Aggregate Behaviour of Investment in China, 1953-96: An Analysis of Investment Hunger and Fluctuation (Institute of Social Studies)

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Aggregate Behaviour of Investment in China, 1953-96

Also published in association with the Institute of Social Studies, The Hague Selected titles: An]an Kumar Datta LAND AND LABOUR RELATIONS IN SOUTH-WEST BANGLADESH Resources, Power and Conflict Luis Carlos Jemio DEBT, CRISIS AND REFORM IN BOLIVIA Biting the Bullet Joke Luttik ACCOUNTING FOR THE GLOBAL ECONOMY Measuring World Trade and Investment Linkages S. Parasuraman THE DEVELOPMENT DILEMMA Displacement in India Jan Nederveen Pieterse (editor) WORLD ORDERS IN THE MAKING Humanitarian Intervention and Beyond Laixiang Sun AGGREGATE BEHAVIOUR OF INVESTMENT IN CHINA, 1953-96 An Analysis of Investment Hunger and Fluctuation Howard White (editor) AID AND MACROECONOMIC PERFORMANCE Theory, Empirical Evidence and Four Country Cases

Institute of Social Studies, The Hague Series Standing Order ISBN 0-333-71477-6 (outside North America only)

You can receive future titles in this series as they are published by placing a standing order. Please contact your bookseller or, in case of difficulty, write to us at the address below with your name and address, the title of the series and the ISBN quoted above. Customer Services Department, Macmillan Distribution Ltd, Houndmills, Basingstoke, Hampshire RG21 6XS, England

Aggregate Behaviour of Investment in China, 1953-96 An Analysis of Investment Hunger and Fluctuation Laixiang Sun Economist International Institute for Applied Systems Analysis Austria

in association with Institute of Social Studies

© Institute of Social Studies 2001 All rights reserved. No reproduction, copy or transmission of this publication may be made without written permission. No paragraph of this publication may be reproduced, copied or transmitted save with written permission or in accordance with the provisions of the Copyright, Designs and Patents Act 1988, or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, 90 Tottenham Court Road, London W1P0LP. Any person who does any unauthorised act in relation to this publication may be liable to criminal prosecution and civil claims for damages. The author has asserted his right to be identified as the author of this work in accordance with the Copyright, Designs and Patents Act 1988. First published 2001 by PALGRAVE Houndmills, Basingstoke, Hampshire RG21 6XS and 175 Fifth Avenue, New York, N. Y. 10010 Companies and representatives throughout the world PALGRAVE is the new global academic imprint of St. Martin's Press LLC Scholarly and Reference Division and Palgrave Publishers Ltd (formerly Macmillan Press Ltd). ISBN 0-333-94809-2 This book is printed on paper suitable for recycling and made from fully managed and sustained forest sources. A catalogue record for this book is available from the British Library. Library of Congress Cataloging-in-Publication Data Sun, Laixiang, 1957Aggregate behaviour of investment in China, 1953-96: an analysis of investment hunger and fluctuation / Laixiang Sun. p. cm. Includes bibliographical references and index. ISBN 0-333-94809-2 1. Investments—China. I. Title. HG5782 .S87 2001 332.6'0951—dc21

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Printed and bound in Great Britain by Antony Rowe Ltd, Chippenham, Wiltshire

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Contents

List of Tables and Figures Preface

IX

xii

1

Introduction

1.1 1.2 1.3 1.4 1.5 1.6 1.7

Key Questions Growth Cycle Model Background Cycles or Fluctuation? Methodological Issues Scope and Notes on the Database Organization of this Book

1 3 8 15 20 24 27

2

Integrating Selected Theories Based on China's Experiences

29

2.1 Introduction 2.2 Incorporating the Investment Cycle Theory of the 'Hungarian School' with China's Experiences 2.2.1 Soft budget constraint, expansion drive and investment hunger 2.2.2 Bauer's four-phase theory and China's investment cycles 2.2.3 Single control equation model and the relevant problems 2.3 Kaleckian Economic Growth Theory and China's Capital Accumulation Mechanism 2.3.1 The causality line: From growth rate to investment to saving to bottleneck constraints 2.3.2 Generalizing the Kaleckian labour constraint equation to a key factor constraint equation 2.3.3 China's capital accumulation mechanism and Kaleckian agricultural-determining growth theory

29 31 31 35 38 45 45 48 51

vi

Contents

23 A Equilibrium growth path and fluctuations 2.4 Existing Researches on China's Investment Cycles 2.4.1 The efforts to link investment cycles to agricultural fluctuations 2.4.2 Reform cycle theory and the persistence of substitution between growth and bottleneck 2.5 Insights from Western Business Cycle Theories 2.6 Summary: The Implications for Modelling Investment Cycles in China 3

The State Investment System and its Response to Reform

3.1 Introduction 3.2 A Historical Overview of the State Investment System 3.3 The Project Approval System and Project Approval Norms 3.3.1 The project approval system: Formal procedure versus real practice 3.3.2 Project approval norms and locality's evasion by collusion 3.4 Credit Plan and Government Control over Financial Resources 3.4.1 Investment plan, credit plan and mandatory loans 3.4.2 Central government investment hunger and key state projects 3.4.3 Lending outside the credit plan 3.4.4 Adjustments of nominal interest rates and patterns of real interest rates 3.4.5 Recurring cycle of inflation and retrenchment during the reform period 3.5 The Material Supply System 3.5.1 Material supply system: Function and characteristics 3.5.2 Declining importance and its special focus since reform 3.6 Soft Budget Constraint and Investment Hunger in State-owned Enterprises 3.7 The Development Drive and Investment Hunger of Local Governments 3.8 Summary: Insatiable Investment Demand Exists at All Levels 4

Agricultural Constraint to the Insatiable Investment Demand

4.1 Introduction

55 57 57 59 63 69

71 71 72 79 79 82 84 84 86 89 90 95 97 97 100 102 106 109

112 112

Contents 4.2 Contribution of the Agriculture to the National Economy 4.3 The Change of Factor Proportions in China's Agriculture 4.4 The Specific Institutional Setting to Help Minimize Agricultural Fluctuation 4.5 Selection of Indicator System 4.6 Agricultural Fluctuations and Macroeconomic Adjustment: Empirical Evidence 4.7 Agricultural Fluctuations and Macroeconomic Adjustment: Stylized Facts 4.8 Summary Data Appendix 5

Energy as the Representative of Producer Goods Constraints

5.1 5.2 5.3 5.4 5.5

Introduction Energy Situation in China: An Overview Widespread and Chronic Shortage of Energy in China Transport Bottleneck and Effective Energy Supply Energy Constraint to Investment Demand: Some Primary Econometric Evidence 5.6 Summary

6

Estimating Investment Functions Based on Cointegration

vii 114 118 122 124 130 149 156 158

162 162 166 174 179 184 186

188

6.1 Introduction 6.2 Unit Roots, Equilibrium Relationship and Error Correction Mechanism 6.3 Modelling Strategy and Steps: A General Framework 6.4 Estimate of Cointegration and Investment Level Equation 6.5 Estimate of Conditional Investment Growth Rate Equation 6.6 Theoretical and Empirical Implications: A Summary Appendices Al Data A2 Cointegration analysis of the vector system A3 Exogeneity

189 192 195 202 205 208 208 210 217

7

221

Conclusions

7.1 Introduction

188

221

viii

Contents

7.2 Major Theoretical Contributions of the Research 7.3 Aggregate Investment Behaviour in China: Stylized Facts 7.3.1 System-generated insatiable investment demand exists at all levels 7.3.2 Supply and distributive barriers to investment expansion and retrenchment campaigns 7.4 Inefficiency as a Consequence of Investment Hunger and Bureaucratic Coordination 7.5 The Difficulties and Possible Selections of Reforming the State Investment System 7.6 Limitations of the Research 7.7 Summary

222 225

Notes Bibliography Index

246 260 282

225 229 232 235 241 242

List of Tables and Figures

Tables 1.1 a 1.1b 1.1c 1.2 1.3 1.4 3.1 3.2 3.3 3.4 3.5 3.6

4.1 4.2 4.3

Subsectoral shares of investment in state-owned industry, 1981-95 Subsectoral shares of employment in state-owned industry, 1965-92 Subsectoral shares of output in state-owned industry, 1965-95 Subsectoral shares of output and employment in rural industry, 1985-94 A chronology of investment cycles in China, showing the rough correlation between cycles and political campaigns Position of foreign investment in the state section fixed investment, 1977-96 Approval norms for investment projects in the 1970s and 1980s Sources of finance for fixed investment in the state sector, 1953-95 National priority projects, 1982-96 Main interest rates on state bank loans, 1979-96 Percentage of key materials subject to central allocation, 1965-92 Reported financial losses of industrial SOEs with independent accounting systems, fiscal subsidies to loss-making SOEs, and policy loans of the People's Bank of China, 1985-96 The output value of light industry using agricultural products as raw materials The values and shares of the exports of unprocessed and processed agricultural products in China's total exports The changes of factor proportions in China's agriculture, 1949-96

IX

12 13 14 16 19 24 83 84 88 92 100

103 115 117 119

x

AA 4.5 4.6 4.7 4.8 4.9 4.10 4.11 A4.1 A4.2 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 6.1 6.2 6.3 6.4 6.5 7.1


The average annual growth rates of key indicators of agriculture, 1949-96 1954-62: The Great Leap Forward and the agricultural crisis it induced 1962-69: The readjustment after the crisis and construction of inland industrial bases 1969-72: Post-cultural Revolution Advance 1972-77: Initial Attempt at the 'Four Modernizations' 1977-80: Post-Mao import of technology and the subsequent economic readjustment 1980-89: Economic reform and the readjustment induced by stagnation of grain production in 1985-89 1989-96: Three-year retrenchment, 'Deng Whirlwind' and 'soft landing' Basic data on output, labour, and population The source of Tables 4.5-4.11 Primary commercial energy production and consumption, 1953-95 Percentage shares of final energy consumption, 1980-95 Energy intensity by sector, 1980-94 Indexes of energy intensity in relation to physical unit of output in energy intensive industries Plan versus market prices for steam coal (1989) and crude oil (1993) Incremental capacity of power-consuming equipment induced by fixed investment Stock and increment of power-generating and consuming equipment (G We) Average railway traffic density in selected countries, 1979 and 1990 Raw coal reserves and production by region, 1985-2000 Granger causality between real investment and effective energy supply Tests for unit roots Residual misspecification tests of the UVAR model (6.4) The cointegration analysis of model (6.4) Tests for linear restriction on (3 and weak exogeneity The estimation of investment growth rate equation in China Industrial concentration by major industrial sectors in China, USA and Japan, 1985

120 132 135 136 138 13 9 142 146 158 160 165 169 169 170 172 176 176 180 182 186 196 196 199 200 205 233


xi

Figures 1.1 4.1 4.2 4.3 6.1 6.2

Investment ratio and real growth rate of state sector investment The fluctuations of per capita grain output and real value added per peasant Accumulation ratio and investment ratio The correlations between agricultural fluctuation and investment adjustment The graphic tests for cointegration relation P'Z, One-step residuals, ±2 standard errors, and one-step-ahead Chow tests for the investment growth equation

17 126 129 151 200 203

Preface Business cycle theory has been a major branch of macroeconomics in the west. Industrial capital accumulation and investment cycle are recurring subjects in the literature about socialist economies. Agricultural issues in general and the interaction between agricultural development and industrial capital accumulation in particular has been central to macroeconomic analysis in a developing economy. While both the general investment cycle theory of socialist economies and the distributive barrier-constrained growth theory of developing economies can help reveal certain features of pronounced investment cycles in China, an integration of them with the methodological and technical progresses emerging in the western business cycle theory and econometrics would bring us a new advantage. The integration makes it possible to model both investment level (growth) and change (cycle) without artificially imposed separation between them, thus leading to an econometrically advanced growth-cycle framework. This is the major theoretical intention of this book. I believe that this integration will have a sound academic implication, and will be of interest to scholars who work on transition economics and development economics, and to general readers who are concerned about economic transition and development. In addition to the theoretical intention, this book also documents and stylizes the evolutionary dynamics of China's state investment system, the policy trade-off between industrial expansion and agricultural development, and the persistent transportation and energy constraints to the economy, from the perspectives of both political economy and historical viewpoint. This will be of interest to China specialists and other general readers who are interested in China's development and reform. This book is based on my doctoral dissertation, which was submitted to the Institute of Social Studies (ISS) in the Hague in 1997, and on research continued and extended after that date. I am very grateful to ISS for providing financial and institutional supports to my PhD study. I am also grateful to the International Institute for Applied Systems Analysis (IIASA) and the

xn

Preface

xiii

World Institute for Development Economics Research (UNU/WIDER) for their support to the continued research in this field. This book has greatly benefited from the support and advice of many different persons in different areas. I am very grateful to my three supervisors, Professor Ashwani Saith, Professor Marc Wuyts and Dr Karel Jansen, whose complementary expertise covers political economy, monetary and financial economics, applied econometrics and development theory. Their common professional interest in behaviour analysis clearly interacted in a manner that broadened and deepened my understanding of the issues in investigating, stylizing and modelling aggregate investment behaviour in China. I am indebted to many of my former colleagues in Peking University in Beijing, particularly Professors Justin Yifu Lin, Yining Li, Shali Zhu, Liangkun Chen, Liyan Wang and Qiwen Wang, for their unreserved support and help in my field work. During my field work in China, many scholars, officials, friends and relatives granted me interviews and gave me very useful assistance. I would like to express my sincerest gratitude to them all and regret that they are too many to name. On a more personal note I want to thank Hao Lu, Fumin Mo, Kexiang Sun, An Wu and Jie Zhang. I benefited considerably from the comments by the four anonymous referees and the editors and from discussions with John Bonin, Valpy FitzGerald, Bill Wansing Hung, S0ren Johansen, Michiel Keyzer, Peter Nolan, Haris Psaradakis, Servaas Storm, and Rob Vos. I owe gratitude to the ISS Publications Committee and Publications Office for their encouragement and prompt assistance, particularly to George Irvin, Cris Kay, Linda McPhee, Joy Misa, Gary Debus, Sharmini Bissessar and Iris Qureshi. I thank all of my former colleagues at ISS, particularly Ank v.d. Berg and Dita Walenkamp, for their profound concern and constant support. For her enduring love, support and commitment, I thank my wife, Sylvia Ya Xu. Without her continuous assistance and contributions, this book would not have been completed.

Laixiang Sun

1

Introduction

1.1 Key Questions China has maintained fairly high capital accumulation levels and aggregate investment ratios,1 which contribute to its impressive economic growth. At the same time, state-sector fixed investment, which constitutes the main share of gross fixed investment,2 has shown conspicuous cyclical patterns in its annual growth rate since 1953, when a consistent and reliable accounting system for the state sector came into operation. The relevant amplitudes are impressive in comparison with those of other socialist countries, such as the former Soviet Union and Eastern Europe.3 An interesting question arises: which mechanisms produce such persistent high investment ratios and what forces shape the remarkable cyclical patterns of these investment growth rates? The basic purpose of this study is to reveal the nature of these fundamental mechanisms and determinant forces and the way together they generate chronic investment hunger and shape the pronounced investment cycles. The determination of a 'rational scale of investment' has constantly perplexed Chinese economic authorities and scholars. Many of the problems in the development process, such as shortages of consumer goods, energy and raw materials, fiscal deficit, over-expanding credit and inflation are linked to the required yet impossibly high level of investment. Correspondingly, any type of'adjustment' or 're-adjustment' always starts by reducing the scale of investment. This process is reflected repeatedly in the policy documents of the Chinese government and much of the literature written by Chinese economists. The determinants of the investment cycle embody essential characteristics of the Chinese economy, which reflect both the general features of a centrally planned economy and those of a developing econo-

Chapter 1

my. It is impossible to adequately describe and simulate the Chinese investment fluctuation process by analysing the general characteristics of the centrally planned economy only or by analysing those of a developing economy on their own. In other words, investment cycles in China synthesize the characteristics of both without being reducible to either. The former characteristics shape our understanding of the institutional pressure and incentives that drive the mechanism of maximizing growth through investment. The latter draw our attention to the interaction between industrialization and supply constraints in general, and the agrarian question in particular, which are central to the dynamics of a typical developing economy (cf. e.g. Patnaik 1995, Rakshit 1989). Analysing and identifying these characteristics from both the perspectives of political economy and of history also form an important subject of this research. According to the general theory of socialist economies, the system constantly initiates pressures and incentives to maximize investment and cannot generate internally self-imposed restraint to resist expansionary drives (Kornai 1992: Chapter 9). As a result, the realized real investment level has, on the one hand, taken quite a high proportion of the national income, and on the other hand, been constrained by the supply limits of bottleneck sectors. In the case of China, investment is limited by the tolerable adverse distributive impact it has on food supply. Against the background of significant policy change, a more interesting question is: has reform fundamentally altered the investment coordination mechanism? Initial data analysis shows a surprising lack of structural change in state-owned industry prior to 1993, in terms of the subsectoral shares of output, employment and investment when compared with townshipand village-run industry. This structural rigidity may imply that statesector investment was not yet based mainly on market criteria and that the state investment system did not respond actively to the changes in demand occurring in a rapidly growing and transforming economy until 1993. This finding shows the need for more accurate econometric modelling and testing based on recently developed cointegration and error correction approaches. There has been a sustained effort to set up formal models of aggregate investment behaviour and investment cycles in socialist and/or developing socialist economies. Two research strands have been influential. The first is distinguished by Kalecki's 'bottleneck constraint

Introduction

type of growth theory'. Kalecki's model focuses on a stable growth path, which, in a developing socialist economy, typically represents a trade-off balance between the desired high rates of investment growth and the toleration limits to the adverse distributive consequences that a high investment rate puts on agriculture. Understanding this trade-off balance and its functioning mechanism is important in the case of a typical developing economy like China. However, this strand does not pay much attention to demand analysis and cycles. The second one is known as the 'Hungarian School'. This strand bases investment demand and fluctuation analysis on notions of soft budget constraint, expansion drive and investment hunger. These notions are particularly helpful in analysing China's case. In terms of modelling, this school focuses on short-run disequilibrium adjustment behaviour of the planners, which is modelled in a response function of the representative planner. The model does not explain the co-movement between real investment level and its constraints. In addition, the approach based on a representative planner faces the problem of intertemporal inconsistency of the planner's rationality (Mihalyi 1992). The attempt to advance both these approaches and to integrate them into a new growth cycle model is of theoretical interest and importance.

1.2 Growth Cycle Model As mentioned above, Kalecki's growth theory emphasizes an equilibrium steady growth path, rather than demand analysis and cycle. Following Kalecki's theory, the starting point is the constant growth pressure produced by a socialist system. Such pressure determines in turn a desired investment growth rate and a corresponding savings rate. Thanks to the effective wage and price control by the socialist state, the desired savings rate can be insured by the state distributive policy on national income. It is this distribution which causes investment to create its corresponding saving. Thus, the main barriers to growth reside in bottleneck constraints such as shortage of agricultural products, energy and raw materials, problems in balancing foreign trade, and the tolerable limit to the adverse distributive consequences of a rising investment rate on agriculture and urban real wage level. Kalecki's analysis provides insight into the relation between the micro phenomenon of the soft budget constraint faced by state sector and the macro context of the investment/saving relation. Its first prem-

4

Chapter 1

ise is that 'investment finances itself from the perspective of income distribution (Kalecki 1976: 43). According to Kalecki's convenient simplification, workers spend what they earn and capitalists earn what they spend. In the context of China, it is the consolidated state sector that earns what it spends. In a capitalist economy, the expansion of individual capitalist investment can be disastrous if corresponding demand is not forthcoming. Reckless investment spending will lead to bankruptcy, or no earning at all, although this spending still contributes to the income of the capitalist class as a whole. This type of restraint on investment puts a hard budget constraint on individual capitalists. However, the soft budget constraint in a socialist economy takes away the capitalist form of individual sanction on investment decision-making because the volume of investment will generate the necessary income to cross-subsidize investment activities. As a result, the real limit to investment expansion is, besides bottleneck constraints, the distributive barrier of a rising investment rate to the food balance. This is reflected in the form of a shortage barrier during the pre-reform period and as both shortage and inflationary barriers in the post-reform years. The Hungarian School's way of dealing with investment cycles in socialist economies focuses on modelling planners' response functions to one or several key shortage signals.4 To advance this most influential approach, we need to first incorporate the diverse actors into cycle analysis and to introduce the behaviour assumption of their intertemporal rationality. This is because in China, investment decisions are taken at different levels of the central, provincial, municipal, prefectural and county authorities, and in enterprises, partly interactively, partly independently. Different brakes operate at different levels in a complex interactive fashion. The decision-makers at each level have their own specific interests and incentives. These indicate that there may be different response mechanisms operating interactively at the different levels within the system. Moreover, these response mechanisms may not be invariant over time, but instead change significantly with the recurring cycles of decentralization and re-centralization, particularly in the reform period. There is no evidence that there is a specific response mechanism that constitutes the constant characteristics of China's investment behaviour. Hence, it is necessary to step back from the immediate mechanisms so as to be able to observe the deeper constancy prevailing in the determination of the investment cycles.

Introduction

In this connection, the cointegration approach can serve the required advancement. As a statistical expression of a long-run equilibrium relationship between two or more non-stationary time series, cointegration can help to establish cycle analysis based on the behavioural assumption of intertemporal rationality. In other words, based on a cointegration approach, we can assume that the investment decisionmakers at different levels and in different sectors are all rational economic agents and that, because of their own specific interests and incentives, they cannot be considered as representative planners. As a consequence, coordination mechanisms that prevail among economic agents are of decisive importance and cannot be ignored. Full information and rationality of individual choice are not sufficient to preclude the business cycle in a well-functioning market economy. The bureaucratic coordination mechanism that characterizes the state investment system of China has also failed to prevent an investment cycle. Secondly, modelling planners' response functions inevitably involves the definition of a norm (cf. among others, Kornai 1982, Simonovits 1992) which itself is not fully explained (Hare 1982, 1989). The notion of norm is intended to represent determinants behind what could be called planners' perceptions of what is feasible. In this direction, the cointegration approach can support the advancement as well. The cointegration relation presents a long-run equilibrium co-movement between the real investment level, supply and distributive barriers to investment expansion. It shows clearly the long-run equilibrium determined by the fundamental tension between system-generated investment ambition and the supply and distributive barriers to that ambition. Therefore, it can replace the traditional univariate norm concept, which in practice, takes the form of a univariate moving average and may produce spurious deviations as pointed out by Slutsky (1927) and Frisch (1928) (see Morgan 1990). In addition, such a co-movement path also serves as an attractor for the disequilibrium adjustment of the investment decision-makers toward a dynamic equilibrium as shown by the error-correction model. Thirdly, a single response function cannot be used to model interaction between shortage and investment tension. This interaction works in two ways. First, the above-normal shortage intensity makes the planner restrain new investment starts and conversely, if the difficulties caused by shortage have diminished, this will stimulate and support further investment expansion. Second, shortage generates in-

6

Chapter 1

vestment tension and at the same time investment tension is one of the major causes of shortage (Kornai 1980: 201). This two-way interaction is present in both the short and long run, although in the long run the positive correlation may be dominant. In the long run, a strong investment expansion initially increases shortage and inflationary pressures, but it eventually leads to an increase in capacity and potential output, which tends to dampen shortage and inflationary pressures and allows the economy to expand investment again. Here, the demand for modelling such short-run two-way interaction and long-run complementarity between investment expansion and bottleneck constraints can be fully met by applying cointegration and error-correction modelling. The long-run equilibrium co-movement can well represent this long-run complementarity and the short-run error-correction equation(s) highlights the different interaction mechanisms in an effective and intuitive way. Following the above analysis, we may conclude that the strong points of Kalecki's growth theory and the Hungarian School's cycle theory can be combined into a new framework of growth cycle theory. In this new framework, the modelling of investment cycle is based on an understanding of what determines the equilibrium level around which the investment fluctuates. The behavioural logic behind the new framework can be seen in a standard way: almost all investment decision-makers, at all levels, have tried to maximize the investment scale within the supply constraints and the inflationary barrier under their jurisdiction. This type of maximization behaviour, subject to certain constraints, can be modelled and tested by employing recently developed econometric approaches such as cointegration and error-correction. In modelling both growth and cycle, the most desirable advantage of the novel econometric approaches is that they allow researchers to single out the underlying equilibrium relation (attractor) which characterizes the supply constraints on investment demand level while investigating its dynamic structure (change) through error-correction. Thus one can work directly with readily observed data rather than having to guess at the shortage indicators that planners at various disjointed levels may use. Adoption of the new framework of growth cycle is also supported by data analysis. By comparing the input-output relations in China's agriculture with several important distributive relations in the uses of the national income, there are evident two-way causalities to be found

Introduction

between the cycles of the capital accumulation ratio (investment ratio also) and the fluctuations of agricultural value-added per labourer, and especially of grain output per capita. This result is clearly different from those obtained from common linear regressions based on some forms of constructed shortage indicators. This type of learning from data has led to employing the recently developed modelling strategy and approaches, and to modelling the economic agents' investment maximization behaviour subject to the representative bottleneck constraints. The behaviour logic of the growth cycle theory can be intuitively interpreted as follows. Investment hunger, which is initiated by expansion drive and becomes feasible under the soft budget constraint (Kornai 1980, 1992), is the driving force which causes investment behaviour to be constrained by shortage and inflationary barriers in the bottleneck sectors rather than by demand. Investment hunger is not about fine-tuning the level of investment; rather it relentlessly pushes the economy towards overheating, thereby producing tensions which lead to subsequent error corrections. The retrenchments characterized by ad hoc administrative measures can be likened to 'pushing a basketball under water': just as this mechanism rarely seems to involve letting some of the air escape, investment hunger is suppressed but still present; the refused proposals are not swept away but only postponed. On the supply side, food as the typical necessary consumer good and energy as the representative basic producer good together set the limits on the feasible rate of investment growth. Foreign exchange does not form an independent constraint since the food and energy constraints together encompass the dynamics of foreign exchange earnings.5 Together they indicate that probably only by combining what drives the system (investment hunger) with the supply constraints encountered (represented by agriculture and energy) can we come to grips with the nature of investment growth rate cycles in China. The cycles cannot be understood independently from the forces that determine the level of investment. While investment hunger is a constant feature of the economic process, planners' responses to tensions are endogenous and have changed over time depending on the specific economic mechanisms through which they are effected. These specific mechanisms may affect the features of the cycles, but not what causes them to happen in the first place. This is the reason why the cycles may be understood and

8

Chapter 1

modelled in relation to the fundamental tensions between investment hunger and the bottleneck constraints. In addition to the general logic presented in the previous paragraph, two points are worth noting. Firstly, there are different conducting channels from investment demand to the bottlenecks between the prereform and reform periods. In the pre-reform period, over-investment resulted in supply shortages of energy, raw materials and agricultural products through material conduction. During the reform period, in contrast, over-investment usually induces over-expansion of credits first, followed by shortage in the planned component and inflation in the market component of the economy through both material and value conduction. In other words, in the transitional period, looser (or more loosely enforced) price controls on transactions, which take the forms of an administratively negotiated market (white market) and a free market, provide a mechanism for a partial translation of cumulated shortage into higher prices. Secondly, in a bureaucratically coordinated economy, though the increase of price levels may be used as a reason to reduce investment scale, it can also be cited, possibly more significantly and frequently, as a good reason to request extra investment, at least in the short run (Kornai 1992). The research below suggests that with regards to shaping real investment cycles, the effect of the inflation rate as a special short-run shortage signal seems to be outweighed by its ability to offer bargaining power for requesting additional investment (for details, see section 6.5).

