Advances in Input-Output Analysis: Technology, Planning, and Development

ADVANCES IN INPUT-OUTPUT ANALYSIS

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ADVANCES IN INPUT-OUTPUT ANALYSIS Technology, Planning, and Development

Edited by

WILLIAM PETERSON

New York Oxford OXFORD UNIVERSITY PRESS 1991

Oxford University Press Oxford New York Toronto Delhi Bombay Calcutta Madras Karachi Pctaling Jaya Singapore Hong Kong Tokyo Nairobi Dar es Salaam Cape Town Melbourne Auckland and Associated Companies in Berlin Ibadan

Copyrighl (c) 1991 by Oxford University Press, Inc. Published by Oxford University Press, Inc., 200 Madison Avenue, New York, New York 10016 Oxford is a registered trademark of Oxford University Press All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Oxford University Press. Library of Congress Cataloging-in-Publication Data Advances in input output analysis : technology, planning, and development / edited by William Peterson. p. cm. Selected papers from the Eighth International Conference on InputOutput Techniques held in August 1986 in Sapporo. Japan. Includes index. ISBN 0-19-506236-1 1. Input -output analysis —Congresses. I. Peterson, William. II. Internationa] Conference on Input-Output Techniques (8th : 1986: Sapporo-shi, Japan) HB142.A28 1991 658.5'03- dc20 90-40536

1 3579 864 2

Printed in the United States of America on acid-free paper

Preface The chapters in this volume have been selected by the Program Committee from among papers presented at the Eighth International Conference on Input-Output Techniques, held in August 1986 in Sapporo, Japan. Although the task of choosing a small number of presentations from the large number of submissions was not an easy one, the choice was designed to reflect both the wide geographical spread of inputoutput techniques as a tool for economic analysis and planning and the range of economic issues to which they have recently been applied. Thus the volume includes applications to both market and socialist economies, to economies at very different levels of development, and to problems of social policy as well as to problems that are more conventionally economic in nature. The choice also reflects one of the major features of input-output analysis as a branch of economics—the positive interaction between the development of the underlying theoretical framework and the systematic collection and organization of the statistical data needed to ensure that the theory can be applied to the problems for which it was designed. Some of the chapters here represent that fusion of theory and data that is essential for progress in applied economics. Others either put forward new methods of analysis, and hence suggest areas where further improvements in our statistical knowledge are likely to be valuable, or describe the continuing efforts of national statistical offices to refine the data sources that underly our knowledge. The Conference was made possible by the generous financial and organizational help of the sponsors, the United Nations Industrial Development Organization (UNIDO), and the host, the University of Hokkaido. I would like, on behalf of all those who attended the Conference, to thank them both for their efforts. 1 would also like to thank the staff of Oxford University Press for their help with this volume. The Conference coincided with an important milestone in the brief history of input-output analysis, the fiftieth anniversary of the publication in August 1936 of Wassily Leontief s first paper on the topic in the Review of Economic Statistics. In addition the organizers succeeded in ensuring that the Conference coincided with an equally important occasion, Professor Leontief s eightieth birthday. All those present will retain warm memories of the party that was held in honor of this event and marked the close of the Conference. The papers included in this volume show how much has been achieved by those working in the tradition Professor Leontief founded, and the authors join in hoping that he will accept the dedication of this volume as a very belated birthday present. Cambridge, England July 1990

W. P.

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Contents Contributors, ix 1. Introduction, 3 WILLIAM PETERSON

I THEORETICAL DEVELOPMENTS IN INPUT-OUTPUT ANALYSIS 2. On the Mathematical Transformation of Input-Output Matrices over Time or Space, 17 A. GHOSH 3. The Absolute and Relative Joint Stability of Input-Output Production and Allocation Coefficients, 25 CHIA-YON CHEN AND ADAM ROSE

II THE COMPILATION OF INPUT -OUTPUT TABLES 4. Considerations on Revising Input-Output Concepts in the System of National Accounts and the European System of Integrated Economic Accounts, 39 CARSTEN STAHMER

5. The Simultaneous Compilation of Current Price and Deflated Input-Output Tables, 53 S. DE BOER AND G. BROESTERHUIZEN

III INPUT-OUTPUT AND THE ANALYSIS OF TECHNICAL PROGRESS 6. Technical Progress in an Input- Output Framework with Special Reference to Japan's High-Technology Industries, 69 SHUNTARO SHISHIDO, KIYO HARADA, AND YUJI MATSUMURA

7. Explaining Cost Differences Between Germany, Japan, and the United States, 108 SHINICHIRO NAKAMURA

8. Price Behavior with Vintage Capital, 121 P. N. MATHUR

9. Input-Output, Technical Change, and Long Waves, 137 E. FONTELA AND A. PULIDO

10. Private-Led Technical Change in Prewar Japanese Agriculture, 149 SHIN NAGATA

viii

Contents

IV INPUT-OUTPUT AND THE ANALYSIS OF SOCIALIST ECONOMIES 11. The SYRENA (SYnthesis of REgional and NAtional Models) Model Complex, 161 A. G. GRANBERG, V. E. SELIVERSTOV, V. I. SUSLOV, AND A. G. RUBINSHTEIN

12. A Planning Scheme Combining Input-Output Techniques with a Consumer Demand Analysis: A Concept and Preliminary Estimates for Poland, 173 LEON PODKAMINER, BOHDAN WYZNIKIEWICZ, AND LESZEK ZIENKOWSKI

13. Some Macroeconomic Features of the Hungarian Economy Since 1970, 187 !,. HAI.PERN AND G. MOLNAR

V INPUT-OUTPUT AND DEVELOPING COUNTRIES 14. Key Sectors, Comparative Advantage, and Internationa! Shifts in Employment: A Case Study for Indonesia, South Korea, Mexico, and Pakistan and Their Trade Relations with the European Community, 199 JACOB KOL

15. Import Substitution and Changes in Structural Interdependence: A Decomposition Analysis, 211 D. P. Pal VI THE ANALYSIS OF SOCIAL AND ENVIRONMENTAL PROBLEMS 16. A Long-Term Projection of the Industrial and Environmental Aspects of the Hokkaido Economy: 1985-2005, 223 FUMIMASA HAMADA

17. An Application of Input-Output Techniques to Labor Force Allocation in the Health and Medical and the Social Welfare Service Sectors, 236 YOSIIIKO KIDO

Index, 244

Contributors

S. DE BOER

YOSHIKO KIDO

Central Bureau of Statistics Voorburg, The Netherlands

Social Development Research Institute Tokyo, Japan

G. BROESTERHUIZEN

Central Bureau of Statistics Voorburg, The Netherlands CHIA-YON CHEN

Department of Mining and Petroleum Engineering National Cheng Kung University Tainan, Taiwan E. FONTELA

Department of Economics University of Geneva Geneva, Switzerland A. GHOSH Department of Economics Jadavpur University Calcutta, India

JACOB KOL Department of Economics Erasmus University Rotterdam, The Netherlands P. N. MATHUR

Department of Economics University College of Wales Aberystwyth, Wales YUJI MATSUMURA

Department of Economics University of Tsukuba Tsukuba, Japan G. MOLNAR

Institute of Economics Budapest, Hungary

A. G. GRANBERG

U.S.S.R. Academy of Sciences Novosibirsk, Soviet Union L. HALPERN

Institute of Economics Budapest, Hungary FUMIMASA HAMADA

Department of Economics Keio University Tokyo, Japan KIYO HARADA

Foundation for Advancement of International Sciences Tokyo, Japan

SHIN NAGATA

Department of Economics University of Hokkaido Sapporo, Hokkaido, Japan SHINICHIRO NAKAMURA

Department of Economics Waseda University Tokyo, Japan o. p. PAL Department of Economics University of Kalyani Kalyani, India

ADVANCES IN INPUT-OUTPUT ANALYSIS

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1 Introduction WILLIAM PETERSON

The papers presented as chapters in this volume have been selected by the Program Committee from among those presented at the Eighth International Conference on Input-Output Techniques, held in August 1986 in Sapporo, Japan. The Conference was made possible by the United Nations Industrial Development Organization (UNIDO), which gave financial and organizational help, and the University of Hokkaido, which hosted the event. On behalf of all those who attended the Conference I would like to thank both for their efforts. It is clear from the wide range of papers presented at the Conference and from the worldwide home bases of the participants that input-output analysis has become an essential tool of applied economics. Yet it would be misleading in this introduction to ignore the fact that, particularly in the developed market economies, the combination of the evolution of macroeconomic theory since the early 1970s and the methodological insistence that models must be based on analysis of the optimizing behavior of rational agents threatens to distance input-output analysis from "mainstream" economics. Since I believe that both of these fields would lose from such a development, it seems appropriate here both to discuss factors that have contributed to it and to suggest how input-output economics can learn from, and also contribute to, the more general development of empirical economics. The field in which the danger of divorce between the input-output tradition and mainstream economics is most acute and most clearly visible is one that has occupied a number of researchers in input-output analysis for many years: the integration of a disaggregated input-output-based model of production and employment with a macroeconomic model explaining the evolution of final demand. These integrated models have been seen as the appropriate tool for detailed business forecasting, for the investigation of economic policy in the medium and long term, and for the analysis of changes in industrial structure brought about by exogenous supply shocks such as natural resource discoveries. Improvements in computing facilities and data availability have allowed the technical problems involved in constructing such models to be largely overcome, and the models are now in use on a regular basis in several developed countries (see, for example, Barker and Peterson [1987] for the United Kingdom, or Almon, Buckler, Horwitz, and Reimbold [1974] for the United States). Yet, as suggested here, two important components of these models are at

4

Introduction

variance with the newly established orthodoxies of economic theory. First, the recent theoretical revolution in macroeconomics, and in particular the move away from a traditional Keynesian framework in which the level of activity is determined by effective demand (itself a function of a relatively small number of "flow" variables and policy instruments), has enlarged enormously the range and complexity of the behavioral relationships that must be modeled. In particular it is clear that both "forward-looking" variables (such as asset prices and exchange rates) and "stockflow" identities (such as the links between current sectoral budget deficits and financial accumulation) must be analyzed if such models are to generate plausible long-run forecasts of macroeconomic developments and (conditional on these macroeconomic forecasts) of the structural evolution of industry. The difficulty is, of course, that such an analysis is essentially part of the research program of macroeconomics rather than of input-output analysis. In addition, a substantial and influential school of macroeconomists (of which Lucas and Sargent [1979] are representative) believes that, in future, macroeconomic models will become both much smaller and more heavily based on purely theoretical reasoning. Such an evolution, which has already started in the United States (Kydland and Prescott, 1982), clearly will make it harder to integrate the industrial detail provided by inputoutput analysis. Second, and perhaps partly for ideological reasons (since the phenomenon is more noticeable among English-speaking economists), there has been renewed emphasis on the importance of reconciling empirical observation with the fundamental assumption made by economists in the free-market (as opposed to the socialist) tradition, that of rational optimizing behavior. This assumption fits ill with the key assumption of conventional input-output analysis—that producing firms will be prepared to supply whatever is demanded at a price that reflects average production costs (which include an allowance for "normal" profits). The perfectly elastic supply curve which this type of behavior implies can be reconciled with profit maximization only if the economy being modeled satisfies the conditions of the nonsubstitution theorem (Koopmans, 1951), in particular constant returns to scale and a single primary factor of production. If this is not the case, then not only does it become necessary to model the behavior of individual firms (which will of course depend on the competitive structure of their markets), but it also becomes impossible to argue that the industry, rather than the decision-making enterprise, is the appropriate unit for analysis. However, assuming that the behavior of individual optimizing agents can be modeled by assuming a single representative firm (whose output is aggregate gross domestic product) is as least as unsatisfactory as building models that lack foundations based on the postulate of rational maximization. Thus if the task of reconstructing economics on this methodologically purist basis is to proceed, it will at some stage have to attack the problem of modeling market relationships between producers. Here input-output analysis clearly has a large contribution to make, since it provides the statistical framework within which the interactions between enterprises through the supply of intermediate goods can be studied. Indeed the efforts made to date to model economic behavior in terms of the actions of enterprises (for example the work of Eliasson [1985] on the Swedish economy) have relied heavily on input-output data for their implementation. A second field in which the significance attached by input-output analysis to

Introduction

5

industrial interdependence and diversity carries an important lesson for conventional economics is that concerned with the interrelation of macroeconomics and industrial organization. It is obvious that the highly aggregate models favored by the "new classical macroeconomics" can only be a starting point for analysis, and that the perfect-competition assumption that these models embody is in many respects extremely misleading. A number of recent papers (for example, Hall [1986] and Bils [1987]) have employed disaggregated industry data to argue that important macroeconomic phenomena, such as the cyclical behavior of profit margins, cannot easily be rationalized in terms of a perfectly competitive model. The input-output tradition, with its stress on modeling technology in terms of the production relationship seen by the individual producer (that between gross output and all distinguishable inputs) rather than in terms of a net output measure constructed after the event by statisticians, is clearly relevant to such research. The fact that, despite these problems of consonance with economic theory, input-output analysis is now a well-established branch of economics means that research has moved away from trying to refine the theoretical structure of inputoutput models toward extending the range of applications and improving their realism and accuracy. Thus only two chapters with primarily theoretical contents are included in this volume, and both of these are directed explicitly at practical problems. The first, by A. Ghosh (Chapter 2), addresses an issue that has long been a concern of those engaged in the design of planning or forecasting models based on an inputoutput table. The inevitable delays in the collection and processing of production statistics mean that such models are typically based on an input-output table that is already several years out of data. Further, such obsolete information must also be used to forecast over a horizon that in some applications will extend well into the next century. Thus the need for simple techniques that can be used to extrapolate inputoutput coefficients is a long-standing one. The most common solution to this problem, the RAS technique (Bacharach, 1970), which assumes the original and extrapolated matrices to be biproportional to each other, has the advantage of computational simplicity and minimal data requirements. Professor Ghosh examines the conditions under which an alternative solution, based on the use of similarity transforms, is useful. Clearly the condition that two input-output systems should be similar is a stringent one. For example, it is easy to see that if one system is more productive in the sense of requiring less intermediate input per unit of output for any commodity, then (because the maximal eigenvalue of the more productive system is higher) similarity is ruled out. This means that the second approach is more likely to be useful in interregional comparisons or comparisons between countries at a similar level of development than in comparisons over time. However, if the basic model is extended to allow for linear error components in the technology matrices, an empirical measure of similarity based on the goodness of fit of a regression of intermediate demand on gross output can be derived. It is important to stress that the purpose of such a regression is not the indirect computation of the input-output coefficients, since it is well known (Arrow and Hoffenberg, 1959) that such coefficients cannot be estimated reliably from highly collinear time series. The fact that individual coefficients cannot be estimated, however, does not rule out computation of the similarity indices suggested here, just as multicollinearity in a statistical problem does not rule out the computation of a subset of regression functions.

6

Introduction

The second theoretical chapter, by Chia-yon Chen and Adam Rose (Chapter 3), explores a problem that has arisen in the use of input-output methods in planning, especially under conditions of physical shortage. Input-output systems are usually treated as demand driven in the sense that the composition and level of final output are determined elsewhere in the model; the activity levels in the various production sectors are adjusted to ensure that supply is adequate to meet demand. In such a framework the nonsubstitution theorem provides a justification for the key assumption that production coefficients are invariant with respect to changes in the scale of output. Under certain circumstances, however, it may be appropriate to assume instead that the allocation of output across consuming sectors is stable, possibly because of bureaucratic inertia in the planning process, and to look at the behavior of a supply-oriented system in which output levels are determined jointly by the accepted allocation rules and the availability of a small number of "key" inputs in short supply. The obvious difficulty with this approach is that it may imply a violation of the technological constraints represented by the conventional production coefficients. The aim of Chapter 3 is to analyze the practical importance of this difficulty by relating the changes in the two types of coefficients to each other, thus establishing the extent of technological substitution necessary for a system trying to cope with (possibly short-term) physical shortages by simple allocation rules. In their empirical application, Professors Chen and Rose consider the effects on the Taiwanese economy of a 50 percent cut in the availability of aluminum, under the assumption of proportional supply rationing, and show that the implied changes in technology for the majority of industries (except for metal products, which is relatively aluminum intensive) are comparatively small. The two chapters on the statistical compilation of input-output tables that follow (Chapters 4 and 5) both show the importance of integrating the analysis of productive relationships into the framework of national accounts. Such an approach is clearly essential if input-output data are to be used as the basis for efforts at disaggregated modeling of the national economy, either for forecasting and policy analysis or for planning purposes. However, such integration also makes it possible to focus on tracing the source of errors in national accounts and making needed improvements in reporting procedures. The development of international conventions for the compilation of inputoutput tables and national accounts statistics has provided applied economists with a huge amount of data on which to base the estimation of key relationships and the testing of alternative hypotheses. It has also offered politicians the scope for comparisons that often are regrettably misleading and chauvinistic. Carsten Stahmer in Chapter 4 addresses problems that have arisen in revising two of the most important of these statistical conventions, the U.N. System of National Accounts (the SNA) and the European System of Accounts (the ESA). Both of these are based on a tabular presentation of the various transactions in the economy that draws directly on the concepts of input-output analysis. One important proposal put forward by Professor Stahmer concerns the appropriate statistical unit for which input-output tables should be constructed. Historically such tables have been based on the concept of the establishment, with establishments producing similar principal products being classified to the same industry. Although this approach seems to fit with the idea that input-output tables

Introduction

1

express the fundamental technological relationships of the economy, it is at variance both with the increasing importance to the economy of conglomerate firms and with the tendency, already discussed, of economists to analyze the behavior of the economy in terms of the choices of the agents who actually make decisions. Both of these factors would suggest that input-output tables should move toward employing the enterprise as the fundamental statistical unit, and this is the course of action recommended in Chapter 4. Clearly such a move would be likely to increase the importance of joint production in the input-output representation of the economy, but this is a problem already built into the commodity-industry distinction employed by the existing SNA. Chapter 5, by S. de Boer and G. Broesterhuizen, describes the process of constructing input-output tables in the Netherlands, where the integration of inputoutput concepts and national accounts is well established. The major advance set out here is the simultaneous use of a wide range of sources to produce consistent measures of both the volume and value of transactions and the prices at which these are carried out. In theory these three measures are related by the equation value = volume x price, but it is well known that real-world data derived from a wide range of inconsistent sources require substantial modification before this equation is satisfied. Elimination of this type of discrepancy at the stage of data collection is an essential step if economists are to ensure that their empirical findings are robust to the choice of alternative data sources. Without such a step it is hard to see how they can achieve any progress toward a body of generally accepted propositions in economics. Five of the chapters in this volume apply concepts and techniques drawn from input-output analysis to the problems of measuring technical change across countries or through time, and of evaluating the impact of such change on the economy. This is an application where what I have argued to be the central feature of input-output analysis—the use of a statistical framework sufficiently flexible to represent both the individual production process and the national aggregate—is peculiarly valuble. Given sufficiently detailed data, observed differences in input-output coefficients can be identified in principle with the effects of particular innovations; at the same time, as was first shown by Domar (1961), macroeconomic measures of differences in total factor productivity can also be interpreted as weighted averages of all the individual coefficient changes. These weights sum to more than 1, reflecting the fact that productivity improvement in industries producing intermediate or capital goods has a favorable impact on all "downstream" activities (Peterson, 1978). In Chapter 6, Shuntaro Shishido, Kiyo Harada, and Yuji Matsumura apply this methodology to the analysis of high-technology industry in Japan. In doing so they are fortunate to be able to use the extremely detailed input-output tables, distinguishing over 500 intermediate inputs, that have been published by the Government of Japan. At the level of disaggregation they studied, a conventional examination of interindustry differences in the productivity of primary inputs would be difficult to interpret, since each producing sector will rely heavily on intermediate rather than primary inputs. As new industries develop and grow, their reliance on outside sources for components and specialized services changes, and although national accounts statisticians attempt to allow for such structural changes in constructing measures of net output, they may not always do so adequately. The work in Chapter 6 shows that during the 1970s most observed changes in input-output coefficients can be accounted for by two factors: differing rates of total factor productivity growth in the individual sectors of the economy and the response

8

Introduction

of firms to price changes that arise largely as a result of such differential technical change. As is well known, these factors can be identified with the S and R components, respectively, of the RAS method for updating input-output coefficients. By using expert opinion on the probable evolution of factor productivity in individual sectors of the economy, it becomes possible to forecast the technological structure of the Japanese economy in the year 2000 and the implication of these changes for employment and international trade. In Chapter 7, Shinichiro Nakamura applies a similar methodology to the comparison of productivity levels and changes in the United States, Japan, and West Germany. As one might expect, the need to ensure cross-country data comparability means that the high level of disaggregation used in the previous chapter must be sacrificed. The aim of Chapter 7 is an important one—to establish how far the comparative cost differences that are largely responsible for determining the direction of international trade reflect differences in productivity and how far they reflect differences in the rewards paid to primary factors. Clearly, comparisons of this kind cannot be based on prevailing exchange rates, and purchasing power parity indices are used instead. Professor Nakamura's findings show that, for both Japan and Germany, the industries with comparatively low unit costs were those for which total factor productivity was relatively high, while the United States was competitive in those industries in which input prices were low. Another finding is that Japan still possesses some characteristics of a "dual" economy, since Japanese industry falls clearly into "high-productivity" and "low-productivity" sectors. The value of the conceptual framework provided by the input-output model of technology, with its focus on aggregate productivity measures as summarizing the combination of a number of distinct production processes, is also shown in the chapter by P. N. Mathur (Chapter 8). In a pioneering study, Carter (1953) showed how the evolution of technology in the U.S. cotton industry could be modeled as resulting from the gradual introduction of new plant. This initial insight into the role played by different vintages of equipment has become an essential building block in many models of productivity growth. Unfortunately, only in very rare cases are economists in a position to apply this model of innovation and its effects directly. Since it is exceptional for production techniques to evolve in such a way that at any particular time only one type of capital good is purchased by all producers, even complete information on the composition of plant in terms of vintages would not be sufficient to characterize the range of attainable input coefficients. In addition, the information about utilization that is needed can only be provided on the basis of returns by individual firms, and hence is usually inaccessible to economists because of confidentiality rules. In these circumstances the investigator is thrown back on indirect tests of the hypothesis that technical change is embodied in particular vintages of equipment. The procedure followed by Professor Mathur is to assess the rate of technical change through time by evaluating how the profitability of individual sectors would have changed if the observed changes in input prices had occurred without any alteration in the input-output coefficients for the industry in question. If technical change were occurring in a competitive industry we would expect that the computed profits accruing to a particular technology would decline as new and more efficient plants came into operation. Indeed, this pattern characterized approximately half of the U.S. industries included in Professor Mathur's study.

Introduction

9

Although these figures are illuminating, it is important not to claim too much for the method of analysis. In the first place, since what is observed is the average inputoutput coefficient rather than that relating to a specific piece of equipment, the method cannot distinguish between decreases in the average coefficient that are directly related to the introduction of new plant and decreases that occur as firms gain experience and improve the efficiency with which existing plant is utilized. Second, although the study finds that there is some correlation between industries with low rates of technical obsolescence (in the sense that old technology remains profitable over a long period) and high concentration ratios, the correlation may arise because it is easier to maintain stable oligopolies and limit entry in "mature" industries where the technology is well known and innovations are rare. Chapter 9, by E. Fontela and A. Pulido, adopts a Schumpeterian approach to the study of technical change and its role in determining the "long waves" of economic growth. The fact that input-output analysis offers a methodology for looking both at individual innovations and at their influence on economic aggregates means that it can in principle be used to quantify such an approach. However, there are substantial problems in applying such concepts in practice, as Fontela and Pulido point out. In particular the dissemination of new technology is surprisingly slow, even when this technology offers substantial cost advantages, and this means that distinguishing changes in coefficients that arise because of innovations from those that reflect changes in product mix or, regrettably, in statistical conventions is extremely difficult. Furthermore, in countries where input-output data are collected within a national accounts framework, the conventional input-output matrix is only one component of the system of accounts, and innovations may show up in the consumption or investment converters as well as in the input coefficients. Nonetheless, the productivity indices for individual French industries quoted in Chapter 9 do provide some support for Schumpeterian theories of the long wave, which predict that the bunching of innovations will lead deviations of productivity growth from trend to be strongly correlated across industries. The final chapter in this section, that by Shin Nagata (Chapter 10), represents an application to a historical problem, the prewar development of Japanese agriculture, which is of considerable relevance to developing countries today. Professor Nagata applies mixed estimation techniques, exploiting both cross-section and time-series data, to the problem of estimating a production function. An important feature of his analysis is the significance attached to intermediate inputs, both purchased inputs such as chemical fertilizers and "productivity-increasing activities" undertaken by individual farmers, in explaining the evolution of agricultural productivity during this period. Traditionally, one major application of input-output analysis has been to the construction of disaggregated models of individual national economies. In the countries of the Socialist bloc, and in those developing countries where the government has committed itself to a major role in the organization of production either directly through public ownership of key enterprises or indirectly through administrative controls, such models are seen as an essential part of the planning process, and their behavioral (as opposed to technical) content is often limited. In contrast, economists constructing such models for developed market economies have seen them as more detailed versions of conventional macroeconomic models, with which they have many behavioral relationships in common. Such models are usually

10

Introduction

designed for forecasting and policy analysis exercises as well as for studying the evolution of industrial structure. Although a number of such models were discussed in papers presented at the Conference, for a variety of reasons it has not proved possible to include any of the papers concerned in this volume. Nor is there any example of what has become known as the "computable general equilibrium" approach to economic modeling (as surveyed, for example, by Waelbroeck [1987]), although such models draw heavily on the concepts and data sources associated with input-output analysis. Instead the three chapters in Part IV of this book illustrate some of the uses made of input output techniques in socialist countries. A. G. Granberg and his collaborators report on SYRENA in Chapter 11. SYRENA is an extremely ambitious complex of planning models for the Soviet Union that incorporates both regional and national components. Here also the list of prospective model developments, such as the construction of linked macroeconomic and financial models, shows the flexibility of the inputoutput framework as a basis for more general modeling exercises. Leon Podkaminer, Bohdan Wyznikiewicz, and Leszek Zienkowski in Chapter 12 show how a relatively simple extension of the basic input-output model to incorporate a consumer-demand system in which the composition of consumption is sensitive to relative prices can be employed to analyze some of the critical problems of economic management that have faced Polish planners during the past decade. Their methodology makes possible the assessment of the extent of disequilibrium in consumer markets by comparing "market-clearing" prices for the postulated structure of consumer preferences with the prices that were actually observed. One important contribution of such a study is that it explicitly incorporates both intermarket spillovers in consumption and the "general equilibrium" implications for production and employment of attempts to equilibrate supply and demand. It is thus likely to provide a more accurate assessment of the shortages facing the economy, albeit on a rather aggregated basis, than casual observation of queues and waiting lists. In particular the authors' finding that in 1977 food prices were not disproportionately low is at variance with the assumptions that have lain behind subsequent policy choices. Realistically the authors do not see either a drastic price reform, involving the universal adoption of market-clearing prices, or the adjustment of supply to meet demand at the current administratively set prices as being a feasible or sensible response to the problem. Instead they conduct an exploratory analysis, based on a linear programming model of the Polish economy, to find solutions that are, if not optimal in the conventional sense, at least superior to the actual outcome. Although the model they employ is a static one, by treating fixed capital formation as exogenously given and by regarding the current balance of trade (equivalent to the change in Poland's net foreign assets) as an objective variable the authors can ensure that such an improvement is not achieved at the expense of future consumption levels. The most important of their results is not the quantitative improvement in consumption attainable by moving to a different set of relative prices, but the qualitative finding that their recommendation of a relative fall in food prices is diametrically opposed to the course that policy has actually followed. The final chapter in this section, by L. Halpern and G. Molnar (Chapter 13), again focuses on the problems raised for socialist planners by the discrepancy between administratively imposed and economically appropriate prices. In their application, to the Hungarian economy during the 1970s, the dual prices (costs of production)

Introduction

11

emerging from a closed input-output model are compared with observed prices to assess the extent of overvaluation or undervaluation affecting particular sectors of the economy. Such deviations are extremely significant, both because under the New Economic Mechanism that operated in Hungary after 1968 there were tendencies for overvaluation to be associated with overproduction, and because they may have serious adverse implications for the planners' ability to attain their desired income distribution. The chapter can thus be seen as a "supply-oriented" version of that by Professor Podkaminer and his collaborators. Setting prices incorrectly has adverse effects not because it distorts consumer choice (which is not modeled in their paper) but because it encourages excessive investment and expansion in the sectors concerned. The major contribution that input-output concepts and data have made to the analysis of economic development was reflected both in the large number of Conference participants from developing countries and in the generous sponsorship provided by UNIDO. Out of a large number of papers in this area we selected two that focus in particular on the use of input-output techniques as tools for the analysis of trade patterns and comparative advantage. Jacob Kol (Chapter 14) considers the probable effects on employment in the European Community and a group of (relatively industrialized) developing countries of a balanced increase in trade in manufactures. In this example there is no analogy of the Leontief paradox (Leontief, 1954), since the developing countries concerned are found to export labor-intensive goods as well as to use processes that are much more labor intensive than those employed by the corresponding industries in the EC. A trade expansion would therefore lead to substantial employment gains in the developing countries at the cost of a small employment decline in the EC as production shifted to the output of nonlabor-intensive exports. One important limitation of this result is worth noting: It neglects any macroeconomic effects that might result from the trade expansion by assuming the structure and level of domestic final demand in both countries to be unaffected by the expansion. There is thus an implicit assumption that EC governments would compensate for the fall in labor income associated with lower employment by cutting taxes, while developing country governments would be required to raise taxes in order to avoid higher labor incomes leading to higher consumption. Professor Kol also considers the extent of intersectoral linkages in the four developing countries under study and confirms earlier findings that forward linkages are relatively important for primary products, backward linkages for manufacturing. D. P. Pal in Chapter 15 uses input-output data for India to decompose import substitution into two components: direct import substitution—the change in the import ratio for industry i that would occur if 1 unit of this industry's output were supplied by domestic production rather than imports, with the output of all other industries remaining constant—and indirect import substitution—the additional effects that arise because the expansion of industry i affects other industries in the economy and hence feeds back to the originating industry. This decomposition is in a sense the quantity analogue of the distinction in the international trade literature between nominal and effective protection rates. Professor Pal's estimates show that the Indian economy has displayed increasing import substitution over time, with only the nonferrous metal sector showing evidence of a significant indirect substitution effect. The final two chapters in this volume apply input-output techniques to

12

Introduction

environmental and social, rather than purely economic, problems. Here the major contribution input-output methodology can make is the incorporation of technical and scientific information in a fashion that is explicit and easily understood by specialists working in the areas concerned. In addition, insofar as the output of pollutants or the demand for skilled labor of various types responds to economic developments, it is plausible to argue that changes in economic structure, defined in terms of variables such as the industrial composition of output, are likely to be more important determinants than macroeconomic growth. Fumimasa Hamada (Chapter 16) considers the environmental problems likely to arise over the next twenty years as a result of economic development of the island of Hokkaido (the northern island of Japan, of which Sapporo, the location of the Conference, is the capital). Although Hokkaido is currently relatively underpopulated and underindustrialized compared with the rest of Japan, it is expected to grow rapidly over the period of Professor Hamada's study, with an especially large expansion of residential construction. Professor Hamada links a small Keynesian macroeconomic model for Hokkaido (which treats economic developments in the rest of Japan as exogenous) with an input-output model to determine industrial structure and with additional technical relationships to determine local demand for fuel, power, and water and the output of four major pollutants. The rapid expansion of employment and manufacturing industry implies that technical progress in pollution control is needed if environmental standards are not to decline severely. In Chapter 17 Yoshiko Kido addresses a problem of great importance in Japan and other developed countries—the growing proportion of elderly and inactive members of the population. This has major implications for the allocation of the labor force between health care and industrial activities. Professor Kido uses input-output techniques to analyze the linkages between industrial and "social service" sectors. In practice these linkages are effectively one-way (so that the relevant block of the Leontief inverse is triangular), since social services supply almost all their output directly to households. Although the expansion of demand for social services has been rapid, technical progress (particularly in health care) has in the recent past slowed the increase in numbers employed. It is hard to know whether this will continue in the future: Although much high-technology medicine, particularly the widely publicized forms of it, are extremely labor intensive, other developments, such as the discovery of effective drugs for mental illness, have had labor-saving effects. REFERENCES Almon, C., M. B. Buckler, L. M. Horwitz, and T. C. Reimbold. 1974. 1985: Interindustry Forecasts of the American Economy. Lexington, Mass.: Heath. Arrow, K. J., and M. Hoffenberg. 1959. A Time Series Analysis of Interindustry Demands. Amsterdam: North-Holland. Bacharach, M. O. L. 1970. Biproportional Matrices and Input- Output Change. Cambridge: Cambridge University Press. Barker, T. S., and A. W. A. Peterson. 1987. The Cambridge Multisectoral Dynamic Model of the British Economy. Cambridge: Cambridge University Press. Bils, M. 1987. "The cyclical behaviour of marginal cost and price." American Economic Review 77: 838-855. Carter, A. P. [A. P. Grosse]. 1953. "The technological structure of the cotton textile industry." In W. W. Leontief et al., Studies in the Structure of the American Economy. New York: Oxford University Press.

Introduction

13

Domar, E. D. 1961. "On the measurement of technical change." Economic Journal 71: 709-729. Eliasson, G. 1985. The Firm and Financial Markets in the Swedish Micro-to-Macro Model. Stockholm: Almqvist & Wiksell. Hall, R. E. 1986. "Market structure and macroeconomic performance." Brookings Papers on Economic Activity 2: 285-322. Koopmans, T. C. 1951. Activity Analysis of Production and Allocation. New York: Wiley. Kydland, F. E., and E. S. Prescott. 1982. "Time to build and aggregate fluctuations." Econometrica 50: 1345-1370. Leontief, W. W. 1936. "Quantitative input and output relations in the economic system of the United States." Review of Economic Statistics 18: 105-125. Leontief, W. W. 1954. "Domestic production and foreign trade: The American capital position reconsidered." In R.E. Caves and H. G. Johnson (Eds.), Readings in International Economics. London: George Allen and Unwin. Lucas, R. E., and T. J. Sargent. 1979. "After Keynesian macroeconomics." In R. E. Lucas and T. J. Sargent (Eds.), Rational Expectations and Econometric Practice. London: George Allen and Unwin. Peterson, A. W. A. 1978. "Total factor productivity: A disaggregated analysis." In K. D. Patterson and K. E. Schott (Eds.), The Measurement of Capital. London: Macmillan. Waelbroeck, J. 1987. "Some pitfalls in applied general equilibrium modelling." In T. F. Bewley (Ed.), Advances in Econometrics—Fifth World Congress. Vol. 2. Cambridge: Cambridge University Press.

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I THEORETICAL DEVELOPMENTS IN INPUT-OUTPUT ANALYSIS

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2 On the Mathematical Transformation of Input-Output Matrices over Time or Space A. GHOSH

THE TRANSFORMATION RULE One feature of input-output matrices is that if they are of the same order and have the same classification it may be possible to find a transformation between any two of them. If such a transformation can be related to the output vectors concerned by a scalar or a diagonal matrix, then the resulting transformation of the matrices can easily be computed and can be expressed in canonical form, and the implicit technological transformation that lies behind this transformation can be brought out clearly. The object of this chapter is to demonstrate some useful properties of inputoutput matrices of the same order, conceptualized as forming a group. A group is defined as a set of elements, and a method of combining them, satisfying the following conditions. Let (At), the typical input-output matrix,1 be a member of the group. 1. Then if (Ai) and (Aj) are members of the group, the product ( A ) ( A j ) and (Aj)(Ai) are also members of the group, that is, the product sets also satisfy the usual conditions on the coefficient matrix. 2. [(A l -A ] yiA3]= [ A . i ( A j - A k ) ] 3. There is an element I such that I-Ai = Ai-I and for every (At) we have

(ACAr*) = 1.

A subgroup is any subset of elements made up of the members of a group that itself satisfies the definition of a group. A right or left co-set of a subgroup g or a group G is the set of elements of G obtained by multiplying each of the elements of g in turn (using right or left multiplication, respectively) by some element of G not in g. Using the above properties we can conceptually formulate that the elements (A() are members of the group G. Under the conventional restrictions on the input-output coefficients, the existence of the Leontief inverse has been proved by various authors. The associative law also follows from the property of matrix multiplication itself.

18

Theoretical Developments in Input-Output Analysis

The commutative property (1) states that (At • Aj) and (Aj • At) are both members of the group. Here we assume that the group G contains not only A( and Aj but also their products, A^Aj and At- Ah which are not necessarily equal. Intuitively we conjecture that this will be true under certain conditions and will follow from the invariance property of the Leontief matrix under nonsingular transformations, in the case where such transformations lead to the formation of another matrix of the Leontief type. The group G thus contains not only At and Aj but also their products. Let us assume that G contains a subgroup g that is a co-set of G, and that (AJ, ( A 2 ) , . . . , (Ak) belong to G and T t , T 2 , . . . , T k belong to g such that Ti(Ai)= A k and so on, that is, that the co-set group contains matrices Tf that transform Ai to Ak, where Ai ,Ak belong to G but not to g. We may thus associate a co-set of transformation matrices Ti that, when premultiplied into Ai, results in Ak, one of the members of G not in g. It is thus possible at least conceptually to move from any matrix Ai to Aj using a suitable member from the co-set group. The physical significance of this, assuming the existence of co-set g, is that at least theoretically we may move from any (7 — Ai) to (I — Aj), provided the corresponding member from the co-set g is estimated. In this paper we demonstrate that a sequence of output vectors, and the corresponding row vectors of transaction flows, can be used to find a set of regression equations giving estimates of the coefficients a ij . These regression matrices, designated R, can be used to estimate the corresponding transformation matrix T using a similarity transformation. (See the appendix for a list of symbols used in this chapter.) Obviously, if we conceive of continuous groups then any output vector X and its neighborhood vector X + dX will give rise to a T + dT in g, which will give a very close approximation to T, and in the limiting case the T will produce the exact A matrix with which we started. But as X and X + dX move away from each other, the estimation of / — Aj from I — (Ai + dAi) will become more and more approximate. In theory, therefore, the input-output space is conceived of as an affine space, permitting movement from any point denoted by I — Aj to any other point I — Ak by estimating the relationship of the corresponding regression coefficients from the row vectors whose sums result in Xj and Xk, and which give rise to the regression systems Aj Xj and Ak Xj, where Aj and Ak have been estimated from historically given sets of transaction flows and their totals. We may now proceed to a demonstration of the properties of such transformations and their possible uses.

PROPERTIES OF TRANSFORMATIONS Let us assume that in two countries P and Q there are two input-output systems, relating to the same years, expressed as

y = Ax

for P

and

n = BO for Q

where A and B are the input -output coefficient matrices and x and 6 are the outputs, y and n being the total intermediate outputs. Let us assume (2) that there is a transformation matrix T such that

x = TO

and

y = T/i

In other words, let us assume that over a historical series of x for P, 0 for Q, y for P,

Mathematical Transformation of Input-Output Matrices

19

and ju for Q a unique transformation exists that satisfies the above conditions. Also, we have already assumed that y = Ax, fj, = B9. Then

by our earlier relation Then A and B are similar matrices, with the same eigenvectors and eigenvalues and identical traces. Assuming that a unique transformation exists such that the matrix Tcan be obtained, we can use T to obtain the matrix B from the matrix A. Since the matrix A and the matrix B are matrices of constants, of the same order and with inverses, there is a transformation T such that AT = TB. Suppose we regress the set \n as functions of the set x over the historical period for which the date has been generated, the regression being constrained to pass through the origin. Let the regression coefficient set R so obtained be

But for any specific year we also have the relation

by virtue of our input-output relation

Or or Thus the regression coefficient matrix suitably transformed gives the A matrix or and as before

We have, therefore,

Thus the regression, on the assumption that the data were generated by the inputoutput model, gives a coefficient matrix which, after using the transformation T, gives us back the B matrix. If we can estimate R, and if we know A, then the transformation T is exactly determined. Several comments regarding our assumptions are now in order. What do these assumptions imply? Assumption (1) states that

This is simply a statement of the input-output model, without the final demand

20

Theoretical Developments in Input -Output Analysis

vector, so that y and /i are intermediate outputs and it is assumed that total intermediate output can be derived by the familiar input-output equations. But assumption (2) implies that the intermediate output vector fj, in Q can be regressed on the set of outputs x of P, and x the total output of P can be regressed on the set of outputs 0 of Q, both sets of outputs being considered over a period of time. What is the justification for this assumption, in the case where assumption (1) is accepted? A characterization of the transformation matrix T can be obtained as follows. Consider the following three-sector input-output matrix, where y is intermediate demand and x is total output, and the aij's are fixed constants with respect to a base period

Let us assume that this model is used to generate a time series xc1(t), xc2(t), xc3(t) for given y,(z), y2(t), y3(t) for n years. Let us now assume that this time series so generated consists of n values of xct(t) and yt(t). If we use these series of x-(f) and y i (t) to form a linear regression system for each equation of the form y, = £a,.Xy with the condition that the equation passes through the origin, then the regression expression of each (say the first one) equation will be of the following form

In a similar way we can also find « 2i , «,; by using the other sets of regression equations. From the series of generated values so obtained we can again form the variance-covariancc matrix consisting of, say, £(X)2 and Exjoc', when xi, xj denote generated values and the generated values will satisfy

Comparing the two systems—the original input-output equations and the regression using the generated values—it is obvious that the terms involving x are identical since the series themselves have been generated by using the theoretical model. Therefore, for any nontrivial solution (e.g., a; / 0), the two systems are identical and the input-output matrix A and the matrix fitted from the generated series (i.e., a) are identical. Let us consider two such input-output systems, one for country (P) and the other for country (Q), behaving exactly as an input-output model. Then we can consider the values of x(P) and y(P), and also of 0(Q) and n(Q), as giving rise to

Mathematical Transformation of Input-Output Matrices

21

Let us also assume that there is a unique transformation T so that

If there is such a unique transformation T we can then move from A(P) to B(Q\ as has been demonstrated earlier, by using the similarity transformation, for example If, therefore, there are two economies whose behavior is exactly described by the input-output rules, and if the total outputs and total intermediate outputs have an exact functional relation that transforms the total output and total intermediate output of one country to those of the other country, then we can pass from one of the input-output matrices to the other by obtaining the similarity transformation using a regression. We may now consider the fact that in the most plausible case both the inputoutput rules and the transformation obtained from the regression are subject to errors. Therefore, we have three sets of errors entering the relationship. We may call them eA, EB, and ^T. We thus have in effect the relation We may write this or

Taking expectations, and assuming eA,eB, er are independent random errors with zero mean, we have or or

The corresponding forms for correlated errors can also be easily be computed. It is to be noted, however, that while the relation will hold on the average, the error components will disturb specific cases depending on their size. If, however, we assume that the error components are also linear functions of time, then we have, on the assumption that &A = EA(Q) + f$A(l) Separating this expression into two components, one involving t and the other involving e,-(0), we have

22

Theoretical Developments in Input-Output Analysis

Again taking expectations we will be left with the original relationship, along with a variance or covariance component for the product of the E;'S, as well as a linear component of the errors in e f (f). Depending on the size of /J this will introduce a larger divergence in the relationship for larger values of t. For short periods, however, the results of the earlier experiments will still hold. The preceding results thus follow from our assumption of the operation of two input-output models over historical periods. If we know one of them, we can find the other using regression methods. But it is generally known that input-output coefficient matrices are not strictly constant. They can be affected by both systematic and random change over time. If we ignore systematic change, then the random change in the A or B matrix over time will give rise to errors in the coefficients, with an expected value of the regression coefficients that will again be equal to A or B. Hence if we ignore the impact of technological change for reasonable periods, we can find a specific matrix with the help of the expected values, as estimated by the regression coefficients, and we can find a transformation matrix T that, as a first approximation, will help us to move from one of the matrices to the other. It is obvious that, using the same kind of arguments and assumptions, one can find a matrix B for the same country referring to the year (t + k) using the matrix A referring to the year t and the historical series of y and x for t and (t + k). It is often our experience that total output vectors and total intermediate demand vectors are obtainable for each sector over time but that the coefficient matrix is not. It is also known that generally the total output and total intermediate output have a stable ratio for the different sectors, so that the coefficients of the transformation appropriate to outputs and those of that appropriate to intermediate outputs are not likely to diverge very significantly. Using this method, therefore, one can derive an input-output matrix for one country from that of another, or for one period from that of another, provided the regression error is not very large. In other words, for this method to be of practical use the observed values, and the computed values from the regression, should have a high correlation. Large changes over time, either random or systematic, between periods or between countries over a single period are potential sources of inaccuracy. The economic interpretation of the mathematical relation just described follows from the fact that, although it is estimated at a point in time, an input-output matrix is assumed to reflect a structural relation that is relatively invariant over time. To the extent that input-output coefficients are invariant, the time series generated by the system will follow the same rule, so that the expected values estimated by regression methods and those estimated from the input-output relation should be reasonably correlated. These two influences—the stability of the structural relationships and the nature of the change over time between them—determine the new coefficient matrix. If the change is random in character, the regression attempts to give an average structural relation over time. The present method therefore seeks to adjust the original structural interdependence using the historical transformation matrix between t and t + k for the same country, or between A and B, two related countries. We now move to the relationship (set out in the following section) between the estimated B matrix and a capital matrix. The B matrix now refers to a later period and the A matrix to a base period. We have from the dynamic balance equation with a capital matrix K,

Mathematical Transformation of Input-Output Matrices

23

where Ax = xt+r — xt, the unit interval being r and K being the capital coefficient matrix being defined for this unit interval. or

But we have B = T A T - l = R - 1 A R . Therefore or and

as also In this way the incremental output is related to the capital coefficient matrix as well as the transformation matrix T and its counterpart, the regression coefficient matrix R. As before, let us consider the effect of error in T.

Then

RT Ms the error term in the historical transformation between A and B. Since Therefore

Using the same regression matrix R for year t + k, we are committing an error given by the terms associated with e in the capital coefficient matrix. This error may be evaluated by correcting for c. USES OF TRANSFORMATION What is the use of this approach for input-output analysis? First, it says that all input-output matrices of the same order and classification can be transformed, if

24

Theoretical Developments in Input Output Analysis

there exists a nonsingular T. The existence of the latter allows a unique growth of the components of the vectors x or Ax that transforms one into the other. The existence of T thus illustrates the existence of a unique transformation, and also the existence of a balanced rate of growth by which one vector may be transformed into the other. The results given here may be easily applied to matrices for different regions, where the regional outputs and final demands for a period are known but where an input-output table exists only for one region and not for the other. The nature of the transformation matrix T also shows that we may have a balanced growth rate from one matrix to the other if we choose a suitable eigenvector and eigenvalue that will help us pass from A to B. This result is due to the similarity of the A and B matrices.

APPENDIX Symbols used in this chapter: A B K T R B

Input-output coefficient matrix for country P or for base period t. Input -output coefficient matrix for country Q or for period t + r. Capital coefficient matrix related to the incremental production between t + r and t. Transformation matrix relating A and B. Corresponding regression coefficient matrix used to estimate the coefficients by means of historical series on total intermediate output x and y, respectively. Error matrix. NOTE

To simplify notation, we have used the convention that the symbols Ah Aj represent the Leontief matrices I — At, I — Aj, respectively.

3 The Absolute and Relative Joint Stability of Input-Output Production and Allocation Coefficients CHIA-YON CHEN and ADAM ROSE

An important variant of the standard input-output model has been developed by Ghosh (1958). In contrast to the fixed input requirements of the Leontief production function, Ghosh's allocation function approach calls for fixed output, or sales, distributions across sectors. Rather than a demand-driven model with fixed coefficients in relation to column sums, the new formulation is a supply-driven model with fixed coefficients in relation to row sums. Applications of the allocation model have been numerous. One set deals with the direct and indirect impacts of natural resource supply shortages (see Davis and Salkin 1984; Giarratani, 1976). Another set of applications pertains to the calculation of Hirschman's (1958) concept of forward linkages (see Buhner-Thomas, 1982; Jones, 1976). Yet another pertains to the formulation of multiregional input-output models (see Bon, 1984, 1988). The conceptual soundness of the allocation function approach in several contexts has been supported by Ghosh's (1958) characterization of the behavior of monopolies and planned economies as dominated by supply considerations. Empirical support for the use of the model emanates from studies that have shown allocation coefficients to be as stable over time as are production coefficients (see Augustinovics, 1970; Bon, 1986; Giarratani, 1981). This chapter addresses a remaining concern of the legitimacy of the allocation model—what we refer to as the joint stability of the production and allocation versions of the I-O model. This concern emanates from the fact that an I-O system cannot be operated with both production and allocation coefficients simultaneously fixed, except for the most trivial cases. Stated another way, when using the allocation model does the constancy of allocation functions implicitly result in production coefficient changes that are unreasonable? Will the results of a simulation of the impacts of, say, an oil embargo help yield a meaningful distribution of the burden of the ensuing oil shortage, but at the same time possibly call for oil input substitutions that are technologically or economically infeasible? The aforementioned empirical tests shed little light on this question because they provide no theoretical linkage

26

Theoretical Developments in Input-Output Analysis

between the two types of coefficients and examine cases of small changes in basic conditions, unlike the sizeable changes that arise when the allocation model is applied to resource crises or economic development. THE BASIC ALLOCATION MODEL The supply-driven I-O model is based on an equilibrium condition of interacting forces through allocation functions. The basic balance equation of this model can be represented as follows:

where sij is the allocation coefficient defined as

and Xi is total supply of sector i, Xj is domestic output of sector j, Fi is total final demand of sector i, Vj is total primary input in sector j, and x(j is the amount of output of sector / purchased by sector j. Rewriting equation (1) in abbreviated matrix form yields where / is an identity matrix. Equation (2) can be formally solved by post multiplying both sides by (/ — S)~ ', yielding the result Given a change in primary inputs, the direct and higher order impacts on domestic output can be determined by equation (3). Row sums of (/ — S)~1 are supply multipliers, representing the total output change in the entire economy given unit changes in primary inputs. Ghosh (1958) justified the allocation model as being appropriate to cases of central planning and monopoly. These situations can be characterized as cases of rationing, which can also encompass instances in inherently competitive economies where a resource disruption is dealt with by administering the remaining supply. Ghosh noted: In economies of rationing since every sector registers a high demand for the scarce factors the general tendency of the rationing authorities is not to change the relative shares of each sector in the short-run since such relative shares are determined by a delicate balancing of different sectors' claims and counter-claims. This tendency considered from the problem of projection makes the allocation coefficient more stable in the short-run than production coefficients.

Ghosh went on to state that the relative instability of production functions may not be a serious problem because of the likelihood of substitution opportunities. However, this dismissal overlooks two important considerations. First, the allocation model is being used increasingly to analyze short-run responses to supply disruptions, and substitution possibilities bear a direct relationship to time. Second, until recently there was no indication that the production coefficient changes brought about by invoking fixed distribution patterns are anything but random, inefficient, or even beyond the range of substitution possibilities.1

Joint Stability of Production and Allocation Coefficients

27

THE BASIS OF THE JOINT STABILITY RELATIONSHIP As already mentioned, the substitution among inputs (i.e., changes in input coefficients) implicit in the solution of the allocation model may be unreasonable. This section presents the theoretical basis of the joint stability of allocation and production coefficients. First, given the possibility that a reduction of a primary factor results in an adjustment in direct requirement coefficients during application of the supply-driven model (see equation 3), we define the changes in conventional input requirement coeffcients as follows:

where a|$ is the new input coefficient, atj is the original input coefficient, x* is the new amount purchased by sector) as input from sector i, and Xf is the new output level in sector j. At the same time, the supply-driven I-O model carries over its fixed allocation coefficients from the original to the simulated situation:

and

Incorporating equations (5aj and (5b) into equation (4) and rewriting it yields

If we define

equation (6) can be written as follows:

Then, we may write the relationship between ay and a* as

28

Theoretical Developments in Input-Output Analysis

Equation (8) enables us to state as a theorem: The stability of production input combinations implicit in the solution of the supply-driven model depends on the ratio of relative changes in corresponding sectoral gross outputs. 2

In other words, as long as the impact based on the supply-driven model does not cause much difference among relative changes in sectoral gross outputs, production coefficients will exhibit stability. The theorem follows from the basic properties of the allocation model, which spreads the initial impact of changes in basic factors proportionally across all sectors. We note also the mathematical condition in equation (8) that exerts downward pressure on coefficient changes—percentage changes in production coefficients are related to relative, or weighted, values of sectoral outputs based on equal proportional allocations. For example, if gross output levels for two goods each equal 100, and then change to 125 and 120, the 25-percent difference in growth rates translates into only a 4.2-percent increase in the corresponding production coefficient.3 It is possible to make an a priori judgment about joint stability on the basis of the basic character of a given I-O model. The direct proportional increase or decrease in the distribution of a factor across all sectors helps move the solution toward equal proportional output changes. Any differences will be caused by indirect and induced supply-side effects. This enables us to state a corollary of our theorem: Joint stability will bear an inverse relationship to the size of the allocation model sectoral multipliers.

This is due to the fact that multipliers are a combination of direct effects (that involve the same proportional change across all sectors) and higher order impacts. The smaller the secondary impacts, the more dominant are the equal proportional impacts of the direct effects in the end result.4

ABSOLUTE VERSUS RELATIVE JOINT STABILITY There is some confusion over the definition and implications of the joint stability property, first elucidated by Chen and Rose (1986).5 Therefore, we offer a clarification, following Rose and Allison (1989). We define absolute joint stability as the requirement that both production and allocation coefficients remain constant after an application of either the production or allocation version of the inputoutput model.

As such, joint stability represents an ideal property. As pointed out earlier, it will hold only in the trivial cases where all percentage changes in sectoral supply stimuli/restrictions are equal or all percentage changes in sectoral final demand increases/decreases are equal. Though the joint stability property will typically not hold in an absolute sense, it is possible that coefficient changes will be rather small and within tolerable limits of accuracy in many contexts. Therefore, we define the more operational concept of "relative joint stability" as the degree to which production coefficients of an input-output model approximate their original value after an application of either the production or allocation version of the model.

