parenthesis are the shares of each factor growth.

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Chapter 7

ICT AND EFFICIENCY IN CHINESE REGIONS

7.1 Introduction

In the previous chapter, the contribution of ICT to China’s economic growth using data

at the national level was examined. This chapter will discuss issues related to regional

growth and disparities in China, using data at the provincial level. The role of ICT in

propelling China’s regional growth is, however, very rarely discussed, despite the

country’s emergence as one of the world’s largest ICT market. This chapter contributes

to existing literature by looking at the pattern of disparity in ICT investments in China.

It will provide a background review of how the pattern of regional disparity in China

has changed as far as ICT is concerned. The chapter attempts to look at the impact of

ICT on technical efficiency in Chinese regions. No previous work in this area has been

reported. There are two main objectives: a) to estimate China’s regional ICT capital

stock and b) to examine the impact of ICT investment on technical efficiency in China’s

regions.

The chapter begins with an account of the pattern of ICT investment in different

regions over the past decade (from 1996 to 2004). This is followed by a discussion of

literature concerned with the concepts of technical efficiency and its relationship with

ICT investment. The next section deals with the method applied in this study to assess

the impact of ICT capital stock as well as other inputs on technical efficiency using

regional data. Data for ICT and other forms of capital stock are derived from investment

figures. Finally, based on the estimation results, the pattern of change in the effect of

ICT on technical efficiency among Chinese regions is illustrated.

7.2 ICT investment in Chinese regions

Testifying the increasing importance of ICT to China’s economy, the ICT sector has

been placed among the ten categories for high priority development by the National

Development and Reform Commission and the Ministry of Science and Technology.1

1 The other sectors include bio-technology and new medicines, new materials, manufacturing, resource development, environmental protection, aeronautics and astronautics, agriculture and transportation. See “High-tech industries gain State priority”, China Daily (North American ed.), New York: July 9, 2004.

161

Among the eight most important tasks to be achieved during the Tenth Five Year Plan

(2001-2005) in former Chinese Premier Zhu Rongji’s ‘Report on National Economic

and Social Development during the Tenth Five Year Plan’, are ‘changing the structure

of industry towards more high-technology industry’, ‘developing the western region in a

strategy of regionally balanced economic development’ and ‘investing in human capital

in a strategy of promoting science, technology and education for the betterment of the

nation’ (Chow, 2002). The report thus marked a gradual shift of attention from

‘focusing on the rapid development of the coastal (eastern) region’ to one of ‘promoting

development of the interior’ (Lai, 2002).2 As China seeks to develop its inner (central

and western) regions, a key question is whether investment in ICT will help narrow the

country’s regional disparity.

To further boost the development of ICT industry, and to develop China into an

internationally competitive ICT powerhouse instead of being merely a manufacturing

centre, the Ministry of Information Industry (MII) set up several information industry

bases in 2004.3 In addition, the MII has also outlined development schemes for 23

special ICT sectors, such as digital television, mobile telecommunications and

automobile electronics, a part of the Eleventh Five-Year Plan (2006-2010) programme.4

The ICT industry has shown robust growth in different regions in the country,

supported by preferential policies for regional development.5 For instance, the ICT

industries in northeastern Jilin province and southwestern Guizhou province grew by

almost 30% and more than 50% respectively.6 To illustrate the relationship between

ICT investment and labour productivity in China, a scatter diagram plotting the

correlation between ICT investment per worker and GDP per worker among China’s

provinces and other regions is drawn (Figure 7.1). In 2004, the majority of provinces or

regions in China have a GDP per worker below 5,000 yuan, and investment in ICT per 2 After two decades of pursuing coastal development, the western development strategy was proposed by former Chinese President Jiang Zemin during the Ninth National People’s Congress (NPC) in March 1999 and the policy was officially endorsed in June the same year in which the phrase ‘great western development’ (xibu da kaifa) was used in Jiang’s ‘Xi’an speech’ (Lai, 2002: 436). 3 Aimed at nurturing China for home-grown leading technologies, these ICT bases would focus on the development of mobile telecommunications, digital TV, softare, as well as semiconductor technologies and products. See “Launch of IT bases planned”, China Daily (North American ed.), New York: August 16, 2004. 4 Ibid. 5 The preferential policies are listed in the state publication A Catalogue of Advantaged Industries for Foreign Investment in the Central and Western Region, in which ‘provinces in the central and western regions may upgrade an existing developmental zone in the capital cities into a national economic and technological development zone’ (Lai, 2002: 457). 6 “IT industry to maintain fast growth”, China Daily, Beijing: December 6, 2004.

162

worker below 50 yuan (Figure 7.1).7 Although most provinces are shown to cluster

close to the point of origin in the graph, it can be seen that there is a generally positive

correlation between ICT investment and labour productivity in China.

The municipal city of Shanghai is the outlier in this model, having GDP per

worker and ICT investment per worker of almost 98,000 yuan and 254 yuan in 2004

respectively. In descending order of the level of ICT investment per worker, Shanghai is

followed by Jiangsu (having less than half of Shanghai’s investment and the second

highest GDP per worker among the provinces after Guangdong), Tianjin, Guangdong

(the province with highest GDP per worker) and Beijing. Interestingly, in 2004, the two

provinces of Jiangsu and Guangdong and the three municipal cities are the only areas

with an ICT investment per worker at above 50 yuan, which is considerably higher than

the national average of about 24 yuan per worker, and they account for three-quarters of

total ICT investment in the whole of China. (For ICT investment in individual

province/municipal city, see Table A7.1 in the Appendix to Chapter 7).

ICT investment (in real terms) in China had increased ninefold in a decade

between 1993 and 2004, from 1.25 billion to 11.21 billion yuan (figures obtained from

Chapter 5). In terms of aggregate ICT investment, the highest is found in the eastern

region which had two-third of the national ICT investment in 2004 (Figure 7.2). The

three municipal cities of Beijing, Tianjin and Shanghai made up the bulk of ICT

investment during the 1990s, but their share was gradually displaced by the eastern

region since 1999. However, it should be noted that investment in the eastern region

was mainly concentrated in a few provinces such as Guangdong, Fujian, Jiangsu and

Zhejiang. Between 1996 and 2004, it appears that the share of ICT investment has risen

only in the eastern region, from 43% to 65%. The shares of the municipal cities, central

and western regions had dropped from 33% to 24%, 16% to 5%, and 8% to 5%

respectively. Yet, China, on average, still has a proportionately low ratio of ICT

investment to GDP – 0.2% in 2000 compared with Japan’s 4.5%8, and 0.34% in 2004.9

7 Regional division used in this paper is based on China Statistical Yearbook. The municipal cities are Beijing, Tianjin and Shanghai. Eastern region consists of Hebei, Liaoning, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Guangxi and Hainan provinces. The central region is made up of the provinces of Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei and Hunan. The western region makes up the remaining provinces and autonomous regions of Sichuan (includes Chongqing municipality), Guizhou, Yunnan, Shaanxi, Gansu, Ningxia and Xinjiang. As data on ICT investment is not available for Tibet and Qinghai, these 2 autonomous regions are omitted from the analysis. 8 See Miyagawa et al. (2004) for Japanese IT investment figures.

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The shares of ICT investment to GDP in the regions are much lower. For instance, in

2004, the respective shares for the municipal cities, eastern, central and western regions

are 0.08%, 0.22%, 0.019% and 0.017%.

7.3 ICT and technical efficiency: a review

7.3.1 Conceptual issues

The concepts of productivity and efficiency are among the most commonly used

measures of firm or economic performance in economics literature. Productivity

generally refers to the ratio of output to inputs, i.e. the amount of output produced by a

given amount of inputs such as capital and labour (discussed in Chapter 3). Efficiency,

on the other hand, usually means the difference or gap between the actual and potential

output produced from a given unit of input. Any variation in productivity can be

attributed to differences in production technology or differences in the efficiency of the

production process (Lovell, 1993: 3). The term ‘efficiency’ is used interchangeably with

‘productive efficiency’, which is made up of two components: technical efficiency and

allocative efficiency. The former is concerned with ‘maximising output for given inputs,

or minimising inputs for a given output’, while the latter is concerned with ‘the

allocation of resources in such a way that consumers could not be better off without

making anybody else worse off’ (Black, 1997). This chapter focuses on examining the

effects of ICT on technical efficiency in the Chinese regions.

7.3.2 Efficiency measurement

Many studies have provided definitions and measures of technical efficiency. For

instance, in Koopmans (1951), ‘a producer is technically efficient if an increase in any

output requires a reduction in at least one other output or an increase in at least one

input, and if a reduction in any input requires an increase in at least one other input or a

reduction in at least one output.’ The key emphasis is on efficient production relative to

the ‘production possibility frontier’. Several approaches have been developed to

measure efficiency. The earliest study that calculates efficiency measures is found in

Farrell (1957) who analysed technical efficiency ‘in terms of realized deviations from

an idealized frontier isoquant’ (Greene, 1993). The econometric approach bears two

9 Note that the shares reported in this chapter are considerably lower than those in Chapter 5 as the latter looks at the share of ICT capital stock to GDP instead of investment.

Figure 7.1 Correlation between GDP per worker and ICT investment per worker in China's provinces, 2004

lny = 0.2204 lnx + 9.7341R2 = 0.4601

0

2

4

6

8

10

12

14

-3 -2 -1 0 1 2 3 4 5 6

Log of ICT investment per worker

Log

of G

DP

per w

orke

r


165

Figure 7.2 Total ICT investment in China's regions, 1996-2004

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

1996 1997 1998 1999 2000 2001 2002 2003 2004

Mill

ion

yuan

Municipals East Central West


166

Figure 7.3 Ratio of ICT investment to GDP in China's regions, 1996-2004

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

1996 1997 1998 1999 2000 2001 2002 2003 2004

%

Municipal cities Eastern region Central region Western region


167

main characteristics, i.e. stochastic which ‘distinguishes the effects of noise from the

effects of inefficiency’ and parametric implying that ‘the effects arising from

misspecification of a functional form is confused with inefficiency’ (Lovell, 1993: 19).

A typical stochastic frontier production function can be expressed as:

yi = f(xi; β) exp{vi - ui} (7.1)

where output yi is the dependent variable, the inputs xi represent a set of explanatory

variables, and β is a vector of production technology parameters to be estimated for

producer i. The random disturbance term given by vi captures statistical noise and is

assumed to be independently and identically distributed as N (0, σ2v). The disturbance

term ui is a measure of technical inefficiency, a positive error component assumed to be

independently distributed of vi. Technical efficiency is given by the ratio of actual

output, yi, to the maximum potential output which is given by the stochastic production

frontier, represented by [f(xi; β) exp{vi}]. The stochastic production frontier is in turn

determined by the structure of production technology, i.e. the deterministic production

frontier (Lovell, 1993: 20). Therefore, technical efficiency (TE) can be measured as:

}exp{}exp{);( i

ii

ii u

vxfy

TE ==β

(7.2)

Having devised a technique for measuring technical efficiency, the outcome is

normally generated as ‘efficiency scores’. The distribution of efficiency scores can be

evaluated using a one-stage analysis, where the efficiency scores are obtained from a

regression of the dependent variable against a vector of explanatory variables.

Efficiency scores are bounded by zero and one or below one. Some studies have

attempted to transform efficiency scores for use as a dependent variable in a two-stage

analysis (Kalirajan and Shand, 1988). Lovell (1993) however advised caution on the use

of explanatory variables in the second stage, which are those that the decision maker has

no control over during the period under consideration, including quasi-fixed variables,

socioeconomic and demographic characteristics, or the weather, etc.

The relationship between technical efficiency and other explanatory variables

can be given as follows:

168

exp{ui} = g(zi; γ) exp{ei} (7.3)

where exp{ui}= TEi, as given in Equation (7.2). The application of this model for

empirical analysis of the relationship between ICT and technical efficiency in the

Chinese regions will be further discussed in section 7.4.

7.3.3 ICT and technical efficiency

How does ICT affect economic performance and technical efficiency (TE)? From an

organizational perspective, communication effectiveness is enhanced with computer

networks transferring information at a reduced time and transaction costs required for

task accomplishment (Shao and Lin, 2001). This in turn enables management to make

sound decisions and better utilize resources, which would enhance a firm’s capability to

produce more output with the same amount of input (Shao and Lin, 2002).

As shown in Chapters 3 and 4, there is extensive research documenting the

positive correlation between ICT investments and productivity growth. However, there

is relatively less literature that examines the impact of ICT on technical efficiency. Shao

and Lin (2001) found that ‘the increase in technical efficiency incurred by ICT is one

source for the productivity growth witnessed in previous studies.’ In theory, technical

inefficiency occurs when a country or firm produces output below its production

possibility frontier curve, given input. Measuring the impact of ICT on firm

performance by using the stochastic production frontier method, Shao and Lin (2001)

found that ICT indeed has a positive impact on a firm’s technical efficiency.