1.3 Background China's development model and experience, particularly its economic reforms and the resultant rapid economic growth and structural changes, have been of major interest to economists. At a practical level, its development has a significant impact on the well-being of over one fifth of the world's people, on international trade, on the evolution of the Asian-Pacific economy and on the international balance of economic and political power. On a theoretical level, a better understanding of its development and transition would provide useful lessons for other transitional and/or developing countries. It would also contribute insights into the processes involved in the transformation of economic systems, the relationship between economic development and institutional changes, the interaction between industrialization and the agrar-

Introduction

ian question and the nature of 'Asian' models of development. Moreover, the fact that China's impressive development and reform occurred despite many sharp policy oscillations, economic fluctuations, internal political conflicts and a lack of coherent reform strategy has compounded the challenges for economic analysis (Chow 1997, Lin et al. 1995 & 1996, Watson 1994, Zhang & Yi 1995). For the pre-reform period, as identified by World Bank (1983),6 China's development strategy comprises two basic but often conflicting objectives. The first is poverty reduction or securing basic needs. This has been realized in the initial stages through land reform and collectivization in agriculture and nationalization in the non-agricultural sectors, and later through a programme of comprehensive rural development and provision of basic social services utilizing local resources and initiatives. The second objective is industrialization, particularly the development of a heavy industrial basis, mainly based on a massive infusion of centrally mobilized resources with less concern about cost effectiveness, and relying on technology largely descended from Soviet designs of the 1950s. Persistent tension between these two objectives has contributed to sharp policy oscillations. The dominant policy trend, however, has been in favour of industrialization. During this period the overall development of China was impressive. With adjustments for international comparability, GNP per capital appears to have grown at 2.0-2.5 per cent per annum in the 1957-77 period and 2.5-3.0 per cent per annum in 1957-79. The former rate is significantly above the average of 1.6 per cent for other low-income countries, though the latter is well below the average of 3.7 per cent for middle-income developing countries. China's most remarkable achievement is regarded as making its low-income groups far better off in terms of basic needs compared with their counterparts in most other poor countries. This was accomplished despite a relatively high population growth rate (2.0 per cent) and an unprecedented degree of international isolation, which means that development has been almost entirely self-financed. At the same time, China's impressive economic growth has been characterized by profound structural unbalances and gross inefficiencies. Driven by persistent industrialization bias, the net output of industry grows at an annual average of 10.2 per cent (1957— 79) in real terms, far above the average for other low-income countries (5.4 per cent) and well above the average for middle-income developing countries (7.5 per cent). Industry accounts for about 40 per cent of

10

Chapter 1

GDP, similar to the average for middle-income developing countries, although China's per capita GDP (US$279 in 1980) remains one of the lowest in the world. Its rapid industrialization provides China with a very comprehensive and basically self-sufficient industrial system. On the other hand, the cost of rapid industrialization has been very high. The two most important issues are as follows. First, apart from the agricultural crisis and the consequent great famine (1959-61) induced by the Great Leap Forward,7 the inadequate agricultural output growth contrasts sharply with the rapid industrial growth. Agricultural gross output has increased by only 2.1 per cent (1957-77) despite significant progress in developing sources of intensive growth (e.g. multiple cropping, irrigation, flood control, new high-yielding seed varieties and chemical fertilizer application). Growth of foodgrain output in particular has been slower than that of gross agricultural output. In 1980, after a remarkable increase in output following the agricultural reform introduced in late 1978, per capita grain output is only 7 per cent higher than in 1957. Rural per capita incomes have hardly risen, with net output per capita dropping by 12 per cent between 1957 and 1977. Secondly, from 1957 to 1979, industrial labour productivity grew slowly while capital productivity declined, and the total factor productivity in industry either stagnated or declined. These facts may indicate that industrial expansion in the pre-reform period was achieved mainly by increasing the quantity of factor inputs (i.e. extensive growth) and not by increased efficiency (intensive growth). Another startling fact is that about 70 per cent of total commercial energy use is accounted for by industry, which on a per capita basis is nearly four times the average for other low-income countries. Energy consumption of per dollar of GDP is also about 2.5 times the average for other developing countries or for industrialized market economies, and about 1.5 times the average for other centrally planned economies. The stylized facts of the pre-reform period can be spelt out from the literature. China's economy achieves a rapid industrial growth, but this is impaired by sharp fluctuations in growth rates, extraordinarily high and oscillating investment rates, declining total factor productivity and inadequate agricultural output growth. There has been a remarkable stagnation in per capita levels of consumption of key items (e.g. grain, vegetable oils, cotton cloth) and in rural living standards over a twodecade period (1957-78) (see, among others, Ishikawa 1983, Lin 1988, Lin et al. 1996, World Bank 1983, Yeh 1984). This necessitates a re-

Introduction

11

examination of the fundamental capital accumulation mechanism and the process which generates the impressive industrial growth, an analysis of the incentive sources of the industrialization drive and relevant supply constraints, and modelling the co-movement between real investment and the supply frontier of representative bottleneck sectors and investment cycles. This will provide a deeper understanding of China's development model and experience. An analysis based on historical and institutional perspectives would also be desirable. Since the economic reforms and open-door policies began in the late 1970s, China's economic performance has been much better than in the pre-reform period. The average annual growth rate of GDP from 1978 to 1997 was 9.8 per cent. This is arguably the fastest in the world and rivals the record achieved by the four Small Dragons - the creators of the East-Asian Miracle in their fast-growing period. On a per capita basis, the annual growth rate of GDP is more than double that of the prereform period {People's Daily, 25 September 1998). While the industrial net output has grown at a rate of above 12 per cent, there have been important changes in the efficiency of the resource use. In stateowned industry there has been an evident reversal of the long-term decline in total factor productivity. The typical Stalinist relationship between the growth rates of heavy and light industry has been reversed, with explosive growth of the rural light industry in township and village enterprises (TVEs). The agricultural growth rate accelerated far ahead of that achieved during the 1957-78 period, and with much more efficiency in resource use. The growth of commerce, transport and communication is also much more impressive than in the pre-reform years. In addition, China remains relatively unburdened by foreign debt and has achieved fast growth with relatively low inflation (see, e.g. Chang & Nolan 1996, Jefferson & Rawski 1994, Lin et al. 1995, 1996, McKinnon 1994, World Bank 1994). Such phenomenal growth, however, has also been impaired by the recurring grain problems, the increasingly sharp energy shortage and crises, and in particular by the pronounced cycles of reform and growth (cf. e.g. Ash 1992, Kambara 1992, Lin et al. 1996, Oppers 1997, Sicular 1989 & 1993, Zhou & Chu 1992). A typical reform and growth cycle is usually stylized as follows (cf. Lin et al. 1996, Oppers 1997, Watson 1994, World Bank 1994, Yusuf 1994, among others). The scenario commences with a series of reform experiments and relaxation of control. As their scope is gradually

12

Chapter 1

broadened, investment and growth accelerate and are accommodated by credit expansion. Soon the economy is expanding by double-digit rates. The decentralization of administrative and economic authority decreases the power of the central government to enforce macroeconomic control. Inflation follows the overheating, and the increasing rent-seeking by those with the opportunity to exploit the institutional rents between the plan and market components of the economy increases corruption. Once the fear of inflation and corruption is widely acknowledged, the government is empowered to contain reform initiatives and to curb growth by severe austerity measures. There is still a lack of the sophisticated mechanisms required to direct an increasingly complicated economy. After a period of 'restoring order and rectifying the economic environment', the call for a resumption of reforms and faster growth becomes irresistible. This is because the benefits of reform and faster growth have been widely recognized, and also a large number of state enterprises cannot exist without subsidies and face real difficulties in circumstances of deflation. Thus new reform experiments are initiated, policies are relaxed and a new cycle begins.

Table 1.1a

Subsectoral shares of investment in state-owned industry, 1981-95 Shares of Investment

Metallurgy Power Coal & coke Petroleum Chemical Machinery Building materials Food Forest Textile & clothing Paper & cultural articles Others Total

1981

1984

1987

1989

1992

1995

11.4 12.5 10.4 14.2 8.8 11.8 4.5 5.3 2.2 10.3 1.6 1.2

12.3 13.5 13.0 16.0 9.7 11.6 5.7 5.0 1.8 7.1 1.4 1.3 100.0

12.7 16.7 8.2 13.7 11.3 12.5 5.5 7.4 1.4 7.3 1.9 1.5

12.0 18.5 8.9 16.8 11.9 11.0 3.9 5.5 1.1 7.3 1.9 1.7

11.4 19.4 8.3 14.4 11.3 13.6 4.5 5.6 0.7 6.4 2.4 2.0

14.1 23.0 7.1 14.6 14.1 9.4 4.1 5.2 0.7 3.5 1.5 2.5

100.0

100.0

100.0

100.0

100.0

Introduction Table 1.1b

13

Subsectoral shares of employment in state-owned industry, 1965-92 Shares of Employment

Metallurgy Power Coal & coke Petroleum Chemical Machinery Building materials Food Forest Textile & clothing Paper & cultural articles Others Total

1965

1978

1985

1990

1992

8.6 2.5 12.4 1.4 6.3 26.0 5.3 8.3 5.8 12.3a 1.3b n.a.

10.2 2.5 12.4 1.6 9.5 30.9 6.0 6.7 3.9 8.9a 1.3b n.a.

9.3 3.0 11.7 2.1 9.4 27.8 6.6 8.1 3.6 11. T 1.5b 1.3

9.3 3.6 11.4 2.7 10.2 26.1 6.2 8.1 3.2 12.3a 1.7b 1.5

9.2 3.8 11.3 2.9 10.4 25.7 6.4 8.4 3.0 11.9a 1.7b 1.5

100.0

100.0

100.0

100.0

100.0

Compared with the conspicuous reform and growth cycle, the structural adjustment of state-owned industry seems to be relatively insignificant, although the state sector monopolies represent 60-70 per cent of total fixed investment and national industry underwent a profound structural change between the 1970s and the early 1990s. Tables 1.1 a-c and 1.2 present the subsectoral shares of output, employment and investment for the state-owned industry, and output and employment for township and village-run industry (TVI). Different patterns of structural change between state industry and TVI are evident in these two tables, particularly, during the period before 1992 (including 1992). Within seven years (1985-92) - quite a short period - the TVI sector underwent a significant structural adjustment. The output shares of textiles and clothing, papermaking and cultural articles, chemicals, metallurgy and, especially, machinery increased by 3-7 percentage points; correspondingly, the output shares of coal and building materials decrease remarkably. The same applies to the employment structure, with a profound change in shares. However, for a fairly long period before 1992, the subsectoral structure of state-owned industry had remained stable. For example, machinery led with 27.7 per cent of gross output value of state-owned industry in 1975 and remained the lead sector

Chapter 1

14

with 26.6 per cent of gross output in 1992, 17 years later. The output shares of textiles, chemicals, food and metallurgy, the next four most important subsectors, have also changed little, since 1975.

Table 1.1c

Subsectoral shares of output in state-owned industry, 1965-95 Shares of Output Value

Metallurgy Power Coal & coke Petroleum Chemical Machinery Building materials Food Forest Textile & clothing Paper & cultural articles Others Total Notes:

1965

1975

1980

1985

1990

1992

1995

10.7 3.1 2.6 3.2 12.9 22.3 2.8 2.9 12.6 19.2 3.9 3.8

9.0 3.9 2.8 5.6 11.3 27.7 3.1 1.9 12.0 15.4 3.2 4.1

8.6 3.8 2.3 5.1 12.5 25.5 3.6 1.7 11.4 18.4 3.5 3.4

8.0 3.3 2.5 4.5 11.2 27.1 4.2 1.6 11.5 18.7 3.9 3.3

7.2 3.0 2.1 3.8 12.7 28.9 4.1 1.3 10.5 18.0 3.7 3.8

10.7 3.7 2.5 5.5 12.4 26.6 5.6 1.4 10.6 15.4 4.3 n.a.

13.6 7.3 3.7 12.2 11.7 20.0 4.2 1.0 13.7 8.8 2.3 n.a.

100.0

100.0

100.0

100.0

100.0

100.0

100.0

(a) Textile only, (b) Papermaking only. For translation between 'new1 and 'old' industrial classifications, see Statistics on Labour and Wages of China, 1949-1985 (1987: 275-82). Some rounding errors are larger than normal because those shares in some small subsectors are missing, as indicated by (a), (b) and n.a.

Sources: For investment: The data for 1981-91 are from Statistics on Fixed Investment of China (1987: 23 for 1981-85; 1989: 31-32 for 1986 & 1987; 1991: 30-31 for 1988 & 1989; and 1993: 39-40 for 1990 & 1991), and for 1992-95 are taken from China Statistical Yearbook on Investment in Fixed Assets, 1950-1995 (1997: 54-55). For employment: The data for 1965 & 1978 are taken from Statistics on Labour and Wages of China, 1949-1985 (1987: 37), and others are from Statistical Yearbook of China (1991: 392; 1993: 410). For output: The figures for 1965-90 are taken from Almanac of China Industry (1991: 969), the data for 1965 are in 1957 constant prices, for 1975 and 1980 are in 1970 constant prices, and for 1985 and 1990 are in 1980 constant prices. The numbers for 1992 and 1995 are inferred from Statistical Yearbook of China (1993: 417; 1996: 418), and in current prices. Columns may not tally due to rounding.

Introduction

15

Other data on changes in subsectoral shares of employment and investment in state-owned industry reveal a similar stability over the period of 1978-92. Taking into consideration that the proportion of state industry dropped from 78.5 per cent in 1978 to 48.1 per cent in 1992 {Statistical Yearbook of China 1993: 414) and that the economic reforms had been carried out for one-and-a-half decades, the lack of change in the structure of state-owned industry during 1978-92 is surprising. This remarkable structural rigidity may imply that before the most radical reform in the state sector was initiated in mid-1992, the state sector investment was not yet based mainly on market criteria, and that the state investment system did not actively respond to changes in demand occurring in a rapidly growing and transforming economy. In other words, the investment approval process in the state sector might still have been dominated by bureaucratic negotiation and coordination based on vested sectoral interests and structural inertia, although the concrete mechanisms had changed. This rigidity, combined with the similarity between the reform/growth cycle in the reform years and the policy/growth cycle in the pre-reform period may justify the persistence of the fundamental tension between investment hunger and the supply possibility for both pre- and post-reform periods. Although there have been numerous changes in miscellaneous policy details and concrete mechanism. The absolute increase of supply possibility in key bottleneck sectors will certainly induce higher rates of economic growth, but the tension between growth drive and supply possibility may remain. This fundamental tension may determine the co-movement between the real investment level and the supply frontier of the representative bottleneck sectors and shape the investment cycles.

1.4 Cycles or Fluctuation? Economists define 'cycle' using the triplex of recurrence, reinforcement and regularity. Recurrence means that the phases of expansion and contraction of certain time series data follow each other. Reinforcement and regularity indicate that each phase 'produces the conditions which usher in the next phase of the cycle' and 'successive cycles ought to resemble each other' (Ickes 1986: 43, Simonovits 1991: 466).

16 Table 1.2

Chapter 1 Subsectoral shares of output and employment in rural industry, 1985-94 1985

1989

1992

1994

2.63 0.30 3.90 n.a. 7.44 20.00 18.64 2.67 7.80 17.02 4.12 14.76

5.14 0.31 3.05 0.23 10.25 23.93 17.43 2.46 8.45 18.50 6.20 4.05

6.34 0.27 2.24 0.27 11.11 27.64 14.49 2.30 7.75 19.99 6.36 1.24

100.00

100.00

100.00

8.23 2.33 1.66 1.08 9.52 24.02 13.82 3.42 8.93 18.86 3.87 n.a. 100.00

2.18 0.45 5.45 n.a. 5.68 14.62 30.64 3.08 7.81 13.81 5.21 10.72

2.91 0.44 5.10 0.09 7.06 18.56 28.86 2.84 6.97 14.60 8.56 4.00

3.03 0.41 4.89 0.11 7.96 19.67 24.41 2.73 6.77 17.07 8.65 4.29

6.87 3.15 4.14 1.85 6.28 19.42 19.57 5.44 6.99 17.36 4.80 n.a.

100.00

100.00

100.00

100.00

Shares of Output Value Metallurgy Power Coal & coke Petroleum Chemical Machinery Building materials Forest Food, beverages & tobacco Textile, clothing & leather Papermaking & cultural articles Others Total Shares of Employment Metallurgy Power Coal & coke Petroleum Chemical Machinery Building materials Forest Food, beverages & tobacco Textile, clothing & leather Papermaking & cultural articles Others Total Note:

For translation between 'new' and 'old* industrial classifications, see Statistics on Labour and Wages of China, 1949-1985 (1987: 275-82). Sources: Statistical Yearbook of China, (English version) 1987: 226-7; 1991:419-20; 1993: 441-2; and 1995: 399.

But the periodicity, or replication, should not be explained in terms of abstract mathematics. Instead, it should be understood from the perspective of the behaviour of investment decision-makers. In other words, one should attempt to understand why investment decisionmakers and, especially, planners in a socialist economy, replicate their

Introduction

17

expansion and retrenchment behaviours, and what forces drive them to do so repeatedly. Figure 1.1 provides a clear picture of investment fluctuations in China by employing both time series of investment ratio (cf. note 1) and real investment growth rate. In Figure 1.1 the investment ratio series presents the cycle of investment levels in terms of national income distribution, and the real investment growth rate exhibits its own cycle. It is clear that the cycles show recurrence, reinforcement and regularity; the relevant amplitudes are also very impressive. What should be emphasized here is that the recurrence, reinforcement, and regularity of planners' expansion and entrenchment behaviours can be clearly spelt out as follows.

Figure 1.1

Investment ratio and real growth rate of state sector investment

Investment ratio

-- 80 - 60

- Growth rate of real Investment

40 -- 20

g E 1/5 >

_20

--40 --60 --80

*&

2

2

o

-100 ( 0 ( D O ( M * ( D 0 0 O C M V 1 0 I O ( 0 ( 0 ( 0 ( 0 ( O N N . K 0 ) 0 ) 0 ) 0 ) 0 ) 0 ) 0 ) 0 ) 0 ) 0 )

Note:

The traditional National Income statistics has been abandoned since 1994.

Source:

Investment ratio series are taken from Table A4.2; real investment growth rate is derived from Table A6.1.

18

Chapter 1

Once we measure the cyclical pattern of investment ratio from trough to trough, it can be seen that there is a rough correlation between investment fluctuations and political/economic campaigns, as shown in Table 1.3. The political economy logic behind this preliminary correlation is as follows. Expansion in each cycle begins with the economy in good condition and is accelerated by the corresponding political/economic campaign. Once danger signals appear the expansion is interrupted, ameliorative measures are initiated, and a retrenchment campaign, which is usually called 'readjustment' or 'rectification' follows. The political/economic campaigns are essentially endogenous within the system (Kornai 1992), which will be analysed further in Chapters 2 and 3 and in section 4.6. In order to identify more precisely the behavioural characteristics of investment decision-makers in each phase of each cycle, it is helpful to employ Bauer's (1978) four-phase description. According to this description, each investment cycle begins with a 'run up' phase, in which a new round of investment expansion is initiated and driven by growth pressure and institutional incentives. In the accelerating or 'rush' phase investment expansion is increased by the high outlay commitments made in the 'run up' phase as well as by the political/economic campaigns. The upper turning point in each cycle, the 'halt', is accompanied by the sharpening of shortages and other danger signals. Finally, the retrenchment phase, or 'slowdown', is usually brought about by administrative coercion in retrenchment campaigns. A chronology for each investment cycle and its corresponding phases is presented in Table 1.3. The relevant detailed description and analysis will be presented in sections 2.2 and 4.6. The behavioural characteristics of planners and the demand-supply forces behind planners' activities, as one of central concerns of the research, will be examined step by step in the following chapters. This introductory section aims to show that China's investment ratio and real investment growth rate are indeed characterized by a pronounced cyclical pattern, with a periodicity of about five years. In mathematical terms, this cyclical pattern is not very regular. However, in the sense of economic behaviour, the pattern represents a typical 'business cycle'.

The 'Socialist High Tide' The Great Leap Forward Construction of inland industrial bases Advance after Cultural Revolution chaos Initial attempt at the 'Four Modernizations' The'Foreign Leap Forward'and 3-year readjustment Rural and urban economic reforms 3-year retrenchment and'Deng Whirlwind'

1 2 3 4

1955-57 1957-62 1962-68 1968-73 1973-77 1977-81 1981-89 1989-96

Notes:

Initial expansion Accelerating expansion Upper turning point Retrenchment

Political/economic campaign 1955 1957-58 1962-63 1968-69 1973 1977 1981-82 1989-90

Run up

1

Phases

1956 1958-59 1964-65 1969-70 1974-75 1978 1983-86 1991-92

Rush

2

1956 1960 1966 1971 1975 1978 1987 1993

Halt3

1957 1961-62 1967-68 1972-73 1976-77 1979-81 1988-89 1994-96

Slowdown4

A chronology of investment cycles in China, showing the rough correlation between cycles and political campaigns

Period

Table 1.3

8

20

Chapter 1

1.5 Methodological Issues8 The growth cycle framework established in this book is rooted in the application of the recently developed methodological framework as summarised in Spanos (1990, 1995). It has been symbolically labelled the LSE (London School of Economics) methodology in the literature though many eminent contributors are not directly linked with the LSE. Within the LSE framework, both theoretical analysis and the probabilistic structure of observed (nonexperimental) data have a common denominator in the form of a data-generating process (DGP) which demarcates the framework of analysis. Theoretical analysis proceeds not for the sake of theorizing but in order to understand certain observable economic phenomena. A reliable summary of the probabilistic information in the data enable us to relate the empirical econometric model to the actual mechanism underlying the observable phenomena of interest and not just to the existing theory. Grounded in this framework, the aim of econometric modelling is no longer the quantification of the existing theoretical model, irrespective of the observed data chosen. The probabilistic structural model of the data plays an important role in interpreting the actual mechanisms that give rise to the observed data, while theoretical analysis serves to clarify the behaviour rules of the economic agents, and suggests relevant observable variables and theoretical parameterization of interest. The reformulation of the wellspecified estimated statistical model in view of the relevant theoretical analysis generates the economically well-interpreted econometric model. Based on this framework, political economy analysis is used to reveal the institutional pressure and incentives confronting the decisionmakers at each level of the central and local governments and the enterprises for maximizing investment growth. It also shows why there is a lack of internal, self-imposed restraint capable of resisting the investment drive, and how this has allowed real investment to move along the supply possibilities frontier of the economy. The Sims's type of unrestricted vector autoregressive (UVAR) representation is then employed to parameterize the joint dynamic structure of the basic data system suggested by the political economy analysis and some primary data exploration. Following the 'probabilistic reduction' (Spanos 1995) type of 'general to specific' procedure and based on cointegration and error correction approaches, we finally obtain a well-specified, well-

Introduction

21

fitted, and economically meaningful long-run equilibrium investment level equation and short-run investment growth rate adjustment equation. Just as the methodological framework predicts, these two equations are adjustment equations rather than traditional demand/supplytype functions. More concretely, based on this framework and relevant techniques we can seek direct empirical proof for the existence of equilibrium relationships without having to tackle such difficult problems as how to set up separate demand and supply functions first. The shortrun adjustment behaviour can then be modelled as an error correction process with the equilibrium relationship as an attractor. The major advantage of modelling the adjustment equations is that it enables us to avoid tackling certain thorny issues around 'bridging' the gap between theory and data which arise when the usual equilibrium assumption is prior-imposed. In fact, such an ex ante assumption implies the imposition of a behaviour rule for at least one of the original 'control' (exogenous) variables in the corresponding demand and supply functions (e.g. price in the common demand and supply schedules), suggesting that the situation envisaged by theory differs greatly from the mechanism underlying the DGP.9 By contrast, based on the LSE framework, the political economy analysis for state investment system, primary data exploration and cointegration modelling can be consistently synthesized within the process of specifying the dynamic adjustment functions without imposing any ex ante behavioural assumption. The expression 'political economy analysis' is used here to mean that besides discussing economic issues about investment decisionmaking, financing, implementation and so on, this book will also examine many other problems beyond the boundary of economics. These include the orientation, interests and decisions of China's top and regional authorities, the central ministries and local economic bureaucracies; conflicts of interest between the centre and the locality, and between different bureaucracies; the correlation between the recurring cycles of decentralizing and recentralizing and the investment cycles led by the local and/or the central; the collusive behaviour between local governments and local enterprises, between enterprise and its immediate supervisory agency, and among enterprises, local governments and the local branches of state banks. As a result, the analysis extends into the fields of political science, history, sociology, and social psy-

22

Chapter 1

chology. This extension is what the term 'political economy' is intended to encompass. The choice of a concrete modelling strategy is taken based on the following two observations. First, as mentioned in section 1.2, in the literature on modelling planners' reaction function to shortage, scholars formerly employed univariate moving averages to represent the normal growth path (norm) which served as an attractor similar to the equilibrium path in Walras' economics (Kornai 1982). The econometric problems linked with this approach are: (a) univariate moving averages may produce spurious cycles and (b) the normal path should be a structural relationship when one deals with investment control function and should be determined by the interaction between relevant demand and supply forces rather than by such univariate techniques as moving averages. In order to overcome these two problems, it is necessary to introduce the concept of an equilibrium relationship or a structural equilibrium so that we can talk about equilibrium against the background of supply shortage (Banerjee et al. 1993).10 Secondly, to regress one nonstationary process on another may result in a spurious (even nonsensical) regression, as discussed in detail by Yule (1926), Granger & Newbold (1974), Granger (1981), Nelson & Kang (1981), and Nelson & Plosser (1982), among others. The idea behind this discussion is that if no bounded combination of the levels of several series exists, then the error term in the regression must be non-stationary under the null hypothesis so that known distributive results do not apply. To obtain an economically sound normal path (norm) or long-term equilibrium relationship between real investment and the representative bottleneck supplies, it helps to employ the recently developed cointegration approaches. This approach enables us to detect and test long-term stable relationships among non-stationary variables based on the interaction between theoretical exploration and statistical testing. The cointegration analysis based on Johansen's (1988, 1992a, 1992b, 1992c) procedure can also help to check weak exogeneity of relevant variables. Once this type of long-term norm relation is found, it is possible to model economic agents' dynamic adjustment function toward the longterm steady-state target in the form of an error correction mechanism. A practical modelling strategy based on dynamic cointegration is recently proposed in Hendry & Mizon (1993). This strategy can be characterized as a 'probabilistic reduction' type of 'general to specific' approach (Spanos 1995), and allows us to simultaneously model the

Introduction

23

dynamics of both short-term (changes) and long-term (levels) adjustment processes. In concrete terms, the modelling starts from a wellspecified, log-linear, and unrestricted vector autoregressive (UVAR) representation of the data. The cointegration relation is detected based on the UVAR and introduced into a reduction process. The modelling then gradually reduces and reparameterizes the UVAR into a most concise conditional error correction model and other marginal models. The work in this book shows that this strategy works well in the context of modelling aggregate behaviour of investment in China. Therefore, based on the LSE methodological framework and the corresponding modelling strategy and approaches, we can model the economic agents' dynamic investment-maximization behaviour subject to the representative bottleneck constraints directly without looking for and constructing complex shortage indicators and artificial normal paths. In this model set-up, the cyclical adjustment processes of real investment can also be better understood. The cointegration relation represents the long-run equilibrium relationship or 'norm' path (hyperplane) between real investment and relevant bottleneck constraints. On the supply side, so long as the supply of energy and agricultural products (as the representative bottleneck sectors) increases along this longrun equilibrium path, the basic demand of real investment for them can be satisfied, and real investment thus proceeds regularly under the restriction of the normal bottleneck constraints or normal shortage states. If, say, the supply of agricultural products exceeds this norm path, it means that the shortage of agricultural products is relaxed, and the 'bottleneck' is widened; thus with the help of those long-run and shortrun adjustment coefficients (bottleneck multipliers) real investment can be accelerated. If the supply falls lower than the norm path, it means that the shortage is intensified, and the 'bottleneck' is narrowed; thus again with the help of those adjustment coefficients, the growth rate of real investment will decline. On the demand side, if an industrialization drive is initiated by a political/economic campaign and real investment exceeds this norm path, in the short-run it is possible to support such an investment rush by drawing on stock and overloading the supply fertilities of bottleneck sectors. However, eventually shortages in bottleneck sectors will re-emerge and be rapidly sharpened by the exhaustion of stock and the overloading of supply capacity. As a result, the 'brake may suddenly be pulled' by the central authorities and the administrative hierarchy in their struggle to cope with emerging danger signals,

24

Chapter 1

and the growth rates of both planned and actual investment outlays will fall, in some case even becoming negative. Thus, by means of the cointegration norm (attractor) and the error correction mechanism we can incorporate Bauer's four-phase description of investment cycles into a more comprehensive and formalised framework.