Joint Stability oj Production and Allocation Coefficients s

29

Employing our previous notation, absolute joint stability can then be stated as where, in effect, expression (9) will hold only when ei = ej Relative joint stability can be defined as the extent to which Some confusion has arisen in the literature to the extent that expression (8) is referred to as the Chen-Rose joint stability condition. This may stem from the related use of the terms consistency and joint stability in our original paper (see Chen and Rose, 1986, pp. 1-2). The derivation of expression (8) does indicate that there is a consistent relationship between afij and aij via sij. But this holds by definition whether Aaij is large or small. Our contention is that the degree of instability is likely to be small in most instances and well within tolerable limits in comparison to other areas where approximation is used. Approximation methods have an honorable tradition in mathematics, economics, and regional science. Examples include entire fields such as calculus and statistical inference, as well as special techniques such as translog forms to approximate twice differentiable expenditure or cost functions, the estimation of consumer surplus using ordinary demand curves, and biproportional matrix methods for adjusting input-output coefficients. Of course the question of what constitutes a tolerable error must still be addressed. There is no definitive cutoff level for all models and applications. However, given established practices in the literature on non-survey I-O model construction, errors of a few percent are explicitly viewed as tolerable (see, e.g., the review by Jackson and West [1989]), and, given the established practice of applying a model calibrated for one time period to another at least a few years hence, small errors are implicitly condoned.6

AN ILLUSTRATION To examine the relative joint stability of allocation and direct requirement coefficients empirically, we applied the supply-driven I-O model to the case of an aluminum shortage in Taiwan.7 Aluminum is considered a strategic material in the economy of Taiwan because of its role as an input into the metals, machinery, transport equipment, and electrical supply industries and because of its limited number of substitutes. Moreover, Taiwan is completely dependent on imports for its bauxite, alumina, and scrap, and is able to supply only 50 percent of its processed aluminum (Chen, 1984). The supply shortage we simulated was on the order of 50 percent of Taiwan's 115.9-million-ton consumption of aluminum in 1979. This level was chosen because it represents a significant supply shock and thus has great potential to destabilize production coefficients. We also note that while an allocation system implies an equal proportional direct sharing of the shortage across sectors, there are other worthy policy approaches and models that are applicable.8 In this case study, the aluminum sector is treated as an exogenous sector and put into the primary input group so that we can evaluate the impact of aluminum supply restrictions. Consequently, equation (3) is changed to

TABLE 3.1 Comparison (in absolute % difference") of original and new input coefficients in major aluminum-using sectors Miscellaneous Metals

Metal Products

Machinery

Electrical Equipment

Transport Equipment

Construction

Sector

Original

New

Original

New

Original

New

Original

New

Original

New

Original

New

01 02 03 04 05 06

0.000000 0.000000 0.000091 0.000000 0.005937

0.000000 0.000000 0.000092* 0.000000 0.005950

0.000000 0.000000 0.000000 0.000000 0.003960

0.000000 0.000000 0.000000 0.000000 0.004052*

0.000000 0.000000 0.000002 0.000000 0.003207

0.000000 0.000000 0.000002 0.000000 0.003219

0.000001 0.000000 0.000000 0.000000 0.000667

0.000001 0.000000 0.000000 0.000000 0.000673

0.000000 0.000000 0.000253 0.000000 0.000232

0.000000 0.000000 0.000255 0.000000 0.000234

0.000370 0.000000 0.001528 0.000000 0.000000

0.000371 0.000000 0.001531 0.000000 0.000000

0.000062 0.110533 0.000000

0.000062 0.110773 0.000000

0.000098 0.000479 0.000017

0.000101* 0.000490* 0.000017

0.000240 0.000140 0.000005

0.000241 0.000141 0.000005

0.000624 0.000218 0.000015

0.000629 0.000220 0.000015

0.000000 0.000083 0.000000

0.000000 0.000084* 0.000000

0.000000 0.036893 0.000000

0.000000 0.036942 0.000000

0.000260

0.000261

0.002077

0.002126*

0.001223

0.001228

0.002864

0.002890

0.000907

0.000915

0.001192

0.001194

0.000244

0.000245

0.005252

0.005377*

0.004878

0.004899

0.008963

0.009046

0.008081

0.008153

0.060429

0.060541

0.001080

0.001082

0.007669

0.007845*

0.003056

0.003067

0.010477

0.010567

0.003489

0.003518

0.001757

0.001759

0.000248 0.000030

0.000248 0.000030

0.000802 0.000171

0.000820* 0.000175*

0.003101 0.000306

0.003114 0.000307

0.002853 0.000539

0.002879 0.000543

0.021426 0.000282

0.021612 0.000284

0.000638 0.000000

0.000639 0.000000

0.000538

0.000539

0.015960

0.016330*

0.000917

0.000921

0.003969

0.004004

0.002656

0.002678

0.000087

0.000087

0.000000

0.000000

0.000002

0.000002

0.000000

0.000000

0.000038

0.000039*

0.000002

0.000002

0.000000

0.000000

0.000538

0.000539

0.007485

0.007655*

0.004665

0.004680

0.039077

0.039403

0.010969

0.011056

0.021879

0.021898

Agriculture Livestock Forestry Fisheries Coal 7 products Crude oil and gas 07 Other minerals 08 Food manufacturing ig 09 Textile and apparel 10 Wood and wood products 11 Pulp, paper, and products 12 Rubber and products 13 Petrochemicals 14 Industrial chemicals 15 Chemical fertilizer 16 Plastics and products

17 Miscellaneous chemicals 18 Petroleum refining 19 Cement and products 20 Nonmetal mineral products 21 Iron and steel 22 Aluminum 23 Miscellaneous metals 24 Metallic products 25 Machinery 26 Electrical apparatus and equipment 27 Transport equipment 28 Miscellaneous manufactures 29 Construction 30 Electricity 31 Gas and city water 32 Transport and communications 33 Trade 34 Services Average absolute % difference1"

0.002511

0.002514

0.014476

0.014796*

0.002661

0.002668

0.006780

0.006832

0.008165

0.008225

0.010521

0.010524

0.008244

0.008263

0.005682

0.005814*

0.004754

0.004772

0.003301

0.003330

0.005709

0.005757

0.008051

0.008063

0.000071

0.000071

0.000117

0.000120*

0.000069

0.000069

0.000000

0.000000

0.000000

0.000000

0.128983

0.129171

0.002773 0.074054 0.001562

0.002894 0.002780 0.074198 0.241486 0.000784** 0.041026

0.269429 0.043342 0.010929

0.269429 0.042454* 0.010912

0.070464 0.101386 0.008532

0.071938* 0.101386 0.008697

0.021298 0.000503

0.021158 0.000500

0.002777 0.001415

0.000698 0.000268 0.011260

0.000698 0.000268 0.011291

0.000135 0.012067 0.041723 0.068107

0.010335 0.003900 0.041469 0.303338 0.002737** 0.008760

0.004693 0.010431 0.041824 0.092000 0.004423** 0.011117

0.054958 0.004735 0.092753 0.115933 0.005612** 0.001240

0.055056 0.116061 0.000622*

0.017386 0.033292 0.109015

0.017414 0.032662* 0.109015

0.021980 0.052535 0.005282

0.022125 0.051799* 0.005308

0.003432 0.024170 0.048272

0.003453 0.023822* 0.048496

0.000186 0.039082 0.003049

0.000186 0.038249* 0.003041

0.002817 0.001436

0.024461 0.001802

0.024340 0.001794

0.392184 0.000634

0.392184 0.000634

0.013412 0.282836

0.013407 0.282836

0.034819 0.000929

0.034561 0.000922

0.001258 0.001653 0.022259

0.001284* 0.001689* 0.022786*

0.001738 0.001431 0.013967

0.001740 0.001434 0.014027

0.003673 0.002161 0.008583

0.003696 0.002177 0.008663

0.002204 0.001717 0.009402

0.002217 0.001729 0.009486

0.000851 0.000852 0.003043

0.000850 0.000852 0.003048

0.000135

0.001536

0.001572*

0.000424

0.000426

0.001019

0.001029

0.000798

0.000805

0.000611

0.000612

0.012095 0.041848 0.068290

0.011921 0.035591 0.036667

0.012199* 0.036445* 0.037535*

0.013681 0.035600 0.045536

0.013736 0.035764 0.045731

0.011647 0.018505 0.048082

0.017752 0.018684 0.048529

0.011593 0.049793 0.050404

0.011693 0.050255 0.050855

0.032192 0.040676 0.033486

0.032241 0.040764 0.033548

0.74

0.26

0.29

0.002963* 0.003884 0.247017* 0.302268 0.021011** 0.005446

1.98

"**Greater than 10.0%; "between 1.0 and 10.0%; all others less than 1.0%. Does not include the aluminum sector coefficients or coefficients with 0.00000 values.

b

0.40

0.78

32

Theoretical Developments in Input-Output Analysis

where Xm is a direct aluminum allocation vector and X and S the new supply vector and allocation matrix, respectively, excluding the newly defined exogenous sector— aluminum. The elements amj of the vector xm are defined as follows:

where m indicates the aluminum sector. For the case of a 50-percent supply reduction in 1979, we utilize equation (3') to compute a new set of gross outputs.9 These new gross outputs and the constant allocation coefficients can be used to calculate a new set of intersectoral flows, x*-, and new production coefficients, a*. The results of our simulation for the major aluminum-using sectors are shown in Table 3.1. Of course the aluminum input coefficients (row 22) are reduced by nearly 50 percent, or the size of the shortage. The reason that the aluminum input coefficient reductions are less than 50 percent is because the new gross outputs for all sectors of the economy have been lowered. We note that the percentage change in other coefficients is less than 1 percent for all but a few cases in the six major aluminumusing sectors, except metal products. This sector is the most aluminum intensive of the group and requires greater substitution throughout to compensate for the lack of aluminum input. Even then the average absolute percent difference in coefficients in the metal products sector is less than 2 percent. The range of average absolute percent differences for other aluminum-intensive sectors is 0.26 to 0.78, and is lower still for the remaining sectors in the economy. Moreover, no single input coefficient change, except those associated with aluminum or metal products, in any of the remaining sectors exceeds 1.0 percent. In general, nearly all the coefficient changes are positive, the major exception being input coefficients for metal products. The negative changes for these coefficients are due to the significant decrease in production of metal products due to the aluminum shortage that cannot be overcome by supply-side adjustments. Coefficient increases indicate a substitution for aluminum or a rearrangement of relative input combinations due to the workings of the supply-driven model. For the two major substitutes for aluminum—plastic and copper—the production input coefficients of the former increase in each case, while the coefficients of the latter increase in five cases and are constant in one.10 Before imparting too much behavioral interpretation to the model, we should point out alternative explanations for the vast number of upward changes in input coefficients. They could signify meaningful substitutions. They could also represent productivity increases associated with making available aluminum go farther, thereby making all other inputs higher proportions of the new reduced cost of production. On the other hand, the changes could be attributable to absurd machinations of the supply model if they represent, for example, a substitution of transportation for aluminum. Finally, however, there is Gruver's (1989) convincing demonstration that, for small changes, the supply-side model is a reasonable linear approximation of the actual production function.11 We can, however, conclude that in our example the production coefficients are remarkably stable.12 Any curious coefficient changes are still within the range of substitution possibilities or are so small that they would not jeopardize any real-world

Joint Stability of Production and Allocation Coefficients

33

coping strategy. Admittedly, part of the stability is due to the small sectoral gross output decreases in reaction to the 50-percent embargo, and the fact that the direct and indirect shortfall is spread across thirty-four sectors.13 However, that is the beauty of the supply model—it indicates how a supply shock can be cushioned by maintaining stability in distribution patterns.14

CONCLUSION This chapter has provided a theoretical understanding and empirical test of the joint stability of production and allocation versions of the I-O model. An empirical example showed a remarkable stability for the case of a sizeable supply disruption. The evidence supports a conclusion that use of the supply-driven, or allocation, version of the I-O model will not necessarily violate the basic production conditions of its conventional counterpart.

NOTES 1. Giarratani (1980) has argued that the allocation model is not as theoretically sound as the production model because the latter is grounded on a behavioral assumption stemming from a branch of production theory whereas the former "rests on behavior about which we have little knowledge" (p. 188). However, Giarratani (1981) has found the allocation model to be as accurate as the conventional model in projections of the U.S. economy. Even stronger criticisms of the conceptual soundness of the allocation model have been offered by Oosterhaven (1981, 1988). Recent work by Gruver (1989) rebuts one of Oosterhaven's major charges concerning the economic sense of production input coefficient changes stemming from applications of the corresponding allocation version of an I-O model. 2. Note that the results of equation (8) are symmetric with respect to explaining changes in allocation coefficients as well. Note also that equation (8) is not the definition of joint stability, but rather a relationship on which it depends. 3. Since writing the first draft of this chapter in the summer of 1985, we have discovered two alternative derivations of equation (8) by Miller and Blair (1985) and Deman (1986). However, in the case of the former there was no discussion of the joint stability issue, and in the case of the latter there was only passing mention, followed by added confusion in Deman (1988). The reader is referred to Miller (1989) for a discussion of the restrictiveness of Deman's stability relationship based on biproportional matrix properties. We are also especially grateful to Miller for prodding us to clarify some of our original concepts. 4. Small multipliers are attributable to a lack of interdependence or a lack of self-sufficiency of an economy. Moreover, in regional economies, coefficient stability may be less crucial when intraregional trade coefficients are used. These coefficients represent purchases and sales between firms in the region and thus are typically smaller than full technical requirements. As Davis and Salkin (1984) pointed out, a significant increase in one of these coefficients may readiiy be made up by imports. Note also that the smaller the variance of the allocation multipliers, the more stable we expect production coefficients to be. However, it is more difficult to tie this condition to the structure of the economy, as we did in the case of the multiplier size. A balanced economic structure is not, for example, a sufficient condition for a low variance. 5. The first version of this chapter was circulated as a paper in 1985 and presented at the University of Pittsburgh Modeling and Simulation Conference in 1986, in addition to the International Conference on Input-Output Techniques. Since that time there has been a strong renewed interest in the supply-side I-O model. A number of contributions to the literature have referred to the Chen-Rose joint stability property. Several of these references

34

Theoretical Developments in Input-Output Analysis

indicate some confusion over the property and its implications. This is due in part to our ambiguous use of some terminology and our lack of elaboration on joint stability. Here we seek to clarify the issue by adding precise definitions to two different forms of joint stability. We wish to thank Ronald Miller, Frank Giarratani, Jan Oosterhaven, Faye Duchin, and H. Craig Davis for their helpful comments on earlier drafts of the paper. We accept responsibility for any remaining errors and omissions. 6. See Gruver (1989) for a linear programming formulation of the supply-side model that constrains coefficient changes. 7. Another confusion has arisen over the purpose of our empirical analysis (see, e.g., Oosterhaven, 1988). This analysis is not intended as a policy simulation of the impact of an aluminum restriction, but as a straightforward test of the joint stability property. To be useful, a simulation of a supply restriction for real-world policymaking would require explicit consideration of substitution behavior and economy-wide optimizing strategies, as well as the several other considerations noted by Oosterhaven (1988). For an example of a modified supply-driven model incorporating elasticities of substitution and also transformed to a linear programming format, the reader is referred to Chen (1984, 1986). The most rudimentary form of the allocation model is used here in order to focus on the inherent properties of the model and avoid biasing the results by complicating factors. 8. One alternative is an optimization strategy based on a linear programming model with and without input substitution (see Chen 1984, 1986). Another approach, following Stone (1961), involves the conventional I-O production model partitioned into unconstrained and constrained components. The formulation for the latter partition simply reverses the roles of gross output and final demand, that is, the gross output of the constrained sector is fixed and its final demand is unknown. The solution proceeds simply with fixed production coefficients and straightforward matrix multiplication. Thus it does not allow for any input substitution and is likely to exaggerate the impact of a shortage (see also Miller and Blair, 1985). 9. A thirty-four-sector Taiwan I-O table, developed by the government of the Republic of China (1981) was used in the simulations. A detailed description of the table and simulations are contained in Chen (1984). 10. Copper is included in the miscellaneous metals industry, and the aggregation may be responsible for this counterintuitive result. 11. Note that it is possible to include explicit substitution possibilities into the model if ownprice (and cross-price) elasticities of demand for aluminum (and between aluminum and its substitutes) can be calculated. A simulation including explicit substitutions such as these in an I -O-allocation-model framework resulted in a total gross output reduction due to a 50percent shortage of 17.5 billion Taiwan dollars, as opposed to the 18.8-billion Taiwan dollar reduction of the simple allocation model. Changes in input coefficients were remarkably similar to those depicted in Table 3.1 as well (see Chen, 1986). 12. Our results are supported by more recent work (see Allison, 1989; Rose and Allison, 1989) in which the relative joint stability of both technical and regional input coefficients in various Washington State I-O tables was tested. The results for a 50-percent aluminum restriction, as well as equally severe restrictions on other sectors, were very similar to those presented here. For example, in the case of the aluminum supply restriction in the 51-sector 1972 table, the vast majority of individual coefficient changes were less than 1.0 percent and no coefficient change outside the aluminum input row exceeded 4.0 percent. 13. Even with the modest aluminum input intensities of the Taiwan economy, the standard demand-driven I-O model would have reduced gross output in all aluminum-using sectors by 50 percent as well as by subsequent multiplier effects! Total gross output in the economy in our supply-driven model simulation was reduced by only 0.6 percent. 14. One shortcoming of the basic allocation version of the I-O model is that the final demands of the solution may be untenable, that is, may depart too much from society's needs. In cases where only a portion of the sectors are supply constrained, a partitioned supply-

Joint Stability of Production and Allocation Coefficients

35

driven model analogous to Stone's partitioned demand-driven model, discussed previously, can partially alleviate this concern. This formulation, developed by Davis and Salkin (1984), involves the use of allocation coefficients throughout while allowing the final demands for unconstrained sectors to be fixed (see Cronin [1984] for a classification and test of hybrid models, and Mizrahi [1989] for a theoretical and empirical examination of them). Our conclusions apply to such modified versions of the allocation model as well.

REFERENCES Allison, T. 1989. "The stability of input structures in a supply-driven input-output model: A regional analysis." M.S. thesis, Department of Mineral Resource Economics, West Virginia University, Morgantown. Augustinovics, M. 1970. "Methods of international and intertemporal comparison of structure." In A. P. Carter and A. Brody (Eds.), Contribution to Input-Output Analysis. Amsterdam: North-Holland. Bon, R. 1984. "Comparative stability analysis of multiregional input-output models." Quarterly Journal of Economics 99: 791-815. Bon, R. 1986. "Comparative stability analysis of demand-side and supply-side input-output models." International Journal of Forecasting 2: 231-235. Bon, R. 1988. "Supply-side regional input-output models." Journal of Regional Science 28: 41 50. Buhner-Thomas, V. 1982. Input-Output Analysis in Developing Countries. New York: Wiley. Chen, C. Y. 1984. "The potential impact of optimal adjustment associated with an aluminum supply restriction." Ph.D. diss., Department of Mineral Resource Economics, West Virginia University, Morgantown. Chen, C. Y. 1986. "The optimal adjustment of mineral supply disruptions." Journal of Policy Modeling 8: 199-221. Chen, C. Y., and A. Rose. 1986. "The joint stability of input -output production and allocation coefficients." Modeling and Simulation 17: 251-255. Cronin, F. J. 1984. "Analytical assumptions and causal ordering in interindustry modeling." Southern Economic Journal 50: 521-529. Davis, H. C., and E. L. Salkin. 1984. "Alternative approaches to the estimation of economic impacts resulting from supply constraints." Annals of Regional Science 18: 25-34. Deman, S. 1986. "Notes and comments: Production and allocation consistency of input-output coefficients." Mimeo, Department of Economics, University of Pittsburgh. Deman, S. 1988. "Stability of supply coefficients and consistency of supply-driven and demanddriven input-output models." Environment and Planning A 20: 811-816. Ghosh, A. 1958. "Input-output approach to an allocative system." Economica 25: 58-64. Giarratani, F. 1976. "Application of an interindustry supply model to energy issues." Environment and Planning A 8: 447-454. Giarratani, F. 1980. "The scientific basis for explanation in regional analysis." Papers of the Regional Science Association 45: 185-196. Giarratani, F. 1981. "A supply-constrained interindustry model: Forecasting performance and an evaluation." In W. Buhr and P. Friedrich (Eds.), Regional Development Under Stagnation. Baden-Baden: Nomos Verlagsgesellshaft. Gruver, G. 1989. "A comment on the plausibility of supply-driven input-output models." Journal of Regional Science 29: 441-450. Hirschman, A. O. 1958. The Strategy of Economic Development. New Haven, Conn.: Yale University Press. Jackson, R., and G. West. 1989. "Perspectives on probabilistic input-output analysis." in R. Miller, K. Polenske, and A. Rose (Eds.), Frontiers of Input-Output Analysis. New York: Oxford University Press.

36

Theoretical Developments in Input-Output

Analysis

Jones, L. P. 1976. "The measurement of Hirschmanian linkages." Quarterly Journal of Economics 90: 323-333. Miller, R. and P. Blair. 1985. Input -Output Analysis: Foundations and Extensions. Englewood Cliffs, N.J.: Prentice-Hall. Miller, R. E. 1989. "Stability of supply coefficients and consistency of supply-driven and demand-driven input-output models: A comment." Environment and Planning A 21: 11131120. Mizrahi, L. 1989. "A generalized input-output model: Combining demand- and supply-side systems." Ph.D. diss., Department of Urban Studies and Planning, Massachusetts Institute of Technology. Oosterhaven, J. 1981. Interregional Input-Output Analysis and Dutch Regional Policy Problems. Aldershot, England: Gower. Oosterhaven, J. 1988. "On the plausibility of the supply-driven input-output model." Journal of Regional Science 28: 203-217. Rose, A., and T. Allison. 1989. "On the plausibility of the supply-driven input-output model: Empirical evidence on joint stability." Journal of Regional Science 29: 451-458. Republic of China. 1981. Executive Yuan, Council for Economic Planning and Development, Taiwan Input-Output Tables. Taipei, Taiwan. Stone, J. R. N. 1961. Input-Output Models and National Accounts. Paris: Organization for Economic Cooperation and Development.

II THE COMPILATION OF INPUT-OUTPUT TABLES

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4 Considerations on Revising Input-Output Concepts in the System of National Accounts and the European System of Integrated Economic Accounts CARSTEN STAHMER

Although an intensive debate on the revision of international systems of national accounts (NA) has been going on for several years, the role of input-output (I-O) in the system has only recently been addressed (see Chantraine and Newson, 1985; Kurabayashi, 1985). An important contribution in this respect has been a 1985 paper by Vu Viet (Statistical Office of the United Nations). Apart from describing the impact of the revision of the NA systems on I-O, Viet gave a distinct overview about the problems arising in I-O compilation. In addition, proposals for a revision of I-O concepts have been put forward both by Stahmer (1985a) and by van den Bos (1985). This chapter is restricted to the revision of input-output concepts in the framework of the System of National Accounts (SNA) of the United Nations and the European System of Integrated Economic Accounts (ESA) (see United Nations, 1968, 1973; EUROSTAT, 1979). It does not cover the revision of I-O in the Material Product System (MPS) (see United Nations, 1971). The first part of the chapter focuses on the requirements for future input-output concepts. This is followed by a comparison of these requirements with the existing concepts. Finally, the chapter outlines proposals for future input-output concepts that meet the requirements as far as possible. Since all conceptual questions cannot be treated in a short chapter, the focus here is on connecting I-O data with other NA subsystems. This integration depends on two crucial points: the statistical units chosen and the treatment of transactions. While in the SNA and the ESA the concept of transaction is uniform throughout all subsystems, problems arise due to the varying statistical unit. Therefore the choice of the statistical unit will be considered here in depth. A revision of the concept of transaction is only touched on, taking into account a suggestion of the Dutch Statistical Office (see van Bochove and van Tuinen, 1985). The question of an appropriate price concept will not be dealt with at all (Vu Viet, 1985).

40

The Compilation of Input-Output Tables

OBJECTIVES FOR A REVISION OF I-O CONCEPTS Having a clear idea from the beginning about what is to be achieved by a revision of I-O will greatly facilitate our discussion. Thus five objectives are delineated here: (1) integration, (2) harmonization, (3) statistical basis, (4) evaluation, and (5) continuity. Some of these are conflicting goals, however, and the international discussion will have to decide on priorities.

Integration One of the most important objectives of the revision of I-O concepts is the further integration of I-O data into the system of NA (see Drechsler, 1985; Lal, 1985; Young, 1985). At present I-O is regarded as a special subsystem of NA, describing production activities and commodity flows. Because the statistical unit used in I-O differs from that in other NA subsystems, however, there is only a loose connexion, and a comprehensive description of all transactions—from production to capital finance— is rendered difficult. Possibilities of unifying the statistical units are discussed here. Nevertheless, it should be mentioned that such a unification might reduce the information value of an individual subsystem. This has been pointed out by Stone (1962) as a "solution of Procrustes."

Harmonization Since the SNA as well as the ESA comprise I-O concepts, harmonization of both concepts should be considered an important goal. As will be shown in the following section, I-O concepts now differ substantially in the two systems. This is true particularly for the presentation of I-O data but also for the statistical units as well as the classifications. Harmonization could substantially improve the international comparability of I-O results.

Statistical Basis Strict application of SNA and ESA recommendations for I-O compilation has caused problems for many countries, since the statistical basis is inappropriate or even missing. Thus, as a third objective of revising I-O, the concepts should take into account the available data. This would be the case particularly if I-O compilation concentrates on observable facts and if the macro concepts of NA measures are aligned more closely to (micro) business accountancy concepts (see Lutzel, 1985; Ruggles and Ruggles, 1982; van Bochove and van Tuinen, 1985). Evaluation On the other hand, an adjustment of I-O concepts in this direction could involve difficulties for the evaluation of the results. For the analysis of production activities and commodity flows, data are needed relating to homogeneous commodity groups. In reality, however, such data are not readily available. To bridge this gap, different solutions are recommended in NA systems. While the I-O concept of the SNA is aligned more to the collectability of data, the ESA focuses on the requirements of the

Revising Input-Output Concepts in the SNA and ESA

41

user. As a further goal of revising I-O concepts, a compromise between both aspects should be envisaged.

Continuity In addition to these four objectives, a fifth, more restrictive point has to be taken into account. Many countries reject a fundamental change of the SNA and the ESA. Instead, continuity of concepts is preferred to facilitate the work of both the producer and the user. However, development and continuity of I-O concepts are not mutually exclusive goals. Proposals have been elaborated to maintain the existing concepts while developing them by additional and more detailed presentations (see Chantraine and Newson, 1985; Kurabayashi, 1985). But it has to be stressed that in this way both the clearness and the coherence of the NA system might be affected.

INPUT-OUTPUT CONCEPTS IN PRESENT NA SYSTEMS

The SNA The SNA has been developed in such a way that the definition of transaction is uniform throughout the whole system. On the other hand, the classification of transactions varies between subsystems, particularly with respect to the statistical unit. For instance, production activities are presented for an establishment basis in principle, whereas for other transactions (e.g., income, capital, and finance) the enterprise or an equivalent unit are chosen. In support of the unit of establishment, two main reasons have been put forward: (1) A statistical unit should be homogeneous in its activities as far as feasible and (2) for a statistical unit, data on outputs and inputs should be available. In the SNA, transactions on the origin and use of national product are connected by a comprehensive description of production activities and commodity flows. A basic feature is the indirect, two-stage presentation of the interrelationship between the producing and the consuming unit. First, the output of the producing unit is shown in a commodity breakdown (make matrix). In a second step, the output by commodity group is allocated to the users (use matrix). There are two main reasons for this presentation. On the one hand, basic statistics customarily allow a breakdown of a producer's inputs by commodity, but not a subdivision of inputs according to the supplying unit. Furthermore, since production analysis aims at showing intercommodity relationships on the basis of homogeneous units, the rowwise breakdown of inputs into commodity groups is advantageous. In a simplified form, Table 4.1 shows the presentation of production activities and commodity flows in the "complete system" of the SNA. Since the production and use of commodities are combined at a deeply disaggregated level, this presentation may be considered as I-O data (though not in the form of a traditional I-O table with a uniform classification of rows and columns). Table 4.1 indicates that the SNA presentation of I-O data is much more complex than in traditional I-O tables. The combination of a commodity with an activity classification restricts the comprehensibility of the SNA concept. This difficulty is reinforced by the fact that this presentation applies to market production only. In

TABLE 4.1 Input-output data in the System of National Accounts Activities

Commodities

Activities Market Nonmarket

Components of value added

E

Commodities

Market

Nonmarket

—

Intermediate consumption

Intermediate consumption

Purposes

Purposes

Final consumption expenditure of households

£

Activities Fixed Increase capital in in stocks formation

Exports

Outputs Outputs (market)

— Imports Total supply

Total uses

Total outputs —

Value added Total inputs

Source: United Nations, Table 2.1, rows/columns 5 to 21.

—

Value added Imports Total supply

Final consumption expenditure of government and private nonprofit institutions

Exports

Total uses

Revising Input-Output Concepts in the SNA and ESA

43

contrast, nonmarket production is subdivided by using the same classification for both the production and the use side. Furthermore, in the SNA, final consumption expenditure is supposed to be broken down by purpose, and gross fixed capital formation should be subdivided by activity. Apart from presenting production activities and commodity flows in a matrix form (e.g., Table 4.1), it is also possible to use the form of accounts. Data on commodities from Table 4.1 may be used to establish commodity accounts (for different commodity groups). Data on market and nonmarket activities in this table are the basis for production accounts. The columns of Table 4.1 give the left-hand side ("outgoings") and the rows give the right-hand side ("incomings") of the accounts. An aggregation of such accounts provides for standard accounts as required in Appendix 8.2 of the SNA. As has been mentioned, institutional units like enterprises or their equivalent are used as the statistical unit in the SNA to present transactions other than production. It would have been appropriate to the SNA basic concept if the transition from establishment data to enterprise data were shown in a combined classification. In particular, the transition from establishments to enterprise for value added (including its components) and gross fixed capital formation would be of interest. In the SNA these items are shown separately according to both an establishment and an enterprise classification, but not in the combined presentation of a transition table.1 The lack of transition from establishment data to enterprise data at a disaggregated level represents a missing link between production entries and transactions on income and capital finance. Instead, only the totals can be compared. "The SNA resembles two pillars, leaning against each other for support, but joined together only at the very top" (see van Bochove and van Tuinen, 1985, p. 28). For purposes of I-O analysis the SNA presentation of production in a combined classification of commodity groups and industries is not directly helpful. Instead, I-O tables with a uniform classification of rows and columns are required. Chapter 3 of the SNA provides information about procedures for obtaining I-O tables by transferring I-O data. Starting from tables in a combined classification, and accepting certain assumptions, one is able to establish I-O tables with a uniform classification of rows and columns. The uniform classification may be either a commodity or an industry classification. These I-O tables, however, are not included in the "complete system" (Table 2.1) of the SNA. Practical experience with the I-O concepts of the SNA has revealed different points, which may be advantages as well as weaknesses: 1. The presentation of production activities and commodity flows with use of disaggregated I-O data has to be considered an essential advantage in comparison with earlier versions of the SNA, showing production activities only at an aggregated level. Further integration of I-O data with the SNA system has been obstructed by both varying statistical units and missing linkages between the different SNA subsystems. 2. The combined presentation by commodity group and institutional-type unit has proved successful. By this means, in many countries the basic statistics could be exploited without the need for extensive recalculations. However, according to an inquiry by Franz (1985) on behalf of the Organization for Economic Cooperation and Development (OECD), the statistical unit underlying industries—the

44

The Compilation of Input- Output Tables

establishment—has caused problems. In many countries data on establishments exist for the manufacturing sector only. Moreover, the data normally refer only to assignable direct costs. Difficulties arise when overhead costs, for example of a multiunit enterprise, have to be allocated to various establishments. In some countries, therefore, ancillary units like a central administration office are considered separate establishments, serving the other establishments of the enterprise. Because of these statistical difficulties, to apply the establishment concept countries have more or less to use enterprise data. 3. The transformation procedures given in the SNA are appropriate to compile I-O tables with a uniform classification of rows and columns on the basis of data in a combined classification. But the assumptions proposed (commodity technology and industry technology) are often insufficient for obtaining plausible results, even if they are combined. Some countries therefore have developed modifications of the SNA transformation methods in recent years.

The ESA The national accounting system for EC member countries (ESA) was developed parallel to the SNA in the 1960s. Although work was not strongly coordinated, as it is in the present revision, most concepts and definitions match. As has been pointed out here, a reduction in the remaining differences could be envisaged as an objective for the present revision. A comparison of I-O presentation in the SNA with that in the ESA reveals equivalences as well as major differences. Both systems recommend a distinction between production and other transactions (e.g., income, capital finance). In both systems the compilation of production activities and commodity flows is to be based on I-O data, the presentation of other NA subsystems on institutional sectors (comprising enterprise-type units). Differences mainly result from the fact that the SNA focuses on the collectability of data whereas the ESA stresses the requirements of the user. Consequently, for the presentation of production in the ESA, commodity x commodity tables are used. In the SNA, this type of I-O table is not included in the "complete system" (Table 2.1) but is presented as additional tables. The presentation of I-O tables with a commodity classification of rows and columns implies statistical units that are homogeneous in respect to their output. In reality, however, such units of homogeneous production for the most part do not exist. Often the subdivision of an enterprise's activities into units of homogeneous production is possible only on the grounds of assumptions. If the allocation of overhead costs to individual establishments is difficult already, it is worse for units of homogeneous production. The treatment of by-products and adjacent products is an exception from this idea of homogeneity in the ESA. These are transferred in a second step. Units of homogeneous production are aggregated to form a branch, that is, the branch produces all goods and services specified in a product classification, and only that classification. Table 4.2 shows the presentation of I-O data in the ESA. Since the definition of branches is uniform for columns and rows—apart from the treatment of by-products—it is possible to connect the supply and the use of commodities directly. Thus the intermediate step of the SNA make matrix, which reclassifies the output of

TABLE 4.2 Input-output data in the European System of Accounts Branches Market Commodities

Nonmarket

Intermediate consumption

Components of Value added Actual output value added Transfers of by-products, etc. Distributed output Imports I Total resources Source: EUROSTAT, 1979, Tables T4, T6b, T7c, T13.

Purposes Final consumption of households

Purposes Collective consumption of general government and private nonprofit institutions

Branch of Ownership Fixed capital formation

E Change in stocks

Exports

Total uses

46

The Compilation of Input-Output Tables

establishments to respective commodity groups, is redundant. The following calculation explains the linkages: intermediate consumption of branch A + gross value added of branch A = actual output of branch A + transfers of ordinary by-products and adjacent products = distributed output of branch A + imports of similar products = total resources of commodity group A = total uses of commodity group A Both systems provide a breakdown of private final consumption expenditure by purpose and a subdivision of gross fixed capital formation by kind of economic activity of the owner (in the ESA by branch of ownership). These subdivisions are presented in additional tables in the ESA. They have been included in the I-O system to allow a comparison between Tables 4.1 and 4.2. As for the SNA, in addition to the presentation in tables, accounts are used to show transactions in the ESA. However, a complete system like Table 2.1 of the SNA, from which both tables and accounts may be derived, is missing. The structure of the system of accounts differs substantially between ESA and SNA. In the SNA, production accounting is quite separate from the income and finance part. Furthermore, the presentation of production is based exclusively on I-O data. The ESA recommends a presentation of production by accounts, in a breakdown of both branch and institutional sector. In this case, I-O data are only the basis for the presentation by branch. The level of breakdown differs in the ESA for branches (fortyfour or twenty-five) and institutional sectors (eight), thus preventing a combination of both. The following accounts are assigned according to the ESA: The goods and services account (CO) of the economy. This account can be derived from the I-O table, which is classified on a product basis. It contains information on total resources and uses of goods and services, without a breakdown by commodity groups or by branch. The production account (Cl) for branches and for institutional sectors. Production accounts show the output of goods and services, intermediate consumption, and value added for branches and institutional sectors. Output is not broken down by user, and intermediate consumption is not shown by commodity group. The generation of income account (C2) for branches for institutional sectors. This account divides value added into its components (consumption of fixed capital, production taxes, subsidies, compensation of employees and operating surplus). Since the generation of income account has to be established for institutional sectors as well, these data are the basis for the presentation of further distributive and financial transactions. This procedure links production with other NA subsystems— at least at an aggregate level. However, at a disaggregated level transitions from data for institutional sectors to branches (I-O tables and accounts) are missing. Table 4.3 shows the ESA concept of presenting production. The appropriate SNA concept is shown for comparison. From practical work with the ESA, experience has been gained that should be

TABLE 4.3 The presentation of production in the System of National Accounts (SNA) and the European System of Accounts (ESA) SNA Tables i [ I-O data in a combined classification by commodity group and activity

ESA Accounts

Tables

Commodity accounts for commodity groups

I-O tables in a uniform commodity classification of rows and columns

Accounts Goods and services account for the economy Productionaccouaccounts Production accounts for branches for institutional sectors

cf. Table 4.1

Production accounts for activities

Production ] i i ] [

I-O data in a uniform ] commodity or an >! industry classification of rows and columns

Generation of income accounts for branches

Generation of income accounts for instit. sectors

Production

Income/ap ' 't 1 fi e

!

cf. Table 4.2

1

Accounts on income transactions (institutional sectors) Accounts on capital finance (institutional sectors)

I / alo 'tfin1 fi ncomecap

i Distribution of income accounts (instit, sectors) financial

Capital and financial financial accounts (instit, sectors)

48

The Compilation of Input-Output Tables

taken into account for a revision. In particular, in respect to the presentation of production the following points have been observed: The ESA concept of I-O tables in a product subdivision has proved successful from the point of view of the user. Comparisons in time and space as well as particular sector studies and I-O analyses could be based directly on such I-O data, without the need for recalculations. A further support has been the I-O methodology issued by EUROSTAT (1976). A problem that remains is the treatment of byproducts and adjacent products: On the one hand, I-O models require a transfer of such products in proportion to the change in output. On the other hand, this procedure does not guarantee an equality of the transfer, that is, the value taken out of the producing branch and the value included with the distributing branch. The restriction of the ESA, that I-O tables are presented only in a product classification, has recently caused critical comments on both sides, from the producer and the user of NA figures. In particular the question of whether production should be presented on the basis of—mostly—fictitious data instead of transactions actually observed has been stressed. In the latter case, the presentation should preferably be based on data of institutions like enterprises and establishments instead of units of homogeneous production. Nevertheless, apart from a presentation of data for institutional sectors, data for commodity groups and branches are needed. Only a combination of both concepts would allow the full spectrum of NA analyses to be applied. Serious reservations concerning the I-O concept of the ESA have been expressed by the producers. In most cases, the recommended breakdown of activities into units of homogeneous production cannot be derived directly from basic statistics. Therefore recalculations of basic data using theoretical assumptions are necessary. But since the ESA offers no assistance in this respect, each country applies its own method of recalculating its basic data to meet the EUROSTAT requirement for an appropriate I-O table. PROPOSAL FOR FUTURE I-O CONCEPTS OF THE SNA AND ESA The practical experience gained in applying the SNA and the ESA may be used as a starting point for a revision of the respective I-O concepts. The five objectives previously mentioned may serve as a guide: 1. 2. 3. 4. 5.

Integration of I-O data with the NA system Harmonization between the I-O concept of SNA and ESA Improved connection of I-O aggregates to basic statistics User-oriented I-O concepts Continuity of I-O concepts

A proposed revision is elaborated here. This proposal centers on continuity and dispenses with a full harmonization between the I-O concept of SNA and ESA. It aims to increase the degree of integration of I-O data with the NA system, to improve the connection between I-O data and basic statistics, and to allow further analysis by providing additional data. These objectives are to be fulfilled by developing more precise definitions, additional tables, and a more detailed description of procedures.

Revising Input-Output Concepts in the SNA and ESA

49

The proposal is closely in line with considerations of Dutch colleagues (see van Bochove and van Tuinen, 1985). The proposal initiates more precise and extended I-O concepts of the SNA and ESA. Accordingly, in the SNA a combined classification of commodity groups and establishments is maintained for presenting production. In addition, the ESA description of production activities and commodity flows would continue to be based on an I-O table with a homogeneous product classification.

The SNA Maintaining the establishment as the statistical unit to present production requires clearing up some questions. A major problem is the treatment of certain central activities of a multiestablishment enterprise (like separate administration and research activities). The SNA recommends (see United Nations, 1968, paragraph 5.19) distribution of these costs of activities (overhead costs) among the individual separate establishments. This corresponds to the ESA regulation (see EUROSTAT, 1979, paragraph 267). However, in different countries this common international regulation is not practiced. Instead, central activities are regarded as a separate establishment serving other producing units. From the point of view of regional analysis this is an advantage. Central activities are regarded as a resident unit of the respective region, without the need for distributing them (proportionately) among other regions. But even the treatment of central activities as separate establishments does not solve the allocation problem. Rather, an output of this establishment has to be determined (possibly by adding up costs), and to be allocated subsequently among the producing units as intermediate consumption. Two points are important for the treatment of central activities. First, the allocation procedure must be understandable. Second, the users must be able to get separate information on observed data and more or less fictitious allocations. As has been stressed, in the SNA the connection between production and other NA subsystems is insufficient. If the SNA maintains industries for production and institutional sectors for other transactions, transition tables should be envisaged. For certain flows such tables could show the detailed transition from an establishment to an enterprise classification. Such a "classification converter" meets a basic idea of the SNA: Subsystems of the NA system may have a different outlay according to the needs, but in such cases links should be established going beyond the aggregate level. Considering Table 2.1 of the "complete system" of the SNA, transition tables should be established to serve as a junction between production and income transactions. Transition tables could be included for value added (and its components) and gross fixed capital formation (possibly in a breakdown by categories), showing these items in a combined classification of industries and institutional sectors. Since many countries already dispose of a register connecting establishments to the owning enterprises, the statistical obstacles seem surmountable. Additionally, the transformation methods from I-O data in a combined classification to I-O tables with uniform classification of rows and columns should be improved. In practice, the methods described in Chapter 3 of the present SNA (United Nations, 1968) have turned out to be insufficient. Applying one of the proposed assumptions rigorously may result in implausible figures. Instead, some possibility of influencing the transformation procedure is necessary. This would be allowed by the

50

The Compilation of Input-Output Tables

following two-stage transformation method: (1) A special transformation matrix is established by applying a technology assumption to each row (column) to be transferred. The row and column totals of this matrix show the initial data and the results after transformation, respectively. (2) The elements of a special transformation matrix could be adjusted selectively in such a way that the margin vector containing the data to be transformed remains unchanged (see Stahmer, 1985b).

The ESA The ESA concept for I-O tables with a uniform product classification has proved reasonable, and no extensive revision seems necessary. However, the future treatment of by-products and adjacent products has to be discussed. As has been pointed out above, the present treatment involves difficulties for I-O analysis. As a solution, the transformation of such products could be based on the idea of the SNA make matrix. Accordingly, the transition from actual output (i.e., before transferring) to distributed output (i.e., after transferring) should be shown in a matrix instead of just a row. This output matrix would give the actual outputs by branch as column totals and the distributed outputs by commodity group as row totals. Thus the transfer of byproducts and so on is carried out within a column vector of this matrix. Table 4.4 outlines this new structure of I-O tables. It can be shown that such output tables are a framework for I-O models allowing for a consistent coverage of by-products. In this context, the output table serves as a transformation matrix from the row to the column classification (and vice versa) in each step of the iterative procedure of I-O models. In the ESA, a connection between the I-O table—showing data classified by branch (commodity groups)—and the production as well as income accounts— classified by institutional sector—is possible only at the aggregate level. For a closer connection a more disaggregated presentation of the production activities of institutional sectors is needed. Such a disaggregated presentation, in combination with a breakdown of output and intermediate consumption by commodity group, would TABLE 4.4 Commodity-classified input-output in a revised European System of Accounts Branches Commodities

E

Intermediate consumption

Final uses

Total uses

Imports

Total resources

Value added

X

Actual output

Commodities

Outputs

I

Actual output

Distributed output

+

Revising Input-Output Concepts in the SNA and ESA

51

provide for the connection to the commodity flows given in I-O tables. In disaggregating institutional sectors, a further question relating to the legal form of business enterprises has to be considered. A decision should be made whether data for nonfinancial corporate and quasi-corporate enterprises (sector S10) are shown separately from data for sole proprietorships and so on (included with the sector households S80), or whether the sectoral disaggregation should start from the totals of the nonfinancial enterprises without taking into account their legal form. From the statistical point of view it should be easier nowadays for most EC member countries to establish tables in a disaggregated, combined classification of institutional sectors and commodity groups than it was at the time the present ESA was developed. Both the extension of statistics on enterprises and the increasing use of computerized facilities for combining different statistics are in support of this suggestion. The extension of production and generation of income accounts for institutional sectors could be carried out in three steps: (1) The accounts could be given in a finer institutional breakdown in accordance with an ESA proposal (see EUROSTAT, 1979, paragraph 124). (2) and (3) The sectoral output and intermediate consumption could be broken down by commodity group. In general, the present ESA does not discuss the problem of deriving NA data from basic statistics. Accordingly a compilation methodology to establish I-O tables in a product classification is missing. This could be included in a future I-O methodology (see EUROSTAT, 1976). In addition, if production accounts for disaggregated sectors were extended as mentioned previously, a transition procedure to obtain data for homogeneous units based on institutional data should be recommended. Since the production activities of enterprises are usually more heterogeneous than those of establishments, the SNA procedures developed for transforming establishment data need modification in any case. Applying the twostage procedure described has proved successful—at least for the Federal Republic of Germany—even in the case of enterprise data. An overview of all suggestions given in this section for completing the SNA and the ESA is given in Table 4.5.

TABLE 4.5 Proposals for completion of present System of National Accounts (SNA)/European System of Accounts (ESA) input-output concepts SNA

ESA

More precise definition of establishment (e.g., treatment of separate central ancillary units like administration, research)

Presentation of by-products and adjacent products in output table

Transformation tables from activities to institutional sectors (e.g., for value added and fixed capital formation)

Further institutional breakdown of production accounts; presentation of outputs and intermediate consumption in tables with a combined classification of commodity groups and institutional sectors

Improved procedures for transforming I-O data in combined classification to I-O tables in uniform classification

Description of transformation procedures from data in institutional classification to data in commodity classification

52

The Compilation of Input -Output Tables

NOTE 1. SNA (1968), Table 2.1, rows (columns) 29 to 32 and rows (columns) 56 to 70, respectively.

REFERENCES Chantraine, A., and B. Newson. August 1985. "Progress on the revision of the European System of Accounts." Paper presented at 19th Conference of the International Association for Research in Income and Wealth (IARIW), Noordwijkerhout, Netherlands. Drechsler, L. August 1985. "Statistical integration, necessity and conflict." Paper presented at 19th IARIW Conference, Noordwijkerhout, Netherlands. EUROSTAT. 1976. Community Input-Output Tables 1970-1975 Methodology. Special Series 1. Luxembourg: EUROSTAT. EUROSTAT. 1979. European System of Integrated Economic Accounts-ESA. 2nd ed. Luxembourg: EUROSTAT. Franz, A. August 1985. "National accounts sectoring and statistical units of reporting and classification." Paper presented at 19th IARIW Conference, Noordwijkerhout, Netherlands. Kurabayashi, Y. August 1985. "United Nations Statistical Office progress report on the review of the System of National Accounts." Paper presented at 19th IARIW Conference, Noordwijkerhout, Netherlands. Lal, K. May 1985. "Canadian input-output tables and their integration with other subsystems of the National Accounts." Paper presented at International Meeting on Problems of Compilation of Input-Output Tables, Baden, Austria. Lutzel, H. August 1985. "Market transactions in the national accounts." Paper presented at 19th IARIW Conference, Noordwijkerhout, Netherlands. Ruggles, N., and R. Ruggles. 1982. "Integrated economic accounts for the United States, 194780." Survey of Current Business 625: 1-53. Stahmer, C. May 1985a. "Integration of input-output with the international system of national accounts." Paper presented at Conference on Input-Output Compilation, Baden, Austria. Stahmer, C. 1985b. "Transformation matrices in input-output compilation." In A Smyshlyaev (Ed.), Input-Output Modeling. Proceedings of the Fifth International Institute for Applied Systems Analysis Task Force Meeting (Laxenburg, October 1984). Berlin: Springer-Verlag. Stone, R. 1962. "Multiple classification in social accounting." Bulletin of the International Statistical Institute 39, Part 3. United Nations. 1968. A System of National Accounts. Studies in Methods, Series F, No. 2, Rev. 3. New York. United Nations. 1971. Basic Principles of the System of Balances of the National Economy. Studies in Methods, Series F, No. 17, New York. United Nations. 1973. Input-Output Tables and Analysis. Studies in Methods, Series F, No. 14, Rev. 1. New York, van Bochove, C. A., and H. van Tuinen. August 1985. "Building block approach to flexibility of the SNA." Paper presented at 19th IARIW Conference, Noordwijkerhout, Netherlands, van den Bos, C. August 1985. "Integration of input-output tables and sector accounts: The possible solution." Paper presented at 19th IARIW Conference, Noordwijkerhout, Netherlands. Vu Viet, Q. August 1985. "Input-output standards in the SNA framework." Paper presented at 19th IARIW Conference, Noordwijkerhout, Netherlands. Young, P. C. May 1985. "The U.S. input-output experience, present status and future prospects." Paper presented at International Meeting on Problems of Compilation of Input-Output Tables, Baden, Austria.

5 The Simultaneous Compilation of Current Price and Deflated Input-Output Tables S. DE BOER and G. BROESTERHUIZEN

This chapter discusses a number of aspects of the procedure by which input-output tables are compiled in the Netherlands. These tables are compiled annually and are fully integrated in the System of National Accounts. A few years ago the method of compiling the input-output tables underwent a revision that, in our opinion, led to great improvement. The most significant improvement is that during the entire statistical process, from the processing and analysis of the basic data up to and including the balancing of the input-output tables, current-price data and deflated data are obtained simultaneously and in consistency with each other. We believe that this innovation may be of interest to other countries that currently compile inputoutput tables or wish to do so in the future, because many countries still—as the Netherlands used to do—first compile data in current prices and afterward calculate data in constant prices and changes in volume and prices, often on a higher level of aggregation. This means that an important opportunity for analysis of the interrelations between various kinds of data, and thus for better estimates, is used little or not at all. In cases where input-output tables are drawn up periodically, the comparability in time of the estimates is an important requirement. For an-extensive discussion on this matter the reader is referred to Al and Broesterhuizen (1985). Plausibility checks of observed changes between two periods can be carried out more easily and more meaningfully if the change in value is split into a volume change and a price change. In addition, with the informational aspect of economic statistics in mind, it is also important that there be complete consistency between deflated input-output tables and the tables with volume and price indices. This can be achieved by compiling both current-price data and deflated data simultaneously. Figure 5.1 shows part of the series of tables that then become available in the course of time. In the Netherlands, deflated data pertaining to a period t are always expressed in prices of the period t — 1. In this way the weighting schemes that are used to combine detailed indices to define changes at a higher aggregation level remain up to date. In this process the volume changes are combined using Laspeyres' formula and the price

54

The Compilation of Input-Output Tables

changes using Paasche's formula. The problems concerning the appropriate choice of index number formulae are not discussed in this chapter (the reader is referred to Central Bureau voor de Statistick, 1984, and Al, Balk, de Boer, and den Bakker, 1984). The Dutch input-output tables are currently still relatively limited in size. They consist of about 225 rows and 135 columns. The rows refer to industry groups, or to commodity groups that add up to industry groups. The columns refer to industry groups and final expenditure categories. The published tables—industry group x industry group tables—are derived from these tables by aggregation. In the last few years a number of developments in economic statistics have led, or will lead

Compilation of Current Price and Deflated Input-Output Tables

55

to, an improvement in the basic data available for the input-output table, or to better conditions for the processing and analysis of the basic data. Three aspects of this improvement are as follows: 1. The introduction of a standardized goods nomenclature for a large number of basic statistics, and in particular for price statistics 2. The start of annual statistics for the trade and service sectors 3. Automation of the processing and analysis of the basic material and the balancing of the input-output table These developments make it easier than it has been up to now to utilize volume and price data in the analysis and processing of the basic data. In the near future the Netherlands will probably switch over to compiling separate make and use matrices, in accordance with the recommendations of the SNA with respect to the compilation of input-output information. Therefore we have opted here to explain the procedure involving volume and price information with the aid of this widely known scheme of make and use matrices. The following sections contain a step-by-step discussion of the various stages within the statistical process. In this discussion we stress the simultaneous compilation of data in current prices and deflated data. The chapter concludes with a short look at the possibilities of applying the system we have described in various situations that differ with respect to the basic statistical material available.

THE STATISTICAL PROCESS IN THE NETHERLANDS General Remarks A number of phases can be distinguished in the compilation process of make and use matrices: 1. 2. 3. 4.

Data collection phase Adjustment phase Processing phase Balancing phase

For the sake of simplicity we here give the impression that these phases are passed through separately and consecutively. Actually the process is more an iterative one, in which the operations of a certain phase are followed by or carried out at the same time as operations in other phases. For example, during the balancing phase the basic material may still have to undergo some corrections, in which case the adjustment and processing phases will have to be repeated. In this section we shall look briefly at phases 1 and 2. Attention will be given to the available basic information that is used in the compilation of the make matrix and the use matrix. Phases 3 and 4 will be examined separately in the following sections. Available Basic Information The information used in compiling make and use matrices relates to a multitude of aspects of the economic process. There are data relating the production and input structure of industry groups, imports and exports of goods and services, household

56

The Compilation of Input-Output Tables

and government consumption, gross fixed capital formation, and stocks. These sources are not gone into here in detail; only the most important sources are outlined. The most important group of statistics involving the compilation of information by sector of industry is that covering annual production. These statistics are compiled or are currently in process of being developed for over 100 groups in manufacturing, 8 groups in construction, and more than 50 groups in the services sector, including a large number in trade. Among other things, these statistics give detailed information on sales (split into domestic sales and exports), purchases, and initial and final stocks of the goods manufactured and consumed. The data are usually specified by commodity, though on the user side in particular unspecified or only roughly specified transactions do occur. Where sales or purchases are specified by commodity, both values and volumes are given. In many cases, particularly for manufacturing, the results of the production statistics relate to a proportion of all the enterprises in the industry group concerned; results cover enterprises with at least ten employees. In the trade and services sector results usually relate to all enterprises; they are obtained by grossing up the results of the sample survey. For industry groups for which production statistics are lacking, many various, and often external, sources are used. In the case of agriculture, for example, there are ample functional statistics, that is, statistics relating to markets for certain goods (but not to producers). Often the only information for large parts of the services sector is the number of persons practicing a certain profession and the amount of wages and salaries. With respect to foreign trade, there are very detailed estimates of imported and exported goods and rougher estimates of imported and exported services. Data on consumption are based mainly on household budget surveys and figures for turnover in retail trade. Fixed capital formation is observed partly by means of direct surveys but mainly through indirect measurement in the context of the commodity-flow method of the input-output table. Finally, price statistics provide very detailed information on the prices of goods: imported goods, exported goods, goods produced domestically, and goods and services consumed by the public.

The Adjustment Phase Continuity In constructing the national accounts, great importance is attached not only to the accurate estimation of levels but also, to an even greater extent, to the accurate estimation of trends. Users of the national accounts and input-output tables require long time series of data, for example to estimate econometric models or to make comparisons over time. As has been explained elsewhere, the objectives of accurate levels and accurate changes cannot be achieved simultaneously (see for example, Algera, Mantelaers, and van Tuinen, 1982; Al and Broesterhuizen, 1985). Adjustment of the basic material should be carried out in such a way that the data for the year under review can be compared with those of the preceding year (in other words so that the change that can be calculated from the consecutive levels does in fact reflect the actual development of the variables in question). The specific problems involved in these categories of adjustment have already been described in detail in the references just given.

Compilation of Current Price and Deflated Input-Output Tables

57

Adjustment Because of Incomplete Data The basic statistics used are not always complete. We have already stated, for example, that production statistics for the manufacturing industry refer only to enterprises employing ten or more persons. These results therefore have to be grossed up to include enterprises employing fewer than ten people and self-employed persons. For this group of enterprises, production, consumption, and value added are estimated on the basis of the number of self-employed workers and the totals of wages and salaries paid, relative to figures relating to enterprises with more than ten employees. Another form of incompleteness may occur when the basic statistics are subject to systematic distortion. This could be the case for figures based entirely on tax returns from persons or enterprises. In these cases the basic data are grossed up by a certain percentage to take into account some degree of fraud. This involves an explicit grossing up of value added by nearly 1 percent of the gross domestic product (see also Broesterhuizen, 1984). Adjustment Because of Differences Between Company Accounts and the National Accounts The great majority of statistics used in compiling the national accounts are based on statements by enterprises. The information these enterprises supply to the Central Bureau is derived from their accounts. This means that inevitably there are differences between the information supplied and the information required in the context of the national accounts. Adjustments have to be made to translate figures for the financial year of the enterprise concerned into figures for the calendar year. The greatest differences, however, occur as a consequence of differences in valuation. In the framework of the national accounts, changes in stocks are valued in terms of actual prices. These changes cannot be expressed as the difference between the values of the initial and final stocks as stated by the enterprise if the enterprise does not assign the true value to stocks. The stated values of initial and final stocks then have to be corrected. These very laborious corrections are, if possible, carried out for each commodity by making use of volume data, where stated; the prices of the commodity; and knowledge of the valuation method applied in the industry group concerned.

THE PROCESSING AND ANALYSIS PHASE Once the adjustments described in the last section have been carried out, we have certain basic information that covers the entire field concerned; is comparable with that of the previous year; and, as far as definitions and registration are concerned, is in accordance with the guidelines of the national accounts. In the processing phase, an overview of receipts and expenditures is compiled for each industry group. To this end, specialists draw up estimates of volume and price changes in production, consumption, and value added. Plausibility checks are carried out on the results in each of these areas. In some cases, in particular in agriculture and the food industry and in some parts of the services sector, the available information consists only of these data on volumes and prices. The calculation of values, which is fairly laborious, can then be completed. In the majority of cases, however, and particularly in manufacturing, information

58

The Compilation of Input-Output Tables

currently available relates to production, consumption, and value added in current prices, while additional information is available on volumes and prices. Here an iterative process of analysis and adjustment of the data is used to arrive at estimates in current prices and in prices of the previous year. The procedure is roughly as follows: The most detailed basic data relating to values in current prices and volumes are used to determine the production or intermediate consumption of a large number of commodity (groups), in current prices and in prices of the previous year. If no volume data are available, price developments based on price statistics are used. Plausibility checks on the values of production and intermediate consumption thus obtained take place at the commodity (group) level, and also through comparisons of volume changes in production and intermediate consumption with each other and with other sources (viz., short-term statistics, employment data, etc.). If these volume changes turn out to be implausible, the derived data are subjected to further analysis. The sector specialist may conclude that a different deflator is required for a certain commodity group or that the original-value figures in current prices are incorrect. Adjustments are then made and new plausibility checks carried out at the level of total production and intermediate consumption. This process is repeated until definitive estimates are determined. It can be stated that the simultaneous compilation of data in current prices and in prices of the previous year results in an improvement in both sorts of data, compared with the results achieved by compilation of figures in current prices followed by deflation after the make and use matrices in current prices have been completed. This conclusion becomes even more obvious when we take into account the role played by price information in determining trade and transport margins on consumption goods and in allocating unspecified or incompletely specified items to commodity groups. Examples of these items are "other raw materials," "other costs," "other metalware," "wood products," and so on. Most of these used to be specified further on the basis of the allocation in the previous year, without specific price developments being taken into account. This can lead to serious distortions, as became very evident at the time of sharply increasing energy prices in the 1970s. For some time now a method of allocation that has been used in Denmark for a much longer period of time (see Thage, 1985) has been applied. In this method, the allocation is carried out on the basis of 1. 2. 3. 4.

The allocation of the corresponding item in the previous year Price changes of all the goods involved The value changes of the total item The assumption that all the components undergo the same volume change

The form of the make and use matrices does not strictly require consumption to be broken down into producers' values and the trade and transport margins. However, for the definitive balancing procedure (i.e., reconciling demand and supply for goods and services), such a breakdown is necessary. The data available to make this breakdown are limited, and it must therefore be carried out on the basis of assumptions. The method is completely identical to the allocation of the unclassified items described above. Particularly when sharp price fluctuations are involved, it seems more likely that the volume of trade and transport margins is proportional to the volume of consumption than that the nominal values of the margins are proportional to the nominal values of consumption. If the first assumption is taken as

Compilation of Current Price and Deflated Input-Output Tables

59

a starting point, then price changes for the trade margins will have to be incorporated separately. The allocation methods described above give only provisional results. Corrections can still be made during the balancing process. It should be clear that these methods entail an improvement with respect to the situation where the only allocation was carried out on the basis of the allocation of the previous year in current prices. In general, however, it is appropriate to give a word of warning about such allocation processes, which are, it should be stressed, necessary. As Thage (1985) has stated, the use of such classification routines in the make and use matrices may lead to a distortion in the direction of imposing greater constancy on the underlying economic structures. This conservatism, in the sense that the statistician will usually assume a gradual rather than an extreme structural change when faced with a lack of information, is justified because his objective must be the minimization of errors in his estimates. As he does not know in such cases the direction which the effects will take, he opts for an average.

THE BASIC SCHEME The basic scheme is formed by a use matrix and a make matrix (Figure 5.2). The contents of the use matrix are as follows: Columns: Relate to industry groups, including any subsidiary activities, and categories of final expenditure (export, final consumption, gross fixed capital formation, and changes in stock) Rows: Relate to Consumption of goods and services classified by standard commodity groups. Transactions are valued at purchasers' prices, including trade and transport margins and commodity taxes and subsidies Commodity taxes and subsidies paid to the government by each industry group, classified by type of tax Noncommodity taxes and subsidies paid to the government by each industry group, classified by type of tax Compensation paid to primary production factors for each industry group classified by category (wages and salaries, social insurance contributions paid by the employer, operating surplus) The column totals of the use matrix give the gross output for each industry group, valued at producers' prices, including the balance of product-linked taxes and subsidies and the totals for each category of final expenditure, respectively. The contexts of the make matrix are as follows: Columns: Contain the production classified by commodity group for each industry group, valued at the approximate basic value Rows: Give the produced trade and transport margins (this relates to subsidiary activities where industry groups outside trade are concerned) As a consequence of the fact that the inputs in the use matrix are valued at purchasers' prices including margins, the margins' row is empty there and the total of the row is therefore 0. In the make matrix the row for margins should also total 0. For this reason a balancing entry for margins is entered on the diagonal, and the margins are

FIGURE 5.2 sEE TEXT FOR DISCUSSION.