The same conclusion is also found in Becchetti et al. (2003) who used a

stochastic frontier approach to estimate the impact of ICT investment on efficiency for

small and medium sized firms in Italy. The authors found that ICT investment affects

firm efficiency by increasing the demand for skilled labour as well as average labour

productivity, introducing new products or processes of communications, and increasing

the average capacity utilization of telecommunications networks.

One empirical work that investigates the impact of ICT on regional economies is

Susiluoto (2003) who defined regional efficiency as ‘a region’s ability to use its basic

productive resources in an economic way to produce well being. In recognising the

difference in the resource base of regions, a region with a good knowledge base, for

169

instance, must produce more than its poorer neighbour in order to be equally efficient.’

By applying the Data Development Analysis (DEA) method to examine the effects of

the ICT sector on economic efficiency among the regions of Finland, Susiluoto showed

that ‘raising ICT in the regional economy increases the performance level or efficiency

of the regions.’10

7.3.4 China-related studies

Regional studies tend to look at the ‘catch up’ hypothesis of Abramovitz (1986) which

postulates that technologically backward countries or regions (followers) have the

potential for catching up with the more advanced (leaders) through faster growth in

productivity. The narrowing of such technological gap between the leaders and

followers, or in other words, convergence, rests on the condition that ‘improvement of

social capabilities in backward regions attracts advanced technology and other

production factors into these regions’ (Jia, 1998).

Evidence of convergence in China’s regional economies on the basis of

technical efficiency performance is found in Wu (1999), who applied a stochastic

frontier model to examine productivity growth among China’s regions for the period of

1981-1995. In an earlier study of China’s state enterprises, Kalirajan and Zhao (1997)

showed improvement in technical efficiency from 1986 to 1989 due to economic reform.

They found an increasing trend in technical efficiency in all provinces during the 4-year

period, the highest being in Shanghai with an average TE score of 0.98. Recent studies

that investigated convergence among China’s regions included Yao and Zhang (2001)

and Bhalla, Yao and Zhang (2003). The latter found evidence of convergence within

‘pre-defined geo-economic sub-regions’ such as the ‘east’, ‘central’ and ‘west’, but not

between the sub-regions.

Other authors have focused on studies of industrial efficiency. For example,

Kong et al. (1998) estimated a stochastic frontier production function for four Chinese

industries (i.e. building materials, chemicals, machinery and textiles) for the period of

1990-1994. Using regional dummies to capture the efficiency differences between the

state-owned enterprises in three provinces, Sichuan, Shanxi and Jilin and those in

Jiangsu province, they found firms in the latter province, which is more developed, to 10 The DEA is an alternative method for modeling the relationship between inputs and output in the production process, and has become popular especially in the study of public sectors such as school and hospitals (Susiluoto, 2003).

170

be more efficient. In a study on China’s iron and steel industry using data from the 1995

industrial census, Zhang and Zhang (2001) measured technical efficiency of all large

and medium-sized enterprises with a stochastic frontier production function. They found

that location has not much impact on technical efficiency, although enterprises in the

eastern region tend to be more efficient than those in other regions. One of their most

important findings is technical efficiency being closely related to the vintage of an

enterprise’s fixed capital assets, as efficient enterprises are those that use relatively new

capital equipment. This could suggest that investment in new ICT equipment is crucial

to improving firm efficiency.

Finally, in another study on China’s iron and steel industry, Movshuk (2004)

examined technological progress and changes in productive efficiency for about 100

large and medium enterprises during 1988-2000 using a stochastic frontier model with

panel data. This chapter will build up the existing literature by including ICT capital in

the production function, and provide new empirical findings of technical efficiency

scores for the period of 1995 to 2004.

7.4 Modelling framework

This section proposes a stochastic frontier model and applies it to test for the effect of

ICT on technical efficiency in China’s regions. The stochastic frontier model takes into

account the differences between the ideal and actual output, thereby seeking to

maximize technical efficiency theoretically (i.e. minimizing the differences). These

differences are attributed to factors ‘that might not be under the control of the agent

being studied’, such as bad weather, breakdown of equipment, or any other random

factors that might be construed as inefficiency (Greene, 1993: 76).

The model proposed by Battese and Coelli (1995) postulates the existence of

technical inefficiency in the production process. The stochastic frontier model (referred

to as the BC model) is conventionally expressed as follows:

lnYit = ln{f (Xit, β)} + εit

εit = vit – uit (7.4)

171

where X and β are the respective vectors in the independent variables and unknown

parameters to be estimated. The disturbance term, εit, is defined as the sum of vit, a

random measurement error assumed to be iid N(0, σ2v), independently distributed of uit;

and uit, a non-negative random variable associated with technical inefficiency in

production which is assumed to be independently distributed such that uit is truncated at

zero of the normal distribution with mean, µ, and variance, σ2 (Battese and Coelli,

1995).

The BC model was further extended to analyse the influence of ‘firm-specific

environmental conditions’ on economic performance in Wu (2001). By developing a

model which examines the effect of environmental variables on technical inefficiency,

the model in (7.4) is rewritten as (adapted from Wu, 2001):

lnYit = ln f(xit, zit, t) + vit – uit (xit, zit, t) (7.5)

where zit represents the ‘environmental variables’, such as ICT capital stock in this

model, xit represents all other explanatory variables and t is a time-trend variable. This

model can be used to test the influence of the environmental variable on technical

efficiency in the form of uit = uit (zit, t), as proposed by Battese and Coelli (1995). In this

model, the estimates of the unknown parameters of the frontier production function can

be obtained using the maximum-likelihood (ML) method (O’Donnell, Rao and Battese,

2005).

To investigate the impact of ICT on technical efficiency among China’s regions,

a hypothesis is formulated as follows:

H1: ICT investment has a positive effect on regional technical efficiency in the

production process.

In this chapter, a one-stage method is used to capture the effect of ICT on technical

efficiency. The stochastic frontier model is designed to capture the effects of efficiency

change resulting from factor inputs which incorporate the ICT capital stock. Following

the model of Kumbhakar and Wang (2005), the efficiency effect of ICT in a specific

region is determined by its endowment of ICT capital per worker, given by the ratio of

172

ICT to labour in logarithmic form (ICTit - Lit). By applying the KW model to equation

(7.4), the Cobb-Douglas production function is specified as follows:

lnY =β1 + β2lnICTit + β3lnKNit + β4lnLit + vit - uit (7.6)

uit = δ0 + δ1 (ICTit - Lit)

i = 1, 2, … , 28 (provinces)

t = 1, 2, … , 10 (time: 1995, … , 2004)

where Y, ICT, KN and L stand for real output, ICT capital stock, non-ICT capital stock

and employment respectively.

This chapter will also apply the more flexible translog production function

specified as follows:

lnYit = β0 + β1lnICTit + β2lnKNit + β3lnLit + γ1(lnICTit) 2 + γ2 (lnKNit) 2 +

γ3 (lnLit) 2 + η1 (lnICTit lnKNit) + η2 (lnICTit lnLit) + η3 (lnKNit lnLit) +

vit - uit (7.7)

uit = δ0 + δ1ln(ICTit - Lit)

i = 1, 2, … , 28 (provinces)

t = 1, 2, … , 10 (time: 1995, … , 2004)

where β, γ and η are the parameters to be estimated.

7.5 Description of data

Output and labour

Output is defined as real GDP, which is derived from nominal GDP deflated by the

consumer price index (CPI) in 1978 constant prices. The data for GDP and employment

for the period of 1995-2004 is obtained from China Statistical Yearbook. In order to

take into account the inter-regional differences in price level, the regional CPI at

constant prices for each municipal city, province and autonomous regions is derived by

dividing the national CPI in constant prices by the individual region’s CPI in current

prices.

173

ICT-capital stock

The ICT capital stock is estimated based on real ICT investment data that is derived

from ‘investment in capital construction’ and ‘investment in innovation’ from the

communications equipment, and computer (hardware and software) industries, obtained

from China Statistical Yearbook on High Technology Industry, deflated by the regional

fixed asset price index which is obtained from China Statistical Yearbook. As data for

investment in the ICT industry by region is only available for the period of 1996-2004,

the dissertation will only cover this period. It should be noted that data for Qinghai

province and Tibet are not available, and therefore the analysis will omit these two

regions altogether. To calculate the regional CPI in constant prices, the fixed asset price

index for each province/municipal city is derived from dividing the national fixed asset

price index by the individual region’s fixed asset investment price index.

The estimation of initial ICT capital stock is similar to that used in Chapter 5, by

applying the following formula, which has also been used by Shinjo and Zhang (2003)

and Miyagawa et al. (2004) for the estimation of Japanese ICT capital stock:

δγ += +1t

tI

K (7.8)

where γ is the average annual growth rate of ICT capital investment (I) and δ is the

weighted average rate of depreciation. The real ICT capital stock is then derived as

follows:

Kt = It + (1–δ) Kt-1 (7.9)

where the capital stock, K, at year t is dependent on the level of ICT investment, It in

the same year and capital stock level in the preceding year which is deflated by the rate

of depreciation, δ. The non-ICT capital stock series is derived from non-ICT investment

figures, which is the difference between total fixed asset investment and real ICT

investment.

Figures for ICT capital investment are deflated by the fixed asset price index, to

be consistent with the method of estimation used in Chapter 6. Similar to Chapter 6, the

choice of the capital depreciation rate, δ, for ICT capital stock is based on empirical

174

175

studies of Kim (2002) and Miyagawa et al. (2004), while that of non-ICT capital stock

is based on Islam and Dai (2005). The rate of depreciation for non-ICT capital stock is

assumed to be 5%, based on the rate for total capital stock used in Islam and Dai (2005).

Since ICT equipment turns obsolete faster than other forms of capital, this study adopts

15% as the proxy depreciation rate for China’s ICT capital stock in 1992-2003, i.e. δ =

0.15, used in the previous chapters.

The ICT capital stock is estimated for the municipal cities, eastern, central and

western regions (Figure 7.4). The share of ICT capital stock has changed remarkably

over the past ten years. In 1995, the municipal cities and eastern region took up one-

third of ICT capital stock respectively, with the central region having another one-fifth

of the total. However, by 2004, while the share of the eastern region has increased to

64%, those of the municipal cities and central region have dropped to 24% and 7%

respectively. The share of the western region has declined slightly during the same

period, from 8% in 1995 to about 5% in 2004.

7.6 Estimation results and interpretation

7.6.1 Estimation results

The empirical work begins with a regression of output (real GDP) against factor

accumulation, that is, ICT capital, non-ICT capital and labour, expressed in equations

(7.6) and (7.7) of the Cobb-Douglas and translog model respectively. The sample has

280 observations for the period of 1995-2004. The initial estimates of the parameters

are presented in Table 7.1. All coefficients of the parameters are statistically significant

at the correct sign. The results show that the growths of ICT capital as well as physical

capital and labour are positively related to China’s economic growth in the 1990s and

the beginning of the 21st century. The maximum likelihood estimates (MLE) of the

stochastic frontier model generated by the Cobb-Douglas and translog production

function are reported in Table 7.1. Using the likelihood ratio test, it is proven the

hypothesis that the production is better described by the Cobb-Douglas function for the

MLE specification, i.e. γ1 = γ2 = γ3 = η1 = η2 = η3 = 0 is rejected. The test statistic for

MLE is χ2(6) = 57.677. 11 Therefore, the Cobb-Douglas assumption is rejected in this

finding.

11 The likelihood ratio (LR) test statistic is given by λ = -2(LLRestricted – LLUnrestricted).

0

10000

20000

30000

40000

50000

60000

70000

80000

Mill

ion

yuan

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Figure 7.4 ICT capital stock in China's regions, 1995-2004

Municipal East Central West


176

Table 7.1 MLE estimates of the stochastic frontier models

Dependent variable = lnGDP

Observations = 280

Cobb-Douglas Translog Parameter t-statistic Parameter t-statistic

Production frontier Intercept lnICT lnKN lnL (lnICT)2

(lnKN)2

(lnL)2

lnICTlnKN lnICTlnL lnKNlnL Efficiency effects Intercept (δ0) ln(ICTit - Lit) (δ1) σ2

u γ Log L

2.5827 0.0446 0.5894 0.3868 0.2081 -0.0364 0.0634 0.7834 61.5448

9.394 3.383 22.834 20.723

1.329 -1.847 2.943 8.143

11.4319 0.0999 -1.0772

1.7736 0.0375 0.0809 -0.0163 -0.0379

-0.0372 -0.0747 0.9624 -0.2673 0.0453 0.8392 90.3834

3.090 0.521 -1.542 5.394 5.976 2.397 -0.637 -2.055 -2.211 -2.148 6.156 -5.183 9.010 17.254

7.6.2 ICT and technical efficiency in China

The next objective of this chapter is to examine the effect of ICT on technical

efficiency. The estimation results obtained from the MLE estimates of the stochastic

frontier method in equation (7.7) are used to examine the effects of ICT on technical

efficiency (TE) among China’s regions. The technical efficiency term is given as δ1,

estimated by the maximum likelihood method, using the computer programme

FRONTIER 4.1.12 A region that is totally efficient in production will have a TE score of

one, or technical inefficiency (U) score of zero (Tong and Chan, 2003). In both of the

Cobb-Douglas and translog models obtained from the MLE specification, δ1 is found to

be negative, therefore implying that ICT has a negative impact on technical inefficiency;

in other words, it has a positive impact on technical efficiency. Based on the unrestricted

frontier model specified by equation (7.7), δ1 is found to be statistically significant at all

levels, thus proving that ICT has had an important impact on technical efficiency across

the country during the past decade.