1.6 Scope and Notes on the Database In consideration of relevant data limitation and the emphasis of the research, investment efficiency and sectoral breakdown of investment will not be discussed in detail.11 Because of data limitations (cf. note 2), this research can only deal with fixed investment in the state sector, for the period of 1953-96. For simplicity, 'investment' hereafter represents the state-sector fixed investment, i.e. 'investment in fixed assets of state-owned units' as described by the Statistical Yearbook of China. Although the over-investment of township and village enterprises (TVEs) is also reported frequently in China's media, both the scale and significance are secondary. In addition, TVE investment hunger may be fuelled mainly by very low real interest rates of credits, by their using the collective lands and facilities with less costs or even without costs, and by local protectionism (Chen 1993, Saith 1995). Dealing with TVE investment drive goes beyond the scope of this book. However, so long as TVEs over-investment exists, the supply constraints on state sector investment expansion are bound to become more binding.

Table 1.4

Position of foreign investment in the state sector fixed investment, 1977-96 1977

1981

1990

1992

1993

1996

n.a. n.a.

3.64 3.80

28.46 6.30

46.87 5.80

95.43 7.30

274.74 11.96

1.31 2.40 n.a.

3.61 5.40 99.26

27.18 9.10 95.50

43.99 8.00 93.86

48.35 6.10 50.67

81.11 6.73 29.52

Total foreign investment Y billions % of national total fixed investment Foreign investment in the state sector Y billions % of state sector fixed investment % of total foreign investment

Sources: China Statistical Yearbook on Investment in Fixed Assets, 1950-1995 (1997: 19-24); Statistical Yearbook of China (1997:151-54).

Introduction

25

Foreign investment has been counted as a component of fixed investment statistics. Table 1.4 presents the relative position of foreign investment in the national total of fixed investment and in the statesector fixed investment. It can be seen from the table that: (a) Before 1993, foreign investment was concentrated in the state sector, which mainly consisted of official loans from OECD countries and international organizations such as the World Bank and Asian Development Bank. It financed the purchase of capital goods from the lending countries (Lees 1997, Chapter 10; Lardy 1994, 1995). (b) Starting from 1993, foreign direct investment and portfolio investment have appeared to constitute the bulk of foreign investment. Because state enterprise joint ventures and shareholding companies are counted as 'other ownership enterprises' in China's statistics, foreign direct investment and portfolio investment have statistically entered the non-state sector. (c) Foreign investment has accounted for a small proportion of China's fixed investment in both the state and non-state sectors, though its contribution to the transfer of advanced technologies and management experiences, to the expansion of China' trade, and to the alleviation of foreign exchange shortage should not be underestimated. In this book, foreign investment is also treated as a component of fixed investment. While foreign investment may not bring direct demand pressure upon domestic investment goods, in the short run it will certainly put pressures on energy supply and on the consumer goods market. The intention of this book is to identify and model the critical forces that determine the equilibrium investment level and investment cycle. Therefore, some important macroeconomic components, such as international trade in general, and export and import of energy and agricultural products in particular, play only implied roles in the modelling process. The economic reason behind this is that China has, in most years, depended on net export of energy and agricultural products for gaining foreign exchange, which, together with capital inflows, has in turn served as a important means for importing advanced techniques and equipment. This implies that the foreign exchange constraint to investment expansion has been transferred to the representative bottleneck constraints of energy and agriculture, which is, to a large extent, encompassed by the modelling process. This transfer process and its

26

Chapter 1

quantitative magnitude will be analysed in some detail in Chapters 4 and 5. The technical reason for this simplification is that many econometric experiments with several relevant international trade indicators and net foreign savings reveal that none of them have statistical significance.12 Due to an almost complete statistical blackout from approximately 1960 until 1980, the reliability of complete sets of hundreds of time series, going back to the early 1950s and which later appeared in the early 1980s, is questioned by most Western scholars. It is unclear whether the data before 1980 come from recent estimations or are actually based on professional statistics. In fact, many people believe that China simply stopped collecting data during the more chaotic years of the Cultural Revolution. However, many confidential and/or unpublished statistical books and documents that were based on professional statistics were written prior to 1980. Recent publications are mainly based on these then-confidential or unpublished data. Apparently exaggerated output data as a result of political campaigns were usually corrected in the following year and/or in the consequent rectification period, rather than in 1980s. For instance, on 14 April 1959 the State Statistical Bureau published 1958 grain output figures double the 1957 total, amounting to 380 million tons {People's Daily, 14 April 1959, p. 1). However, in the same year, the State Statistical Bureau published the 'Revision of Agricultural Statistics of 1958', in which grain output was aggregated to 250 million tons {People's Daily, 26 August 1959). After 1960, the official figure for grain output in 1958 was 200 million tons. During the Cultural Revolution, most data was collected within functional systems, as a part of the day-to-day management of the economy. Li Chengrui (1984), then Director of the State Statistical Bureau, explained that statistics were collected even during the Cultural Revolution and declared that they are 'basically reliable' and 'basically conform to the political and economic changes of the times' (Li Chengrui 1984: 23). The problems related to China's statistical data are mainly caused by the following two factors. First, China's statistical system has undergone several reorganizations over the years, and these reorganizations may have affected the quality, coverage, and definition of individual data series. Secondly, many changes in coverage and definition of data series, particularly structural ones induced by institutional changes and reform and/or by structural shifts of production and con-

Introduction

27

sumption, have not been, and may continue not to be, corrected. For example, in terms of fixed investment, consistent statistical distinctions between in-budget and out-budget financing, between central and local financing, and between fiscal appropriation and bank's policy lending, are hard to obtain. This is due to recurring cycles of decentralization and recentralization, considerable overlap of fiscal and monetary operations, and adjustments of budget coverage. Therefore, before any data can be used in econometric exercises, the relevant institutional context and the definitions used need to be clearly understood. The data series employed in this book in general, and in the modelling exercise in particular, are those considered to meet minimum criteria of reliability and consistency. The modelling process uses aggregate data such as total fixed investment of the state sector, grain output, energy consumption, total population, and a deflator of accumulation in fixed assets by the state sector. Those are all relatively independent of the changes of coverage and definition. Data on state sector fixed investment have always been closely monitored by planners. A single and most powerful institution - the State Planning Commission - has always borne responsibility for collecting these data. Because of the specific position of the State Planning Commission in comprehensive planning over the years, it has maintained the capacity to collect and aggregate investment data on the state sector in a meaningful fashion. The other data used in the primary data analyses are also mainly derived from aggregate figures such as national income, total output of agriculture and agricultural national income, total cultivated land and labour force engaged in agriculture. Where problems in coverage and definition arise, explanations and corrections are provided.

1.7 Organization of this Book Chapter 2 attempts to select the most relevant theories from the literature and to incorporate them into a discussion of China's experiences such as its heavy industry-oriented development strategy and the rigidity of this strategy, the fundamental mechanism of capital accumulation and the reform cycle. Chapter 3 presents the basic characteristics of China's state investment system and its response to reform. It shows that at all levels of central and local government and in enterprises, there is a ubiquitous growth drive and investment hunger. Both of them are endogenously generated within the political/economic system and

28

Chapter 1

structure, and have created pressure and incentives to maximize the likelihood of expanding investment. Due to a lack of internal, selfimposed restraint capable of resisting the expansion drive, real investment will expand until it overshoots the supply possibility frontier of bottleneck sectors. Chapters 4 and 5 present primary evidence of key bottleneck constraints to investment expansion. Chapter 4 aims to explore the adverse distributive consequences that a rising investment rate puts upon agriculture and to examine the short-run two-way interaction between agricultural fluctuation and macroeconomic adjustment in general and investment modification in particular. Chapter 5 shows how energy shortage has checked China's economic growth and played a representative role among producer goods constraints. The short-run two-way dependence between the growth rate of real investment and effective energy supply is also tested in this chapter by using the Granger causality test. In Chapter 6, a comprehensive econometric exercise based on dynamic cointegration approach is carried out. The integration and cointegration properties of the data are analysed. A long-run investment-level function and a conditional error-correction model of investment growth rate are presented. The possibility of the existence of a structural break caused by reform or other policy shifts is examined by the one-step-ahead Chow test (standard structural break test), and by one-step residuals and the corresponding standard errors in a recursive estimation. Finally, Chapter 7 summarizes the book, explores further the theoretical and policy implications of the research, reviews the major limitation of the analysis in this book and discusses the most recent development of China's state sector reform beyond 1995 and 1996.


2.1 Introduction The purpose of this chapter is to select complementary theories based on China's specific experiences and to reveal the possibility of integrating these selected theories into a new growth cycle framework. Because this book intends to undertake a theoretical exercise grounded on empirical and historical analyses, the interaction between empirical examination and theoretical searching has played a very important role in the process of understanding problems and difficulties, in looking for the right modelling strategy and approaches, and in exploring the theoretical and policy implications of the findings. Among the numerous publications dealing with investment cycles in socialist economies such as the former Soviet Union and Eastern Europe, the works of the 'Hungarian School', particularly, Kornai and Bauer, are most influential. These works present a brilliant demand analysis and vividly descriptive explorations of the investment behaviour of planners and the formation mechanism of investment cycles. Such new terms as 'soft budget constraint', 'expansion drive', and 'investment hunger', coined by Kornai and his colleagues, have become key concepts in the discussion of the socialist economy. In particular, the 'soft budget constraint' concept has gone beyond the boundary of the socialist economy and become one of central metaphors in the debates about the dilemmas of 'limited liability', 'the central bank', and 'strategic bankruptcy' as well (Cui 1993). The next section introduces the demand analysis of the 'Hungarian School' based on the concepts of soft budget constraint, expansion drive and investment hunger first, and then uses Bauer's (1978) description of four-phase investment cycles in

29

30

Chapter 2

a socialist economy to sketch out the cyclical patterns of investment in China. As noted in section 1.2, in terms of modelling, a key tradition in the literature is to analyse and estimate the planners' reaction function to several shortage signals, which represent the economy's supply capacity constraints on planners who are seeking to maximize the growth rate. This tradition also dominates the relevant research on China (Imai 1990, 1994a; Naughton 1986, 1987). However, as will be shown below, this modelling tradition suffers from certain theoretical and statistical difficulties. These include the absence of intertemporal rationality of the planner, statistical possibility and macro-indivisibility assumptions of investment commitments, two-way dependency between investment tension and shortage strength (and thus the weak exogeneity assumption of shortage), the effectiveness of planners' control, and the definition and measurement of shortage in consumption and investment goods sectors. In order to overcome these contradictions and limitations and to advance the standard investment cycle theory, we need to find complementary theories and advanced econometric methodology and approaches. A parallel theoretical strand, which attempts to build up the 'bottleneck constraint type of growth theory' for a socialist and/or developing economy, is distinguished in Kalecki's (1972, 1976) seminal works. Kalecki's theory is insightful for directly examining bottleneck constraints on a fairly high rate of investment growth, as in the case in China. He focuses on the trade-off balance between the desired high rates of investment growth and the limits to tolerate the adverse distributive consequences that a high investment rate puts on agriculture. He interprets the problem of financing development as more than an adjustment of planned aggregate output to the available supply of necessities. By incorporating the comparative advantages of both the Hungarian School and Kalecki's theory it is possible to set up a new 'growth cycle' framework (Goodwin 1967 & 1982, Dore 1993). Business-cycle theory has been a major branch of the Western economics. Many approaches have been established to model business cycles in the west. Among them, the modern approach employed by the proponents of real business cycle theory has followed the tradition of stochastic analysis of Frisch (1933) and Slutsky (1937). It distinguishes shocks from a steady growth path of an economy, and more importantly it distinguishes between the shocks that cause variables to differ


31

from their steady state values and the propagation mechanisms that convert the shocks into longer-lived divergences from steady state values (Fischer, 1988). The modelling strategy and approach used in this book is, to a great extent, rooted in the real business cycle approach. This chapter is organized as follows. Section 2.2 gives a brief introduction of the investment demand analysis of the Hungarian School, and then sketches the cyclical patterns of investment in China based on Bauer's four-phase theory. It also reviews the tradition of establishing single control equation models, and shows the relevant problems in that tradition. Section 2.3 discusses and extends Kaleckian 'bottleneck constraints' growth theory in a socialist developing economy, and then explores the mechanism of fluctuation around the Kaleckian equilibrium steady growth path. Section 2.4 traces the existing researches on China's investment cycles and reviews Justin Lin's (1995, 1996) reform cycle theory. Section 2.5 outlines the relevance of the Western business cycle theories and highlights the methodological significance of real business cycle approach and Goodwin's growth cycle model to the research of this book. Section 2.6 summarizes this chapter.

2.2

Incorporating the Investment Cycle Theory of the 'Hungarian School' with China's Experiences 2.2.1 Soft budget constraint, expansion drive and investment hunger

The term 'budget constraint' is familiar from microeconomic theory of the household, in which the sum available to a decision-maker places a constraint on consumer spending, with own expenses covered by income generated by selling household output and/or by earning a return on assets. Clearly, the budget constraint presents a behaviourial characteristic of the decision-maker. It is a constraint on ex ante variables and primarily on demand. The 'softening' of the budget constraint appears when the decision-maker expects that excess expenditure over earnings will be paid by some other institution. In other words, external assistance is highly probable and this probability is firmly built into decision-making behaviour (Kornai 1986).

32

Chapter 2

In socialist economies, such as Hungary and China, state-owned enterprises (SOEs) used to receive regular external assistance. Four main forms of this assistance can be distinguished: (a) Soft appropriation and subsidies granted by national or local governments. These are soft because their amount is negotiable, subject to bargaining, lobbying, etc. (b) Soft taxation, where the taxation rate is not low, but the amount of tax payment is subject to prior and/or subsequent bargaining, where the fulfilment of tax obligations is not strictly enforced, and where there are leaks, ad hoc exemptions, postponements, etc. (c) Soft credit, where again the fulfilment of credit contracts becomes the subject of bargaining, contract fulfilment is not enforced, unreliable debt service is tolerated, and postponement and rescheduling are in order. And (d) Soft administrative pricing, in which administrative pricing, which dominated the price-setting processes before reform and still plays a significant role during transition, can be 'softened' by vertical bargaining with the price authorities according to some permissive 'cost plus' principle. Such external assistance is not granted automatically and some effort is needed to obtain it. Influence costs (Milgrom & Roberts 1988, Milgrom 1988), rent-seeking costs (Krueger 1974) and bargaining costs should be take into account. Therefore, even a softened budget constraint exerts some influence on the behaviour of the enterprise (Kornai 1986). The concept of soft budget constraint illustrates the collective experience of a large group of enterprises and, in China, the sum of the SOEs. It reflects in financial form a deeper, socio-economic phenomenon, which in Marxian terms would be indicative of a certain social relationship between the state and the economic micro-units, particularly the SOEs. One of the most impressive consequences of the soft budget constraint syndrome may be the formation of investment hunger. For example, consider an economy where hard budget constraints dominate. With hard budget constraints a firm will start a project only if it seriously believes that the flow of revenues from the sale of output generated by the new project will cover the flow of expenditures needed to


33

accomplish it. It is true that in a world of uncertainty, different decision-makers will have different degrees of risk-aversion. Nevertheless, given the distribution of risk-aversion over all investment decisionmakers, the total demand for investment resources will be constrained. There will be self-restraint in capital formation decisions because of genuine fear of financial failure. Therefore there will be a relatively symmetrical relationship between demand for investment resources and the supply generated by the same investment resources.1 In contrast, in an economy where a sufficiently large number of decision-makers enjoy soft budget constraints, this kind of symmetry breaks down because external financial support can be obtained with little cost. An enterprise might start a project even though it may suspect that the cost might exceed planned levels by a significant margin or that the revenue might be much less than estimated. In case of financial failure it can expect to be bailed out by the state. In such a situation there is no internal, selfimposed restraint in investment intentions and the demand is not counterbalanced by a 'dead serious' consideration of revenues or of supply. Therefore, as long as an expansion drive exists, investment hunger is inevitable. Expansion drive is found at all levels of the economic hierarchy in a socialist country. The major motivations behind it can be divided into two categories. The first relates to the internal and external pressure to provide evidence of socialist superiority, which should be demonstrated by the fact that a socialist economy can quickly catch up with the developed countries. This belief is a major aspect of official ideology, and has been constantly repeated since the victory of the socialist revolution. As a result, the revolutionaries themselves are not only impatient, but they also feel pressure from large masses of the public. On the other hand, this drive to catch up is reinforced by military and defence considerations. Modernization and economic strength are needed to create a powerful army (Kornai 1992). The second category relates to the motives of the bureaucracy. These motives create such a strong inner expansion drive that the top-level leadership does not need to impose a forced growth policy on the mid-level and lower-level leaders. Among the seven motivations listed by Kornai (1992, section 7.4), five provide an explanation of bureaucracy's powerful inner expansion drive: political and moral conviction, identification with the job, power, prestige and material benefit. The members of the bureaucracy hold the same political convictions as the top-level leaders, who perceive the need to

34

Chapter 2

catch up with increasing speed. Identification with one's job also inclines one toward expansion due to the conviction that the activity of the unit under one's charge is important and that it has to be extended. Someone burdened with the internal problems of a unit also often believes that these could be solved by investment. Leaders at all levels believe that their power and prestige grow along with the expansion of the unit under their charge, and in many cases there are material rewards involved. Finally, expansion drive is further stimulated by shortage. There is a waiting list for the firm's products; buyers increasingly demand more, and investment is needed to increase output. The difficulties the firm suffers in obtaining its own inputs may prompt it to internalize the production of these inputs, which again requires investment (Kornai 1980, 1992). Expansion drive is not, in fact, system-specific. Capitalist entrepreneurs also expect expansion to bring greater profits, and so greater power, prestige and financial rewards. The main system-specific distinction lies not in the actual effort to expand but in the internally generated self-restraints that run counter to the expansion drive. For a capitalist firm even with limited liability, any loss caused by a faulty investment decision hits both owners and managers. Expansion is an attraction but also a high risk because although they expect to make good investments, the risk of bad investment limits unbridled expansion. By contrast, in a socialist economy subject to a soft budget constraint, an SOE can suppose that liquidation will not follow from any defective investment decision, however high the costs and financial losses may be. For an investment project, many people at different levels of the hierarchy play a role in the decision-making (i.e. project approval) process so that it is almost impossible to lay the blame on anyone in particular. The loss incurred will not affect the income or assets of any of the bureaucrats. Since there is an almost total absence of internally generated self-restraint that could check expanding, expansion drive becomes a 'natural instinct' for the bureaucracy and investment hunger is ubiquitous. The above distinction may be alternatively explained by incorporating some thoughts of Kalecki and Kaldor. For each class, from the perspective of income distribution, 'capitalists earn what they spend, and workers spend what they earn' (Kalecki, cited in Kaldor 1955). For individuals, however, the expansion of capitalist investment can be disastrous if corresponding demand is not forthcoming. By contrast, in a


35

socialist economy, the consolidated state sector (state enterprises and government bodies) earns what it spends. In other words, the volume of investment spending will generate the necessary income to cross-subsidize reckless investment activities. Such a soft budget constraint means that the abovementioned individual reluctance is groundless, and the fear of failure is largely eliminated. As a result, the limits to investment expansion are bound to be resource constraints and distributive barriers between investment and consumption in general, and industrial expansion and requisite incentives to food production in particular.

2.2.2 Bauer's four-phase theory and China's investment cycles Section 1.4 presents a preliminary indication of the existence of investment cycles in China. This section shows that the cyclical patterns of both investment ratio and investment growth rate as well as the linkage between them can be adequately described by Bauer's four-phase model of investment cycles in a socialist' economy (Bauer 1978: 24360 & 1988). Bauer's model is instructive for an intuitive understanding of the fundamental mechanism generating investment cycles in China.2 Bauer's initial or run-up phase (cf. Table 1.3) corresponds to the years 1955, 1957-58, 1962-63, 1968-69, 1973, 1977, 1981-82, and 1989-90. During these years, a large number of projects were approved. According to Bauer's model, newly started projects initially involve only a relatively moderate increase in investment outlays. This does not lead to tensions, although clusters of new projects may cause a rapid extension in the stock of investment projects in progress. This leads to a significant increase in the 'investment engagement' or the unspent part of the investment budget allotted to these projects. In the second or rush phase, namely the years 1956, 1958-59, 196465, 1969-70, 1974-75, 1978, 1983-86, and 1991-92 (cf. Table 1.3), new investment projects were continuously being approved and the high outlays committed in the run-up phase were beginning to be felt. Since the investment outlays were systematically understated in the previous plan bargaining process, actual investment outlays exceeded planned volume. The growth rate of investment reached its maximum in this cycle, and exceeded the planned growth rate even if the latter was excessive due to political or economic campaigns (for example, the 'High Tide of Socialist Construction' in 1955-57, the Great Leap For-

36

Chapter 2

ward in 1958-60, Construction of Inland Industrial Bases in 1962-68, the 'Foreign Leap Forward' i.e. 'Yang Yuejirf in 1978, and the 'Deng Whirlwind' in 1992-93). Excess demand in the investment sector increased, the shortage of investment goods and services re-emerged or became sharper, and the shares of fixed investment and of capital accumulation in the national income rose. Other sectors sacrificed their growth rates; in the case of China these were agriculture and consumption sectors, which are particularly vulnerable. According to Bauer's model, the upper turning point in the cycle occurs in the third or halt phase. This occurred in China in the years 1956, 1960, 1966, 1971, 1975, 1978, 1987 and 1993 (cf. Table 1.3), during which there was a sharpening of the shortages of investment goods and services, and planners, especially central planners, became more vigilant and began to turn down a large proportion of investment requests. In the four-phase model, investment requests do not decrease during this phase, but a relatively smaller proportion of them is approved. That is, the 'approved coefficient' falls. At the same time, work on existing projects is hastened even at the sacrifice of a shift between the uses of national income, since 'the planners intend to work off the investment engagement through forcing a rapid growth of investment outlays and of construction' (Bauer 1978: 250). Owing to increased spending on existing projects the shortages may be exacerbated and the investment ratio may rise further, though compared to the previous phase, the growth rate may fall. Once such danger signals as bottlenecks become more frequent in the investment sector, as the supply of investment goods and services for the implementation of ongoing projects becomes more irregular and uncertain, the authorities begin to fear the development of social disturbances as a consequence of growing dissatisfaction with stagnating agricultural production and increased shortages in the consumption sector. As a result, the 'brake may suddenly be pulled'. The collective action of the authorities initiates the fourth or 'slowdown' phase, seen in China in 1957, 1961-62, 1966-67, 1972-73, 1976-77, 1979-81, 1988-89, and 1994-96 (cf. Table 1.3). Not only does the 'approval coefficient' continue to fall, but the annual limits of investment outlays are also cut. In extreme cases, planners may even retroactively cancel investment projects approved during the 'rush' and 'halt' phases, in their struggle to cope with emerging danger signals (which appears to be especially significant in China):


37

The most important feature of this phase is the fall in the planned and actual growth rate of investment outlays (in some cases even negative growth rates). Due to this the rate of investment and of accumulation falls, and an opposite shift takes place among the uses of national income. (Bauer 1978: 252) This situation continues until the tensions in the utilization of national income and the shortages of investment goods and services are alleviated. As the shortages of investment goods and services are relieved, the investment ratio falls and the situation improves with respect to those uses of national income that were suppressed earlier. As a result, restrictions on starting new investment projects begin to weigh more heavily on supervisory planners who are authorized to approve them. As pointed out by Ickes (1986: 47), 'in a resource-constrained economy all projects will appear socially beneficial'. At the micro-level the factory and shop managers say they cannot produce more under the given conditions, and the desire to increase production comes up against one bottleneck after another. Therefore, it seems logical to open the bottlenecks through expansion of capacity. Each investment project, viewed in isolation, appears worthy of investment funds which are needed to alleviate shortages. No measure can be used to compare the opportunity costs of different projects, which means that while each project by itself seems deserving of funds, not all projects can be funded. This means that there are only 'important' and 'more important' investment projects in bureaucratic-coordinated economies. In this context, if selection becomes more rigorous and the approval coefficient falls (in the halt and slowdown phases), then the refused proposals are not denied outright, but are only postponed. Hence, the pressure of these postponed claims increases in the slowdown phase. At the same time, a certain alleviation of tensions and shortages also stimulates the growth-related goals of planners, who become less sensitive to efforts endangering macroeconomic equilibrium. Thus, the selection of investment projects is less strict, the approval coefficient rises again and a new cycle begins. The end point of slowdown marks the lower turning point in the cycle. It is worth noting that the run-up phase usually provides suitable conditions for initiating political/economic expansion campaigns and such campaigns typically accelerate the run-up phase to rush. However, the concrete radical extent of each political/economic campaign, which cannot be folly explained by the general process examined above and is

38

Chapter 2

to a great extent exogenous, seems to play a leading role in determining the amplitude of each cycle. Most significantly, the most radical 'Great Leap Forward' (1958-60) and the resultant economic collapse (196162) and recovery (1963-65) resulted in the widest cycle (1957-62) and the recovery type of high growth rates in 1963-65. The extremely radical 'Cultural Revolution' brought in the second biggest dip (1967-68) and a subsequent recovery rush (1969-70) (cf. Figure 1.1). It may be thanks to such tragic lessons that China has avoided similarly radical political/economic campaigns since 1970. However, less radical economic/political campaigns have been generated within the system (cf. Table 1.3) and may also occur cyclically in the near future. While the radical extent of these political/economic campaigns determines the amplitude of each corresponding cycle, it also determines the nature of interaction between investment expansion and bottleneck constraints. Radical campaigns induce extraordinarily high investment demands, creating the unsustainable exhaustion of stock and an overloading of supply capacity of bottleneck sectors, leading finally to the collapse of some bottleneck sectors or even of the entire economy. In brief, Bauer's investment cycle model is an important component of the Hungarian School's work. The conclusion is that investment fluctuations are endogenous to the centrally planned economic system and are caused largely by planners' response to shortage signals and to internal systematic tensions. The shortage signals represent resource constraints on planners who are seeking to maximize the investment growth. The internal tensions epitomize investment hunger and the expansion drive which stem from incentives of local and departmental institutions and enterprises to attain the objectives of the output plan and to expand their economic and administrative power. On the other hand, it is clear that Bauer's theory focuses on short-run disequilibrium only and does not tackle the question of what determines the level of investment.