Compilation of Current Price and Deflated Input-Output Tables

61

allocated to the commodity groups to which they relate in the column "Trade and transport margins." The commodity taxes, levies, and subsidies entered as costs in the use matrix are included again in the make matrix, this time as a component of the value of production for industry groups. In the corresponding columns they are allocated to commodities, so that for each commodity group in the make matrix the row total is the total valued at purchasers' prices. For the sake of completeness the total of noncommodity taxes, levies, and subsidies and the components of value added are entered in a diagonal cell in the make matrix. Naturally there is a column with imports broken down by commodity group in addition to the domestic production of that commodity group. The column totals of the make matrix give the gross output of each industry group valued at producers' prices, total imports, and the totals for each category of primary costs. A characteristic of the scheme is that (after balancing) the corresponding totals for each commodity group and each industry group in the use matrix and the make matrix are equal. The choice of valuation for entries in the use and make matrices is made on the basis of the following considerations: Valuations should link up as closely as possible with the basic information. This consideration applies both to the money values of the flows and to the nature of the deflators to be used. As far as possible the basis of valuation within the use matrix and the make matrix should be the same. As many other desired valuations as possible should be capable of being derived as simply as possible from the chosen valuation. The precise dimensionality of the use and make matrices is not known at present. We expect that the standardized commodity groups will number about 1500, with about 200 industry groups. To simplify the necessary surveys, the number of final expenditure categories will remain limited. Detailed information on final expenditure (exports by group of countries, fixed capital formation by industry group, private consumption by trade channel, type of household, or income group) will probably be elaborated on in separate systems, which will naturally be related to the use matrix. A characteristic feature of the method of compiling input-output information described here is that for each period for which an input-output table is added to the existing series, transaction tables are compiled simultaneously with deflated values, volume indices, and deflators. This means that for each element of the make matrix and the use matrix five numbers play a part: the figures for t and t — 1 expressed in current prices; the figure for t in prices of t — 1, the volume index, and the deflator. Another way of putting this is that each time, five use matrices and five make matrices go to make up the system. Figure 5.3 gives an outline of this relationship. In principle, the information set out in this figure can be derived in a number of ways. It is necessary that data be present for three of the five pairs of tables. The "normal" situation is that A is known from the calculations of the preceding period and that B and C are derived from basic data that have been processed and checked in previous phases. Tables D and E are derived subsequently. In a situation with incomplete information, for example in the case of estimates for recent periods (in the Netherlands, the two most recent years

62

The Compilation of Input -Output Tables

FIGURE 5.3 See text for discussion. under review), the data are usually based on indicators for volume changes and deflators. In this case A, D, and E are entered and B and C are subsequently derived. The scheme can further be used for restoring continuity in the series of input-output tables following a general revision. Here the procedure is the other way around: C is the known quantity and it will usually be possible to retain previous values for E. If the revision also affects previously estimated volume indices, D should be adjusted accordingly. Revised versions of A and B can then be derived from C, D, and E. THE BALANCING PROCESS When all the stages of the statistical process described in the previous sections have been completed, the use matrix is completely filled, whereas in the make matrix only the columns referring to the production of industry groups and the imports of goods and services are filled. The trade and transport margins, commodity taxes, and subsidies for each industry group are then added to the make matrix. These data can be estimated with the aid of, for example, figures for the gross profit margins of commercial companies, turnover of transport companies, tax rates, and so on. The balancing of the use matrix and the make matrix is, in the first instance, related to the data as they are set out in Figure 5.2. For various reasons—the need for transformation to derive various types of input-output tables for example—it is necessary that the tables be balanced in each desired valuation. To this end each element in the use matrix has to be broken down into trade margin, transport margin, indirect taxes and subsidies, and approximate basic value. When the data on the margins and indirect taxes and so on have been entered in the make matrix, the structure to be balanced is complete (see Figures 5.2 and 5.3). Ex post, the totals of the use matrix and make matrix should be equal, both in current

Compilation of Current Price and Deflated Input-Output Tables

63

prices and in previous-year prices. Initially these equalities will be satisfied in only a very few cases. Various methods can be conceived of for eliminating the differences, the choice depending on factors specific to the country concerned (e.g., the availability of manpower and time) and on the nature of the basic statistical material. For a discussion of these methods, such as those used in Denmark and Norway, the reader is referred to Thage (1985) and to Furunes and R0geberg (1982). A number of characteristics of the balancing process to be applied in the Netherlands in the near future are given in the following. On the one hand the aim is the simultaneous compilation of estimates in current prices and prices of the previous year; on the other hand the aim is the balancing of commodity markets in two phases.

Simultaneous Balancing of Current Price and Deflated Data As in the preceding phases of the statistical process, the procedure in the balancing phase takes place as far as possible simultaneously for the data in current prices and the deflated data. Differences for a commodity group are eliminated by adjusting elements in either the use matrix or the make matrix. If a figure in current prices is adjusted, the consequences for the corresponding figure in prices of the previous year, in volume index, and in the deflator are examined. If a deflated figure is adjusted, a similar procedure takes place. This allows the possibility of checking the plausibility of an intended correction. Deflators that can be found in the various columns of the use matrix and the make matrix for a single commodity group constitute an important starting point for analysis of differences. These deflators were determined independently of each other in previous phases of the statistical process. Now they are compared and their consistency with each other is checked. Such checks can point to where corrections are needed. Some differences can only be eliminated by means of corrections on important aggregates: the gross output or the total input of goods and services of an industry group. As a consequence of such corrections, the value added as determined in the stages preceding the balancing stage must also undergo correction. In this respect, the simultaneous correction of data in current prices and deflated data makes analysis of the effect of such a correction on the change in operating surplus and on the volume change possible at the same time. It would often seem obvious to eliminate differences by making corrections in the consumption of households or in fixed capital formation. The method described here presents the possibility of directly analyzing the consequences of such corrections on the volume of final expenditure. If, according to statistical experts, intended corrections to value added or expenditure in either current prices or volumes turn out to lead to improbable results, alternative ways should be sought to eliminate the original discrepancy. It may be expected that balancing simultaneously in current prices and in constant prices will result in a different allocation of corrections than balancing only in current prices. This is due to the fact that simultaneous balancing allows the consequences of an intended correction to be seen more clearly. Balancing in Two Phases The first balancing phase consists of an analysis of the most important differences as far as size is concerned. This first phase may best be described as the search for

64

The Compilation of Input-Output Tables

inconsistencies in the figures collected and processed in the phases leading up to the balancing phase. Such inconsistencies may come about as a result of, for example, uncorrected errors in the observation of data, incomplete observations (e.g., of changes in stocks), the use of invalid assumptions in the allocation of unspecified items or in grossing up data for missing companies, and the use of unrepresentative deflators. Any or all of these errors can occur in previous phases of the process. In this first balancing phase, the elimination of differences takes place completely on the subjective grounds of human judgment. Both the use matrix and the make matrix are divided into blocks of interrelated commodity groups (e.g., metal products, foodstuffs, services). The idea behind this is that the consequences of corrections due to elimination of differences will be noticeable mainly within one such block. This first balancing phase continues until, in the opinion of statisticians, the remaining differences are so small that their elimination will have relatively insignificant consequences. For elimination of these remaining differences, which are likely to be numerous, a mechanical computer-based algorithm may be helpful. The fact that the matrices to be balanced are so large makes automation of part of the procedure in the balancing phase an absolute necessity. In the Netherlands we are currently experimenting with a method of eliminating differences in which each element of the make matrix and use matrix is assigned a confidence margin. When the balancing phase has been completed, the user has at his or her disposal a system of tables containing consistent and detailed information on the levels, volume changes, and price changes of goods and services transactions, in and between two periods. In addition this system includes detailed information on levels and trends in primary incomes and final expenditure in both nominal and real terms. All this is significant for various categories of users: policymakers, builders of macromodels and models of components of the economy (industry groups, final expenditure categories), market researchers, and others. Many users of national accounts data require long series of data on volume and price indices. This need can be met by means of a series of tables, as shown in Figure 5.1. Apart from this, users of input-output tables require information on the interindustrial relations of groups of economic agents and on the direct and indirect relations within the production structure of a certain country. To this end, they need input-output tables of the type industryxindustry and homogeneous activity x homogeneous activity. This information does not occur as such in the make matrix and the use matrix, since these are commodity x industry tables. The required input-output tables can, however, be derived from the use matrix and the make matrix by simple transformations. A description of these transformations falls outside the scope of this chapter; the reader is referred to the literature, for example United Nations (1968).

CONCLUSION Thus chapter describes how the quality of input-output information can be improved by making use of information relating to volume and price changes at each stage of the statistical process. This is explained with the aid of a system of make and use matrices such as the Netherlands is aiming to achieve in the near future. It should be mentioned, however, that the principle of applying price and volume data to the entire statistical process has already been in use for a number of years. The system presented here for the compilation and presentation of input-output

Compilation of Current Price and Deflated Input-Output Tables

65

data can be applied in a wide variety of statistical environments. Some relevant aspects of this environment are: The nature and specification of the available basic data The quantity and quality of the available manpower The time available In practice, statistical environments vary widely. On the one hand there are differences between different countries; on the other, there are differences in the completeness and detailed classification of data between recent and earlier years within countries. Depending on the availability of basic information, manpower, and computer capacity, the compilation of make and use matrices must to a greater or lesser extent always involve assumptions. These assumptions may relate, for example, to the allocation of unspecified items and to the relation between input and output. Making these assumptions always leads to loss of detail, since the tendency is to use averages. In each statistical environment statisticians have to determine how much loss of detail they consider acceptable. More assumptions are allowed with provisional data than with definitive data. The objective of the information to be obtained is also significant. If this objective is study of the input structure of industry groups or the composition of final expenditure, less reliance may be placed on the assumptions used than when the aim is estimates of the level, volume, or price changes of goods and services transactions in the context of the national accounts. Less loss of detail with use of assumptions can be expected if differences in price changes between various goods and services are taken into account. One condition for this is that there be sufficient relevant price information. The resulting disaggregation is, however, probably of a better quality than when disaggregation is carried out on the basis of nominal value data alone.

REFERENCES Al, P. G., B. M. Balk, S. de Boer, and G. P. den Bakker. 1984. "The use of chain indices for deflating the national accounts." Study prepared by the Dutch Central Bureau of Statistics under contract with EUROSTAT, Voorburg, Netherlands. Al, Pieter, and Guus Broesterhuizen. May 1985. "Comparability of input-output tables in time." Paper presented at International Meeting on Problems of Compilation of InputOutput Tables, Baden, Austria. Algera, S. B., P. A. H. M. Mantelaers, and H. K. van Tuinen. 1982. "Problems in the compilation of input-output tables in the Netherlands." In J. Skolka (Ed.), Compilation of Input-Output Tables. Berlin: Springer-Verlag. Broesterhuizen, Guus, 1984. "The unobserved economy and the National Accounts in the Netherlands: A sensitivity analysis." In W. Gaertner and A. Wenig (Eds.), The Economics of the Shadow Economy. Berlin: Springer-Verlag. Central Bureau voor de Statiskiek. 1984. "Input-outputtabellen 1981 in prijzen van 1980." In De Produktiestructuur van de Nederlandse Volkshuishouding. Vol. 12. Dutch Central Bureau of Statistics, Voorburg, Netherlands. Furunes, Nils Terje, and Svein Lusse R0geberg. 1982. "Compilation of input- output tables in Norway." In J. Skolka (Ed.), Compilation of Input-Output Tables. Berlin: Springer-Verlag. Thage, Bent. May 1985. "Balancing procedures in the detailed commodity flow system used as a basis for annual input-output tables in Denmark." Paper presented at International Meeting on Problems of Compilation of Input- Output Tables, Baden, Austria. United Nations. 1968. A System of National Accounts. Studies in methods series F, no. 2, Rev. 3. New York: United Nations.

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III INPUT-OUTPUT AND THE ANALYSIS OF TECHNICAL PROGRESS

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6 Technical Progress in an Input-Output Framework with Special Reference to Japan's High-Technology Industries SHUNTARO SHISHIDO, KIYO HARADA, and YUJI MATSUMURA

Rapid technical progress in Japan's manufacturing industries in recent years, especially after the oil price shock in 1973, has attracted much international concern among economists and policymakers. Although technical progress at the macroeconomic level has suffered from a deterioration since 1973, the manufacturing industries have experienced a remarkable annual increase in productivity, especially the high-technology industries such as robotics, microelectronics, fine chemicals, fine ceramics, and biotechnology. The rise in these new sectors, which have replaced the traditional ones such as steel, petrochemicals, ship building, and heavy electrical appliances, facilitated a rapid structural change during the 1970s that was characterized by a shift from capital-intensive and energy-using sectors to knowledge-intensive and energy-saving sectors. This chapter analyzes the mechanism behind this structural change in an inputoutput framework. It also analyzes factor productivity on a detailed sectoral basis with special reference to the high-technology industries. A new approach is presented in this context. It integrates factor productivity and relative price analysis with the input-output model by using the V-RAS method, a modified RAS method that includes primary factors (the V matrix) in a consistent framework. An empirical test of this approach over the period 1975 to 1980 provided fairly satisfactory results, and the approach was thus judged a reasonable basis for the analysis of a rapid change in industrial structures. Finally we present in this chapter alternative scenarios for the Japanese economy in the year 2000, with special reference to the high-technology industries, by using alternative technology assumptions. The international impacts of these scenarios are also discussed by combining the I-O model with a multicountry model. The technology studies and forecasts are based on detailed government I-O tables, comprising 544 x 409 matrices, which have recently been made available in terms of both current and constant prices (see Government of Japan, 1985).

70

Input-Output and the Analysis of Technical Progress

THE V-RAS METHOD: A MODIFIED APPROACH INTEGRATING TOTAL FACTOR PRODUCTIVITIES AND RELATIVE PRICES Since Stone's approach in 1963, a number of variants of the RAS method for analyzing and forecasting the input-output coefficient matrix have been attempted. In the context of the study of high-technology sectors showing rapid technical progress in Japan, we have developed a new approach that integrates traditional total factor productivity and related relative price analysis by expanding the traditional RAS method to cover both intermediate and primary input matrices. This method has the advantage of allowing analysis of technical changes in a consistent framework on the basis of an input-output matrix including real value added, that is, its component primary factors such as labor and capital. Our I-O coefficient matrix a*, therefore, is rectangular with dimension (n + m) x n, instead of the ordinary n x n, including m row vectors of primary input, which is specified, as below, in terms of constant prices of the base year.

where aij denotes an intermediate input coefficient and vmj a primary input coefficient, such as labor and capital costs; and r denotes total factor productivity (TFP), since both intermediate and primary input coefficients are expressed in constant prices of the base year. The basic idea for this definition of a£j lies in the fact that technical change can best be studied by analyzing both types of input factors simultaneously in a consistent framework. The use of a£,- has another advantage, since its summation over k gives the inverse of TFP, a most useful and comprehensive concept for measuring productivity (see Jorgenson and Griliches, 1967; Jorgenson, Kuroda, and Nishimizu, 1985). As shown in the following, the ordinary RAS method can be strengthened by this extension of the matrix of aij to the matrix a*, and by integrating the a*j with relative price analysis. This method may conveniently be termed V-RAS, since it includes the V matrix. Our model for technical change can be formulated in the following way.

Technical Progress in Japan's High-Technology Industries

71

where r = substitution parameter a = a* in base year s = efficiency parameter rf = r for intermediate input rs = r for primary input p = relative input price change over base year p = input price of intermediate input p = average input price p = p in base year \JL = import dependency ratio px = output price pm = import price pv = price of value added v = input coefficient of primary factor pf — price of primary factor n = profit margin per unit of output Given exogenous values for TFP (t), the price of primary factors (pf), the profit margin (71), import prices (pm), and the base year I-O matrix (akj\ the model can determine simultaneously technical change for target year, as expressed by the variables a*, r, and s, and the price level variables px, p, p, p. We shall be more specific about individual equations and the causal relationship behind them. As mentioned before, the efficiency parameters (s) in the conventional RAS system are now approximated by the inverse of TFP, so that s can be easily accounted for by the change in TFP as shown in equation (4). These TFP changes are usually estimated using information relating to technology, such as R&D expenditures, diffusion of new technologies, and relative factor prices; changes in these directly affect s parameters. The r parameters, representing substitution among factor inputs, are mostly explained by the changes in relative prices as in (6), since we assume that the elasticities of substitution of a specific product are approximately equal among different users. A detailed analysis discussed later, however, indicates that additional explanatory variables, such as dummy variables representing rate of technical progress and other nonprice factors, are required. In any case, we expect that 0 > a > — 1 in most cases, where a = 6 In r/8 In p. Equations (8), (9), and (10) determine output and input prices that are affected by changes in the parameters s and r, that is, changes in the a* matrix. Therefore, our

72

Input -Output and the Analysis of Technical Progress

causal relationship indicates simultaneous interdependence between a* and p, mostly arising through changes in r. A representation of this interdependence is given below.

where

> = endogenous flows > = exogenous flows A, A* = matrices comprising akj, a*j

Rapid technical growth in high-technology products, for example, lowers their s parameters and prices, which in turn raises their r parameters due to substitution, stimulating demand for them (shown later). We now discuss the empirical implementation of this new approach, which we have called V-RAS. EMPIRICAL ASPECTS OF TECHNICAL CHANGES FROM 1970 TO 1980 In July 1985, the Japanese government published I-O tables for 1970, 1975, and 1980, including a 544 x 409 matrix on a perfectly comparable basis in both current and 1980 prices (Government of Japan, 1985). On the basis of this detailed I-O data base, employment (distinguishing between employees and others) and capital stock data were estimated on a 406 sectoral basis for these three benchmark years by the University of Tsukuba and Foundation for Advancement of International Science (FAIS) (Shishido et al., 1981). Primary input coefficients for labor and capital in both current and 1980 prices were also estimated, and the latter series in real terms served as a basis for obtaining sectoral TFP, as specified in equation (2). TFP changes for major high-technology industries are indicated in Table 6.1; full details of the changes are available on request. Although no adjustment was made for differences in the rates of capacity utilization, the table shows the rapidity of technical progress in Japan's high-technology sectors during the 1970s. In view of the fact that the average annual rate of technical progress in terms of TFP for all industries was 1.3 percent during the period 1970 to 1975 and 1.0 percent during the period 1975 to 1980, and that there was a marked tendency in the average rate of technical progress to decline in the latter half of the 1970s due to oil price increases, it is surprising that there was no such general tendency (rather signs of an acceleration) in some sectors of the high-technology industries. Technical progress is particularly conspicuous for office machinery, computers, semiconductor devices and integrated circuits, and computer rental services, which show rates of increase about ten times or more the average rate. Rapid increases are also observed for new material- and biotechnologyrelated sectors such as acrilonitrile fiber, electric wires and cables, and medicines. Table 6.2 shows a picture of the whole economy during the 1970s. The table was

Technical Progress in Japan's High-Technology Industries

73

TABLE 6.1 Total factor productivity of high-technology industries on 409-sector basis Annual Growth Rate (%)

Level

1970

1975

1980

1975/1970

1980/1975

0.844

0.909

0.995

1.49

1.82

0.789 0.213

0.845 0.502

1.007 1.018

1.38 18.70

3.57 15.19

0.767 0.502 0.469 0.670 0.302 0.731 0.510

0.865 0.736 0.723 0.839 0.583 0.820 0.714

0.990 1.089 1.156 1.078 1.086 1.055 1.073

2.43 7.95 9.04 4.60 14.06 2.32 6.96

2.74 8.15 9.84 5.14 13.25 5.17 8.49

0.254

0.528

1.111

15.76

16.04

0.624 0.743 0.609 0.547 0.592 0.755

0.809 0.857 0.700 0.728 0.751 0.863

1.016 1.025 1.004 1.024 0.985

5.33 2.90 2.82 5.88

4.66 3.65 7.48 7.06

4.87

1.135

2.71

5.57 5.63

0.185

0.324

0.564

11.86

11.72

New materials Acrilonitrile fiber Other glass and glass products Other basic nonferrous metal products Metal doors and shutters Electric wires and cables

0.480 0.666 0.786 0.691 0.622

0.787 0.784 0.917 0.953 0.884

0.986 0.945 1.071 1.014 1.111

10.39 3.32 3.13 6.64 7.28

4.61 3.81 3.15 1.25 4.68

Biotechnology Agricultural chemicals Medical preparations Toilet preparations and dentifrice

0.403 0.690 0.685

0.619 0.921 0.920

0.834 1.167 1.158

8.96 5.95 6.08

6.14 4.85 4.71

Industry Electronics Machine tools Other general industrial machinery and equipment Office machinery Sewing machines and wool knitting machinery Electric sounders Radio and television sets Electric equipment for home use Electric computers and accessory devices Other applied electronic equipment Electronic tubes Semiconductor devices and integrated circuits Telecommunication machinery and related equipment Electric measuring instruments Medical instruments Cameras Watches and clocks Musical instruments Rental and leasing of electric computers and accessory devices

obtained by aggregating the original table calculated on a 409-sector basis. Rapid technical progress is also noted for high-technology industries such as 46, light electrical applicances (mostly electronics); 49, precision instruments; and 34, other chemical products (fine chemicals), their rates of increase being four to five times the average. It should be noted that rates for these sectors tended to accelerate during the latter half of the observation period, probably as a result of higher energy prices. Also notable is the fact that several industries declined or were stagnant in terms of technical progress: agricultural crops, fisheries, crude oil and natural gas, printing and publishing, basic petrochemicals, coal products, and building construction. They all tended to marked decline after the oil price shock. It can be stated, therefore, that the oil price increase in the latter half of the period under consideration resulted in accelerating the imbalances in technical progress between different sectors, especially

Input- Output and the Analysis of Technical Progress

74

TABLE 6.2 Total factor productivity of all industry on 72-sector basis Annual Growth Rate (%)

Level Sector a 1 2 3 4 5 6 7 8 9 10 11

12 13

14 15

16b 17

18 19 20

21 22 23 24 25 26 27 28 29 30 31

32 33 34 35 36 37 38

1970

1975

1980

1975/1970

1980/1975

0.771 0.490 0.923 1.212 0.906 0.812 0.825 1.465 0.752 0.893 0.791 0.831 0.960 0.860 1.426 (2.516) 0.674 0.725 0.694 0.874 0.877 0.908 0.876 0.847 0.998 1.200 1.022 0.981 0.572 0.662 0.519 0.470 0.563 0.735 1.069 1.119 0.853 0.954

0.739 0.485 0.732 1.065 0.860 0.776 1.063 1.292 0.866 0.969 0.983 0.922 0.995 0.876 1.556 (3.307) 0.741 0.806 0.800 0.957 0.905 0.963 0.934 0.890 0.956 1.029 1.059 0.867 0.541 0.777 0.838 0.657 0.584 0.890 1.099 1.029 0.908 0.967

0.685 0.508 0.742 1.102 0.729 0.833 1.118 0.986 1.114 1.022 1.033 0.841 1.028 0.975 1.594 (3.352) 0.830 0.912 0.871 0.945 0.954 0.976 0.987 0.930 1.009 1.005 0.937 0.945 0.693 0.755 0.986 0.797 0.778 1.106 1.114 1.022 0.962 0.906

-0.84 -0.20 -4.53 -2.55 -1.04 -0.90 5.20 -2.48 2.86 1.65 4.44 2.10 0.72 0.37 1.76 (5.62) 1.91 2.14 2.88 1.83 0.63 1.23 1.29 1.13 -0.86 -3.03 0.71 -2.44 -1.10 -3.15 10.06 6.93 0.74 3.90 0.56 -1.66 1.26 0.27

-1.51 0.93 0.02 0.68 -3.25 1.12 1.01 -5.26 5.17 1.07 1.00 -1.82 0.66 2.16 0.48 (0.27) 2.29 2.50 1.72 -0.25 1.06 0.27 1.11 0.88 1.08 -0.47 -2.42 1.74 5.08 -0.57 3.31 3.94 5.90 4.44 0.27 -0.14 1.16 -1.30

between high-technology and other industries. These imbalances also affect relative prices and demand components for intermediate inputs, an effect which is discussed in the following section.

TESTING V-RAS ESTIMATES OF PRICES AND I-O COEFFICIENTS For the empirical implementation of our model for technical change, we first estimated r and s parameters of the (544 + 4) x 409 matrices for 1975 and 1980 by using

Technical Progress in Japan's High-Technology Industries TABLE 6.2

(Continued) Annual Growth Rate (%)

Level Sector a

39 40 41 42 43 44 45 46

47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66

67 68 69 70

71 72 Total

75

7970

1975

1980

0.846 0.892 0.920 0.695 0.836 0.830 0.793 0.604 0.909 0.949 0.630 0.835 1.059 1.156 1.071 0.714 0.933 1.048 0.836 1.027 1.468 1.944 0.796

0.0

0.907 0.896 1.153 0.871 0.869 0.867 0.949 0.768 0.951 0.977 0.764 0.874 1.127 1.265 1.127 0.791 1.065 1.046 0.954 1.125 1.767 2.493 0.806

1.055 1.040 0.993 1.065 0.997 1.015 1.023 1.070 1.013 1.022 1.011 0.990 1.044 1.145 1.014 0.787 1.126 0.959 1.019 1.212 1.852 4.354 0.854

0.0

0.0

1.021 0.933 1.023 0.947 0.975 1.002 1.035 1.083 0.944 1.530

1.683 0.960 0.986 0.927 1.042 1.071 0.954 0.984 1.002 1.055

1.521 1.035 1.075 0.982 1.146 1.046 0.950 1.000 1.087 1.040

10.51 0.57 -0.73 3 -0.43 3 1.34 1.34 -1.62 2 -1.900 1.20 -7.177

0.909

0.970

1.021

1.31

1975/1970

1.40 0.09 4.62 4.62 0.78 0.88 3.66 4.92 0.91 0.58 3.93 0.92 1.25 1.82 1.02 2.07 2.68 0.38 2.68 1.84 3.78 5.10 0.25

0.0

1980/1975

3.07 3.03 -2.944 4.10 2.79 3.20 1.51 6.86 1.27 0.91 5.76 2.52 2 -1.527 -1.977 9 -2.09 9 -0.100 1.12 -1.722 1.33 2.00 0.94 11.80 1.16

0.0

-2.00 0 1.52 1.74 1.16 1.92 -0.47 7 -0.08 8 0.32 1.64 -0.299 1.30

a

See Table 6.6 for sectoral classification. Excluding real capital input.

b

the V-RAS method in equation (3). The primary input coefficients in the value-added matrix are composed of four row vectors: (1) real cost for business consumption, (2) real cost for employees, (3) real cost for entrepreneurs and family workers, and (4) real user cost of capital. The real cost (v) in equation (1) denotes the unit-factor cost deflated by base year prices, that is, 1980 prices.1 The results for high-technology industries are shown in Table 6.3 (full details are available on request). As can easily be seen, the s parameters are closely related to the inverse of TFP, whereas the r parameters seem to correspond to the changes in relative prices, as anticipated in our theoretical model. Broadly speaking, it is clear

76

Input -Output and the Analysis of Technical Progress

TABLe 6.3 r and s parameters of high-technology industries on 548 x 409 sectoral basisa

Industry Electronics Machine tools Other general industrial machinery and equipment Office machinery Sewing machines and wool knitting machinery Electric sounders Radio and television sets Electric equipment for home use Electric computers and accessory devices Other applied electronic equipment Electric tubes Semiconductor devices and integrated equipment Telecommunication machinery and related equipment Electric measuring instruments Medical instruments Cameras Watches and clocks Musical instruments Rental and leasing of electric computers and accessory devices

1970-1975

1975-1980

r

s

r

s

0.866 1.537 3.171 0.999 1.568 1.253 1.852 1.682 1.572 1.224 2.030 1.210 1.291 1.793 1.285 1.133 0.888

0.996 0.936 0.424 0.949 0.686 0.649 0.772 0.511 0.889 0.736 0.484 0.787 0.889 0.912 0.789 0.833 1.013

1.914 1.237 1.883 1.586 2.034 1.063 1.423 2.623 1.956 0.924 3.094 1.585 1.320 1.393 1.139 1.341 1.225

0.814 0.831 0.428 0.804 0.626 0.563 0.748 0.486 0.753 0.714 0.494 0.695 0.812 0.721 0.755 0.761 0.812

1.695

0.588

1.346

0.613

0.494 1.006 1.111 1.731

0.539 0.908 0.843 0.784 0.636

2.316 1.132 0.992 1.110

0.808 0.834 1.140 0.945 0.954

New materials Acrilonitrile fiber Other glass and glass products Other basic nonferrous metal products Metal doors and shutters Electric wires and cables Copper electric wires and cables Aluminum electric wires and cables

0.858 0.872

Biotechnology Agricultural chemicals Medical preparations Toilet preparations and dentifrice

1.898 1.375 1.146

1.364 0.956

0.680 0.790 0.777

1.025 1.586 1.201

0.753 0.805 0.798

"See equation (3) and following for explanation of r and s parameters.

that the lower the value of s, the higher the value of r becomes. (e.g., office machinery and semiconductor devices compared with electrical tubes and musical instruments). On the basis of these parameters, we estimated two types of structural equations, that is, equations (4) and (5), for the parameters s and r, respectively, on the basis of cross-sectional data for 1975 and 1980. The results are shown in Tables 6.4 and 6.5. The following specifications were used for the s and r functions.

whereZi and i zkj are industrial dummtyy variables.

TABLE 6.4 s functiona s=

a0

«! W*7S

-0.063

1.137 (78.14) *28

(3116-10) -0.198 (-2.57)

*2l

(0014-20) 0.086 (1.12) <*29

(3210-00) 0.157 (2.04)

CC22

<*23

<*24

(2012-30) -0.330 (-4.30)

(2070-00) -0.230 (-2.99)

<*25

(2092-00) -0.176

(2306-00) -0.243 (-3.17)

<*30

(3416-00) -0.096

(-1.25)

aSee equation (11) and appendix for sectoral classification code.

«31

(3421-10) -0.548 (-7.13)

(-2.29) «32

(3421-20) 0.330 (4.30)

«33

(3605-10) -0.069 (-0.90)

*26

(2313-00) -0.267 (-3.48) «34

(3840-00) -0.225 (-2.92) R2 = 0.9399 S = 0.0767 d = 1.3256

«27

(3112-30) -0.147 (-1.91) *35

(5120-00) -0.166 (-2.17)

TABLE 6.5 r functiona log r= 1. Agriculture, forestry and fisheries

a0

ai

«21

*22

<*23

*24

C*25

(0011-240) 0.880 (3.00)

(0014-310) 1.278 (4.33)

(0014-610) 0.528 (1.76)

«26

-0.095

logp -0.618 ( 4 3-4.38)

(0014-990) 0.345 (1.17)

(0016-190) -0.830 (-2.83)

(0016-410) -0.603 (-2.05)

y.21 (0016-910) 0.330 (1.13)

(0016-920) 1.368 (4.47)

3d log p -0.615 (-3.50)

(1101-030) 0.569 (3.14)

a0 2. Mining

-0.085

*28

*21

X

29

a

30

«31

«32

(0212-220) -1.590 (-5.43)

(0220-020) 0.413 (1.38)

(0017-020) -4.058 (-13.85)

(0020-090) 0.326 (1.11)

a22 (1101-040) 0.358 (2.01)

«23

«24

«25

(1210-010) -0.588 (-3.24)

(1220-100) 1.521 (8.44)

(1301-020) 0.178 (0.93)

a 27 (1990-900) -0.633 (-3.46) a0 3. Food and tobacco manufacturing

0.075

a! logp -0.399 ( — 3.21)

R2 = 0.8280 S = 0.2898 rf = 2.1059 £Z

26

(1302-200) 1.679 (8.37) R2 = 0.8999 S = 0.1707 d = 1.5858

*21

«22

*23

«24

(2011-030) 0.906 (5.00)

(2040-100) -0.347 (-1.90)

(2040-210) -0.483 (-2.59)

(2040-220) -0.215 (-1.18)

«27

*28

a.2g

(2091-700) -0.522 (-2.88)

(2110-100) -0.237

(2110-300) -0.206

(-1.30)

(-1.13)

=<30

«31

(2110-500) -0.289 (-1.58)

(2200-000) 0.385 (2.06)

«25

«26

(2040-320) -0.174 (-0.95)

(2050-120) - 1.754 (-9.20) R2 = 0.7324 5 = 0.1789 d = 1.5453

logr = 4. Textile, wood, furniture, pulp, and printing industries

a0 0.091

a0

5. Chemical product manufacturing

0.200

«i logp -0.517 (-2.09)

(2301-100) -0.628 (-2.67)

a 27 (2520-020) -0.117 (-0.50)

*21

*22

*23

(2311-100) 1.006 (4.23)

(2316-000) 0.302 (1.26)

(2720-120) 0.239 (1.02)

a 29 (2720-200) -0.141 (-0.60)

(2720-300) -0.377 (-1.61)

*i logp -0.538 (-6.57)

a 21 (3112-110) -0.335 (-2.09)

(3112,150) -0.443 (-2.75)

a 27 (3118-112) 0.552 (3.43)

(3118-113) 0.728 (4.55)

*28

«28

<*22

Z 29

(3118-114) 0.307 (1.93)

*30

«23

(3112-310) 0.311 (1.93) <*30

(3118-200) -0.241 (-1.51)

<*24

(2320-000) 0.797 (3.39) «31

(2720-400) -0.638 (-2.67) <*24

(3116-300) 0.664 (4.17) =<31

(3119-120) -0.363 (-2.23)

«25

(2390-400) -0.676 (-2.69) «32

(2800-910) -0.081 (-0.34) «25

(3116-400) 0.586 (3.67) 1*32

(3192-500) -1.061 (-6.62)

<*34

6. Ordinary machinery, electrical machinery, and transport equipment manufacturing

a0 0.086

0.1 logp -1.118 (-10.03)

R2 = 0.2322 S = 0.4764 d = 2.2278 *26

(3116-900) -0.631 (-3.91) 1*33

(3192-611) -0.355 (-2.23) R2 = 0.7174 S = 0.1580 d = 2.1132

(3192-619) -0.268 (-1.68)

log r=

*26

(2510-100) -0.126 (-0.53)

a2j (3602-100) 0.518 (3.03)

(3603-100) -0.288 (-1.68)

(3603-571) -0.396 (-2.24)

(3604-151) 0.320 (1.86)

(3604-170) -0.383 (-2.24)

(3701-100) 0.452 (2.64)

a 27

<*28

a29

«30

*31

«32

1*33

(3702-220) -0.593 (-3.37)

(3704-230) -0.460 (-2.68)

(3704-240) 0.426 (2.40)

(3810-100) 0.830 (4.82)

(3850-200) -0.313 (-1.83)

(3850-300) -0.381 (-2.23)

(3910-100) 0.272 (1.58)

*34

(3920-100) -0.338 (-1.96)

<*35

(3920-200) -0.406 (-2.37)

<*22

<*23

<*24

<*25

«26

R2 = 0.7552 S = 0.1693 d = 1.9134

TABLE 6.5 a0

7. Other manufacturing (mostly, basic industries)

0.107

«! logp -0.346 ( — 3.38) «27

(3390-100) -0.412 (-2.15)

«21

(3210-010) -0.243 (-1.27) X

2S

(3390-410) -0.133 (-0.69)

(Continued) «22

(3210-020) 0.348 (1.76) "29

(3390-421)

-0.512 (-2.67)

*23

(3210-060) -0.825 (-4.06) «30

(3390-429) -0.128 (-0.67)

<*24

(3210-091) -0.264 (-1.33) «31

(3411-000) -0.589 (-3.06)

«25

(3210-092) 0.448 (2.28) «32

(3412-000) -0.573 (-2.93)

a 34 (3501-110) -2.194 (-11.43) log r=

8. Transport

aa

0.024

«0

9. Wholesale, finance and insurance; other Services, Industrie**; industries

0.012

a. logp -0.538 (-2.62)

«1

«21

«22

<*23

*24

(7110-010) -0.220 (-1.41)

(7110-020) -0.260 (-1.80)

(7121-021) -0.167 (-1.17)

*25

(7170-011) 0.144 (0.97)

(7170-210) 0.920 (6.41)

«21

(5110-100) 0.211 (1.14)

(8213-210)

(8290-200) 0.594 (3.25)

»28

*34

(8700-000) 0.500 (2.74) a

«33

(3416-020) 1.348 (6.87) R2 = 0.7735 5 = 0.1903 d = 1.7206

logp -0.520 ( — 4.50)

0.798 (4.36)

*26

(3340-000) -0.096 (-0.50)

See equation (12) and appendix for sectoral classification code.

<*22

(5120-000) 0.250 (1.36) «29

(8300-200) 0.695 (3.80)

«23

(5130-000) 0.736 (3.90)

«24

(8210-050) -0.618 (-3.38)

<*30

<*31

(8300-400) 0.321 (1.76)

(8300-600) 0.482 (2.64)

«25

a

26

(7190-000) 0.338 (2.35) R2 = 0.8024 5 = 0.1378 d = 2.5728 «26

(8210-060) -0.520 (-2.84)

(8213-110) -1.273 (-6.96)

a32 (8300-900) 0.518 (2.83)

(8302-300) 0.529 (2.89)

«33

J?2 = 0.7263 S = 0.1809 d = 1.7076

Technical Progress in Japan's High-Technology Industries

81

As shown in the table, results for the s function were fairly satisfactory with twelve dummy variables for 409 cross-sectional data points. al is almost unity, as expected, and negative values for the dummy variables represent faster technical progress than given by the standard parameters a0 and aj. As far as the r function is concerned, we separated the economy into nine groups after various attempts using regression analysis on 548 cross-sectional data points. The elasticities of substitution for these groups range from —1.1 in machinery to —0.3 in basic industry, satisfying the requirement of consistency with economic theory in all cases. Positive signs with dummy variables imply positive additional demand accounted for by nonprice factors, as in machine tools and semiconductor devices in the machinery group, for instance, while negative signs are noted for the opposite type of commodities, as in motorcycles and bicycles in the same group. A similar tendency is observed for other groups. Our estimation results are fairly satisfactory, in terms of goodness of fit, for all groups except textiles and other consumption goods. Finally, we tested our model's capacity for ex post prediction for 1980 on the basis of the 1975 I-O coefficients and the parameters just discussed. Two types of variable—sectoral intermediate demand and price levels for both input and output prices—were predicted by solving our model, as shown in equations (1) through (10). As a matter of convenience, pv in (9), the price of value added, was treated as exogenous, which allowed deletion of equation (10). The results for sectoral intermediate demand (Table 6.6) and input prices (Table TABLE 6.6 Ex post testing of V-RAS method: intermediate demand for 1980 prices) Sector 01 02 03 04 05 06 07 08 09 10 11

12 13 14 15

16 17 18 19 20 21 22 23

24

25 26 27 28

Agricultural products Livestock and sericulture Agricultural services Forestry and logging Fishery products Coal Metal mining Crude petroleum and natural gas Other nonmetal mining Slaughtering and dairy products Seafood preserved Grain cleaning and flour Other food products Formula feed Beverages Tobacco Fiber yarn Fabric Knitted fabric Other ready-made textile goods Apparel and accessories Lumber and wooden products Furniture and fixtures Pulp and paper Paper products Printing and publishing Leather and leather products Rubber products

U 7833143.0 2924755.0 405760.0 2994903.0 1795952.0 1275780.0 1773949.0 13356630.0 2369862.0 1264759.0 531357.0 974225.0 3225513.0 1353881.0 1113210.0 60672.0 1295278.0 2830808.0 107014.0 992393.0 780824.0 5283631.0 2070721.0 4140746.0 3906856.0 5135162.0 260407.0 1843752.0

V 8183799.6 2946282.9 420430.9 3001511.0 156456.6 1456278.0 1858434.6 13367348.6 2156476.2 1305492.4 547609.7 1025239.0 3240545.1 1294578.2 1103085.8 65396.3 1303315.4 3206492.3 112116.6 1029662.7 828152.3 5414905.4 2177439.3 4357670.6 3909509.2 5122424.6 279589.7 1661597.6

U v-u/u /U 0.04477 0.00736 0.03616 0.00221 -0.12834 0.14148 0.04763 0.00080 -0.09004 0.03221 0.03059 0.05236 0.00466 -0.04380 -0.00909 0.07787 0.00621 0.13271 0.04768 0.03756 0.06061 0.02485 0.05154 0.05239 0.00068 -0.00248 0.07366 -0.09880

TABLE 6.6

U

Sector 29 30 31 32 33 34 35 36 37

(Continued)

u*-u/u

U*

3267753.0 4239956.0 1120388.0 2472421.0 883743.0 550261 1.0 14417762.0 2465361.0 7576598.0 11028720.0 11362084.0 3906409.0 3534475.0 4221381.0 8071673.0 11813944.0 1210866.0 8383650.0 8403180.0 1924745.0 1114685.0 5628922.0

3580172.1 4447411.6 1111527.5 2519678.9 888466.1 5345647.1 14043809.0 2826694.1 7900309.0 10899697.5 11402895.6 4174179.2 3771217.2 4220965.2 8127069.1 12419110.7 1349093.7 8405235.0 8508885.5 2075675.8 1233193.9 5297968.2 0.0 4090399.2 0.0 8236220.6 795514.7 1637452.1 20263631.0 12998469.1 785617.4 4394348.7 10996110.7

0.09561 0.04893 -0.00791 0.01911 0.00534 -0.02853 -0.02594 0.14656 0.04273 -0.01170 0.00359 0.06855 0.06698 -0.00010 0.00686 0.05122 0.11416 0.00257 0.01258 0.07842 0.10632 -0.05880

0.0

0.0

3508139.0

3493145.1

-0.00427

67 68 69 70 71 72 73 74 75 76

Basic chemicals Basic petrochemicals Chemical fiber materials Plastic Chemical manures and pesticides Other chemical products Petroleum refinery products Coal products Nonmetallic mineral products Iron and steel materials Basic iron and steel products Iron and steel casting and forging Nonferrous metal materials Basic nonferrous metal products Metal products General machinery Heavy electric machinery Light electric appliances Motor vehicles Other transport equipments Precision instruments Other industrial products Building construction Building repairing Civil engineering Electric power supply Gas supply Water supply Commerce Financial and insurance services Real estate dealing Real estate rents Transport Self-transport Communication Public administration Education Research Health and social insurance Other public services Other services Office supplies Packing Activities not elsewhere classified Cb*» Yw*a Yu*" Yk*a

1086.0 267216.0 150191.0 1592144.0 13982735.0 1043677.0 2745584.0 9452253.0 10186331.0 130541696.0 22944384.0 75484096.0

972.2 273354.0 121085.9 1648115.8 14087904.9 1004326.8 2981481.3 9757252.7 10226726.4 128630319.2 22422597.1 75319354.4

-0.10482 0.02297 -0.19379 0.03515 0.00752 -0.03770 0.08592 0.03227 0.00397 -0.01464 -0.02274 -0.00218

76

Total of intermediate sectors

292976207.0

295055144.9

0.00710

38

39 40

41 42

43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

66

0.0 4235178.0 0.0 8131465.0 787780.0 1646123.0 22057149.0 12183432.0 980273.0 4778253.0 10980259.0

0.0 -0.03418 0.0 0.01288 0.00982 -0.00527 -0.08131 0.06690 -0.19857 -0.08034 0.00144

0.0

Error ratio (in terms of mean absolute deviation) = 0.04727 a

Cb* = Real unit cost for business consumption; Yw* = real unit cost of employees; Yu* = real unit cost of entrepreneurs and family workers; Yk* = real unit user cost of capital; U* = estimated intermediate demand; U = actual intermediate demand. 82

Technical Progress in Japan's High-Technology Industries

83

6.7) prove to be fairly satisfactory on a seventy-two-sectoral basis. The error of total intermediate demand is only 0.7 percent, and the average of absolute percentage errors is 4.7 percent. Forty of sixty-eight sectors (excluding sectors without intermediate demand) have absolute percentage errors less than 5 percent. Considering the predictions for price levels, both input and output price deflators indicate a satisfactory performance. Weighted averages of predicted values are 99.8 percent of the actual values for both deflators. Fifty-six sectors (of seventy-two) have errors less than 5 percent for input price deflators, and fifty-four for output price deflators. In view of the fact that there were rapid increases in energy prices and sharp changes in relative prices during the late 1970s, our results on the ex post prediction of sectoral demand and prices seem to indicate the practicability of our V-RAS method, in which prices and technology can be simultaneously determined.

LONG-TERM IMPACTS OF THE ACCELERATION OF TECHNICAL PROGRESS IN HIGH-TECHNOLOGY INDUSTRIES Our next objectives are to make long-term forecasts of technology, output, and employment on the basis of the V-RAS method tested previously, and to evaluate the long-term impacts of accelerating technical progress in high-technology industries. These are projected as an alternative scenario. A technology forecast for TFP in the 544 x 409 I-O coefficient matrix was conducted by a multidisciplinary group consisting of economists, natural scientists, engineers, and government experts, most of whom were residing in Tsukuba Science City.2 The research project has been funded by the National Institute for Research Advancement since 1984 (see Shishido et al., 1986) and is still going on with international collaboration, especially with the University of Pennsylvania. A statistical data bank for industrial technology and marketing (ITOM), on a detailed I-O basis, has recently been established, and this was fully utilized for the present study. Because this is a long-term projection and tentative in nature, we used a simplified version of the V-RAS approach where the r parameters were determined exogenously. This is because of the difficulty involved in forecasting prices for primary factors pf for the year 2000. Thus we focused our attention only on equations (1) through (5), though some parameters in equation (6) were used for side analysis. The framework of our forecasting process is summarized as follows, each stage being given in sequence. 1. Macroeconomic forecasting of Japan and other countries for 1990 with a world model, and extrapolation of Japan to the year 2000 2. Revision of Japan's conversion matrix (72 x 7) of final demand for 1990 and 2000 3. Revision of Japan's afj in the context of TFP, pv, and pm on a 544 x 409 matrix basis for 1990 and 2000, and aggregation to a 76 x 72 matrix basis 4. Forecasting of sectoral output, employment, and imports for Japan on the basis of (1) through (3) (standard scenario) 5. For the alternative scenario for accelerated technical progress, further adjustment of conversion matrix and afo in (2) and (3), respectively 6. Upward revision of Japan's GNP growth potential on the basis of (4), and related adjustment of other macroeconomic parameters such as those for investment, prices, exchange rate, primary import coefficients, and so on for 1990 and 2000

TABLE 6.7 Ex post testing of V-RAS method: levels of input price and output price deflators for 1980 (1980 = 100) Code

01 02 03 04 05 06 07 08 09 10 11 12 13

14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

30 31 32

33

34 35 36 37 38

39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54

84

Name of Sector Agricultural products Livestock and sericulture Agricultural services Forestry and logging Fishery products Coal Metal mining Crude petroleum and natural gas Other nonmetal mining Slaughtering and dairy products Seafood preserved Grain cleaning and flour Other food products Formula feed Beverages Tobacco Fiber yarn Fabric Knitted fabric Other ready-made textile goods Apparel and accessories Lumber and wooden products Furniture and fixtures Pulp and paper Paper products Printing and publishing Leather and leather products Rubber products Basic chemicals Basic petrochemicals Chemical fiber materials Plastic Chemical manures and pesticides Other chemical products Petroleum refinery products Coal products Nonmetallic mineral products Iron and steel materials Basic iron and steel products Iron and steel casting and forging Nonferrous metal materials Basic nonferrous metal products Metal products General machinery Heavy electric machinery Light electric appliances Motor vehicles Other transport equipments Precision instruments Other industrial products Building construction Building repairing Civil engineering Electric power supply

P*°

P*}"

99.8 97.8 88.6 97.9 101.0 99.2 98.7 100.0 94.7 99.6 96.8 102.8 108.0 93.3 101.2 100.3 101.0 99.5 105.5 97.8 105.4 100.3 102.7 104.7 103.0 99.8 107.3 102.0 92.6 94.7 97.1 94.3 96.7 102.6 98.3 103.5 95.1 96.5 102.8 92.7 97.2 93.8 96.7 102.7 101.8 103.2 107.2 101.6 97.7 99.1 104.1 101.9 100.6 97.3

99.7 97.8 88.6 96.4 101.1 96.2 94.5 102.8 94.1 99.8 96.4 102.8 108.5 93.3 101.3 100.3 101.2 99.4 106.1 97.7 105.7 100.4 102.7 105.1 103.1 99.8 108.6 102.1 91.8 94.5 97.0 94.2 96.2 102.8 98.2 103.5 95.0 96.4 102.8 92.7 96.8 93.6 96.6 102.8 101.9 103.4 107.2 101.8 97.5 99.0 104.1 101.9 100.6 97.5

TABLE 6.7 Code 55 56

57 58 59 60 61 62 63 64

65 66 67 68 69 70

71 72 73

(Continued)

Name of Sector

Gas supply

P*"

Ptf

101.2

101.2 95.6 96.8 97.7 99.4 98.3 96.3 0.0 96.4 103.2 96.4 91.7 99.0 89.3 99.0 102.1 92.3 104.5 99.8

95.6 96.8 97.7 99.4 98.3 96.5 0.0

Water supply

Commerce Financial and insurance services Real estate dealing Real estate rents Transport Self-transport Communication Public administration Education Research Health and social insurance Other public services Other services Office supplies

96.4

103.2 96.4 91.7 99.0 89.3 99.0 102.1 92.3 104.0 99.8

Packing Activities not elsewhere classified Total of intermediate sectors

v= Estimated input price deflator; pj,. = estimated output price deflator.

TABLE 6.8 Change in total factor productivity: standard scenario Annual Rate (%)

Level Sector Code"

1980

1990

2000

1

0.68456 0.50824

0.76971

0.52450

0.80230 0.51613

3 4

0.74201 1.10182

0.77026

0.76035

0.72866 0.83347 1.11837

1.21536 0.75902 0.92186 1.00476

0.98592

0.96828

1.02229 1.03341 0.84113 1.02807 0.97511 1.59353 3.35202 0.83018 0.91200 0.87091 0.96481 0.95413

1.06533 0.90902 1.03135 1.14588 1.57373 3.31655 0.90308 1.00280 0.90802 1.04279 0.99959

2

5 6

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

1.11423

1.38546 1.02537

1.27140 0.76217 0.95356 0.83084 0.93159 1.56100 0.98574 1.04560 0.91888 0.99032 1.25374

1.50304

3.20861

0.88915

0.94609 0.84076 0.95458 0.93993

1990/1980

1.18

0.32 0.37 0.99 0.41 1.01 -1.07 -0.18 2.20 0.03 0.30 0.78

0.03 1.63

-0.12 -0.11 0.85

0.95

0.42

0.78 0.47

2000/1990

0.42 -0.16 -0.13 0.45 0.04 0.34 -1.88 -0.39 1.20 -0.39 -0.19 0.11 -0.41 0.90

-0.46

-0.33 3 -0.16 6 -0.588 7 -0.77 8 -0.88 -0.611

85

TABLE 6.8

(Continued) Annual Rate (%)

Level Sector Codea

1980

1990

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72

0.96733 0.98712 0.92986 1.00870 1.00501 0.93665 0.94547 0.69339 0.75511 0.98563 0.79700 0.77811 1.10590 1.11419 1.02219 0.96241 0.90560 1.05552 1.03974 0.99277 1.06471 0.99689 1.01515 1.02342 1.07010 1.01275 1.02233 1.01079 0.99001 1.04431 1.14459 1.01427 0.78703 1.12631 0.95870 1.01941 1.24154 1.85219 4.35436 0.85425 0.0 1.52093 1.03522 1.07162 0.98157 1.14553 1.04597 0.94998 1.00000 1.08678 1.03982

1.01662 1.04654 0.95597 1.02506 1.07077 0.95539 1.03155 0.87336 0.81417 1.12474 0.94056 1.02444 1.35957 1.18036 0.98664 1.00531 0.95940 1.19831 1.16894 1.02542 1.32219 1.15553 1.16409 1.13160 1.32903 1.22022 1.05728 1.34277 1.09532 1.13622 1.17171 1.10920 0.79819 1.28137 0.95062 1.20684 1.51209 1.90102 8.12797 1.01228 0.0 2.00317 1.18820 1.30608 1.15053 1.30966 1.22925 1.07564 0.89000 1.14233 0.95457

1.02325 1.04942 0.95325 1.00903 1.07588 0.93085 1.05629 0.98491 0.83724 1.19738 1.03339 1.18120 1.49760 1.21472 0.95321 0.98300 0.96315 1.27848 1.22859 0.89885 1.49324 1.23417 1.19773 1.15817 1.32403 1.33287 1.02149 1.53801 1.12456 1.16401 1.14196 1.12942 0.79679 1.34661 0.91367 1.31353 1.64849 1.91936 11.93223 1.07979 0.0 2.32102 1.24889 1.45243 1.23483 1.36359 1.31632 1.11587 0.78775 1.15753 0.86170

Total

1.02062

1.15426

1.20026

aSee Table 6.6 for sectoral classification. 86

2000

1990/1980

2000/1990

0.41 0.59 0.28 0.16 0.64 0.20 0.88 2.33 0.76 1.33 1.67 2.79 2.09 0.58 -0.35 0.44 0.58 1.28 1.18 0.32 2.19 1.49 1.38 1.01 2.19 1.88 0.34 2.88 1.02 0.85 0.23 0.90 0.14 1.30 -0.088 1.70 1.99 0.26 6.44 1.71 0.0 2.79 1.39 1.97 1.60 1.35 1.63 1.25 -1.16 0.50 -0.85

0.07 0.03 -0.033 -0.16 0.05 -0.266 0.24 1.21 0.28 0.63 0.95 1.43 0.97 0.29 -0.34 -0.222 0.04 0.65 0.50 -1.31 1.22 0.66 0.29 0.23 -0.044 0.89 -0.344 1.37 0.26 0.24 -0.266 0.18 -0.022 0.50 -0.400 0.85 0.87 0.10 3.91 0.65 0.0 1.48 0.50 1.07 0.71 0.40 0.69 0.37 -1.21 0.13 -1.02

1.24

0.39

TABLE 6.9 Change of total factor productivity: accelerated scenario Annual Rate (%)

Level

Sector Code'

1

2 3 4

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54

1980

1990

2000

1990/1980

2000/1990

0.68456 0.50824 0.74201 1.10182 0.72866 0.83347 1.11837 0.98592 1.11423 1.02229 1.03341 0.84113 1.02807 0.97511 1.59353 3.35202 0.83018 0.91200 0.87091 0.96481 0.95413 0.97633 0.98712 0.92986 ' 1.00870 1.00501 0.93665 0.94547 0.69339 0.75511 0.98563 0.79700 0.77811 1.10590 1.11419 1.02219 0.96241 0.90560 1.05552 1.03974 0.99277 1.06471 0.99689 1.01515 1.02342 1.07010 1.01275 1.02233 1.01079 0.99001 1.04431 1.14459 1.01427 0.78703

0.79155 0.53745 0.78946 1.24543 0.77808 0.94930 1.03006 0.99234 1.42010 1.05124 1.09415 0.93165 1.05859 1.17416 1.61058 3.39922 0.92256 1.02734 0.93099 1.06804 1.02477 1.04290 1.08246 0.97997 1.05037 1.09850 0.98889 1.06482 0.89493 0.83426 1.16023 0.96411 1.05242 1.39920 1.21003 1.01102 1.04065 0.98284 1.22777 1.19777 1.06761 1.36687 1.18564 1.20172 1.17371 1.39254 1.25646 1.09018 1.40449 1.18783 1.18716 1.22296 1.14835 0.81491

0.90775 0.56180 0.79188 1.32422 0.79438 1.00264 0.86625 0.97044 1.62664 1.02567 1.09406 0.95148 1.06967 1.44633 1.68940 3.34271 0.91652 0.98475 0.87649 0.99122 0.97973 1.06802 1.11176 0.99350 1.05089 1.12334 0.98850 1.11269 1.02554 0.92143 1.25802 1.07461 1.27789 1.60221 1.26627 0.99275 1.03995 1.00269 1.33132 1.27945 0.95850 1.58205 1.28801 1.26059 1.23309 1.41440 1.39868 1.07510 1.65381 1.28974 1.25416 1.22727 1.19820 0.85152

1.46 0.56 0.62 1.23 0.66 1.31 -0.822 0.06 2.46 0.28 0.57 1.03 0.29 1.88 0.11 0.14 1.06 1.20 0.67 1.02 0.72 0.66 0.93 0.53 0.41 0.89 0.54 1.20 2.58 1.00 1.64 1.92 3.07 2.38 0.83 -0.11 0.78 0.82 1.52 1.42 0.73 2.53 1.75 1.70 1.38 2.67 2.18 0.64 3.34 1.84 1.29 0.66 1.25

1.38 0.44 0.03 0.62 0.21 0.55 2 -1.72 2 -0.22 1.37 -0.255 -0.00 0 0.21 0.10 2.11 0.48 -0.177 7 -0.07 -0.42 2 -0.600 4 -0.74 5 -0.45 0.24 0.27 0.14 0.00 0.22 -0.00 0 0.44 1.37 1.00 0.81 1.09 1.96 1.36 0.46 -0.188 -0.011 0.20 0.81 0.66 -1.07 1.47 0.83 0.48 0.49 0.16 1.08 -0.144 1.65 0.83 0.55 0.04 0.43 0.44

0.35

87

88

Input-Output and the Analysis of Technical Progress TABLE 6.9

(Continued) Annual Rate (%)

Level Sector Codea

1980

1990

71 72

1.12631 0.95870 1.01941 1.24154 1.85219 4.35436 0.85425 0.0 1.52093 1.03522 1.07462 0.98157 1.14553 1.04597 0.94998 1.00000 1.08678 1.03982

1.30606 0.98171 1.23684 1.55006 1.94900 8.33720 1.03724 0.0 2.05252 1.21678 1.33814 1.17890 1.34204 1.25993 1.10384 0.91201 1.18559 0.98043

Total

1.02062

1.19221

55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70

1990/1980

2000/1990

1.38932 1.06445 1.36840 1.71833 2.00102 12.45016 1.12437 0.0 2.41683 1.29835 1.51198 1.28559 1.41514 1.37157 1.16440 0.81937 1.23611 0.89883

1.49 0.24 1.95 2.24 0.51 6.71 1.96 0.0 3.04 1.63 2.22 1.85 1.60 1.88 1.51 -0.92 0.87 -0.59

0.62 0.81 1.02 1.04 0.26 4.09 0.81 0.0 1.65 0.65 1.23 0.87 0.53 0.85 0.54 -1.07 7 0.42 -0.87

1.27317

1.57

2000

0.66

aSee Table 6.6 for sectoral classification.