12 The instructions for the programme can be found in Coelli (1996).

177

Table 7.2 Average technical efficiency (TE) in China’s regions Region/Province Average TE, 1986-89

(Kalirajan and Zhao, 1997)aAverage TE, 1995-2004

(this study) Beijing Tianjin Shanghai

0.9207 0.9098 0.9802

0.9014 0.9610 0.9468

Municipal cities 0.9369 0.9364 Hebei Liaoning Jiangsu Zhejiang Fujian Shandong Guangdong Guangxi Hainan

0.8444 0.8759 0.8999 0.9599 0.9241 0.9514 0.8967 0.8615 0.8085

0.7163 0.8699 0.9048 0.7832 0.9265 0.8832 0.9196 0.4695 0.5651

Eastern region 0.8914 0.7820 Shanxi Inner Mongolia Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan

0.7235 0.6324 0.7810 0.8971 0.9121 0.7330 0.7711 0.8393 0.8516

0.5239 0.6004 0.7724 0.9121 0.5131 0.6094 0.6395 0.6949 0.6517

Central region 0.7934 0.6575 Sichuan Guizhou Yunnan Shaanxi Gansu Ningxia Xinjiang

0.6716 0.7464 0.9668 0.6170 0.7282 0.6846 0.7539

0.6465 0.3407 0.2323 0.5787 0.4400 0.4331 0.2894

Western region 0.7384 0.4230 National

0.8265

0.6997

Note: a. TE scores for state enterprises only.

The impact of ICT on technical efficiency can be examined by dividing the

sample period into two, i.e. during the second half of the 1990s and the first half of this

decade. The former covers the Asian financial crisis. By plotting the average TE scores

in each region over the years 1995-2004, it can be seen that all regions have experienced

178

a gradually increasing trend in technical efficiency over the past decade (Figure 7.5).13

However, there was a slight decline in technical efficiency in 1995-1997, which

suggests that the financial crisis had a negative impact on technical efficiency. This can

also be attributed to a fall in ICT investment occurring in many regions in 1997, except

for Liaoning, Guangxi, Hainan, Shanxi, Heilongjiang and Ningxia (see Table A7.1 in

the appendix to this chapter). It can also be noticed that only the municipal cities and

eastern regions have average TE scores which are consistently above the national

average since the mid-1990s. The central region has a TE score that approximated the

national average in 2004 though.

Regionally, the municipal cities have the highest average TE score of 0.92 over

the period of 1995-2004. The highest average TE for an individual area is found in

Tianjin and Shanghai, followed by Fujian, Guangdong, Heilongjiang, Jiangsu and

Beijing, which are the only areas with TE scores of 0.9 and above (Table 7.2). As of

2004, the highest TE scores were found in Tianjin (0.98), Beijing, Shanghai and

Guangdong (0.97 each), followed by Jiangsu (0.96), Fujian (0.95) and Shandong (0.94).

The lowest TE scores, in ascending order, were found in Xinjiang (0.29), Yunnan (0.41)

and Guizhou (0.47) of the western region. The northeastern provinces of Liaoning, Jilin

and Heilongjiang have performed comparatively well, having TE scores over 0.91 in

2004, while Liaoning and Heilongjiang have consistently scored over 0.9 since 2001.

These provinces could be further boosted with the implementation of the ‘Northeast

revitalization’ programme which has produced positive effects for economic growth in

the region. 14 As a matter of fact, the Northeast Revitalization Office of the State

Council approved over 260 ICT projects amounting to four billion yuan (US$481

million) in an overall plan for the development of the ICT sector as part of the economic

revitalization of the northeastern region.15 In the western region, the effect of ICT on

technical efficiency is lifted by the higher scores achieved by Sichuan and Shaanxi

provinces.

13 Figure 7.5 illustrates the contribution of ICT to efficiency in the Chinese regions. The efficiency scores are generated by FRONTIER 4.1. 14 Headed by the Chinese Premier Wen Jiabao, the ‘Northeast revitalization’ programme was initiated when an office in charge of affairs was formed in the State Council in October 2003. The programme provides preferential policies and financial support aimed at reviving the industrial bases and spurring the economic growth of Northeast China (Dong, 2006). 15 “IT giants to assist in Northeast revitalization”, China Daily (New York: July 23, 2004).

179

180

7.7 Conclusion

This chapter finds evidence that ICT investment has a significantly positive effect on

regional technical efficiency in China during the 1990s and early years of the 21st

century. As such, ICT investment is expected to be an important driver of China’s

economic growth. Although most of the investment is pumped into the coastal region

and municipal cities, the rising technical efficiency of the central and western regions

suggest a rapid catch-up of the latter with the more developed regions within the next

decade. The exceptional performance of the three north-eastern provinces indicates the

strong priority given to development in these areas.

There is thus a case for greater investment in infrastructure and ICT equipment,

especially in the central and western regions. While the Japanese experience has shown

the rate of return on ICT capital stock to be higher than that on other forms of capital,

thereby encouraging policies which stimulate ICT investment (Miyagawa et al., 2004),

there is no reason why China, having the potential for training of a much larger base of

skilled labour to better utilize its ICT resources, could not do the same.

There are areas for further research on this field of study. China’s economic

efficiency could be better evaluated using industrial or firm-level data. The issue of

factor re-allocation between ICT and non-ICT capital, as has been studied for developed

economies, has so far been unaccounted for. It is unknown as to whether there is any

substitution of ICT capital for non-ICT capital, as data on the price of ICT capital is

unavailable. Thus it still remains to be seen whether the same substitution has taken

place as shown in the developed countries. Finally, the inclusion of other variables in

the analysis such as openness, infrastructure and human capital could be taken into

consideration so that the impact of these factors on China’s growth can be assessed.

Figure 7.5 The effect of ICT on technical efficiency in China’s regions, 1995-2004

0

0.2

0.4

0.6

0.8

1

1.2

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

TE e

stim

ates

Municipal cities Eastern region Central region Western region National

181


Table A7.1 Real ICT investment in China’s regions, 1996-2004 (million yuan) Region/Province 1996 1997 1998 1999 2000 2001 2002 2003 2004

Beijing Tianjin

Shanghai Hebei

Liaoning Jiangsu

Zhejiang Fujian

Shandong Guangdong

Guangxi Hainan Shanxi

Inner Mongolia Jilin

Heilongjiang Anhui

Jiangxi Henan Hubei Hunan

Sichuan Guizhou Yunnan Shaanxi

Gansu Ningxia

Xinjiang

212.91 334.79 374.14 135.86 34.75 229.20 39.18 76.79 175.62 509.84 5.76 0.26 3.00 10.27 24.09 172.35 9.23 21.62 128.34 28.38 60.34 142.74 5.73 0.27 61.21 6.78 0.00 0.55

130.39 210.27 162.74 46.42 36.24 138.12 23.07 51.51 126.26 386.62 17.25 7.75 6.50 1.21 12.69 376.90 9.23 8.93 20.68 15.35 37.88 108.03 2.46 1.07 22.71 2.86 0.41 0.10

213.60 1388.91 761.11 96.83 97.05 129.34 47.74 120.20 89.70 843.91 16.16 4.04 13.52 0.94 8.38 51.91 15.72 21.10 54.85 56.78 34.82 164.74 8.78 2.52 65.39 8.81 0.24 0.24

189.27 485.92 474.82 81.75 105.94 138.84 198.67 89.08 126.27 2429.24 9.00 6.39 6.22 8.20 158.98 54.64 14.31 6.50 9.84 55.10 68.91 179.38 9.44 2.58 73.97 7.73 1.84 0.26

244.27 476.47 675.86 38.38 144.21 184.76 115.36 158.79 178.90 1526.41 8.57 6.82 11.01 0.00 43.66 24.45 16.92 3.78 43.95 37.49 143.46 250.71 18.93 2.12 151.84 16.34 2.94 0.00

156.20 1019.56 1993.04 136.30 188.98 359.24 336.56 302.67 370.50 2360.05 24.05 16.34 14.92 0.30 66.64 23.15 82.87 19.91 141.64 225.54 122.64 392.16 39.06 1.78 203.59 28.75 20.26 2.56

131.61 324.59 1653.83 118.79 186.35 661.19 374.88 224.43 403.67 2701.66 3.96 6.59 10.12 2.32 100.14 1.29 78.13 49.34 76.49 203.06 119.98 349.60 30.14 1.56 96.85 14.29 2.13 0.09

121.03 274.28 1221.36 259.01 213.00 3381.34 484.77 424.48 622.30 2478.10 121.15 19.22 5.31 14.37 16.46 7.82 182.85 78.90 95.11 236.83 17.84 185.85 124.84 12.34 50.37 50.60 1.10 0.00

506.43 343.54 2063.06 142.40 161.77 3682.07 421.60 298.47 571.43 2499.45 65.04 1.72 148.97 59.09 51.67 6.96 54.86 125.27 69.72 91.73 41.20 338.48 135.96 18.22 65.58 45.64 0.27 0.00

Source: China Statistical Yearbook on High Technology Industry 2002-2004.

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Table A7.2 ICT capital stock in China’s regions, 1995-2004 (million yuan) Region/Province 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Beijing Tianjin

Shanghai Hebei

Liaoning Jiangsu

Zhejiang Fujian

Shandong Guangdong

Guangxi Hainan Shanxi

Inner Mongolia Jilin

Heilongjiang Anhui

Jiangxi Henan Hubei Hunan

Sichuan Guizhou Yunnan Shaanxi

Gansu Ningxia

Xinjiang

566.85 799.80 902.69 325.74 95.65 516.59 84.04 157.91 413.95 1174.06 12.72 0.68 8.17 27.27 62.58 435.50 20.25 53.33 295.53 68.82 146.14 349.02 16.10 0.72 171.63 20.28 1.06 1.40

694.73 1014.63 1141.43 412.74 116.05 668.30 110.62 211.01 527.47 1507.79 16.57 0.84 9.93 33.46 77.29 542.53 26.44 66.95 379.54 86.88 184.55 439.41 19.41 0.89 207.09 24.03 0.90 1.74

720.91 1072.70 1132.96 397.25 134.88 706.18 117.10 230.86 574.61 1668.24 31.33 8.46 14.94 29.65 78.38 838.05 31.71 65.83 343.29 89.20 194.75 481.53 18.96 1.82 198.74 23.28 1.18 1.58

826.37 2300.70 1724.13 434.50 211.69 729.59 147.28 316.43 578.12 2261.92 42.79 11.23 26.22 26.14 75.01 764.25 42.67 77.06 346.65 132.60 200.36 574.04 24.90 4.07 234.32 28.60 1.24 1.58

891.68 2441.52 1940.32 451.07 285.88 758.99 323.85 358.05 617.67 4351.87 45.37 15.94 28.50 30.42 222.74 704.26 50.58 72.00 304.49 167.81 239.22 667.31 30.61 6.04 273.15 32.04 2.90 1.60

1002.20 2551.76 2325.14 421.79 387.20 829.90 390.63 463.13 703.92 5225.50 47.14 20.37 35.23 25.86 232.99 623.07 59.92 49.61 302.77 180.13 346.79 817.92 44.95 7.26 384.01 43.57 5.41 1.36

1008.07 3188.56 3969.41 494.82 518.10 1064.66 668.60 696.33 968.84 6801.72 64.12 33.66 44.87 22.28 264.68 552.76 133.80 75.14 399.00 378.65 417.41 1087.39 77.26 7.95 530.00 65.78 24.86 3.72

988.47 3034.86 5027.83 539.39 626.73 1566.15 943.19 816.31 1227.18 8483.12 58.46 35.20 48.25 21.26 325.12 471.14 191.85 113.21 415.63 524.91 474.78 1273.88 95.81 8.32 547.35 70.20 23.26 3.25

961.23 2853.91 5495.02 717.49 745.71 4712.57 1286.48 1118.34 1665.40 9688.75 170.85 49.14 46.33 32.44 292.81 408.28 345.92 175.13 448.40 683.01 421.40 1268.65 206.28 19.41 515.61 110.26 20.87 2.76

1323.47 2769.36 6733.82 752.27 795.63 7687.76 1515.11 1249.06 1987.02 10734.89 210.26 43.49 188.34 86.67 300.55 329.35 348.89 274.13 450.86 672.29 399.39 1416.84 311.29 34.72 503.85 139.36 18.01 2.35

183

Chapter 8

DEMAND FOR ICT SERVICES IN CHINA 8.1 Introduction

Up till now, the dissertation has been focusing on examining how information and

communications technology (ICT) contributes to economic growth in China. Output

growth generated through technological progress and increases in inputs is known as the

‘supply-side’ effect. However, this is only half of the story. Growth is also generated by

aggregate demand, as production capacity can only be fully utilized if there is sufficient

demand (Seiter, 2005).