2.2.3 Single control equation model and the relevant problems The work of the 'Hungarian School' implies that our understanding of investment cycles in socialist countries should be improved by trying to model planners' behaviour. In fact, analysing and estimating the planners' reaction function have occupied central positions in the literature


39

(Mihalyi 1992, Chapter 2), in which the works of Kornai (1982), Roland (1987), and Christin & Short (1991) are most influential and therefore of greatest interest to us. Nevertheless, the successful elaboration of a testable model would not be an easy task, particularly when the necessary econometric approach were not yet available. Kornai (1982: 38-53) establishes a control equation governing the investment process by the planners in which the dependent variable, namely the volume of what he calls investment vintages (new projects), is determined by the normal volume of investment vintages, and the deviations of consumption, investment commitment, and shortage intensity from their normal values or averages (e.g. five-year moving average). This model can be written as:

where M(t) represents the volume of the investment vintage and symbolizes the value of machines and buildings put into operation in future as a result of the projects started in year t, H(t-\) represents consumption lagged by one year, K(t) represents the investment commitment, that is, the total scale of investment required for completing all projects under construction in the year /, and Z(t) indicates the shortage intensity. The (*) designation presents normal levels of M(t), H(t-\), K(t) and Z(t), respectively. The \i's (>0) represent the feedback strength coefficients. Kornai affirms that as a result of the influence of the three non-price feedback signals, decision-makers cause M(t) to deviate from its normal value M (t) - new investment starts - so as to drive the system back to the normal paths of consumption, investment and shortage. Clearly, this model also focuses on short-run disequilibrium behaviour without tackling the question of what determines the investment level, because in equilibrium M(t) = M*(t). Apart from this, the theoretical and statistical confusion inherent in equation (2.1) makes it impossible to test this single equation. First of all, there is an absence of intertemporal rationality of the representative planner in the model setup. Secondly, as pointed out by Mihalyi (1992, section 2.2.2), both K(t) and M(t) are stock variables; the measurement of K(t) is both theoretically and statistically confusing because it represents a mixture of actual (i.e. past expense at mixed price level) and planned (i.e. future expenditure estimated) costs; and

40

Chapter 2

M(t) is completely an ex ante estimate of the expenditure needed to complete the given investment projects started in the year /, based only on engineers' calculations. In fact, the relevant actual costs are always much higher than the pre-estimates (Kornai 1980: 195-8). In addition, as in other socialist countries, data on new investment projects are not available in China. Thirdly, perhaps more importantly, there exists a type of 'fallacy of composition' within the theoretical derivation related to investment commitment. The concept of investment commitment has played a central role in quantifying effective demand for investment goods. 'The investment decision-maker is - to a large extent - the prisoner of his own previous decisions' (Kornai 1982: 54). The basic assumption here is that current decisions concerning the volume of investment in the next period are determined by past decisions on investment starts. The intuitive rationale behind the derivation is based on numerous observations, which indicate that once an investment project has been started, it will sooner or later be completed regardless of profitability considerations, as a half-ready factory is a guaranteed loss from the point of view of the planners. As a theoretical abstraction, this can be expressed as an assumption of the indivisibility of investment decisions. Although at microeconomic level an investment decision usually remains indivisible in a socialist economy, on the macroeconomic level, projects can and do substitute and complement one another. If the plans for opening a new coal mine are shelved, there will be no need for a new coal-fired power station. In this sense, the idea of investment commitment provides a theory of demand for investment goods only at the individual firm level rather than at the macroeconomic level (Mihalyi 1992). Fourthly, using a univariate moving average as a proxy of the 'normal path' may, itself, produce spurious cycles as discussed in details by Slutsky (1927) and Frich (1928). In addition, if the 'normal path' does represent the equilibrium state of planners' behaviour, there should be a structural relationship determined by the interaction among the relevant demand (investment, here) and supply (e.g. supply of investment goods and consumer goods) variables. In fact, this type of structural equilibrium relationship presents the determining function of investment level. Fifthly, the notion of shortage is theoretically powerful but statistically difficult to handle. In a market economic framework ex ante demand would be simulated and modelled through structural behaviour relationships, e.g. consumption should express the 'revealed preferen-


41

ces' of consumers given their income and prices. In case of insufficient supply (shortage) price adjustment would lead to an adjustment of both demand and supply to equilibrium direction. Here one may define 'shortage' as excess demand for goods or factors and trust that price and/or quantity adjustment will clear the shortage. But even in a market economy we are not always able to be certain that we have modelled ex ante behaviour properly. In a centrally planned economy, we may have some certainties, but also some uncertainties. For example, in the area of consumption it is difficult to know the 'revealed preferences' of the agents if consumer goods have been rationed. Certainty used to be linked to the central planners' 'revealed preferences' as expressed in economic plans, but these plans are also argued to be endogenous to economic performance. Such uncertainties imply that it is almost impossible to estimate ex ante demand functions in the traditional manner. In the literature, the term 'shortage' has been linked to several different situations: (a) bureaucratic inefficiencies, (b) real bottlenecks such as insufficient supply of investment goods and consumer goods, (c) financial bottlenecks such as insufficient foreign exchange or domestic finance to meet investment requirements, (d) sectoral imbalances, and (e) gestation lags or duration of the 'construction period'. There are various measuring exercises focused on (b), showing diverse results. For example, in China's case, there are about ten competitive indicators measuring shortage in consumption markets (Portes & Santorum 1987, Table 6; Peebles 1991, Table III.l & III.2; Peebles 1992; Imai 1994a & b). Finally, due to the simultaneous interaction between shortage and investment tension, the sign before |i z is less certain than might be wished. On one hand, more intense shortage reflects the fact that the system meets its own resource constraints more and more frequently and suffers increasing losses. Abovenormal shortage intensity therefore induces the decision-makers to restrain new investment starts. Conversely, if the difficulties caused by shortage have diminished and complaints about under-utilization are beginning to be voiced, this will provide a stimulus to expand investment activity. (Kornai 1982: 49-50) Thus, the sign before \iz would be negative. But proceeding from the perspective of correlation analysis for time series, we will obtain an opposite conclusion. Shortages and investment tension

42

Chapter 2 are in close interaction: they form a special 'vicious circle'. Awareness of shortage is one of the main motives for expansion drive and the associated investment hunger. Shortage signals play an important role in the selection of investments. Thus, shortage generates investment tension... At the same time investment tension is one of the main causes of general shortage. Since investment hunger is insatiable, it creates an almost-insatiable demand. This extends as far as the resource constraints on investment activities, and even goes beyond them... The stronger the investment tension, the more it is felt that investment demand tries to draw resources away from other fields of utilization thus amplifying general shortages. (Kornai 1980: 201)

This circle suggests that there is a positive correlation between investment tension and shortage intensity in general, and the shortage of investment goods in particular, because investment tension results in shortages of investment goods almost without lags. These two opposite forces make the sign before \iz uncertain. Roland (1987) develops a more practical model based on equation (2.1) to display the former Soviet planners' behaviour with regard to macroeconomic investment growth. In his model the growth rate of investment is a function of structural pressure for investment and the measures of shortages in the investment goods, foreign trade, and consumption sectors. The structural pressure is measured in the first specification by the constant term and, in the second one, by the five-year moving average. He estimates the model using Soviet data for the period 1960-80 and concludes that with the exception of a positive coefficient for consumption tension, his results are consistent with the Hungarian School's explanation of investment cycles. Christin & Short (1991) improve Roland's model by removing trend and average indicators and employing more appropriate and theoretically well-established measures of disequilibrium in foreign trade and the consumer goods market. However, both works use 'unfinished construction' as a proxy for shortage in the investment goods sector which may be misleading. 'Unfinished construction' is the stock of unfinished capacity at historical cost, and there is no way to build a meaningful price index to deflate this stock series (Mihalyi 1992: 81).3 Following Bauer's (1978) line of analysis, the ratio of unfinished investment stock in current investment is much more sensitive to the 'approval coefficient' and 'the planners' intention to work off the investment engagement'. In other words, planners are more responsive to shortage signals and investment engagement at the beginning of the cause-effect chain; they then decide whether or not to change the approval coefficient and to hasten the


43

work on existing projects even at the sacrifice of growth in agricultural and consumption sectors. As a result, the ratio of unfinished investment rises or decreases passively and cannot be considered causative. Finally, as Roland (1987) argued, 'increased shortage in this sector leads to a lengthening of construction periods and thus to a rise in the rate of unfinished construction' (Rolands 1987: 197). As a theoretical concept, the construction period should be determined by technical characteristics of the investment process and the shortage intensity of investment goods. However, in the light of statistics, it is almost fully determined by investment starts and current investment outlays. In fact, the relevant statistical formulas of the construction periods, which have been used in China and in the other socialist countries (Statistics on Fixed Investment in China, 1989-1990, 1991: 347; Chen & Niu 1990: 916), are as follows: Construction period = [Number of projects under construction in this year] [Number of projects completed in this year]

(2.2)

or Construction period = [Plan target of investment in all projects under construction this year] [Investment outlays in this year]

(2.3)

Relating formulae (2.2) and (2.3) to Bauer's (1978) description of the investment cycle (section 2.2.2), it can be found that there is a strong negative correlation between the construction period cycle and that of the investment growth rate. In the 'run-up' phase of an investment cycle, a large number of projects are approved, and the number of projects under construction and the planned investment in all projects under construction rise rapidly. However, because 'the newly started projects in the first period of construction involve only a relatively moderate increase in investment outlays' (Bauer 1978: 249), the construction period reaches its maximum in the cycle while both growth rates of investment outlays and the number of completed projects are still very low. In the 'rush' phase, actual investment outlays exceed planned volume by a large margin and the growth rate of investment gradually reaches its maximum in the cycle. However, there is a slowdown both

44

Chapter 2

in the growth of the number of projects under construction and of planned investment. Therefore, the construction period gradually shortens. In the 'halt' phase, planners begin to refuse a larger proportion of investment requests, and at the same time, work on existing projects is hastened even at the sacrifice of growth of other sectors, so that the construction period reaches its minimum. In the 'slowdown' phase, the approved coefficient continues to fall, both the number of projects under construction and planned investment in all projects stagnate, and the annual limits of investment outlays are cut. Thus the construction period begins to stretch out. The above analyses suggest that the empirical correlation between investment growth and the uncompleted construction rate presented in Roland (1987) and Christin & Short (1991) may represent this type of negative correlation stemming from the statistical formulae (2.2) and (2.3) rather than the expected relationship between the investment growth and the shortage intensity of investment goods. Grosfeld (1987) models a planner's reaction function based on the notion that a planner's reaction to shortage signals is not smooth and continuous. She incorporates thresholds into her model, above and below which the planner has different intensities of reactions to shortages. In this way she effectively models Kornai's idea of shortage indicators (1980, 1982) as departures from normal levels. Furthermore, by allowing the reaction threshold to vary, Grosfeld tries to eliminate the problem of attempting to specify the subjective normal level of shortage. Nevertheless, there have been criticisms of (a) her proxies for the shortages of consumer goods and foreign trade sectors, which face the same contradictions as in the Roland's model (Christin & Short 1991), and (b) the impossibility to test planner's synthetic reactions to more than two shortage signals using her model. The latter would involve some very complicated econometric models switching among more than four regimes. Two other implied assumptions in the modelling tradition should also be considered. The first is that all shortage variables are assumed to be at least weakly exogenous. Taking into consideration the close interaction between investment tension and shortage intensity, this assumption has to be tested at the outset, because without weak exogeneity, the single reaction function is bound to be misspecified (Engle et al. 1983, Engle & Hendry 1993). The other assumption is that the planners' control is effectual, at least in the 'halt' and 'slowdown' phases of

Integrating Selected Theories Based on China fs Experiences

45

the investment cycle. However, as shown in their empirical estimates by Brada & King (1992), in a number of markets in former Soviet Union and Poland there was an appreciable, long-lasting, and increasing excess demand, most notably for grain, although the planners had made efforts to achieve market equilibration. The separation of motives and effects suggests that empirical testing based only on the single reaction equation might become secondary or insignificant in statistical tests.

2.3

Kaleckian Economic Growth Theory and China's Capital Accumulation Mechanism

2.3.1

The causality line: From growth rate to investment to saving to bottleneck constraints

By generalizing his experience of perspective planning, Kalecki (1972) established an outline of an economic growth model for a socialist economy. His basic equations are as followings. r200 < 10-200 >50 < 30-50 Ho) is Johansen's (1992c) LR statistics for testing H a in Ho: r = 1, asymptotically x2(3) distributed on the null of p = (1, - 1 , -2, -1)' and a 2 = a 3 = a 4 = 0; and x 2 (1) distributed on the null of the others. The figures in parentheses are the probability of the LR test.' * ' in p means no restriction on the constant element.


201

Equation (6.7) indicates that the long-run real investment level in China has moved along the supply possibilities frontier of bottleneck sectors and along the distributive barrier between industrial expansion and necessary agricultural growth, represented by the effective energy supply and per capita grain output, respectively. In other words, the key binding constraints on the long-run level of real investment demand are the effective supply of representative consumer goods and producer goods as well as the distributive barrier which industrial expansion poses to necessary agricultural development. Another important economic implication of equation (6.7) is that it serves as a statistical expression of an equilibrium or stationary relationship between real investment (corresponding to the demand side in a typical equilibrium equation in an ex ante sense) and energy supply and grain output (corresponding to the supply side). This forms an empirical proof of the existence of the equilibrium relationship without the aid of first modelling separately the demand and supply functions. As a consequence, the error term, {z -p - e - 2.0g7w},_i, represents the previous disequilibrium and should be a useful explanatory variable for the next direction of movement of // or (z -p)t. More concretely, when the error is positive (negative), z,_i and (z -p)t-\ are too high (low) relative to the equilibrium comovement path and the economic agents will in general reduce (increase) / in future periods relative to the comovement path given in equation (6.7). It is the error-correcting behaviour on the majority of economic agents that induces the cointegrating relationship among the corresponding economic time series. The estimated adjustment coefficient vector a suggests that the disequilibrium error P'(Z'/-i 1)' has an important impact on A/, while its influence on Ae, Agm and Ap is less significant. This is confirmed by running Johansen's (1992c) LR tests of linear restrictions on a. Table 6.4 also records these tests for the hypothesis a, = 0 (z = 1, 2, 3, 4), where a, is the z-th element of a. As proved by Johansen (1992a, 1992b) and Urbain (1992), a, = 0 is equivalent to the z-th component of Zt being weakly exogenous for the long-run parameters in P as well as for the short-run parameters in the conditional equation (6.5). The test statistics in Table 6.4 show consistently that the weights of the cointegration relationship in the Ae, Agm and Ap equations are not significantly different from zero, implying that energy supply, grain output and inflation are weakly exogenous in the UVAR system. Thus it becomes possible to construct a well-specified conditional investment determi-

202

Chapter 6

nation equation based on (6.5) to fully capture the dynamic of investment determination represented in Model (6.4).

6.5 Estimate of Conditional Investment Growth Rate Equation Based on the findings in the last section, the conditional representation (6.5) of the data can now be used directly as the starting point for sequential reductions and reparameterization aiming at the development of the well-specified conditional investment determination equation. It should be pointed out that the UVAR model (6.4) has been mapped into 7(0) space through differencing and cointegration transformation (restricting n = ccp'), thereby allowing OLS estimation and statistical inference to proceed along the lines of standard Gaussian asymptotic theory. This being the case, and taking into consideration the price homogeneity of degree one, we can rearrange model (6.5) into real investment terms so that we can conveniently interpret the economic implication of the induced error correction equation. The rearranged Model (6.5), as an 7(0) equation, is estimated by OLS, and is subsequently simplified in order to obtain a more concise, yet congruent data characterization. The results of estimation with the usual standard errors are reported in Table 6.5. The statistics listed in Table 6.5 indicate that the goodness of fit measured by R2 and a is highly significant, and all of diagnostic tests suggest the equation appears to be well-specified. The overall parameter constancy of this conditional equation is confirmed by recursive estimation. Figure 6.2 shows the sequence of one-step-ahead Chow tests, that is, the standard structural break test, none of which are significant. Figure 6.2 also records the one-step residuals and the corresponding ±2 standard errors to show that the standard error a is almost constant over the sample. The constancy of each coefficient, except the Ap's, is also confirmed by the same recursive estimation; the relevant figures have been omitted here for simplicity. The recursive ^-statistics for Ap's coefficient show that the Ap's impact is dominated by the striking outlier after 1985 when the industrial price reform was initiated; before 1985 the impact was not statistically significant. Together these suggest that the equation given in Table 6.5 appears to be a wellspecified and well-fitted investment growth rate determination equation


203

with parameter constancy (i.e. no structural break) over the sample period (1953-95).

Figure 6.2

Note:

One-step residuals, ±2 standard errors, and one-stepahead Chow tests for the investment growth equation

Res1 Step is the one-step residual; S.E. is the standard error. 11 CHOW is the one-step-ahead Chow test; 5% crit. is the critical value at the 5% significant level.

The investment growth rate equation has a clear economic interpretation in all of its explanatory variables. First, the estimated coefficient of the cointegration relationship (i.e. error correction terms) reveals a large and significant adjustment to disequilibrium deviations of real investment level from their norm level determined by the long-run investment determination function. In other words, on average the investment agents decrease (increase) their real investment outlays by about 83 per cent of the last year's over- (under-) investment. Second, notwithstanding this disequilibrium impact, the short-run bottleneck multipliers of effective energy supply (representative producer goods) and grain output (necessary consumer goods) are significantly greater than zero; their values are fairly close to the corresponding long-run multipliers in equation (6.7). Third, previous real investment growth has a significant short-run inertia as discussed by Bauer (1978) and others, which is indeed consistent with certain characteristics of a socialist economy. Fourth, the significant positive correlation between A(i-p) and Apt-\ reflects the basic fact that, in a bureaucratically coordinated

204

Chapter 6

economy, an increase in the price level is not only a result of overinvestment but also, perhaps more significantly, a reason to request extra investment at least in the short run because shifting the blame of extra expenditure to rising input prices is straightforward and appears to be more reasonable (Kornai 1992: 548-52). This effect was relatively limited before reform, but has played an increasingly important role during reform, as shown by the recursive /-statistics mentioned in last paragraph. The variable A/?,_i also represents a special short-run shortage signal translated by the market component of the economy. However, the estimated investment growth rate equation implies that this effect is overbalanced by the impact of offering bargaining reasons for requesting additional investment. Finally, the parameter constancy indicates that there is a lack of structural break induced by the reform. It should be emphasized that the deeper constancy prevailing in the determination of the investment cycles does not imply the concrete and immediate adjustment mechanism remaining unchanged over time. In fact, as sections 2.4.2, 3.4.5 and 4.6 reveal, the concrete adjustment processes can be quite different due to the changing patterns in distribution of power among different political groups and between the central and local governments. Several significant changes have taken place during the reforming years. First, the inflation barrier has played an increasingly important role in the investment adjustment process. This may make Kaleck's growth theory more relevant to China. Second, since credit has begun to play a major role for financing investment, the investment cycles have shown a new scenario during the reform period. Over-investment first induces over-expansion of bank credit, followed by excessive money creation, high inflation in the market component and shortage in the planned component of the economy. This in turn justifies retrenchment based on ad hoc administrative measures (cf. section 3.4.5). Third, the rigour/chaos characterizes the reform cycle and the widespread rent-seeking activities (cf. section 2.4.2, Lin et al. 1996). However, a key feature has been persistent. This is the investment hunger and the resulting tensions between investment expansion and the supply and distributive barriers to the expansion. This fundamental feature is exactly what has been captured by both the long-run investment level equation and the short-run investment growth equation.

Energy as the Representative of Producer Goods Constraints 205

Table 6.5

The estimation of investment growth rate equation in China Dependent Variable: A(/-p) t

Fxnlanatorv \7ariablp Â.L/ICII I d l l / I V VCIIICIUIw

A(/-P)M

Aet &gmt Apf-i

(/-p-e-2.0fifm;M Constant Type of Test 2

Coefficient

Standard Error

0.233 1.547 2.311 1.154 0.833 11.437

0.072 0.110 0.424 0.277 0.124 1.696

Test Value

Probability

R

0.90

G

0.09 1.86 65.70 0.42 0.61 0.06 1.11 1.24 1.63

DW F(5, 35) Normality %2(2) AR 1-2 F(2, 33) A R C H 1 F(1,33) HeteroF(10, 24) Functional Form F(20,14) RESET F(1, 34) Notes:

— 0.00 0.81 0.55 0.82 0.40 0.34 0.21

d is the estimated standard deviation of residuals. The normality x2(2) is the Jaris the Lagrange Multiplier test for /th- to que-Bera statistic. AR i-j F(q, T-K-q) yth-order residual autocorrelation (q=j-i+ 1; T = the number of observations, and K = the number of regressors in the equation). ARCH 1 - q F(q, T-K-2q) is the qfth AutoRegressive Conditional Heteroscedasticity test. Hetero F(q, T-K-q - 1) is White's test for heteroscedasticity and tests the joint significance in a regression of the squared residuals on the regressors and their squares. Functional Form F(q, r - K - c / - 1 ) i s a general test for functional form mis-specification/ is Ramsey's test for specification heteroscedastic errors. RESET F(q, T-K-q) error. Probability in test-panel is the probability values of the test statistics under the relevant null hypothesis.

6.6 Theoretical and Empirical Implications: A Summary Industrial capital accumulation and investment cycles is a recurring subject in the literature which seeks to explain the working of a socialist and/or developing economy. This chapter advances the standard investment cycle theory of a socialist economy by simultaneously modelling both investment level and growth rate based on the framework of

206

Chapter 6

cointegration and error correction, and by incorporating the distributive barrier theory related to a typical dual developing economy. It is shown empirically that a persistent tension exists between system-generated investment ambitions and the supply and distributive barriers to the ambitions for both pre- and post-reform periods and in the presence of significant policy changes. As a result, first, the longrun investment function is characterized by equilibrium co-movement among the real fixed investment level, grain output per capita as a proxy for necessary consumer goods and effective energy supply per capita as a proxy for basic producer goods. Because the annual real investment level has persistently moved along the supply possibilities frontier of bottleneck sectors, it has been possible to maintain a constant high level of real investment. Second, much of the cyclical pattern of investment growth rate can be explained by the adjustment to the comovement path and by the relevant change rates of energy supply and agricultural output. Third, the fact that both functions maintain parameter constancy over the sample period indicates that there is a lack of structural break induced by the reform. This constancy, together with the structural rigidity of the state industrial sector (cf. section 1.3), may suggest that China's state investment system has still followed its own logic. Until very recently, it has not actively responded to the significant changes in demand occurring in a rapidly growing and transforming economy. Furthermore, the investment decision-making process is still dominated by bureaucratic negotiation based on vested sectoral interests and structural inertia, although the concrete mechanisms have changed. This finding is consistent with the recognition of China's top officials. For example, in his annual report to the 1997 National People's Congress, Chen Jinhua, head of the State Planning Commission, once again blamed the poor performance of state-owned enterprises on overheated fixed investment. He claimed that the persistent overheated investment led to excessive production capacity and insufficient use of facilities in most of the state industrial sectors, as well as to low efficiency, high costs and low competitiveness of the whole state sector {People's Daily, 4 March 1997). The concepts of 'norm' and 'control by norm' play a central role in the traditional modelling practice of a socialist investment cycle. A fundamental assumption in the practice is that control is based on 'negative feedback'. Because of excessive intended investment and output,


207

tensions arise, leading to reduced investment and output. Within the framework of negative feedback, a 'normal' value needs to be defined for each variable. The deviations of the control variables from their norms depend on the deviations of the controlled variables from their norms, simultaneously and/or with lags. The difficulties in defining and measuring 'norm' has been widely acknowledged by modellers.6 As a compromise, univariate mean or moving average has been employed as the norm for each variable (e.g. Kornai 1982, Kornai & Martos 1981, Roland 1987, Simonovits 1992). The question of what determines the interaction among such norms and how can we deal with it has been left untackled. In this regard, this chapter may make an important advancement. The co-movement equation (6.7) depicted by the cointegration relation acts as an equilibrium attractor of the investment adjustment behaviour of the economic agents toward the dynamic equilibrium, as shown by the error correction equation. This cointegration attractor is determined by tensions between investment ambition and the corresponding barriers rather than by habit or convention. The attractor has an intuitive interpretation of disequilibrium adjustment behaviour and can be modelled. The disequilibrium error correction is, in terms of economic behaviour, also more informative and instructive than univariate negative feedback. This new framework avoids the spurious deviation from the 'norm' by a filter such as a moving average (Fishman 1969), and determines naturally the long-run interaction among relevant univariate levels.

208

Chapter 6

Appendices

Al Data See Table A1 on next page.

Notations: Investment

=

Grain Energy Population Price index

= = = =

Fixed investment by state-owned units at current prices, and in billion yuan. Total output of grain, in million tons. Overall energy consumption, in million tons coal equivalent. Total population, in millions. Price index of fixed investment by state-owned units.

Sources and Notes: Investment, grain, energy, and population: Data for 1951-96 are directly taken from Yearbook 1993: 149, 364, 477, 81; 1994: 140; 1995: 137; 1997: 150, 383, 215, 69). Price index: Since the official statistics of this index started in 1989, we have to look for a proper proxy for it before 1989. Figures for 1952-85 are the deflator of accumulation on fixed assets of state-owned units (AFAS). The deflator can be generated based on AFAS's value series at current prices and index series at comparative prices taken from Statistics on National Income: 1949-1985 (1987: 37, 40). Because capital accumulation is the newly added fixed assets (less depreciation of the total fixed assets) plus the newly acquired circulating fund, and the latter only accounts for about 25 per cent of the total (Yearbook 1993: 76, 48), we claim that this deflator is the best proxy for the price index of fixed investment by state sector in China's statistics. The most suitable proxy data for 1986-88 are price indices of state-owned construction output values which were published after 1982 (Survey 1987: 75; 1988: 73; 1990: 82; 1994: 89), because construction accounts for about 60 per cent of the investment (Yearbook 1993: 150). The figure for 1989 is from Chinese Economic Yearbook (1990: 11-32). Data for 1990-93 are from Economic Situation and Prospect of China, 1991-92 (1992: 202, 209); 1992-93 (1993: 207); 1993-94 (1994: 3) and for 1994-96, data are from People's Daily (2 March 1995: 2; 5 March 1996: 2; 5 April 1997). a

According to Yearbook (1997: 215), 1996 figure of energy consumption was estimated one.