7. Forecasting of Japan's sectoral output, employment, and imports for 1990 and 2000 on the basis of (5) and (6) (accelerated scenario) 8. Forecasting macroeconomic variables with a world model for Japan and other countries for 1990 on the basis of (6) The results of our forecast for TFP for 1990 and 2000 are shown in Tables 6.8 (standard scenario) and 6.9 (accelerated scenario). Both forecasts are based on the engineering study and price analysis made by our research group. Roughly speaking, the microelectronics sector, including optoelectronics and mechatronics, is assumed to continue the present rapid technical progress for the period 1980-1990, but the rate tends to level off gradually later on. Other high-technology industries such as new materials, biotechnology, and new energy resources are assumed to catch up gradually with microelectronics for 1990-2000. Sensitivity analysis provided upper and lower bounds for the rates of progress in those industries by taking account of changes in relative prices, especially for energy resources where we assumed a falling trend in real prices until 1990-1995 and a gradual U turn thereafter. (For details on forecasting technology, see Shishido et al. [1986].) After making allowances for changes in relative prices, we estimated two alternative sets of parameters describing the sectoral components for consumption, investment, and exports. We then used the estimates to break down macroeconomic variables for 1990 and 2000. The macroeconomic forecasts in Table 6.10 were obtained using the World Eccnometric Model (T-FAIS VI) jointly developed by the University of Tsukuba and

89

Technical Progress in Japan's High-Technology Industries TABLE 6.10

Growth rates for major macroeconomic variables in 1980 prices 1980-1990 Aa

B

A

B

3.72 2.97 5.02 0.54 4.22

4.99 4.24 6.31 1.78 5.46

5.21 3.01 5.21 3.01 4.74

5.97 3.75 5.97 3.75 5.49

Variable Private consumption Government consumption Private fixed investment Government fixed investment GNP a

1990-2000

A = standard scenario, B = accelerated scenario.

the Foundation of Advancement of International Science.3 In the standard scenario the economy is assumed to grow at 4.2 and 4.7 percent for the 1980s and 1990s, respectively, whereas in the accelerated scenario it is assumed to grow at 5.5 percent for the entire period, that is, about 1 percent faster than in the standard scenario. This 1-percent acceleration in GNP growth rate is derived from an assumed 0.3percent acceleration in TFP, and consequential changes in the I-O coefficient matrix in real terms, over the twenty-year period, as shown by the figures that follow. A rough approximation can be made, using the following neoclassical growth assumption (V = K), which allows the conversion of the TFP growth rate to the macroeconomic growth rate.

where ' = rate of change X = total output V = GNP Mr = total intermediate input K = total capital input L = total labor input T = TFP on an I-O basis Equation (13) represents the definitional identity between gross output, value added, and intermediate input, whereas the parameters a and b denote the cost shares Level

Standard Accelerated Difference ( = 1. -2.)

Annual Rate (%)

1980

1990

2000

1980-1990

1990-2000

1.02 1.02

1.15 1.19

1.20 1.27

1.24 1.57 0.33

0.39 0.66 0.27

V

0.30

90

Input Output and the Analysis of Technical Progress

of capital and labor input, respectively, in total output. Equation (14) indicates the ordinary input-output technical relationship with TFP as a shift variable. Both equations are aggregative, covering the whole economy, and are shown in terms of the rate of change. From equations (13), (14), and (15) we obtain the following formula:

Under the assumption of full-employment growth, this implies that the impact of TFP on an I-O basis affects the GNP growth rate in the following way:

where b has a value of approximately 0.3 in the case of Japan, implying a multiplier effect of 3.3. Our assumption of a 1-percent acceleration of GNP is based on this multiplier and the average acceleration of TFP on an I-O basis. Final demand components for this accelerated scenario were derived on the basis of this basic assumption by using the revised component ratios discussed previously. Details of output, employment, and imports for 1990 and 2000 are shown in Tables 6.11, 6.12, and 6.13. Several interesting findings are summarized below. First, the acceleration of growth caused by stimulated technical progress, especially in high-technology industries, promotes an increase in the overall growth rate throughout the economy. Employment and imports also increase. The rate of acceleration is significantly higher in the 1980s than in the 1990s, because of a slight deceleration in technical progress in the latter period, particularly in the electronic sectors. Real GNP in the year 2000 is 10.6 percent higher in the accelerated scenario. Second, the manufacturing sector as a whole accelerates its growth rate from 4.8 to 5.3 percent in the 1980s and from 5.6 to 5.9 percent in the 1990s. Particularly notable increases for manufacturing are in light electrical applicances (mostly electronics), general machinery, and precision instruments. The acceleration is also conspicuous in biotechnology sectors such as agriculture, food, finished chemicals, and medical services. With respect to new materials, ceramics and fine chemicals indicate fairly rapid growth rates, while material sectors such as steel and nonferrous metals show relatively slower growth. Third, the growth rate of total employment also improves from —0.03 to 0.51 percent in the 1980s and from 2.25 to 2.45 percent in the 1990s. The improvement is particularly notable for tertiary industries. A slightly declining tendency, however, is found for employment in pulp and paper, basic chemicals, nonmetallic mineral products, iron and steel, metal products, and agriculture. Fourth, imports increased slightly in the accelerated scenario, with a higher component for manufactured imports. Although it is feared that the acceleration in high-technology industries might substantially reduce raw material imports from less developed countries and other primary export countries, our results indicate no significant change in such primary imports. Because of accelerated income growth, the substitution effect caused by high technology is offset by increased demand for those primary products. Finally, we turn to the international impacts of our scenarios. The analysis was conducted for 1990 with the World Econometric Model (T-FAIS VI), by feeding the same exogenous assumptions into the model in order to evaluate the impacts of high-

TABLE 6.11 Alternative scenarios for 1990 and 2000 Output (standard scenario)

Sector Codea

1 2 3 4 5 6 7

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49

1980 7839 3329 423 1825 2695 255 128 100 2118 3942 2336 3726 9081 1360 4196 2313 1375 3858 1306 1736 4035 4953 3244 3908 4025 6346 501 2541 3297 4300 1420 2623 931 7197 15437 2469 8255 10579 14497 3869 2418 4618 10409 22908 3488 19115 20400 5090 3428

Level (billion yen) — ._. -. 2000 1990 8174 2894 488 2117 3445 264 90 83 2981 2438 2867 4298 12292 1368 6011 2965 1809 5930 1637 2519 5362 6450 4842 5625 5977 8508 522 4303 5511 6926 3321 4853 1058 13229 19967 3362 11225 13357 22390 5514 3142 7907 16407 43238 6211 47721 35651 6372 6607

10743 5519 788 2821 5013 320 95 91 4892 5618 3746 5888 18107 2853 8987 4093 2584 9513 2157 4095 7718

9412 7579 8785 9960 11375 634 6953 9706 11996 7277 8923 1407 25597 29693 5116 17114 19441 35925 8360 4518 13818 26614 78243 10883 113505 58241 9644 11692

AnnualRate (%) 1990/1980

2000/1990

0.42 -1.39 1.44 1.50 2.49 0.37 -3.46 -1.86 3.47 -4.69 2.07 1.44 3.07 0.06 3.66 2.51 2.78 4.39 2.28 3.79 2.88 2.68 4.09 3.71 4.03 2.98 0.42 5.41 5.27 4.88 8.87 6.35 1.28 6.28 2.61 3.13 3.12 2.36 4.44 3.61 2.66 5.52 4.66 6.56 5.94 9.58 5.74 2.27 6.78

2.77 6.67 4.90 2.91 3.82 1.95 0.49 0.96 5.08 8.71 2.71 3.20 3.95 7.62 4.10 3.28 3.63 4.84 2.80 4.98 3.71 3.85 4.58 4.56 5.24 2.95 1.96 4.92 5.82 5.65 8.16 6.28 2.89 6.82 4.05 4.29 4.31 3.82 4.84 4.25 3.70 5.74 4.96

6.11 5.77 9.05 5.03 4.23 5.87

Component (%) — — — 2000 1990 1980 1.44 0.61 0.08 0.34 0.50 0.05 0.02 0.02 0.39 0.73 0.43 0.69 1.67 0.25 0.77 0.43 0.25 0.71 0.24 0.32 0.74 0.91 0.60 0.72 0.74 1.17 0.09 0.47 0.61 0.79 0.26 0.48 0.17 1.32 2.84 0.45 1.52 1.95 2.67 0.71 0.44 0.85 1.92 4.22 0.64 3.52 3.75 0.94 0.63

0.98 0.35 0.06 0.25 0.41 0.03 0.01 0.01 0.36 0.29 0.34 0.52 1.48 0.16 0.72 0.36 0.22 0.71 0.20 0.30 0.64 0.77 0.58 0.68 0.72 1.02 0.06 0.52 0.66 0.83 0.40 0.58 0.13 1.59 2.40 0.40 1.35 1.60 2.69 0.66 0.38 0.95 1.97 5.19 0.75 5.73 4.28 0.76 0.79

0.77 0.39 0.06 0.20 0.36 0.02 0.01 0.01 0.35 0.40 0.27 0.42 1.29 0.20 0.64 0.29 0.18 0.68 0.15 0.29 0.55 0.67 0.54 0.63 0.71 0.81 0.05 0.50 0.69 0.86 0.52 0.64 0.10 1.83 2.12 0.37 1.22 1.39 2.56 0.60 0.32 0.99 1.90 5.59 0.78 8.10 4.16 0.69 0.83 91

TABLE 6. 11 Output (standard scenario) (Continued) Level (billion yen)

-— — — — 1990 1980

Sector Code"

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77

2000

Annual Rate (%) — -_ — 2000/1990 1990/1980

Component (%) .._ _ — 1980 2000 1990

8123 29837 4235 21185 10157 1441 3153 51518 15804 21849 4778 20605 0 4899 13913 10242 592 15624 3504 38422 1044 2895 9287 10186 130542 22937 75484 782197

14592 45446 5970 25869 15287 2269 4039 80672 23812 31625 7439 31002 0 6717 18670 14756 888 26175 5922 61879 1499 5109 13250 13421 132674 20385 103846 1103442

26936 72686 9640 37815 25053 3984 5519 134519 40619 54032 12584 47426 0 10587 25271 22270 1389 49951 10701 109000 2398 9521 20816 20149 166601 24652 164888 1777090

6.03 4.30 3.49 2.02 4.17 4.65 2.51 4.59 4.18 3.77 4.53 4.17 0.0 3.21 2.98 3.72 4.13 5.30 5.39 4.88 3.69 5.85 3.62 2.80 0.16 -1.17 3.24 3.50

6.32 4.81 4.91 3.87 5.06 5.79 3.17 5.25 5.49 5.50 5.40 4.34 0.0 4.66 3.07 4.20 4.57 6.68 6.09 5.82 4.81 6.42 4.62 4.15 2.30 1.92 4.73 4.88

1.49 5.49 0.78 3.90 1.87 0.27 0.58 9.48 2.91 4.02 0.88 3.79 0.0 0.90 2.56 1.89 0.11 2.88 0.64 7.07 0.19 0.53 1.71

1.75 5.45 0.72 3.11 1.83 0.27 0.48 9.68 2.86 3.80 0.89 3.72 0.0 0.81 2.24 1.77 0.11 3.14 0.71 7.43 0.18 0.61 1.59

1.92 5.19 0.69 2.70 1.79 0.28 0.39 9.61 2.90 3.86 0.90 3.39 0.0 0.76 1.80 1.59 0.10 3.57 0.76 7.78 0.17 0.68 1.49

543348

833116

1400800

4.37

5.33

100.00

100.00

100.00

239652

384281

664707

4.84

5.63

44.11

46.13

47.45

Total 1-72

Total 10-50

- -

Output (accelerated scenario) —-

Level (billion yen)

Annual Rate (%)

Component(%)

,

rt

ijCCLOr

92

Code

1980

1 2 3 4 5 6 7

7839 3329

1990 8856 3091

2000

11865 6016

423

516

791

1825 2695

2155 3758

2853 5700

255 128

253 83

297 82

1990/1980

1.23 0.74 2.00 1.67 3.38 -0.05 5 -4.24 4

2000/1990

2.97 6.89 4.38 2.85 4.25 1.59 -0.15 5

1980

1990

2000

1.44 0.61 0.08 0.34 0.50 0.05 0.02

0.99 0.35 0.06 0.24 0.42 0.03 0.01

0.77 0.39 0.05 0.18 0.37 0.02 0.01

TABLE 6.11 Output (accelerated scenario) (Continued) Level (billion yen)

Component(%)

Annual Rate (%)

^pcjnr

Code

1980

1990

2000

1990/1980

8 9 10 11

100 2118 3942 2336 3726 9081 1360 4196 2313 1375 3858 1306 1736 4035 4953 3244 3908 4025 6346 501 2541 3297 4300 1420 2623 931 7197 15437 2469 8255 10579 14497 3869 2418 4618 10409 22908 3488 19115 20400 5090 3428 8123 29837 4235 21185 10157 1441 3153 51518

84 3130 2626 3161 4767 13578 1428 6685 3333 1872 6185 1806 2714 5942 6716 5224 5673 6085 9039 566 4413 5482 6548 3358 4545 1090 14202 20874 3251 11804 12817 22369 5479 3051 7893 16194 47910 6582 52730 36554 6706 7992 14330 49174 7376 29234 15897 2459 4390 87470

89 5200 6194 4343 6806 20583 2958 10582 4912 2715 10075 2518 4597 9037 9855 8446 8702 9983 12198 716 7147 9441 10248 7403 7905 1375 28645 31064 4819 18200 18142 35712 8169 4249 13612 25618 90508 11766 134513 60008 10455 13944 25747 80815 10610 45906 26301 4489

-1.71 1 3.98 -3.98 3.07 2.49 4.10 0.49 4.77 3.72 3.14 4.83 3.29 4.57 3.95 3.09 4.88 3.80 4.22 3.60 1.23 5.68 5.22 4.30 8.99 5.65 1.59 7.03 3.06 2.79 3.64 1.94 4.43 3.54 2.35 5.51 4.52 7.66 6.56 10.68 6.01 2.80 7.84 5.84 5.12 4.18 3.27 4.58 5.49

12

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49

50 51 52 53 54 55 56

57

6247

3.37

151634

5.44

2000/1990

1980

1990

2000

0.59 5.21 8.96 3.23 3.63 4.25 7.55 4.70 3.96 3.79 5.00 3.38 5.41 4.28 3.91 4.92 4.37 5.07 3.04 2.38 4.94 5.59 4.58 8.23 5.69 2.35 7.27 4.06 4.02 4.43 3.54 4.79 4.07 3.37 5.60 4.69 6.57 5.98 9.82 5.08 4.54 6.70 6.03 5.09 5.22 4.62 5.16 6.20 3.59 5.66

0.02 0.39 0.73 0.43 0.69 1.67 0.25 0.77 0.43 0.25 0.71 0.24 0.32 0.74 0.91 0.60 0.72 0.74 1.17 0.09 0.47 0.61 0.79 0.26 0.48 0.17 1.32 2.84 0.45 1.52 1.95 2.67 0.71 0.44 0.85 1.92 4.22 0.64 3.52 3.75 0.94 0.63 1.49 5.49 0.78 3.90 1.87 0.27 0.58 9.48

0.01 0.35 0.29 0.35 0.53 1.52 0.16 0.75 0.37 0.21 0.69 0.20 0.30 0.67 0.75 0.59 0.64 0.68 1.01 0.06 0.49 0.61 0.73 0.38 0.51 0.12 1.59 2.34 0.36 1.32 1.44 2.51 0.61 0.34 0.89 1.82 5.37 0.74 5.91 4.10 0.75 0.82 1.61 5.52 0.72 3.28 1.78 0.28 0.49 9.81

0.01 0.34 0.40 0.28 0.44 1.33 0.19 0.68 0.32 0.18 0.65 0.16 0.30 0.58 0.64 0.54 0.56 0.64 0.79 0.05 0.46 0.61 0.66 0.48 0.51 0.09 1.85 2.00 0.31 1.17 1.17 2.30 0.53 0.27 0.88 1.65 5.84 0.76 8.68 3.87

0.67 0.90 1.66 5.21 0.68 2.96 1.70 0.29 0.40 9.78 93

TABLE 6.11 Output (accelerated scenario) (Continued) Level (billion yen) Sector Code 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77

Total 1-72 Total 10-50

Component(%)

Annual Rate (%)

1980

1990

2000

15804 21849 4778 20605 0 4899 13913 10242 592 15624 3504 38422 1044 2895 9287 10186 130542 22937 75484 782197

25216 35516 7809 32674 0 7211 21056 16663 959 29486 6439 68206 1567 5257 13703 14024 140005 21538 107610 1174772

543348 239652

1980

2000

1990

1990/1980

2000/1990

44236 64789 13461 50699 0 11829 30562 26971 1546 60211 12128 126561 2544 9772 21692 21314 179513 26331 171169 1918130

4.78 4.98 5.04 4.72 0.0 3.94 4.23 4.99 4.93 6.56 6.27 5.91 4.15 6.15 3.97 3.25 0.70 -0.63 3.61 4.15

5.78 6.20 5.60 4.49 0.0 5.07 3.80 4.93 4.89 7.40 6.54 6.38 4.97 6.40 4.70 4.27 2.52 2.03 4.75 5.19

2.91 4.02 0.88 3.79 0.0 0.90 2.56 1.89 0.11 2.88 0.64 7.07 0.19 0.53 1.71

2.83 3.98 0.88 3.66 0.0 0.81 2.36 1.87 0.11 3.31 0.72 7.65 0.18 0.59 1.51

2.85 4.18 0.87 3.27 0.0 0.76 1.97 1.74 0.10 3.89 0.78 8.17 0.16 0.63 1.40

891566

1519803

5.08

5.68

100.00

100.00

100.00

402876

713908

5.33

5.89

14.11

15.19

16.06

a

See Table; 6.6 for se<;toral classif;ication. TABLE 6.12

Alternative scenarios for 1990 and 2000

Employment (standard scenario) Component (%)

Annual Rate (%)

Level ^\pct fir

JtZLLUl

Code" 1 2 3 4 5 7 7 8 9 10 11 12 13 14

94

1980 3200 1709 255 534 841 139 49 27 320 314 340 68 1856 73

1990 2065 962 218 447

766 102

28 17 259 132 285

65

1732 38

2000

1982 1389 288 479 892 98 27 15

295

237 292 79 2013 50

1990/1980

2000/1990

-4.28 8 -5.59 9 -1.57 7 - 1.77

-0.41 3.74 2.83 0.70 1.52 8 -0.38 -0.22 2 -0.900 1.29 6.05 0.24 1.94 1.51 2.96

-0.93 3 -3.04 4 -5.53 3 -4.56 6 -2.099 -8.300 -1.755 2 -0.42 -0.69 9 8 -6.38

1980

1990

2000

2.08 1.11 0.17 0.35 0.55 0.09 0.03 0.02 0.21 0.20 0.22 0.04 1.21 0.05

1.35 0.63 0.14 0.29 0.50 0.07 0.02 0.01 0.17 0.09 0.19 0.04 1.13 0.02

1.04 0.73 0.15 0.25 0.47 0.05 0.01 0.01 0.15 0.12 0.15 0.04 1.05 0,03

TABLE 6.12 Employment (standard scenario) (Continued)

Code3 15 16 17

18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Component (%)

Annual Rate (%)

Level

c I Or ij£C 1980

1990

2000

336 175 388 1046 449 537 1291 1028 1059 470 677 2189 160 730 449 162 196 217 118 1202 150 73 1733 451 1332 671 127 504 2941 5055 830 3449 3461 1269 994 1930 7705 796 6930 832 191 1495 26403 6313 603 344 9329 0 2148 9201 8107

362 163 286 908 324 490 1063 906 1071 478 669 2092 115 791 413 180 251 216 71 1193 151 73 1569 437 1118 609 126 504 2765 5192 902 3708 3707 1059 989 2031 7549 722 5477 886 171 1546 28607 6392 585 209 9185 0 1926 9173 8861

456 181 282 1063 296 621 1117 1021 1302 595 851 2245 110 948 494 244 369 262 62 1538 189 89 1855 536 1204 686 165 625 3180 6338 1142 5118 4462 1237 1131 2631 9004 869 5995 1154 206 1837 37359 8505 767 189 10736 0 2302 10186 11129

1990/1980 2000/1990 0.74 -0.72 -3.03 -1.41 -3.21 -0.91 -1.93 -1.25 0.12 0.16 -0.11 -0.45 -3.22 0.79 -0.84 1.05 2.47 -0.06 -5.02 -0.08 0.02 -0.06 -0.98 -0.30 -1.73 -0.97 -0.07 0.01 -0.62 0.27 0.83 0.73 0.69 -1.57 -0.05 0.51 -0.20 -0.97 -2.33 0.63 -1.08 0.34 0.81 0.12 -0.31 -4.86 -0.16 0.0 -1.08 -0.03 0.89

2.34 1.09 -0.13 1.59 -0.90 2.39 0.50 1.20 1.97 2.22 2.44 0.71 -0.51 1.83 1.81 3.09 3.95 1.97 -1.33 2.57 2.29 2.12 1.69 2.06 0.74 1.19 2.70 2.17 1.41 2.02 2.39 3.27 1.87 1.79 1.35 2.62 1.78 1.87 0.91 2.68 1.89 1.74 2.71 2.90 2.74 -1.00 1.57 0.0 1.80 1.05 2.31

1980

1990

2000

0.22 0.11 0.25 0.68 0.29 0.35 0.84 0.67 0.69 0.31 0.44 1.43 0.10 0.48 0.29 0.11 0.13 0.14 0.08 0.78 0.10 0.05 1.13 0.29 0.87 0.44 0.08 0.33 1.92 3.29 0.54 2.25 2.25 0.83 0.65 1.26 5.02 0.52 4.52 0.54 0.12 0.97 17.20 4.11 0.39 0.22 6.08 0.0 1.40 5.99 5.28

0.24 0.11 0.19 0.59 0.21 0.32 0.69 0.59 0.70 0.31 0.44 1.37 0.08 0.52 0.27 0.12 0.16 0.14 0.05 0.78 0.10 0.05 1.03 0.29 0.73 0.40 0.08 0.33 1.81 3.39 0.59 2.42 2.42 0.69 0.65 1.33 4.93 0.47 3.58 0.58 0.11 1.01 18.69 4.18 0.38 0.14 6.00 0.0 1.26 5.99 5.79

0.24 0.09 0.15 0.56 0.15 0.32 0.58 0.53 0.68 0.31 0.45 1.17 0.06 0.50 0.26 0.13 0.19 0.14 0.03 0.80 0.10 0.05 0.97 0.28 0.63 0.36 0.09 0.33 1.66 3.31 0.60 2.68 2.33 0.65 0.59 1.38 4.71 0.45 3.13 0.60 0.11 0.96 19.53 4.45 0.40 0.10 5.61 0.0 1.20 5.33 5.82

95

TABLE 6.12 Employment (standard scenario) (Continued) Annual Rate (%)

Level

Component (%)

C i -i ijCC I Or

Code"

1980

1990

2000

1990/1980

2000/1990

1980

1990

2000

0.90 0.87 1.33 0.73

2.42 3.77 3.37 3.09

0.26 4.35 1.13 10.46

0.29 4.75 1.30 11.28

0.29 5.51 1.45 12.25

400

437

556

6675 1740 16054

7276 1987 17273

10536 2767 23427

0 370 270

0

0.0

0.0

0.0

0.0

260

0 635 314

0.0

442

1.80 -0.38 8

3.69 1.91

0.24 0.18

0.29 0.17

0.33 0.16

Primary industry Secondary industry Tertiary industry

7075 40500 105904

4864 39231 108964

5465 47316 138471

-3.68 8 -0.32 2 0.29

1.17 1.89 2.43

4.61 26.39 69.00

3.18 25.63 71.19

2.86 24.74 72.40

Total

153478

153059

191253

-0.03 3

2.25

100.00

100.00

100.00

66 67

68 69 70 71 72

Employment (accelerated scenario) Annual Rate (%)

Level

Component (%)

C
Code

1 2 3

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

19 20 21 22 23 24 25 26 27 28

96

1980

3200 1709 255 534 841 139 49 27 320 314 340 68 1856 73 336 175

388 1046 449 537

1291 1028 1059 470 677 2189 160 730

1990

2178 1002 225 444 816 95 25 17 266 139 307 70 1867 38 393 178 288 924 349 516 1149 921 1128 470 665 2169 122 791

2000 1930 1384 278 466 973 87

22 14 301 251 325 87 2099

45 473

209 284 1081 332 669

1255 1027 1393 566 819 2312 119 936

1990/1980 2000/1990 -3.77 -5.20 -1.28 -1.83 -0.31 -3.68 -6.52 -4.65 -1.85 -7.84 -1.03 0.37 0.06 -6.21 1.57 0.20 -2.93 -1.23 -2.48 -0.40 -1.16 -1.09 0.63 -0.00 -0.18 -0.09 -2.67 0.80

-1.20 3.28 2.15 0.47 1.78 -0.88 -1.02 - 1.42 1.25 6.12 0.58 2.19 1.18 1.68 1.87 1.59 -0.14 1.58 -0.51 2.64 0.89 1.09 2.13 1.87 2.11 0.64 -0.26 1.69

1980 2.08 1.11 0.17 0.35 0.55 0.09 0.03 0.02 0.21 0.20 0.22 0.04 1.21 0.05 0.22 0.11 0.25 0.68 0.29 0.35 0.84 0.67 0.69 0.31 0.44 1.43 0.10 0.48

1990

1.35 0.62 0.14 0.27 0.51 0.06 0.02 0.01 0.16 0.09 0.19 0.04 1.16 0.02 0.24 0.11 0.18 0.57 0.22 0.32 0.71 0.57 0.70 0.29 0.41 1.31 0.08 0.49

2000 0.94 0.67 0.14 0.23 0.47 0.04 0.01 0.01 0.15 0.12 0.16 0.01 1.02 0.02 0.23 0.10 0.11 0.53 0.16 0.33 0.61 0.50 0.68 0.27 0.40 1.12 0.06 0.45

TABLE 6.12 Employment (accelerated scenario) (Continued) Annual Rate (%)

Level Sector Code

1980

1990

2000

1.42 1.37 3.80 1.24 -2.32 2.70 2.13 1.69 1.54 1.61 0.52 0.86 2.08 1.77 0.99 2.26 2.43 3.67 1.75 1.71 1.87 1.89 1.89 2.02 1.47 2.61 2.12 0.94 2.94 3.02 3.25 -0.98 1.55 0.0 2.04 1.59 2.86 2.57 4.31 3.63 3.46 0.0 3.50 1.82

0.29 0.11 0.13 0.14 0.08 0.78 0.10 0.05 1.13 0.29 0.87 0.44 0.08 0.33 1.92 3.29 0.54 2.25 2.25 0.83 0.65 1.26 5.02 0.52 4.52 0.54 0.12 0.97 17.20 4.11 0.39 0.22 6.08 0.0 1.40 5.99 5.28 0.26 4.35 1.13 10.46 0.0 0.24 0.18

0.25 0.10 0.15 0.12 0.04 0.77 0.10 0.04 0.98 0.25 0.67 0.37 0.07 0.30 1.65 3.46 0.58 2.41 2.30 0.67 0.65 1.16 4.93 0.47 3.74 0.56 0.11 1.01 18.74 4.09 0.40 0.13 5.85 0.0 1.25 6.25 6.04 0.29 4.95 1.31 11.49 0.0 0.27 0.16

0.22 0.09 0.17 0.11 0.03 0.79 0.09 0.04 0.90 0.23 0.56 0.31 0.07 0.28 1.43 3.40 0.58 2.71 2.14 0.63 0.61 1.10 4.67 0.45 3.39 0.57 0.11 0.87 19.64 4.32 0.43 0.09 5.35 0.0 1.20 5.75 6.29 0.29 5.92 1.46 12.66 0.0 0.30 0.15

-3.28 -0.10 0.95

0.74 1.86 2.72

4.61 26.39 69.00

3.14 24.83 72.04

2.65 23.43 73.92

0.51

2.45

100.00

1990/1980 2000/1990

1980

1990

2000

449 162 196 217 118 1202 150 73 1733 451 1332 671 127 504 2941 5055 830 3449 3461 1269 994 1930 7705 796 6930 832 191 1495 26403 6313 603 344 9329 0 2148 9201 8107 400 6675 1740 16054 0 370 270

101 166 246 197 71 1250 154 68 1586 409 1090 590 117 486 2663 5588 933 3896 3709 1087 1046 1869 7972 752 6040 899 181 1636 30271 6606 641 214 9447 0 2018 10096 9765 461 7998 2108 18557 0 443 262

462 190 357 223 56 1631 190 81 1848 481 1149 643 144 579 2939 6989 1186 5585 1414 1288 1259 2255 9612 918 6987 1164 223 1797 40425 8893 883 194 11020 0 2469 11827 12941 594 12194 3011 26065 0 625 314

-1.13 0.21 2.26 -0.96 -4.96 0.39 0.22 -0.61 -0.88 -0.95 -1.98 -1.27 -0.83 -0.35 -0.99 1.01 1.17 1.23 0.70 -1.53 0.52 -0.32 0.34 -0.56 -1.37 0.78 -0.53 0.90 1.38 0.46 0.61 -4.63 0.13 0.0 -0.62 0.93 1.88 1.43 1.83 1.94 1.46 0.0 1.84 -0.29

Tertiary industry

7075 40500 105904

5067 40108 116368

5456 48230 152158

Total

153478

161543

205844

29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71

72 Primary industry Secondary industry

Component (%)

100.00 100.00

aSeeTable 6.6 for sectoral classification.

97

TABLE 6.13 Alternative scenarios for 1990 and 2000 Imports (standard scenario) Level (billion yen)

Code1'

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

39 40 41 42 43 44 45 46

47 48 49

98

1980 2320 188 0 1333 266 1010 1602 13524 264 637 544 15 836 6 246 85 142 196 156 97 295 557 64 317 61 45 115 102 432 149 33 96 107 605 2212 8 142 227 88 9 1352 168 118 620 102 793 150 475 301

1990

2000

3187 202 0 1884 344 1426 1659 16175 447 478 1032 21 1167 8 300 148 198 373 245 206 670 1128 174 646 143 68 182 292 826 304 92 242 143 1230 3793 14 305 400 156 25 2010 442 213 1167 211 1908 285 860

5263 496 0 2962 501 2242 2320 23265 733 1404 1873 41 1810 17 629 245 335 761 431 462 1427 2164 405 1317 328 91 304 695 1650 647 254 588 232 2687 6829 28 652

792

Component (%)

Annual Rate (%)

o , tj£CiOr

777

287 56 3252 1077

425 2112 413 4540 553 1639 1870

1990/1980

2000/1990

1980

1990

2000

3.23 0.72 0.0 3.52 2.60 3.51 0.35 1.81 5.41 -2.65 6.61 3.42 3.39 2.92 2.00 5.70 3.38 6.65 4.62 7.82 8.55 7.31 10.52 7.38 8.89 4.21 4.70 11.09 6.70 7.39 10.80 9.69 2.94 7.35 5.54 5.76 7.94 5.83 5.89 10.76 4.05 10.16 6.08 6.53 7.54

5.14 9.40 0.0 4.63 3.83 4.63 3.41 3.70 5.07 11.17 6.14 6.92 4.49 7.83 7.68 5.17 5.40 7.39 5.81 8.41 7.85 6.73 8.82 7.38 8.66 2.96 5.26 9.06 7.16 7.85 10.69 9.28 4.96 8.13 6.06 7.18 7.89 6.87 6.29 8.40 4.93 9.31 7.15 6.11 6.95 9.06 6.85 6.66 8.97

6.05 0.49 0.0 3.48 0.69 2.63 4.18 35.27 0.69 1.66 1.42 0.04 2.18 0.02 0.64 0.22 0.37 0.51 0.41 0.25 0.77 1.45 0.17 0.83 0.16 0.12 0.30 0.27 1.13 0.39 0.09 0.25 0.28 1.58 5.77 0.02 0.37 0.59 0.23 0.02 3.53 0.44 0.31 1.62 0.27 2.07 0.39 1.24 0.78

5.46 0.35 0.0 3.23 0.59 2.44 2.84 27.73 0.77 0.83 1.77 0.04 2.00 0.01 0.51 0.25 0.34 0.64 0.42 0.35 1.15 1.93 0.30 1.11 0.25 0.12 0.31 0.50 1.42 0.52 0.16 0.41 0.25 2.11 6.50 0.02 0.52 0.69 0.27 0.04 3.45 0.76 0.37 2.00 0.36 3.27 0.49 1.47 1.36

5.08 0.48 0.0 2.86 0.48 2.16 2.24 22.46 0.71 1.36 1.81 0.04 1.75 0.02 0.61 0.24 0.32 0.73 0.42 0.45 1.38 2.09 0.39 1.27 0.32 0.09 0.29 0.67 1.59 0.62 0.25 0.57 0.22 2.59 6.59 0.03 0.63 0.75 0.28 0.05 3.14 1.04 0.41 2.04 0.40 4.38 0.53 1.58 1.80

9.18

6.63 6.12 10.16

TABLE 6.13 Imports (standard scenario) (Continued) Level (billion yen)

c jeClOi

Annual Rate (%)

Code"

1980

1990

2000

50 51 52

517 0 0 0 1 0 0 632 309 1 0

1167

2558

0 0 0 1 1 0

0

53 54 55 56 57 58 59

60 61 62 63 64 65 66 67 68 69 70 71

72

10-50 1-72

1371

1839

0.0 0.0 0.0 0.0

2

1980

2000/1990

1.35

8.16

8.48

0.0 0.0

0 0

1990

2.00

2000

2.47

0.0 0.0 0.0

0.0 0.0 0.0

0.0 0.0 0.0

0.0 0.0 0.0

7.18 7.18

0.00

0.00 0.00

0.0

0.0 0.0

8.05 7.19 11.61

8.00 8.03 5.24

1.65 0.81 0.00

2.35 1.06 0.01

0.00 0.00 0.00 2.86 1.29 0.00

0.0

0.0

0.0

0.0

0.0

7.03

6.97

4.80

0.0

6.87

0.0

0.0

6.22

57

4.67

6.63

0.05

0.05

0 0 0 0 0

0.0

0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0

2 1

619

2960 1340

3 0

5 0

3627

7113

0 30 0 0 0 0 0

0 19 0 0 0 0 0 724 0 0

1990/1980

Component (%)

0

0.0

0.0 0.0 0.0

0.0 0.0

0.0

0.0

2.97

0.0

0.06

0.0 0.0 0.0 0.0 0.0

3.89

8.82

1.89

0.0 0.0

0.0 0.0

0.0 0.0

0.0 0.0

0.0

2443

3.60

4.62

2.85

2.67

2.36

47865 103603

6.18 4.29

7.11 5.91

34.48 100.00

41.27 100.00

46.20 100.00

1732

4033

0 0

0 0

1092

1555

13220 38344

24073 58336

9.11

0.0

Imports (accelerated scenario)

Sector Code

1 2 3 4 5 6 7 8 9 10 11 12 13

_ 1980

._

Level (billion yen)

1990

2000

2320

3453

188 0

5813

216 0

541 0

1333

1917

2995

266

375

569

1010 1602 13524

1367 1529 16423

2076 2006 22777

264 637 544 15 836

469 525 1137

780 1548 2171

23

47

1289

2058

Annual Rate (%)

—

_

1990/1980

2000/1990

1980

Component (%) —— 1990 2000

4.06 1.40

5.35 9.62

6.05 0.49

0.0

0.0

5.71 0.36

0.0

0.0

3.70 3.49 3.07 -0.47 1.96 5.91 -1.92 7.65 4.37 4.43

4.56 4.26 4.27 2.75 3.32 5.22 11.42 6.68 7.41 4.79

3.48 0.69 2.63 4.18 35.27 0.69 1.66 1.42 0.04 2.18

3.17 0.62 2.62 2.53 27.14 0.77 0.87 1.88 0.04 2.13

5.36 0.50

0.0 2.76 0.52 1.91 1.85 21.01 0.72 1.43 2.00 0.04 1.90

99

TABLE 6.13 Imports (accelerated scenario) (Continued) Level (billion yen) Sector Code

14 15

16 17

18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

36 37 38 39 40 41 42

43 44 45 46 47 48 49 50 51 52 53 54 55 56

1980

2212

8 142 227 88 9

1352 168 118 620 102 793 150 475 301 517 0 0 0

1

0 0

8 334 166 205 389 270 222 742 1175 188 652 146 72 198 300 822 288 94 227 148 1320 3966 14 321 384 156 25 1952 442 210 1293 223 2109

292 905 875 1146 0

0 0 1 1 0

57

632

1486

58 59 60 61 62 63

309 1 0 1839 0 19 0

655 3 0 3822 0 33 0

64

100

1990/1980

2000

1990

6 246 85 142 196 156 97 295 557 64 317 61 45 115 102 432 149 33 96 107 605

Annual Rate (%)

17 740 294 352 806 503 519 1671 2266 451 1305 329 97 343 714 1604 553 259 521 226 3007 7144 26 693 725 285 55 3059 1061 409 2443 447 5380 570 1777 2231 2445 0 0 0

2 2 1 3335 1459 6 0 7604 0 63 0

2.92 3.11 6.92 3.74 7.10 5.64 8.63 9.66 7.75 11.38 7.48 9.12 4.81 5.58 11.39 6.64 6.81 11.04 8.99 3.30 8.11 6.01 5.76 8.50 5.40 5.89 10.76 3.74 10.16 5.93 7.63 8.14 10.28 6.89 6.66 11.26 8.29 0.0 0.0 0.0 0.0 0.0 0.0 8.93 7.80 11.61 0.0 7.59 0.0 5.68 0.0

2000/1990 7.83 8.28 5.88 5.56 7.56 6.42 8.86 8.46 6.79 9.14 7.19 8.46 3.03 5.65 9.06 6.91 6.74 10.67 8.66 4.32 8.58 6.06 6.39 8.00 6.56 6.21 8.20 4.59 9.15 6.89 6.57 7.20 9.82 6.92 6.98 9.81 7.87 0.0 0.0 0.0 7.18 7.18 0.0 8.42 8.34 7.18 0.0 7.12 0.0 6.68 0.0

Component (%)

1980

1990

2000

0.02 0.37 0.59 0.23 0.02 3.53 0.44 0.31 1.62 0.27 2.07 0.39 1.24 0.78 1.35 0.0 0.0 0.0 0.00 0.0 0.0

0.01 0.55 0.27 0.34 0.64 0.45 0.37 1.23 1.94 0.31 1.08 0.21 0.12 0.33 0.50 1.36 0.48 0.16 0.38 0.24 2.18 6.55 0.02 0.53 0.63 0.26 0.04 3.23 0.73 0.35 2.14 0.37 3.48 0.48 1.50 1.45 1.89 0.0 0.0 0.0 0.00 0.00 0.0

0.02 0.68 0.27 0.32 0.74 0.46 0.48 1.54 2.09 0.42 1.20 0.30 0.09 0.32 0.66 1.48 0.51 0.24 0.48 0.21 2.77 6.59 0.02 0.64 0.67 0.26 0.05 2.82 0.98 0.38 2.25 0.41 4.96 0.53 1.64 2.06 2.26 0.0 0.0 0.0 0.00 0.00 0.00

0.81 0.00 0.0 4.80 0.0 0.05 0.0

1.08 0.00 0.0 6.32 0.0 0.05 0.0

1.35 0.01 0.0 7.01 0.0 0.06 0.0

0.02 0.64 0.22 0.37 0.51 0.41 0.25 0.77 1.45 0.17 0.83 0.16 0.12 0.30 0.27 1.13 0.39 0.09 0.25 0.28 1.58 5.77

Table 6.13 Imports (accelerated scenario) (Continued)

Sector Code

Level (billion yen) — — — 1990

1980

65 66 67 68 69

0 0 0 0 724

70

1-72

Annual Rate (%)

1990/1980

0 0 0 0 4682

1092

1608

0 2546

13220 38344

25253 60520

51151 108408

0

10-50

2000

0 0 0 0 1909 0 0

0

71 72

— -— — 0.0 0.0 0.0 0.0 10.18 0.0 0.0

0

3.95

6.69 4.67

2000/1990 0.0 0.0 0.0 0.0

Component (%) 1980 0.0

0.0 4.70

0.0 0.0 0.0 1.89 0.0 0.0 2.85

7.31 6.00

34.48 100.00

9.39 0.0

2000

1990

0.0 2.66

0.0 0.0 0.0 0.0 4.32 0.0 0.0 2.35

41.73 100.00

100.00

0.0 0.0 0.0 0.0 3.15

0.0

47.18

aSee Table 6.6 for sectoral classification.

technology industries in Japan on developed and developing countries. Specifically, adjustments were made for related equations in the model consistent with the increase in technical progress. Export and other price equations were adjusted downward; the business investment equation was adjusted upward; the import function for primary products was adjusted downward; and the exchange rate of the yen was gradually adjusted upward. All of these adjustments were made on the basis of side studies, and are consistent with the sectoral changes in technical progress. A summary of the results, shown in Tables 6.14, 6.15, and 6.16, indicate a fairly substantial impact, although the figures are limited to a ten-year period. Accelerated technical progress in Japan gives rise to higher economic growth and imports and a TABLE 6.14 International impacts of Japan's acceleration of high technology: real GNP (%)) Annual Growth Rai'.e, 1980-1990 Country Japan United States Canada United Kingdom France Germany Italy Australia Other developing countries All developing countries OPEC Non-oil-developing countries World

Standard

Accelerated

4.41 2.91 3.32 1.99 1.78 1.97 2.29 3.05 2.31 2.82 3.56 3.95 3.12

5.58 2.93 3.52 2.07 2.1! 2.13 2.47 3.22 2.46 3.10 3.60 3.98

3.33

1990 Deviation from Standard Scenario 11.77 0.16 1.93 0.78 3.29 1.64 1.81 1.63 1.41 2.78 0.42 0.34 2.05

101

TABLE 6.15

International impacts of Japan's acceleration of high technology: real GNP components (%)

Macro Country

Variablesaarianleses3

Japan

4.41 3.65 5.22 7.83 6.20 2.91 3.36 3.26 1.81 5.21 1.99 2.08 4.02 3.28 5.03 1.78 -0.43 -2.40 6.43 2.19 1.97 0.63 0.23 3.72 0.76

V C IP

E

M V

United States

C

IPE United Kingdom

M V C

IPE

M

V

France

C

IPE M V C

Germany

Annual Growth Rate, 1980-1990 - —Standard Accelerated

IP

E

M

1990 Deviation from Standard Scenario 11.77 14.00 18.40 1.21 10.98 0.16 -0.08 0.17 2.10 0.24 0.78 0.70 0.62 2.37 1.70 3.29 2.81 6.34 4.31 4.01 1.64 1.32 2.22 2.58 1.77

5.58 5.02 7.01 7.96 7.31 2.93 3.35 3.27 2.02 5.23 2.07 2.15 4.08 3.52 5.21 2.11 -0.15 -1.80 6.89 2.59 2.13 0.76 0.45 3.99 0.94

aV= real GNP, C = real private consumption, Ip = real private investment (including residential investment), E = real exports, M = real imports. Japanese variables are in 1970 prices and are not exactly comparable with those in 1980 prices in Table 6.10.

TABLE 6.16

Country Japan United States Canada United Kingdom France Germany Italy Australia

102

International impact of Japan's acceleration of high technology: GNP deflator (%) Annual Growth Rate, 1980 -1990 _- _. _ _ _ _ Standard Accelerated 2.30 4.07 5.44 5.37 6.67 1.44 10.01 6.44

2.15 4.11 5.61 5.54 6.74 1.62 10.08 6.70

1990 Deviation from Standard Scenario -1.42 0.38 1.63 1.58 0.67 1.79 0.61 2.54

Technical Progress in Japan's High-Technology Industries

103

lower rate of inflation, which appreciates the value of the yen. Because of the effects of multicountry multipliers and price effects, the Japanese growth rate tends to rise a little faster in the accelerated scenario. As shown in Table 6.14, the world economic growth rate is accelerated by 0.2 percent, with OECD countries' growth increasing by 0.3 percent, oil and petroleumexporting countries (OPEC) by 0.04 percent, and the non-oil-developing countries (NODC) by 0.03 percent. Among the OECD countries, the United States is least affected, with growth increasing by 0.02 percent, but other industrial countries are affected significantly, by 0.10 to 0.20 percent in terms of the growth rate. In Table 6.15 it is notable that exports are the demand component with the greatest increase, except for Japan where the yen appreciates by 4 percent in 1990. The Japanese current account declines by $12.2 billion in 1990, whereas the U.S. current account rises by $4.4 billion. The rates of inflation in OECD countries, shown in Table 6.16, are surprisingly low, despite the acceleration in their growth rates. The inflation rate is probably affected by the decline in Japanese export prices caused by higher technical progress, although this is partly offset by the appreciation of the yen. Primary exports of the world, for which a fall due to technical progress might be feared, show a positive response for almost all countries. The increases experienced are 0.34 percent for the United States, 0.33 percent for Australia, 0.24 percent for OPEC, 0.22 percent for the NODC, and 0.23 percent for the world as a whole. This again clearly implies that the income effect of technical progress outweighs the substitution effect. CONCLUDING REMARKS AND FUTURE RESEARCH The analysis of long-term trends discussed so far is still preliminary, especially in the area of sectoral relative price forecasting. The new approach, the V-RAS method, whose ex post forecast proves to be fairly satisfactory in terms of output and prices, should be further extended from cross-section data to time series with the aim of analyzing more dynamic changes in factor prices and output prices. This type of dynamic adjustment I-O model, with sixty-four sectors, is now being developed and is to be linked to a similar U.S. I-O model developed at the University of Pennsylvania. A dynamic version of V-RAS, which enables a complete integration of technology and prices within a dynamic input-output framework, is being tested. Although the extrapolation to the year 2000 is tentative, our sensitivity analysis of technical changes in high technology industries indicates 1. An acceleration in technical progress gives rise to a higher rate of investment and economic growth, with more imports and a lower trade surplus. 2. The demand for primary imports also remains unchanged in the accelerated scenario, although the import content of output tends to fall: the resource-saving effect is offset by the effect of higher income. 3. The long-term international impacts are also significant and favorable in terms of trade, economic growth, and the rate of inflation. The structural analysis and forecasts of relative prices, problems that remain to be solved, will be carried out using the new dynamic model discussed in this chapter.

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Input- Output and the Analysis of Technical Progress

APPENDIX s Function 0014-20 Miscellaneous cereals 2012-30 Animal oil and fat 2070-00 Sugar manufacturing 2092-00 Prepared feed for animal and poultry 2306-00 Synthetic fiber yarn 2313-00 Synthetic fiber fabric 3112-30 Methanol derivatives 3116-10 Synthetic resin for fiber 3210-00 Petroleum refinery products (including grease and lubricating oil) 3416-00 Steel pipes and tubes 3421-10 Copper 3421-20 Lead 3605-10 Office machinery 3840-00 Repair of motor vehicles 5120-00 Gas supply r Function 1. Agriculture, Forestry, and Fisheries 0011-240 0014-310 0014-610 0014-990 0016-190 0016-410 0016-910 0016-920 0017-020 0020-090 0212-220 0220-020

Wheat (domestic) Soybeans (domestic) Coffee beans and cocoa beans (imported) Field crops for feed Other dairy products Beef cattle meat Wool Meat by other livestock raising By-products of sericulture Other agricultural services Firewood Logs (imported)

2. Mining 1101-030 1101-040 1210-010 1220-100 1301-020 1302-200 1990-900

Coal for general use, and lignite Anthracite Iron ore (domestic) Copper ore Crude petroleum (imported) Natural gas (imported) Other nonmetal ores

3. Food and Tobacco Manufacturing 2011 -030 By-products of slaughtering 2040-100 Canned or bottled marine products

Technical Progress in Japan's High-Technology Industries

2040-210 2040-220 2040-320 2050-120 2091-700 2110-100 2110-300 2110-500 2200-000

Kneaded fish meat Other processed fish meat Salted, dried, and smoked marine products Cleaned rice (imported) Ice Brewed sake Beer Ethyl alcohol for liquor manufacturing Tobacco products

4. Textile, Wood, Furniture, Pulp, and Printing Industries 2301-100 Silk-reeling 2311-100 Silk fabric 2316-000 Yarn and fabric dyeing and finishing (entrusted processing only) 2320-000 Knitted fabric 2390-400 Ropes and fish nets 2510-100 Lumber 2520-020 Other wooden products 2720-120 Coated paper and paper converted for construction use 2720-200 Paper containers 2720-300 Other paper articles 2720-400 Cellophane 2800-910 Printing 5. Chemical Product Manufacturing 3112-110 3112-150 3112-310 3116-300 3116-400 3116-900 3118-112 3118-113 3118-114 3118-200 3119-120 3192-500 3192-611 3192-619

Pure benzol Pitch Refined methanol Nylon fiber Acrilonitrile fiber Other synthetic fiber materials Urea Ammonium chloride Ammonium nitrate Agricultural chemicals Zinc oxide Matches Powder for industrial use Powder for other use

6. Ordinary Machinery, Electrical Machinery, and Transport Equipment Manufacturing 3602-100 3603-100 3603-571 3604-151

Machine tools Agricultural machinery Cast equipments Machinery for service industry

105

106

Input-Output and the Analysis of Technical Progress

3604-170 3701-100 3702-220 3704-230 3704-240 3810-100 3850-200 3850-300 3910-100 3920-100 3920-200

Industrial furnace Generators Radio and television sets Electronic tubes Semiconductor devices and integrated circuits Steel ships Two-wheel motor vehicles Bicycles and rear cars Physical and chemical instruments Cameras Other photographic and optical instruments

7. Other Manufacturing (mostly, basic industries) 3210-010 3210-020 3210-060 3210-091 3210-092 3340-000 3390-100 3390-410 3390-421 3390-429 3411-000 3412-000 3416-020 3501-110

Gasoline Jet fuel oil Heavy oil B Naphtha LPG (liquified petroleum gas) Cement Carbon products Raw concrete Concrete panels Other cement products Pig iron Iron scrap Steel pipes and tubes (special steel) Panels of steel-frame structure

8. Transport 7110-010 7110-020 7121-021 7170-011 7170-210 7190-000

National railways transport (passengers) National railway transport (freight) Local railway and tramway transport (passengers) International air transport Services relating to air transport (public) Other transport services

9. Wholesale, Finance and Insurance, Other Services Industries 5110-100 5120-000 5130-000 8210-050 8210-060 8213-110 8213-210 8290-200 8300-200 8300-400

Electric power Gas supply Steam and hot water supply School research institute (private, natural) School research institute (private, cultural) Research institute (public, natural) Research institute (industrial, natural) Private nonprofit institutions serving enterprises Research, data processing, and calculation services Building maintenance service

Technical Progress in Japan's High-Technology Industries

8300-600 8300-900 8302-300 8700-000

107

Civil engineering and construction services Other business services Car renting Packing

ACKNOWLEDGMENT This research is partly funded by the National Institute for Research Advancement.

NOTES 1. This large-scale computation was implemented using the FACOM M-380 of the University of Tsukuba. 2. Tsukuba Science City is located 60km northeast of Tokyo. There are two national universities, about forty-six national research laboratories, and about sixty-five private research institutes and high-technology industries. 3. See S. Shishido et al. (1981). This world model covers twenty-four countries and regions with the macroeconomic variables linked by two sets of trade matrices—primary and industrial. The model was used for forecasting 1990: from this forecast an extrapolation was made for the year 2000, mostly on the basis of trends.

REFERENCES Government of Japan. 1985. 1970-1975-1980 Link Input-Output Tables. Vols. 1 and 2. Tokyo: Government of Japan. Jorgenson, D. W., and Z. Griliches. 1967. "The explanation of productivity change." Review of Economic Studies 34: 249-283. Jorgenson, D. W., M. Kuroda, and M. Nishimizu. August 1985. "Japan-U.S. industry-level productivity comparison, 1960-1975." Paper presented at National Bureau of Economic Research (NBER) Conference on Research in Income and Wealth: U.S.-Japan Productivity Conference, Cambridge, Mass. Shishido, S., et al. 1981. Tsukuba-FAIS World Econometric Model (T-FAIS V). University of Tsukuba, Foundation of International Science and Tokyo Scientific Center-IBM, Tsukuba, Japan. Shishido, S., et al. 1986. Studies on Long-Term International Impacts of Japan's High-Technology Industries. Foundation for Advancement of International Science, Tsukuba Science City, Japan. Stone, R., et al. 1963. Input-Output Relationships 1954-1966. Vol. 3 of Programme for Growth. Cambridge, Department of Applied Economics. London: Chapman and Hall.

7 Explaining Cost Differences Between Germany, Japan, and the United States SHINICHIRO NAKAMURA

This chapter explains differences in the unit cost of production between the United States, Japan, and Germany for twenty-five industrial sectors for the period 19601979. My colleagues and I consider the differences in the level of total factor productivity (TFP) and in input price as two major factors generating these differences in the level of unit production costs. We thus break down intercountry differences in unit production cost into the differences in TFP and in input price. We use the methodology that was originally developed by Jorgenson and Nishimizu (1978) in terms of the production function within a bilateral framework; this methodology has been extended by Caves, Christensen, and Diewert (1982) within a multilateral framework. Our approach represents a dual extension in terms of the cost function. We estimate TFP on the basis of cost function instead of production function. While the choice between production function and cost function is quite irrelevant for the measurement of TFP itself, the cost function has the advantage of relating the unit cost level to the level of TFP and input price directly. Using the same set of data as ours, Jorgenson, Kuroda, and Nishimizu (1985) measured TFP for the United States and Japan, whereas Conrad and Jorgenson (1984) and Conrad (1985a) measured TFP for all three countries. All these studies used the index number approach based on the production function, and the main concern was the measurement of TFP per se. For five manufacturing sectors taken from the same data set, Conrad (1985b), using the index number approach, measured TFP differences between the three countries on the basis of the cost function. However, he made no attempt to explain intercountry unit cost differences. Hence the study reported here can be said to be the first with the explicit aim of explaining unit cost differences between the United States, Japan, and Germany at the level of disaggregation of twenty-five sectors. Since the data for each of the three countries are measured in the domestic currency, it was necessary to convert them into a common unit of measurement, for example U.S. dollars, to make an international comparison. We used purchasing power parities (PPP) for this purpose.

Cost Differences Between Germany, Japan, and the United States

109

THE MODEL We assume that the twenty-five industrial sectors in the United States, Japan, and Germany minimize the cost of production for given levels of input prices and output. Under this assumption we can represent the sectoral technology of production by the cost function (see Shephard, 1970). We now assume that the technology of each sector is represented by a translog cost function that is linear homogeneous in output. We further assume that for a given sector the intercountry difference in the structure of technology is represented by the difference in the zero- and first-order translog parameters. In other words, the sectoral cost functions for the three countries are identical up to the linear terms. This assumption is essential for the measurement of TFP levels based on the exact index number approach, that is, for measurement without econometric estimation of cost function parameters (see Denny and Fuss, 1983). Let x, p, y, and t be the m x 1 vector (m = 3) of the quantities of inputs, the corresponding vector of input prices, the level of positive output, and time (which is used as a proxy for the state of technology), respectively. Indicating Germany, Japan, and the United States by the suffixes G, J, and A, respectively, we now write the cost function of a given sector for the three countries Cr, r = A, G, J, as follows:

where br, brt, and btt are scalars, brp and btp are m x 1 matrices, and B is an m x m matrix with the properties i'brp = 1, B = B', B = (0,0,0), i'btp = 0, and z'Bz ^ 0 with i and z being the m x 1 unit vector and an arbitrary m x 1 nonzero vector, respectively. Under the assumption of a constant returns-to-scale technology, the difference between countries in the unit cost of production emerges as a result of differences in TFP and in input price levels. Therefore, if we put the same values of p and t in equation (1) for two countries, say r and s, the difference in unit cost between the two countries would measure the difference in their TFP levels. The resulting measure of TFP difference is given by

If the right-hand side of (2) takes a positive (negative) value, at the price level p country r has a higher (lower) unit cost than country s, implying that at that price level the former has a lower (higher) TFP than the latter. We now turn to the general case, where p is different among the three countries. In this case intercountry differences in unit costs arise both from differences in TFP and from differences in input price. To measure the TFP difference among the three countries we use a measure that is dual to the translog multilateral TFP index introduced by Caves et al. (1982). For this purpose we first define the mean unit cost function c as follows:

where Applying Shephard's lemma to (3), with the price level being evaluated at

110

Input- Output and the Analysis of Technical Progress

ln p = (ln(pApapj))/3, we get the arithmetic mean over the three countries of the value shares:

where D refers to the gradient vector. From equations (1) and (4) we get the following expression for the logarithmic difference between the unit cost of country r, r = A, G, J and the mean unit cost evaluated at ln p:

where the last equality follows from The unit cost difference between countries r and s can then be expressed as

where Brs refers to the terms in the first square brackets and Qrs to the remaining terms. Brs represents the difference in TFP between r and s; Qrs represents the difference in input price. We now define Brs and Qrs as the multilateral dual TFP index and the multilateral input price index, respectively. In the special case where r and s have the same input price level, Brs indeed reduces to the expression given on the righthand side of equation (2). From this definition it follows that

Therefore, if the multilateral indexes were available for two different pairs of the three countries, the index for the last pair could be obtained from the first two simply by using this property. Equation (6) shows the parametric representation of the multilateral indices. If the parameters of the translog cost function (1) were known, they could be used for computing the multilateral indices, for analyzing the factors determining their development over time, or even for projecting their possible future development. Here, however, we choose to proceed without estimating the parameters of the cost function and to use the index number approach instead. In particular, we try to dispense with

Cost Differences Between Germany, Japan, and the United States

111

estimation of the unknown parameters of the cost function by assuming that the realized values of factor shares, as well as the observed unit cost of production, actually coincide with the theoretical optimal values (this procedure is in line with the current practice of TFP measurement; see Christensen, Cummings, and Jorgenson, 1981; Conrad and Jorgenson, 1984; Jorgenson, Kuroda, and Nishimizu, 1985). Under this assumption we can obtain the value of Qrs using the realized factor shares and input prices. Substituting the value of Qrs obtained in this way into the right-hand side of equation (6) and the realized unit costs into the left-hand side, we can obtain the value of Brs. We now turn to the measurement of the levels of TFP and input prices for each of the three countries using the multilateral indices introduced above. Without loss of generality we use the United States as the base country, and we estimate the TFP as well as the input price levels for Japan and Germany relative to the corresponding U.S. levels. Since multilateral indices satisfy the circularity condition, the choice of the base country is quite irrelevant for analyzing the relative positions of the three countries in terms of TFP, input prices, and unit cost levels. We first estimate the aggregate input price index for the United States using the Tornqvist index:

where pi refers to the ith element of p and (— 1) to the previous year's value of the variable. This index is the only price index that is exact for a linear homogeneous translog cost function (see Diewert, 1976). Putting PX = 1 in (7) for the base year (1970 in our case), we obtain a time series of PX for the United States, PXA, over the sample period. We next estimate TFP for the United States, following the approach of Jorgenson and Griliches (1967):

Normalizing the index so that it equals 1.0 in a particular year, say 1960, and accumulating the measure in accordance with equation (8) provides a time series of TFP for the United States, TFPA, over the sample period. We now turn to the measurement of TFP and the aggregate input price for Germany and Japan. We recall that the multilateral indices Brs and Qrs approximate the bilateral differences in TFP and input price, although the approximation is in general not exact because of their multilateral nature:

Therefore, by using these formulae the indices of aggregate input price and TFP for Germany and Japan are given by

112

Input-Output and the Analysis of Technical Progress

THE DATA We apply the model just described to time-series data covering the period 1960-1979 for Germany, Japan, and the United States. Our data consist of time series on the prices and quantities of outputs and three types of inputs (capital services, labor services, and materials) for twenty-five producing sectors, ranging from agriculture to trade and including nineteen manufacturing sectors. (The sectoral classification is given in Table 7.3). These data have been compiled using the methodology developed by Gollop and Jorgenson (1980). In particular, we use the U.S. data developed by Jorgenson as an extension to Gollop and Jorgenson (1980); the German data developed by Conrad (Conrad, 1985a, b), by Conrad and Jorgenson (1984), and by Conrad and Unger (1984); and the Japanese data developed by Kuroda (Kuroda, Yoshioka, and Jorgenson, 1984) and by Imamura and Kuroda (1984). Details of the process of data compilation are given in these studies. However, we point out here that the data on labor and capital services have been constructed using detailed information on labor input classified by education levels and other quality factors and on capital stock classified by asset types. In the previous section we implicitly assumed that all the prices and quantities of the three countries are mutually comparable, that is, that they are measured in terms of a common unit. Since our data for the three countries are measured in domestic currencies, however, it is necessary to convert them into a common unit, say into U.S. dollars. The simple procedure of conversion using exchange rates is known to be quite inappropriate for making international comparisons because it completely neglects intercountry differences in relative price levels. We use purchasing power parities (PPP) for the inputs and outputs of Germany, Japan, and the United States to convert the data into the common U.S. base. In particular, we use the time series of PPP for inputs and outputs in German marks per dollar developed by Conrad and Jorgenson (1984) and those in yen per dollar developed by Jorgenson et al. (1985) to convert the German as well as the Japanese data into the U.S. dollar base. Let us denote by JP, JX, JPPP, and exJA the price index and quantity of a particular good (input or output) measured in yen taken from the Japanese data, the PPP for this good in yen per dollar (the price of one dollar's worth of this good in terms of yen), and the rate of exchange for the yen against the dollar, respectively. The quantity of this good in dollars, xJ, is then given by

and the nominal expenditure on this good in U.S. dollars by

Dividing the expenditure in dollars by the quantity in dollars, we obtain the corresponding price index in dollars, pj:

We use the price index and expenditure figures obtained in this way for the empirical application of our model. In an analogous manner we obtain the corresponding data for Germany. The data on exchange rates are taken from Bank of Japan (1975, 1983).