China has built up a massive network of ICT infrastructure over the past few

decades to meet the strong growth in demand for telecommunications and other ICT

services. The definition of what is covered by ‘ICT services’ has changed dramatically

within the recent few years with a broad range of applications being introduced in the

market, which include gaming, multimedia messaging, emails, fax, voicemails and

streaming video.1 As examined in Chapter 2, the application of 3G (third generation)

technology will be a main force driving demand for a wide range of ICT products such

as mobile phones, personal digital assistants (PDAs) and other advanced electronic

communications devices.2 Therefore it is important to establish a model that estimates

and forecasts the demand for ICT services in China in the near future.

This chapter has two main objectives, i.e. to estimate the demand elasticity of

ICT services and compare China’s with other countries; and to project demand for ICT

services in the near future till 2010. It contributes to current literature that has largely

focused on the supply-side growth accounting framework, mainly the contribution of

ICT capital/investment to economic and labour productivity growth. Projection of ICT

demand would be useful for ICT policy makers in China as well as businesses making

inroads into the Chinese market.

The chapter is structured as follows: It first reviews the literature that focuses on

estimating demand for ICT services, which generally looks at the telecommunication 1 “3G License to Drive Demand for Wireless Test Equipment in China”, PR Newswire (New York: September 27, 2006). 2 Ibid.

184

and computer markets. The chapter then proceeds with estimating demand functions for

telecommunications and computers in China. The estimation results are then employed

to predict demand for telecommunications and computers in China till the year 2010.

Finally, the chapter rounds up with a discussion of the growth prospects for China’s ICT

market in the near future.

8.2 Literature review

How is demand for ICT defined and measured? Broadly speaking, there are two main

components of demand for ICT, namely, telecommunications and computers (which

covers hardware equipment and software application services). Theoretically, the

demand curve for ICT assumes the ordinary downward sloping form, where quantity

demanded is inversely dependent on price and positively on income (Brynjolfsson,

1994). Castro and Jensen-Butler (2003), who examined the notion that ICT will

promote regional economic convergence by developing a regional demand model,

define the potential demand as ‘the utility for the set of services when the number of

subscribers and the traffic that they generate is perceived as approaching infinity.’ In

other words, demand for ICT could be measured in terms of the number of subscribers

or calls traffic.

8.2.1 Demand for telecommunications

The notions of network externalities as a feature of telecommunications demand are

explored by Blonski (2002) and Varian (2003). The former pointed out two important

features of the telecommunications market - the size of a network which ‘represents a

force towards uniformity and thus monopoly’ and non-linear pricing owing to ‘the

possibility of price discrimination between heterogeneous customers’ (Blonski, 2002:

96). Similarly, Varian (2003) distinguished between the effects of ‘direct and indirect

network effects’ on price discrimination in high technology industries.

Authors who examine demand models for ‘telecommunications’ of specific

countries usually refer to fixed line telephones (Nadiri and Nandi, 1997; Das and

Srinivasan, 1999). Nadiri and Nandi (1997) estimated the determinants of demand for

the US telecommunications industry over the period 1935-87. They pointed out that

price and income alone cannot explain the exponential growth in telephone demand, and

therefore introduced a variable, i.e. the share of tertiary sector to non-agricultural

185

employment, to capture the effect of the changing structure of the US economy on

telecommunications demand. Das and Srinivasan (1999) estimated price elasticities of

demand for telephone usage in India for the period of 1992-97 using both national level

time-series and pooled state level cross-section data. Similar to Nadiri and Nandi (1997),

the authors also included the share of tertiary services in GDP as an explanatory

variable for telephone usage in their model.

A comparison of the two papers yields some interesting findings concerning the

price and income elasticities of telephone demand. Both regression results have shown

negative price elasticity and positive income elasticity, as postulated in the theory of

demand. The magnitude of elasticity coefficients with respect to price and income in the

US, however, are lower (-0.34 and 0.12) compared with those of India (-0.46 and 1.9)

respectively (Table 8.1). In another analysis of price elasticity of telecommunications

demand, Hackl and Westlund (1995) observed a range of -0.26 to -0.51 for the UK,

Germany and three Scandinavian countries. The evidence about the price elasticity of

demand being higher in less developed countries is supported by Martins (2003), who

found that the price elasticity of telecommunications demand range from -1.2 in richer

countries to -1.4 in poorer countries, which support the underlying theory that demand

is more elastic in smaller markets where the diffusion process is in its initial phase.

With the changing structure of the telecommunications industry as a result of

innovation and convergence with other ICT sectors, the telecommunications market has

become more differentiated ever since the last two decades, most evidently the rapid

growth of the mobile phone market. Therefore, literature that focuses on the demand for

mobile telephony has emerged in recent years. Madden and Coble-Neal (2004)

investigated the impact of fixed-line network on mobile telephone subscription for 58

countries from 1995-2000. They found the price elasticities with respect to fixed-line

telephones and mobile phones to be 0.12 and -0.05 respectively, and income elasticity

to be 0.03. Iimi (2005) estimated the demand for cellular phone services in Japan for the

period 1996-99 using a nested logit model, focusing on the effects of product

differentiation and network externalities on demand. It was found that the market for

cellular phone services to be highly product-differentiated and that the demand for such

services is highly price-elastic, with estimated elasticity ranging from 1.30 to 2.43.

Other authors focused on different segments of the telecommunications market, such as

the demand for second or additional telephone lines (Duffy-Deno, 2001; Eisner and

186

Waldon, 2001) and demand for international message telephone services in Europe

(Madden, Savage and Tipping, 2001).

Table 8.1 A comparison of price and income elasticity in the telecommunications

market Author Country Price elasticity Income elasticity Hackl and Westlund (1995) Nadiri and Nandi (1997) Das and Srinivasan (1999) Martins (2003)

Europe US India Developed countries Less developed countries

-0.26-0.51 -0.34 -0.46 -1.21 -1.38

0.18 1.9 0.40-0.92

The starting point in any demand analysis is normally to derive the consumer’s

utility function based on the consumer choice model. Shy (2001) constructed a demand

curve for telecommunications services derived from the utility function for η consumers

who are connected to the service, as follows:

UC = ⎩⎨⎧ −0

pqα

⎭⎬⎫

eddisconnectconnected

where q represents the number of subscribers connecting to the service, p is the

connection fee to the service, and α measures the degree of importance of the service to

a consumer. The consumer obtains a positive utility when UC = αq – p > 0. In a more

general form, if total subscription to a phone service is represented by δi, the size of a

network, N, can be defined as:

∑=

=M

iiN

1δ

where M is the population size that represents the maximum number of potential

subscribers to the telecommunications service (Lee and Lee, 2006).

187

The theory of network externalities tells us that the number of consumers

connected to the telephone network has a positive effect on the demand for access to

telephone services, since the utility of each consumer increases with each increase in the

total number of other consumers using the same products or services (Shy, 2001). Being

a ‘network good’, telecommunications services are subject to network externality

effects in which higher utility can be offered to customers with a larger size of the

network. This means that the utility of current subscribers will be increased by new

subscribers joining the network as the latter ‘enables existing subscribers to obtain

additional benefits from the ability to make or receive calls from him/her’ (Lee and Lee,

2006; Madden, Coble-Neal and Dalzell, 2004). In other words, one would not subscribe

to a phone service, especially the mobile, if there is nobody to talk to!

Another type of externality in the literature of telecommunications is the ‘call

externality’. In many countries, the caller pays for making calls, but not the receiving

party. In making an outgoing call, the caller considers only his/her own benefits and the

price of the call, but the receiving party benefits from answering the calls as well (Lee

and Lee, 2006). Assume that a consumer seeks to maximize his/her utility function from

making calls, subject to an income constraint. Further assume that call externality is

incorporated in the utility of an individual, which implies that the number of existing

subscribers making and receiving calls affects the demand for calls. The utility function

of a consumer, U, can be expressed as follows:

U = U(q, y, N) (8.1)

where q represents the number of calls, y is income of the consumer and N is the size of

network. The budget constraint further depends on access (or connection) and call

charges, given by:

(pa + pcq) + pzz = y (8.2)

where pa and pc are the access charge and call charges respectively, and pzz refers to the

total expenditure on all other goods and services. By aggregating all individual demand

for calls in the entire telecommunications market, the total demand for calls, Q, can be

expressed as:

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Q = f(pa, pc, pz, N, Y) (8.3)

where N and Y represent the size of network and gross income respectively.

From Equation (8.3), a basic model of the demand for telecommunications,

which is a function of a set of composite prices, income and the size of network, can be

formulated. Assuming the demand curve for telecommunications service in a log-linear

form, Equation (8.3) can be re-expressed as:

(8.4) 43210 ααααα YNPPeQ ca=

where Pa and Pc are the access and call charges divided by the consumer price index

(CPI) respectively (and therefore internalizing the prices of other goods and services in

the model), N is the size of the network, Y is gross income deflated by the CPI, and α0,

α1, α2, α3 and α4 are the parameters to be estimated.

There has been some recent works which focus on estimating the growth of

mobile telephony (Madden and Coble-Neal, 2004; Madden, Coble-Neal and Dalzell,

2004). Madden and Coble-Neal (2004) expressed the utility of a telephone network

subscriber with income Y and a network of size N, as u(Y, NF, NM) where NF and NM

represent the number of fixed-line and mobile phone network subscribers respectively.

The equilibrium mobile telephony network size was then given as:

lnNMt = μ + α lnλt + β lnYt (8.5a)

lnNMt = μ + α lnλt + β lnYt + δ lnNFt (8.5b)

where λt is the price of subscription for fixed line and mobile services.3 Equation (8.5a)

estimates the demand for telecommunications by examining the factors affecting the

growth of mobile telephony. Equation (8.5b) examines the substitution effect between

fixed-line and mobile telephony by including the network size (or subscriber base) of

the former in the model. The models derived from Equations (8.4) and (8.5) are relevant

3 See Madden and Coble-Neal (2004: 521) for the meaning and derivation of μ and λt.

189

to this dissertation in determining the demand functions to be estimated for China’s

telecommunications market in section 8.3.

8.2.2 Demand for computers

Attempts to estimate the demand for computers went back to the 1960s when Gregory C.

Chow estimated a demand equation of ‘US-made general-purpose digital computers’ for

the period of 1955-65, which was to be used for forecasting (Chow, 1967). Using a

Gompertz growth curve that shows how computer users adjust their computers to an

equilibrium level, which is constantly moved upward due to the price of computers

falling by about 20% per year, Chow obtained an estimate of the price elasticity of

demand to be -1.44.

The demand function for computers has also been estimated by Brynjolfsson

(1994) and Stavins (1997). Brynjolfsson (1994) estimated the demand function for

computers based on the Marshallian and Exact consumer surplus calculations. Using

data of eight sectors on ‘office, computing and accounting machinery’ (OCAM) in the

US from 1970 to 1990, the author estimated the price elasticity of -1.33, and income

elasticity of 3.45. Stavins (1997) estimated the demand elasticity for US personal

computers from 1976 to 1988. Using the two-stage least squares (2SLS) estimation

method, the price elasticity of demand averaged -6.3, ranging from -2.9 in 1977 to -7.2

in 1988.

Other works have focused on the determinants of demand for computers

(Pohjola, 2003; Chinn and Fairlie, 2004). Pohjola (2003) reviewed the determinants of

the cross-country diffusion and adoption of ICT, and estimated a statistical model to

identify the most important determinants of real spending on computer hardware per

capita in a panel of 49 countries for the period 1993-2000. He found the most important

determinants of computer use to be the level of income, the relative price of computers

and the stock of human capital. The elasticity of human capital with respect to computer

spending is relatively large, at 2.97-3.02, compared to income elasticity of 0.92-1.08

and price elasticity of -1.10 (Table 8.2). Such results emphasize the importance of

education for the adoption of ICT, as Pohjola suggested a 10% increase in the years of

schooling is associated with a 30% increase in computer spending per capita.