209

Table A1 Year 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

Investment

__ 9.159 10.268 10.524 16.084 15.123 27.906 36.802 41.658 15.606 8.728 11.666 16.589 21.690 25.480 18.772 15.157 24.692 36.808 41.731 41.281 43.812 46.319 54.494 52.394 54.830 66.872 69.936 74.590 66.751 84.531 95.196 118.518 168.051 197.850 229.799 276.276 253.548 291.864 362.811 527.364 765.797 932.249 1089.820 1205.620

Grain

Energy

143.69 163.92 166.83 169.52 183.94 192.75 195.05 200.00 170.00 143.50 147.50 160.00 170.00 187.50 194.53 214.00 217.82 209.06 210.97 239.96 250.14 240.48 264.94 275.27 284.52 286.31 282.73 304.77 332.12 320.56 325.02 354.50 387.28 407.31 379.11 391.51 402.98 394.08 407.55 446.24 435.29 442.66 456.44 445.10 466.62 504.54

__ __ 54.11 62.34 69.68 88.00 96.44 175.99 239.26 301.88 203.90 165.40 155.67 166.37 189.01 202.69 183.28 184.05 227.30 292.91 344.96 372.73 391.09 401.44 454.25 478.31 523.54 571.44 585.88 602.75 594.47 620.67 660.40 709.04 766.82 808.50 866.32 929.97 969.34 987.03 1037.83 1091.70 1159.93 1227.37 1311.76 1388.11*

Population

Price index

563.00 574.82 587.96 602.66 614.65 628.28 646.53 659.94 672.07 662.07 658.59 672.95 691.72 704.99 725.38 745.42 763.68 785.34 806.71 829.92 852.29 871.77 892.11 908.59 924.20 937.17 949.74 962.59 975.42 987.05 1000.72 1016.54 1030.08 1043.57 1058.51 1075.07 1093.00 1110.26 1127.04 1143.33 1158.23 1171.71 1185.17 1198.50 1211.21 1223.89

«... 100.00 98.64 97.79 94.82 91.95 88.74 90.48 95.85 99.36 100.66 101.17 100.59 99.37 97.39 96.57 96.51 94.49 94.10 93.21 94.22 95.01 95.25 95.16 96.09 96.97 98.13 98.78 100.78 102.66 106.30 108.85 111.29 116.17 129.22 142.14 154.08 175.34 192.87 214.86 235.27 271.27 352.38 394.67 425.45 448.85

210

A2

Chapter 6

Cointegration Analysis of the Vector System

Unit Root Test Most econometric tests are built upon the assumption of stationary, ergodic stochastic processes. In general, the statistical properties of regression analysis using non-stationary time series are dubious. On the other hand, although most economic time series are not stationary, many of them, in fact, can at least be approximated by stationary processes if they are differenced. If a non-stationary series must be differenced d times to make it stationary, it is said to be integrated of order d. This is expressed by writing yt ~ I(d) (Engle & Granger 1987). Clearly, it may be important to pretest the stationarity and integration order of each relevant time series before considering their long-run stable relationships. Moreover, the unit root test has its own independent value in terms of detecting some basic features of an individual series. Various tests have been suggested for testing stationarity. A straightforward procedure for annual time series is to test p = 1 in the following equations: yâi

+ axt + x,

x,= PxM + e,

(f = 0 , 1 , 2 , - ) ,

(t=l,293,.~).1

(A2J) (A2.2)

If the error term in equation (A2.2) is a white noise process then (A2.1) and (A2.2) can be interpreted as a random walk about a linear trend when P = 1, and as an asymptotically stationary first-order autoregressive (AR(1)) process about a linear trend when | P | < 1. However, in this case we are not using a conventional Student's f-test. An appropriate and simple approach is suggested by Dickey & Fuller (1979, 1981). The so-called Dickey-Fuller test (DF) relies on the OLS estimates of any of the following regressions, which are equivalent to equations (A2.1) and (A2.2), namely: Ay, = Ty M + £,

(A2.3)

Ay, = T0 + Ty M + e,

(A2.4)

Ay, = T0 + ixt + xy M + e,

(A2.5)

AV/ = T0 + 1\t + T2t2 + Ty M + £,

(A2.6)


111

where A is the difference operator (Ay, = yt-yhi). x = (M. Equation (A2.3) makes sense for x < 0 only if yt has (population) mean zero, (A2.4) allows yt to have a nonzero mean, (A2.5) allows it to have a trend, and (A2.6) allows it to have a trend that changes over time.2 The DF test consists of testing that x is negative and significantly different from zero; x < 0 implies that p < 1 and that the series is stationary (with or without drift, trends, etc.). Under the unit root hypothesis, the DF statistics have nonstandard distributions. Especially, the conventional ^-statistic is not asymptotically distributed as the standard normal distribution. We have to use the special tables of critical value given by Dickey (1976) and Fuller (1976), and particularly, by MacKinnon (1991). MacKinnon implemented a much larger set of replications than did Dickey and Fuller, and he estimated response surface regressions more accurately over these replications. Asymptotic critical values can be read off directly from these regressions, and critical values for any finite sample size can be easily computed with a hand calculator. The main drawback of the original DF test is that it does not take account of possible autocorrelation in the error process. If et is autocorrelated then the OLS estimates of equations (A2.3)-(A2.6) are not efficient. In order to overcome the above problem we can employ AR(p) model: p

A

T

yt=

+

*A yt-i+*

yt-\ Z

(A2-7)

i=\

which corresponds to equation (A2.3). We also can add the second term Ex, Ay,./ into equations (A2.4)-(A2.6) to construct relevant AR(p) models. Where p should be relatively small in order to save degrees of freedom, but large enough to allow for removing the autocorrelation in et. The distribution of the '/-statistic' associated with>v-i is the same as that given in the DickeyFuller tables for the AR(1) model, and the test for a unit root is known as the 'augmented Dickey-Fuller test' (ADF). On the other hand, it can be shown that the /-statistics associated with AyM (/ = 1, 2, ..., p) are asymptotically standard normal distributions (Harvey 1990: 81-2, Engle & Yoo 1987). In the case of important structural changes in the trend function of a time series, Perron's innovational outlier model and additive outlier model have shown an evident comparative advantage (Perron 1994). For a given series {yt, / = 0, 1, ..., T}, the approach is generalized to allow a one-time change in the structure occurring at time Tb (1 < Tb < T). Three different models are considered under the null hypothesis: one that permits an exogenous change in the level of the series (a 'crash'), one that allows an exogenous change in the rate of growth and one that permits both changes. For the structural changes to the trend function one can view them as 'big shocks' or infrequent events that have permanent effects on the level of the series. In order to

212

Chapter 6

distinguish the way these 'big shocks' affect the level of the series, two other models are introduced (Perron 1989). The first, the 'additive outlier model', specifies that the change to the new trend function occurs instantaneously. The second, the 'innovational outlier model', specifies that the change to the new trend function is gradual. The corresponding test procedures are based on simple autoregressions (estimated by OLS), which are appropriately augmented with trend and dummy components. For the innovational outlier models, the regressions are as follows:

i=i

and

p + 8D(Tb ) t + x yt_x + X T, A yM + 8 ,

(A2.9)

/=i

where DUt = 1 and Dft = {t- Tb) if t > Tb (0 otherwise), and D(Tb)t = 1 if t = Tb + 1 (0 otherwise). Equation (A2.8), the crash model, allows for a one-time change in the intercept of the trend function. Equation (A2.9) permits both a change in the intercept and the slope of the trend function to take place simultaneously. The statistic of interest is the /-statistic for test that T = (J-l = 0 as before. In the case where the break point Tb can be treated as known, it allows the /-statistic for testing x = (}-l = 0 to be invariant (in finite samples) to the value of the change in the intercept under the null hypothesis. For the additive outlier models, the procedures consist of two-step approaches. In the first step, the trend function of the series is estimated and removed from the original series through the following regressions: yt = \i + r|/ + yDUt + xt yt = \i + x\t + QDUt + yDT*t + x, ^ = ^1 + 11/ + yDT*t + xt

(A2.10) (A2.ll) (A2.12)

where xt is defined as the detrended series. The second step also differs according to whether or not the first step involves DUt, i.e. the dummy associated with a change in intercept. For equations (A2.10) and (A2.11), the test is exercised in the following autoregression applied to the estimated detrended component xt:


A

^ = iVt-1+ £ dj D(Tb)t.j 7=0

+ E T/ A yt_t + s,

213

(A2.13)

/=1

For equation (A2.12) where no change in intercept is involved and the two segments of the trend are joined at the time of break, there is therefore no need to introduce the dummies in the second step regression. The second step form of the regression is thus the same as equation (A2.7), but applied to the estimated detrended series xt. Under the null hypothesis of a unit root, the ^-statistic for x = (M = 0 also has a non-standard distribution. Furthermore, the critical values to be used depend on the particular model selected. For models (A2.8), (A2.9) and (A2.13) the critical values can be found in Perron (1989: Tables IV.B, VLB) and are the same in the innovational or additive outlier versions. For the additive outlier model based on (A2.12), the critical values can be found in Perron (1993).

Cointegration and Error Correction Model The recent development associated with testing the long-run equilibrium behaviours of time series that are difference stationary processes is inextricably related to the concept of cointegration. The notion that in the long run two or more variables might have convergent values, i.e. the deviations from this long-run co-movement path are stationary, has received considerable empirical testing following the work of Granger (1981), and others (see Hendry 1986, Banerjee et al. 1993), on cointegration. Cointegration is defined as follows (Engle & Granger 1987): The components of the ^-dimensional vector Z, are said to be cointegrated of order d, b, denoted Zt ~ CI(d, b), if (a) all components of Z, are I(d); (b) there exists a vector p (* 0) so that ut = p>, ~ l{d-b\ b>0. The vector p is called the cointegration vector. In the d = b > 0 case, cointegration would mean that although the components of Z, were all I(d), the equilibrium error would be 7(0), and ut will rarely drift far from zero if it has zero mean and ut will often cross the zero line. If Zt was not cointegrated, then ut could wander widely and zerocrossings would be very rare, suggesting that in this case the equilibrium and/or co-movement concepts have no practical implications. There may be r cointegration vectors, with r < n. When 1 < r < n, the cointegrating vectors may be denoted p1? ..., pr and may be gathered together into the nxr matrix B = (p b ..., pr). By construction, the rank of B is r, which is termed the cointegrating rank of Z,.

214

Chapter 6

It is apparent that in the case of three or more variables it is possible to have a subset of the variables which are integrated at a higher order than remaining variables and still have a valid cointegrating relationship if one linear combination of all variables in the subset is integrated at the same order as the remaining variables. There are many test procedures for cointegration. The computationally simple one is the Engle-Granger two-step estimator. In the first step, the parameters of the cointegrating vector are estimated by running the static regression in the levels of the variables. In the second step, these are used in the error correction form. Both steps require only OLS. The central feature is testing whether the error series ut of the static regression is 7(0) based on the Dickey-Fuller unit root test procedure but using different critical values (Engle & Granger 1987). The results may be shown to be consistent for all the parameters. However, the Engle-Granger approach suffers from several problems. First, inference about the p vector depends upon nuisance parameters, and large finite-sample biases can result when p is estimated by a static regression (Banerjee et al. 1986). Second, unit-root tests applied directly to ut usually lack power because the approach ignores the dynamics of the system (Kremers et al. 1992). Third, the number of cointegrating vectors is often of interest, and many hypotheses of interest relate to the complete conditional model specification and concern speeds of adjustment and the constancy of p over time, but the Engle-Granger two-step approach lacks means to deal with these issues. Finally, the choice of normalization in regression affects the finite-sample properties of the Engle-Granger procedure. Johansen (1988) and Johansen & Juselius (1990) develop a maximumlikelihood-based testing procedure for estimating and testing multiple cointegrating vectors based on the dynamics of the system. The Johansen procedure shows significant advantages over the Engle-Granger technique in terms of all issues mentioned in last paragraph, and therefore, has attracted the closest attention of empirical researchers. The Johansen procedure starts from the ^-dimensional vector autoregressive process Zt as defined by the model (6.3). The error correction form of model (6.3) is model (6.4). The model (6.4) is expressed as a traditional first difference VAR model except for the term n Z M . The main purpose of the Johansen procedure is to investigate whether the coefficient matrix II contains information about long-run relationships between the variables in the data vector. For r = 0, 1, . . . , « , the hypothesis of at most r cointegrating vectors is defined as the reduced rank condition: # r : I I = <xP'

(A2.14)


215

where a and p are n x r matrices. Thus, (a) Hn specifies rank (II) = n, i.e. the matrix II has full rank, indicating that the vector process Z, is stationary; (b) Ho specifies rank (II) = 0, i.e. the matrix II is the null matrix, model (6.4) corresponds to a traditional differenced vector time series model, and there are n unit roots in | II(z ~l)\ = 0; and (c) 0 < rank (II) = r < n indicates that there are r cointegrating vectors and n-r unit roots in | II(z~l)\ = 0. P' is the matrix of cointegrating vectors, and a is the matrix of 'weighting elements'. Each 1 x n row pi of p1 is an individual cointegrating vector. Each 1 x r row ay of a is the set of weights for the r cointegrating terms appearing in theyth equation of model (6.4). The rank r is exactly the number of cointegrating vectors in the system. While a and p themselves are not unique, p uniquely defines the cointegration space, and suitable normalization for a and p are available. The essence of aP'ZM is that it contains all the long-run (levels) information on the process for Z,. The only other terms in model (6.4) are current and lagged AZ,. The vector P'ZM measures the extent to which observed data deviate from the long-run stationary relation(s) among the variables in the system. The statistical analysis of model (6.4) under the restriction of reduced rank of the matrix II = aP' can be performed by reduced rank regression (Johansen 1988). The variables AZ, and Z,_i are regressed on the lagged values AZ,_i, AZ,_2, ..., AZt-k+i and 1 to form residuals /?0/ and Rlh and residual product moment matrices:

The cointegrating relationships are then estimated as the eigenvectors corresponding to the r largest eigenvalues of the equation: lA.Sn-S'io SoUoil = O

(A2.1S)

The likelihood ratio test statistic of the hypothesis Hr in Hn is given by the so-called trace statistic: n

Qr = -T Y,

ln

H ~ h ) S i v e n t h e eigenvalues ij>.-.>in.

(A2.16)

i=r+l

Similarly the likelihood ratio test statistic for testing Hr in Hr+\ is given by so-called 1^^ statistic: ir+l)

(A2.17)

216

Chapter 6

Under the assumption that the number of cointegrating vectors is r and that the coefficient a'ljx * 0, such that there is a linear trend in the data, the limit distribution of both Qr and Qr/r+u which only depends on the degree of freedom n — r, is nonstandard and tabulated by simulation in Johansen & Juselius (1990: Table Al) and in Osterwald-Lenum (1992: Table 1). The hypothesis H*r that the trend is absent (a'±(i = 0) can be analysed by another reduced rank regression, the relevant trace and / l ^ statistics have the same forms as in equations (A2.16) and (A2.17), and their limit distribution is given by Table A3 in Johansen & Juselius (1990) and by Table 1* in Osterwald-Lenum (1992). The idea of the error correction model is relatively simple, i.e. that a proportion of the disequilibrium from one period is corrected in the next period. Error correction mechanisms can be derived as optimal behaviour with some kinds of adjustment costs or incomplete information. Engle & Granger (1987) defined a general error correction representation for a vector system as follows: A vector time series Z, has an error correction representation if it can be expressed as A(B)(l-B)Zt = -awM + 8,

(A2.18)

where 8/ is a stationary multivariate disturbance, with A(0) = /, ,4(1) has all elements finite, B is the backshift operator (BZt = ZM), utA = $Zt_x presents the cointegrating error, and a * 0. It is clear that by rearranging terms, any set of lags of the u can be written in this form, therefore equation (A2.18) permits any type of gradual adjustment toward a new equilibrium. As has been proven in Engle & Granger (1987) as the Granger Representation Theorem and again in Engle & Yoo (1991) using polynomial matrix functions, cointegration implies that the system follows an error correction representation, and conversely an error correction system has cointegrated variables. The error correction model provides a way of modelling, simultaneously, the dynamics both of short-run (changes) and long-run (levels) adjustment processes. Particularly, the idea of incorporating the dynamic adjustment to steady-state targets in the form of error correction seems to have introduced a quite useful approach to modelling dynamic adjustment behaviour of economic agents.

Estimating Investment Functions Based on Cointegration A3

217

Exogeneity

Concepts and Structure A clear understanding of exogeneity is critical for analysing the implications of cointegration for statistical inference, policy analysis and forecasting. Whether a variable is exogenous depends upon whether the variable can be treated as 'given' without losing information for the purpose concerned. The distinct purposes of statistical inference, policy analysis and forecasting define the concepts of weak, super and strong exogeneity. Ericsson (1992) presents a detailed and clear overview on these concepts. The following explanations are mainly based on Engle, Hendry & Richard (1983) and Ericsson (1992). Weak exogeneity is one of the essential concepts in this research, and provides the basis for efficient inference in our conditional model. For simplicity I would start from a bivariate normal case. Consider two variables, yt and xh which are jointly normally distributed and serially independent:

zMyt\~

IN (ii, Q)

(A3.1)

in which ' - IN(\i, £2)' denotes 'is distributed independently and normally, with mean vector \i and covariance matrix Q\ Without loss of generality, equation (A3.1) can be factored into the conditional density of yt given xt and the marginal density of xt ,3 as follows:

Fz(zt; v) = Fy\x(yt I xt; W • Fx(xt; x2)

(A3.2)

where F?(-) represents the density function for variable '?'. Thus, Fz(Zt; \j/) is the joint density of Z{, Fy\x(yt\ xt; Xx) is the conditional density ofyt given xh and Fx(xt; X2) is the marginal density of xt. The parameter vector \|/ is the full set of parameters in the joint process whereas X{ and X2 are the parameters of the conditional and marginal processes, and their respective parameter spaces are *F, A h and A2. Defining X as (V, V)' and denoting its parameter space as A, then there is a one-to-one function g(-) such that X = g(\|/). It should be noted that though this factorization (A3.2) is without loss of generality, analysing the conditional density Fy\x(yt\xt; Xi) while ignoring the corresponding marginal density Fx(xt; X2) is with the loss of generality, and in general implies a loss of information about the conditional process being modelled, unless xt is weakly exogenous over the sample period for the parameters of interest.

218

Chapter 6

Definition: The variable xt is weakly exogenous over the sample period for the parameters of interest 0 if and only if there exists a reparameterization of \|/ as X, with X = (V> V)\ such that (a) 8 is a function of A! alone, and (b) the factorization in equation (A3.2) operates a sequential cut, that is, (Xu A^) belong to Ai x A2, the product of their individual parameter spaces. (Engle, Hendry & Richard 1983: 282) That (Xh X2) belong to A! x A2 is called 'variation free'. It means that the parameter space Ai is not a function of the parameter X2, and the parameter space A2 is not a function of the parameter Xx. In other words, knowledge about the value of one parameter provides no information on the other parameter's range of potential values. If weak exogeneity holds, then efficient estimation and testing can be conducted by analysing only the conditional model (e.g. equation (6.5) in our case), ignoring the information of the marginal process (e.g. equation (6.6) in our case). Strong exogeneity is the conjunction of weak exogeneity and Granger noncausality, and it insures valid conditional forecasting. Consider a first-order vector autoregressive as a simple situation extended from equation (A3.1) in following form: Z/ = n Z / - i + e , ,

s,~/W(0,Q)

(A3.3)

Model (A3.3) carl be factored into the conditional model of yt given xt and the marginal model of xt as follows:

yt = box(

+ V M

+btyt_x +vlt , vl(

xt = T I 2 2 X M +TI 2 1 ^ M + e 2/ ,

~IN(0,G2)

e 2/ ~/AT(0,CD 2 2 )

(A3.4a) (A3.4b)

where Ti/, presents the (/,y)th element of n, and (b0, bh b2) are derived from II and £1 with b0 = co12/co22, bx = nn - (COI2/CO22)TC22, and b2 = nn - (COI2/CO22)TC21 (see, Engle, Hendry & Richard 1983: 297). Valid prediction of yt from its conditional model (A3.4a) requires more than weak exogeneity. With weak exogeneity alone, >V-i influences xt if n2X * 0 in the marginal model (A3.4b). In this case xt in the conditional model (A3.4a) cannot be treated as 'given' for prediction of yt. The additional restriction required is that 7i21 = 0, or in general that y does not Granger-cause x. Weak exogeneity plus Granger noncausality generates strong exogeneity. Concretely speaking, strong exogeneity permits valid multi-step ahead prediction of y from (A3.4a), conditional on predictions of x generated from (A3.4b) with n2X = 0, where the predictions of x depends upon their own lags only.


219

Super exogeneity is the conjunction of weak exogeneity and 'invariance', and insures valid policy simulations. The concept of invariance can be introduced as follows. The reduced form (A3.3) may be empirically nonconstant because of changes in policy rules, unexpected shocks, innovations, etc. The factorization (A3.2) may aim to isolate those nonconstancies into the sub-set of parameters, X2, leaving the parameters of the conditional model Xx invariant to the changes that have occurred. Thus we can define the parameter Xx as invariant to a class of interventions to the marginal process of xt if Xi is not a function of X2 for the class of interventions. Policy analysis (or counterfactual analysis) often involves changing the marginal process of xt. Valid analysis of a conditional model under such changes requires that the parameters X\ be invariant to those changes (or interventions). The relevant concept is super exogeneity. Super exogeneity means that xt is weakly exogenous for the parameters of interest 0, and X\ is invariant to the class of interventions to X2 under consideration. It should be understood that when a variable is super exogenous with respect to a specific class of interventions, the variable need not to be super exogenous with respect to those interventions outside this class, although it may be.

Testing and Inference Following the definition of weak exogeneity, if we are interested in the longrun cointegration parameters given by p in model (6.4), it can be easily seen, based on models (6.5) and (6.6), that the weak exogeneity of Xt with respect to p is equivalent to the condition that ax=0.Jn this case p and the remaining adjustment coefficients Vy enter only in the conditional model (6.5), and the properties of the Gaussian distribution show that the parameters in the models (6.5) and (6.6) are variation free (see Johansen & Juselius 1990; Johansen 1992a, 1992b; Boswijk 1990). Thus the hypothesis of weak exogeneity of Xt for p in n = aP' can be formulated as: H: ax = 0 This hypothesis is a linear restriction on a and is discussed in Johansen & Juselius (1990), in which it is shown that under the hypothesis H the maximum likelihood estimation of the parameters could be performed by reduced rank regression and that the test ofHinHr consists of comparing the eigenvalues calculated without the restriction to those with the restriction (cf. equation A2.16). The test statistic is: QWH=T

220

Chapter 6

It is asymptotically distributed as y?(rnx), where «;r denotes the dimensions of Xt. Urbain (1992) proves that the H above is also the hypothesis of weak exogeneity of Xt for all of the long-run and short-run coefficients in the conditional equation (6.5) as parameters of interest. Thus equation (A3.5) can also serve this extended purpose. In addition, the weak exogeneity for all coefficients in the conditional equation can be tested via the tests of super exogeneity of Engle & Hendry (1993). Two common tests for super exogeneity are as follows. (1) If the conditional equation has constant parameters, but the marginal models have nonconstant parameters, then the conditional equation parameters could not depend upon the marginal model parameters. This is the test of Hendry (1988). (2) If the marginal processes are constant, we can use Wu-Hausman tests for independence between the conditioning variables and the residuals; that is, to test the significance of the residuals from the marginal models, or reduced forms, in the conditional equation. If the marginal processes have non-constancy we can further develop the marginal model for Xh by adding dummy and/or other variables, until it is empirically constant. Then we can test for the significance of those dummy and/or other variables when they are added to the conditional model. Their insignificance in the conditional model demonstrates invariance of the conditional model's parameters to the changes in the marginal processes; thus we test both weak and super exogeneity (Engle & Hendry 1993). It is clear that tests of parameter constancy are central to the tests of super exogeneity.

7

Conclusions

7.1 Introduction This closing chapter presents the general conclusions and explores further the theoretical and policy implications of this research. The theoretical implication is related to the recognition that we have reached a stage in which we are able to advance the standard investment cycle theory of a socialist economy by integrating both the Hungarian School's standard investment cycle theory and Kalecki's distributive barrier theory into a new framework of growth cycle. The policy implication involves such issues as the investment inefficiency of the state sector, capital accumulation strategy, structural adjustment and reform of the state investment system. Section 7.2 presents the theoretical contribution of the research. Section 7.3 summarizes the stylized facts about aggregate investment behaviour in China both from a perspective that features the sources of investment hunger at the central, local and enterprise level, and from a perspective that emphasizes the link between key bottleneck constraints and retrenchment campaigns. Section 7.4 discusses the inefficiency caused by investment hunger and bureaucratic coordination, and examines how China's state sector has suffered from investment inefficiency induced by the rigid state investment system. Section 7.5 deals with the difficulties and possible options for reforming the state investment system with reference to the renewed reform package of late 1993 and the evolution of the reform in 1994-98. Section 7.6 reviews what can be perceived as the major limitations of the research. Finally, section 7.7 makes some further comments on the critical issues highlighted by the research.

221

222

Chapter 7

7.2 Major Theoretical Contributions of the Research The first major theoretical contribution of the research is that it supplies a new framework for conceptualizing the aggregate investment behaviour and modelling investment cycles in a socialist and/or developing economy. The new framework is established by integrating the existing theories of investment cycle and bottleneck constrained growth in a socialist and/or developing economy with the cointegration and error correction mechanism. Three advancements brought about by this integration are worth mentioning. First, although the concrete investment adjustment processes have been changed following the changing patterns in power distribution among different political apparatuses and in particular, the significant changes which have taken place in the reforming years (cf. section 6.6), the investment hunger and its resultant tensions between investment expansion and the supply and distributive barriers have persisted. This fundamental feature has been highlighted by the both long-run investment level function and the short-run investment growth function. As long as the annual real investment level has unceasingly moved along the supply possibilities frontier of bottleneck sectors, a relatively high level of real investment has been constantly maintained. Secondly, this integration highlights the importance of coordination mechanisms that prevail among economic institutions and their agents. There have been different investment approval and brake apparatuses operating at different levels in a complex, interactive fashion, and the operation and interaction have been altered by dramatic political and economic changes (sections 1.3 and 1.4 and Chapter 3). However, one fundamental feature of these operations and interaction, interest conflicts and accompanying bureaucratic coordination, has persisted. All bureaucrats are rational economic agents, and they have tried to maximize the interests of their own institutions subject to certain internal and external supply and distributive constraints. They cannot be considered as homogeneous actors. If business cycles in the market economy can be partly attributed to market co-ordination failure according to Keynesians and new Keynesians (Fischer 1988), investment cycles in China can be also considered as a type of bureaucratic coordination failure.

Conclusions

223

Thirdly, the traditional univariate 'norm' and 'negative feedback' concepts can be updated, respectively, by introducing the notions of the equilibrium co-movement between real investment level and the supply and distributive barriers to investment expansion, and of the disequilibrium adjustment behaviour of economic agents toward the equilibrium attractor. The comparative advantage of the attractor notion is that a comovement attractor is directly linked with the real tension between system-generated investment ambition and the supply and distributional barriers to the ambition, and thus it can be modelled. The advantage of the error correction notion is that it represents the disequilibrium adjustment behaviour of the economic agents toward an equilibrium comovement path, which is economically more active and intuitive in the context of there being many non-homogeneous bureaucrats. As mentioned before, the study of industrial accumulation mechanisms and investment cycles has been a recurring and popular subject in the literature on the dynamics of the socialist and/or developing economy. In practice, the determination of a 'rational scale of investment' has also been a critical question, which has perplexed economic authorities and scholars in economies like China's. Following the typical textbook framework, scholars might need to focus on building up demand and supply functions to address the investment issue. They may ignore the question of how to bridge the gap between theoretical (usually unobservable) variables and the observed (non-experimental) data (cf. sections 1.5). With reference to the literature dealing with investment cycles in the former Soviet Union, Eastern Europe and China, it seems that no one has tried to set up a demand/supply function. Instead, most scholars have concentrated on building up and testing planners' response functions to one or several representative shortage signals. This departure from the standard supply and demand functions framework comes out of learning from data and direct economic experiences. In this connection, the framework suggested by this book can be also justified by learning from data and direct experiences. The variable system is selected based on a political economy analysis of China's state investment system and on comprehensive data analyses. The probabilistic structure exploration of the selected time series is established through modelling an unrestricted vector autoregression system, without any pre-imposed equilibrium conditions or behavioural assumption. The empirical findings can also be interpreted in the terminology of probabilistic structure exploration. First, there statistically exists a long-

224

Chapter 7

run stable comovement among investment outlays, grain output, effective energy supply and the price level of investment. Within these four variable information systems investment is endogenous whereas all the others are weakly exogenous. The cointegration relation is homogenous of degree one in price level. The cointegration relation thus represents a typical equilibrium path shaping real investment level. Secondly, the conditional error correction model developed from the equilibrium level equation is a well-defined and well-fitted investment growth rate adjustment equation. Much of the cyclical pattern of investment growth rate can be explained by the error correction towards the co-movement path and by the relevant change rates of energy supply and agricultural output. Thirdly, the standard one-step-ahead Chow test does not detect any statistically significant structural break induced by policy changes or reform within the sample period. This indicates that both investment level and growth rate equations have parameter constancy over the sample period. Besides the contributions presented above, the research provides an alternative way to investigate the interaction between agricultural fluctuation and investment adjustment without econometric modelling (cf. section 2.4.1 and Chapter 4). It shows that in China's case, common linear regression based on the notion of the 'negative feedback' between variations of agricultural harvests, procurement and variations in investment outlays may be unable to find the expected 'negative feedback'. However, once a comprehensive indicator system is selected based on an examination of the main proportional relations in the economy to which the decision-makers are most sensitive, and once the major input-output relations in agriculture and the distributive relations in the uses of the national income are figured out, a picture of two-way interaction (not only 'negative feedback') emerges. The two-way interaction indicates that the interaction between investment expansion and agricultural development cannot be simplified as substitution alone. Investment expansion may be at the expense of agricultural development, but the good harvests of agriculture have stimulated and supported investment expansion. This appears to be true for both the short and long run, though it is more likely that complementarity may be dominant in the long run (cf. sections 1.2 and 4.1).