Cost Differences

113

Between Germany, Japan, and the United States

EMPIRICAL RESULTS Since we are dealing with twenty-five sectors for three countries over twenty years, and our space is limited, we choose to limit ourselves to presenting an overall picture of the results. This implies that for the most part we treat the twenty-five sectors as a whole, without going into sectoral details. (Because data on PPP for the German petroleum sector are not available, comparisons involving Germany were possible for only twenty-four sectors.) The only exceptions to this restriction are Tables 7.3 and 7.5, which give information at the sectoral level. We first compare the relative position of the United States, Japan, and Germany with respect to TFP, input price, and unit cost for 1960, 1970, and 1979. Table 7.1 shows for each country the ranking, expressed in terms of the number of sectors, for TFP, input price, and unit cost, while Table 7.2 shows the unweighted arithmetic mean over the sectors of unit cost, input price, and TFP, together with their average annual growth rates. In 1960 the United States had the highest TFP level for almost all the sectors, while Japan had the lowest level for two-thirds of the sectors. Germany was between the two. This pattern continued throughout the 1960s, although there was a tendency TABLE 7.1 Comparison of ranking order of United States, Japan, and Germany for twenty-five sectors with respect to total factor productivity (TFP), input prices, and unit production costsa Year Ranking Order

1970

1960

I

2

3

1

1979

2

3

16

9

(64)

(36) 9 (36) 7 (29)

0 (0) 13 (52) 11 (46)

1

2

3

TFP Levels United States Japan Germanyb

23 (92) 1

(4) 1 (4)

2

(8) 8 (32) 15 (63)

0 (0) 16 (64)

8 (33)

3

(12) 6 (25)

3 (12)

11 (44)

11

(46)

14

8

(56) 3 (12) 8 (33)

(32) 11 (44)

5 (21)

Input Price Levels United States Japan Germanyb

United States Japan Germanyb

23 (92) 1 (4) 1 (4)

1 24 (96) (4) 1 0 (0) (4) (4) 1 22 23 (92) (96) (4) (4) Unit Cost Levels 2 (8) 1

0 (0) 23 (92) 1

4

5

1 (4) 16

(16)

(20)

(64)

4

13 (54)

7

7

11

(29)

(29)

(46)

17

7

(68)

(28)

(17)

0 (0) 24 (96) 0 (0)

1 (4) 3 (12) 21

15 (60) 9 (36) 1

(88)

(4)

15

7

3

2

15

(60)

(28)

(12)

(60)

3

7

(8)

8

6

(12)

(28)

(32)

(24)

15

(60) 6

15

(25)

(63)

4

(17)

9

(36) 13 (52) 2 (8)

8 (32) 11 (44) 5 (20)

aEach number in the table gives the number of sectors with the ranking order in the first row. The numbers in parentheses are percentages. b Twenty-four sectors only.

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Input-Output and the Analysis of Technical Progress

TABU; 7.2 Arithmetic means over sectors of the unit costs, input prices, and total factor productivity, and their annual growth rates in the United States (A), Japan (J), and Germany (G)

Year(s) 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976

1977

1978 1979

1960-70 1970-79

1960-79

TFP

Input Price

Unit cost

A

J

G

A

J

G

0.8521

0.7118 0.7055 0.6975 0.7197 0.7250 0.7365

0.7175

0.7758

0.8320

0.3379 0.3584 0.3551 0.3738 0.3909

0.4445 0.4891 0.5147 0.5311

0.7539 0.7592 0.7552

0.8210 0.8111

0.4064 0.4253 0.4571 0.5131

0.8152

0.8295

1.0545

1.0557

0.5467 0.6004 0.6150

0.5952 0.6095 0.6064 0.6177

0.8393 0.9550

0.8567 0.8850 0.8950 0.9355

1.7391

1.4934 1.7424 1.8648

1.3405

0.7241 1.0782 1.4001

0.8502 0.8540 0.8523 0.8548 0.8684 0.8934 0.9080 0.9321 0.9657 1.0000 1.0413 1.0855 1.1783 1.3951 1.5424 1.6381 1.7735 1.9137 2.1708 1.46 7.46 4.68

0.7435

0.9509 1.3102 1.7530 1.8671 2.0435 2.4735

2.3419 1.23 10.55

5.95

0.7674 0.7804 0.7937 0.8185 0.8368

0.8377

1.1926

1.8453

1.9799 2.2218

0.7832 0.7987 0.8149

0.9755 1.0000 1.1156 1.2048

1.4741 1.5856 1.7056 1.8373

1.3869 1.5037 1.5819

2.0431

0.5642

0.6745 0.7999 0.9092 1.0551 1.3959 1.7331

1.8574 1.8820

2.0572 2.3308

2.7552 2.0214 Annual Grow th Rates (%)) 5.34 2.31 2.55 5.23 12.37 12.14 6.99 9.45 9.12 8.94 4.76 6.21

2.4838

2.0119

A

J

G

1.0000

0.5818 0.6044 0.5078

0.7082

1.0115 1.0266 1.0488 1.0570 1.0814 1.0859

1.0800 1.0990 1.1065 1 .0948 1.1099 1.1258 1.1225 1.0525 1.0521

1.0759

1.0851 1.0841

1.0659 0.82

-0.13 0.32

0.6434

0.7259 0.7481 0.7589 0.7821

0.6497 0.5700 0.7096 0.7804

0.8080 0.8285 0.8408 0.8705

0.5157

0.8200 0.8409 0.8539 0.8885

0.9148 0.9350 0.9719

1.0854

0.9994 1.0581 1.1159 1.1115 1.1405 1.1521 1.1824 1.2481

3.35 2.56

2.53 2.89

3.12

2.83

0.9489 0.9652

0.9854 1.0154 1.0729 1.0543

for the United States gradually to lose its leading position, in particular relative to Germany. This tendency became stronger in the 1970s, and by 1979 the United States had lost its leading position for twenty of the twenty-three sectors. As a consequence, in 1979 the United States had the highest TFP level for only three sectors. The leading position was shared equally between Germany and Japan: each country had eleven sectors with the highest TFP level. We supplement these qualitative findings with the numerical results of Table 7.2. From the table we find that in 1960 the Japanese TFP level was on average 50 percent of the U.S. level, while the German level was 70 percent of it. Because the growth rates of TFP for Germany and Japan were significantly higher than those for the United States, however, by 1970 this TFP gap had been reduced to 23 percent for Japan and 15 percent for Germany. As this process continued Germany reached the U.S. overall TFP level around 1974, and finally by 1979 its TFP level was higher than the U.S. level by 17 percent. On the other hand, the Japanese overall TFP level approached the U.S. level around 1977 and indeed exceeded the U.S. level in 1979 by a small amount (2 percent). We note here that this last comparison holds for the unweighted arithmetic mean but not for the weighted mean using sectoral value added as the weights. In fact, in terms of the latter measure the Japanese overall TFP level (1.0256)

Cost Differences Between Germany, Japan, and the United Slates

115

is still behind the U.S. level (1.1001), whereas the German level (1.2781) continues to be the highest. While Germany and Japan shared an equal number of sectors with the highest TFP level, the pattern of TFP levels differed significantly between the two countries: Japan had the largest number of sectors with the lowest TFP level and Germany had the smallest. The proportion of sectors with the lowest TFP level is 44 percent for Japan, 33 percent for the United States, and 21 percent for Germany. Thus the Japanese economy appears to have a kind of dichotomy in terms of the sectoral TFP levels. We now turn to price comparisons. In 1960 the United States had the highest input price level for almost all the sectors, and Japan had the lowest level; Germany was located in between. This pattern remained almost unchanged throughout the 1960s. In the 1970s, and in particular after 1973, however, this pattern underwent a substantial change, especially with respect to the relative positions of the United States and Germany. In 1979 it was not the United States but Germany that had the highest input price level for almost all the sectors. A majority of the U.S. sectors moved into the second position behind Germany. While in 1979 Japan still had the largest proportion of sectors with the lowest input price level, at the same time it had a larger number of sectors with the highest input price level than the United States. In fact, in terms of the arithmetic mean over the sectors, the Japanese overall price level has been higher than the U.S. level since 1978, whereas in terms of the mean weighted by sectoral value added, the Japanese level (1.9780) is still behind the U.S. level (2.0078). We next compare unit cost levels. In 1960 the United States had the highest unit cost level for more than two-thirds of the sectors, Japan had the lowest level for almost the same proportion of sectors, and Germany was located in between. This pattern of unit cost levels continued without substantial change throughout the 1960s. However, as a result of the substantial changes in TFP mentioned above, as well as changes in input price levels, during the 1970s this pattern also underwent a substantial change. In 1979 Germany had the highest unit cost level for two-thirds of the sectors and had thus taken the position the United States held in the 1960s. Japan continued to retain its position as the country with the largest proportion of sectors with the lowest unit cost level. On the other hand, Japan had a larger number of sectors (eight) with the highest unit cost than did the United States (two). In fact from Table 7.2 we find that since 1973 the Japanese unit cost level was on average higher than the U.S. level. We have discussed the overall pattern of the relative position of the three countries with respect to TFP, input price, and unit cost, and the changes that have occurred over twenty years. In our model, unit cost is determined solely by TFP and input prices. Hence the cost advantage (or disadvantage) of the three countries in a particular industry can also be explained by their relative position with respect to TFP levels and input price in that sector. Since TFP and input price can have either mutually reinforcing or offsetting effects on unit cost, their roles as determinants of cost advantage or disadvantage may differ significantly among the three countries. In the following section we consider the roles of TFP and input price as determinants of cost advantage or disadvantage for the year 1979. We start by considering cost advantage. Table 7.3 shows for 1979 for each country the sectors with the lowest unit cost (among the three countries), together with the corresponding relative levels of TFP and input price. We find that of the eight

116

Input-Output and the Analysis of Technical Progress TABLE 7.3 Sectors with lowest unit costs and its factors, 1979 Ranking Order" of Sector United 3 4 5 6 14 22 25 27 Japan 2 9 11 15 16 19 20 23 24 26 27

States Construction Foods Textiles Apparels Leather products Transportation equipment Transportation and communication Trade services Mining Paper products Chemicals Stone and clay Primary metals Machinery Electrical machinery Precision instrument Miscellaneous manufacturingg Utility Motor vehicles

Germany 1 Agriculture 7 Wood products 10 Printing 13 Rubber 18 Fabricated metals

TFP Levels

Input Price Levels

2

2 3 2

1 1 3 2

3 3 2

3 2

3 2

1 1 1 1 1 3 1 1 1 1

2 3

2

3 3 2 3 3

2

3

2 3 3

1 1 1 1 1

3 1 1 1 1

aRefers to ranking order in comparison of United States, Japan, and Germany.

sectors for which the United States had the lowest unit cost, only two sectors had the highest TFP levels and none had the highest input price level. In particular, for two sectors U.S. unit cost was the lowest as a result of the offsetting effects of the lowest TFP level and the lowest input price level. We can say that the cost advantage of the United States for these eight sectors is for the most part based on moderate TFP levels and moderate input price levels. As a quantitative illustration of this we show in Table 7.4 the arithmetic means, over the sectors with the lowest unit cost, of unit cost, input prices, and TFP. We find that the eight U.S. sectors just considered are indeed characterized by the lowest mean input price and a TFP level lying between those of Germany and Japan. Germany has the lowest unit cost for five sectors. For all of these sectors Germany has the highest TFP level, and for all but one sector the highest input price level as well. Germany cost advantage for these five sectors (with one exception) is thus based on high TFP levels, which are sufficient to more than offset the highest input price level. This is consistent with our finding above, based on Table 7.1, and can

Cost Differences

117

Between Germany, Japan, and the United Slates

further be confirmed by Table 7.4. Row (c) of this table shows that these five German sectors indeed have the highest mean TFP, and the highest mean input price level as well. Japan has eleven sectors with the lowest unit cost. For nine of these Japan has the highest TFP level, while all eleven sectors have input price levels of the second (four sectors) or the third (seven sectors) order. We can say that Japanese cost advantage rests to a large extent on the highest TFP level and low input price levels. Table 7.4 (row (b)) shows that in fact these eleven Japanese sectors have on average the highest TFP and the lowest input price level. The findings on TFP and input price as factors of cost advantage set out above can be summarized as follows. Japanese cost advantage is based on the mutually reinforcing effects of high TFP levels and low input price levels. German cost advantage is based on high TFP levels, which are high enough to more than offset high input price levels. U.S. cost advantage rests on the mixed effects of moderate TFP levels and moderate or low input price levels. We next turn to the analysis of factors of cost disadvantage. Table 7.5 shows for each country the sectors with the highest unit cost (among the three countries) together with the corresponding relative levels of TFP and input price for 1979. The two sectors for which the United States has the highest unit costs are characterized by the lowest TFP levels, while their input price levels are as high as the German levels. Germany has the highest unit cost for fifteen sectors. All these sectors also have the highest input price level. Of them, four have the highest TFP level, two the second highest, and five the lowest level. Thus, in contrast to the United States, there is no TABLE 7.4

Comparative characteristics of sectors with lowest and highest unit cost, 1979, for United States (A), Japan (J), and Germany (G)a Unit Cost A

Sectors with lowest unit cost (a) United States (8 sectors) (b) Japan (11 sectors) (c) Germany (5 sectors) Sectors with highest unit cost (d) United States (2 sectors) (e) Japan (8 sectors) (f) Germany (15 sectors)

J

1.847b8 7b 2.590

Input Price

TFP

G

A

J

G

A

J

G

2.551

1.910

2.037

2.862

1.125

0.888

1.212

2.227

1.511

2.819

2.022

1.865

2.801

1.046

1.408

1.089

2.052

3.139

1.638

2.131

1.967

2.483

1.131

0.759

1.657

2.339

1.931

1.567

2.132

2.087

2.136

0.920

1.105

1.459

2.337

3.576

1.762

2.058

2.172

2.223

1.020

0.675

1.204

2.060

1.738

2.825

1.971

1.933

2.938

1.110

1.303

1.160

aRows (a) and (d) refer to sectors for which United States has lowest (highest) unit cost. Rows (b) and (e) refer to sectors for which Japan has lowest (highest) unit cost. Rows (c) and (f) refer to sectors for which Germany has lowest (highest) unit cost. bArithmetic means over the sectors characterized by the first column.

18

Input-Output and the Analysis of Technical Progress TABLE 7.5 Sectors with highest unit costs and its factors, 1979 Ranking Ordera of TFP Levels

Input Price Levels

United States 7 Wood products 16 Primary metals

3 3

3 1

Japan 1 Agriculture 3 Construction 4 Foods 10 Printing Petroleumb b Petroleumb 12 13 Rubber 18 Fabricated metals 22 Transportation equipment

3 3 3 3 2 3 3 3

1 3 1 3 1 2 3 3

Germany 2 Mining 5 Textiles 6 Apparels 9 Paper products 11 Chemicals 14 Leather products 15 Stone and clay 19 Machinery 20 Electrical machinery 27 Motor vehicles 23 Precision instruments 24 Miscellaneous manufacturing 25 Transportation and communication 26 Utility 27 Trade services

2 2 1 3 2 1 2 3 3 1 3 3 2 2 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Sector

aRefers to ranking order in comparison of United States, Japan, and Germany. b Comparisons of this sector were possible only for the United States and Japan, due to the unavailability of the German PPP data for this sector.

overall pattern characterizing the TFP levels of the German sectors with the highest unit cost. In fact the mean TFP level of these high-cost German sectors is higher than the corresponding Japanese level. Japan has the highest unit cost for eight sectors. All these sectors are characterized by the lowest TFP level, while their mean input price is higher than the corresponding U.S. level. We conclude that high unit costs for the United States and Japan arise because the sector concerned has the lowest TFP level, whereas for Germany they arise because Germany has the highest input price.

CONCLUDING REMARKS We have tried to explain differences in the sectoral unit production cost between the United States, Japan, and Germany by differences in TFP and in input price. Our methodology is based on the index number approach. We found that the levels of TFP

Cost Differences

Between Germany, Japan, and the United States

119

and input price, as well as their effects on unit cost, are indeed quite different among the three countries. For 1979 (the most recent point in our data) we can summarize the above finding of country-specific features as follows. Germany has on average the highest TFP level, as well as the highest input price level. It has a cost advantage in those sectors whose TFP levels are high enough to offset the highest input price level. Germany has a cost disadvantage in sectors in which this offsetting process does not work. While Japan has as many sectors with the highest TFP level as Germany, it also has an equally large number of sectors with the lowest TFP level. Although on average the Japanese input price level exceeds the U.S. level in 1979, Japan has the remarkable feature that the sectors with the highest TFP level have also, on average, the lowest input price level. Therefore, the Japanese cost advantage is based on the mutually reinforcing effects of high TFP and low input price levels. The United States has the smallest number of sectors with the highest TFP level, and the smallest number with the highest input price level as well. The United States is thus characterized by moderate TFP and input price levels. As a result this country has a cost advantage in sectors for which Germany has too high input prices and Japan too low TFP levels. Both the United States and Japan have a cost disadvantage in sectors in which the TFP level is on average the lowest and the input price level is of a moderate level.

REFERENCES Bank of Japan. 1975, 1983. Annual Statistics for International Comparisons. Tokyo: Bank of Japan. Caves, D. W., L. R. Christensen, and W. E. Diewert. 1982. "Multilateral comparison of output, input, and productivity using superlative index numbers." Economic Journal 92: 73-86. Christensen, L. R., D. Cummings, and D. W. Jorgenson. 1981. "Relative productivity levels, 1947-1973, an international comparison." European Economic Review 16: 61-94. Conrad, K. 1985a. Produktivitaetsluecken nach Wirtschaftszweigen im internationalen Vergleich. Berlin: Springer-Verlag. Conrad, K. 1985b. "Theory and measurement of productivity and cost gaps in manufacturing industries in U.S., Japan and Germany." Discussion Paper 301-85, University of Mannheim, Germany. Conrad, K., and D. W. Jorgenson. 1984. "Sectoral productivity gaps between the United States, Japan and Germany, 1960-1979." Paper presented at annual meeting of Verein fur Socialpolitik, Travemunde, Germany. Conrad, K., and R. Unger. 1984. "Dynamische Allokation von Produktionsfaktoren." Mimeo, University of Mannheim, Germany. Denny, M., and M. Fuss. 1983. "A general approach to intertemporal and interspatial productivity comparisons." Journal of Econometrics 23: 315-330. Diewert, W. E. 1976. "Exact and superlative index numbers." Journal of Econometrics 4: 115145. Gollop, F., and D. W. Jorgenson. 1980. "U.S. productivity growth by industry, 1947-73." In ,J. W. Kendrick and B. Vaccara (Eds.), New Developments in Productivity Measurement. Studies in Income and Wealth 41. New York: Columbia University Press. Imamura, H., and M. Kuroda. 1984. "Quality change of labor input in Japan." Occasional Paper E-l, Keio Economic Observatory, Keio University, Tokyo, Japan.

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Jorgenson, D. W., and Z. Griliches. 1967. "The explanation of productivity change." Review of Economic Studies 34: 249-283. Jorgenson, D. W., M. Kuroda, and M. Nishimizu. August 1985. "Japan-U.S. industry level productivity comparison, 1960- 1979." Paper presented at National Bureau of Economic Research (NBER) U.S. -Japan Productivity Conference. Cambridge, Mass. Jorgenson, D. W., and M. Nishimizu. 1978. "U.S. and Japanese economic growth, 1952-1974: An international comparison." Economic Journal 88: 707-726. Kuroda, M., K. Yosioka, and D. W. Jorgenson. 1984. "Relative price changes and biases of technical change in Japan." Economic Studies Quarterly 35: 116-138. Shephard, R. W. 1970. Theory of Cost and Production Functions. Princeton, N.J.: Princeton University Press.

8 Price Behavior with Vintage Capital P. N. MATHUR

An economy experiencing continuous technical advance will necessarily be embodying part of its improving know-how in new capital equipment. Equipment of different vintages will work with different efficiencies and may require different amounts of inputs, labor, and working stocks to produce a unit of output. At a particular time, we may expect fixed capital equipment of several vintages to be used for producing the same commodity. Investment is made in equipment of the latest technique, but the older equipment may also continue production, even if it is likely to earn lower returns than new equipment. The old equipment will continue to be used, however, until enough capital of the newer vintages is accumulated to satisfy total demand for the commodity being produced. In a competitive industry with free entry, innovators with new, more efficient techniques can start production units; if demand does not increase pari passu, they will be able to lower the price of the commodity, which in turn will displace a requisite number of the most inefficient production units from the market. In a monopoly, however, the producer may deliberately delay the introduction of the new process, thus giving older capital equipment more time to survive economically than would otherwise have been possible. LAYERS OF TECHNIQUES Thus in a state of technological change we expect to witness a spectrum of technologies of different vintages existing and working simultaneously. We can define the technology associated with a vintage of capacity for the production of the j th commodity as follows: c(kj)

)

A(kj), S(kj)

l(kj) j)

Capacity Input and working stock vectors per unit of capacity Labor coefficient

Further, let be the balance left after the prime costs per unit of output are met. We may call this

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Input-Output and the Analysis of Technical Progress

balance the residual. It may be noted that while prices (P), wage rate (w), and interest rate (r) can be assumed to be the same for all units, irrespective of their equipment vintage or technique of production, the residual is different for each vintage. It is on the value of this residual that the actions of an individual unit depend. When an investment is being made in equipment pertaining to a new technology, the expected residual should be large enough to cover not only the interest and depreciation charges, the risk premium, and so on but also profit expectations. The residual is not like a fixed annuity over the physical lifetime of the equipment, as is the case if there is no technological progress and thus no obsolescence. In our age of advancing technology, the value of the residual should decline progressively, and the investor needs to take this into account in any investment decision. When production using particular fixed capital equipment eventually becomes not economically worthwhile (the residual has declined too far), the equipment can only fetch scrap value. Thus its opportunity cost is almost zero. In deciding whether to continue the production process, the unit of production therefore will not consider whether continuing will provide returns on the fixed capital. It should continue production so long as it can cover the prime cost of production, that is, as long as its residual is not negative. Let A, S, and L be the input-output and capital coefficient matrices and labor vector, respectively, representing the technology of the marginal units producing each commodity. As the residual is zero for all of these we should have

Given the wage rate and interest rate, this gives price as

We let X denote the output of these units using marginal techniques. Then the net output available for use is given by P(I — A)X. rPSX represents interest payments, and thus P[(I - A — rS)X]/LX is the wage rate. The marginal technology determines both the price structure and the real wage rate in the ecomomy. It can be shown, similarly, that given the real wage rate, the marginal technology will determine the interest rate as well as the price structure. There is 1 degree of freedom, implying that either the wage rate or the interest rate can be independently determined. The marginal technology itself will be determined in such a way that total savings in the economy are equal to total investment and other autonomous demand. Shortterm increases in demand will bring less and less efficient technologies into production, thus increasing employment in the economy. These techniques will be economically viable only if the real wage rate or the interest rate, or both, decrease. Such a fall will in turn increase the residuals of all the units. The savings rate is likely to be higher from the residual income than from wage or interest income. This redistribution of income in favor of the residual earners from the working units will therefore increase total savings, even from the older-unit production. Over and above this will be some savings by income recipients because of the increased production. Thus, the bringing of more and more marginal techniques into production will increase total savings in the economy. Similarly, the opposite case of removal of more and more marginal firms from production will decrease total savings in the economy. The number of producing units in the economy will thus depend on the condition that

Price Behavior with Vintage Capital

123

the savings of their cooperating factors matches investment and other autonomous demand.

DIFFERENT TYPES OF PRICE BEHAVIOR The previous derivation relies crucially on the competitiveness of the industry. In a monopolistic industry the selection of techniques for current production can be an administrative act; the price system functions to achieve other aims of the monopolist. Therefore the price of a commodity produced under monopolistic control will not be determined by the production cost of the least efficient technique in use. It will depend both on the movement of the demand curve determining the marginal revenue curve and the movement of marginal costs. The increase or reduction in marginal costs will therefore not be sufficient to allow one to predict change in price or production. Similarly, in agriculture and mining, where short-term supplies do not depend on current prices, the relationship between cost of production and price will be weak. In these cases the price elasticity of demand is likely to be less than 1, sometimes substantially so. Even small changes in demand are likely to result in large changes in price. If demand decreases, in the short term producers do not have to be able to recover even their normal wages and other costs, while if demand increases they may reap windfall gains. Thus we get four types of price determination in the economy: 1. Traditional marginal cost pricing of competitive industry, where the cost of production of the least efficient techniques required to meet demand determines prices 2. Monopoly pricing, where marginal revenue is equated with marginal cost and the price structure does not work as a decentralized communicator, informing firms whether they should produce or suspend production. In monopolistic industry that can be achieved by administrative action 3. Pricing of commodities whose short-run supply is given, and prices are determined in such a way that this supply can be absorbed by the market. Though in the longrun these prices are likely to affect supply, recurring crises in world commodity markets and the agricultural price support policies adopted in developed countries are indications of the inefficiency of this kind of pricing 4. Pricing of imports, determined only in the context of the international economy If we introduce these three additional types of commodity—monopolies, restricted supply, and imports—the price equation given previously will not describe the process of price formation in the economy. Then the formula for determining the prices of the competitive commodities in the economy will have to be modified to take into account the cost of inputs of the commodities whose prices are determined in other ways. We shall first see below how temporary equilibrium is established in an economy that consists of different layers of techniques in each industry and where all prices are determined on the marginal cost principle.

AN ECONOMY WITH LAYERS OF TECHNIQUES Such an economy can be conceived of as a collection of sets of different types of the economic units.

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Input Output and the. Analysis of Technical Progress

Production Set The production units at time t form a set Z(t), consisting of units producing all the commodities in the economy. A production unit can either produce nothing or work at full capacity, since it is guided only by the decentralized signals of price structures, wages, and interest rates. It will produce to its full capacity if the output price can meet its prime costs, otherwise it will not produce at all. Each working production unit requires working stocks and also has to enter into a coalition with a set of laborers. The residual income of a unit at time t is then where superscripts give the period of residual income, wage and interest rates, and prices; subscript j refers to the commodity; and k in brackets refers to the unit of production. The production unit produces zero if the residual for it at time t is less than zero; otherwise it produces to the full extent of its capacity. The set Z(t) consists of subsets Z(tj), such that each unit of Z(tj) produces a net output of the jth commodity. To keep the exposition simple, we shall assume that each production unit produces a net positive quantity of one commodity only. Given prices and the wage and interest rates, the production units in the set Z(tj) can be ordered in accordance with the decreasing per-unit value of the residual. It will be be seen that the set Z(tj) is a convex set. This implies that global decreasing returns to scale prevail for the set Z(tj) as a whole, though the individual production units have only the alternatives of producing to their full capacity or not producing at all. (If we want an exact solution, strict convexity may have to be assumed. This will not be seriously wrong if the number of units is large.)

Labor-Consumer Set The units of labor form another set of economic units designated as L. Individual labor units may be attached to individual production units as workers; when the production unit is producing at a non-zero level, these labor units earn the prevailing wage rate. Some of these labor units may own a share in one or more production units and may earn a part of the residual income. Further, some of them may have savings in the banking units and earn interest on them. Labor units form small consumption coalitions called households whose total income consists of wages earned by its members, interest earned on the savings of its members, and its members' share in the residual income of production units. To each household is attached a utility function, having the usual convenient properties, whose arguments are not only commodities and monetary balances but also savings.

Banking Set Banks form another set of economic units designated as B. Their primary function is to take the deposits of savers and pay interest to them. Furthermore, they advance loans to production units and stockholders for purchasing working stocks, to new production units for investment, to governments, and so on. Thus one of the main characteristics of the monetary-cum-banking system is the provision of credit. The value of loans advanced need not be less than or equal to the savings deposited but can be more. How much money should be created depends on the judgment of the

Price Behavior with Vintage Capital

125

government and the demand and creditworthiness of other credit receivers, as well as on the liquidity judgment of the individual banking units.

Autonomous Expenditure Set This set consists of those economic units that can purchase commodities without first earning an amount equivalent to the commodities' value. These purchases are paid for with loans from the banking set. The autonomous expenditure set may comprise government, production units both new and old, and stockholding units, as well as consumer units that are able to get consumer credit. They are a distinctive institution of the market monetary-cum-credit economy. The expenditure of this set is not constrained by the budget constraint, as is that of the labor-consumer set L.

Stockholder's Set Stockholders hold the stock of commodities consisting of investment goods, intermediate goods, and consumer goods. However, the fixed capital stock of different vintages is held only by the production set. Whenever members of the autonomous expenditure set require a commodity it is supplied to them by stockholders. Similarly, whenever producers or consumers demand a commodity it is also provided only by members of the stockholder set. Let us assume that they charge a price that is proportional to the price they have paid to the producer. (In the following, to keep the exposition simple, we shall proceed as if that proportion is 1. The necessary changes can easily be made by the reader.) However, if the particular commodity is not in stock it must be ordered, and the price then charged relates to when the commodity is actually delivered. Stockholders order from producers approximately the amount of commodities they are able to sell in the current period.

MOVING EQUILIBRIUM Broad Conceptualization Resembling Economic Contours In period t all commodity demand is met by stockholders at the price determined by the marginal cost of production of the previous period. If demand for a particular commodity is greater than current stocks, meeting of the extra demand is deferred to the next period to be met at the next period's price. On meeting that demand stockholders place the order for production to the production units of set Z(t). Suppose in the production set Z(tj) the unit Z(tj1) had a residual of zero in the last period, but now orders for the commodity j are such that not only the newly commissioned units (producing a positive amount of commodity j) should produce in this period but also units that were not required to produce in the last period. Then the following two things will happen: (1) These new units will ask for a loan from the banking set to purchase their current working capital needs from the stockholders. This implies that the autonomous demand of the period will increase more than otherwise. (2) The prices at which the units will deliver the goods will be such that they are able to meet their prime cost of production, that is, the cost of inputs, wage costs, and the interest on working capital. This price will be more than the current price of the commodity j, since otherwise in a competitive market nothing would have prevented them from producing in the last period.

126

Input Output and the Analysis of Technical Progress

Suppose, on the other hand, that total orders placed for this period by stockholders are less than the net production of the commodity j in the last period. This will mean zero production in some of the units that produced in the last period. In a competitive decentralized economy this can only be achieved by reducing the price considerably, so that the required number of units find they cannot meet their prime cost of production. This will also mean that the units going out of production will not repurchase their raw materials and other supplies from the stockholders in the current period, which will further reduce demand. If we ignore for the time being the complications arising out of the increased or reduced purchase of working capital stocks by the incoming or outgoing production units, a temporary equilibrium of the economy within the period is indicated. The involuntary savings or dissavings required during the period will be carried out by the stockholders by reducing or increasing their stocks. They will try to adjust their position in the next period by placing a suitable number of orders with the production set. We can further assume that within a given period the wage rate and interest rate remain fixed. Thus in period t the price structure, wage rate, and interest rate will be given respectively as Pt, wt and rt. However, Pt+l will be the price of the goods produced, as they will be sold in the next period at that period's price. Once the prices of all inputs are given, the production units for the commodity j can be ordered in accordance with increasing cost, and as soon as the volume of orders placed for the commodity by the stockholders is known, a list of the most efficient production units that will be able to fulfill those orders will be determined. As the stockholders would bring down the quoted delivery price of the commodity as far as possible, the price will be determined in such a way that the most inefficient unit on that list is just able to cover its prime cost. Thus where A and S represent the matrices formed by collecting per-unit intermediate input vectors of the least efficient working unit for each commodity, the current stock vector required by each marginal unit to be able to undertake production, and L represents their labor requirements.

Okun's Law: Economic Expansion and Concomitant Inflation From the foregoing considerations it is obvious that for prices to remain constant over two periods the stockholders' orders should increase by an amount just equivalent to the new creation of capacity in the period. Then all the units producing in the last period will go on producing in the current period. However, if current autonomous demand is more than that necessary to ensure this, less efficient units, which were not producing in the last period, will have to start production again. This implies not only an extra increase in total production and employment but also an increase in prices. This conforms with the conclusions of Keynes: "The increase in the effective demand will, generally speaking, spend itself partly in increasing the quantity of employment and partly in raising the level of prices." Thus, "Instead of constant prices in the condition of unemployment, and of prices rising in proportion to the quantity of money in conditions of full employment, we have in fact a condition of prices rising

Price Behavior with Vintage Capital

127

gradually as employment increases." (Keynes, 1936, p. 296). Arthur M. Okun (1981, pp. 348-352), from his study of real-world business fluctuations, had come to the conclusion that in the modern United States around 90 percent of the first-year response to a spending shock shows up in output and 10 percent shows up in prices.

Real Wage and Interest Rates: Their Counterinflationary Movements Another consequence of the foregoing formulation is that higher output and employment can only be supported by lower real value added per unit of production by the marginal unit, because the production units required to come into production are less efficient. Wage earners and interest receivers of period t spend their income of that period in the next period at the prices prevailing in the t + 1th period. By the proportion that prices in that period are higher than in the tth period, their real income decreases. This decrease conforms with the received wisdom of empirical investigators. As far back as 1913, after a careful study of business cycles up to that time, W. C. Mitchell (1913, p. 16) wrote, "During business revivals the prices of labour rise less than the price of commodities," and in crisis, "The prices of labour fall less rapidly than the prices of commodities" (p. 135). It is well known that in the 1930s the real hourly earnings in manufacturing continued to rise at about 3 percent per annum. "From 1929 to 1933 real wages actually rose by between 7 and 11 per cent. There was no appreciable decline in nominal wage rates observed between 1929 and 1930 despite the sharp rise in unemployment and the rapid decline in industrial production" (Baily, 1983). For the effect on interest income, I shall just quote Modigliani's (1983) comments on Summers's paper: "Summers's evidence is overwhelming for the period 1860 to 1940, when the nominal rate, whether short or long, seemed hardly to respond to inflation (and in the first half actually responded perversely). But even for the post war period, though the response is appreciably positive, specially over longer spans of time, it remains well below unity—except for the subperiod 1954-71." After all, the well-known Gibson paradox states that a strong positive correlation between the price levels and interest rates is empirically observable. This finding is, of course, inconsistent with monetary theory. Thus we can say that the above formulation of the moving competitive equilibrium with layers of techniques throws up the hypothesis that an increase in economic activity that is faster than the creation of new capacity will not only increase employment but also lead to increases in prices and decreases in the real wage and real interest rates. This has been found to accord with facts of economic life in certain periods.

Stability of Moving Equilibrium This system of moving equilibrium will only be stable if wage earners or interest earners, or both, acquiesce in the reduction of their real income and if the increase in economic activity and redistribution of income is such that sufficient voluntary savings can be generated to cover the extra autonomous expenditure. As shown previously, with the opening of the closed units the real wage and/or real interest rate will be reduced. This will result in an increase in the residual income of all the

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Input -Output and the Analysis of Technical Progress

nonmarginal units of production. If their saving propensity is higher than that of wage and interest receivers, savings from the income generated by these units should increase. These savings will be in addition to what is saved from the income of the newly producing units. Hence, using the above savings assumption, we can say that total savings in the economy will increase. The extent of the increase will also be affected by the proportion to which wage earners and interest earners take the cut, since the saving propensity of the two is likely to differ. These saving propensities, together with the distribution of income between different factors of production, should determine the number of production units to be reopened so that the extra savings generated will become equal to the extra autonomous demand that started the whole process. Thus there is only 1 degree of freedom within the system, and that too is severely constrained. Once the minimum acceptable real wage rate or acceptable real interest rate is determined, the condition for stability of the system will be the acquiescence of the rest of the economic system in the consequences thereof. If the system does not acquiesce and has the means to adopt effective countermeasures, cost-push inflation will result. (Let us call this factor-push inflation.) It may be noted that such inflation may result not only from the actions of labor, but also of banks in trying to push up nominal interest rates, which in turn can only be brought in line with the real interest rate by inflation. The real interest rate will of course be that which is appropriate for the real wage rate and the marginal technology in production, that is, the rate that gives Then r is the appropriate real interest rate for the real wage w. It may be noted further that if there is acquiescence in the reduction, but not in appropriate proportions, then the price structure may change. The change will be much greater if there is no such acquiescence. In this case: where r* is the nominal interest rate. If P is the equilibrium value corresponding to w, r, then With the nominal interest rate higher than the real rate, the second term on the righthand side of the equation will be positive, indicating not only a price rise but also changes in the price structure. The prices of the commodities having higher requirements of working stocks will rise higher. In an open economy this will imply penetration in the market for capital intensive goods of the countries having higher nominal interest rates by the economies where this difference is lower or nonexistent. The penetration of U.S. and U.K. manufacturing markets by Japan and Germany during the 1970s and early 1980s may be due partly to this phenomenon.

Working Stocks Requirements: Overheating of the Economy? Now we can look to the effects of working capital requirements on restarting production and thus on increasing employment. As already noted, this requirement will tend to enhance the effects of the increase in autonomous demand. This increase

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129

may be considered an instantaneous multiplier, as against the traditional multiplier operating through extra consumption demand. For this process to work, the requirements for working stocks of marginal units should not be greater than the production of those units during the period. (Otherwise this instantaneous multiplier will tend to infinity.) This is improbable because total stocks in a period are normally expected to be less than the inputs required for production during the period, and in times of high demand it is unlikely that there are considerable stocks of the produced commodity. During the downturn the situation may be different. Thus if stockholders have to meet the extra autonomous demand and also the demand for working stocks of all units that have to restart production to achieve this, the stockholders may have to advance the following amount of commodities: where X is the extra autonomous demand, S is the working stock requirements of the marginal techniques, and g is the reaction coefficient of stockholders. The stockholders may not try to replace their stocks totally in the next period, probably believing that a large part of the extra autonomous demand is a temporary phenomenon. The proportion that they tend to replace is given by g. This gives the value of the instantaneous multiplier as (/ — g S ) - 1 . If the extra autonomous demand is considered to be too large, it may trigger a reaction among the stockholders, making g too large. This may increase the instantaneous multiplier to such an extent that the necessary cut in the real wage and real interest income involved may be unacceptable. Such a result will only "overheat" the economy, even if it is far from a state of full employment. It will be equivalent to having too large an extra autonomous demand. In an open economy, of course, there is another way out of this dilemma—to import some of the extra requirements. As long as such action does not lead to a foreign exchange crisis it may well ease the situation. The stockholders' reaction coefficient g is not a constant. Its value is likely to increase with the absolute size of the increase or decrease in autonomous demand. Its value will also depend on the state of the economy at the time. During expansionary phases, its value may become higher and higher as the duration of the phase continues. The same may be true for stationary and declining phases. However, the value of g is likely to be quite small at the start of such a phase. In passing we may also note that this extra demand for working capital may tend to increase the demand for intermediate goods faster than that for consumer goods Further, the autonomous demand is likely to consist mainly of investment goods. This, coupled with the higher demand for fixed capital goods due to a better investment climate generated by the decrease in the real wage and interest rates accompanying the increase in the profit rates, will show itself in the higher growth of fixed capital and intermediate goods industries. Return of the Working Stocks: A Path to Accelerated Depression? In times of decreasing autonomous demand, the opposite effects should be evident. In competitive industries having layers of techniques in the form of already invested fixed capital of different vintages, the real wage rate and/or interest rate should increase. The increase gives market signals to the least efficient firms to close down. We should

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thus see rising rates of factor payments coupled with rising unemployment of those factors themselves. But that is not all. The units stopping production in the period will not repurchase their usual working stocks from the stockholders. "The reduction in working capital, which is necessarily attendant on the decline in output in the downward phase, represents a further element of disinvestment, which may be large; and once a recession has begun, this exerts a strong cumulative influence in the downward direction" (Keynes, 1936, p. 318). Further, summarizing the experience of business downturns, W. C. Mitchell wrote: As the rise in prices that accompanied revival, so the fall that accompanies depression is characterized by certain regularly recurring differences in degree. Wholesale prices fall faster than retail, the prices of producer goods faster than those of consumer goods, the prices of raw materials faster than those of manufactured products. (1913, pp. 160-161)

Sectors Having Different Price Behavior How are sectors having different modes of price formation affected by these gyrations of the economy? Of course the sectors with monopoly pricing need not reduce or increase the prices of their goods just to ensure that the right amount is produced. Even the effect on employment can be somewhat cushioned in the short run. However these sectors will get the advantage of the reduction in real wage rates as well as that of the reduction in real interest payments. That may increase their residual income even without increased productivity or technological advance. For sectors whose market equilibrium is determined by price adjustments rather than demand adjustments, the situation will be different. Naturally price changes will be greater, but production and employment will hardly be affected. This also implies that the instantaneous multiplier will hardly be operative in their case. In the areas in which these sectors are not dependent on the goods of other sectors, they may even be better off during a depression. In such a case, their main undoing is due to their monetary obligations, such as payments of rent and interest and repayments on their borrowings, since these obligations are determined in a monetary unit that is heavily overvalued in terms of production. In the case where a support price system is operative and the public authority takes up the burden of stock adjustment, these sectors may help with stability. Such stock management operations on the part of public authorities would imply creation of extra autonomous demand during contraction. So we can say that this sector, together with the fixed-income salariat, provides a primary stabilizing force in an economy, and the banking sector with its tendency to foreclosures during downturns and to overlending during upturns provides the opposite propulsion. Those state taxing activities that are not sensitive to the price level reinforce these opposing tendencies. MOVEMENT OF RESIDUAL SHARE OVER TIME In the jth competitive industry, the residual for the kth unit has been defined previously as

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131

where l(kj), A(kj), and S(kj) are, respectively, the labor coefficient, the input coefficient vector, and the working stock coefficient vector for the kth unit of the industry producing the j th commodity. P is the price vector, w the wage rate, and r the interest rate for stock holding. So the share of the residual will be b(kj)/pj. With continuous technological progress, embodied in the new fixed capital equipment of the later vintages, the average cost of production of the commodity will go on declining continuously, since the new processes should be capable of producing the commodities at less cost per unit than the existing best technique (the cost of course being evaluated at the current price structure). This should be true not only for the average total cost per unit of output but also for the average direct cost. If the direct cost of the unit is more than that of older units, it can be competed out of production when the older unit is threatened with closure, since to remain open it needs only to meet its direct costs. Further, in the period of technical change, the new investment will not only be made to meet increasing demand but also to take advantage of the possibility of reaping high profits opened up by the technological advance. In the latter case, however, investment will be made at the cost of closing down some other units, since total capacity would then exceed demand. As pointed out previously, the way in which this can be achieved in a competitive industry is by changing the price-wage-interest structure so as to make the least efficient units nonfeasible. This implies that their residual per unit of output becomes negative. If the price structure does not change, closing down of older units could be achieved only through increases in real wages or interest rates. But even if technological change were similar in all industries, the different modes of pricing for competitive, monopolistic, supply-determined, and imported commodities will make this outcome very unlikely. With almost continuous technical change, and with most of the new investment being undertaken to take advantage of the higher profitability thus achievable, we can expect that the residual per unit of output for a production unit will go on diminishing over time in competitive industries. The process may not be smooth over a time period experiencing economic shocks. For instance, a big and sudden price rise of an input, such as that of oil in 1974, may give a jolt to the efficiency ordering of the various techniques. Before 1974 a technique would have been made more efficient by replacing the costlier input of labor by oil. The sharp change in relative prices in 1974 would have made the new technique less efficient than the older one. Similarly, a large increase in the nominal interest rate may also change the efficiency order. Thus the smooth process of declining per-unit residual will have occasional hiccups. On the other hand, if the industry is monopolistic, new capacity will be installed only (1) if extra demand is sufficient to justify it and the extra revenues that can be generated by increasing prices are expected to be less than those achieved by increasing the capacity, or (2) if the cost advantage is so great that it outweighs the loss of abandoning some old working capacity. Further, there is no role for the price mechanism in giving decentralized signals to a comparatively inefficient firm to close down. This can be achieved by administrative decisions. The relative price of the commodity produced is likely to be reduced only when demand is such that there is likely to be a net gain due to an increase in the market size. For the present purpose the main point to note is that there is no simple way in which the prices of commodities produced under monopoly conditions will behave with continuous

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Input-Output and the Analysis of Technical Progress

technological change. We thus cannot say how the share of the residual income of a unit producing such a commodity will behave over time. At times it may decrease, it may remain constant, or it may even increase.

ANOTHER INDICATOR OF MONOPOLY POWER This generates another indicator of monopoly power within an industry. In these decades of rapid innovation in almost all industries and of comparatively slower increase in demand, we can expect that for a particular production unit with a given technology, the share of residual income in the price of a competititive industrial good will decline almost continuously over time. Thus by monitoring the movement in the residual over time, we can have an indication of whether that industry is working in a competitive market or not. However, over any significant length of time hardly any production unit keeps its technology frozen. Minor improvements are being implemented almost continuously, increasing the productivity of the production unit, although major innovations requiring the incorporation of significant technological changes may require the creation of a new production unit. Further, following the economic history of a firm for a considerable period requires sufficient research resources, and results will be available only after a considerable period of time, even if the problem of collecting firm-specific information can be overcome. What is really required is to revalue the input, labor, and stock coefficients of a unit and to evaluate the changing values of these over time. Then we can find out the behavior of the residual over time. In practice we need not find out these coefficients for an individual production unit. We can take the average coefficients for one year and adjust them for the changes in price, wage, and interest rate for future years. Thus we can find out what would have been the value of the residual in those years if the technology of the industry had not changed over the period. A declining residual will give us an indication that the particular commodity is facing a competititive market, and vice versa.

COMPETITIVE AND NONCOMPETITIVE INDUSTRIES IN THE UNITED STATES We have used the preceding indicative technique to distinguish between competitive and noncompetitive industries in the United States. For this purpose we considered three average technologies of U.S. industries, given by the input-output tables of the years 1958, 1963, and 1967. The residuals for each industry have been calculated for the price-wage-interest structures prevailing in each year after that of the construction of the table to the year 1981. Input-output tables and the wage rates have been taken from various issues of the Survey of Current Business, inventories from the study "Industry inventory requirements: an input-output analysis" from the November 1973 issue of the same journal. Interest rates have been taken from various issues of the Federal Reserve Bulletin. Price data are from the Bureau of Labour Statistics, "Time series data for input-output industries—output, price and employment." Ideally, we require information about the new capacity created in each industry to match the extra demand. Though the information about new investment is readily

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133

available, that about newly created capacity is hard to come by. Nevertheless it can be assumed that there is the potential for new innovations to induce capacity-creating activity faster than the growth in demand. So if we find a residual in many industries, we may surmise that the industries where this is not occurring are experiencing monopoly power. We found that just over half of the seventy-nine sectors show a continuously declining residual, forty-four for the 1958 technique, forty-four for the 1963 technique, and forty-three for the average technique of the year 1967. However, they are not always the same sectors. There are thirty-four sectors for which the residual falls continuously for the average technique of all three periods. In a further nine sectors it falls for two techniques out of three. In five sectors of these nine, it falls for both 1963 and 1967 average techniques. The thirty-four industries for which the residual falls continuously can be considered as operating in competitive conditions. A further five can be thought of as operating under those conditions in the two decades the 1960s and 1970s. These sectors are given in Tables 8.1 and 8.2. Of ten agriculture and mining sectors, two show declining residuals—iron ore mining and stone and clay mining. This is all the more significant because usually we expect the unit cost of mining to increase over time as more and more mining resources are exploited. Great strides in improving mining techniques during this period are indicated in these two sectors. Since the state fixes the price of main agricultural products and bears the burden of the stock adjustments, we of course do not expect it to come under the competitive banner. Otherwise its supply would be determined before the market had a chance to determine its price. Therefore there is no reason to expect its residual to follow the diminishing trend. Construction, and nine of twelve service sectors, do not show declining residuals over this period. These are labor-intensive activities, and it seems that the factors that lead to diminishing residual, as discussed above, play no significant part in industries where fixed capital equipment has rather a subsidiary role. Both transport and communication show themselves as competitive industries with almost continuous technological change, and twenty-seven of fifty-one manufacturing sectors show the same trend. Nine behave in a way expected of noncompetitive industries. The rest did not show similar price formation characteristics for all the three technologies analyzed. Some exhibited a completely different pattern after 1973-1974. These must have been the industries whose cost pattern was significantly affected by the oil price shock experienced in those years.

CONCENTRATION RATIO AND MONOPOLISTIC PRICE BEHAVIOR

The usual indicator of monopoly power is the concentration ratio. It is suggested that a higher concentration ratio implies greater power to distort the price that would have prevailed in a competitive environment, to the advantage of the controllers of that enterprise. The method described in this chapter is an alternative way to judge monopoly power from revealed price behavior. We shall examine here the correspondence between these two approaches. The Bureau of the Census calculates concentration ratios for manufacturing industries on a regular basis. To make practical application possible, these ratios are

TABLE: 8. 1 Sectors with consistent pattern of residuals for three U.S. technologiesa No.

Competitive Sectors (declining residual)

Noncompetitive Sectors (nondeclining residual)

No.

I. Agriculture, Mining, Construction 9

1 2 3 4 6 7 8 10 11 12

Stone and clay mining

Livestock and products Agricultural products Forestry and fishery Agricultural services Nonferrous metal mining Coal mining Crude petroleum and gas Chemical mineral mining New construction Maintenance and repairs

11. Manufacturing 14 16 17 18 19 20 22 26 28 29 30 34 40 44 46 51 52 53 54 56 57 58 59 60 61 62 63 64

Food and kindred products Yarn and fabric Miscellaneous textiles and carpets Apparel Fabricated textile products Wood products, exc. containers Household furniture Printing and publishing Plastic and synthetic material Drugs and toilet preparation Paints and allied products Footwear and leather products Structural metal products Farm machinery and equipment Material handling machinery Office and accounting machines Service industry machines Electric industrial equipment Household appliances Radio, T.V., and communication equipment Electronic components, etc. Miscellaneous electric machinery, etc. Motor vehicles and equipment Aircraft and parts Other transportation equipment Scientific and controlling equipment Optical and photographic equipment Miscellaneous manufacturing

15 32 35 37 38 39 43 45 55

Tobacco manufactures Rubber and miscellaneous plastic products Glass and products Primary iron manufactures Primary nonferrous products Metal containers Engines and turbines Construction and mining machinery Electric lighting and wiring equipment

III. Transport, Communication, Services 65 66 69 76 78

Transportation, warehousing Communications Wholesale and retail trade Amusement Federal government enterprises

aGiven by input- output tables of 1958, 1963, and 1967. 134

67 68 70 72 73 75 77

Radio and T.V. broadcasting Electricity, gas, water, etc. Finance and insurance Hotels and personal services Business services Automobile repair and services Medical, educational, etc.

135

Price Behavior with Vintage Capital TABLE 8.2

No.

Sectors with consistent pattern of residuals for at least two technologies of U.S. industrya Competitive Sectors (declining residual)

Noncompetitive Sectors (nondeclining residual)

No.

1. Agriculture, Mining, Construction 4 5 10

Agricultural services Iron and ferroalloy mining Chemical mineral mining

6

13 23 24 25 33 42

Ordinance and accessories Nonhousehold furniture, etc. Paper and allied products Paper board containers Leather tanning and products Other metal fabricated products

Nonferrous metal ore mining

II. Manufacturing 27 31 36 41 47 48 49 50

Chemicals and chemical products Petroleum refining, etc. Stone and clay products Stamping, screw machine products Metal working machinery, etc. Special industrial machinery General industrial machinery Machine shop products

III. Transport, Communication, Services 71 79

Real estate and rental State and local government enterprises

aGiven by input-output tables of 1958, 1963, and 1967.

naturally calculated for quite a detailed industrial classification. In our exploratory study we could not do the analysis in such detail. For this comparison we therefore constructed from these detailed figures approximate mean concentration ratios for the input-output manufacturing sectors. Competition for an industry is not only internal. Imports provide as much competition as the production of another firm in the country itself. Not only actual imports but just the threat of imports should induce the firms in an industry to undertake competitive price behavior, lest the market share of the foreign firms may increase to their cost. It is thus useful to adjust the concentration ratios for imports. They thus become the ratios of the production of the largest firms to the total availability of the product within the country, rather than to total production. The concentration ratio is not a unique indicator. Usually alternative values are calculated, taking the production of the four, eight, twenty, and fifty largest firms as the numerator. All these together help to give a general idea of the monopolistic nature of a particular industry. In Table 8.3 we have cross-classified the sectors determined competitive or monopolistic by their price behavior according to their concentration ratios. We see from the table that there is a high degree of correspondence between the indications given by the two criteria. However, there are also significant divergences. More sectors are likely to be judged as monopolistic by the criterion of the concentration ratio than by the criterion of price behavior. Obviously the criterion of the concentration ratio cannot take into account the threat of higher import penetration of their market, or that of entry of new firms into their industry.

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Input-Output and the Analysis of Technical Progress

TABLEi 8.3

Average import-adjusted concentration ratios for declining and nondeclining residual sectors Concentration Ratioa 0.6-0.8

>0.8 No. 50 20 8 4

Residual Declining Nondeclining Declining Nondeclining Declining Nondeclining Declining Nondeclining

No.

%

No.

%

No.

1

26 100 7 67 0 22 0 0

17 0 15

63

3

33 11 56 0 33

3 0 9 0 15 2 11 6

9 2 6 0 ••) 2.

0 0

3 5

0 3

0 56

<0.2

0.2-0.4

0.4-0.6 %

11

0

33 0 56

22 41 67

No.

%

No.

%

0 0 1

0 0 4

0 0 0

0 0 0

0

0

0

0

9 0

33

0

0

0 52

0

0

2 0

7 0

14 0

0

aBased on N firms

REFERENCES Baily, M. N. 1983. "The labor market in the 1930s." In J. Tobin (Ed.), Macroeconomics, Prices and Quantities. Washington, D.C.: Brookings Institution. Keynes, J. M. 1936. The General Theory of Employment, Interest and Money. London: Macmillan. Mitchell, W. C. 1913. Business Cycles. Berkeley: University of California Press. Modigliani, F. M. 1983. Comment on L. H. Summers, "The nonadjustment of nominal interest rates: A study of the Fisher effect." In J. Tobin (Ed.), Macroeconomics, Prices and Quantities. Washington, D.C.: Brookings Institution. Okun, A. M. 1981. Prices and Quantities: A Macroeconomic Analysis. Washington, D.C.: Brookings Institution.

9 Input-Output., Technical Change, and Long Waves E. FONTELA and A. PULIDO

ON CYCLES AND LONG WAVES During the 1950s and 1960s research on long-term fluctuations of economic activity was given very low priority. If cycles and long waves had ever existed, they were preKeynesian; they could be avoided by appropriate use of macroeconomic management tools. But the 1970s and early 1980s showed that this policy concept has serious limitations. Many advanced industrialized countries with excellent economic advisors and powerful resources were unable to adjust to major changes in the environment (flexible exchange rates and oil price shocks) and accepted stagnation and painful levels of unemployment, sometimes even conceding that these unsatisfactory situations were part of a necessary adjustment mechanism. In these circumstances there has been a revival of research on cycles and long waves. Van Duijn (1983) listed the following cycles from the most frequent analysis of the subject: 1. 2. 3. 4.