190

Table 8.2: A comparison of price and other elasticity in the computer market Author Country Price elasticity Income

elasticity Human capital elasticity

Chow (1967) Brynjolfsson (1994) Pohjola (2003)

US (1955-65) US (1970-90) 49 countries (1993-2000)

-1.44 -1.33 -1.10

3.45 0.92-1.08

2.97-3.02

In another study, Chinn and Fairlie (2004) identified the determinants of cross-

country disparities in personal computer and Internet penetration by examining a panel

of 161 countries over the period 1999-2001. In contrast to Pohjola (2003) who used the

US price of computers as that for all economies, the authors estimated a reduced form

equation for computer penetration rates, as it was not clear to them that the underlying

structural parameters could be identified since the price index looks similar to a

downward sloping linear trend. As such, Chinn and Fairlie included several sets of

variables, such as economic variables (income per capita, years of schooling, illiteracy,

trade openness), demographic variables (dependency ratio, urbanization rate),

infrastructure indicators (telephone density, electricity consumption),

telecommunications pricing measures and regulatory quality. Their results suggest that

public investment in human capital, telecommunications infrastructure and the

regulatory infrastructure can mitigate the gap in PC and Internet use. This chapter draws

on existing literature to estimate a demand function for the telecommunications and

computer markets in China respectively. The estimation results will generate elasticity

values for the Chinese ICT market with respect to price, income and other exogenous

variables.

8.3 Modeling demand

8.3.1 Modeling demand for fixed-line telecommunications

The telecommunications market has been changing dramatically over the past decades

with the rapid introduction of new services such as the mobile and Internet phone

services. In China, as in other countries, the adoption of new technologies such as the

code division multiple access (CDMA) has created a fast-growing mobile telephony

market since the 1990s. Telecommunications demand is normally described in terms of

191

subscription and usage, which can be defined as access service and call service

respectively (Lee and Lee, 2006). However, subscription usually comes before usage, as

the consumer has to first pay for connection and subscription fees, and purchase

telephone equipment as well, prior to using the services provided by the

telecommunications carrier. This dissertation proposes to use subscription as the proxy

for telecommunications demand. To be more specific, the demand for fixed line and

mobile telephony is estimated separately.

Several factors can be attributed to account for the increase in demand for ICT

services in China. First, telecommunications demand is fuelled by rapid economic

growth which has stimulated increased demand for communications brought about by

rising investment and trade. The rise in demand for computers is also attributed to rising

income and educational standards of the population. The decline in phone connection

charges and falling prices of telecommunications and computing equipment has made

ICT services more affordable and accessible to a greater segment of China’s population.

As a measure of the aggregate size of the telecommunications market in China,

both fixed-line and mobile subscribers are used as the dependent variable in different

models. A general form of the demand equation based on the framework of Lee and Lee

(2006) is used in this chapter. In order to derive the respective price and income

elasticity of telecommunications demand, a model specified in Equation (8.4) is

transformed into a log-linear form, where the total number of telecom subscribers is

regressed against the price as well as income:

tit

m

ii

tt u

PP

PY

Q +⎟⎠⎞

⎜⎝⎛+⎟

⎠⎞

⎜⎝⎛+= ∑

=

lnlnln1

10 βββ (8.6)

where Qt refers to total telecommunications subscribers (either fixed or mobile), Y/P is

gross domestic product divided by the consumer price index (CPI) in constant prices,

and Pit/P represents the set of prices deflated by the CPI. In this case, in order to take

into account the price trend in China, the ICT hedonic price index obtained from the

US’ Bureau of Economic Analysis is divided by the Chinese CPI in constant prices. To

estimate the demand for fixed-line telephones in China, Equation (8.6) is further re-

written as:

192

lnFSt = α + βlnPt + δ lnYt + ut (8.7)

where lnFSt represents the number of fixed-line subscribers in China, Pt refers to the

ICT price index for China and Yt denotes real GDP per capita in constant prices, while β

and δ are the coefficients which give us the price and income elasticity respectively.

The model can further incorporate control variables that account for dynamic

changes occurring in the Chinese telecommunications market, based on the framework

of Vagliasindi et al. (2006). Both fixed and mobile phone services are increasingly

complemented by the use of Internet which is accessible not only through the fixed

network, but now with enhanced mobiles that offer IP telephony. A dummy variable, Dt

(equals to 1 when Internet usage began in 1994) captures the increasing use of Internet

by Chinese subscribers. By including the dummy variable, the model now takes the

following form:

lnFSt = α + βlnPt + δ lnYt + λDt + ut (8.8)

where Dt = 1 for observations in 1994-2005

= 0, otherwise (i.e. for observations in 1978-1993)

To further test for mobile-fixed substitution effects, the dissertation will also

include mobile subscription in Equation (8.8), which is then re-written as:

lnFSt = α + βlnPt + δlnYt + γlnMSt + λDt + ut (8.9)

where MSt refers to the number of mobile subscribers in China. The inclusion of the

latter may bring about endogeneity in the above model which will be dealt with in the

empirical exercise.

8.3.2 Modeling demand for mobile telecommunications

Next, the chapter goes on to estimate the demand for mobile phones in China. Annual

data for mobile phone subscription is available for the period of 1988-2005, as the

mobile network was introduced in China only from 1987 onwards. A model similar to

that specified by Equation (8.7) is formulated, using the number of mobile phone

subscribers (MSt) as the dependent variable.

193

To reflect the telecommunications reforms that took place in China during the

1990s, a dummy variable is introduced for the competition resulting from the first

break-up of China Telecom in 1999. Competition was further intensified in 1999 when

China Unicom was given the privilege to compete against the incumbent China Mobile

by giving 10 per cent discounts on prices offered by the latter (Lu and Wong, 2003: 46).

Therefore, a dummy variable, Dt (equal to 1 if there is more than one operator in the

market) captures the presence of competition in the telecommunications market. The

demand for mobile phones is then expressed as:

lnMSt = α + βlnPt + δ lnYt + λD2 + ut (8.10)



To further test for the presence (or absence) of fixed-mobile substitution effects,

the framework of Vagliasindi et al. (2006) is also applied in the model, by adding the

number of fixed-line subscribers as an explanatory variable. The model described in

Equation (8.10) is re-expressed as:

lnMSt = α + βlnPt + δ lnYt + γlnFSt + λDt + ut (8.11)

where MSt is dependent on price (Pt) which is given by the ICT hedonic price index

deflated by the Chinese CPI as in Equation (8.6), real income per capita in constant

prices (Yt) and fixed-line subscription (FSt), while β, δ and γ are the parameters to be

estimated. Again, the inclusion of the latter may bring about endogeneity in the above

model which will be dealt with in the empirical exercise.

8.3.3 Modeling demand for computers

Stavins (1997) specified the utility function of a computer consumer, assuming that

each consumer, i, chooses a computer model, m, to maximize his utility, uim, which is

positively related to the quantity of embodied characteristics, zm, and negatively related

to the model price, Pm, such that:

uim = δi zm - αPm + εim (8.12)

194

where δi represents the consumer’s valuation of quality of that model, and εim is a

random component.

Chow (2002) has estimated a model for forecasting demand for computers in

China. The model, however, looks at only the effect of price and income on the demand

for computers, but it does not take into account other factors that have been discussed in

some literature, such as the level of education, trade openness and infrastructure

indicators such as telephone density and electricity consumption (Pohjola, 2003; Chinn

and Fairlie, 2004).

Demand for computers is assumed to be a derived demand from firms and a

final demand from consumers (Chinn and Fairlie, 2004: 8). To integrate both forms of

demand into an estimation model for the computer industry in China, we first look at

the various factors identified in Chinn and Fairlie (2004) as the determinants of

computer use. Owing to limited data available that can be used as an indicator of

computer demand, and taking into account the utility function in Equation (8.12), the

demand model for computers is specified as follows:

lnCt = α + βlnPt + δlnYt + μt (8.13)

where the dependent variable, Ct, is taken to be the sales volume of computers, which is

influenced by price (Pt) and income (Yt). The sales volume of computers is also used as

an indicator of demand in a presentation by the State Science and Technology

Commission of China.4 One problem that arose is that an adequate time series data on

sales of computers is not available from statistical sources. However, in an analysis on

the supply side statistics of China’s ICT products, Katsuno (2005) has shown that sales

of PCs (desktops and laptops) are approximately close to their production volume

during the period of 1998-2001. Since a time series data on the production of PCs is

available from China Statistical Yearbook for the period of 1990-2005 (see Figure 2.8

in Chapter 2), it shall be used as a proxy for computer sales.

4 A powerpoint presentation by Mr Wu Yingjian, Director of Torch High Technology Industry Development Center, Ministry of Science & Technology of China. See http://www.ercim.org/ HPCN/docs/B4.pdf

195

As data on the price of computers in China is not available, the model uses the

ICT hedonic price index obtained from Chapter 5. The choice of using the US computer

price index is based on the assumption that computer prices are the same in all countries,

owing to the global nature of the computer market and competitiveness of the computer

industry (Pohjola, 2003). Yt is real GDP per capita in 1978 constant prices. To test the

robustness of the model, a dummy variable, Dt, which reflects the use of Internet, and

thus taking on the value of one from 1994 onwards is added to Equation (8.13). The

estimation model would now look like:

lnCt = α + βlnPt + δlnYt + ηDt + εt (8.14)



8.4 Data issues

As shown in Figure 2.4 of Chapter 2, the mobile penetration rate exceeded that of the

fixed telephone in 2003. Figure 8.1 further shows that the mobile phone market has

grown much more rapidly compared with the fixed line market as well as the national

output since its inception in the Chinese market in 1988. It has been established in

theory that ‘the growth of mobile can be expected to cause an initial increase in fixed

network traffic (due to complementarities) and subsequently a decline in the fixed

network (due to substitution effects ) (Vagliasindi et al., 2006). Therefore it would be

interesting to test whether such a case has occurred in China. To propose a model that

captures the substitution effect depends on the choice of a dependent variable which can

be used as a measure of telecommunications demand. One common proxy of

telecommunications demand is the usage of telecom services. Yang and Olfman (2006)

discussed two dimensions of telecommunications usage, that is, the actual traffic or

traffic intensity, measured in number of calls going through a network; and the number

of lines or subscribers registered on the network which reflects the availability of

telecom infrastructure or the size of the market. The call traffic was used as a dependent

variable in Das and Srinivasan (1999), but such data is unavailable in Chinese statistical

sources.

196

197

Annual data for fixed line and mobile subscription are obtained from China

Statistical Yearbook and the Ministry of Information Industry website, which is

available for the period of 1978 to 2005. Data for access charges and call charges is not

available from any Chinese statistical sources. Therefore the dissertation will use the

ICT hedonic price index obtained from the US national accounts as a proxy variable for

price. The application of this principle is based on the assumption that Chinese

machines are manufactured using foreign technology and therefore any pattern of price

changes should resemble that of the US. The demand curve for Chinese

telecommunications is assumed to be downward sloping, reflecting a market that is

facing increased demand and decline in prices. This assumption is made since the

introduction of competition into the mobile (as well as fixed line in 2001) telephony

market has provided incentives for carriers in China to lower prices. The negative

correlation between pricing and the fixed-line as well as mobile phone markets are

illustrated in Figures 8.2 and 8.3 respectively.

A positive relationship between income (measured in terms of either gross GDP

or GDP per capita) and telecommunications demand is expected, as greater income

implies greater affordability to subscribe to a phone service (Madden, Coble-Neal and

Dalzell, 2004). This proposition is well illustrated in Figure 8.4 which shows that both

the fixed line and mobile subscription have generally increased in tandem with rising

income in China, with the mobile market expanding at a much faster pace. Income

(GDP) is expressed in real terms, deflated by the consumer price index in constant

prices obtained from China Statistical Abstract 2006.

The total demand for telecommunications services constitutes residential and

industrial demand. However, no separate data are available for residential and industrial

telecommunications. Therefore the demand model is based on a single equation. Data

available from China Statistical Yearbook and the Ministry of Information Industry

(MII) website suggests that either the expansion of local switchboard capacity or the

number of phone subscribers could be used as an indicator of the demand for telecom

services as it reflects the expanding subscriber base and increasing complexity of the

telecom market. Data for the number of phone subscribers can be obtained from Lu and

Wong (2003) and China Statistical Abstract 2006. Data for all variables is available for

the period 1978-2005, whereas that of mobile phone subscription only appeared from

1988 onwards.

Figure 8.1 Growth rate of fixed-line, mobile and GDP in China, 1979-2005

-20

0

20

40

60

80

100

120

140

1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

%

Fixed line growth Mobile growth GDP growth

Source: State Statistical Bureau, China Statistical Yearbook, Yearbook of China’s Electronics Industry and China Statistics Yearbook on High Technology Industry (various issues).