Conclusions

7.3

225

Aggregate Investment Behaviour in China: Stylized Facts

This section intends to summarize the stylized facts on aggregate investment behaviour in China, which have been analysed in detail in Chapters 2 to 5, and represent the main contribution of this research to the literature on the issue of investment behaviour analysis in a socialist developing economy like China. The facts are grouped under two headings. The first is that 'system-generated insatiable investment demand exists at all levels', and the second reviews supply and distributive barriers to investment expansion and consequent retrenchment campaigns.

7.3.1 System-generated insatiable investment demand exists at all levels The process of investment decision-making in the state sector is in fact a distribution process of rights to possess and use certain scarce state assets such as budget funds, bank loans, land, quotas of power, oil and other key materials in shortage. The primary intention of ministries, local governments and firms is to try to obtain as much investment and corresponding other properties from the bureaucratic distribution process as possible so that they can benefit in the future and justify their existence and power base. A simple but insightful example is that if a state enterprise is assigned a building or a piece of land in the commercial centre of a city by negotiation or just by chance, it can obtain more benefit and its employees can get more bonuses just from renting the building or land. Such intention is the essential source of investment hunger at each level. It is based on these motivations that the relevant decision-makers accustomed to pay only secondary attention to the future profitability of any new investment project. It is fine if the project turns out to be profitable, but if not, the loss will be borne by the state anyway. Such investment expansion drive, combined with the soft budget constraint, will always lead to investment hunger and inefficiency. At central government level, the most immediate reason for expansion drive and investment hunger is the internal and external pressure to provide evidence of socialist superiority, to catch up with the industrialized powers as fast as possible, and to create a modernized army force for national security. In addition to these general motivations, one more China-specific and increasingly important stimulation comes

226

Chapter 7

from the divergent priorities of the central and local governments. Such divergence, generally linked to the great regional differences and diversity, has increased considerably during the reform period, following the general trend of decentralization. Before the reform, the central government used to depend on political/economic campaigns and its ability to control the state budget to implement and finance its investment ambitions. During the reform period, besides using various direct and indirect administrative measures to bring local investment decisions in line with national priorities, and with its greater access to information and a wider range of tools at its disposal, the central government created and has since expanded a special programme of national 'priority' projects mainly related to energy, transport and communication. These projects have been planned with a 'rational time-table and guaranteed supply of materials', and thus have first claim on materials still under state control. In order to ensure the implementation of these priority projects, the central government employs such strategies as concentrating its own resources on these projects, harnessing the financial resources of the state bank to them, and drawing on local financial resources through a version of 'matching funds' for them (cf. section 3.4.2). This shows how the central government has played a dual role as both the highest administrator of the economy and as an active investor with its own interests. When employing ad hoc administrative measures to suppress the overall over-investment, the central government has forced local authorities to cut their investment outlays but allowed its own plans to go ahead. At local level, besides expansion motivations such as providing evidence of socialist superiority, catching up with more developed regions and expanding the power, prestige and material benefits of local bureaucrats, local governments have to bear responsibility for their own regional development, employment, stability and welfare. In fact, local governments have been faced with much stronger pressure to promote development than the central government. For instance, they have to accept liability for social disturbances arising from unemployment, housing shortage, infrastructure deficiency and growing dissatisfaction in consumer sector. They must struggle to meet expenditure obligations imposed by central policies and commands, and they have to promote a more rapid rate of local economic growth so as to raise their own negotiating position within the bureaucracy. In order to promote local economic development and extend employment, local governments

Conclusions

227

have been eager to set up new factories under their jurisdiction and to expand their own enterprises, without regard for duplication of construction or economy of scale at the national level. While they rely on their own state enterprises (i.e. local SOEs) for providing a critical social safety net which will provide grants for housing, schooling, health care and retirement benefits for the urban population, local governments are willing to prevent their enterprises from bankruptcy even in the event that chronic losses are occurring. Faced with intense expenditure pressure and a tax system that depends on industry for the generation of over two-thirds of total revenues (both in- and extra-budget), local governments seem to have little choice but to engage in industrial expansion at the cost of agricultural and infrastructure development. At enterprise level, state-owned enterprises (SOEs) have received various forms of external assistance from different government bodies, both before and after reform. They can request, through bargaining and lobbying, soft appropriations and subsidies from national or local budgets, ad hoc exemptions and postponements of tax payments from taxation authorities and soft credit from state banks and their local branches. They can also enjoy soft administrative pricing. It is widely acknowledged that in the pre-reform period the budget constraint of SOEs had been quite soft. Surprisingly, the empirical evidence presented in Table 3.8 shows that the core of SOEs, 'the industrial SOEs with independent accounting systems', had also made increasing losses in the period of industrial reform. The ratios of their losses to their pre-tax profits increased dramatically, from 2.4 per cent in 1985 to around 20 per cent in the period of 1991-96. Such an overall level of loss is not possible without 'soft' subsidies from the government budget and 'soft' credits from the state banks, in view of the fact that the share of the planned component has rapidly declined. It is the lasting soft budget constraint, combined with the increasing autonomy, that has stimulated SOEs' expansion drive and investment hunger, and has encouraged SOEs to take too much risk in their investment processes during the transition period (cf. Chapter 3, Zou & Sun 1996). In addition to the soft budget constraint, SOE expansion drive and investment hunger can also be explained partly by the managers' bureaucratic motives such as policy and moral conviction, identification with the job, expanding power, prestige and material benefits. However, the soft budget constraint is the most essential and system-specific source of SOEs' investment hunger.

228

Chapter 7

Official documents indicate the existence of a strict project approval system and a sound distribution of project approval limits among local governments at different levels. In practice, however, the situation is very different. SOEs used to work together with their immediate supervisory agency on many issues and could therefore easily find ways to obtain approval or even to bypass the limitations. Meanwhile, in order to promote their own regional growth and create employment, the local governments at lower levels are willing to collude with their SOEs and local state bank branches to circumvent project approval requirements at higher levels. The most popular collusion in the investment planning process is separating projects into smaller components with underestimated costs so as to evade or simplify project review and approval procedures. The superficially strict credit plan system has functioned in a similar way. Under the institutional arrangement of 'dual subordination' (shuangchong lingdao) along the lines of both vertical and regional accountability, the local branches of state banks have been defenceless against pressure from local governments. Additionally, the local branches have had to depend on their local governments for basic welfare such as housing, children's schooling and employment for bank employees. Therefore the local branches have strong incentives to follow the local credit plan and to collude with local authorities to extend credit, formally or informally. Succinctly, the expansion drive and investment hunger are endogenously generated within the political/economic system, and exist ubiquitously at all levels. In the meantime, there is no genuine fear of a financial failure nor any other internal restraint to resist the expansion drive. The credit system is far from independent, and financial obligation is not binding unless some strict ad hoc administrative measures are imposed by political/economic campaign or through the hierarchy. The first significant outcome of such an institutional set-up and incentive mechanism is that real investment is bound to move along the supply possibility frontier of the bottleneck sectors and the distributive barrier between investment expansion and necessary agricultural growth, as discussed in detail in previous chapters. The second important consequence is widespread investment inefficiency, which will be discussed in section 7.4.

Conclusions

229

7.3.2 Supply and distributive barriers to investment expansion and retrenchment campaigns As summarized above, at all of the central, local and enterprise levels, the political/economic system constantly produces pressure and incentives to maximize the investment possibility, but it cannot initiate internal and/or self-imposed restraint to resist the expansion drive. As a result, real investment is bound to expand until it comes up against the shortage and inflationary barriers of the bottleneck sectors. Meanwhile, the intuitive data explorations in Chapters 4 and 5 and the cointegration analysis in Chapter 6 indicate that the grain supply as the necessary consumer good and effective energy supply as the representative producer good have repeatedly checked investment booming and industrial expansion. The decision-makers at different levels understand and are constrained by the shortage and inflationary barriers of basic consumer goods and producer goods. A bad project may be able to ensure the funds it needs but may be screened out at the very beginning because of the shortage. The interactive relationship between agricultural fluctuation and investment adjustment has attracted major attention from Chinese leadership, scholars and the public. As shown in Chapters 4 and 6, swings in agricultural production and marketing have been followed by dramatic swings in industrial accumulation and investment. The increase of agricultural surpluses has stimulated and supported rapid investment expansion, but will be followed by major shifts in the distribution of national income and significant reductions in incentives (e.g. material rewards) and prices (or the margin of raising prices) for agriculture. On the other hand, agricultural shortfalls have led to a passive reversion to increases in incentives and prices, and to cutbacks in industrial accumulation and investment. The basic force which determines these interactions is the conflict between the active industrialization drive and the reactive agricultural policy at different levels, which has been characterized by China's basic capital accumulation mechanism. This accumulation mechanism had successfully served the heavy-industry oriented development strategy in the pre-reform period, by depending on the distorted macroeconomic environment, planned allocation system, and induced institutional arrangements to guarantee the high profits of industry and low consumption of both workers and peasants. During the reform period,

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however, the essential characteristic of the accumulation mechanism extracting surplus from agriculture by coercion - seems to have persisted. Although agriculture's terms of trade significantly improved between 1979 and 1984, the coercive procurement system in rural areas and the food rationing system in urban areas have continued. The government tried to abolish low-price rationing in 1993, but revived it in most cities in 1994 and 1995. Moreover, the urban and industrial biases of local governments have been reinforced by the intense expenditure pressure they face and by the tax system, which depends on industry for the generation of over two thirds of total revenues (cf. section 3.7). As a consequence, the agricultural policy has been, in practice rather than according to official documents, guided by budgetary considerations and local industrialization drives. The combination of budgetary and inflationary pressure encourages the governments to lower agriculture's terms of trade and to reduce other material rewards whenever possible, and to increase procurement prices and other incentive rewards only when it is absolutely necessary. Although signs appeared during the 'soft landing' period of 1994-97, that the Chinese government started to give agriculture priority through the enforcement of 'provincial governor's "grain bag" responsibility system' and of protecting procurement prices, the duration of the policy package is not clear. Therefore, we may conclude that the agricultural problem in China is not only a problem of theoretical understanding of agriculture's extreme importance in economic development, but also, and perhaps more significantly, a practical issue which is related to state investment and accumulation systems, and to the political negotiation position of the peasantry. The energy shortage in China is, to some extent, similar to labour shortages in former Eastern European socialist economies. In China the widespread shortage of primary energy has been chronic and cannot be anticipated to ease in the near future. It is widely acknowledged that because of power shortages over 20 per cent of power-driven equipment remains idle. Meanwhile, the coal shortage forces power stations to stop generators, and the transport constraint means that coal shortage in industrial centres and coal stocks above storage capacity in the mining bases have usually co-existed. During the reform period, the government gave first priority to coal transport railway construction and improvement. However, along with the continuous and high-speed

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economic growth, the incremental gaps between coal demand over coal freight volume and between coal output and freight volume continued to grow. This indicates that the growth rate of demand for coal has been greater than the growth of effective coal supply during rapid economic growth. As a result, the tension between demand for and effective supply of basic energy (mainly coal) has remained and even been aggravated during the reform period. The econometric evidence given in Chapters 5 and 6 (based on the Granger causality test and cointegration analysis) shows that there is an evident two-way dependence between real investment and effective energy supply in terms of both level and growth rate, and that within the information system consisting of nominal investment, deflator of investment, grain output and effective energy supply, energy is weakly but not strongly exogenous. The weak exogeneity of effective energy supply here can be attributed to such factors as the location of energy bases, distribution patterns of energy production and consumption, the geographical difficulty of constructing railways and roads, as well as other factors which are beyond the policy trade-off of how to allocate investment funds. Due to the persistent energy shortage, China's macroeconomy has progressed steadily within a normal state of shortage. Usually, even the state annual plan would leave a significant 'plan gap' for energy distribution, which was normally equal to 5 per cent of the realized supply (section 5.3). The government used to assume that the gap would not damage the steady progress of the macroeconomy if the planned economic growth targets were not exceeded. Indeed, in the interval around trough point of each cycle, these 'plan gaps' did not undermine the progressing and shortage was even relieved. However, in the interval around the peak point of each cycle, the realized growth rates of output and investment have certainly exceeded the planned targets by a significant margin. Meanwhile, neither the effective energy supply nor energy production are able to grow so dramatically. As a consequence, energy stocks in consuming areas are bound to decrease considerably. A transportation crisis emerges and becomes increasingly severe. The supply of energy and raw materials for the implementation of ongoing investment projects and even for maintaining simple reproduction becomes irregular and uncertain. Inflation pressure re-emerges and increases. These danger signals in turn cause social disturbances and force the central government to initiate a retrenchment campaign and to reinforce this by ad hoc administrative measures, such as sending cen-

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tral delegations to each province and using the Party hierarchy to review and supervise the implementation of these retrenchment policies. The first theme of such retrenchment campaigns has been to control the scale of fixed investment (cf. Chapter 3, section 5.3).

7.4 Inefficiency as a Consequence of Investment Hunger and Bureaucratic Coordination Investment inefficiency becomes fundamentally important when the discussion moves from theoretical modelling to policy concern. China's state sector has suffered from investment inefficiency which, as has been mentioned, is a consequence of its state investment system. This system is characterized by complex bureaucratic co-ordination at and across different levels. When dealing with investment inefficiency in the state sector, the most famous and impressive evidence is the lasting and large-scale duplication of construction (chongfu jianshe) at national level. The situation has deteriorated since the fiscal reform and relevant decentralization in the early 1980s (Wong 1992, World Bank 1994). There are numerous examples of this. By the end of 1990 China had built up 167 production lines for colour television sets with an annual production capacity of 20 million sets. The actual output was only 10 million, which means that half of the production capacity was idle. In 1993, China had 126 automobile factories and 5,000 re-equipping automobile factories theoretically capable of producing one million automobiles annually. However, most of these factories had no economy of scale by any standard, and the average utilization ratio of production capacity was again about 50 per cent. Similar situations exist in almost all sectors. For instance, in the raw material sector about 40 per cent of soda ash production capacity was idle in 1992. In the textile sector, the wool spinning industry suffered from a remarkable cycle of redundant construction from 1980 to 1990. The most impressive cycles of such construction seem to appear in durable consumer goods production such as wrist watches, radio sets, sewing machines (in the early 1980s), TV sets, washing machines and tape recorders (in the late 1980s). While most such construction projects were completed, over 50-90 per cent of production capacity came to a standstill. The fundamental mechanism behind the recurring repetitionary construction cycles is that each local government is competing with others

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to obtain more investment from the centre and/or state bank to establish more seemingly profitable construction projects under its jurisdiction when they perceive the possibility of making quick profits. However, by the time the projects are completed, it is often discovered that there are already too many such projects at the national level, and that there is not enough market for the products. The opportunity cost of such duplicated construction is also high. Initiation of too many projects is certainly at the expense of technical updating of existing assets, inducing greater prominence of gestation lags between the initiation and completion of investment projects. An international comparison of industrial concentration by major industrial sectors in China's state sector, USA and Japan in 1985 (Table 7.1) indicates that many of China's SOEs remain small in scale compared with their counterparts in the major industrialized countries. It is well known that the concentration of production is much higher in former socialist economies of Eastern Europe than in capitalist ones (Kornai 1992: Chapter 17), Table 7.1 thus indicates that China's economy, in terms of economy of scale, is remarkably different from its socialist counterparts in Central and Eastern Europe, where under communism large-scale state monopolies and central direct control were generally favoured. The existence of a large number of small, duplicated production facilities with few plants able to reap full economies of scale may represent a unique feature of China's state investment system. Table 7.1

Industrial concentration by major industrial sectors in China, USA and Japan, 1985 (percentages) USA

China Sector

Largest firms

Iron & steel Motor vehicles Chemicals Engineering Textiles Note: Source:

38 7 7 6 2

Market shares 46.7 35.6 10.2

2.9 1.5

Largest firms

7 13 19 9 5

Japan

Market shares

Largest firms

83.7 94.6 84.9 58.4 40.7

Largest firms mean that they have the largest market shares. Lin, S. (1990:123).

20 10 15 10 10

Market shares 84.0 74.1 48.0 53.9 68.4

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At the aggregate level the investment inefficiency in the state sector is partly evident in the incremental capital-profit ratio. During 1985— 92, the net value of fixed assets of industrial SOEs with independent accounting systems increased from 398 billion yuan to 1,098 billion yuan, an increase of 700 billion yuan, while the realized pre-tax profits only increased 61 billion yuan from 133 billion yuan to 194 billion yuan. The incremental ratio of fixed assets to pre-tax profit is 11.5, indicating only a one yuan increase in pre-tax profits for every 11.5 yuan increase in fixed assets (net of depreciation). The ratio of pre-tax profits to total capital decreased from 23.8 per cent to 9.7 per cent, and the realized pre-tax profits per 100 yuan fixed assets went down to 12.4 yuan in 1992 from 22.4 yuan in 1985 {Yearbook 1993: 430, 437). As in the case of shortage and inflation pressures, the over-large scale of duplicated construction is also interpreted as a danger signal, because it not only indicates the deteriorating investment inefficiency but also the impending scale of over-commitment and wasting outputs. In official reports on the retrenchment campaign, the number of smallscale and duplicated investment projects (usually half-constructed) that have faced forced closure has been treated as an important measurement to evaluate retrenchment {A Collection of Documents on Fixed Investment and Construction). In this sense, the retrenchment campaigns do help improve investment efficiency through curbing further waste. However, given the basic investment system, the retrenchment campaigns are essentially reactive rather than active. A large number of these closed projects will sooner or later, be revived. During the reform period, alongside increasing decentralization, the problem of smallscale and duplicated construction seems to have been exacerbated by local governments and SOEs using their greater autonomy to build even more small-scale plants. This has led to yet more duplicated production facilities and thus to over-capacity in many industries {People's Daily, 26 April 1994; Yearbook, 1997: 454-5). The abovementioned facts indicate that a state investment system characterized by bureaucratic negotiation may create 'the wrong kind of wealth' as analysed in Naughton (1995a). That is, the bureaucratic investment system builds many factories and other facilities each of which, if taken in isolation, appears viable and productive in the context of the bureaucratic component of the economy, but turns out to have little value once its products have to face market demand and competition. In spite of the obvious improvement in the overall incen-

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tive environment during the reform periods, China's rigid state investment system seems to continue to build and expand factories that ultimately prove to be unviable and have to be closed. This 'wrong kind of wealth' implies that a significant part of the impressive economic growth of the last decade is offset by ill-conceived and wasteful investment projects, and will not only turn out to be useless but will also impose conversion costs on the next stage of reform and development. In addition, the remarkable resistance of the state investment system to reform may create serious problems for the future macroeconomic stability and the reform of SOEs and the state banking system.

7.5 The Difficulties and Possible Selections of Reforming the State Investment System The economic reform of the last two decades has undoubtedly helped China achieve her remarkably high rates of economic growth, and shows ex post coherence to some extent although such coherence is not the result of a carefully planned reform strategy (Lin et al. 1994, 1995; Naughton 1995a & b). On the other hand, some profound shortcomings remain and a series of uncompleted essential tasks still confront the reformers. Among the most obvious shortcoming has been the continuing failure to develop institutions and impartial rules that apply to all economic organisations. One of the most essential tasks is to remove the resistance of the state investment system to reform. The difficulties of reforming the state investment system involve not only determining how to tackle the twin problems of central-local relations and relations among government, enterprises, and banks, but also how to deal with the soft budget constraint of the state sector, particularly with regard to state enterprises (SOEs). On the one hand, SOEs continue to have access to the bulk of capital under conditions in which their liability and ability to repay are unclear, and the efficiency of their capital utilization is obviously low. On the other hand, SOEs have born the responsibility of supporting an extensive work force (over 100 million) by keeping redundant workers on their payroll, and of providing a number of goods and services (including housing, health care, child care and schooling, etc.) with positive externalities to society. Therefore, the SOE sector should not be allowed to collapse, despite the fact that a significant number of them need to rely on soft subsidies and credits to remain in business. The social consequences of

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the their collapse would be too dire and the political and economic costs too great. How to harden SOEs' budget constraint while avoiding SOEs' collapse and/or large-scale unemployment will continue to be the most difficult theoretical and practical challenge that Chinese reformers must face in the transitional period (Gu 1999, Jefferson 1998, Perotti et al. 1998, Sun 1999, World Bank 1997). In terms of the central-local relations, China's reform has not yet found a way of maintaining central effective control over macroeconomic policy while also preserving the benefits of decentralized decision-making and greater sub-national autonomy. In particular, on the issues of profit-making economic management, China's central and local governments have no clear division of functions. During the reform period, through gradual decentralization, local governments have obtained increasing economic management power from the central authorities. At the same time, the central government has retained some control over industry, commerce, trade, prices, wages and credit allocation. As the central and local governments undertake the same functions, their differences have devolved mainly to ownership, production types, and quota allocation. For instance, the central government is responsible for managing large-scale coal enterprises at the central level, and local governments are responsible for those smaller-scale coal enterprises at local levels. In foreign trade, providing the foreign exchange quotas set by the centre are fulfilled, local governments can take measures to encourage export on their own. Import and export of some kinds of products are monopolized by the central agents, while other categories are open to both central and local foreign trade agents, and so on. Such division in turn determines the composition and scope of expenditure of the central and the local authorities, which is usually known as responsibility for fiscal expenditures. While local governments have certain administrative intervention powers in many economic issues and in their own enterprise businesses, they must be provided with certain sources of revenue as an incentive to use their power to good effect. In other words, if local governments exercise their power inappropriately, they are punished by a reduction of revenues. Similarly, the central government also holds concrete administrative intervention powers and needs its own independent sources of fiscal revenue.

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Against this background we can understand why the arrangement and structure of the fiscal system has played a leading role in the distribution of administrative power among the governments at different levels, and why other measures of administrative decentralization are gradually built around it. This indicates that tackling central-local relations requires a broad effort to define systematically the respective economic roles of central and local governments at each level. Basically, this requires the development of national consensus on a delineation of the economic responsibilities of central and local governments. As long as the local governments are fearful that their discretionary authority may be revoked, and as long as there remains widespread opportunities for bargaining, it would be difficult to reverse the tendency of local governments to focus on quick revenues without being concerned about the negative externalities of their actions on the national economy, and/or to depend on ad hoc measures for retaining control over local resources (Lou 1992, World Bank 1994). The challenging issue with regards to the relations among government, enterprises and banks is how to cope with their collusion in investment and financing affairs (cf. Chapter 3), which also forms one of most important avenues for realizing the soft budget constraint enjoyed by industrial ministries, local governments, and their enterprises. The co-mingling of government and enterprise activities, and the pervading interference of governments at all levels in the activities of the state banks, have created incentives and opportunities for collusion. As a result, the leveraged financial position of SOEs is increasing at a good pace.2 The governments at different levels and their SOEs monopolize the benefits from soft borrowing while distributing the associated costs over the rest of the economy. For example, in 1995, the SOE sector consumed about 80 per cent of state band credit funds but created less than 45 per cent of China's GDP (Perotti et al. 1998). Soft lending to the SOE sector has resulted in a rapid accumulation of bad debts in the state bank system. It is estimated that China's four main state banks had bad debts equal to a crippling 22 per cent of their lending by mid1997 (The Economist, 13 September 1997: 24). Tackling this problem is as difficult as dealing with how to harden SOEs' soft budget constraint and transforming most of the state banks into pure commercial banks. To design a detailed reform schedule is beyond the scope of this research. Instead of dealing with specific reform programmes, in the sue-

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ceeding paragraphs we will follow a 'positive-predictive approach' (Kornai 1995) to examine the most relevant or feasible strategies for the future reforming of China's state investment system. The core of the strategies may be significantly different from the type of reform that has prevailed in Europe. The impact of the SOEs' soft budget constraint can be reduced, and a major part of the problem can be solved by gradual 'denationalization' in the future. Denationalization in a narrow sense represents a transitional process that mainly includes successful non-state enterprises taking over or merging with poorly performing SOEs. SOEs are converted into joint ventures with either domestic non-state or foreign enterprises. SOEs are reorganised into joint stock companies or transformed into joint stock cooperatives, and small-sized SOEs and/or some medium-sized SOEs are sold or leased. In a broad sense, denationalization means that the relative reduction of the state sector can also be achieved through the growth of the non-state sector, composed mainly of township and village enterprises (Sun 1989, Lin et al. 1995, Qian&Xu 1993a). In fact, denationalization in a broad sense is already practised and has shown a significant achievement in China since the early 1980s. In recent years, reorganization of large and medium SOEs into joint stock companies and transformation of small and medium SOEs into joint stock cooperatives has become a focus of SOEs' reform. Takeovers and mergers of SOEs by non-state enterprises have emerged as well (Parker & Pan 1996; Sun 1998b, 1999). Certain limitations exist and some difficult outstanding issues remain, which are mainly related to the SOEs' unrelieved social burden. This in turn encourages more far-reaching social security and housing reforms. As a result, the total output proportion of pure, state-owned industry decreased from 77.6 per cent in 1978 to 34.1 per cent in 1994 and further to 28.5 per cent in 1996 {Statistical Yearbook of China 1997: 413).3 In comparison with the high social and economic cost of massive and fast privatization in the former Soviet Union and Eastern European countries, denationalization may be an easier, less costly, and more suitable reform strategy in China. In addition, denationalization has led to a natural industrial structural adjustment in China, whereas the privatization process per se does not automatically bring about economic structural modification (Lavigne 1995, Sun 1998b).