The The The The

Kitchin, or inventory, cycle (3-5 years) Juglar, or investment, cycle (7-11 years) Kuznets, or building, cycle (15-25 years) Kondratieff, or long wave (45-60 years)

Van Ewijk (1982), using spectral analysis, has shown convincingly that "the long wave is not a general characteristic of real economic growth in industrialized countries," although there may be some supporting statistical evidence for the Kuznets cycle, particularly in the United States. Adelman in the 1960s (1954, 1965) had already reached a rather similar conclusion, but also demonstrated that "in a simulation of the ordinary business cycles of the U.S. economy by a randomly shocked Klein-Goldberger model, long swings were generated which corresponded in all important respects to the extended waves observed in the U.S. economy."

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Input-Output and the Analysis of Technical Progress

These studies and others have shifted the emphasis of long-wave research away from its traditional topics, which were: The existence and relation of two types of cycles, one dealing with prices and the other with real variables The regularities of these cycles The explanation of the changes from each phase of the cycle to the next one. It is now widely accepted that the economic mechanisms in a modern economy with proper macroeconomic management do not generate regular long cycles, but (following Adelman) that major shocks may product long-lasting changes in growth rates. Avoiding, therefore, the sterile debate on the history of economies with different institutional frameworks and on the regularities of fatalist determinstic cycles, it is possible to revert to the study of long "swings," concentrating on the key issue of passage from a long period of stagnation or depression to a new period of growth, and giving priority to the process of changes of pace in the long run (Melnyk, 1970). Macroeconomic management, Keynesian or monetarist, is particularly efficient at keeping an economy on a given growth path, but how then it is possible to shift from a lower growth path to a higher one, assuming that the latter is desirable? Certainly in Europe the issue is relevant at the present moment; the critics of growth in the late 1960s (the zero-growth supporters) had not anticipated the coming levels of unemployment and the lower quality of life offered to the younger generations. Better quality of life and income distribution are associated with higher economic growth, but the composition of output does not necessarily imply the waste of materials, and energy and environmental degradation, that were the main reasons for the popular criticism of economic growth. Modern research on long waves is thus to be associated with research on the determinants of economic growth, and in particular with the behavior of the "residual" in the aggregated production functions. Most studies on long waves are neo-Schumpeterian, essentially concerned with the process of generation and diffusion of innovations and of the technologies supporting them. This is clearly the case for the work of Mensch (1979), Kleinknecht (1981), Van Duijn (1983), Freeman (1977), Freeman, Clark, and Soete (1982), Nelson and Winter (1982), Seidl (1984), and others for whom changes in the rate of innovation are among the main causal factors of changes in the phases of the long waves. Authors such as Forrester (1981), Mandel (1981), and Sterman (1985) believe that other factors are more active in generating long waves of economic activity, but that the innovation process varies substantially following the cycle. It would be too simplistic to state that an economy moves from stagnation to expansion as a result of an acceleration of its innovation rate; this type of change takes place together with changes in the final demand markets and in the investment climate. But high growth has to correspond to high innovation rates. Recent research has emphasized that at certain points in time there are sudden accelerations of the innovation rate, a clustering of major innovations playing a very active role during each wave. The evidence used to prove this statement (usually derived from highly debatable lists of innovations like the one established by Mahdavi [1972]) is not always convincing, but as indicated by Rosenberg and Frischtak (1984), there are sufficient economic and technological resons to justify the synchronization of different diffusion paths of new technologies.

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Input-Output, Technical Change, and Long Waves

TABLE 9.1 Propensity to innovate during different phases of the long wave

Product innovation (new industries) Product innovation (existing industries) Process innovations (existing industries) Process innovations (basic sectors) a

Depression

Recovery

Prosperity

Recession

+a

++++

++

+

+++

+++

+

+

+++

+

++

++

+

++

+ ++

++

+ denotes a low rate of innovation ; + + + + , a high rate.

There are also reasons to assume that this clustering of innovations is of a different nature during different phases of the process of change of pace. According to Mensch's metamorphosis model (Mensch, 1979), a clustering of basic innovations at the end of a depression leads to an expansion period during which new products are introduced; during the following recession, increased competition between producers for a stable or declining market leads to more innovation, this time in new processes (cost reduction). A rather similar view is expressed by Van Duijn (1983), as summarized in Table 9.1. Neo-Schumpeterian researchers have accordingly introduced a number of new ideas that are relevant for the understanding of economic growth processes. They do, however, require serious testing, yet to be done. For Rosenberg and colleagues (1982, 1984) it should be necessary to establish the existence of clusters of innovations, and further, to demonstrate that they occupy a strategic position in the economy in terms of backward and forward linkages. They are convinced that there is an "inter-industry flow of new materials, components and equipment" generating a "vastly disproportionate amount of technological change, productivity improvement and output growth in the economy" (Rosenberg and Frischtak, 1984, p. 18). Two other ideas are also interesting in this context: 1. Pavitt (1984) has shown that out of 2000 innovations introduced in Great Britain, only 40 percent originated in the innovating sector. Many industries like agriculture, construction, and services rely for their innovations on technologies developed in other sectors. 2. A study by Oppenlander and Scholz (1981) has shown that German firms consider that only 36 percent of new products are technology-based innovations—most new products are essentially "market innovations," that is, the opening of new end uses for already existing products. All these statements bring the technological change argument into the field of input-output analysis. On Input—Output and Technical Change Most input-output analysts will agree that despite the many limitations of available statistics (forcing the use of very heterogeneous product mixes for the sectors) the technical coefficients for intermediate and value-added inputs do convey a technolog-

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Input Output and the Analysis of Technical Progress

ical notion (also implicit in all cost breakdowns of any producing firm). Consequently it has always been assumed that the observation of changes over time in these coefficients should provide evidence of changes in production functions and therefore of technological changes. Still, Vaccara (1970) clearly indicated that "it is important to remember that changes in input coefficients, as reflected in an input-output table, can be caused by many factors, only one of which may be technological change." Vaccara's list includes: Changing product mix Degree of capacity utilization Statistical conventions relating to secondary production and competitive imports Changes in consumer tastes Changes in relative prices Technological innovation Because of the high production cost of detailed input-output tables, very few countries have made available annual time series of these tables, and changes in relative prices severely complicate intertemporal comparisons of single tables, Nevertheless, some conclusions are sufficiently general to be acceptable by the majority of those who have worked in this area. These conclusions can be summarized as follows: 1. In the advanced industrialized countries with a very complete technological structure, changes in technical coefficients take place very slowly. The main reason for this slow evolution of the coefficients can easily be explained: the technical coefficients can change substantially only if all the production technologies of a sector are changed. Change of production technologies, however, takes place only with new investment, and therefore many years are needed before the entire production structure has adopted the innovation. 2. The importance of each single coefficient change for the accuracy of the output estimates is very different; indeed the number of coefficients that can substantially influence any sectoral output is very small. 3. When a change in technical coefficients is induced by a change of relative prices (i.e., a substitution process in the input structure), the change takes place in the same direction all along the rows of the I-O table. This observation is intuitively understandable: when the relative price of petroleum was decreasing, all industries tended to shift their energy consumption toward this product. 4. When changes induced by relative prices along a row take place, they tend to be faster in those sectors where output grows quickly. Thus the penetration of plastics into a fast-growing industry (e.g., automobile, electronics) tends to occur faster than in a slow-growing industry (e.g., construction). Again, this observation can be easily understood, since fast-growing sectors are those with the fastest investment rate, and consequently those that can more quickly adapt new production technologies. 5. Despite many research efforts to compare international input structures, there is little or no evidence of any trend for similar columns in different nations. This may appear as a surprising statement, when it is intuitively obvious that industries in different countries apparently do adopt similar technologies (e.g., multinational corporations). The fact is that the column structures depend on "product mix" and that this product mix is extremely different in each country. These conclusions are derived from studies of matrices of technical coefficients for intermediate (domestic and imported) inputs (the A matrix) at constant prices, often

Input-Output, Technical Change, and Long Waves

141

with little reference to the matrices for primary inputs (the V matrix), with vkj being the coefficient of primary input k per unit of output of sector j; this introduces additional difficulties for an analysis at constant prices. As already shown by Fontela (1987), these studies do not usually take into consideration changes in coefficients in the complementary tables related to final demand: The consumer spending table (or consumption table) and the investment table. The consumption table is a matrix in which are reported the different goods and services needed to meet a given consumption function; consumption functions classify household final consumption expenditure according to the major object (the purpose) for which the goods and service are acquired. The standard classification of consumption functions is Food, beverages, and tobacco Clothing and footwear Gross rent, fuel, and power Furniture, finishings, and household equipment and operation Medical care and health expenses Transport and communication Recreation, entertainment, education, and cultural services Miscellaneous goods and services Changes in the goods and services required to satisfy these consumption functions are obviously heavily dictated by technology: new conservation techniques change the proportion of fresh fruit to preserves; new durable goods change the requirements for household equipment, and so on. If we portray the consumption table by a matrix B, with m columns corresponding to the consumption functions, and n rows corresponding to the n goods and services available (e.g., those considered in the A matrix), the column coefficient bij will indicate the proportion of product i needed for a unit consumption of function j. The matrix of investment allocation describes the decomposition in terms of goods and services of the fixed gross capital formation of producing sectors. For each sectoral investment (e.g., chemical sector investment) it provides the product content (e.g., the machines, electronic equipment, buildings, etc., required by the investment). If we portray the investment table by a matrix C, with k columns corresponding to the investment demanding sectors, and n rows corresponding to the n goods and services available (e.g., those considered in the A matrix), the column coefficients cij indicate the proportion of commodity i needed for a unit of investment of sector j. This broader input-output framework is required if a test of the hypothesis put forward by long-wave analysts is to be attempted. The general conclusions relating to the A matrix do not necessarily apply to the V, B, or C matrices. Thus there are many reasons for the cij coefficients to change very rapidly. While the aij and bij coefficients portray average situations (the aij coefficients mix old firms with new firms producing commodity j; the bij mix families with traditional demand patterns with "leaders" in high income groups), the cij coefficients relate only to the latest investments and therefore to the most modern available production technology. New processes, and new products for investment, first appear in the C matrix and substantially modify its content. If we turn to the V matrix, it summarizes the key substitutions between labor and capital; furthermore, if computed at constant prices it allows for the computation of a "residual" productivity gain for each sector. The sum of the columns of A and V,

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Input-Outpul and the Analysis of Technical Progress

which totals 1 in an input- output system in value terms, is generally different from 1 (less) if computed at prices of a base year, owing to the fact that less primary and intermediate inputs (in real terms) are used per unit of output.

Deflating the primary input tables—a difficult statistical task—is a necessary step for understanding technological change using input-output tables. Long-wave analysts suppose that at a certain point in time there are clusters of innovations; these clusters may induce changes in the coefficients of the four matrices A, V, B, and C. If we are to follow Mensch and colleagues (1981) or Van Duijn (1983), then, at different phases of the cycle, coefficients should change differently in tables B and C in particular, with relatively more important changes in B during expansions and in C during recessions. Time series for A are available in some countries; for V those already available, at current prices, could be deflated; but B and C tables are even more difficult to obtain, and there are no known time series of them with a reasonable level of detail in any industrialized country. In Spain an effort is currently underway to draw up time series for the period 1960-1984 for all four matrices at constant and current prices within a national accounts framework, but with a very small degree of sectoral disaggregation (nine sectors for production and investment, three consumption functions, two primary inputs). Pulido (1986) is planning econometric tests of different models to explain changes in the coefficients of these tables. The column coefficients of the A and V matrices can be related to prices through a neoclassical cost function incorporating a price possibility frontier. Obviously changes in relative prices should provide a basic explanation of changes in these coefficients, as well as of changes of coefficients in the consumption matrix and in the matrix of investment allocation. One could further improve the explanation of changes over time of these coefficients by introducing a logistic curve. Many authors have noted the existence of an S-shaped pattern in the diffusion of innovations, and in particular, Romeo (1975) has shown the possibility of using this diffusion model for a similar innovation in different industries. Logistic curves have already been fitted to historical data of aii coefficients for the United States and some European countries in the framework of the Wharton long-term model, the INFORUM (University of Maryland), and the EXPLOR-FORSYS (Battelle Institute) projects. Following the Wharton practice, we could estimate a relative price-sensitive logistic model:

where aij = technical coefficient, year 0 (base year) or t Z(t) = logistic function pi = price of industry i with a lag distribution wh for the impact of relative prices.

Input-Output, Technical Change, and Long Waves

143

As already indicated by Kennedy and Thirlwall (1972) in their survey of research on technical progress, there is a possibility of further explaining changes in technology using research and development efforts as a causal factor. Of particular relevance to input-output analysis is the work of Scherer (1982), who computed R&D data both by industry of origin (industry performing the R&D expenditure) and industry of use (allocation of R&D data of each industry of origin according to its output structure). Pulido (1986) suggested using technological capital by sector (KRj), a stock variable computed with an inflow of new technology (a lag distribution of past R&D and the total of transfers for technology imports MT) and an outflow of old technologies (computed as a rate of depreciation (d) of technological capital):

To take into consideration the technology flows outlined by Scherer (1982) we can define another technological stock variable as

and further replace the logistic function component of equation (1) by an expression based on the index Rtj/Rj°. This specific introduction of a technology variable should in principle provide a better explanation of changes in the coefficients of both the A and the V matrices, but where it is expected to bring substantial improvements is in the explanation of the C matrix coefficients, that is, of the structures of investment for each industry. As already noted, this matrix is particularly sensitive to changes in technology, since its coefficients are not affected by the existing capital stock (as is the case for the A and V matrices). In the case of the consumption matrix (B matrix), obviously this approach is not relevant since the columns of the matrix refer to consumption functions, and not to industries. Research on the impact of technological change on the consumption matrix should probably be conducted along rather different lines, starting with the semiaggregated system of demand equations with relative prices and household income as explanatory variables, and including progressively additional factors related to the diffusion process of new products. Returning to the issue of cycles and long waves, while it is obvious that the inputoutput framework theoretically provides all the necessary elements for a serious study of the question, it is also evident that we are still far away from having the data and the models required for such a study, although current efforts to construct long time series of input-output tables provide an important initial step in the right direction. ON PRODUCTIVITY AND INPUT-OUTPUT Another way of looking at the long-wave problem is provided by the analysis of absolute productivity growth, a subject of continuous interest for applied economic research (see recent publications by Baumol and Wolff, 1984; Cette and Joly, 1984; Denison, 1983; Dubois, 1985; Griliches and Mairesse, 1983; and Kendrick, 1981). Nelson (1981) noted the need to overcome the neoclassical model and to explore a heterodox literature that is putting more emphasis on the dynamic process of

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technological change as a driving force for evolutionary growth, thus reverting to Schumpeterian economics. Activity analysis with input-output models can provide interesting insights into the process of change of absolute productivity at sector level. This is a subject to which French researchers have devoted considerable effort over the past decade, following the pioneering work of Courbis and Temple (1975). Let us consider a base-year measure of absolute productivity in a given sector h:

where i = 1 , . . . , n commodities (outputs) j = 1 , . . . , m factors (inputs) q, p = quantities and prices of commodities produced by sector h w, /' = factors used for production by sector h We can compute a productivity gain, or "surplus" as

In an input-output framework, we can replace the sum of the factor inputs at prices of the base year by the sum of the technical coefficients and the value-added coefficients multiplied by the total output of the industry, all at base-year prices. We can also replace the sum of commodities at base-year prices by the total output also at baseyear prices:

Obviously for S j , t ^ 0

which is not the case for the base year. Thus this measure of productivity gains depends on the base year chosen, and a better interpretation of the results of the computations is obtained if the base year is regularly shifted (year 0 = year t — 1), that is, if the computations are made in "volume" terms. Through a set of simple algebraic transformations it is possible to derive from equation (5) the following relation:

which traces the distribution of the productivity gains through price changes of the final products or of the inputs. In the input-output framework we can write

and thus compute the distribution of productivity gains, which either reduce the value

TABLE 9.2 Interindustry flows of productivity gains (millions of FF 1962), France, 1959-1969 ] 1. 2. 3. 4. 5. 6. 7. 8.

Agriculture Food Energy Intermediate Capital goods Consumption Construction Services

(Total C) Distributed gains (R-C)

2

-815 -651 -218 -857 -1 -83 + 78

4

3 __ 5

-19 -255 -2639

5

6 -33 -158 -471 -2705 -81

7

+ 560

-209 -290 -24 -67 + 48 + 507

-545 -327 -65 -58 -171

-452 -599 -12 + 31

-1224 + 28 + 244

+ 44 + 477

+ 1266

-1172

-850

-1171

-3945

-7057

-2927

-1299

-712

+ 594

-4816

-7689

+ 4605

+ 738

+ 2022

-580 -5525

-418 -1276 -626 -245

8 -1017 + 815 -902

Trade

Total R

+ 849

-1884 -256 -5987 -11634 -2452 -2189 + 723 + 3763

-1217

-278

-19916

+ 4980

+ 278

0

-173 -935 + 422 + 573

_2 -550 -263

-6 -328 + 22

Example: Row 3 (energy) and column 4 (intermediate goods): The amount in this cell, —2639, means that the energy industry has distributed this productivity gain to the intermediate goods industry through a decrease of its relative price during the 1959-1969 period. Source: Courbis and Temple, 1975.

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Input Output and the Analysis of Technical Progress

of outputs (price reduction) or increase wages and capital returns, or transfer the gains to other sectors with increasing prices. Obviously some adjustments must be made to avoid the distorting effect of overall inflation by using, in applied work, relative prices (i.e., relative to a national average price index). To summarize this argument, we could say that 1. An industry makes a productivity gain or surplus if its output at constant prices is lower than the sum of its inputs at constant prices. 2. An industry gains additional surplus for redistribution if it has increased its output prices or if its input prices have decreased. 3. An industry redistributes its total productivity gain plus the additional surplus mentioned in (2) either by decreasing its output prices or by increasing its remuneration of intermediate or primary inputs. A highly competitive framework will favor redistribution to the market through relative decreases of output prices, and indeed it can be verified (at least in France) that high absolute productivity gains are found in industries with decreasing relative prices, and vice versa. Exceptions correspond to more imperfect markets (e.g., agricultural products). Table 9.2, computed by the French statistical office, gives a good description of the flows of productivity gains among industries. In total, the intermediate goods industry (4) has substantially decreased its relative prices during the period, transfering productivity gains to other sectors for a total of 11,634, but it has also benefited from decreases of the prices of its intermediate inputs, for a total of 3945. Thus its net contribution to the productivity of the overall interindustry structure is estimated at 7689 (11,634-3945). During the high-growth period 1959-1969, the French intermediate goods industry, as well as the energy sector, was strongly stimulating the rest of the economy through its relative price decreases. Those results in terms of relative prices correspond to the absolute productivity trends indicated in Table 9.3. During the boom period of the 1960s, industries producing intermediate, equipment, and consumer goods were showing high absolute productivity growth and decreasing relative prices. The reverse was true for services (for which, however, there are special difficulties in measuring productivity, due to the complexity of trying to separate volume growth from price changes in the output of this industry). If we return to the theory of long waves, and we characterize the period of the 1970s and early 1980s as one of depression, then it appears that all manufacturing sectors have substantially decreased their rate of absolute productivity growth (and therefore of innovation) during that period. An inspection of the twelve-year moving TABLE 9.3

Absolute productivity trends by industry in France (yearly averages)

Industry Intermediate goods Equipment goods Consumer goods Services

1960-1971

1974-1985

2.4 3.0 2.9 0.6

1.1 1.1 0.9 0.4

Input-Output, Technical Change, and Long Waves

147

FIGURE 9.1 Absolute productivity trends: twelve-year moving average of annual growth rates of global productivity.

average of the average annual rate of absolute productivity growth in these industries (Figure 9.1) also shows the fastest decrease in the consumer goods industry, while for investment goods the rate of introduction of more productive technologies has remained at a high level for a long period. In the early 1980s, the bottom seems to have been reached for intermediate goods and consumer goods, whereas for investment goods on the contrary the decrease in absolute productivity growth rates seems to accelerate slightly. This evidence is rather in line with van Duijn's propositions, but is certainly insufficient for one to reach a conclusion on the subject. Nevertheless, these attempts to use input-output models for better understanding of long waves of innovation and productivity are promising enough to encourage further applied research in countries that have constructed time series of comparable input-output tables.

REFERENCES Adelman, I. 1964. "Long swings, a simulation experiment." In F. Balderston and A. Hoggatt (Eds.), Proceedings of a Conference on Simulation. Cincinnati: South-Western. Adelman, I. 1965. "Long cycles, fact or artifact." American Economic Review 55: 444-463. Baumol, W. J., and E. N. Wolff. 1984. "On interindustry differences in absolute productivity." Journal of Political Economy 92: 1017-1034. Cette, G., and P. Joly. 1984. "La productivite industrielle en crise: Une interpretation." Economie et Statistique 166. Courbis, R., and P. Temple. July 1975. "La methode des comptes de surplus et ses applications macroeconomiques." Collections de l'INSEE 35 C.

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Denison, E. F. 1983. "The interruption of productivity growth in the United States." Economic Journal 93: 56-77. Dubois, P. 1985. "Ruptures de croissance et progres technique." Economie et Statistique 181. Fontela, E. 1987. "Technology as a factor of economic leadership." In O. Hieronymi (Ed.), Technology and International Relations. London: Macmillan. Forrester, J. W. 1981. "Innovation and technical change." Futures 13: 323-331. Freeman, C. 1977. "The Kondratiev long waves, technical change and unemployment." In Structural Determinants of Employment and Unemployment. Paris: Organisation for Economic Cooperation and Development. Freeman, C., J. Clark, and L. Soete. 1982. Unemployment and Technical Innovation. London: F. Pinter. Griliches, Z., and J. Mairesse. 1983. "Comparing productivity growth." European Economic Review 21: 89 -119. Kendrick, J. W. 1981. "International comparisons of recent productivity trends." In W. Fellner (Ed.), Essays in Contemporary Economic Problems. Washington, D.C., and London: American Economic Institute for Public Policy Research. Kennedy, C., and A. P. Thirlwall. 1972. "Surveys in applied economics: Technical progress." Economic Journal 82: 11 - 72. Kleinknecht, A. 1981. "Observations on the Schumpeterian swarming of innovations." Futures 13: 293- 307. Mahdavi, K. B. 1972. Technological Innovation: An Efficiency Investigation. Stockholm: Beckmans. Mandel, E. 1981. "Explaining long waves of capitalist development." Futures 13: 332-338. Melnyk, M. 1970. Long Fluctuations in Real Series of the American Economy. Kent, Ohio: Kent State University Press. Mensch, G. 1979. Stalemate in Technology. Cambridge, Mass.: Ballinger. Mensch, G., C. Coutinho, and K. Kaasch. 1981. "Changing capital values and the propensity to innovate." Futures 13: 276 292. Nelson, R. N. 1981. "Research on productivity growth and productivity differences: Dead ends and new departures." Journal of Economic Literature 19: 1029-1064. Nelson, R. N., and S. G. Winter. 1982. "The Schumpeterian tradeoff revisited." American Economic Review 72: 114-132. Oppenlander, K. M., and L. Scholz. 1981. "Innovation test: A new survey of the IFO institute." Paper presented at the 15th CIRET Conference, Athens. Pavitt, K. 1984. "Sectoral patterns of technical change: Towards a taxonomy and theory." Research Policy 13: 343-373. Pulido, A. January 1986. "Crecimento economico y esfuerzo tecnologico. Un enfoque integrado, modelo economctrico y modelo input -output con coeficientes variables." Universidad Autonoma de Madrid. Romeo, A. A. (1975) "Interindustry and interfirm differences in the rate of diffusion of an invention." Review of Economics and Statistics 57: 311-319. Rosenberg, N. 1982. Inside the Black Box: Technology and Economics. Cambridge: Cambridge University Press. Rosenberg, N., and C. R. Frischtak. 1984. "Technological innovation and long waves." Cambridge Journal of Economics 8: 7-24. Scherer, F. M. 1982. "Inter-industry technology flows and productivity growth." Review of Economics and Statistics 64: 627- 734. Seidl, C. 1984. Lectures on Schumpeterian Economics. Berlin: Springer-Verlag. Sterman, J. D. 1985. "An integrated theory of the economic long wave." Futures 17: 104-130. Vaccara, B. N. 1970. "Changes over time in input-output coefficients for the United States." In A. P. Carter and A. Brody (Eds.), Applications of Input-Output Analysis. Vol. 2. Amsterdam: North-Holland. van Ewijk, C. 1982. "Spectral analysis of the Kondratieff-cycle." Kyklos 35: 468-499. van Duijn, J. J. 1983. The Long Wave in Economic Life. London: Allen and Unwin.

10 Private-Led Technical Change in Prewar Japanese Agriculture SHIN NAGATA

The purpose of our research is twofold: to build a model in which agricultural producers themselves carry out innovative activities, and to apply the model to prewar Japanese agriculture in order to test its empirical validity. We believe that the construction of such a model, and its empirical verification, are essential to acquiring a deeper understanding of prewar Japanese agricultural development. One of the reasons we study prewar Japanese agriculture is that such study is an important prerequisite to understanding the Japanese economy today. More important, it helps us in our study of many contemporary less-developed countries (LDCs), where the agricultural sector still constitutes, as it did in prewar Japan, a sizeable proportion of the economy. This view follows the tradition of regarding Japan as a model of economic development (Kelley and Williamson, 1974; Lockwood, 1954), on the one hand, and of seeing agriculture as a strategic sector (Ishikawa, 1967), on the other. Prewar Japanese agriculture showed remarkably rapid productivity growth from an international perspective (Hayami and Ruttan, 1971), considering Japan's poor natural resource endowment. We believe that contemporary LDCs can learn from this Japanese experience. In this chapter, however, we wish to focus on acquiring a deeper understanding of the facts of the Japanese case, and on presenting a model relevant to those facts. The available data (Ohkawa, Shinohara, and Meissner, 1979) show that about half of the total output growth in Japanese agriculture may be attributed to total factor productivity (TFP) growth and that this growth, and hence total production itself, decelerated suddenly around 1920 (Hayami, 1975). We realized the importance of the development of new techniques by private farmers—a point that is also relevant to the thesis of this chapter—while studying changes in actual techniques. For example, in considering rice strains, a particularly important item in Japanese agriculture, we have found that all the major varieties of the 1920s were selected after the Meiji Restoration and before 1920 by individual private farmers (Nogyo Hattatsu-shi Chosa Kai, 1953-58). The role played by private farmers in the innovation process was not confined to improvement of the "best technology." In fact each farmer used his resources, especially labor, to improve "average practice." This process is usually recognized as

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Input-Output and the Analysis of Technical Progress

technological diffusion. This hypothesis—that private farmers themselves carried out productivity-increasing activities, rather than regarding technical change as an exogenous factor—provides a consistent interpretation of the pattern of technological change in prewar Japanese agriculture. By 1920 the labor surplus condition no longer held (Fei and Ranis, 1964), and hence labor allocation to productivity-increasing activity decreased, resulting in a deceleration of productivity growth. These facts, and this line of reasoning, may sound strange to those who are familiar with the experiences of contemporary LDCs and those who regard public expenditure as the major source of technical progress in agriculture, as well as to those who believe that labor scarcity will induce labor-saving technical change and may thus accelerate productivity growth. We do not deny these lines of argument, and we recognize that they may apply to postwar Japanese agriculture, and to a lesser extent to prewar Japan as well, especially in the 1930s. But we wish to emphasize here that the Japanese case was different, and that our argument for the importance of private innovation provides a consistent additional explanatory dimension of agricultural productivity growth in post-Meiji Japan. One major emphasis here is institutional, in that Meiji Japan did not have adequate public institutions for agricultural research and extension, in both quantitative and qualitative terms. Public expenditure on agricultural research and extension was small and directed toward importing Western techniques, which were not "appropriate" for Japanese agriculture as it was then. At that time Japanese agriculture was based on rice and on small-scale farming; it made little use of animals. The Japanese endowment of productive factors, particularly the land-labor ratio, was significantly different from that in Western nations. Thus Japanese agriculture had to find a way to develop by its own efforts, with little help from government or from agriculture abroad. These considerations suggest that we need a theory of private innovation to understand the Japanese experience. Furthermore, since the adoption of new techniques is almost always a private activity, in that the government cannot force private farmers to innovate without considerable cost, such a model of private innovation is vital to understanding technical progress, especially diffusion processes, in general. In this respect we can argue that prewar Japanese agriculture provides a pure case for studying the private aspects of technical progress, because the "distortionary" impact of public activities on technical change was minimal. The notion of private technical change in agriculture has not been explored thoroughly before, leaving a vacuum that needs to be filled. In the next section of this TABLE 10.1 Characteristics of the optimal solution"

h <0 K = 1 c< 1

a decreases toward zero a decreases toward zero

/! = 0

a remains constant a decreases toward zero

h>0 No unique solution exists a gradually approaches its long-run optimal ratio

"h = rate of labor growth (measured in productivity-increasing activities [PIA] efficiency units); c = production elasticity of the technological level in PIA; and a = optimal labor allocation ratio devoted to PIAs out of the total labor available.

Private-Led Technical Change in Prewar Japanese Agriculture

151

chapter we present what we call the productivity increasing activity (PIA) model. The construction of this model can be seen as a contribution to the theory of endogenous technical change. The empirical validity of the model is tested, with positive results, and reviewed in the following section. The testing procedure can also be regarded as an exercise in estimating the production function of prewar Japanese agriculture. We will review our empirical work both as an empirical verification of the model (and hence of our hypothesis) and as new estimates of this production function.

THE THEORETICAL MODEL Model Specification Farmers produce agricultural goods Q with one part of their labor input La and other inputs / at a historically given technical level P. We further specify this production relation to be Cobb-Douglas The other part of the labor force is used to improve production techniques.

In formulation (2) we assume that the rate of technical advancement depends on the level of technology. We normally expect it to be harder to improve technology if the technical level attained so far is higher (i.e., e < 1). We assume that the total labor available (La + Lp) and the "other inputs" (/) increase at given rates, respectively, and that farmers maximize the present value of the future output stream (discounted at a given rate) by allocating their labor between agricultural goods production and the productivity-increasing activities.

We can study this type of problem by applying Pontryagin's Maximum Principle.

Existence of an Optimal Solution Our first theoretical result concerns the existence of an optimal labor allocation plan. If the rate of productivity increase depends positively on the level of productivity (i.e., c > 1), many solutions make the present value of the output stream infinite. If the rate of productivity increase depends inversely on the level of productivity (i.e., e < 1), there exists a unique solution, subject to the qualification that the long-run output growth rate does not exceed the interest rate. If the rate of productivity increase is independent of the productivity level (i.e., e = 1), there exists a unique solution provided the labor growth rate (measured in terms of efficiency units allowing for PI As) is not positive: the qualification stated previously, on the relation between the growth rate and the interest rate, also applies here. If, on the other hand, the labor growth rate is positive, the solution ceases to be unique and the present value of the output stream is infinite, as in the case where e > 1. The general characteristics of the solutions can be illustrated in Table 10.1.

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Input- Output and the Analysis of Technical Progress

"Comparative Static" Properties of the Solution We conducted a detailed study of the properties of this model, and of how changes in the parameters and the initial conditions affect the solution, particularly in terms of the optimal labor allocation. Here, however, we summarize only what we consider the most important aspects of these theoretical results. These findings are incorporated into the econometric tests presented in the next section. Our theory supports the following hypotheses: 1. In the "simple" model a. The level of productivity has no effect on the optimal labor allocation ratio to PIAs (a) b. The size of the total labor force in the agricultural sector has a positive effect on the optimal labor allocation ratio to PIAs 2. In the "general" model a. The productivity level has negative effects on the optimal labor allocation ratio to PIAs b. The total labor force has a positive effect on this ratio, as in the simple model The simple model is denned as a special case of our model, when the growth rate of productivity does not depend on the productivity level (e = 1) and when labor (in efficiency units in PIA activities) is perceived as remaining constant (h = 0). This simple model should be regarded as an approximation (of lower order) of the general model, which is itself, of course, an approximation of reality.

Relation to Previous Models of Endogenous Technical Change In this subsection we will try to clarify the relationship of our model to the models of Lucas (1967) and Uzawa (1965), which we believe should be called models of endogenous technical change. In a sense, our purpose in this subsection is to make clear the contribution we have made to the field of theories of endogenous technical change through the development of our model. It is clear that our model, and the models of Lucas and Uzawa, have certain similar features. In particular each model regards a productivity increase as the output of activities that consume production factors, with the level of these activities being optimally determined to maximize the future output stream. However, these models also contain features that differ, so that each model has an advantage over the others for some purposes. In other words, none of the models includes any of the others as a special case. We would like to make the differences clear, and to search for directions in which our model should be enriched. Productivity-increasing activity in the Uzawa model is specified in a form that is more suitable as a representation of education than of R&D, in that the share of labor devoted to this activity determines the rate of productivity increase. The other feature we felt to be inadequate was that the level of productivity (or technology) has no influence on the determination of the rate of technical change (i.e., e is assumed to be unity). The Lucas model has corrected these deficiencies. His model, however, is applicable to a competitive microeconomic agent in the sense that perfectly elastic

Private-Led Technical Change in Prewar Japanese Agriculture

153

supplies of labor (and other production factors) are assumed. This assumption is not really acceptable for studying prewar Japanese agricultural development. Furthermore, we think that Lucas' assumption of constant factor prices (e.g., wage rates) over time is inadequate, even in a more general context. Our approach can be taken as being another way to remedy the shortcomings of the Uzawa model, or as a variant of the Lucas model, with perfectly inelastic factor supplies that are increasing at given constant rates. Our approach, however, also has shortcomings. First, our analysis is within the framework of constant elasticity (in fact, of Cobb-Douglas) production relations. In this characteristic our model is inferior to those of Lucas and Uzawa. Second, if our model is to be a macroeconomic growth model, we should treat savings (or investment) as a choice variable, as is done by Uzawa. If these two extensions were made to our model, we could assert that the Uzawa model is a special case of ours.

Interpretation of the Model We implicitly treat productivity-increasing activities (PIAs) as private R&D, both advancing the best practices and enhancing average practices by imitating the techniques used by neighbors. This involves an element of trial and error, an essential component of R&D activities. Yet we have no intention of confining the PIAs to private R&D. More specifically, we would like to include investment in latent capital (e.g., land improvement projects such as building and improving irrigation and drainage facilities) and private educational activities1 (such as attending farmers' meetings) as components of the PIAs.2 We can show that the latent capital model and the private education model are special cases of our PIA model. In the first of these models there is another type of capital, which is not included in the official measure of capital (and is thus latent) and is produced using production factors, notably labor. Farmers are assumed to maximize the present value of the future output stream by allocating their labor between agricultural goods production and latent capital production. In the private education model, technical change is a function of the share of labor in educational activities, rather than of the amount of labor devoted to PIAs. ECONOMETRIC TESTING Methodology In applying our theoretical model to econometric testing, two difficulties arise. These difficulties, and the way in which we overcame them, are as follows: 1. Our models cannot be transformed into linear relationships between observable quantities without assuming away the key features of the model. Hence we cannot estimate the model using ordinary least squares. We thus resorted to maximum likelihood (or nonlinear least squares) estimation. 2. The time series on inputs, as well as those on outputs, have strong time trends so that parameter estimates are very unstable (i.e., we have a serious case of multicollinearity). We thus adopted the mixed estimation technique. This method allows us to combine cross-sectional data, which are free from time trends, with the time series data, which alone can provide legitimate measures of technical change.

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Time-Series Estimation Without Mixed Estimation Ordinary least squares estimates of Cobb-Douglas production functions, without using the mixed estimation procedure, can hardly be regarded as representing production relations3: the land coefficients were around 3 to 4, and the coefficients of nonfarm current inputs were negative, although insignificantly so. We can interpret these results if we take account of PIAs. 1. The increase in the land area is another product of PIAs4. Hence when PIAs are undertaken, the land area increases at the same time that productivity increases. The land area thus functions as a productivity indicator as well as an input for agricultural production. 2. The negative value for the coefficient of "nonfarm current inputs" is especially troublesome, since fertilizers, a major component of nonfarm current inputs, are considered leading inputs in the phase of biological-chemical technology change, in which the level of fertilizer application and the technology level are expected to more or less correspond to each other. The purchased fertilizers were almost complete substitutes for self-supplied fertilizers, which constitute self-supplied investment of "liquid" capital, as one element of latent capital. Hence when PIAs are undertaken, self-supplied capital increases to substitute for purchased fertilizers. Therefore the relation between purchased fertilizers and output becomes muted. 3. The foregoing observations can be regarded as evidence that PIAs seem to be an important component of the agricultural production relations under study. Estimation of the PIA Model To implement the mixed-estimation procedure, we first ran a cross-sectional regression, using a specification based on a straightforward Cobb-Douglas production function. The data used were those given in Akino (1972). The differences from previous studies using these data (Hayami, 1975) are that we tested the hypothesis of constant returns to scale (with the finding that decreasing returns to scale prevailed) and that we used climactic variables to improve estimation. Utilizing the cross-sectional production function estimates as prior information, we computed time-series estimates of both the simple model and the general model, applying the method of mixed estimation. 1. We first estimated an ordinary Cobb-Douglas production function, using the mixed-estimation method with and without a time-trend variable. The estimation results without a time-trend term could not be interpreted as a production relationship, even using the mixed-estimation procedure. The time trend was statistically significant, as expected, indicating the existence of technical change. 2. We then estimated our models, which incorporated PIAs into the production function. The results were statistically superior to the estimates incorporating a simple time trend. The difference between the two versions (simple and general) of our models was not statistically significant. This implies that we need more detailed data to distinguish between the contributions of diffusion, and of the advancement of best practices, to technical change at the aggregate level. 3. Because there was no guarantee that the cross-sectional and time-series production

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functions were the same, we investigated whether they actually were. Furthermore, the coverage of capital and "nonfarm current inputs" are not the same for crosssectional and time-series data. The cross-sectional data are less complete because of data deficiency, so there are grounds for scepticism about using cross-sectional estimates as prior information for mixed estimation of the time-series production function. Accordingly we performed the compatibility test suggested by Theil (1963). The results were well within the acceptance range for any reasonable significance level. Hence we may conclude that the incompatibility between the two data sets was not serious. 4. One question we would like to address is whether our model can explain the observed timing patterns. This is equivalent to the question whether the estimation residuals have the same pattern as the timing sequence we set out earlier. If we can explain the timing using the models, the residuals from the models should be random and have no significant pattern. Our models turned out to be powerful in this respect, in that statistically we could not discern a kink around 1920 in our residuals. In contrast, the simple time-trend model failed in this respect: there was a statistically very significant kink around 1920. 5. The estimates of the elasticity of optimum labor allocation to PIAs with regard to the total agricultural labor force were about 6 to 9, depending on the specification. These estimates may seem too high. In the period 1914 to 1920 the agricultural labor force decreased from 13.5 million to 12.0 million in terms of male equivalent workers, a decrease of 11 percent. The parameter estimates imply that the labor devoted to PIAs decreased by between 51 and 69 percent. We interpret these results as follows: a. Our theoretical model implies that these values are reasonable if the labor allocation ratio was around 5 to 10 percent and the production elasticity of labor for the PIAs was around 85 to 90 percent, or for other combinations with smaller (or larger) values for the PIA labor allocation ratio and lower (higher) PIA production elasticities. b. The PIAs are specified in net terms. For example, maintenance of irrigation and drainage facilities is not included in PIAs but rather in ordinary agricultural goods production. Therefore in the same period, the change in the labor allocation to gross PIAs was modest, but the change in net terms was drastic, as the preceding figures indicate. 6. The estimates of the production elasticities for each of the four input categories are practically identical, regardless of whether we use the simple or the general model, and are reported in Table 10.2. TABLE 10.2 Mixed-estimation production elasticity estimates Labor Land Capital Nonfarm current inputs

0.15 0.42 0.11 0.18

•Figures in parentheses are standard errors.

(0.04)°

(0.04) (0.02) (0.02)

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Comparison with Previous Estimates Previous estimates of the production function for prewar Japanese agriculture are summarized in Table 10.3. Although his work was based on postwar data, Tsuchiya's estimates are also included, on the grounds that the conditions of production in 1951 were still similar to those prevailing prewar. We recognize a high degree of similarity among the estimates, including ours. The exception is the set of estimates by Akino (1972), which serve as the basis for the studies on technical change in Hayami (1975). The similarity is particularly remarkable because of the fact that their data bases are fairly independent. Ohkawa's classic study (1945) and Tsuchiya's study (1955) are based on production-cost surveys of different dates; both of Shintani's studies (1970,1971) are based on farm household economy survey data of different dates; and Akino's study and ours are based on the aggregate data, which covers all farming units. We think that this similarity indicates the soundness of our estimates. One difference that clearly distinguishes our estimates from the others is that we found decreasing returns to scale (indicated by the fact that the sum of the production function coefficients is less than unity). We should compare our results only with those of Ohkawa and Tsuchiya, because all the others simply assumed constant returns to scale without testing the hypothesis. We think that there are two sources for the difference. First, their studies cover only rice production. As Tsuchiya (1955) has shown, the degree of scale economies (or diseconomies) depends on the commodity studied: rice is subject to constant returns to scale, whereas other commodities are subject to increasing returns or decreasing returns. Our study covers aggregate agricultural production, and although rice is the main commodity, it is not a priori clear whether constant returns prevail. Second, the data on which their studies depend came from homogeneous regions, whereas our data covered all regions. Thus the irrigation and drainage facilities were rather uniform in their data base, but this latent capital is one of the main sources of decreasing returns to scale, and hence their estimates failed to capture decreasing returns to scale. This difference, however, does not necessarily indicate any inconsistency between their estimates and ours. They estimated microeconomic production functions, with the aim of comparing coefficients with factor shares and of studying the behavior of individual farmers, whereas TABLK 10.3 Previous estimates of prewar agricultural production function Shintani

Labor Land Capital Nonfarm Current input Sum Years covered Output covered

Ohkawa (1945)

(1970)

(1971)

Akino (1972)

Tsuchiya (1955)

0.23 0.56 —

0.22(0.09)" 0.63(0.10) —

0.3-0.5 0.3-0.5 0.2

0.41(0.07) 0.17(0.06) 0.14(0.04)

0.19 0.56

0.18 0.98 1937-39 Rice

0.15(0.05) 1.00 1888-1900 Rice

— 1.00 1925-36 Agri. output

0.28(0.04) 1.00 1928-37 Agri. output

0.25

"Figures in parentheses arc standard errors.

1.00 1951 Rice

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our aim was to estimate a time-series production function and to study the dynamic property of technical change. Akino's estimates are quite different from the others, although our data and his are basically the same: we actually used his cross-sectional data, along with time-series and climactic data. One of the most important sources of the difference between our estimates and Akino's is that we included climactic variables at the stage of crosssectional estimation. We think that the influence of climactic factors in cross-sectional estimation should be eliminated when one studies technical change. Such change appears legitimately only in time-series data and has little to do with climactic factors.

NOTES 1. Formal education can be regarded as this type of activity if the effort devoted to it is a choice variable from the private farmers' point of view. 2. Obviously these activities are interrelated. For example, in the case of imitating the practices of others, acquisition of the necessary information can be classified as the third type of activity (i.e., education), whereas trying the new techniques is an activity of the first type. But in addition, improved irrigation, an element of the second type of PIA, may also be required. 3. Straightforward time-series estimation of Cobb-Douglas functions based on Long-Term Economic Statistics (LTES) data has been tried before, but resulted in unreasonable estimates and was therefore discarded (Yamada, 1967). We report the results here because we think they are supportive to our views. 4. One purpose of land replotment projects was to increase land area, although the main purposes were to simplify operations and to improve (or to build) irrigation and drainage facilities.

REFERENCES Akino, Masakatsu. 1972. "Nogyo Seisan Kansu no Keisoku" (Estimation of the agricultural production function). Nogyo Sogo Kenkyu 26: 163-200. Fei, J. H., and G. Ranis. 1964. Development of the Labor Surplus Economy: Theory and Policy. Homewood, III.: Irwin. Hayami, Yujiro. 1975. A Century of Agricultural Growth in Japan. Minneapolis: University of Tokyo Press and University of Minnesota Press. Hayami, Yujiro, and V. W. Ruttan. 1971. Agricultural Development: An International Perspective. Baltimore: Johns Hopkins University Press. Ishikawa, Shigeru. 1967. Economic Development in Asian Perspective. Tokyo: Kinokuniya. Kelley, A. C., and J. G. Williamson. 1974. Lessons from Japanese Development: An Analytical Economic History. Chicago: University of Chicago Press. Lockwood, W. W. 1954. The Economic Development of Japan. Princeton, N.J.: Princeton University Press. Lucas, R. E. Jr. 1967. "Tests of a capital theoretic model of technical change." Review of Economic Studies 34: 175 180. Nogyo Hattatsu-shi Chosa Kai. 1953-1958. Nihon Nogyo Hattatsu Shi (History of Japanese Agricultural Development). l0vols. Tokyo: Chuokoronsha. Ohkawa, Kazushi. 1945. Shokuryo Keizai no Riron to Keisoku (Theory and Measurement of the. Food Economy). Tokyo: Nihon Hyoronsha. Ohkawa, K., M. Shinihara, with L. Meissner. 1979. Patterns of Japanese Economic Development: A Quantitative Approach. New Haven, Conn., and London: Yale University Press.

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Shintani, Masahiko. 1970. "Meiji Chuki Suito Seisan ni Kansuru Suryo Bunseki" (Econometric analysis of paddy rice production in the middle Meiji era). Nogyo Keizai Kenkyu 42: 97101. Shintani, Masahiko. 1971. "Senzen Nihon Nogyo no Gijutsu Shimpo to Fukyu ni Kansuru Benseki" (Technological innovation and its diffusion in prewar Japanese agriculture). Paper presented at the annual meeting of the Japanese Association of Theoretical Economics, Tokyo. Theil, H. 1963. "On the use of incomplete prior information in regression analysis." Journal of the American Statistical Association 58: 401-414. Tsuchiya, Keizo. 1955. "Nogyo ni Okeru Seisan Kansu no Kenkyu" (A study of the agricultural production function). Nogyo Sogo Kenkyu 9: 209-262. Uzawa, H. 1965. "Optimum technical change in an aggregative model of economic growth." International Economic Review 6: 18 31. Yamada, S. 1967. "Changes in output and in conventional and nonconventional inputs in Japanese agriculture since 1880." Stanford University Food Research Institute Studies 1: 371-413.

IV INPUT-OUTPUT AND THE ANALYSIS OF SOCIALIST ECONOMIES

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11 The SYRENA (SYnthesis of REgional and NAtional Models) Model Complex A. G. GRANBERG, V. E. SELIVERSTOV, V. I. SUSLOV, and A. G. RUBINSHTEIN

GENERAL PRINCIPLES Studies of multiregional input-output models and models of two-level economic systems—distinguishing between the "national economy" and the "regions"—have been conducted in the Institute of Economics and Industrial Engineering (IEIE) for more than fifteen years. During the first stage of this work, multiregional models were developed separately from regional models and were considered only as an instrument for the computation of centrally planned projections and analyses. As the studies devoted to regional models have advanced, however, the situation for a growing number of regions has changed: multiregional models have become an important instrument for the synthesis and coordinated solution of individual regional models. The main construction principle of the SYRENA (SYnthesis of REgional and NAtional economic models) model complex is as follows. A detailed description of the "central" elements (which are of special interest) is completed by an aggregated presentation of the remaining elements of the national economy (Figure 11.1). The SYRENA model complex consists of national-level models (with and without a regional specification) and models of the first-level regions (macrozones, union republics, and economic regions). There are three types of multiregional model in the SYRENA complex: 1. Different versions of the balanced multiregional input-output model (Nauka, 1983) 2. Optimizing multiregional input-output models (OMIOM) with a scalar or vector objective function (in particular cases, these models contain relationships specifying production, consumption, and incomes for the population concerned) (Granberg, 1973). 3. Models of optimal economic interaction of regions with local objective functions (Rubinshtein, 1983).

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FIGURE 11.1 Structure of the SYRENA (SYnthesis of REgional and NAtional economic models) model complex.

Moreover, within the SYRENA framework studies were conducted on the coordination of solutions for two-level systems on the basis of 1. A decomposition procedure giving the optimal multiregional distribution of centralized resources1. 2. The application of regional response functions.

OPTIMIZATION OF A MULTIREGIONAL INPUT-OUTPUT MODEL OMIOM is a result of the development and integration of the system of regional input-output balances described by

where XR, YR = gross and final output vectors of the Rth region AR = matrix of input-output coefficients in the Rth region S = number of regions This system is modified for inclusion in OMIOM by two operations: 1. The output is disaggregated into two types: output on old fixed assets set up before the plan period (denoted by a superscript °) and output on new fixed assets set up during the plan period (denoted by a superscript"). 2. Some components of final output are fixed exogenously (QR, which includes the change in stocks, the replacement of losses, the trade balance, and other elements of final output), whereas other components are endogenous (VR, UR, repairs to capital and investment for production; ZR, nonproduction consumption, the inflowoutflow balance; XRS, the outflow of production from the Rth region to the Sth region; XSR, the corresponding inflow of production).

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The model takes into account linkages between neighboring (bordering) regions, with $(R) denoting a set of regions that are neighbors of the jRth region. The set of equations given by (1) are transformed into the following set:

where aR is the sectoral structure of nonproduction consumption and Efs, MSRR are the inflow-outflow matrices. Investment requirements are formalized as where DOR, D R are the matrices of dimension 2 x n giving machinery and construction input needed for repairs to capital. Regional balances for productive investments are given by where BOR, B R are the matrices of total investment output ratios for a period and HR is the total production investment for a period. The variables giving investment for the last year UR and total investment over a given period HR are related by a formula derived from the assumption of an exponential growth rate 0 (constant for all years of the period) for each type of investment:

The regional structure of labor resources is exogenous, and the labor constraints are where LOR, L R are the labor output ratios and LR is the projected limit on labor resources. The regional structure of nonproduction consumption is expressed as follows: where AR is the share of the Rih region in the national total of nonproduction consumption, S« = 1/1K = 1, and Z is the national total of nonproduction consumption. The volume of output on old fixed assets is limited by upper bounds: where NR is a vector of production capacities available at the beginning of the plan period. Some components of the vectors denoting output produced using new fixed assets can be limited as follows: For extractive industries the upper bounds correspond to the amount of resources available.

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In 162 Input-Output and the Analysis of Socialist Economises

FIGURE 11.2 Structure of the model regional block. The objective function is to maximize the national total of nonproduction consumption: The structure of the regional block of the model is shown in Figure 11.2. The coefficients 7, ft are parameters of the approximation formula used for condition (5), with £t, £2 being the corresponding variables. The structure of the coordination block linking the Rth and 5th regions is shown in Figure 11.3. In the interregional block there is also a column vector As describing the allocation of consumption across regions.

BALANCED MULTIREGIONAL INPUT-OUTPUT MODELS The main assumption used in OMIOM is that units of output of the same good are substitutes, whether they are produced in the region or imported from other regions. As the sectoral and regional classifications used are highly aggregated, it is reasonable to use an alternative version of the multiregional input-output model of the balanced type (a modification of the Moses-Isard model [Moses, 1960]). In this version of the model the inflow of production to each region is considered to be noncompetitive, that is, units of output of the same good are regarded as being different if they come from different regions. The two principal equations characterizing the multiregional balances are

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FIGURE 11.3 Structure of the model coordination block. where ARXR + YR is a vector of total consumption in the Rih region. Two types of balance models can be distinguished, denoted the W-model and the V-model. To build the W-model one defines, on the basis of equation (12), a system of so-called trade coefficients GRS (a diagonal matrix) so that Obviously, 1,RGRS = I. Since these coefficients are fixed, the VF-model equations are the result of substituting values from equation (13) for the variables XRS into equation (11) for each region: where G = block matrix, with typical block {GRS}; A= block-diagonal matrix, with typical block {AR}; X,Y = vectors composed of XR and YR, respectively. This model is known as the Moses model (Moses, 1960). To build the V-model one applies the "inverse procedure." From (11) one finds coefficients \SR (a diagonal matrix) so that

Obviously, £ S A M = /. These coefficients are also fixed so that one substitutes values for the variables XRS from equation (14) into equation (12) for each region. The Kmodel equations are where A is a block matrix with typical block {ASR}. The two models complement one another in the sense that in the first model the structure of production consumed in each region is constant for all regions acting as suppliers, and in the second the structure of production produced in each region is constant for all regions acting as consumers.

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An analysis of the productivity of the matrices in the models considered gives the following results: The matrix GA in the FF-model is productive, that is, (7 — GA) ' > 0, under every system of trade coefficients G if and only if there exists a price vector P ^ 0, with P the same for all regions, satisfying the condition PAR < P for R = 1,S. To prove that this condition is necessary a separation theorem can be applied. Let us call matrices AR, R = 1,S, possessing the property mentioned interproductive. In contrast, the matrix GA is nonproductive under every G if and only if there exists a price vector P ^ 0 satisfying PAK > P for R = 1,S. In this case let us call the matrices AR internonproductive. If the matrices AR are neither interproductive nor internonproductive, then the matrix GA can be both productive and nonproductive, with the outcome depending on G. For example, if matrices AR, R = 1,S, are productive but not interproductive, then under some G matrix GA can be nonproductive. Similarly, if the matrices AR, R = \,S, are nonproductive but not internonproductive, under some matrices G the matrix GA can be productive. The last example is a good illustration of the interaction effect: regions with nonproductive matrices AK, which cannot function under autarchy, can produce a viable system through cooperation. Similar conditions of matrix productivity were not derived for the K-model. In other words, the existence of a nonnegative solution to this model depends not only on A but also on A and Y. The importance of the concepts of interproductivity and internonproductivity goes beyond the framework of the FF-model. The concepts can be considered concepts of productivity in multiregional systems in general. Specifically, the productivity properties of the mean input^ output matrix for the system 1IRARRR (where RR is a diagonal matrix giving the weights of the Rth region in total production) are similar to those of the matrix GA. Internonproductivity of the regional matrices AR results in the nonexistence of a solution to the optimization multiregional model (under a nonnegative right-hand side). Conversely, a solution of this model can exist even if all regional matrices AR are nonproductive but are not internonproductive. The balance models can be applied both to the analysis of the hypothetical territorial proportions and for projections. In the second case, they can be used on the one hand as a "tool" for analyzing solutions of more sophisticated optimization models. On the other hand, optimization models can be added for a number of sectors and regions in the block of interregional exchange of production, with relationships describing multiregional linkages and using exogenous parameters being obtained from balance models. ECONOMIC INTERACTION MODEL By using the optimal solution generated by OMIOM one can calculate regional production exchange balances measured in shadow prices. These estimates can be used as indicative indices to measure the "pure contribution" of a region to a national aggregate of nonproduction consumption and therefore to characterize the efficiency of the regional economy. Thus, for a fixed interregional structure of nonproduction consumption, the OMIOM determines the corresponding vector of regional production exchange balances (the vector of regional pure contributions).

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The economic interaction model (EIM) solves a problem that is precisely opposite. Under a priori given regional balances DR one is to define the interregional structure of nonproduction consumption (values A*). Thus in the EIM, the consumption of a region is a function of its pure contribution to the national aggregate of nonproduction consumption. The EIM structure is as follows. Under fixed prices Pfs and Pf* for the /th product exported from the Sth region and imported to it, respectively, each region maximizes its regional aggregate of nonproduction consumption ZR subject to the constraints given by equations (2) through (6), (8), and (9) and the supplementary conditions: The price vector P = (Pfs, P?R) is an equilibrium if there exist equilibrium optimal solutions of the regional subproblems, that is, if there are optimal solutions satisfying the conditions for all tradeable products / and trading regions S and R2. If equilibrium prices do not exist, individual regions cannot provide the given values of balances DR, that is, the a priori demand for a "pure contribution" of these regions to the national aggregate of consumption in the model framework is exaggerated. In this case one can consider quasi-equilibrium solutions in the following sense.3 Regions providing the given balances DR maximize their aggregate of nonproduction consumption, as in the initial problem, but regions that cannot provide the given DR minimize their imbalances (i.e., minimize the difference from the given DR). Thus quasi-equilibrium solutions ensure the minimum difference from the a priori given values of the resulting pure contributions of regions to the national aggregate in the case when the latter cannot be achieved. Equilibrium and quasi-equilibrium solutions of the EIM, like OMIOM optimal solutions, are Pareto optimal, that is, no region can attain a higher value for its aggregate fund of nonproduction consumption without decreasing its value in at least one other region. The EIM solutions, in contrast to the OMIOM solutions, satisfy a more strict set of requirements for optimality in problems with multiple criteria, in that they belong to the so-called feasible nondistinct core.4 The OMIOM and EIM solutions are closely related. If one fixes the balances DR obtained through the OMIOM solution in the EIM, then the EIM equilibrium or quasi-equilibrium solution coincides with one of the OMIOM optimal solutions. Moreover, equilibrium (or quasi-equilibrium) prices are proportional to the corresponding optimal dual variables of equations (2) in the OMIOM solution. The inverse relationship is the following. If one defines the interregional structure of nonproduction consumption by using the EIM solution and fixes it as the vector giving the interregional structure of consumption in the OMIOM, then the optimal OMIOM solution coincides with one of the EIM solutions. The relationship just described is applied in the algorithm used for the EIM solution, which is based on the iterative solution of problems like the OMIOM with repeated adjustment of the interregional structure of consumption described by A*.

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APPLICATION AND DEVELOPMENT OF THE SYRENA MODEL COMPLEX The main elements of the SYRENA model complex considered above have much in common. First, the models use the same data base, and the regional input-output blocks are presented in a similar way. Second, the models are formally (mathematically) interrelated and their software has many common components. We regard the different types of models described here as complementary instruments for applied investigations, and we consider that the best way to apply them is as an interrelated complex. The object that is under study using the SYRENA model complex is the process of multiregional economic interaction and the development of regions in the framework of the national economy of the Soviet Union. The SYRENA model complex is oriented to solve the following problems: 1. To estimate hypothetical territorial proportions from the point of view of improving national economic efficiency. 2. To project long-trend perspectives for territorial proportions in the Soviet Union. 3. To study trends and perspectives of the development of the large regions in the framework of the national economy. 4. To analyze processes for narrowing the gap in the levels of economic development and living standards between regions. 5. To study the territorial aspects of intensifying development and improving efficiency. 6. To study the impact of large-scale national and regional economic programs on the process of territorial development in the Soviet Union. 7. To estimate the effects, from the national economic point of view, of the formation of territorial-production complexes of national importance. The SYRENA complex has been used for a number of years to develop regional sections for the complex program of technological progress (covering a period of two decades), and to carry out preplanning studies of the problems of large regions, specifically those of the Russian Republic and Siberia, in the framework of the national economy. Recently, the organizations of a number of Union republics (the Ukraine, Kazakhstan) and regions of the Russian Republic (the Urals, the Far East, etc.) have joined the SYRENA complex as external cooperating bodies. For this purpose coordinated studies are conducted to create specialized multiregional USSR models with corresponding regional blocks (the Urals, Kazakhstan, Far East, etc.) and to install these models on the computers of the organizations mentioned. This work will lead to advanced studies on the modeling of regional development perspectives in the framework of the national economy. The construction of the SYRENA complex provided opportunities to gain a deeper insight into a number of different problems: the interaction of social and technological factors, the interaction of production and transport, the development of individual regions, and others. The principles of construction and analysis used for the SYRENA model complex were applied to the development of models of the world economy and socialist economic integration (Granberg and Menshikov, 1983; Granberg and Rubinshtein, 1984). This study was conducted in cooperation with the Projections and Perspective Studies branch of the U.N. Secretariat. In some respects

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the studies on the modeling of world economic development and socialist economic integration were more advanced than those of interregional interactions in the USSR economy. Here we should mention: 1. 2. 3. 4.