198

Figure 8.2 Log-linear relationship between fixed line subscribers and ICT price index in China, 1978-2005

y = -1.201x + 4.9009R2 = 0.978

0

1

2

3

4

5

6

7

-1 0 1 2 3 4 5

log (ICT price index)

log

(Fix

ed li

ne su

bscr

iber

s)


199

Figure 8.3 Log-linear relationship between mobile subscribers and ICT price index in China, 1988-2005

y = -3.0356x + 4.1825R2 = 0.9465

-6

-4

-2

0

2

4

6

8

-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

log (ICT price index)

log

(Mob

ile su

bscr

iber

s)


200

Figure 8.4 Correlations between fixed-line subscription, mobile subscription, and income per capita in China (1978-2005)

-8

-6

-4

-2

0

2

4

6

8

10

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

log

Fixed line Mobile GDP per capita


201

202

8.5 Estimation results

8.5.1 Estimation results for fixed-line telecommunications demand

The chapter begins with estimating the demand for fixed line telephone market, using

annual data for the period of 1978-2005. The initial estimates of the parameters

specified by Equations (8.8) and (8.9) are presented in Table 8.3. The first model

specification (1) includes Internet use as the dummy variable, as described by Equation

(8.8). The estimation results show price and Internet use to be statistically significant all

levels, but not income. As the Durbin’s d-statistic of 0.4406 is less than dL = 1.181,

there is evidence of positive first-order serial correlation at all levels of significance.

Therefore, a corrective measure using the Cochrane-Orcutt Method is proposed to

specification (1) above. The test result shows no conclusive evidence of the presence of

positive first-order serial correlation as the d-statistic of 1.2712 lies between dL = 0.933

and dU = 1.696. In this model specification (2), income is found to be statistically

significant at the 10% level.

Next, the estimates based on Equation (8.9) which includes mobile subscription

as an explanatory variable to test for mobile-fixed substitution effects is obtained, i.e.

specification (3). Note that in this case, there are only 18 observations (against 28 in the

previous two model specifications) as mobile subscription only started in 1988.

Compared with specification (1), all explanatory variables except the intercept term in

(3) are statistically significant at the 5% level or lower, but the d-statistic of 0.9112 is

below dL = 0.933, suggesting evidence of a positive first-order serial correlation.

Therefore, the model is further tested by including both mobile subscription and the

dummy variable for Internet use into Equation (8.9), specified as (5). All explanatory

and dummy variables are found to be statistically significant at all levels. There is no

evidence of positive first-order serial correlation at all levels of significance as the d-

statistic of 2.1772 is greater than dU = 1.604.

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Table 8.3 Estimation results: fixed-line telephone demand

Model specification Explanatory Variables (1) OLS (2) GLS (3) OLS# (4) OLS# (5) GLS# (6) OLS#

Constant Price Income per capita Mobile subscription Mobile subscription (lagged) Dummy Internet use R2

Adjusted R2


2.1092 (0.533) -0.6694*** (-3.850) 0.1532 (0.280) - 0.7012*** (4.037) 0.9898 0.9885 0.1986 28 0.4406

0.2765 (0.149) -0.8027*** (-8.885) 0.4365* (1.699) - 0.1676** (2.368) 0.9990 0.9989 0.0621 28 1.2712

0.2462 (0.188) -0.5029*** (-6.757) 0.4535** (2.476) 0.1326*** (6.383) - 0.9987 0.9984 0.0590 18 0.9112

-1.1438 (-1.502) -0.4817*** (-11.695) 0.6340*** (5.995) 0.0977*** (7.545) 0.2088*** (5.750) 0.9996 0.9995 0.0325 18 2.1772

-1.3087 (-1.599) -0.4799*** (-12.623) 0.6562*** (5.770) 0.0946*** (5.817) 0.2228*** (5.649) 0.9996 0.9994 0.0349 18 2.0495

-0.5477 (-0.616) -0.4032*** (-7.879) 0.5653*** (4.610) - 0.1328*** (5.716) 0.1601*** (3.528) 0.9996 0.9994 0.0332 17 2.3227

Note: Figures in parentheses are the t-ratios. *, ** and *** indicate significance at 10%; 5% and1%. # The observed time series period for model specifications (3)-(5) is 1988-2005, as they take into account the mobile subscription which started only in 1988.

A final modification to specification (4) by using the Cochrane-Orcutt method

produced similar results. However, specification (4) is preferred as it has the lowest

standard error of regression and the highest value of d-statistic among all of the

specifications. Based on specification (4), this study reveals the price and income

elasticity of fixed line demand in China to be approximately -0.5 and 0.63 respectively.

The price elasticity is found to be exceptionally low even when compared with that of

developed countries, while income elasticity is lower than that of India, but slightly

higher than those of developed countries (see Table 8.1).

Due to inconclusive evidence concerning the presence of positive first-order

serial correlation in model specifications (1), (2) and (3) based on the d-statistic, another

test, the Breusch-Godfrey (BG) test is conducted using EView 5.0. For specifications (1)

and (2), the null hypothesis of no serial correlation is rejected at all levels of

significance. For specification (3), the test statistic of χ2(1) = 5.3301 (with p-value of

0.021) is obtained, suggesting that the null hypothesis of no serial correlation is rejected

at 5%, but not rejected at 1% level of significance.

In addition, the regression models are tested for structural stability by

introducing interactive dummy variables. Equations (8.8) and (8.9) are respectively

expressed as:

lnFSt = α + βlnPt + δ lnYt + λ1 DPt + λ2 DYt + ut (8.15)

and lnFSt = α + βlnPt + δlnYt + γlnMSt + λ1 DPt + λ2 DYt + λ3 DMSt + ut (8.16)



All of the interactive dummies are found to be statistically insignificant at all levels, and

therefore the null hypothesis of no structural change is not rejected.

To take into consideration of potential endogeneity in specifications (3), (4) and

(5), a one-lagged period of mobile phone subscription (MSt-1) and a dummy variable for

Internet usage ( ) are included as explanatory variables in the regression model as

follows:

tD1

204

tttttt uDMSYPFS +++++= − 1413210 lnlnlnln ααααα (8.17)

where D1t = 1 for observations in 1994-2005


As shown in Table 8.3 as model specification (6), there is no evidence of

positive first-order serial correlation at all levels of significance as the d-statistic of

2.3227 is greater than dU = 1.900. This test has found all variables to be statistically

significant at all levels, including the dummy variable for Internet usage, except the

constant term.5

8.5.2 Estimation results for mobile telecommunications demand

Next, to estimate the demand for mobile phone market in China, a preliminary

regression is run, based on Equation (8.10) that uses competition introduced in 1999 as

the dummy variable. The estimation results are presented in Table 8.4. Shown as model

specification (1) in Table 8.4, all explanatory as well as the dummy variables are found

to be statistically significant as all levels. However, there is no conclusive evidence of

the presence of positive first-order serial correlation as the d-statistic of 1.1311 lies

between dL = 0.933 and dU = 1.696. To correct for the problem of serial correlation, the

Cochrane-Orcutt method is used, specified as (2). The d-statistic of 1.2811 is obtained,

which still points to indecision concerning the presence of positive first-order serial

correlation.

Finally, the variable of fixed-line subscription is added to the model specified by

Equation (8.11). Based on the OLS estimates, the income variable has become

statistically insignificant in model specification (3). The GLS in specification (4)

corrects for serial correlation, but income is still insignificant. Therefore, as a final

measure, the dissertation includes both fixed-line subscription and Internet use as a

dummy into the model. As a result, all of the explanatory and dummy variables in

model specification (5) are found to be statistically significant, although income is

5 Note that tests for unit root and stationarity are not considered for all empirical exercises in this chapter due to the fact that the results are potentially sensitive to the small sample size which is a limitation of this study. Owing to the limited number of observations, unit root tests would be unreliable with small sample sizes.

205

significant only at the 10% level. Therefore, specification (5) is preferred and will be

used for projection of the demand for mobile phone in China.

Even though specification (5) has the highest d-statistic of 1.5266, there is still

no conclusive evidence of a positive first-order serial correlation, as it lies between dL =

0.820 and dU = 1.872. The standard error of regression in (5) is also the lowest among

all of the specifications. Using specification (5) as the reference model, the price and

income elasticity with respect to mobile phone market in China are thus estimated to be

-0.67 and 1.12 respectively. As the elasticity values are higher in comparison with those

of the fixed-line market, it can be concluded that consumers in the mobile market are

more sensitive to changes in price and income. Higher income elasticity in the mobile

phone market could suggest that Chinese consumers would switch over to using the

mobile phone when affordability is no longer a problem with the improvement in living

standards and increase in income. On the other hand, for any increase in the price level,

consumer would exercise caution on the use of mobile phones, thus making calls on the

fixed-line telephone more often instead.

Due to inconclusive evidence concerning the presence of positive first-order

serial correlation in model specifications (1) to (5) based on the d-statistic, another test,

the Breusch-Godfrey (BG) test is conducted using EView 5.0. The null hypothesis of no

serial correlation is rejected at all levels of significance for specification (3), whereas for

specification (1), the test statistic of χ2(1) = 2.9287 (with p-value of 0.087) is obtained,

suggesting that the null hypothesis of no serial correlation is rejected at 10%, but not

rejected at 5% level of significance. It is proven serial correlation is absent in

specifications (2), (4) and (5) at all levels of significance.

In addition, the regression models are tested for structural stability by

introducing interactive dummy variables. Equations (8.10) and (8.11) are respectively

expressed as:

lnMSt = α + βlnPt + δ lnYt + λ1 DPt + λ2 DYt + ut (8.18)

and lnMSt = α + βlnPt + δlnYt + γlnFSt + λ1 DPt + λ2 DYt + λ3 DFSt + ut (8.19)


206

207

tttttt uDFSYPMS +

As shown in Table 8.4 as model specification (6), there is no conclusive evidence of

positive first-order serial correlation as the d-statistic of 1.6997 lies between dL = 0.779

and dU = 1.900. This test has found all variables to be statistically significant at all

levels, including the dummy variable for mobile competition, except the constant term.

Using the Breusch-Godfrey test, the test statistic of χ2(1) = 0.0584 (with p-value of

0.809) is obtained, suggesting that the null hypothesis of no serial correlation is not

rejected at all levels of significance.

Some other phenomena can also be observed from the regression obtained in

Tables 8.4 and 8.6. Competition and Internet usage have a significant impact on the

mobile phone and fixed-line market respectively. The opening up of the telecom market

to competition has further brought about an increase in consumer choices where the

mobile and Internet services are concerned. Consumers are also more likely to be

attracted to interactive mobile services such as the SMS and MMS, and more recently

the mobile Internet games. Such effects can be attributed to the presence of network

externalities.


To consider potential problems with endogeneity in specifications (3), (4) and (5)

in Table 8.4, a one-lag period of fixed-line subscription (FSt-1) and a dummy variable

for mobile competition (D2t) are included as explanatory variables in the regression

model as follows:




where D2t = 1 for observations in 1999-2005

++++= − 2413210 lnlnlnln α α α α α (8.20)

Model specification Explanatory variables (1) OLS (2) GLS (3) OLS (4) GLS (5) OLS (6) OLS Constant Price lncome Fixed-line subscription Fixed line subscription (lagged) Dummy First divestiture of China Telecom R2

Adjusted R2


-23.6708*** (-8.017) -0.9039*** (-16.140) 3.6481*** (9.013) - - 0.8800*** (3.943) 0.9970 0.9964 0.2335 18 1.1311

-22.5593*** (-5.923) -1.2026*** (-3.618) 3.5454*** (7.039) - - 0.3940* (1.876) 0.9980 0.9973 0.1846 18 1.2811

-9.9346** (-2.263) -0.5446*** (-8.491) 0.8532 (1.119) 1.5958*** (5.492) - - 0.9980 0.9976 0.1910 18 0.7349

-11.3727* (-1.988) -0.5708* (-1.984) 1.1659 (1.141) 1.4003*** (2.965) - - 0.9985 0.9980 0.1591 18 1.1888

-10.3335*** (-3.052) -0.6650*** (-10.768) 1.1223* (1.891) 1.2011*** (4.715) - 0.5203*** (3.251) 0.9989 0.9986 0.1472 18 1.5266

-12.2226 (-3.433)*** -0.8221 (-12.412)*** 1.6610 (2.861)*** - 0.7931 (3.617)*** 0.6271*** (3.470) 0.9984 0.9978 0.1656 17 1.6997


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Table 8.4 Estimation results: mobile telephone demand

8.5.3 Estimation results for computer demand

The demand for computers in China is estimated using the time series data for the

period of 1990-2005. The initial estimates of the parameters in Equation (8.13) are

presented as specification (1) in Table 8.5. Price and income are statistically significant

at the 5% level. With a Durbin d-statistic of 0.981 which is lower than dL = 0.982, there

is evidence of a positive first-order serial correlation. The GLS estimates in

specification (2) raised the value of d-statistic to 1.6159, which is greater than dU =

1.539, hence providing no evidence of serial correlation. However the explanatory

variables have become statistically significant only at the 10% level.

Therefore, the dummy variable for Internet use is added to the model, specified

from Equation (8.14). The estimation results are generated as specification (3) in Table

8.5. The dummy variable is shown to be statistically insignificant. In this case, there is

no conclusive evidence of the presence of positive first-order serial correlation as the d-

statistic of 1.1073 lies between dL = 0.857 and dU = 1.728. Given that specification (2)

has the highest d-statistic and the lowest standard error of regression), it is chosen to

estimate price and income elasticity, since Dt can be omitted from the model. This study

therefore estimates the price and income elasticity to be approximately -0.9 and 2.9

respectively, which are lower than those of the US in the 1990s.