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A better and more feasible framework for re-configuring the relations between central and local authorities may be termed 'market reinforcing decentralization' (McKinnon 1993, Montinola et al. 1995, Qian & Roland 1999, Qian & Weigast 1997, World Bank 1994). Such a framework includes the following four essential characteristics: (1) Shared authority, which means that the locus of economic regulatory authority is shared by the different government levels so that no one level can monopolize the regulatory authority over the entire economy; in order to constrain central government from taking actions that may undermine market development this principle is necessary; (2) Local openness, that is, regional governments, are prevented from using their regulatory authority to build barriers against the development of interregional common markets; this principle is extremely important in terms of breaking down regional monopolies, reducing regional and rural/urban inequality, and promoting healthy interjurisdiction competition; (3) Hard budget constraint to all governments, which ensure that revenue sharing among governments at different levels is limited and borrowing by governments is constrained; this requirement is essential for rationalizing the state investment system because in most cases the recurring over-investment is local- and/or central-government-led (cf. Chapter 3); (4) Institutionalized scope and durability of authority and responsibility, which ensures that each level of government has a delineated and institutionalized scope of authority and responsibility so that it can act autonomously within its own well-defined domain of authority; and secondly, that the durability of authority allocation is also institutionalized so that the allocation cannot be altered by the central government either unilaterally or under pressure from local governments. Based on such an institutionalized framework, the economy is most likely to enjoy the benefits of decentralization while avoiding its most serious negative consequences such as dukedom economies, local-led investment hunger and the resultant macroeconomic imbalance. 'Market reinforcing decentralization' as a fundamental mechanism to achieve efficient institutional structures and to regulate fair competi-

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tiveness is bound to be a long historical process. However, its institutional formulation can be constitutionalized much earlier, based on the emerging development of national consensus on the delineation of the economic responsibilities of central and local governments. China started to commercialize the operations of the state bank in 1994, in part through the creation of 'policy banks'4 that are expected to take over government-directed lending for fixed investment and to help the central government finance its 'priority' expenditures without having funds diverted to unintended uses. Within the design and functioning of the policy bank system, however, several potential problems remain (World Bank 1994, 1997). Among these the most important is that the creation of the policy banks alone cannot make a clear break between policy and commercial lending of the state banks, and thus will not stop the deterioration of the loan portfolio of the state banks. It is impossible for the State Development Bank and Agriculture Development Bank to take over responsibility for all fixed investment loans in their respective sectors. A significant part of fixed investment for 'basic' and 'pillar industries' has still been and will continue to be financed by other state banks at preferential interest rates earmarked by the People's Bank of China (the central bank) specifically for these sectors. Moreover, all working capital loans, including those to loss-making SOEs, have been and will continue to be part of the portfolios of all the state banks. Therefore, more profound reforms need to be undertaken in this area. In particular, denationalization in both the narrow and broad senses is required. In the broad sense, denationalization of the banking system means encouraging the urban credit cooperatives to develop into non-state banks by alliance or merger, prompting the development of other non-state banks, and allowing domestic and foreign joint-ventured banks and/or foreign banks to undertake domestic financial business step by step. This would break the persistent monopoly of state banks and introduce essential competition into the financial area. Denationalization in the narrow sense calls for converting some state banks into joint ventures with either domestic or foreign financial institutions, and to reorganize parts of state banks into joint stock banks (Sun 1989). Such a denationalization of the banking system can in the short term effectively limit the impact of the state banks' soft budget constraint. In the medium term, it can introduce and reinforce fair competition among those different financial institutions characterized by multiple owner-

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ship, and finally, it can generate an efficient and pluralized financial market.

7.6 Limitations of the Research Several limitations of the research can be identified, a majority of them attributable to the inherent data shortage. First of all, discussion of political campaigns is far from complete or sufficient. Discussion has involved the economic conditions for initiating, and the economic consequences of those political campaigns, but does not pay so much attention to the corresponding political reasons and processes. Although a good economic condition usually serves as a necessary premise for initiating a political campaign, each political campaign has its own political reasons and logic. Such political determinants in turn bring in exogenous consequences to the economic processes, which have, to a great extent, shaped the amplitude of investment cycles. A further exploitation in this direction would add new insights into the politics and political economy of investment cycles in China; however, this would be beyond the focus and scope of the research. Secondly, as mentioned earlier, during the pre-reform period overinvestment usually resulted in supply shortages of energy, raw materials and agricultural products through direct material conduction. In the reform period, over-investment led first to over-expanding of credit, followed by shortages in the planned component and inflation in the market component of the economy through both material and value conduction. This research mainly discusses the persistent forces in both periods which shape the investment cycles. It has paid relatively less attention to the particularity of the conducting mechanism in the transition. Thirdly, annual time series data limits the ability of this research to go deeply into detecting the dynamic relationships between investment, credit scale and structure, inflation, and consumer goods supply for the reform period because of too few degrees of freedom. And finally, again, because of the limitation of degrees of freedom based on the annual time series data, the research only deals with the fixed investment made by the state sector. In fact, the investment made by collectives (mainly, township and village enterprises) and individuals, and foreign direct investment are playing an increasingly important role in shaping the macro-scale of investment levels. In this non-state sector the central

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government has to rely heavily on indirect policy instruments to carry out its industrial and other macro-policy objectives. Some of these limitations can be overcome by focusing on the transition period and using quarterly time series data, although the data collection is not at all easy. During the transitional period, the annual series for the fixed investment in the non-state sector has become available, and quarterly data for credit scales and structure, cash supply, retail sales of consumer goods, as well as for fixed investment in the state sector can be collected. Based on this extended database it is possible and inviting to tackle the recurring cycles of over-investment (inflation) and retrenchment in China. To stylize the characteristics of the state investment system in the transition and of the conducting mechanism (from excessive investment demand to over-expanding of credit to inflation and then to the bottleneck constraints) could make a worthwhile contribution to the literature of transitional economics. The establishment of a seasonal norm function of the investment level based on cointegration and the corresponding error correction model of investment growth rate cycles would offer a formal framework in which deeper insight into the cyclical problem might be obtained, and more practical policy implications might be spelt out.

7.7 Summary This book has sought to establish a new framework for conceptualizing the aggregate investment behaviour and modelling investment cycles in a socialist and/or developing economy like China. The new framework is characterized by the integration of the existing theories of the investment cycle and of the bottleneck-constrained growth in a socialist and/ or developing economy with the cointegration and the error correction mechanism. Based on this framework, it is found that although the concrete investment adjustment processes have been subject to changes brought about by power redistribution among different political apparatuses and by the succeeding reform, the investment hunger and its generated tensions between investment expansion and the supply and distributive barriers have persisted. As long as the annual real investment level has unremittingly moved along the supply possibilities frontier of bottleneck sectors, a high level of real investment can be constantly maintained.

Conclusions

243

The importance of the coordination mechanisms that prevail among economic institutions and their agents is highlighted by the research. Different investment approval and brake apparatuses operate at different levels in a complex, interactive fashion, and these are also subject to political and economic changes. However, their fundamental feature remains, which is the persistent interest conflicts and accompanying bureaucratic coordination. We have assumed that all bureaucrats are rational economic agents, that they maximize the interests of their own institutions subject to certain internal and external supply and distributive constraints, and that they are not homogeneous. We have found that the investment cycle in China is a type of bureaucratic coordination failure, rather than a result of the planner's myopic intertemporal irrationality. The long-run investment level function and short-run investment growth rate function are established through modelling an unrestricted vector autoregression system, without any pre-imposed equilibrium conditions or behaviour assumption. The investment level function is represented by a long-run equilibrium comovement among investment outlays, grain output, effective energy supply and the price level of investment. The investment growth rate function is characterized by a conditional error correction model developed from the equilibrium level equation. A large proportion of the cyclical patterns of investment growth rate can be explained by the error correction behaviour towards the co-movement path and by the relevant change rates of energy supply and agricultural output. In the modelling process, we employ the standard one-step-ahead Chow test to detect any statistically significant structural break induced by policy changes or reform within the sample period, and the conclusion is negative. This indicates that both investment level and growth rate functions have parameter constancy over the sample period. In other words, investment hunger and its resulting tension between investment expansion and supply/distributive barriers have persisted within the sample period. The complicated bureaucratic co-ordination and bottleneck constraints have caused widespread investment inefficiency. This has been evident in large-scale duplication of construction at national level, initiation of too many new projects at the expense of technical updating of existing assets, greater prominence of gestation lags between the starting and completing of investment projects, and in other kinds of projects turning out to have little value once their products have to face

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market demand and competition. In order to eliminate the scourge of investment inefficiency, greater reform of the state investment system and related areas is required. Reforming the state investment system involves several difficult issues such as how to tackle the problems of the central-local relationships and relationships among government, enterprises and banks, and how to solve the soft budget constraint of the state sector. These difficulties can be overcome gradually by such reform strategies as the denationalization of state enterprises and state banks, and market reinforced decentralization. Besides reform, four other policy implications can be spelt out: (a) The policies and measures aimed at releasing agricultural, energy and transport bottlenecks and at promoting transformation of traditional agriculture are essentially anti-cyclical. (b) In order to overcome the bottleneck constraints it is necessary to slow down the extensive expansion of general industry, so that more economic resources can be allocated to agriculture, energy and transport.5 (c) The remarkable rigidity of the state investment system, which stands in striking contrast to successful reforms in other areas, indicates that reforming the state investment system is indeed one of most difficult aspects of the transition process. Consequently, this system is and will remain a dominant force in the state sector in the medium term at least. How it is changing and what can be done to adapt it to changes in the dynamic reformed economy remain major policy questions. Because markets and market-related institutions are still in their infancy, complete reliance on them is not possible. However, policies which give greater priority to the development of agriculture, energy, transport, communication, infrastructure and the raw materials sectors, which allow investment to anticipate changes in domestic consumer preferences and demand, promote joining international competition, which attempt to separate investment plans from credit plans and policy lending from commercial lending of the state banks, and which continue to support the growth of collective, township and village enterprises and other non-state sectors, are significantly helpful to the transition of the state investment system in the medium term. (d) With enormous population pressure in both the rural and urban sectors, China must create a high value-added but labour-intensive

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modern agriculture so as to generate full employment at the same time as building modern agriculture. Following this strategy, Chinese agriculture should stimulate the good tradition of intensive and meticulous farming, promote land-saving techniques, and produce processed agricultural products. Clearly, the most problems addressed here are quite common to other transitional economies from central planning to market domination, and a part of the problems is also very typical in other developing economies.

Notes

Chapter 1 1 The average proportions of capital accumulation and state sector fixed investment in the national income used, i.e. the accumulation ratio and investment ratio respectively, are 29.9 with a standard deviation (SD) of 6.65 and 19.5 with a SD of 5.27 during the 1953-93 period; and the same figures as a percentage of GDP are 27.6 (SD 2.45) during 1978-93 and 18.3 (SD 2.19) during 1978-95, when the relevant statistical figures were available (source: Statistical Yearbook of China, 1993: 31, 43, 149; 1995: 137; 1996: 42,139). National income is the sum of net output of agriculture, industry, transportation, construction, and commerce, the five material production sectors of the economy. The coverage of national income in China's statistics excludes value added in 'non-material production sectors.' Thus, national income in China is approximately equivalent to the United Nations' net material products (NMP). The above-defined national income is also known as national income produced. National income used = national income produced - export + import {Statistical Yearbook of China, English version, 1990: 38). While GNP is not available, national income is the best proxy. 2 The percentage shares of state-sector fixed investment in the total are 69.5 in 1981, 65.6 in 1990, 60.6 in 1993 and 54.4 in 1995 {China Statistical Yearbook on Investment in Fixed Assets, 1950-1995: 27). The fact that statistics on total fixed investment covering all ownership types started only in 1981 prevents us from dealing with the patterns of total fixed investment for the pre-1980 period. However, in consideration of the strict restriction on development of the non-state-owned sector before the reform, it is safe to say that the shares of state fixed investment were higher than 70 per cent of the total from 1953 to 1979. 3 There are seven cycles of investment growth rates in China with lengths of three to eight years in the 1953-96 period. The maximum and minimum values of real investment growth rates in China from 1953 to 1996 are

246

Notes

247

84.53 and -62.54 per cent, respectively; in the former Soviet Union 18.2 and 0.7 percent; in Hungary 36.0 and -23.3 percent; in Poland 25.4 and -22.3 percent; and in Bulgaria 51.2 and -2.2 per cent from 1951 to 1989, respectively (source: data for China are based on the first column in Data Appendix of Chapter 6; for the others see Mihalyi 1992: 120). 4 For an extensive survey on this subject, see Mihalyi (1992). For other surveys, see Ickes (1986) and Simonovits (1992). Attempts to apply such response functions to China include Naughton (1986, 1987) and Imai (1994a &b). 5 The most direct reason behind this argument is that various econometric exercises, including several relevant international trade indicators and capital flows, show that none of them are statistically significant. The economic logic behind this non-significance will be analysed in sections 1.6, 4.2, 4.6 and 5.2. 6 This World Bank country study, China: Socialist Economic Development, is reviewed as the earliest and most comprehensive study of China's economy, based on the Bank's privileged and unprecedented access to Chinese data and officials, and from an internationally comparable perspective (Cyril Lin 1988). The statistical data appearing in the following paragraph are taken from this report. 7 The details about this most serious consequence of the Great Leap will be presented in section 4.6. 8 This section involves the ongoing methodological debate in econometric modelling. For those readers who have no interest in or are not familiar with this debate, you can skip over this section. 9 For the detailed discussions on this issue, see Maddala (1983) and Spanos (1990,1995), among others. 10 Roughly speaking, we say that an equilibrium relationship/fo, x2) = 0 holds between two variables X\ and x2 if the amount s, =f(xu,x2t) by which actual observations deviate from this equilibrium is a median-zero stationary process (Banerjee et al. 1993: 4). This implies that the equilibrium relation acts as an 'attractor', of the centre of gravity type. Along the attractor, no further incentive for, say, making extra benefit, can be produced (Engle & Granger 1991: 1-2). 11 The main data limitation for sectoral analysis is lack of a sectoral breakdown of technical updating and transformation investment for the pre-1980 period, although for most years the sectoral data of capital investment is available.

248

Notes

12 Lardy (1995: 1073) raises a strong argument: 'contrary to what one might believe from observing the flood of foreign capital flowing into China, on a net basis such inflows have not contributed to domestic capital formation in China', although they have made a major contribution to China's openness to the world.

Chapter 2 1 The terms of 'self-restraint' in investment decision-making and 'relatively symmetrical relationship' between demand and supply are used here to explain why investment hunger becomes so impressive in socialist economies in comparison with market economies. In a market economy there may also be imbalances between desired investment and supply capacity of the economy, which cannot be fully explained by such 'self-restraint' and 'symmetrical relationship'. The relevant adjustment mechanisms have been discussed intensively in macroeconomic literature. 2 In this section Bauer's four-phase description is employed to sketch the basic and most general features of investment cycle in China. The specific features of a boom-bust reform cycle will be discussed in section 2.4.2. Investment hunger and bottleneck constraints have persisted in both pre- and post-reform periods. 3 In fact, Mihalyi (1992: 81-7) compares four similar indicators in measuring investment dispersion: 'uncompleted investment stock/current investment', 'current investment/current completed investment', 'uncompleted investment stock in / year/investment in t-\ year in uncompleted projects', and 'current investment in unfinished projects/current investment', by using data for Hungary (1950-89) and the former Soviet Union (1958-63). He finds that movements in the four indicators do not exhibit parallel variations; he questions which indicator planners should use (ibid.: 86). 4 In fact, as pointed out by Wuyts (1988), FitzGerald's reconstruction (related to price stability and the food balance) is a little vague. Equation (2.6) has the advantage of being able to be intuitively understood, although it still remains vague. 5 For an excellent survey of Kalecki's research on the socialist economy and of the debates around Kalecki's growth theory, see Osiatynski (1988). 6 Identifying the investment category with Kalecki's industry sector in this section is reasonable when we deal with the planners' trade-offs. In China over 90 per cent of fixed investment by state-owned units has gone into industry (over 60 per cent of the total), construction, transportation, communication, commerce, banking and other social service sectors, which toge-

Notes

249

ther form the so-called modern sector or industrial sector, leaving little over for the traditional agricultural sector. About half of the remaining 10 per cent of fixed investment is shared by water conservation and irrigation projects, with the rest shared by farming, forestry, animal husbandry and fishery. In the period 1981-85 only 3.9 per cent of total fixed investment went to the agricultural sector including water conservancy and from 1986-95, the figure was only 2.5 per cent (Statistics on Fixed Investment in China, vols: 1950-85, 1986-87, 1988-89, 1990-91; China Statistical Yearbook on Investment in Fixed Assets, vol. 1950-95: 42-43). 7 The economic logic behind equation (2.6) for a semi-industrial market economy is well summarized in Jansen (1990: 10-11), where the price mechanism in general and agriculture's terms of trade (price scissors) in particular play a central role in, and export and import also contribute to, avoiding inflation and stagnation of the domestic economy. In China's case, however, coercive procurement of key agricultural products, binding rationing of foodstuffs and other basic consumer goods, and the adjustment of material reward to peasants (rather than price) have played the leading role in determining agriculture's terms of trade in a much broader sense. In addition, for a huge economy like China, the export and import of grain and energy do not play a significant role in influencing the balance between industry and agriculture. This will be demonstrated in Chapters 4 and 5. 8 In a Kaldorian sense, forced saving might be realised by shifts in the distribution of national income from wages to profits at the expense of a tolerable inflation rate. Also industrial concentration may help raise profits through depressing the real wage. However, in the meantime, the reaction of workers to the reduction of the real wage is a demand for higher money wages based upon trade-union bargaining power, causing a price-wage spiral to follow (Kaldor 1955, Kalecki 1976: 44, Thirlwall 1974: 86-96). In China such forced saving is directly collected by the state sector through industrial nationalization and agricultural collectivization. The cost is the stagnation of the urban real wage rate and of consumption by both the urban and the rural population for a long time, as well as inflation. 9 In addition, rural/urban inequality is high in comparison to other nations in Asia (Khan et al. 1993). In 1988 the official estimated ratio of urban to rural income was 2.19, and Khan et al. (1993) put it at 2.42 by taking into account in-kind income and subsidies. Considering that the rural population accounts for almost 80 per cent of total population, it is fair to say that the agricultural sector has provided a significant share of accumulation through its extremely low consumption, although part of this income difference may be explained by the productivity gap. 10 The agricultural constraint to investment demand and the linkage between agricultural fluctuation and macroeconomic adjustment in China will be

250

Notes

discussed from the perspectives of both history and data analysis in Chapter^ 11 The term 'job-off in China indicates those employees who still keep the employment title and corresponding welfare benefits, but have lost their jobs. 12 The dependence ratio in the less developed northwest region is certainly higher than the national average. 13 According to Lin et al., the economic causality in general and development strategy choice in particular is decisive. Once a capitalist economy adopts the same 'leap forward' development strategy, it also has a similar macro policy environment, resource allocation system, and micro management institution, as well as a development performance similar to that of the corresponding socialist economy (Lin et al. 1996: Chapters 2 & 3). 14 There are a large number of official documents on setting up and implementing these five-year plans, most of them unpublished. For published materials, see, for instance, 'Report about the First Five-Year Plans on the Second Conference of the First National Peoples's Congress' by Li Fuchun {Documents of the Second Conference of the First National Peoples's Congress of the People's Republic of China, Beijing: People's Press 1955), 'Recommendation about the Second Five-Year Plan of Developing National Economy' approved by the Eighth National Congress of the Communist Party of China {Documents of the Eighth National Congress of the Communist Party of China, Beijing: People's Press 1956), the news reports and leading article about the design and implementation of the Third FiveYear Plans in People's Daily (28 & 30 November, 3 December 1965; 1 January, 14 August 1966), Compendium of a Ten-Year Plan from 1976 to 1985 about the Developing National Economy approved by the First Conference of the Fifth National People's Congress (Beijing: People's Press, 1978), The Sixth Five-Year Plans of National Economy and Social Development of the People's Republic of China approved by the Fifth Conference of the Fifth National People's Congress (Beijing: People's Press, 1982), and others. 15 For two intensive surveys on this subject, see Sheng (1993a, 1993b). For other surveys, see Knight (1995) and Karshenas (1993). 16 For a rough definition of the equilibrium relation, see note 8 of Chapter 1. For a strict definition and explanation, see section 6.2. 17 A full exploration will depend on cointegration and error correction mechanism analysis. 18 Other statistical weaknesses include that

Notes

251

(a) all behaviour equation estimates show non-constancy of parameter (by one-step-ahead Chow test), (b) the money demand equation presents heteroscedasticity [ARCH test: F(l, 35) = 16.82; heteroscedastic error test: F(4, 31) = 4.50] and misspecification of functional form [White's general functional form misspecification test: F(5,29) = 3.43], (c) the consumption goods demand equation exhibits serial correlation [LM test for 1 to 2 order serial correlation: F(2, 33) = 2.98], heteroscedasticity [ARCH F(l, 33) = 11.56; heteroscedastic F(6, 27) = 5.39], and misspecification of functional form [White test: F(9, 23) = 3.55], and (d) the investment function estimate shows significant autoregressive conditional heteroscedasticity [ARCH F(l, 32) = 5.93]. 19 For relevant English surveys and research on this issue, see Garnaut & Ma (1992), Cyril Lin (1988, 1989), Oppers (1997), Sung & Chan (1987), Watson (1994) and World Bank (1990,1994). 20 In Fischer (1988: 304) four sets of stylized facts and in Chapter 2 of Dore (1993), ten stylized facts (empirical regularities) associated with business cycles in the modern market economies are stated, together with empirical evidence. 21 For the dominant control of investment resource by the state, see note 2 of Chapter 1. The dominance of bureaucratic coordination over investment decision-making in the post-reform period will be dealt with in Chapter 3.

Chapter 3 1 According to the official statistics {China Statistical Yearbook on Investment in Fixed Assets, 1950-1995: 458), China's government set the classification standard of large, medium and small construction projects in 1953 and revised this in 1962, 1977 and 1979, respectively. In principal, the construction projects are classified according to the total designed capacity or the total amount of investment in the design specification or preliminary design approved by authorities at higher levels of the government hierarchy. Usually, the size of a project is defined according to the designed capacity of the main product of the project. If there are many types of products and it is difficult to determine which one is the main product, the size of a project is defined according to its total planned investment. 2 For example, the family farming system, one of most important reforms, now known as the household responsibility system, was first secretly adopted by some peasants in Anhui Province in 1978 and was fully officially recognized in late 1981, when 45 per cent of the former collectives had already been dismantled and the household responsibility system had

252

Notes

been instituted (X. Chen, 1993, Lin et al. 1995). The first steps in enterprise reform, namely introducing profit retention for enterprises, performancerelated bonuses for workers and permitting state enterprises to produce outside the mandatory state plan, were pioneered by Sichuan Province in 1978 and 1979 under the then governor Zhao Ziyang. More recent examples include stock exchanges in Shanghai and Shenzhen, which were also initiated by the local authorities and only later became accepted by the central authority (Chen et al. 1992, Zhang & Yi 1995). 3 Fixed investment by a state-owned unit is divided into 'capital construction', 'technical updating and transformation', other investments (mainly oil field development and repair of highways), and 'commercialized housing construction'. Theoretically, the capital construction investments emphasize the creation of new enterprises or major expansion of existing enterprises while the technical updating and transformation investments are intended for the modification of existing facilities. Historically, capital construction investments related to projects were financed through the government's budget and thus more tightly controlled by the central government, whereas technical updating and transformation investments were financed mainly from depreciation funds and usually controlled at local level. Now the distinction is less clear-cut as budget funds are also channelled through state banks, and technical updating and transformation investments are funded to a large degree by bank loans. But the basic feature has been maintained, i.e. that the former is more under central control and the latter more under local control. 4 The relevant details on the 'national priority projects', will be presented in section 3.4.2. 5 Before 1979, bank loans and working capital were closely interrelated. Working capital was divided into two categories: 'quota working capital' was the amount required by an enterprise for the maintenance of a normal rate of business turnover and was provided by government budgets (to which all profits had to be remitted). 'Non-quota working capital' was the capital requirement allowed above the quota, and consisted of temporary and seasonal needs, plus the financing of goods in transit (Chen & Niu 1990: 1213). 6 For instance, the inflation rates of construction materials are as follows: Year 1988 1989 1990 1991 1992

Inflation rate (%) 16.3 13.0

6.6 8.6

Year 1993 1994 1995 1996

18.8

Source: A Statistical Survey of China 1994: 89; 1997:69.

Inflation rate (%) 61.1 12.5

2.6 2.8

Notes

253

7 For a detailed introduction to the history and reform of China's material supply system, see Eckstein (1977: 134-39) and Wong (1985). 8 Data from a World Bank survey of over 900 SOEs, summarized in Table 2 of Jefferson & Rawski (1994), show the increasing and significant positive correlations between profits and retained earnings and bonuses in the periods 1980-83, 1984-87 and 1988-90. 9 This empirical research is based on the surveys conducted by the Institute of Economics, Chinese Academy of Social Science with assistance of four provinces' System Reform Commissions (Sichuan, Jiangsu, Jilin and Shanxi). Annual data for 1980-89 for 769 SOEs in the surveys give details of the firms' internal incentives, the firms' cost and revenue accounts, and the nature of the relationship between the firms and the state. Because large firms are over-represented in comparison to SOEs in general, the sample may cover the core of the traditional state-run enterprises.

Chapter 4 1 The general meaning of agriculture in China, especially in official statistics, is that it consists of cropping (i.e., agriculture in a narrow sense), forestry, animal husbandry, sideline activities and fishery. The output value of cropping accounted for over 74 per cent of all agriculture before 1979 {Comprehensive Statistics of China's Rural Economy, hereafter Rural Statistics, 1989: 114-5), and in 1992, this share decreased to 55.5 per cent {Yearbook 1993: 335). 2 In Chinese official statistics, 'grain output' covers all types of grain such as rice, wheat, corn, sorghum, millet and other miscellaneous grains, as well as tuber crops and soybeans. The output of beans refers to dry beans without pods. Tuber crops (sweet potato and potato, not including taro and cassava), are included as a grain item converted on a 4:1 ratio, that is four kilograms of fresh tubers was considered equivalent to one kilogram of grain until the end of 1963. Since 1964 a 5:1 ratio has been used {Yearbook 1986: 743). 3 One obvious exception is the agricultural collapse during 1960-62 caused by the ultra communization and the Great Leap Forward, together with the high compulsory grain delivery quotas and bad weather. For the detailed and insightful analyses and the debates on the reasons for this collapse, see, among others, Kueh (1995), Lin (1990, 1993), Liu, M. (1993) and Putterman & Skillman (1993). 4 See note 2 of this chapter.

254

Notes

5 The Soviet figures for 1928 and 1932 are cited by Kueh (1995) from Hoeffding (1959) and Kueh (1984). The other data are from Yearbook (1991: 819, 836; 1993: 81, 364, 609; 1994: 59, 345). 6 The example, cited by Kueh (1995), is from 'The 1931 flood in China: An economic survey', in Nanjing Academic Journal (Jinling Xuebao) 1932 (2/1): 216. 7 Roughly speaking, weak exogeneity means that if the variables in the righthand side of an equation are weakly exogenous, the single equation analysis for explaining the left-hand variable maintains the full information of interest as does the unrestricted vector autoregressive system, when incorporating both long-run and short-run dynamic. The strong exogeneity is much closer to traditional exogeneity than is weak exogeneity. Weak exogeneity plus Granger noncausality from the left-hand variable to the righthand variables generates strong exogeneity. 8 Justin Lin (1989,1990) put the point more bluntly when he tested the commonly accepted explanations of the collapse of agricultural production in 1959-61: The official explanation for the disaster of 1959-61 was bad weather, but it is unlikely that in three successive years bad weather hit every part in a country with a vast territory. In fact, bad weather is the easiest excuse for local officials in China to explain poor agricultural performance caused by other reasons such as the deprivation of the right to withdraw from a collective, mistakes in policy or management, incentive problems, etc. National statistics show a clearly increasing trend in the "areas reported to be hit by natural disasters', which is contradictory to the fact that more acreage was irrigated each year and most of the increased irrigation came from modern engine-powered irrigation. Lin (1989: 15) employed the indicator 'areas hit by drought/areas by flood' to reveal that there is a clear trend for more areas to be reported to be hit by drought than flood. The reason is that floods are harder to fabricate and easier to identify than droughts. Therefore, when agricultural production falls, local officials often used 'drought' as an excuse. 9 The difference between the accumulation ratio and the investment ratio is caused by the fact that the accumulation also includes changes in inventories and non-state investment. Here the non-state investment dominates the difference. For example, during 1981-94, non-state fixed investment accounts for over 30 per cent of the national total, and over 9 per cent of GDP {Statistics on Fixed Investment in China, 1950-85, 1987: 5; Yearbook 1993: 145, 31; 1995: 137, 32). In the retrenchment phase non-state investment is usually cut back first by the state bank's much stricter control over non-state credit than over the state sector; the bank may stop lending to non-state investment projects and so on. This activity goes beyond the scope of this research.