The application of the material-value multiregional classification. The combination of global optimization and economic interaction models. The combination of input-output and macroeconomic models. The incorporation of a national economic model into the initial version of the world multiregional model.

An important line of extension of the range of application of the SYRENA model complex is the incorporation of "second level" regional models and blocks describing large regional programs. This study has started to connect blocks describing the "Krasnoyarski region" and the "Primorye region" with the operating regional blocks for East Siberia and the Far East. The lines along which the SYRENA model complex is being developed include both modification of the basic model complex (the development of material-value multiregional models, of multiregional input-output models with a financial block, of a more sophisticated presentation of dynamic and social aspects of territorial development, etc.), and improvements in the specification of the external linkages of the SYRENA complex with regional and national economic models. The intention is to construct a "buffer" between the SYRENA complex and a system of macroeconomic, econometric, material-value, material-financial, and other models that are used in the perspective studies of economic regions. To combine the SYRENA complex with operating models for Union republics, a model complex developed in Latvia has currently been chosen for a test study.

AN EXAMPLE SCENARIO The principal method of application of the SYRENA model complex is scenario analysis. A scenario is a study of an important perspective problem, or a combination of probable problem situations, which calls for specialized model projections under varying conditions and parameters. The application of the SYRENA model complex to the study of individual regional perspectives in the framework of the national economy involves two types of scenario: national and regional. The former deals with the impact of the pattern of national economic development on regional perspectives, while the latter investigates the consequences of probable changes inside a region, both for the region itself and for the national economy. A scenario studying the regional, multiregional, and national consequences of a change in the efficiency with which material resources are utilized, and the associated costs, is taken as an illustration of the application of scenario studies to the role of the Siberian region in the national economy of the Soviet Union. To investigate the consequences of possible fluctuations in the material-output ratio, a series of variants with varying input-output ratios for fuel, metals, wood, and other materials was compared with the "central" variant. Figure 11.4 characterizes the response of aggregate indices describing economic growth for Siberia and the Soviet Union as a whole to material-output ratio changes within an interval of + 20 percent. For changes in the interval +10 to —20 percent, the national economic indicators are changing monotonically (growth rates of national income and consumption are increasing, whereas the growth rate of gross product is decreasing).

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FIGURE 11.4 Changes in the material-output ratio and their effects on growth rates.

However, if the material-output ratio were to increase by more than 10 percent, the growth rate of gross product would diminish. The responses of the Siberian economy are of a more complicated nature. Siberian growth rates for gross product increase in both cases, that is, both when material-output ratios increase and when they decrease, but for essentially different reasons. In the first case the increase in material-output ratios requires enforced growth of the extractive industries and construction, resulting in a sharp increase in investment demand. In the second case the expansion is due to the accelerated growth of manufacturing in this scenario. Scenario studies enable us to conclude that in future the dependence of the growth rates and economic structure of Eastern areas on the parameters of national economic development, and in particular on the intensive development of industry in the European areas of the Soviet Union, is likely to increase. There are two reasons for this. First, the Eastern areas provide nearly all the incremental output for fuel, nonferrous metals, energy-intensive chemical products, and wood products. Second, because of limited labor resources and investment, fluctuations in traditional "Siberian" branches will lead to substantial changes in growth rates for complementary and associated industries (e.g., machine-building and light industry). If the growth rate of investment diminishes, the share of Siberia and the Far East in total investment also declines. The response of this region to the increase of investment efficiency is an acceleration of growth compared with the national economy as a whole, because Siberia receives investment released from the European area. All the variants with varying investment volumes prove the economic value of investment allocation to Siberia and the Far East in advancing growth.

The SYREN A Model Complex

171

Because of the shortage of labor in Siberia, an equal increase in labor productivity in all regions results in a higher economic effect. The gap in growth rates increases in favor of Siberia. However, labor shortage in the region becomes still more acute, and this has a negative impact on the development prospects of laborintensive industries. This phenomenon can be explained by a specific mechanism characterizing the economic interaction of Siberia with other regions (above all, with European regions): to provide for their advanced development, European regions demand ever greater increases in the extraction and processing of raw materials, which come mainly from the Eastern regions of the Russian Republic. The problem can be resolved if the growth of labor productivity in Siberia outstrips that in other regions. The effectiveness of this strategy of intensive development is much enhanced if it is supported by simultaneous measures to ensure the release of investment and economy in the use of material resources in the European area. The high efficiency of the Siberian economy is proved by computations using the Economic Interaction Model of the regions of the Soviet Union. Two types of computation were undertaken: 1. A search for situations in which the pattern of economic development is characterized by zero regional inflow-outflow balances, where these balances are calculated using the dual variables of the production problem (corresponding to the equilibrium prices). This type of situation is interpreted as a situation of balanced interregional exchange. The computations showed that in the only equilibrium solution, the national aggregate of nonproduction consumption decreases slightly compared with the OMIOM optimal solution, but the consumption share of Siberia increases considerably. 2. An analysis of the core of the economic system was conducted by considering the feasible outcomes for potential coalitions of regions. We found that the total effect of regional interaction (the emergency effect) is more than half of the final indices of national economic development; Siberia provides more than half of the total effect.

NOTES 1. This procedure was implemented for the problem "West-East" described in Granberg and Tchernishev (1970). 2. A model formulated in this way is a particular case of a problem with conventional centers. For each pair of trading regions two centers are considered: one for production exported from the Rth region to the Sth, the other for imported production. Prices Pfs and PS,K in this case correspond to the prices in these centers. 3. This definition is close to the one given in Dantzig, Eaves, and Gale (1979). 4. The core or nondistinct core is defined as a set of solutions satisfying a condition that no coalition R'eR = {1, • • • , S} or nondistinct coalition (Ekeland, 1979, pp. 101-108), which is characterized by nonempty R'eR and fixed regional shares in the coalition Hs[0,1], can improve (blockade) the corresponding vector of values of regional objective functions in the Pareto sense. These (classical) definitions can only be applied for a problem with zero balances DR. For a case with nonzero balances, feasible coalitions or nondistinct coalitions are considered as R' for which RZR. DR < 0 or fi£^. HRDR < 0. We can call solutions that cannot be blocked by feasible coalitions or nondistinct coalitions the feasible core or feasible nondistinct core, respectively.

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Input-Output and the Analysis of Socialist Economies

REFERENCES Dantzig, G. B., B. G. Eaves, and D. Gale. 1979. "An algorithm for a piecewise linear model of trade and production with negative prices and bankruptcy." Mathematical Programming 16: 190-209. Ekeland, I. 1979. Elements d'economie mathematique (in French). Paris: Hermann. Granberg, A. G. 1973. Optimization of Territorial Proportions of the National Economy (in Russian). Moscow: Ekonomika. Granberg, A. G., and S. M. Menshikov (Eds.). 1983. Interregional Inter sectoral Models of the World Economy (in Russian). Novosibirsk: Nauka. Granberg, A., and A. Rubinshtein. 1984. "Some lines of development of the United Nations global input-output model." In Proceedings of the Seventh International Conference on Input-Output Techniques. New York: United Nations, pp. 33-48. Granberg, A. G., and A. A. Tchernishev. 1970. "A problem of optimal territorial planning 'West-East'" (in Russian). Izvestya Sibirskago Otdelenya Akademii Nauk SSSR (Social Sciences series), Vol. 2, No. 6. Moses, L. 1960. "A general equilibrium model of production, interregional trade and location of industry." Review of Economics and Statistics 42: 373-397. Nauka. 1983. Multiregional Input-Output Balances (in Russian). Novosibirsk: Nauka. Rubinshtein, A. G. 1983. Modelling of the Economic Interactions in Territorial Systems (in Russian). Novosibirsk: Nauka.

12 A Planning Scheme Combining Input-Output Techniques with a Consumer Demand Analysis: A Concept and Preliminary Estimates for Poland LEON PODKAMINER, BOHDAN WYZNIKIEWICZ, and LESZEK ZIENKOWSKI Much of the actual central economic planning in the socialist (centrally planned) economies has been based on input-output techniques. Apart from planning and analytical exercises that respect literally the fundamental equation X = (/ — A ) - 1 Y , many multisectoral models of resource allocation are formulated in a more relaxed way, for example as linear programming problems allowing underutilization of capacity in some industries, expansion in others. In either form the application of input-output techniques for centrally planned economies, though undoubtedly adequate in reflecting the supply side of the economies concerned, has tended to be deficient in representing the linkages to the economies' demand side. In particular, the private consumption component C of the vector of final demand Yis usually assumed to be exogenously prescribed—and therefore may not correspond to the true notional demands implied by the circumstances. The ensuing consumer market disequilibria (shortages) may result in more than a subjective feeling of dissatisfaction with the working of the economy. Through a feedback that has been studied by many authors (see the work of Malinvaud, 1977; Muellbauer and Portes, 1978; and Podkaminer, 1986), market disequilibria may spill over into labor markets, reducing labor supply and inducing labor shortages and thereby precipitating a deterioration in production efficiency throughout the economy. If the flexibility of wages and consumer prices is low, some degree of disequilibrium in consumer markets may be unavoidable. (Also, as developments in the market situation in Poland during the period 1980-1985 have proved, high—but misguided—flexibility of prices and wages may bring about accelerating inflation without substantial equilibration of the markets.) However, the structure of the "exogenously" prescribed vector of private consumption and the resulting size of the wage fund may both significantly contribute to market disequilibria. In this chapter we try to elicit alternative policies that could have led to

174

Input -Output and the Analysis of Socialist Economies

equilibrium in Poland's consumer markets in 1977. Apart from the policy of accepting the observed pattern of production and consumption while leaving the burden of adjustment to changes in consumer prices, we try to indicate policies that allow for more or less dramatic changes in the composition of output and employment. THE DEMAND SIDE Private consumer demand is assumed to conform to the ELES (extended linear expenditure system) as studied in Lluch, Powell, and Williams (1977). The notional (Marshallian) demand functions for that system are given by the following expressions:

where qi = quantity demanded of ith good. Pi = price of ith good. ci, bi = good-specific parameters (committed consumption, marginal budget share). d = parameter giving marginal propensity to consume. y = total disposable monetary endowments of consumers. m = number of goods. Although the ELES has been found to lack some theoretically desirable properties, and its estimation may entail some biases (especially when there is little variation in the observed prices), none of its rivals seems to fare much better in actual applications, especially when the level of aggregation is rather high. Following Podkaminer (1982), we specify the equations (1) for Poland on the basis of the comparative studies presented in Lluch et al. (1977). We consider m = 7 aggregates of goods consumed: 1. 2. 3. 4. 5. 6. 7.

Food, alcohol, tobacco ("food"). Clothing and footwear ("clothing"). Rent, fuel, electricity ("housing"). Furniture, domestic utensils ("furniture"). Health, personal services ("health"). Transport and communication ("transport"). All remaining services ("rest").

There are three alternative sets of parameters d, b, c that we shall apply in parallel. These three sets have been estimated on the base of statistical data relating to three West European countries: Ireland, Italy, and the Federal Republic of Germany. These "outside" sources of information reflect better, in our opinion, the "true" demand of the Polish consumer than the parameter estimates that would have been obtained using Polish data, which are affected by permanent market disequilibria. It is worth noting that when the quantities demanded qi are fixed (as equal to the supplies), it is possible to determine the corresponding equilibrium prices. These are given by the following formulae:

Planning Combining Input-Output and Consumer Demand Analysis

175

TABLE 12.1 Estimates of equilibrium prices for actual supplies of 1977 (observed 1977 prices = 1.0) Variant of ELESb Parameters Good"

Food Clothing Housing Furniture Health Transport Rest

1

2

3

0.804 1.118 1.450 3.160 0.464 2.291 2.383

0.934 0.723 2.173 1.366 1.406 1.406 1.281

0.702 0.939 2.505 3.082 1.470 1.332 2.333

a

See text for items included in each category. Extended linear expenditure system.

b

Of course, the formulae (2) are well defined as long as the supplies qi are bigger than the "substance consumption" parameters ci.

PURE STRATEGIES FOR CONSUMER MARKET EQUILIBRATION By applying the formulae (2) to the consumer's monetary endowments of 1977 and the quantities of private consumption observed in 1977 (qio) we arrive at the prices that would have equilibrated consumer markets without any reallocation of resources within the production sphere. These equilibrium prices are reported in Table 12.1. According to the estimates set out in Table 12.1, the restoration of market equilibrium in the consumer market would have required massive increases in the prices of housing, furniture, transport, and rest, while at the same time leaving room for some decrease in food prices. The other extreme equilibrating strategy would have stipulated that prices should be fixed at their actual 1977 levels, and would have computed suitable modifications in supplies to match the notional demands. By applying the formulae (1) to the actual prices of 1977 and the consumers' monetary endowments of 1977 we learn that these equilibrium supplies are rather different from the recorded ones (see Table 12.2). Table 12.2 indicates that the fixed-price strategy for the restoration of equilibrium may not have been realistic, at least in the short run. While it would not have been impossible to reduce the supplies of food and clothing, as suggested by Table 12.2, the twofold to threefold increases in the supplies of housing, transport, and rest seem totally impossible. A LINEAR-PROGRAMMING MODEL COMBINING I-O BALANCES AND MARKET EQUILIBRIUM CONDITIONS Apart from the two extremal pure strategies for the restoration of consumer market equilibrium just outlined, there are many more intermediate ones. The combination of moderate price rises and reallocations of resources from the industries supplying less heavily demanded goods (food, clothing) to those producing the goods with least adequate supplies may provide better policy options (i.e., options that are both

176

Input-Output and the Analysis of Socialist Economies TABLE 12.2 Estimates of supplies equilibrating consumer markets with unchanged 1977 prices (1000 million 1977 zlotys) Variant of ELES b Parameters I

2

3

Actual Consumption, 1977

Food Clothing Housing Furniture Health Transport Rest

484.4 170.4 153.5 134.9 18.5 203.5 87.3

539.4 118.8 215.5 67.8 83.2 113.8 113.2

442.3 156.1 144.7 116.0 50.0 114.3 186.1

560.7 151.7 111.4 47.3 35.5 94.0 91.2

Total

1353.5

1269.7

1310.0

1091.8

Gooda

a

See text for items included in each category. Extended linear expenditure system.

b

realistic, assuming moderate growth rates for individual sectors, and socially more palatable, since they limit the extent of price changes). Another factor worthy of consideration is the possibility of reducing the aggregate fund of wages through a reallocation of some labor from the sectors producing less heavily demanded goods, while paying relatively high wages and using large amounts of intermediate inputs, to other sectors that produce outputs for which there is more demand and have lower direct and full wage input costs. A simultaneous mathematical determination of the best admissible decisions over the set of consumer prices and quantities (produced and consumed) is, at least for the time being, out of our reach. Leaving to one side the purely computational difficulties inherent in such a simultaneous—and highly nonlinear—optimization, we do not feel competent to express satisfactorily the socially desirable trade-offs between price changes and greater resource (i.e., labor) mobility. The approach followed instead proceeds by determining the quantities produced and consumed, satisfying both I-O properties and market equilibrium conditions, for alternative vectors of consumer prices. More specifically, for various "reasonable" consumer price vectors P = (P1, P2, ..., P7), we build and solve versions of the linear-programming (L-P) model with the quantities as decision variables. After analyzing the solutions of these models (dual prices, sensitivity, slack variables) we can either modify the initial assumptions about the price vectors or iteratively grope for other, more promising, price changes. Constraints of Basic L-P Models Input-Output Properties where X = gross output vector. I —A = Leontief matrix. C, G,F,E = vectors of personal consumption, public consumption, fixed capital formation, and exports.

Planning Combining Input-Output and Consumer Demand Analysis

177

The fourteen-coordinate private-consumption vector C is related to the seven consumption goods aggregates through the following linear relationship: where D is a (7,14) disaggregation (or composition) matrix. X, C, Q, and E are treated as vectors of decision variables. G and F are fixed at their 1977 levels. The only exception to this rule is the f12 component of the F vector. /12 (construction investment) is assumed to depend on the size of the demand for housing q3. The semifunctional dependence is expressed by the following continuous piecewise linear formula 1 :

Other Relationships Total employment where L = total employment. Xjj = gross output, jth sector. a] = direct unit labor requirement, jth sector. Total wages and salaries, including other labor incomes where W is the total of wages and wj is the direct unit wage rate, jth sector. Imports where M = total imports. pf = import price, jth sector. aj = unit import requirement, jth sector. Exports where e' is exports, jth sector, and pj is export price, jth sector. Foreign trade balance The magnitudes L, W, M, E, and Bal are decision variables. In various runs of the model they have, or have not been, constrained by upper (or lower) bounds. Similarly, to exclude unrealistic solutions that require radical structural changes and a degree of mobility of labor or transferability of exports that does not exist, we have often

178

Input-Output and the Analysis of Socialist Economies

restricted the gross outputs of particular sectors (allowing a maximum 10 percent growth rate) and exports (allowing a maximum 15 percent change in either direction). Consumer Market Equilibrium Conditions

where qi = private consumption, ith good. ci, d, bi = fixed ELES coefficients. Pi = fixed price, ith good. 5 = that part of population's disposable monetary endowments other than wages (cash balances, grants, pensions, etc.), fixed at observed 1977 level. W = fund of wages. It should be noted that with qi and W variables, equation (11) is a system of linear inequalities with respect to the decision variables. The systems of constraints equations (3) through (11), eventually complemented by additional lower and upper bounds on particular variables, define sets of more or less realistic plans for production, consumption, exports, imports, employment, and wages which could have been implemented in 1977 without substantial violation of the basic I-O properties and without producing market disequilibria with respect to the seven major aggregates of goods. Imposing various optimization criteria that seem worthy of social approval (maximum consumption, maximum foreign trade surplus, etc.) upon the systems of constraints, and resorting to the usual simplex algorithms, we have elicited many examples of overall economic policies seemingly superior (if only in that they guarantee consumer market equilibrium) to the actual policy selected in 1977.

TWO SETS OF RESULTS The utilization of the linear-programming model defined above requires repeated runs of the computer calculations. When prices are set too low, or too-low upper production bounds are assumed, the program is likely to return the answer "no feasible solution." Since we do not intend to overwhelm the reader with a mass of confusing details, we shall report only two sets of runs executed under different assumptions. Set I

The basic assumption for this set doubled the price of housing. However that was not enough. In addition we had to allow for increases in the production of particular sectors. A 10-percent growth limit proved sufficient to produce reasonable results. (However, the necessary growth rate for the production sectors defined as "wood and glass" and "construction" appeared even higher, ranging from 20 to 36 percent). In this set we assumed that the exports of all producing sectors could grow by up to 15 percent. Because we imposed no lower limit on exports, the optimum computed

179

Planning Combining Input-Output and Consumer Demand Analysis

volume of exports fell in some versions of the solutions by 80 percent, which hardly seems to be realistic. The repercussions for other variables (e.g., employment) also made their changes unrealistic in this set of results. Tables 12.3, 12.4, and 12.5 characterize the nature of the solutions to the models of Set I.

Set II The basic assumptions made here differ from those underlying Set I in that the exports of any production sector are not permitted to vary from the actual 1977 levels by more than 15 percent. In addition we "changed" the consumer prices, lowering the food price by 20 percent and raising other prices: housing by 100 percent; furniture by 90 percent; health, transport, and rest each by 30 percent. The rise in the overall price index corresponding to the assumed changes is equal to 10 percent. (This is quite modest when compared with the inflation rate we have learned to live with in the 1980s.) Total employment was assumed not to exceed the actual 1977 level by more than 1 percent. Tables 12.6, 12.7, 12.8, and 12.9 illustrate the economic nature of the solutions to the models constituting Set II.

CONCLUSIONS The research we have conducted so far is certainly more concerned with testing the model and with learning about its manifold possibilities than with actual data. Nevertheless it seems evident that, first, the restoration of equilibrium, although entailing some sacrifices (some price rises), is not out of the economy's reach. Yet at the same time the proposition, often overstressed, that equilibrium should be restored only through the reallocation of resources and the expansion of output (with consumer prices being kept constant) must be characterized as unrealistic. Second, it would be difficult to restore internal equilibrium in the consumer market and at the same time increase the volume of net exports, without a substantial decrease in the TABLE 12.3 Optimum consumption in 1977 (1000 million 1977 zlotys) (first set of assumptions, see text) Criteerion: Maximnum Perso al Consum, ption

Criteerion: Maxinmm Ba lance of Tratde

b Va iant of ELES Pammete,rs

Gooda Food Clothing Housing Furniture Health Transport Rest Total a

1

2

3

438.7 150.9 95.3 118.4 16.4 178.8 166.1

492.1 108.5 113.4 59.7 75.4 118.0 102.7

428.1 150.3 125.1 109.9 47.3 107.3 175.3

1164.6

1069.8

1143.3

See text for items included in each category. Extended linear expenditure system.

b

I

2

3

433.2

486.6

95.3 118.8 16.4 179.3 166.6

113.4 59.7 117.1 118.1 102.8

420.8 219.3 125.4 117.1 47.4 107.6 175.8

1235.6

1111.7

1221.3

219.5

108.5

Actual Consumption, 1977 560.7 151.7 111.4 47.33 35.5 94.0 91.2 1091.8

TABLE 12.4 Optimum gross output in 1977 (1000 million 1977 zlotys) (first set of assumptions, see text) Criterion: Maximum Personal Consumption

Criterion: Maximum Balance of Trade

Variant of ELES a Parameters Sector Coal and energy Fuel (without coal) Transportation equipment Other machinery Chemicals Wood and glass Light industries Food and agriculture Transport and communication Trade Housing Construction Other material goods and services Nonmaterial services Total a

Extended linear expenditure system.

Actual Output. 1977

1

2

3

1

2

3

110.3 108.4 221.3 450.6 213.6 160.0 338.2 905.3 255.3 283.2 40.2 412.7 504.3 208.3

116.3

107.2

203.6 451.9 245.6 121.4 243.1 990.9 243.2 254.9 45.3 575.7 563.3 193.1

126.3 114.8 206.3 535.7 247.0 168.7 311.1 909.8 279.4 49.5 683.5 624.5 215.0

221.4 450.2 174.7 160.0 395.9 908.8 254.6 286.9 40.3 412.4 502.5 208.4

114.3 91.3 203.6 446.9 226.1 121.2 242.3 993.0 242.6 256.6 45.3 575.7 581.2 194.5

123.2 95.7 205.6 495.3 210.4 173.6 406.8 914.2 256.9 283.8 49.7 685.7 614.1 215.0

201.4 117.6 267.2 589.5 245.7 132.0 369.8 1220.3 299.8 272.4

4211.7

4357.2

4729.9

4212.9

4334.6

4730.0

5144.2

108.9

258.1

89.6

45.6 523.3 668.4 191.2

TABLE 12.5 Optimal employment in 1977 (1000 persons) (first set of assumptions) Criterion: Maximum Balance of Trade

Criterion: Maximum Personal Consumption

Variant of ELES a Parameters Sector Coal and energy Fuel (without coal) Transportation equipment Other machinery Chemicals Wood and glass Light industries Food and agriculture Transport and communication Trade Housing Construction Other material goods and services Nonmaterial services Total a

Extended linear expenditure system.

1 262.0 46.1 439.1 1031.1 290.1 536.2 862.5 3451.0 1124.7 1230.2 93.9 1351.6 1022.3 2514.8 14255.6

2 276.4

46.3

404.0 1034.0 333.5

406.8 620.0 3777.3 1071.2 1107.4 105.7 1885.3 1141.8

3

1

300.6 48.8 409.3 1225.8

254.8 38.1 439.3 1030.0 237.3 536.2

335.5 565.2 793.3 3468.2 1137.0 1213.8

1009.6 3464.6 1121.4

1246.5

2332.3

115.6 2238.6 1265.8 2596.1

1350.5 1018.6 2516.0

14542.0

15713.6

14356.9

94.0

2 271.7

38.8 404.0 1022.5 307.0

406.2 618.0 3785.2 1068.7 1114.5 105.7 1885.3

3 292.7 40.7 407.9 1133.3 285.8 581.8 1037.3 3485.1

1131.7 1232.9

115.9

Actual Employment, 1977 478.6 50.0 530.0 1348.9 333.6 442.4 942.9

4652.1 1320.5 1183.3 106.4

1714.2

2349.0

2245.7 1244.7 2596.1

1353.9 2308.4

14554.8

15831.6

16765.2

1178.2

182

Input-Output and the Analysis of Socialist Economies

TABLE 12.6 Optimum consumption in 1977 (1000 million 1977 zlotys) (second set of assumptions, see text) Criterion: Maximum Balance of Trade

Criterion: Maximum Personal Consumption

Actual Consumption,

Variant of ELESb Parameters Good" Food Clothing Housing Furniture Health Transport Rest Total

1

- -_

- -

2

3

1

2

3

1977

566.0

645.7

509.0

566.0

645.2

508.2

560.7

168.2 95.3 73.8 14.6 157.5 148.0

118.1 95.3 32.8 66.6 102.5 90.9

157.4 96.6 68.1 40.0 88.3 145.8

168.2 95.3 73.8 14.8 157.5 148.0

118.4 95.3 32.8 101.4 137.4 90.9

170.9 96.4 72.1 88.6 182.6 145.5

151.7 111.4 47.3 35.5 94.0 91.2

1223.4

1152.4

1105.3

1223.5

1221.4

1264.4

1091.8

"Sec text for items included in each category. b Extended linear expenditure system.

real level of consumption. However if one goes too far in this direction the result may be another episode of social unrest. In the future we shall be extending the scope of the alternative scenarios so as to be able to map comprehensively the eeonomy's responses to various price and other conditions. Also, we plan to repeat the whole study using data for more recent years. The whole study seems to us to be of the utmost practical importance. For, as the statistics for Poland's economic development since 1977 prove, government actions on prices and on production and consumption patterns have taken the opposite course to that which we have found rational. There have been relative increases in the prices of food with the prices of other, less well-supplied, goods falling behind. As far as the reallocation of resources is concerned, we have witnessed a major shift of all resources including the labor force from manufacturing and housing to the primary sectors. To us, therefore, it is not surprising that since 1978 the Polish economy has been displaying a major recession in production and consumption, coupled with hyperinflationary tendencies, and acute and deteriorating consumer market disequilibrium. ACKNOWLEDGMENT We thank Bozena Lopuch for programming work and computer calculations.

NOTE 1. In a static I O analysis, housing construction - an activity directly linked to the fulfilment of current consumer needs —is classified as capital formation. Without the relationship (5) it may appear incorrectly that a significant growth in the consumption of housing is possible without the construction of any new homes. Unfortunately this is not so. The specific numbers f12, q3, a, S appearing in (5) have been established in a separate study.

TABLE 12.7

Optimum gross output in 1977 (1000 million 1977 zlotys) (second set of assumptions, see text) Criterion: Maximum Balance of Trade

Criterion: Maximum Personal Consumption

Variant of ELES a Parameters

1

2

3

1

2

3

Actual Output, 1977

Coal and energy Fuel (without coal) Transportation equipment Other machinery Chemicals Wood and glass Light industries Food and agriculture Transport and communication Trade Housing Construction Other material goods and services Nonmaterial services

186.8 118.5 284.4 568.2 225.7 145.5 382.8 1212.5 314.3 306.2 41.5 438.8 633.5 206.5

193.8 119.1

186.7 117.9

188.5 118.6

285.8 584.9 262.7 115.0 312.0 1336.3 292.0 283.5 50.1 440.3 659.9 192.0

212.5 119.3 282.6 606.5 254.4 146.6 387.1 1156.2 302.0 282.8 50.1 458.2 677.0 206.8

284.4

201.4 117.6 267.2 589.5 245.7

382.8 1212.5 314.3 306.3 41.5 438.8 633.6 206.5

277.3 549.7 270.3 114.5 312.0 1337.1 306.4 291.1 41.3 439.3 665.6

191.2 123.9 293.9 573.8 270.3

193.1

389.7 1131.2 327.0 302.0 42.1 449.8 675.1 208.6

369.8 1220.3 299.8 272.4 45.6 523.3 668.4

Total

5062.2

5127.4

5142.1

5065.7

5104.8

5125.7

5144.2

Sector

a

Extended linear expenditure system.

568.1

225.8

146.5

146.6

132.0

191.2

TABLE 12.8 Optimum export, import, and balance of trade in 1977 (1000 million 1977 zlotys) (second set of assumptions, see text) Criterion: Maximum Personal Consumption

Criterion: Maximum Balance of Trade

Variant of ELES a Parameters Sector

1

2

3

1

Actual Exportsd,

2

3

1977

53.4 13.2 52.7

62.8 15.6 63.1 119.8 38.4 13,2 41.0 49.7 48.7 2.7 —

Exports: Coal and energy Fuel (without coal) Transportation equipment Other machinery Chemicals Wood and glass Light industries Food and agriculture Transport and communication Trade Housing Construction Other material goods and services Nonmaterial services

53.4 13.9 53.7 101.8 32.7 11.2 34.8 42.2 41.4 2.3 — 15.1 32.6 —

57.3 17.9 72.6 137.7 44.2 11.2 34.8 42.2 41.4 2.3 8.9 15.1

53.4 13.2 53.7 101.8 32.7 11.2 34.8 42.2 41.4 2.3 — 15.1 32.6

53.4 13.2 53.7 101.8 32.7 11.2 34.8

32.6

72.2 17.9 72.6 137.7 44.2 13.5 47.1 55.6 56.0 3.1 8.2 20.4 44.1

2.3 — 15.1 32.6

32.7 11.2 34.8 42.2 41.4 2.3 — 15.1 32.6

Total exports

435.0

518.3

592.7

434.4

434.4

434.4

511.0

Total imports

597.2

603.0

608.5

597.0

607.5

621.7

602.7

-162.2

-84.7 7

-15.8

-162.6

-173.1

-187.3

-91.7

Balance of trade a

Extended linear expenditure system.

42.2 41.4

101.8

17.8 38.3

TABLE 12.9 Optimum employment in 1977 (1000 persons) (second set of assumptions, see text) Criterion: Maximum Balance of Trade

Criterion: Maximum Personal Consumption

Variant of ELES a Parameters

Actual Employment,

Sector Coal and energy Fuel (without coal) Transportation equipment Other machinery Chemicals

1 443.8 50.4 564.3 1299.9 306.5

2 460.6 50.6 567.0 1338.2 356.8

385.4

3 504.8 50.7 560.6 1387.7 345.4 491.1 987.0

1 443.7 50.1 564.3 1299.9

2 447.9 50.4 550.2 1257.6

367.0

306.6 491.0 976.2 4622.0 1384.3

383.7 795.8 5097.1

Wood and glass

491.0

Light industries Food and agriculture Transport and communication Trade Housing Construction Other material goods and services Nonmaterial services

976.2 4622.0 1384.4 1330.3 96.9 1437.0 1284.2 2493.1

1228.7 117.0 1500.5 1372.2 2496.7

1330.5

1264.6

117.0 1442.0 1337.6 2317.8

1437.0 1284.3 2493.1

16780.0

16780.0

16780.0

16780.0

Total "Extended linear expenditure system.

795.5 5094.0

1286.1 1231.3

4407.4 1330.2

96.9

3 454.2 52.6

583.1 1312.9 367.0 491.0

993.6

1977 478.6 50.0 530.0

1348.9

333.6

442.4 942.9

1438.6 1349.3 2331.7

4312.1 1440.3 1314.3 98.2 1472.9 1368.5 2519.2

4652.1 1320.5 1183.3 106.4 1714.2

16780.0

16780.0

16765.2

1349.8

96.3

1353.9

2308.4

186

Input-Output and the Analysis of Socialist Economies

REFERENCES Lluch, C., A. A. Powell, and R. Williams. 1977. Patterns in Household Demand and Savings. Oxford: Oxford University Press. Malinvaud, E. 1977. The Theory of Unemployment Reconsidered. London: Blackwell. Muellbauer, J., and R. Portes. 1978. "Macroeconomic models with quantity rationing." Economic Journal 88: 788-821. Podkaminer, L. 1982. "Estimates of the disequilibria in Poland's consumer markets, 19651978." Review of Economics and Statistics 64: 423-431. Podkaminer, L. 1986. "Investment cycles in centrally planned economies: An explanation involving labor shortage and consumer markets' disequilibria." Acta Oeconomica 35: 133-144.

13 Some Macroeconomic Features of the Hungarian Economy Since 1970 L. HALPERN and G. MOLNAR

In introducing the "New Economic Mechanism" in 1968, the reformers of the Hungarian economy hoped to solve the problem of efficiency in a socialist economy. We show that the survival of the structure of prices and accumulation that had existed before the reform, and the interrelationship between them—among other factors— hindered the necessary transformation in the economic structure. In addition, slow adjustment, or in practice the refusal to adjust, to the explosion in world prices in the mid 1970s has led to a critical level of foreign indebtedness for the Hungarian economy. Cheap foreign loans financed investment projects of very low efficiency, while export capacity remained far below the level necessary for debt servicing. A critical point was reached in 1978, when economic policymakers decided to restrict imports, consumption, and investment to be able to service foreign debts. In 1980, simulation of world market prices was introduced to stimulate adjustment. Since then economic activity has been almost stagnant. In this chapter we present the quantitative aspects of this process, using a closed input-output model to analyze the Hungarian economy since 1970. Simultaneous analysis of the primal and dual sides of the model provides relevant results. To apply the closed model to an open economy, we have added a foreign trade sector to the model using an exogenously given export-import ratio. An index is defined to measure changes in the structural efficiency of the economy. This index helps us to demonstrate the efficiency profile of the Hungarian economy and its connection with foreign trade activity. THE MODEL The well-known primal-dual model we used is as follows: and

188

Input-Output and the Analysis of Socialist Economies

where A B X, P /

= = = =

Matrix of input-output coefficients Matrix of capital output ratios Vectors of gross output and price rate of accumulation or profit; assuming constant coefficients it can be interpreted as a long-term rate of growth

The theoretical model is derived from Brody (1970). It contains twenty sectors, fifteen commonly used sectors, foreign trade, and the labor sector, and was computed for every year from 1971 to 1983, using different A and B matrices for each year. In the case of foreign trade, input is volume of exports and output is volume of imports. Thus a closed model was used for ex-post empirical investigations of an open economy. To resolve this contradiction we used exogenous export-import ratios. Since the process describing indebtedness is nonstationary, the changing export/ import ratio reflects the external position of the economy: a ratio greater than 1 shows net debt servicing, whereas a ratio less than 1 implies net loan disbursement. It is supposed that any import surplus will be repaid using the export surplus earned in following years. For any given year the import surplus reduces the costs of total imports in the economic structure. In the opposite case the export surplus is also broken down using the import structure. This procedure reflects a special feature of the Hungarian economy—the fact that the most import-intensive sectors benefited primarily from foreign credits in the 1970s. The labor sector's input is consumption, while its output is the wage bill. A large proportion of consumption is financed by means other than wages, such as allowances and benefits. In measuring the costs of reproduction of the labor force, consumption is a better measure than wages, and therefore, while we kept the sectoral wage structure constant, we multiplied the wage vector by the consumption/wage ratio. These two modifications are sufficient to ensure that the initial data for the model are closed in the way necessary for their use.

COMPUTED PRICES The most characteristic feature of the computed prices, which are shown in Table 13.1, is that they showed high stability all through the period under examination, whereas differences are considerable between the sectors. The same sectors that were overvalued or undervalued 1 in 1971 remained so in 1979. What is more, only three sectors changed their character during the whole period: Mining, undervalued between 1972 and 1974, overvalued in other years Food-processing industry, slightly overvalued in 1973, undervalued in other years Home trade, slightly overvalued in 1981, undervalued in other years All the other sectors were either overvalued or undervalued throughout the period. Furthermore the comparative levels hardly changed either. If, omitting mining, the sectoral price indices were ranked for 1971 and 1979, the ranking of the sectors would be identical, except for two changes of position. It follows, clearly, that while prices deviated permanently from costs during the 1970s, nevertheless they were in some way related to the latter, since prices and costs

TABLE 13.1 Computed prices by sectora (actual price = 1) Sector

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

Mining Electricity Metallurgy Machinery Building material industry Chemical industry Light industry Food-processing industry Industry (aggregate) Construction Agriculture Transport, communication Home trade Foreign trade management Water management Material services Material sectors Personal, etc., services Health, etc., services Communal, etc., services Nonmaterial services Services (aggregate) Consumption Accumulation Exports

0.98 0.81 0.79 0.78 0.88 0.71 0.84 1.03 0.85 0.90 1.18 1.13 0.82

1.02 0.79 0.86 0.82 0.85 0.76 0.89 1.03 0.88 0.90 1.17 1.14 0.79

1.09 0.82 0.87 0.83 0.87 0.81 0.92

1.14 0.84 0.77 0.77 0.86 0.82

0.94

0.86 0.88 0.85 0.80 0.87

0.83 0.90 0.88

0.82 0.84 0.81

0.63 0.73 0.77

0.77

0.77

0.88

0.69

0.71 0.87

0.89 0.69 0.85 1.09 0.84 0.91

0.84 0.86 0.80 0.79 0.89 0.73

0.58 0.68 0.82 0.89 0.87 0.66 0.97

0.56 0.68 0.86 0.88 0.87 0.70 1.00 1.10 0.86 1.00

0.70

0.60

1.93 1.00 0.94 2.04 1.32

1.64 0.96

0.56 0.67 0.85 0.89 0.89 0.66 0.99 1.09 0.86 1.00 1.10 1.08 1.01 0.99 1.48 1.08 0.95 1.64 1.33 1.25

a

1.19 1.48 1.20 1.12 0.93 0.89

0.95 1.82 1.30 1.22 1.43 1.15

1.10 0.95

0.91

0.98

0.89 0.91 1.14 1.11 0.79

0.63 1.74 0.96 0.95 1.77

1.31 1.25 1.42 1.15 1.08 0.95 0.93

0.90 1.04 0.87 0.89 1.18 1.14 0.79 0.53 2.14 0.97 0.95 1.84

1.35 1.22 1.45 1.17 1.10 0.94 0.91

0.92 0.82 0.78 0.87 0.73

0.85 1.08 0.86 0.91 1.19 1.14 0.86 0.51

2.28 1.01

0.95 1.90 1.37 1.20 1.47 1.20 1.11

0.95 0.90

Weighted by gross output at actual current prices: The producers' price level is 1.

0.87 1.06 0.86 0.92 1.17 1.12 0.88 0.59

2.11 1.03 0.95 1.85

1.39 1.25

1.49 1.22 1.12 0.95 0.89

1.10 0.87 0.90 1.15 1.12 0.88 0.52

2.23 1.02

0.95 1.82 1.40 1.26 1.49 1.21 1.12 0.93 0.89

1.17

1.13 0.89 0.50 2.27 1.04 0.94 1.92

1.43 1.24 1.52 1.24

1.14 0.93 0.88

0.89 1.11 0.86 0.91 1.17 1.11

0.88 0.62 1.92 1.02 0.95 1.74 1.38 1.21 1.44 1.20 1.12 0.93 0.89

0.90 0.88

0.63 0.96 1.09 0.85 0.98 1.13 1.15 0.97 0.84 1.40 1.07 0.95 1.72

1.31 1.25 1.42 1.24 1.13

0.98 0.92

1.08 0.85

1.00 1.12 1.11 1.00 0.85 1.50 1.08

0.95 1.64 1.33 1.25 1.40 1.23

1.13 0.98 0.93

1.41

1.23 1.13 0.99 0.93

1.12

1.08 0.98 0.93 1.32 1.05 0.95 1.54 1.33 1.21 1.36 1.20 1.12 0.98 0.93

190

Input-Output and the Analysis of Socialist Economies

moved "in parallel" in a certain sense. The computed price indices changed in a relatively stable fashion between the two extreme points of the period. With a few exceptions, these changes began by a reduction in the undervaluation or overvaluation, and then price indices went back to near to their initial values. During the 1980s there have been considerable changes in industry. Overvaluation has increased in mining, electricity, and (to a smaller extent) the chemical industry. The position for industry as a whole has not changed, since overvaluation in machinery and light industry has decreased. Another change, compared with the 1970s, is that the computed price of construction is equal to the actual price. These changes are due to the introduction of so-called competitive pricing in 1980. This means the simulation of world market prices, implying that enterprises with considerable hard currency exports can use the microprofitability ratio achieved on Western markets on home markets. In the case of primary goods, world market prices were introduced. This led to the reduction of micro and macro profitability for enterprises in the processing industry, and an increase in the relative income of the primary sectors. This is reflected in the changed valuation of the industrial sectors mentioned above. Chemical Industry as an Example of an Overvalued Sector The overvaluation of the chemical industry fell by 11 percent between 1971 and 1974, and then went back to near the original level within a year. At that time the chemical industry was again the most overvalued sector, if foreign trade management, of peripheral importance, is not considered. In 1976 the overvaluation grew further, and then fluctuated gently around the level that had been reached. The reduction in overvaluation between 1971 and 1973 can be explained largely by the fact that the import surplus of the national economy was transformed into an export surplus, and therefore the chemical industry (with the highest intermediate import coefficient) lost some of its relative advantages over the other sectors. The further rise in the computed price index in 1973-1974 was the consequence of the rising costs of imported materials because of changes in foreign prices. In one year, the coefficient of intermediate imported inputs into the chemical industry rose from 30.6 to 40.2 percent. Some factors acted against the reduction in overvaluation. The ratio of fixed capital to output fell considerably, and the actual chemical industry price grew faster than the industrial average from 1973 to 1974. All in all the macro profitability of the chemical industry fell to its lowest level in 1974, but even so it was still among the most profitable industries. The reduction in overvaluation was followed by a considerable price increase, of 17.7 percent, from 1974 to 1975, which restored at one stroke the initial level of overvaluation. The changes in the second half of the 1970s were much smaller, and were related to fluctuations in output prices and the import surplus. In 1980 the introduction of competitive pricing, with a 24-percent price increase, implied a rise of 11 percent in overvaluation. During the early 1980s there was considerable import substitution. Intermediate import costs were practically stagnant, while intermediate domestic input costs rose very fast. By 1983 the level of overvaluation reached 70 percent, a level characteristic of the second half of the 1970s.

Some Macroeconomic Features of the Hungarian Economy Since 1970

191

Agriculture as an Example of an Undervalued Sector The undervaluation of agriculture fluctuated around a level of 18 percent. By 1973 its undervaluation had fallen a little, due partly to a low rate of price increase and partly to reduced relative wage costs. In 1974 its intermediate import costs grew suddenly, although still not reaching half of the industrial average (and compensated by price subsidy). In the following year, however, agricultural prices did not grow—as was the case with the majority of industrial prices—but fell. And yet the extent of undervaluation barely grew, since the industry adjusted successfully, and the intermediate import coefficient was reduced. In 1975 agricultural profitability dropped to a very low level. The price increase of 1976 restored the earlier level of undervaluation, and some of the price subsidies were stopped. The slight price reductions during the rest of the period were neutralized by technological change, which was characteristic not only of these three years but of the whole of the 1970s. The wage coefficient fell from 45 to 37 percent in the course of nine years, the coefficient of intermediate input costs grew from 55.4 to 63 percent, while both wages and the prices of inputs used in agriculture grew faster than agricultural output prices. To sum up the analysis of the computed price indices, we can state that at the beginning of the period, the level of overvaluation in the overvalued sectors, as well as the level of undervaluation in the undervalued sectors, generally fell, implying that the price scissors closed. In the following years this process was reversed and the computed prices stabilized at the level that characterized the early period. We have shown (Halpern and Molnar, 1985) that if the price scissors narrow at the same time that import prices grow and the rate of accumulation starts to increase, the system of income redistribution gets into a difficult situation. In such a case, either this system has to be adjusted flexibly to conform with the new conditions or, more simply, the price scissors must be opened again, implying that the prices of the overvalued sectors should be raised. It is the latter policy that was put into practice. In this interpretation it was the policy of maintaining the trends and proportions of the income redistribution system that led to the increase in the prices of the overvalued sectors. Thus the pattern of sectoral income, accumulation, and prices—closely linked by income redistribution—reproduced itself, resisting changes and ensuring that every effort at a structural transformation directed at one or another component of the complex system was doomed to fail.

COMPUTED PRODUCTION PATTERN At our computed prices, the sectors earn income proportional to their capital, and this enables them to enlarge their capital steadily. For this expansion to be carried out, the pattern of accumulation has to be transformed as well. The computed production pattern obtained as a result of solving the primal problem (1) indicates the direction and dimensions of this transformation. In a simple formulation, sectors whose actual production is larger than their computed production, that is, sectors with an index below 1, are called overproducing sectors, and those whose production is smaller are called underproducing sectors. Table 13.2 presents actual and computed production indices. The sector with the

TABLE 13.2 Computed production by sectora (actual production = 1) Sector

1971

1972

1973

1974

1975

1976

Mining Electricity Metallurgy Machinery Building material industry Chemical industry Light industry Food-processing industry Industry (aggregate) Construction Agriculture Transport, communication Home trade Foreign trade management Water management Material services Material sectors Personal, etc., services Health, etc., services Communal, etc., services Nonmaterial services Services (aggregate) Consumption Exports

0.95 1.01 0.92

0.97 1.00 1.03 0.88 1.09 0.98 1.01 0.98 0.97 1.14 0.99 1.07 1.00 0.97 1.15

0.99 1.00 1.02 0.89 1.03 0.99 0.99 1.00 0.97 1.10 1.01 1.06 0.98 0.98 1.10 1.02 1.00 1.02 1.01 1.00 1.01 1.02 1.01 0.93

0.92 0.99 0.98 0.88 1.02 0.97 0.97 0.99 0.95 1.18 1.02 1.08 0.98 0.96 1.34 1.04 1.00 1.03 1.02 1.01 1.02 1.03 1.01 0.90

0.97 0.98 0.94 0.88 1.01 0.98 0.99 1.02 0.96 1.15 1.01 1.05 0.99 0.97 1.34 1.04 1.00 1.03 1.02 1.01 1.02 1.03 1.01 0.92

3.01 0.99 0.96 0.89 1.07 0.98 0.99 0.97 0.96 1.10 1.03 1.08 0.97 0.96 1.41 1.04 1.00 1.02 1.02 1.01 1.02 1.03 1.02 0.91

a

0.82 0.98

0.95 0.96 0.99 0.93

1.28

1.00 1.11 1.02 0.96 1.38 1.07 1.00 1.05 1.02 1.02 1.03 1.05 1.02 0.91

1.03 1.00 1.03 1.01 1.01

1.02 1.03

1.01

0.92

Computed gross output at the Macro Level is equal to actual gross output.

1977

1978

1979

1980

1981

1982

1983

1.00

1.01 1.00 0.93 0.89 1.03 0.94 0.94 0.96 0.94 1.19 1.02 1.10 1.00 0.93 1.56 1.07 1.00 1.04 1.02 1.02 1.03 1.05 1.02 0.87

1.02 1.01 0.93 0.88 1.02 1.00 1.03 0.99 0.97 1.05 1.03 1.06 1.02 0.98 1.25 1.05 1.00 1.02 1.01 1.00 1.01 1.03 1.01 0.93

0.96 0.97 0.97 0.88 1.04

0.97 0.99 0.97 0.90 1.05 1.00 0.99 0.98 0.97 1.13 1.01 1.04 1.00 0.96 1.29 1.04 1.00 1.02 1.01 1.01 1.01 1.03

0.98 1.01

0.97 1.00 0.98 0.90 1.04 1.01 0.99 1.00 0.97 1.07 1.01 1.04 1.00 0.97 1.22 1.03

0.99

0.95 0.89 1.05 0.98 0.99 0.95 0.95 1.09 1.04 1.08

0.99

0.96 1.48 1.05 1.00 1.03 1.02 1.01 1.02 1.04 1.02 0.89

0.99 0.98

1.00 0.96 1.14 1.00 1.04 1.05 0.98 1.14 1.05 1.00 1.02 1.01 1.01 1.01 1.03 1.01 0.93

1.01 0.94

0.99 0.90 1.04 1.00 0.99 0.98 0.97 1.14 0.99 1.04 1.00 0.97 1.27 1.04 1.00 1.02 1.01 1.01 1.01 1.03 1.01 0.94

1.00

1.04 1.00 1.00 1.02 1.03 1.01 0.97

Some Macroeconomic Features of the Hungarian Economy Since 1970

193

greatest level of overproduction is machinery; that with the greatest level of underproduction, apart from water management, is construction. Additional overproducing sectors, for the whole or a considerable part of this period, are metallurgy, chemical industry, light industry, food-processing industry, and foreign trade management; while permanently underproducing sectors are building material industry, agriculture, transport and communication, water management, and nonmaterial services. The indices of electricity and the home trade sector fluctuate, tending toward overproduction, whereas the mining industry, initially overproducing, becomes an underproducer after 1975 and an overproducer again after 1980. The majority of indices fluctuate rather sharply from one year to another. This fluctuation is mainly influenced by the changing ratio of accumulation and fixed capital formation drawing on the output of the given sector (where the sector concerned delivers to capital formation), since for several reasons this ratio is highly variable. In the case of sectors with a considerable output of such products (equipment, buildings) or sectors closely related to these, the relative extent of accumulation is determined by the investment cycle, which implies, for example, that if machinery output for investment purposes increases significantly the production index of machinery will fall correspondingly. In the sectors where output for accumulation consists mainly of changes in the stocks of the products of the sector concerned—which sometimes even results in a negative demand for accumulation purposes—obviously fluctuations in the index are even more closely linked to the cycle. What interests us primarily is, however, not the cyclical variation of the production indices but their level, which, as with the price indices though to a lesser extent, is rather stable for most sectors. The picture given by the production indices agrees in several of its features with what we know about development preferences. Nonmaterial services, transport and communication, and water management, that is, all services apart from trade, are permanently and to a considerable extent underproducers, so that their economic development (measured by the rate of accumulation relative to the capital stock) is not favored. More information about the preference structure can be found in Halpern and Molnar (1985). There are also important differences that can be observed when indirect interrelationships are taken into account. The most conspicuous feature is the great extent of underproduction in construction, which arises because in the neglected service sectors on average more than three quarters of the fixed capital consists of buildings (in nonmaterial services the figure is more than 90 percent), while in industry this proportion is less than a third. If, therefore, services were to grow in proportion, demand for the output of construction would suddenly grow, while demand for output of machinery would fall. This explains why the production index for machinery is particularly low. It is mainly the underproduction of consumption goods that ensures that agriculture is a considerable underproducer and brings the index for the foodprocessing industry, which appears to have developed faster than average, close to 1. The difference is in the opposite direction for metallurgy, which is an overproducer according to the figures given, although in the late 1970s it experienced a rate of accumulation less than the average. This is because, in spite of a low rate of accumulation, its actual production level is judged to be too high by the model. The computed production pattern consumes fewer imports and more labor than the actual production pattern.

194

Input-Output and the Analysis of Socialist Economies

With a few exceptions the overproducing sectors coincide with the overvalued sectors and the underproducing sectors with the undervalued sectors: this is confirmed by Table 13.3. This rule is the consequence of economic policy toward development and pricing rather than of the characteristics of the model we have applied. The development policy side of this rule during the 1970s was as follows: When accumulation decreases or is growing slowly, the share of industry is low and that of services (especially of nonmaterial services) high. On the other hand, when accumulation grows rapidly, the share of industry (needed for accumulation) is high or increases, while that of services, especially of nonmaterial services, falls. In simple terms this implies that, as a rule, services have a larger share when the volume of accumulation is smaller, while industry's share is higher when accumulation is greater. If, therefore, changes in sectoral growth rates or the sectoral pattern of accumulation are used as distinct arguments or counterarguments in the course of the analysis or formation of macroeconomic policy, this must disguise an intention to promote or hold back the development of one or the other sector. Pricing policy is a necessary tool for the implementation of such a development policy. During the period of socialist industrialization, pricing policy's main function was to ensure the redistribution of incomes. For the most part this role has been maintained. After allocating the resources necessary for the development of the favored industrial sectors, the remaining industrial revenues are reallocated to finance the lower level of development of the other sectors. This linkage between development

TABLE 13.3 Correspondence between valuation and production during years 1971-1983a Overproducer

Overvalued

Mining (71, 75, 80-83) Electricity (73-77, 80-81, 83) Metallurgy (71, 74-83) Machinery Building material industry (72) Chemical industry (71-81) Light industry (71, 73-78, 80-83) Home trade (72-77, 83) Foreign trade management

Mining (72-74) Food-processing industry (71-72, 74, 76-79, 81-83) Agriculture (72, 82) Undervalued <

Underproducer Mining (76-79) Electricity (71-72, 78-79) Metallurgy (72-73) Building material industry (71, 73-83) Chemical industry (82-83) Light industry (72, 79) Food-processing industry (73) Construction (71-82) Home trade (71, 78-81) Food-processing industry (75, 80) Construction (82) Agriculture (71, 73-81, 83) Transport and communication Home trade (82) Water management Personal and business services Health, social, cultural services Communal, Administrative services

"Whsre no year is given for a particular sector, that sector falls into the relevant category for all years of the period 197183. Where a sector is italicized, that categorisation of the sector was dominant over the period 1971-1983.

195

Some Macroeconomic Features of the Hungarian Economy Since 1970

preferences and price distortions is continuously worsening the microeconomic efficiency and the macroeconomic performance of the whole economy.

STRUCTURAL EFFICIENCY The link between the bias in the price (income) pattern and that in the production (accumulation) pattern has been demonstrated qualitatively. We will now develop a quantitative measure of this link. For this we use the multisectoral equilibrium growth rate A, and the growth rate of the Harrod-Domar model f, the rate of capital accumulation. Table 13.4 gives values for these growth rates, as well as the export/import ratio, and the level and rate of growth of the ratio A//. It is evident from the model that A and £ depend on the export/import ratio. The accumulation boom—1971, 1974-1975, 1978—was always financed by a large import surplus. The minimum value of both growth rates is in 1973, when exports considerably exceeded imports. Beside the more or less parallel changes in the rates of growth there are, however, differences in their underlying trends. These differences can be explained by the coincidence of overvalued sectors with overproducers and of undervalued sectors with underproducers. This is the reason A always exceeds £. If the price or production pattern is not biased, that is, there is no over(under)valuation or over(under)production, as is the case when prices or production are in equilibrium, then A and £ are equal. If the sectors were simultaneously undervalued and overproducing, or overvalued and underproducing, £ would exceed A. Since A depends on the level of aggregation, the rate of growth of A// will be interpreted as a move toward or away from the equilibrium situation, and in this context will be used as a measure of structural efficiency. It is clear from Table 13.4 that, in line with our previous statements, structural efficiency was improving between 1971 and 1973, and this was followed by a large deterioration in 1974. During the period 1974-1977 the deterioration continued, at a slower pace, with structural efficiency reaching a minimum in 1978. After 1978 the structural efficiency of the economy improved again. Our computations show that the Hungarian economy could absorb the import surplus only at a decreasing rate of efficiency, and that the finance of investment projects from these resources led to a deterioration in macroeconomic efficiency. This is closely connected with the rule mentioned in this chapter about the interrelationship between the rate of growth of accumulation and its sectoral structure. When gross capital formation increases at a relatively high rate, its structure differs considerably from the structure in the case of a lower rate of growth. In the case TABLE 13.4

/(multisectoral) /(aggregate) Exports/imports Kff Rate of growth of ///

Multisectoral and aggregate rates of growth (growth rates in % per year) 1971

1972

7.2 7.6 82.8 94.5

6.2 6.0 6.8 7.1 6.8 6.8 7.5 6.3 6.1 7.2 7.1 7.3 103.7 113.6 90.4 84.6 89.8 90.1 98.2 99.0 95.5 94.9 94.8 94.3 3.9 0.8 -3.55 -0.6 6 -0.1 1 -0.55

1973

1974

1975

1976

1977 1978 7.3 7.9 80.7 92.2 2.2

1979

1980

1981

6.3 6.6 92.0 95.9 4.0

5.4 5.6 94.4 96.9 1.0

5.1 5.0 4.4 5.3 5.1 4.5 97.3 102.2 104.8 96.7 97.6 96.9 -0.2 2 0.9 -0.7 7

1982

1983

196

Input-Output and the Analysis of Socialist Economies

where the rate of accumulation is low, the relative weight of services is growing and our index of structural efficiency also increases. International comparisons reveal that countries with the same level of GNP per capita spend more of their net income on services than does Hungary. As a result the same differences can be observed in the structure of capital. The great improvement between 1979 and 1980, and the continuing, but slower, growth of structural efficiency show the capability of the Hungarian economy for structural changes and for structural adjustment to changes in world market conditions. Despite improving structural efficiency in the early 1980s, the question remains whether the Hungarian economy is able to grow faster and efficiently at the same time. The need to be both more efficient and more dynamic is almost unavoidable, because of the lack of external resources that could finance a less efficient but greater rate of growth. Economic growth should be accelerated because of the cumulating social tensions, due to the virtual stagnation of consumption since 1979, and because of the deteriorating stock of capital.

NOTE 1. A sector is overvalued if its computed price index remains below 1, that is if the computed price is lower than the actual one, and undervalued if the computed price index is above 1, that is if the actual price is lower than the computed one.

REFERENCES Brody, A. 1970. Proportions, Prices, and Planning. Amsterdam: North-Holland. Halpern, L., and G. Molnar. 1985. "Income formulation, accumulation and price trends in Hungary in the 1970s." Acta Oeconomica 35.