Table 8.5 Estimation results: computer demand

Model specification Explanatory variables (1) OLS (2) GLS (3) OLS Constant Price Income per capita Dummy Internet use R2

Adjusted R2


-19.2730** (-2.230) -0.8462** (-2.364) 2.6705** (2.210) - 0.9765 0.9729 0.3737 16 0.9810

-20.8572* (-1.776) -0.8818* (-1.733) 2.8736* (1.751) - 0.9817 0.9767 0.3311 16 1.6159

-16.7017* (-1.833) -1.0333** (-2.510) 2.3404* (1.851) -0.3515 (-0.935) 0.9781 0.9727 0.3755 16 1.1073


209

By running the Breusch-Godfrey (BG) test on EView 5.0, conclusions on the

presence or absence of serial correlation can be reached. The null hypothesis of no serial

correlation is not rejected at all levels of significance for specification (2). For

specifications (1) and (3), the respective test statistics of χ2(1) = 3.4618 (with p-value of

0.063) and χ2(1) = 3.4324 (p-value of 0.064) are obtained, suggesting that the null

hypothesis of no serial correlation is rejected at 10%, but not rejected at 5% level of

significance.

Finally, the regression models are tested for structural stability by introducing

interactive dummy variables. Equation (8.14) is re-expressed as:

lnCt = α + βlnPt + δ lnYt + λ1 DPt + λ2 DYt + ut (8.21)





8.6 Projection of ICT demand

With the estimation results derived in the preceding section, the next focus of the

chapter is to project demand for the ICT market in China, namely, the fixed-line, mobile

and computer markets. Lanning et al. (1999) attempted to forecast demand for

telecommunications capacity. They present an alternative approach to the study of

telecommunications demand by building aggregate estimates for demand based on the

elasticity of demand for bandwidth. The same principle is applied to computer hardware

and software usage that is experiencing consistent growth in computing power.6 With

high demand elasticity in the ICT industry, as experienced in China, a drop in prices

would lead to a jump in consumer demand at a greater magnitude. Firms will have the

incentive to innovate since the fall in prices does not undermine their profits.

6 According to Moore’s Law, the computing capacity of microprocessors double every 18 months; whereas in optics, bandwidth capacity doubles every 12 months (Lanning, et al., 1999:5).

210

Chow (2002) presented a study on forecasting the demand for personal

computers in China for the period of 1997-98 due to limited data available, using a

mathematical model adapted from his earlier work, Chow (1967). For the purpose of

forecasting computer demand in China up to the year 2015, Chow used three income

elasticity estimates – the low 0.7988 estimate based on the mature US market, the high

1.693 estimate based on the Chinese new market, and the average 1.246 of the two.

8.6.1 Forecasting telecommunications demand

Projections of demand will be made separately for the fixed-line and mobile phone

markets. In the existing literature, two methods could generally be applied for

forecasting. First, the approach suggested by Lanning, et al. (1999) is based on

estimates of the elasticities. A comparison of price and income elasticities derived for

developed and developing countries can be used as a guide to impose the upper and

lower limits of demand elasticity with respect to telecommunications and computer

respectively. Based on this approach, the price elasticity of fixed-line

telecommunications could range from -0.465 to -0.475 for the low-growth, base case

and high-growth scenarios respectively; and income elasticity is estimated to range from

0.48 to 0.49 (Table 8.6).

The second approach is based on projected growth rates of the explanatory

variables. Price is estimated to decline at rates of 15-19% p.a., based on its average rate

of decline over the past years. The projected income growth is based on the average

growth rate over the past five years (from 2001 to 2005), with GDP growth ranging

from 8% to 10% (Tables 8.7).

It can be noted that projections based on the two approaches outlined above

produce a multitude of combinations. For instance, three projections can be derived

from a combination of high price elasticity with a high, base and low income elasticity

respectively. The same goes for projections based on the estimated growth rate of price

and income. This would create a total of 18 possible scenarios. Therefore, the

dissertation seeks to simplify the assumptions by limiting projections to those that

produce the highest and lowest possible as well as the base case scenarios. This is

defined by H-H, B-B, L-L where H, B and L represent the high, base and low elasticity

or growth rates respectively.

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Table 8.6 Estimated price and income elasticity for China’s fixed-line telecom

demand Growth scenario Price elasticity Income elasticity High elasticity -0.475 0.490 Base case -0.470 0.485 Low elasticity -0.465 0.480 Source: This study. Table 8.7 Estimated growth rate of price and income for China’s fixed-line telecom

demand Growth scenario Price (%) Income (%) High growth -0.190 0.100 Base case -0.170 0.090 Low growth -0.150 0.080 Sources: This study.

Projections based on the growth rate and elasticity of price and income is

illustrated in Figure 8.5. The forecast for the base case scenario in 2006 – 389 million

fixed-line subscribers – is slightly higher than the actual figure of 368 million provided

by the Ministry of Information Industry at the end of 2006.7 Fixed-line subscription is

projected to exceed 650 million by 2010 under the base case and low elasticity scenarios,

and 750 million under conditions of high elasticity. Forecast based on growth rates of

price and income generate similar results, with the number of subscribers exceeding 650

million in 2010 with low growth rates, and reaching almost 770 million with high

growth rates.

The forecast for growth of mobile phone subscription is based on the elasticity

and growth rate of price and income, similar to that of the fixed-line (Tables 8.8 and

8.9). It should be noted that although the estimated income elasticity for the mobile

phone market obtained in Table 8.4 is around 1.1, the projection used here is based on

an elasticity value of around 0.95, as using the former figure produced an

extraordinarily explosive growth figure. Projections based on growth rates and elasticity

of explanatory variables is illustrated in Figure 8.6.

7 Ministry of Information Industry, http://www.mii.gov.cn/.

212

Table 8.8 Estimated price and income elasticity for China’s mobile telecom demand

Growth scenario Price elasticity Income elasticity High elasticity -0.620 0.960 Base case -0.615 0.950 Low elasticity -0.610 0.940 Source: This study. Table 8.9 Estimated growth rate of price and income for China’s mobile telecom

demand Growth scenario Price (%) Income (%) High growth -0.190 0.100 Base case -0.170 0.090 Low growth -0.150 0.080 Sources: This study.

The forecast for the base case scenario in 2006 – 470 million mobile phone

subscribers – is slightly higher than the actual figure of 461 million provided by the

Ministry of Information Industry at the end of 2006.8 Mobile subscription is projected

to reach over 950 million by 2010 under the base case and low elasticity scenarios, and

more than 1.1 billion under conditions of high elasticity. Similarly, forecast based on

growth rates of price and income yield projected figures of more than 880 million and

1.1 billion subscribers in 2010 under conditions of low and high growth rates

respectively. A realisation of the high-elasticity or high-growth scenario would mean

that almost every citizen in China would own a mobile phone in 2010.9 The forecast of

this study is consistent with that of another report which projected the total number of

fixed and mobile telephone lines to exceed one billion in 2009.10

8.6.2 Forecasting computer demand

To forecast the demand for computers, using the elasticity approach, the price elasticity

could be estimated to range from -0.86 for the low-growth to -0.9 for the high-growth

scenario; whereas income elasticity is estimated to range between 2.85 to 2.9. The

growth rate for price and income is similar to that used for projection of

telecommunications demand (Tables 8.10 and 8.11).

8 In the first quarter of 2007, mobile subscribers reached 480.65 million in China. See Ministry of Information Industry, http://www.mii.gov.cn. 9 While it is impossible to expect every citizen (which includes dependants aged below 15) to own a mobile phone, this scenario implies that many citizens of working age may own two or more lines. 10 “China – The world’s largest telecom market and more to come”, Online Telecom Reports (May 2006), http://www.hottelecoms.com/cp-article-may2006.htm.

213

214

Table 8.10 Estimated price and income elasticity for China’s computer demand Growth scenario Price elasticity Income elasticity High elasticity -0.900 2.900 Base case -0.880 2.870 Low elasticity -0.860 2.850 Source: This study. Table 8.11 Estimated growth rate of price and income for China’s computer demand Growth scenario Price Income High growth -0.190 0.100 Base case -0.170 0.090 Low growth -0.150 0.080 Source: This study.

Projections based on growth rates and elasticity yield markedly different

scenarios (Figure 8.7). Computer usage is projected to increase from 80 million in 2005

to 106 million in 2006 under the base case scenario.11 By 2010, it is estimated to reach

almost 550 million and exceed 750 million users under conditions of base and high

elasticity; whereas based on the growth rate approach, it is projected to reach almost

700 million users under conditions of high growth rate.

8.7 Conclusion and growth prospects

The demand for telecommunications and computers in China will be shaped by rapid

technological progress that changes the market structure of the entire ICT industry. For

telecommunications demand, the price elasticity is estimated to range between -0.07 and

-0.08 for the fixed-line network, and between -0.47 and -0.67 for mobile phone market;

income elasticity varies between 1.6 and 1.7 for the fixed-line, and between 0.81 and

1.78 for the mobile market. For computer demand, the price elasticity is estimated to be

around -0.10-0.11, whereas income elasticity ranges between 1.89 and 2.10. The

elasticity of human capital with respect to computer demand is found to lie between 6.4

and 7.0. Such results show that rising income and educational attainment, together with

falling prices of telecommunications and computers, will largely increase the appetite of

Chinese consumers for ICT products and services in the foreseeable future. The forecast

of demand for telecommunications and computers in China is based on two approaches

11 Unlike telecommunications demand, the actual figure for computer usage in 2006 was not available at the time of writing this dissertation, and therefore no comparison with the projected figure could be made.

Figure 8.5 Forecast of China's fixed-line telephone demand, 2005-2010

300

350

400

450

500

550

600

650

700

750

800

2005 2006 2007 2008 2009 2010

Mill

ion

subs

crib

ers

Base growth High growth Low growth Base elasticity High elasticity Low elasticity

Note: Complete lines represent projections based on growth rate; dotted lines represent those based on elasticity.

215

Figure 8.6 Forecast of China's mobile telephone demand, 2005-2010

300

400

500

600

700

800

900

1000

1100

1200

2005 2006 2007 2008 2009 2010

Mill

ion

subs

crib

ers



216

Figure 8.7 Forecast of China's computer demand, 2005-2010

0

100

200

300

400

500

600

700

800

2005 2006 2007 2008 2009 2010

Mill

ion

units



217

– the growth rates and elasticity of the explanatory variables. The number of

telecommunications subscribers (including both fixed-line and mobile telephony) is

projected to hit one billion in 2007, while computer sales is estimated to exceed one

trillion yuan by 2010.

There is optimism surrounding the future of the ICT market in 2005-6 for China,

and the Asia-Pacific region in general, driven by the boom in China as well as India.

For instance, both countries are expected to account for 42% of total ICT spending in

Asia-Pacific excluding Japan, with China dominating 33% of the market share.12 This

will provide opportunities for business outsourcing to these countries in the area of ICT

planning, education and training, especially in software application and systems

implementation, as well as human resources, accounting, logistics and risk

management.13 The telecommunications services market will also be driven by growth

in Internet Protocol (IP), broadband and wireless services.14

Following China’s entry into the WTO, and preparation for the 2008 Beijing

Olympic Games, there are strong prospects for the greater adoption of ICT in the years

ahead. Growth in the ICT market will be boosted by strong domestic demand for

telecommunications and computer services.15 A steady growth in the ICT market will

be driven by China’s strong economic growth, the emphasis on e-government (with the

State becoming the biggest ICT spender), the continuous inflow of foreign direct

investment, the potential role of the small and medium-sized cities in alleviating the

pressure of overheating hardware investment in the big cities where the overcapacity of

telecommunication networks have suppressed market demand, and the rise in

importance of the small and medium business market (Liu, 2004a). However, the fact

that the growth rate of fixed-line as well as mobile telecommunications has been

declining in recent years suggests that the Chinese telecommunications market is

approaching maturity, which may occur after 2008.

There is a broad consensus that China’s ICT industry will face a turning point

and achieves maturity in 2008 when the revenue from software and ICT services is

12 “India, China to be technology leaders of 2005”, Knight Ridder Tribune Business News (Washington: January 1, 2005). 13 Ibid. 14 “India, China growth rates may propel IT spending in Asia-Pacific region”, Businessline (Chennai: January 2, 2005). 15 “IT market to see steady growth”, China Daily (Beijing: February 28, 2002).