Notes

255

10 Pragmatically speaking, consider the following specification of a time series >>, (without trend and nonzero mean): yt = qyt.j +s(, where the term et is a sequence of uncorrelated disturbances with mean zero and constant variance. If cp is less than one in absolute value, yt fluctuates around a mean of zero, and thus is said to be stationary. The detailed discussion and test procedure of stationarity will be given in the technical appendix of Chapter 6. 11 The remarkable success of China's collectivization movement from 1952 to 1958 and the sudden collapse of agricultural production in 1959-61 have been the topics most discussed among students of the Chinese economy. The reasons for the sudden collapse are hotly debated in the recent literature (cf. Justin Lin 1990, 1993; a Symposium issue of the Journal of Comparative Economics June 1993; Kueh 1995; Riskin 1987, among others). The focus of this chapter is to deal with the correlation between agricultural fluctuation and investment adjustment, which may indirectly show some reasons for the boom and collapse from the perspective of macroeconomic policy. 12 There is a subcycle of agriculture in 1954—57 as shown in Figure 4.1, which is accompanied by an investment cycle in 1955-57 as indicated in section 1.3. The basic macro-mechanism behind this correlation is fundamentally same as that of the major cycle from 1954-62; therefore I do not discuss it separately (for a detailed discussion about the subcycle, see Eckstein 1968 and Riskin 1987: Chapter 5). 13 Dong (1980), cited by Riskin (1987). 14 On average, at least ten million casual and contract workers worked in state-owned mines, construction sites and other non-farm industries (Lardy 1985: ii), while registered in the rural system. This means they are ineligible for rationed food, the distribution of low-rent houses, retirement insurance and other welfare systems which are only open to 'permanent workers'. They must also purchase food in non-state markets where prices are normally two to four times the prices of rationed commodities and must finance housing and medical costs themselves.

Chapter 5 1 If the conversion of hydropower into SCE is calculated on the basis of an internationally used equivalent coefficient as done by the China State Statistical Bureau in 1993 and 1994, the result will show that the share of hydropower will decrease by more than 3 percentage points and the proportion of coal will increase correspondingly by more than 3 percentage points, reaching 76.8 in 1993 and 77.7 per cent in 1994 (cf. Yearbook 1995: 199).

256

Notes

2 For example, based on the official exchange rate, China's energy intensity (unit of energy use/US$GDP) in 1990 was 2.65 kg oil equivalent per dollar of GDP, whereas the OECD average was 0.25 kg oil equivalent per dollar (Ishiguro & Akiyama 1995: 28). 3 For the detailed discussions of the reasons for the high energy intensity in China, see, among others, Lu (1993: section 3.2) and World Bank (1985: 10-16). 4 A detailed discussion of the 1994 energy price reform and its effect goes beyond the concern of this research. For relevant information, please see Economic Situation and Prospect of China 1994: 40-5; 1997: 61-6, 8 0 ^ ; China Information Daily, 16 Feb. 1994: 1, 3. 5 For relevant reports on this issue written by Chinese officials and researchers, see Energy of China (Zhongguo Nengyuan) (1989 (2): 6-9; (3): 1-4; (6): 31-4; 1990 (3): 4-9; (5): 18-21; 1991 (7): 37-40; (10): 5-15; 1992 (2): 4-5; (3): 5-10; (7): 23-8), China Investment and Construction (Zhongguo Touziyu Jianshe) (1988 (8): 25-7; (12): 18-19; 1989 (3): 10-12). For the relevant literature in English, see Ledic (1989), Jing-Tong Liu (1987), Lu (1993), Smil (1988), and World Bank (1985). 6 It should be noted that in this research the normal shortage level is determined by the co-movement among relevant variables rather than by a univariate moving average. 7 See, for instance, China Information Daily (2 July 1993: 1; 16 February 1994: 1, 3), People's Daily (overseas version) 14 June 1993: 2; 1 March 1994: 2), Statistical Survey of China (1994: 8, 47). 8 A symbolized technical consideration is that, on average, the comprehensive utilization time of representative power-consuming equipment is 2,500 hours a year, whereas that of representative power-generating equipment with the same capacity is 5,000 hours a year. This implies that the reasonable ratio should be 2:1 in terms of technical efficiency (Hu, Zhaoyi 1990). 9 China had the smallest service sector and the largest industrial sector in comparison with the USA, India, Brazil, Japan and South Korea in the early 1980s. This contributes significantly to the high freight intensity. Assuming that the service sector does not generate any freight transport, the freight intensity related to total GNP of an economy like China, where the service sector is 20 per cent, would be double that of an economy like the USA where the service sector accounts for 60 per cent of GNP, with the same intensity of freight for agricultural and industrial sectors (for details, see Yenny & Uy 1985: 9-10). 10 For relevant reports on transport bottleneck and crisis, see China Information Daily (2 July 1993: 1), People's Daily (9 March 1989: 5; People's

Notes

257

Daily (overseas version), 14 June 1993: 2), European Time (18 November 1995: 5) cites Economic Daily (November 1995), and World Bank (1983). 11 For relevant reports on this issue, in addition to those given in note 4 of this chapter, see People's Daily (overseas version) (9 March 1989: 5), China Information Daily (2 July 1993: 1; 16 February 1994: 1, 3), China Investment and Construction (1992: (12): 25-6). 12 In the literature, 'unfinished construction' is used as a proxy for shortage in the investment goods sector. However, as discussed in section 2.2.3, it may be a misleading indicator. Furthermore, this indicator is not available for China.

Chapter 6 1 The following discussion about long-run equilibrium relationship and error correction mechanism is based on Banerjee et al. (1993: Chapters 1 & 5), Engle & Granger (1991: Introduction), Granger & Hallman (1991). 2 The relevant formal definition and discussion will be provided in the technical appendix of this chapter. 3 It is a basic fact that the sample size of macroeconomic variables is relatively small in most developing economies. This, however, should not become an insurmountable barrier to further exploration based on the interaction between theoretical and statistical analysis. One must be cautious to obtain economically meaningful insights. 4 For the technical procedures of the unit root test and other tests discussed in following paragraphs and sections, see the technical appendix of this chapter. 5 Both Schwarz and Hannan-Quinn information criteria are used to choose the proper number of lags. For the technical details, see Doornik (1994: 151-2). I also estimate the relevant system in which the |i is restricted to lie entirely in the space spanned by a, thus only allowing for an intercept in pzt. The test statistics indicate that r = 2 (after Reimers' small-sample correction). Following Johansen's (1992d) sequential testing procedure, which estimates both r and the presence or absence of trends, and guarantees correct size asymptotically, one should accept the null hypothesis associated with the largest eigenvalue for which rank (n) < r is not rejected against the unrestricted alternative rank (n) = 4 in either of the two systems. Therefore, I chose the unrestricted constant in Model (2) because it suggests that r=l, and allows for there being a linear trend in the data.

258

Notes

6 Among others, see Hare (1982, 1989), Kornai (1982, 1992), Kornai & Martos (1981), Kornai & Simonovits (1986), Martos (1990), and Simonovits (1992).

Chapter 6 Appendices 1 Substituting equation (A2.2) into (A2.1) and rearranging it gives yt = \i + bt + By,_i + £t (/ = 1, 2, 3, ...), where î = Oo(l-B) and 5= a 1(1—B). Dickey (1976), Fuller (1976) and Dickey & Fuller (1981) introduced tests of 6 = 1 based on statistics obtained from applying ordinary least squares (OLS) to this rearranged equation of the quasi-first-difference transformation of equation (A2.1). The distribution of the /-statistic for 6 changes if a time trend is excluded in the regression, and if a constant and a time trend are excluded, which was first tabulated by Dickey using the Monte Carlo method and is given in Table 8.5.2 of the book by Fuller (1976). Recently, a formalized generating procedure of these critical values, MacKinnon (1991), has been popularly accepted. 2 Equation (A2.6) is less commonly encountered than (A2.3)-(A2.5), but it may well be a plausible specification in some cases, as argued by Ouliaris etal.(1988). 3 The word 'marginal' in the modelling process is used in its statistical sense as usually employed in textbooks on the probability theory (see, e.g. Kendall & Stuart 1977: 22, Spanos 1986: sections 5.3 and 5.4). 'Marginal' as used in statistics should not be confused with its economic sense, as in 'marginal versus average cost'.

Chapter 7 1 For the relevant reports and surveys on the issue of repetitionary construction, see, among others, Almanac of China's Finance and Banking (1993: 265-6), Statistical Yearbook of China (1997: 454-5), China's Youth Daily (3 February 1993: 2), and Survey Report by the research group (1992). 2 At the beginning of the reform, state industrial enterprises owed banks a sum of credits equal to only about 11 per cent of their book value (depreciated fixed assets plus the value of all inventories). At that time SOEs relied basically on fiscal appropriation for investment funds (cf. sections 3.2 and 3.4, Table 3.4). During the reform period, SOEs have become increasingly dependent upon bank funds for both fixed investment and working capital. By 1988, external liabilities were about 45 per cent of the book value of state industrial enterprises (Naughton 1995a: 264). In June 1995, according to a recent survey by the People's Bank of China (the central bank) of 248 SOEs in various parts of China, the SOEs' average total debt to asset ratio

Notes

259

stood at 69 per cent, while their average debt-to-working capital asset ratio was 98 per cent {Shenzhen's Securities Times, 22 January 1996). 3 When including the output value of state-joined shareholding companies, the output proportion of industrial SOEs in total was 40 per cent in 1994 {Statistical Yearbook of China 1995: 377). 4 The three newly created 'policy banks' are the State Development Bank of China (SDBC), the Agricultural Development Bank of China (ADBC) and the Export Import Bank (EIB). 5 It should be noted that in terms of identifying sectoral priorities, policy concern is clearly different from theoretical modelling. According to the former, transport has been the first bottleneck in the chain of resource and goods circulation. In the modelling experiments, it seems that effective energy supply (energy consumption) is the best representative of these bottlenecks (cf. Chapter 5).

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Index

'Above-norm' projects 79 Adj ustment and re-adj ustment 1 5,13,18 macro-economic 1-5, 30, 48, 57,70,112-8,124-57,249 structural 13, 18, 108-10 Adverse distributive impact 2, 46 consequences 3, 28,30 Agricultural development and industrial expansion 130 Agricultural terms of trade 46-7, 54-5, 70, 129, 143, 230 in kind 129 Agricultural value-added 6 Agriculture first 134 Anti-Rightist Campaign 131 Anti-Right Deviation 131 Asymptotically stationary 210 AutoRegressive Conditional Heteroscedasticity (ARCH) test 205,251 Attractor 5-6,21-4,189-91,207, 223, 247 Autocorrelation 197, 205, 211 residual 205

model 18-24,35-8 theory 18-24,31,35-8 Behaviour assumption 4, 189, 243 Bottleneck constraint 2-7, 23-5, 28, 30, 38, 45-8, 55-6, 623,70, 113, 162-6,174,221, 242-4, 248 type of growth theory 2, 301,55 Bottleneck sectors 2, 7, 11, 15, 23,28,38,56,88,97,111, 143,201,206,222,228-9, 242 Brazil 179-80 Bureaucratic co-ordination 5, 678,81,221-2,232,243,251 failure 222,243 mechanism 5, 68 Business cycle theory 30, 64, 70 Capital accumulation 1, 6, 10, 27,36,52,54-5,70,117-8, 129-34,156,205,221,246 levels 1 mechanism 10, 45-7, 51, 54, 70,117,229 ratio 6,113, 129-34,137, 146-52, 161, 175, 177 Capital construction 73-83, 141, 252 projects 82

Bargaining power 8, 249 Barriers to growth 3, 46 Bauer 18,29,35-8,42-3,69,203 Bauer's four phase description 18-24,29,69, 248

282

Index Central Bank (People's Bank of China) 89-90, 96, 147, 240, 258 Central-local relations 235-7, 244 Centrally planned economy 1 -2, 10,38,41,72-3,81,99100, 102, 171 ChenJinhua 206 China International Engineering Consulting Company (CIECC) 80 China's development model 8, 11 China's state investment system 27,71,206,223,233,238 China's traditional economic system 60 Chinese economy 1, 52, 255 Chow test 28, 189, 202-3, 224, 243,251 one-step ahead 189,202-3, 224,243,251 'Clambering into the plan' 81 Cobweb Theorem 157 Cointegration, see also Attractor 2, 4-6, 20-2, 28, 56, 68, 113,184, 188-203,206-7, 213-9,222,242,250 analysis 22, 196-8,210,229, 231,250 approach 4-5,20-2,28,184, 188-203 norm 24 relation 5,23, 193-4,200-3, 207, 224 vector 198,213 Cointegrated of order 213 Collectivization 9, 47, 53, 123-4, 131, 249,255 Collinearity 189 Collusion 71, 82, 89-90, 105, 110,228,237

283

Commune and brigade enterprises 101 Co-movement, see also Attractor and Cointegration 3, 5-6, 11, 15,188,191, 197,2067,213,223-4,256 Conditional density 217 distributions 194 equation 194-5,201-2,21820 error correction model 23, 28, 189,194,224,243 process 217 variables 189 'Control by norm' 206 Constraints a key factor- 48-50 bottleneck See Bottleneck constraints labour- 46-48,50 Construction Bank of China 74, 86 Co-ordination 2, 5, 67-8, 81, 221-2,232,243,251 mechanism 2, 5, 68 see also Bureaucratic co-ordination Covariance matrix 193,217 Credit plans 72,76,85,244 Cultural Revolution 19, 26, 38, 101, 136-9, 141 Daqing 140 Data-generating process (DGP) 20-1 Decentralization 4, 12, 27, 60, 71,73-7,81,100-1,107, 157,226,232,234,236-9, 244 and re-centralization 4, 27, 71,73,76,96

284 Degrees of freedom 185,198, 211,241 Deng Xiaoping 138-9,146 Deng Whirlwind 19, 36, 146 Denationalization 238, 240, 244 in a broad sense 238, 240 in a narrow sense 238, 240 Developing economy 1-3,30-1, 45-6, 55, 65-6, 163, 205-6, 222-5, 242 countries 8-10,51,171,179 low-income 9 middle-income 9 socialist 3,30-1,46,65,2225,242 Development drive 72, 106 Dickey-Fuller test (DF) 192, 210-1,214 augmented- (ADF) 192-3, 196-7,211,214 Disequilibrium 3, 5, 38-9, 42, 188,192-3,201,203,207, 216,223 adjustment behaviour 3, 5, 39, 203, 207, 223 adjustment mechanism 188, 192-3,203 Distributive barrier 4-5, 35, 47, 201,204,222-3,225,2289, 242-3 theory 206,221 Domestic loans 77, 84 Dual subordination 110, 228 Dual track economy 61 price system 61 Dummy variables 59,197-8 Duplication of construction 227, 232, 243 Eastern Europe 1, 29, 67, 223, 230,233,238 Eckstein's theory 58,112

Index Economic reform 8, 11, 15, 63, 73, 76, 94, 142, 235 Effective energy supply 28, 47, 162, 166, 174, 179, 181, 183-7 Eigenvalue 198-9,215,219,257 Endogenous cycle approach 64-5 Energy commercial 10,163-4, 167 constraints, and shortage 7, 11,28,46-9,88,96,10910,113, 162,164,166, 174-5,183-7,230-1,241, 244 consumption, see also Effective energy supply 10,27,166,169-71,183-7, 195 intensity 169-71 modern 162-3, 178 rationing quota 164 traditional 162-3 England 173 Engle-Granger two-step estimator, and approach 214 Enterprise contract responsibility system 102 Equilibrium business cycle approach 63-4 Error correction, see also Cointegrtion approach 2,20, 184, 188 mechanism 22, 24, 48, 18890,216,222,242,250,257 model 23,68,192,213,216, 224, 242-3 representation 188, 192, 216 Excess kurtosis 196-7 Exogeneity strong 217-8,254 super 195,219-20, weak 22,30,44,187, 189, 195,200,217-9,231,254

Index Expansion drive 3, 7, 28-9, 31-4, 38, 42, 69, 86, 105, 108, 110-1,225,227-9 Factor proportions 118, 131, 152 Factorization 217-9 Fallacy of composition 40, 69 Feedback 39, 69, 194, 206-7, 223-4 Till the gap' 90,96 'Five small industries' 101 Five-year plans 52 first to eighth 75, 106, 167, 250 Foreign investment 24-5, 144 loans 77,84 'Foreign Leap Forward' 19, 36, 140 Formal procedure versus real practice 79,81 Former Soviet Union 1, 29, 45, 73, 98-100, 122-4, 163, 166,180,223,238,247-8 Four Modernizations 19,138 Fundamental tension 5, 7, 15 Gaussian asymptotic distribution 219 asymptotic theory 202 full-information maximum likelihood (FIML) cointegration procedure 194, 198 General to specific 20, 22 Gestation lags 41,233,243 Goodwin's growth cycle model 30-1,68,70 'Grain bag responsibility system' 156 Grain output per capita (GRNPC) 7, 126-7, 131, 142, 195, 198,206 Granger 28, 184,254

285

approach 184 causal analysis 184-5 Causality, and test 28,185-7, 231 see also Strong exogeneity Graphic test 198,200 Great Leap Forward 10, 19, 35, 38,59,74, 131-3,167, 177, 197,253 great famine 10 Growth cycle 11,13,20 framework 6, 20, 29-31, 221 model 3,70 theory 6-7 see also Goodwin's growth cycle model Hannan-Quinn information criteria 257 Hard budget constraint 4, 32, 239 Heavy industry-oriented development strategy 27, 51-2,54,61-2, 117 Heteroscedasticity test 205, 251 Homogeneity 198,202 Hungarian School 3-4, 6, 29-31, 38,42,48,55-6,69-70,221 India 51, 122-4, 128, 167, 170-1, 179-80,256 Indonesia 170-1 Industrial and Commercial Bank of China 86 Industrial concentration 233, 249 Inference 183, 189-90, 196, 202, 214,217,219 In-plan prices 171-2 Input-output relations 6, 66, 112, 124, 224 Integrated of order 191,210 Interest conflicts 71,222,243 rigidity 63, 174

286

vested 89, 174 Intersectoral distributive policy 55 Intertemporal inconsistency 3 Invariant, and invariance 4, 191, 195,212,219-20 Investment behaviour 2, 4, 7, 29, 66,221-2,225,242 aggregate 2,221-2,225,242 Investment commitments 30 engagement 35-6, 42 starts 5,39-41,43 vintages 39 Investment cycle first to seventh 17,19,13155 theory 30-1,205,221 Investment growth rate cycle 7, 17,188,242 Investment fluctuation 2,15-8, 38,58,112-3 Investment hunger 1, 3, 7, 15, 24, 27,29,31-4,38,42,62-3, 67,69,71-2, 144,166,204, 221-2,225-8,232,239, 242-3, 248 of the central government 868, 109-11 of local governments 106-11 of state-owned enterprises 102-5,110-1 of township and village enterprises 24 Investment plans 85, 97, 105, 244 Investment ratio 1, 6, 17-8, 35-7, 69, 113,129-55,177,246, 254 aggregate 1, 17-8, 129-55, 246 Investment/saving relation 3 Institutional

Index changes 8,26,131 pressure and incentives 5, 20 Interest rates 24, 52, 61-2, 78, 90-5 nominal 90-1,94 market 52 real 24,90-5 preferential 240 regulated, or administered 92, 103 IOUs 55, 144, 147 Jarque-Bera test, and statistics 197, 205 Johansen's procedure 5, 194-5, 198-9, 214,257 LR tests 198,200-1 Kaleckian, or Kalecki's distributive barrier theory, see also Distributive barrier 221 growth theory 2-3, 6, 30-1, 45-6,51,55,70 labour constraint equation 48, 187 model 2, 46 profit formation equation 55 'Labour accumulation' 118,124 Lagrange multiplier test 205 Learning from data 7, 223 'Leaving gaps' 99 Lending outside the credit plan 89-90 Likelihood function 198 ratio (LR) test 198,215 Limited liability 29,34 Lin Biao's fall 138 Linear restriction 198, 200-1, 219 Local openness 239

Index Locality's evasion 82 Long-run equilibrium relationship See Attractor, Cointegration, Co-movement LSE methodology, and framework 20-3, 189, 193 Lucas critique 68 monetary misperception model 64 Philips curve model 64 MacKinnon critical values 196, 211,258 Macroeconomic adjustment 28, 48,57,70, 112, 114, 118, 124,130,149, 156,249 Management accountability 104 Mandatory credit plan 85-6 investment plan 85-6, 97 loans 84-5 plans 76,78, 101,252 procurement 143 Mao, or Mao Zedong 130, 139 Marginal density 217 distributions 194-5 model 23, 194,218,220 process 195,217-9,220 Market component of the economy 8, 12,96,102,204,241 criteria 2, 15 co-ordination failure 222 reinforcing decentralization 239 Matching funds 78, 87, 109, 226 Material input level 128 input ratio 121-2, 125, 127, 150, 152, 154

287

supply system 72, 78, 97101,253 Maximal eigenvalue and trace See Eigenvalue Ministry of Finance 85 Misspecification, and test 196-7, 205,251 Modelling strategy, and approach 7,22-3,29,31,189,192-4 Motives of bureaucrats, and governments 33, 109-10, 227 Moving average 5, 22, 39-40, 42, 56,190,195,197,207,256 Multi-layer, multi-regional system 107 Nanjing Yangtse River Bridge 181 National income, and used 2-3, 6, 17,27,36-7,45,47-8,5051,54,63, 112,114, 117, 124, 127-30, 137, 141-3, 150,159,161, 169,224, 229, 246, 249 National People's Congress 146, 206, 250 National priority projects, and programme of 78, 86-8, 90,109,252 Negative feedback See Feedback New Keynesian approach 64 'Nodding approval' 80 Non-bank financial institutions 89-90, 96 Nonexperimental data 20 Non-stationary variables, time series, and process 5, 22, 68,184-5, 188-93, 198,210 Norm (path or normal state) 5, 22-4,56, 174, 189-91,203, 206-7, 223, 242 see also 'Control by norm'

288

Norms (of project approval) 79, 82-3,110 see also 'Above-norm' projects Normal growth path 22, 55-6 Normality test 196-7,205 One-step-ahead Chow test See Chow test One-step residuals 28, 202-3 'Open door' policy 144, 181 Ordinary least squares (OLS) 190,202,210-2,214,258 Over-investment, and expansion 8, 24, 93, 96, 144, 204, 226,239,241-2 Parameter constancy 202-4, 206, 220, 224, 243 Parameterization 20,202 People's Bank (of China) 84-6, 89-90,96, 104,240,258, see also Central Bank People's Daily 11, 26, 49, 83, 88, 115,126,141, 144,147, 167,175,206,234,250, 256-7 Perron critical values 196 Perron's innovational outlier model 193 additive outlier model 193, 197 Philips curve 64 Planned component of the economy 8,96,204,227, 241, see also Dual track economy Planner's rationality 3, 243 reaction or response function 44,58 Policy banks

Index State Development Bank 240, 259 Agricultural Development Bank 240,259 Export Import Bank 259 Policy trade-off 113,130,187, 231 Political economy 2, 18, 20-2, 63,71,117,156,223,241 Political campaigns 19, 26, 38, 241 'Politics in command' 131 Positive-predictive approach 238 Post-Cultural Revolution Advance 19, 136 Power-consuming equipment 175-6,256 Power-generating equipment 176-7,256 Pre-reform, and post-reform 4, 811,15,62,67,72,75,82, 96, 108-9, 122, 197,206, 227,229,241,248,251 Pre-war China 122-4, 128 Privatization 238 Probabilistic information 20 reduction 20,22 structural model 20 structure of observed data 20, 223 Procurement contract 143 price 55,134, 149, 161,230 Project approval 34, 71-2, 78-82, 105,110,164,228 Railway traffic density 180 Ramsey's test for specification error 205 Rational scale of investment 1, 223 Real business cycle

Index approach 31,63-4 models 64, 68 theory 30 Real investment level 2-3, 5, 15, 113,185,198,201,203, 206, 222-4, 242 Real value added per labourer (RVAPL) 112,122,125, 127-8, 132, 135-6, 138-9, 142, 146, 149-50, 152, 1601 'Recovery period' 133, 137, 167 Recursive estimation 28, 189, 202 'Reference rate' 90 Reform cycle theory 31,59, 70 Reimers' small-sample-correction 198-9,257 Rent-seeking 12, 32, 63, 93, 172, 204 Reparameterization 202, 218 Representative planner 3, 5, 39, 69 Resident registration system 1234 Resource-constrained (RC), versus demand-constrained (DC) 37,66 Resource endowments 163 Retrenchment 7, 17-9, 67, 95-6, 71,73,82,95-6, 145-6, 175,178, 186,204,221, 225,229,231-2,234,242, 254 Schwarz information criteria 257 Self-imposed restraint 2, 20, 28, 33, 229 Self-raised funds, and investment 84 Self-sufficient, or sufficiency 10, 148-9, 173 Shared authority 239

289

Shortage index 58, 127 signals 4, 30, 38, 42, 44, 223 Simultaneity 189 Skewness 196 Social relationship 32 'Socialist High Tide' 19,131 ' Socialist market economy' 146 SOE autonomy 104 Soft administrative pricing 32, 227 budget constraint 3-4, 7, 29, 31-5,66,69,72, 102,105, 110, 170-1,225,227,235, 237-8, 240, 244 credit 32,108,227 subsidies 32, 235 taxation 32,227 'Soft landing' 146,156,230 Solow method 57 Special economic development zones 147 Spurious regression 184-5,190 Standard errors, or deviation 28, 58,91,196,199,202-3, 205 State budget 84,226 State compulsory procurement 47 State Council 74, 79, 82-5, 93, 98, 138, 146, 148 State Investment Corporation (SIC) 79-80 State Planning Commission (SPC) 27, 74, 79-83, 85, 90, 97-8, 175,206 State investment system 2, 5, 15, 21,27,71-2,75,79,86, 110,206,221,223,232-5, 238-9, 242, 244 State-owned enterprises (SOEs) 32, 72, 75, 93-5, 101-2,

290

104-6,108-110,164, 171, 206, 227-8, 233-240 Stationarity 210 Stochastic analysis, or approach 30, 64 processes 210 Stock exchanges, of Shanghai and Shenzhen 89 Structural break test See Chow test Structural inertia 15 rigidity 2, 15 unbalance 9 Stylized facts 10, 64-6, 68, 131, 149,156,221,225 Subsistence urge 123-4 Supply constraints 2, 6-7, 11, 24, 47, 111, 124-5, 127 Technical updating (and transformation) investment 74-5, 77-82, 84, 233, 243 Ten-Year Development Programme 146 Ten-Year Plan 138-9 Terms of trade, agriculture's 467, 54-5, 70, 129, 143, 230 Thailand 170-1 Three-year readjustment programme 19, 145 Total factor productivity 10-1, 57,61,112,127 Township and village enterprises (TVEs) 11, 24, 141,143-4,164,238,241, 244 industry 13 Transformation of economic systems 8

Index Transport bottleneck 113, 166, 179,181,183-4,244 t-statistics 58,211 Two-way interaction, and dependence 5-6,28,30,70, 166, 187,224,231 Uncertainty 33 Unfinished construction 42-3, 69 Unit root test 210-1,213-5,257, see also Dichey-Fuller test Unitary elasticity 198 United States 163,166,180 Univariate mean, moving average, or norm 5, 22, 40, 56, 69, 207, 223, 256 Unrestricted vector autoregressive (UVAR) representation 20,23,189,193,195-7, 201-2,254 Urban Credit Cooperatives 89, 240 Variances 189-93,255 'Variation free' 194, 218-9 Walras' economics 22, 65 Well-specified 20, 23, 189, 197, 201-2, White noise process 192, 210 White's test for heteroscedasticity 205 Working capital 84, 90-1, 105, 130,240,252,258-9 Wu-Hausman tests 220 'Young intellectuals' 114, 141 ZhouEnlai 134,138 ZhuDe 139

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