218

predicted to account for more than half of the total ICT market, surpassing that of the

hardware. Another sign of maturity can be seen from the unbundling of software and

ICT services from hardware, i.e. they are no longer offered free-of-charge.16 A final

point to note is that, the concern with overheating of the Chinese economy in the recent

years, in the view of International Data Corporation (IDC), will not have much impact

on the ICT industry, as those affected industries (such as the real estate, steel &

aluminium as well as a few other manufacturing industries) ‘account for only a small

proportion of ICT spending in China’. China’s continued moves toward a market-driven

economy, led by increasing ICT investments in the private sector will further increase

productivity and strengthen its competitiveness internationally.17

16 “Analyst predicts IT sector will mature by 2008”, China Daily (Beijing: November 11, 2004). 17 “IDC expects minimal impact on China IT spending following economic policy shifts”, World IT Report (London: June 21, 2004).

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Chapter 9

CONCLUSION

This chapter serves as the concluding part of the dissertation. It begins by summarizing

the empirical findings related to the impact of ICT on the Chinese economy. Next, it

discusses the prospects for development of the ICT sector in China in the near future, in

particular development outlook for the post-WTO era. Finally, the chapter will briefly

outline those factors that will remain crucial for the future development of ICT in China.

9.1 Summary of findings

The role of ICT as a driver of productivity growth in many countries, especially those of

the developed world has been ascertained. Although there is evidence to suggest that the

continued surge in productivity growth will remain with increased investment in ICT,

the recent drop in productivity growth in the US has raised some questions over how

long such a strong productivity growth can be sustained in ‘old economies’ like the US

which may be reaching their limits in terms of innovation and diffusion of technology

(Atkinson, 2006). However, in the case of China, it may just be the beginning of a

period of strong productivity growth driven by increased investment in ICT, especially

in innovative investment, as proven empirically in the dissertation.

The salient features of findings in this dissertation are summarized as follows.

First, the ICT capital stock of China is estimated to be valued at more than 730 million

yuan in the initial year of 1983, based on an assumed depreciation rate of 15%; and it

grew at almost 20% on average over the past two decades, which is about twice the

growth rate of GDP. Second, ICT capital is proven to be a positive driver for the

Chinese economy, and is estimated to contribute about 25% to the country’s economic

growth, although the percentage varies at different periods. Although non-ICT capital is

still the dominant factor input to China’s economic growth, the contribution of ICT and

TFP has become increasingly important in the first few years of this century.

Third, ICT is found to have a positive and significant impact on technical

efficiency in China. However, a wide disparity still exists as far as the impact on

individual region or province is concerned. The gap between the eastern and western

regions in terms of technical efficiency scores is found to be very wide. This is due to

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the bulk of ICT investment being pumped into the municipal cities and coastal

provinces. On the whole, however, technical efficiency resulting from ICT investment

has been gradually rising over the past few years for all regions.

Lastly, the dissertation has produced some projections of demand for the

telecommunications (fixed line and mobile) and computer markets in China. Using data

that is obtained from Chinese statistical sources, several model specifications are tested

to derive estimates of the price and income elasticity with respect to each of these

markets. Assuming three different scenarios of the base case, low and high

elasticity/growth, it is forecast that the majority of Chinese citizens would have access

to a basic telephone or even the mobile phone in five years from now. As for computer

demand, about half of all the Chinese population is expected to use the computer by

2010.

9.2 Future directions of ICT in China

9.2.1 Prospects after WTO

The year 2007 probably holds special meaning for research in this field of study as it

signifies the beginning of a new round of developments occurring in the ICT sector in

China. One of the most significant events is perhaps the 6th anniversary of China’s

accession to WTO on December 11, 2001. Observers domestic and foreign alike will be

assessing the extent to which the country has fulfilled its commitments of opening up its

telecommunications sector.

The ICT boom in China is attributed to the abolishment of tariffs on ICT imports

in an effort to observe its WTO commitments. China joined the WTO’s Information

Technology Agreement (ITA) on April 24, 2003 which requires it to remove all tariff

barriers to imports of ICT products such as telecom equipment and personal

computers.1 In keeping with the commitments, China had abolished the tariffs for 256

ICT-related taxable items since January 1, 2005 (Zi, 2006).

In accordance with the WTO timetable, with effect from December 11, 2006,

China will have to raise the foreign equity limit to 49%. As stipulated in the Sino-US

1 “China, Egypt join WTO’s Information Technology Agreement”, WTO News, http://www.wto.org/ English/news_e/news03_e/news_china_egypt_25apr03_e.htm

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agreement on the commitment to open up its telecommunications market, China will

allow 49% of foreign ownership in mobile services nationwide five years after

accession to WTO (i.e. by end of 2006), and in fixed-line services six years after

accession (by end of 2007).2 Currently, major foreign companies that have a share in

the Chinese telecommunications market include Vodafone, SK Telecom, Telefonica, the

Commonwealth Bank of Australia and JP Morgan, etc.3 With the issue of 3G licenses

lingering at the start of 2007, the Chinese government is expected to set the legal

framework on new telecommunications standards, such as the 3G standards. In this

respect, the MII Vice-Minister, Jiang Yaoping, was reported to remark that the long-

awaited China’s Telecommunications Law could be promulgated earliest in 2007. 4

Furthermore, China will also remove the restrictions it has imposed on mobile voice and

data communications as well as other domestic and international telecommunications

businesses from 2007.5

The fact that China has become the world’s top ICT producer and exporter since

2004 is evidence of the country’s technological capability and transformation from

being a ‘manufacturing superpower’ to an ‘innovation superpower’ (Zeng and Wang,

2007). Based on empirical evidence found in this study, the dissertation supports the

recommendations put forth by some authors that China’s future ICT policy should focus

on boosting its innovation capacity. This can be achieved through improving the

efficiency and quality of domestic R&D and strengthening financial support for

innovation by promoting the venture capital market. Yusuf and Nabeshima (2007)

recommended that China’s policy effort to strengthen its technological capability should

focus on four specific areas: promoting R&D in large corporations by offering fiscal

incentives; enlarging the contribution of key universities to innovation by creating

linkages between these universities and private businesses; establishing institutional or

organizational channels for focusing research efforts and diffusing research findings to

small and medium-sized enterprises; and creating urban centres to attract innovation

activities and building urban innovation capability, which are taking place in major

cities such as Beijing, Shanghai, Shenzhen, Guangzhou, Chengdu and Xi’an. Zeng and

Wang (2007) suggested that China needs to improve its regulatory regime by

2 “China to Further Open its Telecom Industry”, SinoCast China Business Daily News (London: December 12, 2006). 3 “Innovation and IPR”, USITO Weekly China Summary (December 15, 2006), http://www.usito.org/ news_dls.php?id=200&category=USITO%20Weekly%20China%20Summary 4 Ibid. 5 Ibid.

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restructuring the Ministry of Information Industry (MII) into a ‘State Communication

Commission’ along the lines of the US Federal Communications Commission (FCC).

China’s ICT policies should also aim to address the issue of digital divide

among its regions, as well as that between the urban and rural communities. Despite its

rapid growth in ICT penetration, the regional and urban-rural gaps still remain large

with no evident sign of narrowing. In this respect, China could look into establishing a

financial scheme for providing universal service. Funds to be used for such purpose

may be raised through taxation on the gross revenues of telecom and other ICT

companies, which can be further re-directed to building ‘ICT community centres’ for

the inner and rural regions (Zeng and Wang, 2007). Such policies will have a significant

impact on regional development and alleviating the problems of unemployment and

social inequalities in the poor areas.

Finally, the Chinese government has an important role to play in promoting

greater use of ICT across the country. Known in many countries as ‘e-government’,

introducing online services in the public sector will help to enhance efficiency as well as

transparency of government services and encourage greater participation from citizens.

China should also further tap on its capacity to expand its e-commerce network by

‘improving its credit system and logistic services’ to promote further development of

the ICT sector (Zeng and Wang, 2007).

9.2.2 Moving beyond the Earth: Development of satellite and space technology

The meaning of what constitutes ‘ICT’ in China has changed dramatically over the past

few years. The development of telecommunications has moved in pattern that began

with the construction of fixed-line network, followed by mobile communications and

3G mobile communications, and the convergence among telecommunications and other

forms of information technology applications. Now there is a move to an emphasis on

satellite communications. To identify the future directions of the ICT industry in China,

one may take a cue from two major events that occurred in the last quarter of 2006. The

first event took place on October 29, 2006 at the Xichang Satellite Launch Centre in

Sichuan Province is the launch of China’s first direct broadcasting satellite –

SINOSAT-2, a spacecraft designed to serve the needs for TV broadcasting, direct PC

and broadband multimedia systems in China as well as neighbouring countries for 15

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years.6 Unfortunately, the launch ended in failure due to ‘the solar power panels not

working’.7

Needless to say, China is venturing into the realms of outer space exploration.

The new emphasis on satellite communications has given the Chinese authorities the

impetus for speeding up the development of its space industry. Indeed, the Chinese

government’s plan to boost its space programmes was announced in the State Council’s

white paper on ‘China’s Space Activities’ published in October 2006. The document

highlighted the progresses that have been made, development targets and major policies

for the near future, and prospects for international cooperation. 8 In this chapter, the

salient points pertaining to the development of satellite communications are extracted

from the white paper as follows:

• Progress made in the past five years

Space Technology: China has independently developed and launched 22 different

types of man-made satellites. A new satellite series have been developed, namely,

the Dongfanghong (or The East is Red) telecommunications and broadcasting

satellites.

Space Application: By the end of 2005, China had more than 80 international and

domestic telecommunications and broadcasting earth stations, and 34 satellite

broadcasting and TV link stations. Altogether, about 100 satellite communications

networks and more than 50,000 Very Small Aperture Terminals (VSAT) have been

established in several government departments and large corporations. Satellite

telecommunications and broadcasting technologies have also reached out to the rural

areas.

• Development targets for the next five years

It is stated in the white paper that China aims to set up ‘a relatively complete

satellite telecommunications and broadcasting system, and to enhance the scale and

economic efficiency of the satellite telecommunications and broadcasting industry’.

To achieve the aims, China will ‘develop and launch geostationary orbit

6 “China launches high-power communications, broadcast satellite”, Xinhua Online (October 29, 2006), http://news.xinhuanet.com/english/2006-10/29/content_5261682.htm

7 “China to launch ‘SinoSat-3’ next May”, Xinhua Online (November 28, 2006), http://news.xinhuanet.com/english/2006-11/28/content_5401086.htm 8 “Text of State Council’s 2006 white paper on China’s space activities”, BBC Monitoring Asia Pacific (London: October 12, 2006).

224

telecommunications satellites and direct TV broadcasting satellites with long

operating life, high reliability and large capacity; and develop satellite technologies

for live broadcast, broadband multimedia, emergency telecommunications, and

telecommunications for public service.’

• Major policies in the near future

China will strengthen the development of space application technologies with an

emphasis on telecommunications satellites, satellite remote-sensing, satellite

navigation and carrier rockets. The country will ‘construct a comprehensive chain of

space industry covering satellite manufacturing, launching services, ground

equipment production and operational services.9

9.3 Epilogue

Whether China would successfully achieve its goals and targets rests on overcoming

several barriers (as examined in Chapter two) and implementation of policies which lay

down guidelines that are unambiguous to would-be businesses operating in the Chinese

market. Structural barriers include ‘an ivory tower approach to engineering education,

weak links between universities and business, academic corruption, ineffective

intellectual property protection, the domination of markets by state-owned industries,

and the scarcity of funding for venture capital’. 10 One critical and probably most

pressing matter at hand is the issue of 3G licenses for mobile communications systems

which is a test of the standard of home-grown technologies.

Finally, China’s ICT industry is growing rapidly not only on the manufacturing

sector, but also in the services market. Several factors and trends taking shape at present

will turn China into a potential ICT outsourcing superpower in the near future, as

summarised in Chan (2005) as follows: First, China’s abolishment of tariffs and lifting

of equity restrictions in accordance to WTO commitments will boost competitiveness

for domestic firms as well as attracting greater investment from foreign services

providers. Second, China will assume a world leading position with the development of

home-grown 3G standards, and continued R&D on new computer models and software

9 In what seemed to mark the further advancement in space technology development at the beginning of the year, a Chinese test of an anti-satellite weapon conducted on January 11, 2007 has sparked off protests from the US and other allies such as Australia and Japan. See “U.S., allies protest China’s anti-satellite test”, CNN (January 19, 2007), http://www.cnn.com/2007/WORLD/asiapcf/01/19/ china.missile.ap/index.html?eref=rss_world 10 “To innovate, China needs more than standards”, Financial Times (London: July 13, 2006).

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standards using core technologies. Furthermore, to improve its international reputation,

China will have to impose harsher crackdown on software piracy and infringement of

intellectual property rights. Third, China will adopt a different strategy than that of India

in becoming an ICT superpower. This can be seen from the example of Lenovo which

acquired the PC division of IBM and joint venture with other major multinational

companies. Lastly, China will accelerate its human capital investment by training more

ICT personnel through joint training programs with multinationals such as Microsoft

and IBM. The combined forces of an increasing pool of ICT talents and the return of

overseas graduates will greatly enhance the nation’s chances of achieving its ‘ICT or

high-tech superpower’ status within the next few years (Chan, 2005).

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