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INPUT-OUTPUT AND DEVELOPING COUNTRIES

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14 Key Sectors, Comparative Advantage, and International Shifts in Employment: A Case Study for Indonesia, South Korea, Mexico, and Pakistan and Their Trade Relations with the European Community JACOB KOL

The expansion of trade between developing and industrial countries may lead to an international shift in employment. As developing countries concentrate on laborintensive products in their export package relative to their imports, and would apply labor-intensive techniques in production relative to the techniques used in industrial countries, the resulting net shift in employment would be positive for the developing countries. This chapter describes such a shift in employment for a balanced increase in trade in manufactures between the EC and four developing countries: Indonesia, Korea (S), Mexico, and Pakistan. The chapter also investigates the correlation between revealed performance in trade and factor contents of production for these four developing countries. Finally (but first in the chapter), the factor content of production is related to the selection of key sectors on the basis of linkage effects on employment, income, or output.

LINKAGES AND KEY SECTORS In 1958 Hirschman formulated the concepts of forward and backward linkages. Backward linkages relate to "input provision" and forward linkages to "output utilization." To measure backward linkages Hirschman (1958, p. 108) advocated going beyond first-round effects and including indirect effects computed using the Leontief inverse. In the notation recommended for this conference, and following BulmerThomas (1982), the backward linkages (BL) (which are induced by increased final demand for products of sector j) are calculated as

200

Input-Output and Developing Countries

where rij] is the typical element of the Leontief inverse (R) and n is the number of sectors. The column sum of rij over i in the numerator is the important element in relation (1). Averaging over n in the numerator and taking this average relative to the corresponding average over all sectors in the denominator merely scales the linkage indicator BL on 1.1 Following Jones (1976), and using the notation employed above, we calculate the forward linkages (FL) of sectori i as

where s* represents the total production increase in sector j as a consequence of an extra unit of primary inputs in sector i, assuming that the structure of sales of (any) sector i is constant (see Appendix A for details). The same normalization for numerator and denominator is followed in relation (2) as in (1). If we ignore for the present the detailed discussion in Hirschman (1958) and elsewhere on the merits and qualifications of forward and backward linkages, the conclusion reached by Hirschman is that "forward linkage could never occur in pure form," that is, without considering "pressures of demand." This casts some doubt at least on the desirability of the completely symmetric treatment of forward and backward linkages, as formalized in Jones (1976) and represented in relations (1) and (2).2 Nevertheless, relations (1) and (2) are retained here because no other formalization of the concept of forward linkage seems operational and because a symmetric treatment of both types of linkage makes the outcomes easy to compare. An interesting way of combining the two types of linkage is presented in Lee (1986), where the likelihood of forward linkage realization is also dealt with.

Actual and Potential Linkages As noted by Hirschman (1958, p. 109), "The lack of interdependence and linkage is of course one of the most typical characteristics of underdeveloped economies." One could add that this is precisely the reason for looking at key sectors in terms of high linkages. Actual linkages, however, tend to be low in developing economies; potential linkages, on the other hand, could be selected using data for a (more) advanced economy.3 It is not the purpose here to trace potential linkages but rather to describe actual linkages. On the other hand, the countries studied—Pakistan, Indonesia, Korea, and Mexico, and in addition the EC—represent a range of development levels by themselves. Production, Income, and Employment Linkages In accordance with Hirschman (1958), linkages have been defined so far in terms of output. Of course linkages can be formulated also with respect to income or employment.4 Such linkages are easily calculated from the production linkages as formulated in

Key Sectors, Comparative Advantage, and Shifts in Employment

201

relations (1) and (2), by multiplying the production effects rij and s* by the appropriate sector income—and labor—intensity coefficients in production. In this chapter employment linkages will be reported for the most part in accordance with the following sections on factor contents and the international division of labor, and the general "concern for the employment problem" (Krueger, 1983). Key Sectors of Origin and Destination The backward linkages of a sector have been defined so far as being induced by an increase of final demand for that sector's products. Although the linkages spread through the economy, the original demand impulse is said to be located within the sector of origin. Alternatively one can think of impulses affecting all final demand categories, but with the linkage effects of these impulses being calculated with respect to one sector only: the sector of destination. Formally, the output linkage effects for a sector of origin (j) are given by

and for a sector of destination (i) by

Both relations contain elements rij of the Leontief inverse; both therefore represent backward linkages. The effects on income or employment are easily calculated, as has been indicated in the previous paragraph. The distinction between origin and destination may be important with respect to employment creation, since industrial sectors tend to perform poorly as sectors of destination but rather well as sectors of origin, particularly in view of their linkages with high labor-intensive primary production sectors. For the latter the opposite result would hold (Buhner-Thomas, 1982). Empirical Results on Employment, Income, and Output Linkages The rankings of sectors in order of size of their linkage effects are likely to differ, depending on whether these effects relate to employment, income, or output. This is due, of course, to the relative differences in labor intensity and income creation per unit of output between sectors. To illustrate this point, Table 14.1 reports for Indonesia the ten (of seventy-three) sectors with the largest backward linkage for employment, together with the rankings of these sectors for income and output linkages. The differences in rankings are large: for instance, only four of these top ten employment-creating sectors belong to the top ten income-creating sectors. Similar results are found for the other countries considered, as well as for forward linkages (these results are not shown). Backward and Forward Linkages It is outside the scope of this chapter to consider backward and forward linkages for each sector in detail. Rather, some propositions in Hirschman are investigated (Hirschman, 1958, p. 109): "By definition all primary production should exclude any

Input-Output and Developing Countries

202

TABLE 14.1 Linkage effects on employment, income, and output: rankings of key sectors in Indonesia, 1975 Rankings of Backward Linkages

Sector

Employment

Income

Output

1 2 3 4 5 6 7 8 9 10

10 21 32 38 8 6 12 15 23 9

8 13 65 6 53 21 7 58 9 3

Wood and cork products Fruits and vegetables Agriculture Food products n.e.s.a Other services Other manufacturing Oil and fats Other mineral mining Spinning industries Clothing industries d

n.e.s = not elsewhere specified.

substantial degree of backward linkage" although it is added that modern techniques may require substantial manufactured inputs in agriculture. Second, "the superiority of manufacturing" over agriculture in terms of strong linkages is "crushing" (p. 110). Lastly, it is implied (pp. 106-107) that backward linkages will increase and forward linkages will shrink the more a sector is close to producing for final demand. Table 14.2 reports on average forward and backward linkages for three broad sectors: primary production, manufacturing, and services. As these linkages are for employment rather than for output, as in Hirschman (1958), primary production performs better on average than manufacturing, both in forward and backward linkages. As expected, for primary production forward linkages on average exceed backward linkages, whereas the opposite holds for manufacturing (except in the case of Korea where both are equal). Lastly, intercountry differences in linkage values are to some degree explained by the differences in the number of sectors. Sectors of Origin and Destination Table 14.3 reports on backward linkages for employment in terms of sectors of origin and of destination. As expected, primary production performs much better as a TABLE 14.2 Average forward and backward linkages: Indonesia, Korea, Mexico, and Pakistan, 1975 Sector

Backward

Forward

Primary Manufacturing Servicesa

Indone sia 1.122 1.43 0.92 0.99 0.97 0.93

Primary Manufacturing Servicesa

1.40 0.90 1.05

Mexico

"Including construction and utilities.

1.822 0.799 1.14

Backward

2.08 0.73 0.98

Forward

Korea (S)

Pakistan 1.63 0.95 0.69

2.04 0.75 0.96 1.82 2 0.88 0.84

Key Sectors, Comparative Advantage, and Shifts in Employment

203

TABLE 14.3 Sectors of origin and of destination: Indonesia, Korea, Mexico, and Pakistan, 1975; (% of total employment createda) Sector-

Origin

Destination

Origin

Indonesia Primary Manufacturing Servicesb Total

Korea (S)

16 66 18

40

100

41

23

31 44 25

100

100

100

38 30

32

22 66 12

100

100

37

Mexico Primary Manufacturing Services' Total a b

19 61

20 100

Destination

28 31

Pakistan

36 37

27 100

Total employment created by a unit increase in final demand. Including construction and utilities.

destination than as an origin of employment linkage effects. Clearly the opposite holds for manufacturing. 5

KEY SECTORS AND COMPARATIVE ADVANTAGE One of the weaknesses of the concepts of key sectors and linkages is that the effects on employment or income, for instance, are calculated per unit of output. The prospective volume of output is not taken into account, because estimates of domestic and foreign demand and sales are not incorporated (Hirschman, 1958). Bulmer-Thomas (1982) added that in the selection of key sectors, "there is no consideration of efficiency or comparative costs." In this section sectoral performance in foreign trade is investigated. Two aspects are considered: comparative costs as incorporated in factor intensities and comparative advantage as revealed in foreign trade statistics.

Measuring Factor Intensities or Intrinsic Comparative Advantage In measuring factor contents of production, the indication in Baldwin (1971) and in Hamilton and Svensson (1982) is followed: total (that is, direct and indirect) rather than direct factor intensities should be used in analyzing performance in international trade. Furthermore, the Leontief method of calculating factor contents of production (of exportables and import substitutes) is amended in accordance with Riedel (1975). Total factor intensities are calculated in two stages: 1. Total factor contents are calculated for domestic production, that is, for both the final product and the total requirement of intermediates, insofar as these are produced domestically. 2. For intermediates that are imported, the factor contents of the additional exports needed to keep the trade balance at the same level are calculated.

204

Input- Output and Developing Countries

Formally:

where L'j represents the total labor intensity of one unit of output of sector j, consisting of the total labor intensity of its domestic production content (Lj) and of its import content {Mj [L£/(l — M£)]}. (The details are given in Appendix B.) Capital intensities are found by replacing L by K in relation (5).

Measuring Foreign Trade Performance or Revealed Comparative Advantage

A range of indices has been applied to measure revealed comparative advantage. Possibly the best known of these is the index developed by Balassa (1965). The original version expresses a sector's exports as a share of the country's total (manufacturing) exports relative to the same quotient for a group of competitor countries.6 However, in this chapter the index developed by Michaely (1984) is preferred.7 The index calculates, for each sector i, the share of its exports (X i ) in total exports (X) and the corresponding ratio for imports. Formally:

The value of the Michaely index in (6) ranges from — 1 to + 1, providing a scale of trade performance much easier to interpret than the Balassa index, ranging from 0 to infinity. At the one extreme a value of +1 for the Michaely index indicates that all exports are concentrated in sector i and that there are no imports of this commodity. A value of — 1 represents the other extreme of complete import specialization. TABLE 14.4 Labor intensity and revealed comparative advantage: trade of the EC with Indonesia, Korea, Mexico, and Pakistan, 1975 and 1983 Indonesia

Korea (S)

Mexico

Pakistan

Manufactured sectors with large revealed comparative (dis)advantagea

31%

32%

19%

33%

Sectors with change in sign of revealed comp.advamt advantageb

20%

0%

17%

0%

Average labor intensity c of sectors having revealed comp. advantage relative to sectors having revealed comp. disadvantage

1.24

1.08

1.16

0.66

"Sectors with absolute value of Michaely index >0.02 in 1975 and 1983 or >0.10 in 1983 only. b Sectors in which absolute value of Michaely index changed in sign between 1975 and 1983. c Calculated as the weighted average of sectoral labor intensities, the weight being the sectoral (absolute) values of the Michaely index in 1983.

205

Key Sectors, Comparative Advantage, and Shifts in Employment

Empirical Results Confining the analysis to manufacturing industry, this chapter reports only on summary findings rather than on results at the detailed sectoral level. Table 14.4 reports on trade relations of Indonesia, Korea (S), Mexico, and Pakistan with the EC in 1975 and 1983. The first row in the table reports the percentage of all manufacturing sectors that showed a substantial value of the Michaely index in both years (see table footnote a). The second row states the percentage of these sectors for which the Michaely index changed sign between 1975 and 1983, indicating a change in the character of revealed comparative advantage. Combining these two rows, it follows that only 16 to 33 percent of the manufacturing sectors showed both a substantial and a stable character of revealed comparative advantage. The third row of Table 14.4 reports the average labor intensity of sectors having a substantial trade surplus with the EC, relative to those having a substantial trade deficit (see table footnote c). The results confirm that net exports are labor intensive relative to net imports, although not to a very substantial degree, and not for Pakistan. This last observation is mainly due to large net imports of labor-intensive shipbuilding. Table 14.5 reports the correlations between the rankings of manufacturing industries, taken according to the backward linkage effects (Jones index), the labor intensity of production (Riedel index), and revealed comparative advantage (Michaely index). The only sectors that have been included are those that showed a substantial and stable pattern of revealed comparative advantage, as explained above. Table 14.5 shows (in the first row) that the rank correlation between sectoral performance in trade with the EC and labor intensity is very weak for Indonesia, Korea, and Pakistan and not very strong for Mexico. The second row also shows a weak rank correlation between trade performance and backward linkage effects on employment. As the Riedel index for labor intensity differs from the Jones index for backward employment linkages only with respect to the labor content of imported intermediates, the very poor rank correlation between the two (third row of Table 14.5) is surprising.

TABLE 1 4.5 Correlation between trade performance, labor intensities, and backward linkage effects on employment: Indonesia , Korea, Mexico, and Pakistan, trade with the EC, 1975° Indonesia

Korea

Mexico

Pakistan

Trade performance,b labor intensity c

0.23

0.30

0.71

0.12

Trade performance, backward linkagesd

0.48

0.57

0.20

0.25

Labor intensity, backward linkages

0.14

0.25

0.37

0.13

"Spearman b Measured c Measured d Measured

correlation coefficients for rankings of manufacturing sectors. according to the Michaely index. according to the Riedel index. according to the Jones index.

206

Input-Output and Developing Countries

INTERNATIONAL SHIFTS IN EMPLOYMENT Many studies on the employment effects of North-South trade focus on the effects in industrial countries (ICs) alone (e.g., Schumacher, 1984). These studies usually show that manufactured imports from LDCs (less developed countries) can account only to a small extent for the decline in employment in manufacturing industries in the North during the 1970s, Krueger (1983) has undertaken a study on employment and trade with the focus on developing countries. A few studies (e.g., Lydall, 1975; Glismann and Spinanger, 1982) deal with both sides of the coin, namely, that trade or shifts in production between LDCs and ICs will probably have employment effects in both groups of countries. Summarizing very briefly, the main object of Lydall (1975) was to estimate the employment effects of changes in the pattern of trade between ICs and LDCs. For that purpose, twelve product groups were selected: these were known to be restricted in their access to markets in the ICs, either by tariff or by nontariff barriers. It was assumed that, if these barriers were reduced, exports of these products from LDCs to the ICs would increase substantially; it was assumed further that such an increase would increase production and employment in the former, while replacing it in the latter. Glismann and Spinanger (1982) had a somewhat different focus from that of Lydall. They studied the effects on income and employment in both countries of relocating textile and clothing industries from a developed country (West Germany) to a developing country (Malaysia). Both Lydall (1975) and Glismann and Spinanger (1982) substantiated expectations that production in LDCs requires two to ten times as much labor as production in ICs. Here the analysis deviates from that of the studies reported above, most importantly in the following respects: 1. No selection of products is made in advance; all manufactured products (except oil products) are included. 2. The effects on employment in ICs and LDCs are calculated for trade in both directions. TABLE 14.6 Employment effects of balanced increase in manufactured exports and imports between the EC and four developing countries (LDCs): Indonesia, Korea, Mexico, and Pakistan, 1983° Employment

Effects b Exports from LDCs to EC

In In EC

Indonesia + 9260 -577

Korea + 2639 -569

Mexico + 809 -397

Pakistan + 8949 -660

Exports from EC to LDCs In In EC

Indonesia -6579 + 471

"Increase in manufactured imports and exports of $10 million. b Number of persons employed.

Korea -2062 + 466

Mexico -792 + 421

Pakistan -5648 + 384

207

Key Sectors, Comparative Advantage, and Shifts in Employment t

3. Trade flows are not assumed but are taken as they occurred in reality in 1975 and 1983.

Data and Calculations The effects of trade on employment are calculated using input-output relations, and including only intermediates produced domestically. The input-output tables concerned—for Indonesia, Korea (S), Mexico, Pakistan, and the EC—are for 1975. They were taken from publications of the respective Statistical Offices. These publications also contained sectoral labor input coefficients. Trade data were provided by the U.N. Statistical Office in Geneva in Standard International Trade Classification (SITC) codes; converters linking the SITC with the various I-O classifications were constructed. As the trade data pertain to 1975 and 1983, the latter had to be converted into 1975 prices using unit values so that the I-O tables and technical data for 1975 could be applied properly.

Empirical Results Table 14.6 reports the results for trade between the EC and the four LDCs selected in 1983. Rows 1 and 2 reflect differences in technology between LDCs and the EC, since the LDCs' package of manufactures is produced much more labor intensively than the corresponding package of import substitutes would have been in the EC. Put differently, exports from the LDCs create much more in-country employment than they displace in the EC. It can also be observed that this difference is far larger for Indonesia than for Mexico, and thus declines as the level of development increases. Rows 1 and 3 indicate that, measured in terms of the LDCs' technology, their export package is more labor intensive than their import package in their manufactured trade with the EC. Rows 2 and 4 indicate the same result, measured in terms of EC technology.8 From the figures in Table 14.6 we can deduce that a balanced increase in their mutual trade will cause a shift in employment between the EC and LDCs. The corresponding figures are presented in Table 14.7. From a balanced unit trade increase of $10 million in 1983 between the EC and the four LDCs selected, important gains in employment are reported for Indonesia and Pakistan; employment in the EC shows a small loss, due of course to the assumed shift from the relatively laborintensive production of import substitutes to the production of less labor-intensive exports. Internationally, employment stands to gain (Table 14.7). TABLE 14.7 Shifts in employment between EC and four developing countries — Indonesia, Korea, Mexico, Pakistan -caused by balanced increase in mutual trade in manufactures, 1983a Employment In In EC a

Effect b

Indonesia + 2681 -106

§10 million increase in mutual trade in manufactures. Number of persons employed.

b

Korea +579 -103

Mexico +17 -24

Pakistan +3303 -276

208

Input-Output and Developing Countries

CONCLUSIONS The main findings from an analysis of 1975 interindustry relations in Indonesia, Korea (S), Mexico, and Pakistan can be summarized as follows: 1. The rankings of sectors according to the size of their linkage effects differ considerably, depending on whether these effects refer to output, income, or employment. 2. Linkage effects for employment are stronger for primary production than for manufacturing. 3. For manufacturing sectors, forward linkages are weaker than backward linkages. 4. Primary production sectors perform better as the destination of employment linkage effects than as the origin; the opposite holds for manufacturing sectors (confirming the findings in Bulmer-Thomas, 1982). In addition, an analysis of the trade relations in 1975 and 1983 between the four developing countries (LDCs) discussed in this chapter and the EC reveals: 5. Labor intensities in the manufacturing sectors with a clear revealed comparative advantage in trade with the EC are higher on average than those for the manufacturing sectors with a clear revealed comparative disadvantage. 6. The correlation between the rankings of manufacturing sectors according to labor intensity and to their performance in trade is very weak. The following conclusions can be drawn from the analysis of 1975 interindustry relations in the EC as well as in the LDCs concerned: 7. Production in the four LDCs selected is (far) more labor intensive than production of corresponding products in the EC (confirming findings in Lydall, 1975, and in Glismann and Spinanger 1982). 8. The package of manufactured exports of the four LDCs to the EC is more labor intensive than their package of manufactured imports from the EC, both when LDC technology is applied and when EC technology is applied. 9. A balanced increase of both imports and exports of $10 million, using the product structure of 1983 for trade between the four LDCs selected and the EC, would result in important employment gains in Indonesia and Pakistan, smaller gains in Korea and Mexico, and small employment losses in the EC.

APPENDIX A Measuring Forward Linkages The method for measuring forward linkages developed by Jones (1976) is as follows: In (1) the matrix of intermediate delivery flows (W) is premultiplied by the diagonal matrix of the inverse of production levels ( x ^ 1 ) to represent a stable structure of intermediate sales. Further, let with v' representing the row vector of primary inputs.

key sectprs. cp,[arative advantage amd sjofts om e,[ploymentl o y m e n t

209 209

Ithjkjiui9gtjjklklkjopuuyyyiiiuotyyyyyy follo s wt h a t

The typical element s* represents the total (direct and indirect) production expansion in sector j induced by an additional unit of primary inputs in sector i. Summing s* over j gives the forward linkage of sector i, as expressed in relation (2) in the main text.

APPENDIX B Measuring Factor Intensities The method of calculating factor intensities presented in Riedel (1975) is as follows: Let Lj be the total (direct and indirect) labor requirements for one unit of output of sector j, calculated on the basis of interindustry relations regarding domestically produced intermediates only. Let Mj be the total (direct and indirect) import requirements per unit of output of sector 7. Let Lp be the total (direct and indirect) labor requirements for the production of a unit of exports, where exports represent the vector of current exports of all commodities, scaled to sum to unity. Let Mp be the total (direct and indirect) import requirement per unit of exports produced. Then labor requirements per unit of output (L1,) are given by or, since 0 < Mp < 1

The reasoning behind relation (1) is that factor intensities reflect factor requirements in domestic production for output j plus the factor requirements of domestic production for the additional exports needed to pay for imported intermediates needed directly and indirectly for producing output j.

ACKNOWLEDGMENTS I wish to acknowledge helpful suggestions and comments from Ad ten Kate, Huib Poot, and Ivonia Rebelo and statistical assistance from Bart Kuijpers and Maarten de Zeeuw.

NOTES 1. A value BLj > 1 implies above average backward linkages of sector j; a value BLk < 1 indicates the opposite for sector k. 1. The same doubt, namely whether "original insights are being lost through excessive formalization," has been expressed by Bulmer-Thomas in a book review (Economic Journal, Vol. 95, No. 380, Dec. 1985).

210

Input-Out put and Developing Countries

3. An alternative way of estimating potential linkages would be to start from coefficient matrices of total intermediate deliveries (AT), that is, including imported intermediates (AM), rather than to restrict the analysis to intermediates produced domestically alone (AD). The latter would describe actual linkages only (see also Lee, 1986). 4. The choice is open here, as with the choice of a numeraire in cost benefit analysis, namely whether production, income, or employment (or foreign exchange) would be the target item for optimization. 5. Within manufacturing this applies in particular to agro-based industries (figures not shown). 6. Later on Balassa (1986) included imports as well, to arrive at figures for net exports. 7. This preference is substantiated in Kol and Mennes (1986). 8. Except for trade with Mexico, mostly due to a large share of low-labor-intensive chemicals (medicines) in Mexican exports.

REFERENCES Balassa, B. 1965. "Trade liberalisation and 'revealed' comparative advantage." Manchester School 33: 99-123. Balassa, B. 1986. "Comparative advantage in manufactured goods: A reappraisal." Review of Economics and Statistics 68: 315-319. Baldwin, Robert E. 1971. "Determinants of the commodity structure of U.S. trade." American Economic Review 61: 126-146. Bulmer-Thomas, V. 1982. Input-Output Analysis in Developing Countries. Chichester, England: Wiley. Glismann, H. H., and D. Spinanger. 1982. "Employment and income effects of re-locating textile industries." The World Economy 5: 105-109. Hamilton, Carl, and Lars E. O. Svensson. 1982. "Should direct or total factor intensities be used in tests of the factor proportions hypotheses in international trade theory?" Seminar paper no. 206, Institute for International Economic Studies, University of Stockholm. Hirschman, Albert O. 1958. The Strategy of Economic Development. New Haven, Conn.: Yale University Press. Jones, Leroy P. 1976. "The measurement of Hirschmanian linkage." Quarterly Journal of Economics 90: 323-333. Kol, Jacob, and Loet B. M. Mennes. 1986. "Intra-industry specialization: Some observations on concepts and measurement." Journal of International Economics 21: 173-181. Krueger, Anne O. 1983. Trade and Employment in Developing Countries (part 3). Chicago: University of Chicago Press. Lee, Kuhn C. 1986. "Input-output multipliers with backward, forward and total linkages." Mississippi Research & Development Center, Jackson, Miss. Lydall, H. F. 1975. Trade and Employment. Geneva: International Labor Office. Michaely, M. 1984. Trade, Income Levels and Dependence. Amsterdam: North-Holland. Riedel, James. 1975. "Factor proportions, linkages and the open developing economy." Review of Economics and Statistics, Nov.: 487-494. Schumacher, Dieter. 1984. "North-South trade and shifts in employment." International Labour Review 123: 333-348.

15 Import Substitution and Changes in Structural Interdependence: A Decomposition Analysis D. P. PAL

In an input-output framework, import substitution in a sector influences and is influenced by import substitution in other sectors because of the simultaneity that exists between the sectors. The higher the degree of such simultaneity, the higher the extent of indirect repercussion. Estimates of import substitution made in an interindustry framework may thus be called measures of the total (direct plus indirect) import substitution. In this case the functional interdependence of the different sectors is considered in estimation. The estimates obtained may thus be called functional estimates, which are contrasted with the statistical estimates obtained in the isolated framework. Import substitution is usually defined at two levels. At the absolute level it is the reduction of imports over time. At the relative level it is the reduction of the import coefficient over time. Controversy arises regarding the nature of imports (and hence of import coefficients) to be taken into account. Chenery, Shishido, and Watanabe (1962) treated imports as perfect substitutes for domestic goods and lumped them (i.e., imports and domestic outputs) together to define import substitution as the reduction of the actual import/total supply ratio (i.e., the actual import coefficient) over time. Morley and Smith (1970) extended the Chenery-Shishido-Watanabe measure. They defined the concept of "implicit imports," which are obtained through the transformation of the actual imports of different products, into "the domestic production necessary to substitute completely for imports, holding all final demands constant." Import substitution in the sense of Morley and Smith is the reduction of implicit imports to implicit supply (i.e., the implicit import coefficent). The novelty of the Morley-Smith measure is that it counts both direct and indirect production requirements of imports, and thus imputes import substitution to those sectors that have no imports at all. In the Chenery-Shishido-Watanabe measure (and hence in the Morley-Smith measure) domestic goods and imports are lumped together without distinction in final demand and intermediate demand and hence in the input-output balance equations. Guillaumont (1979) made such a distinction and measured the "import content" of the actual import coefficient (here, import/domestic output ratio, mjx^ of a "given

212

Input Output and Developing Countries

product i, purchased at the final demand stage." The reduction of this "import content coefficient" is, in Guillaumont's sense, import substitution for sector i (at the relative level). Attempts have also been made to treat imports as imperfect substitutes for domestic goods and to define import substitution as the evolution of the ratio of domestic supply ( = domestic output net of exports) to domestic demand ( = domestic final demand + intermediate demand) (Chenery and Syrquin, 1977; Kubo and Robinson, 1979; Syrquin, 1976). Although economists differ with respect to "consistent measures" of import substitution, in all the measures domestic output and imports are counted in some form or other. Import substitution is thus effected through changes in the structure of both domestic outputs and imports. Domestic outputs may change due to changes in the nature of capacity utilization or to changes in the production process (i.e., structural interdependence) of the economy, or both. The contribution of changes in structural interdependence to import substitution is of interest to us. We shall decompose import substitution into two effects: (1) supply effects brought about by structural changes in the production processes and (2) demand effects brought about by changes in demand for imports. In this chapter, using earlier work on structural interdependence (Pal, 1981), a decomposition model is developed and applied experimentally to Indian data. THE ANALYTICAL MODEL Derivation of Total Import Substitution Let us consider an n-sector static open I-O model where X, Y, E, and M are n-element column vectors of gross outputs, final demands, exports, and competitive imports, respectively. A = (aij) is the input coefficient matrix of order n x n. Equation (1) is reduced to the ith equation of which is

We differentiate (3) with respect to mj to get

The elasticity of output i with respect to import j is

Import Substitution and Changes in Structural Interdependence

213

The import elasticity of output i depends on (1) the production process used by sector i, (2) the production processes of all other sectors, because of the simultaneity that exists among the sectors, and (3) the import coefficient m j /x i . So variation in any one of these factors causes variation in the import elasticity. The time derivative of (5) yields

from which follows

This implies that the change in the import elasticity would have been equal to (8) had there been no change in the import coefficient, and equal to (9) had there been no change in production processes. Now for the case i = j, (7) becomes

But by the Chenery measure (Chenery et al., 1962), relative import substitution of output i, denoted by si, is in continuous terms

Also, since m i / z i = 1/(1 + Xj/ffij), log(m,-/Zj) = — log(l + X;/m;). The time derivative of this yields

which, on substitution of (10), reduces to

Import substitution of output i (si) is expressed in terms of the rate of change of the import elasticity as well as that of the technical coefficient. The first term on the righthand side of (13) measures what we call the effect of the change in the import elasticity (M-effect), while the second term measures the effect of changes in the technical coefficient (T-effect). The signs of these effects are not readily known, and therefore their directions cannot be predicted. Regarding time as a discrete variable, (13) ma be approximated over the period 0 to 1 as

214

Input -Output and Developing Countries

Since Ey ^ 0 and ry > 0 for each i,j, and t, the M-effect ^ 0 and the T-effect < 0. This is not surprising. As new and improved technology is adopted in the production process of domestic output, domestic output rises and thus replaces imports. The direction of the M-effect follows from the very nature of the import/domestic output relationship. We write (14) in the multiplicative form (after substituting the value of EH) as where u01 = r/,/rg and v01 = (x?/z?) 2 K/x' - mf/xf). Let us interpret u and v. It follows from (4) that r'u is the amount of the total increase (reduction) in the production of sector i required to support one unit reduction (increase) in its own import at time t, t = 0,1. That is to say, rln is the ith domestic production requirement per unit of the ith import replacement at time t. u01 is, thus, the ratio of domestic production requirements at two points of time, 0 and 1. u01 may be regarded as the supply effect (S-effect) on (or supply component of) import substitution. Alternatively, it follows from the power series expansion of the Leontief inverse R = (ry) = I + A + A2 + ..., that r'ii is the total (direct plus indirect) requirement of the ith output as input by the ith sector at time t. u01 thus indicates the effect of change in the input structure of the economy (i.e., structural change) during the period 0 to 1. Changes in input structure result in changes in domestic output. The S-effect is, therefore, a structural phenomenon: the structural component of import substitution. v01 indicates, on the other hand, the effects of changes in the import coefficients. It is regarded as the demand-effect (D-effect) on import substitution. u01 is always positive and u01 ^ 1, but v01 may be negative or nonnegative. The case where u01 > 1 is interpreted as a favorable supply effect of structural change on import substitution, and the reverse for u01 < 1. When u01 = 1 there occurs no structural change, particularly for the sector under consideration. Import substitution will be positive, nil, or negative depending on whether s01 =| 0. The sign of si will be determined by that of v01. The demand effect (u01) alone sets the direction of import substitution. The degree of sectoral interrelatedness (i.e., the S-effect) has no impact on the directional aspect of import substitution.

Derivation of Direct Import Substitution We have derived an expression for total import substitution in sector i. As already pointed out, import substitution in sector i influences and is influenced by import substitution in other sectors. Total import substitution is thus composed of direct and indirect import substitution. In this section we shall single out the direct component of total import substitution. Suppose a change in mi affects only xi. Then from equation (1)

and

Ea is the direct import elasticity of output i.

Import Substitution and Changes in Structural Interdependence

215

Lemma 1. Let A = (aij)be an indecomposable nonnegative convergent matrix with its Leontief inverse R = (I — A)~ J = (r^). Then

Proof.

It follows from the power series expansion that

where A2 = (a-}), A3 = (a™-), and so on. Also, since au < 1,

But a'-i = afi + 'Lk^iaikaki, a"'i = afi + aii'Eki:iaikaki + "E:kfia"kaki, and so on. Hence it follows that ru > fu.

Application of Lemma 1 yields the result that the total import elasticity is no smaller in absolute value than its direct counterpart, that is, Let us now turn to the concept of direct import substitution. Without going into AND T = 0,1,IN EXPRESSION (14) AND DERIVE THE DIRECT IMPORT SUBSTITUTION IN SECTOR I AS

DETAILS, WE REPLACE

stitution is expressed in terms of a direct supply effect («01) and a demand effect (v0i). Our previous remarks on the direction of total effects also hold good here. Several points of interest now crop up. Will s01 and s01 be identical in direction? Which one will be larger in absolute value? Will the ranking of the sectors be independent of the type of substitution? Equation (15), when divided by equation (18), becomes

which entails the following: 1. sfVs? 1 > 0: direct and total substitution will be identical in direction. 2. s?1/^1 | 1» provided (4/r?-) | (r}Jr§. The relative strength of direct and total substitution depends on the nature and degree of interrelationship among the sectors. 3. Since the ranking of the sectors depends on both direction and magnitude, it may not be independent of the type of import substitution. 4. Indirect import substitution is s?1 = s?1 — s?1 = v01(u01 — u01), which is not independent of the degree of sectoral interrelatedness. ESTIMATES The input-output tables of India for the years 1953-1954, 1960-1961, and 19641965 have been used. All the I-O tables are in 1960-1961 producers' prices. Import

TABLE 15.1 Total import substitution in India (at relative level)

No. a 1 2 3 4 5

6

7 8 9 10 11

12

13 14 a b

u01 0.7926(1 3)b 0.9509(11) 1.0222(3) 0.9772(8) 0.9701(9) 1.0685(2) 1.0114(5) 0.8943(12) 0.9781(7) 1.0170(4) 0.9697(10) 0.7762(14) 0.9841(6) 1.1659(1)

v01 0.0264 0.0429 -0.0092 0.0871 0.2567 0.5500 -0.0252 0.1039 -0.1063 0.0168 -0.0112 -0.0728 -0.0011 0.0109

See text for description of sectors. Figures in parentheses are sector ranks.

1960-61 to 1964-65

1953-54 to 1964-65

1953-54 to 1960-61

cector

s01 0.0209(9) 0.0406(10) -0.0094(5) 0.2806(13) 0.2490(12) 0.5878(14) -0.0255(2) 0.0929(11) -0.0159(3) 0.0171(8) -0.0109(4) -0.0565(1) -0.0011(6) 0.0127(7)

u01 0.8518(14) 0.9526(12) 1.0245(6) 0.9765(10) 1.0522(5) 1.1711(2) 1.0959(4) 0.9278(13) 1.0018(8) 1.0081(7) 0.9852(9) 1.1575(3)

0.9632(11) 1.2429(1)

u01 0.0203 -0.0005 -0.0101 0.1187 0.0143 0.0243 -0.0720 -0.0218 -0.0182 0.0019 -0.0024 -0.0710 -0.0018 0.0132

s01 0.0173(12) -0.0005(8) -0.0103(5) 0.1159(14) 0.0150(10) 0.0285(13) -0.0789(2) -0.0202(3) -0.0182(4) 0.0019(9) -0.0024(6)

-0.0822(1)

-0.0017(7) 0.0164(11)

uo1 1.0746(5) 1.0019(11) 1.0022(10) 0.9993(12) 1.0846(3) 1.0961(2) 1.0835(4) 1.0375(7) 1.0243(8) 0.9852(13) 1.0159(9) 1.4913(1) 0.9787(14) 1.0660(6)

vm -0.0090 -0.0398 -0.0009 -0.0872 -0.1459 -0.1456 -0.0507 -0.0989 -0.0019 -0.0145 0.0089 0.0021 -0.0008 0.0022

s01 -0.0097(8) -0.0399(6) -0.009(10) -0.0871(4) -0.1582(2) -0.1596(1) -0.0549(5) -0.1020(3) -0.0019(9) -0.0143(7) 0.0090(14) 0.0032(13) -0.0008(11) 0.0024(12)

TABLE 15.2 Direct import substitution in India (at relative level) 1953-54 to 1964-65

1953-54 to 1960-61

c* ector

No."

S01

1 2 3 4 5 6

0.7905(13)* 0.9526(11) 1.0209(3) 0.9474(8)

0.9739(9) 1.0746(2)

7 8

1.0080(5) 0.8916(12)

9 10 11 12 13 14

0.9781(7) 1.0164(4) 0.9689(10) 0.7771(14)

0.9842(6) 1.1656(1)

v01 0.0264(9) 0.0429(10) -0.0092(5) 0.2871(13) 0.2567(12) 0.5500(14) -0.0252(2) 0.1039(11)

-0.0163(3) 0.0168(8)

-0.0112(4) -0.0728(1) -0.0011(6) 0.0109(7)

"See text for description of sectors. 'Figures in parentheses indicate sector ranks.

s01 0.0209(9)

0.0409(10) -0.0094(5) 0.2806(13) 0.2500(12) 0.5910(14) -0.0254(2)

0.0926(11)

-0.0159(3) 0.0171(8) -0.0109(4) -0.0566(1) -0.0011(6)

0.0127(7)

S01

0.8486(14) 0.9543(12) 1.0213(5) 0.9774(10) 1.0570(5)

1.1761(2) 1.0973(4)

0.9246(13) 1.0018(7) 1.0016(8) 0.9821(9) 1.1592(3)

0.9634(11) 1.2429(1)

v01

1960-61 to 1964-65

s01

0.0203 -0.0005 -0.0101 0.1187 0.0143 0.0243

0.0172(12) -0.0005(8) -0.0103(5) 0.1160(14)

-0.0182 0.0019

-0.0182(4)

-0.0720 -0.0218 -0.0024 -0.0710 -0.0018 0.0132

0.0151(10) 0.0286(13) -0.0790(2) -0.0202(3)

0.0019(9) -0.0024(6) -0.0823(1) -0.0017(7) 0.0164(11)

u01 1.0735(5) 1.0019(10) 1.0003(11) 1.0000(12) 1.0853(4) 1.0944(2) 1.0886(3) 1.0370(7) 1.0243(8)

0.9854(13) 1.0136(9) 1.4916(1) 0.9616(14)

1.0663(6)

V0i

-0.0090 -0.0398 -0.0009 -0.0872 -0.1459 -0.1456 -0.0507

-0.0989 -0.0019 -0.0145

0.0089 0.0021

-0.0008 0.0022

s01

-0.0097(8) -0.0399(6) -0.0009(10) -0.0872(4) -0.1583(2) -0.1594(1) -0.0552(5)

-0.1026(3) -0.0019(9) -0.0143(7) 0.0090(14) 0.0031(13) -0.0008(11) 0.0023(12)

218

Input -Output and Developing Countries

substitution measures at the relative level (both direct, total, and indirect) experienced by fourteen different aggregated sectors are estimated for three periods: I II III

1953-54 to 1960-61 1960-61 to 1964-65 1953-54 to 1964-65

The aggregated sectors are 1. Agriculture 2. Plantation 3. Coal and coke 4. All other mining 5. Iron and steel 6. Nonferrous metals 7. Engineering 8. Chemicals 9. Cement 10. Glass, wood, and nonmetallic products 11. Food, drink, and tobacco 12. Cotton and other textiles 13. Jute textiles 14. Leather, rubber, and products Table 15.1 gives the estimates of total import substitution, broken down by components, and Table 15.2 the estimates of direct import substitution. The estimates of indirect substitution arc presented in Table 15.3. The following points should be noted. 1. Direct and total import substitution have been identical in direction for all periods. 2. The producing sectors have displayed identical rank ordering with respect to direct and total substitution. TABle 15.3 Sector No.a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 a

Indirect import substitution (s) in India (at relative level)

1953 54 to 1960-61

1953 54 to 1964-65

1960-61 to 1964-65

0.0001 -0.0003 0 0 -0.0010 -0.0032 -0.0001 0.0003 0 0 0 0.0001 0 0

0.0001 0 0 -0.0001 -0.0001 -0.0001 0.0001 0

0 0 0 0.0001 0.0001 -0.0002 0.0003 0.0006 0 0 0 0.0001 0 0.0001

See text for description of sectors.

0 0 0 0.0001 0 0

Import Substitution and Changes in Structural Interdependence

219

3. Import substitution has varied both by sector and by period. During 1953-54 to 1960-61, only six of fourteen sectors enjoyed positive import substitution (both direct and total): coal and coke (sector 3), engineering (sector 7), cement (sector 9), food (sector 11), cotton and other textiles (sector 12), and jute textiles (sector 13). The number increases to eight and eleven during the periods 1953-1954 to 19641965 and 1960-1961 to 1964-1965, respectively. This clearly indicates an increasing tendency over time in the economy toward import substitution. 4. The structural component of import substitution (u01 in the case of total substitution and w01 in the case of direct substitution) has also varied from sector to sector and from period to period. During the first period, 1953-1954 to 1960-1961, w01 and w01 both exceeded unity in the case of only five sectors: coal and coke (sector 3), nonferrous metals (sector 6), engineering (sector 7), glass, wood, and nonmetallic products (sector 10), and leather, rubber, and products (sector 14). Only these five sectors, therefore, have gained from a favorable technological effect on import substitution, leather, rubber, and products (sector 14) being ranked first. But during the period 1960-1961 to 1964-1965, u01 and u01 exceeded unity in the case of twelve sectors, and thus almost all the sectors attained a favorable technological effect on import substitution (direct and total), cotton and other textiles (sector 12) being ranked first. 5. Indirect import substitution for most of the sectors has been either nil or insignificant, as can be seen from Table 15.3. Nonferrous metals (sector 6) deserves special attention. Interestingly, and to an appreciable extent, it has displayed significant positive indirect substitution, although its direct and total substitution have both been negative. CONCLUDING REMARKS First, in this chapter import substitution is estimated only at the relative level. Absolute import substitution is not estimated. Second, imports are taken as perfect substitutes for domestic goods. This is quite consistent with the characteristics of the Indian economy, where all types of imports have domestic substitutes. However, the case of imperfect substitutes may be examined along the lines developed here. Third, competitive imports and domestic outputs are lumped together without distinction, in final demand and intermediate demand, and hence in the input-output balance equations. Intermediate transactions may be decomposed into (1) intermediate transactions of domestic outputs and (2) intermediate transactions of imports, and two separate interindustry transactions matrices may be built, one relating to the interindustry domestic transactions and the other to the interindustry import transactions. Import substitution may then be examined in terms of the "pure" domestic technology matrix (which is formed from the interindustry domestic transaction matrix). We have not experimented with this approach because such a matrix is not available over time for India. NOTE 1. In the case where A is strictly indecomposable so that rj; > 0, Etj = 0 only when nij = 0.

220

Input-Output and Developing Countries

REFERENCES Chenery, H. B., S. Shishido, and T. Watanabe. 1962. "The pattern of Japanese growth, 19141954." Econometrica 30: 98 139. Chenery, H., and M. Syrquin. 1977. "A comparative analysis of industrial growth." Paper presented at 5th World Congress of the International Economic Association, Tokyo. Guillaumont, P. 1979. "More on consistent measures of import substitution." Oxford Economic Papers 31: 324-329. Kubo, Y., and S. Robinson. 1979. "Sources of industrial growth and structural change." Paper presented at 7th International Conference on Input-Output Techniques, Innsbruck, Austria. Morlcy, S., and G. W. Smith. 1970. "On the measurement of import substitution." American Economic Review 60: 728 735. Pal, D. P. 1981. "Structural interdependence in India's economy-an intertemporal analysis." Ph.D. diss., Kalyani University, Kalyani, India. Syrquin, M. 1976. "Sources of industrial growth and change: An alternative measure." Paper presented at the European Meeting of the Econometric Society, Helsinki.

VI THE ANALYSIS OF SOCIAL AND ENVIRONMENTAL PROBLEMS

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16 A Long-Term Projection of the Industrial and Environmental Aspects of the Hokkaido Economy: 1985-2005 FUMIMASA HAMADA

This chapter describes a study that attempts to predict industrial activity and its environmental effects in the central area of Hokkaido for the period 1985 through 2005. An input-output model linked with a small-scale macroeconometric model of Hokkaido was constructed and environmental changes in terms of biological oxygen demand (BOD), nitrogen oxides (NOX), sulfur oxides (SOX), and carbon dioxide (COD) to be emitted by the industrial activities and also by the residents were estimated, to predict the degree of resulting environmental pollution. The study also estimated industrial demands for space, water, fuels, electric power, and workers by individual industries, and the demand of residents in this area for various public utilities. Although many empirical studies of input-output structures have been reported and published since the seminal work of Leontief (1941), an application of inputoutput techniques to industrial and environmental development in a specific area, for which data on economic and environmental circumstances are very limited, is rare. The study reported here is an attempt to develop an analytical method for this type of work, since it is required for the analysis of regional development. First, a macroeconometric model of Hokkaido consisting of seventeen structural equations was constructed. This model determines final demands, such as consumption, investment, and interregional trade, with the levels of local and central government consumption and investment and the real growth rate of the national economy as given. Second, a Hokkaido input-output model was used to determine final demands by fifteen industries. The classification of these industries (Table 16.1) reflects the characteristics of changes in the industrial structure of the Hokkaido area from the viewpoint of the technological production process, such as integrated and nonintegrated, processing and combining, basic and nonbasic transformation, and so forth. This model also determines the real output by these fifteen industries as a whole. Third, by estimating shares of the central area of Hokkaido by industry, real

224

The Analysis of Social and Environmental Problems TABLE 16.1

Industrial classification used in study of Hokkaido-area industries

Name

No.

Industries Included

1

Primary industry

Agriculture, forestry, and fishery

2

Integrated basic materials

Chemicals and petroleum products

3

Nonintegrated basic materials

Textiles, paper and pulps, and ferrous and nonferrous products

4

Integrated process combining

Metal products and general machines

5

Nonintegrated process combining

Transportation and precision machines

6

Integrated other manufactures

Food and wood products

7

Nonintegrated other manufactures

Clothing, printing, leather products, rubber products, etc.

8

Energy

Electric power, gas, and water

Transportation

Transportation

10

Commercial trade

Wholesale and retail

9 11

Services

Services

12

Public services

Public services

13

Financial institutions

Financial intermediaries, insurance, and unclassified

14

Construction

Construction and civil enginering

15

Mining

Mining

output in this area was obtained. Finally, using these estimates for real output by industries, economic and environmental activity levels in this area were predicted on the basis of the assumed growth path of the Japanese economy as a whole for the period 1985 through 2005. OUTLINE OF THE WHOLE SYSTEM As already noted, the aim of this study is to predict the level and structure of industrial activity in the central area of Hokkaido, and its relation to environmental changes. The long prediction horizon (1985-2005) implies that technology will change, as will the tastes and values of consumers, the supply conditions of natural resources, and other locational factors that influence economic activities in this area. The prediction is based on a set of assumptions about the macroeconomic system and technological progress in the field of pollution abatement as well as in the industrial production process. A Macroeconomic Model of Hokkaido A complete system for the determination of income, prices, and output should ideally be constructed as a set of dynamic and interdependent relations between supply of

Industrial and Environmental Aspects of the Hokkaido Economy

225

and demand for outputs. I will, however, assume that the supply elasticity is infinite, and that the scale of the Hokkaido economy is determined from the demand side. I also assume that the volume of final demand depends on that of the Japanese economy as a whole, as well as on the consumption and investment of the Hokkaido government. The price level is assumed to be determined by the national economy through arbitrage. The macroeconomic model of Hokkaido was estimated by ordinary least squares for the sample period 1965 through 1982. The estimated model is as follows: Consumption (CP)

Housing Investment (h)

Fixed Investment (i)

Inventory Investment (j)

Exports to Other Domestic Areas and Abroad (e)

Imports from Other Domestic Areas and Abroad (m)

Implicit Price Deflator for Consumption (pc): 1975 = 1

Implicit Price Deflator for Housing Investment (ph): 1975 = 1

Wholesale Price Index (p w ): 1975 = 1

226

The Analysis of Social and Environmental Problems

Personal Disposable Income (y) at 1975 Constant Prices where pc, ph, and pw are the rate of change in the consumption deflator, housing investment deflator, and wholesale prices, respectively. ra is the long-term rate of interest. Data on the Japanese macro and Hokkaido macro variables for the national and regional accounts produced by the Economic Planning Agency (annual) were used. Other price indices were available from the Bank of Japan (monthly). This macromodel determines real disposable income y, real consumption CP, real housing investment h, real fixed investment i, real inventory investment j, real exports e, real imports m, and consequently real gross output x, which is given by an identity where gc, gi, and gj are the government's consumption, capital formation, and inventory investment, respectively and are assumed to be exogenously determined. As easily seen, this system is basically demand led, so that the level and movement of exogenous demand are very important in determining the course of the economy in question. Among other factors, the scale of the Japanese economy as a whole and its growth rate regulate the scale and growth rate of the Hokkaido economy. This assumption seems too strong to govern the analysis of the actual economic performance of Hokkaido, but by keeping in mind the fact that the relative share of the Hokkaido economy in the Japanese economy as a whole is about 4 percent (on the basis of GNP), and that the main purpose of this study is not to make an intensive investigation of the industrial structure of Hokkaido but to predict what will take place in this area for the next twenty years, this simplification can be permitted as a first step. Since the Hokkaido economy has its own regional characteristics, the composition of final demand may be different from that in other regions, and this difference should be reflected in the industrial structure in Hokkaido. This is a minimum requirement for a macromodel in this study.

An Industrial Model of Hokkaido and Its Central Area Before determining the industrial structure of the central area of Hokkaido, it is necessary first to construct an industrial model of the Hokkaido economy. Final demands by item are decomposed into demands by fifteen industries, using a converter that is a column vector of dimension 15 x 1. Let Xc be a consumption vector and Vc its converter. Then the consumption vector can be written In the same way, the investment vectors and other final demand vectors are written as follows:

Industrial and Environmental Aspects of the Hokkaido Economy

227

where Xt is a final demand vector for the sum of housing investment, fixed investment, and inventory investment; Xe is a final demand vector for exports outside of Hokkaido; and X0 is the sum of other final demands. Ve and V0 are the corresponding converters. Next, real output levels by fifteen industries are determined, using an inverse matrix for the Hokkaido economy:

where X is a real output vector for the Hokkaido industries (15 x 1), and R is the inverse matrix (15 x 15), the classification of which is seen in Table 16.1. Since we do not have any reliable data on the input-output relationships specific to the central area of Hokkaido, the only thing we can do is estimate the relative share of real output by industry in this area to that of Hokkaido, and compute real output by industry in this area by multiplying those relative shares by the real outputs by industries for Hokkaido as a whole. Let [Ski] be the relative share matrix for the central area of Hokkaido, where Ski = RXSki/RXk and RXSki is the real output of the kth industry for the ith city; and Skj = RXj/RX where RXj is total real output for the jth town or village and RX is total real output for the Hokkaido economy. Real output by industry and by area can be determined as follows:

Summing up those real outputs by industries and by areas with respect to the areas located in the central area of Hokkaido, real output by industry k for central Hokkaido can be determined as

Calculation for the primary industry (agriculture, forestry, and fishery) and the last industry (mining) turned out to be impossible because of lack of reliable data. Once real output by industry for the area in question is obtained, the number of workers and the demands for space, water, and energy (including fuel and electric power) can be computed by multiplying the corresponding coefficients by real output by industries:

228

The Analysis of Social and Environmental Problems

where SPk is demand for space (square meters), WTk is demand for water (cubic meters per day), EPk is demand for electric power (1975 constant million yen), FEk is demand for fuel (1975 constant million yen), and LBk is demand for workers (persons) by industry k. a, /?, y, e, and r\ are coefficients corresponding to those demands, and n and v are the rates of increase in efficiency and productivity, respectively. Demand for space by the residents LRESD,, and demand for water by the residents WTLIF,, can be written as follows:

where NPOP, is the number of residents and a and /? are constants. The number of residents is assumed to grow at a constant annual rate, so that

where NPOP 0 is the number of residents at the beginning of the prediction period and 7 is an annual rate of increase in the number of residents in the area in question.

An Environmental System for the Central Area of Hokkaido The environmental profile is assumed to be formed through the degree of air pollution by the quantities of BOD, NOX, SOX, and COD that industrial activities and residents will emit in this area. Technological progress will reduce the quantity of these materials per unit of output and per capita, so that the speed of technical progress should be taken into account. The only information on technological progress in this field is available from the reports of some nonprofit institutions and from the Hokkaido Government Office. The procedure used to estimate the quantities of these materials to be emitted is multiplication of the coefficients by real output by industry and by number of residents in the area. We define t;sk as the coefficient for the quantity of the sth material to be emitted per unit of real output by industry k, and £s as the coefficient for the quantity of the sth material to be emitted by one resident. Then the quantity of materials to be emitted by industry k in period t, Skt, is determined as

where /t is an annual rate of technical progress which reduces the quantity of the polluting materials emitted per unit of output by industry k. The quantity of material s to be emitted by the residents, Spt, can be written as

where NPOP, is the number of residents in period t.

Industrial and Environmental Aspects of the Hokkaido Economy

229

A LONG-RANGE PROJECTION: 1985-2005 This section presents a set of computed results of a long-range projection using the combined macroeconomic, industrial, and environmental models discussed in the previous section. First, however, some basic preconditions of the projection developed here should be stated. Basic Preconditions As already mentioned in the introduction, the whole system used in this study is basically demand led. The leading or exogenous demands of the Hokkaido economy are, among others, the rate of growth of the Japanese economy as a whole, the consumption and investment of the Hokkaido government, and exports outside of Hokkaido. The main features are as follows: 1. The annual growth rate of the Japanese economy is about 3.7 percent during the period 1985 through 1991 and is about 4.5 percent after 1992. 2. The growth rate of Hokkaido government consumption is about 3 to 4 percent through the period of projection. 3. The rate of growth of Hokkaido government investment is about 3 to 4 percent during the same period. 4. The rate of inflation is about 1.5 to 3 percent during the same period. These preconditions seem to agree with the general consensus on the present and future state of the Japanese economy. Summary figures are omitted here, but data in more detail are available on request. An Economic Profile On the basis of the preconditions, and using the whole system together with the figures shown in Tables 16.2, 16.3, and 16.4, the time paths of the control solutions for all the endogenous variables were obtained for the period 1976 through 2005. To save space, only the main features of the computed results will be shown here. Figure 16.1 summarizes the time paths of real output by industry in the central area of Hokkaido in terms of growth rates. In the coming twenty years the growth rate will turn out to be higher in the process combining industries such as general machines, transportation machines, and precision machines; in the nonintegrated light industries such as clothing, printing, rubber, paper and pulps; and in ferrous and nonferrous products and construction. This implies that, contrary to a trend on the mainland, the development of manufacturing industries will dominate in the central area of Hokkaido, so that the demand for labor will increase at rather a high rate in this area compared with the mainland. Figure 16.2 shows this feature very clearly—that labor demands will increase in the same industries as those listed here despite increases in labor productivity in these industries. Figure 16.3 shows movements in the growth rates of total demand for fuel, space, and water during the same period. These three are interesting in that the growth rates seem to follow a similar pattern, despite the fact that the demand for these inputs by industries varies considerably: detailed figures showing this are omitted here because of space restrictions.

TABLE 16.2 Induced production coefficients and share coefficients'I Industry No.b

1 2 3 4 5 6 7

8 9 10 11 12 13 14 15

Rc

R1

Re

K»

RXSk/RXk

0.04627 0.03837 0.01696 0.01849 0.00785 0.06255 0.02061 0.03564 0.03655 0.08947 0.11131 0.00142 0.08914 0.00916 0.01932

0.00702 0.01312 0.11335 0.09988 0.03454 0.01746 0.03003 0.03090 0.03050 0.04786 0.01716 0.00000 0.03203 0.27739 0.03023

0.11603 0.05906 0.20855 0.01730 0.01181 0.07891 0.01107 0.03806 0.04068 0.02507 0.03577 0.00006 0.02682 0.00218 0.05017

0.00065 0.01943 0.02308 0.00788 0.00978 0.01435 0.01296 0.04530 0.02815 0.02255 0.17290 0.17857 0.02269 0.00308 0.01405

— 0.46373 0.77773 0.88273 0.69973 0.65073 0.85173 0.45960 0.45960 0.63751 0.45960 0.45960 0.45960 0.45960 _.

"Input-output table for the Hokkaido economy is available in the report of the Hokkaido government (1984); the 1980 table was used in computing the inverse matrix R and the final demands converters Vc, V1, Ve, and V0. See text for description of these converters. 'See Table 16.1 for names of industries.

TABLE 16.3 Environmental coefficients: annual basis" Industry No. b

2 3 4 5 6 7 8

9

10 11 12

13

14

Residents

BOD c (ton)

COD

NOX

SOX

(ton)

(ton)

(ton)

2.963 11.960 0.507 0.246 12.100 7.940

6.749 30.827 0.449 0.199 11.288 7.126 0.811

32.060 48.390 1.857 1.235 10.466 10.585 125.100 81.510 22.240

— — — — — — 18.250

— — ._. -— .._ —

19.600 17.350

23.630 8.690 0.97

25.0100 38.8400 3.4940 4.9420 9.7030 16.5530 302.5000 287.0000 78.5000 69.1700 61.2400 83.4100 30.8600 0.0600

"All coefficients are per unit of real output in 1975 constant million yen. Figures for the residents are per 1000 persons for one year. The annual rate of technical progress to reduce public pollution is assumed to be 0.076 in all cases. b See Table 16.1 for names of industries. c BOD = biological oxygen demand COD = carbon dioxide NOX = nitrogen oxides SOX = sulfur oxides

230

231

Industrial and Environmental Aspects of the Hokkaido Economy TABLE 16.4 Other economic factor coefficientsa Industry Noh

2 3 4 5 6 7 8 9

10 11 12 13 14

Residents

Labor (persons)

Space (m2)

Water (m3/day)

Electric Powerc

Fuel

0.01517 (0.05)11 0.05752 (0.04) 0.11047 (0.04) 0.12099 (0.06) 0.09097 (0.03) 0.18904 (0.02) 0.03573 (0.04) 0.55534 (0.04) 0.89928 (0.03) 0.46192 (0.03) 0.30276 (0.02) 0.23058 (0.07) 0.61248 (0.06) —

22.49

0.2408

0.01791

0.07562

30.70

2.4685

0.10872

0.11383

16.68

0.1528

0.02040

0.03010

13.26

0.0646

0.01470

0.00680

19.80

0.4450

0.01290

0.02960

6.57

0.0558

0.01240

0.00990

16.90

0.0698

—

—

0.8400

—

3.588

0.0866

—

3.162

0.0763

—

2.799

0.0676

—

3.813

0.0920

—

—

0.0457

—

0.15

10.6

—

"Coefficients in all columns are per unit of real output in 1975 constant million yen. b See Table 16.1 for names of industries. c Unit of coefficients is neutral. d Figures in parentheses are the annual rates of technical progress obtained from direct estimation of the production functions by industry as given by Hamada and Chido (1983). The coefficient of space for residents is km2 per 1000 persons, and the coefficient of water for residents is 10,000 ton per 1000 persons. Annual rate of technical progress to save space, water, electric power, and energy is assumed to be 0.03. Sources: Industries 2-7, Japan Spacing Center, 1984; industries 8 14, Japan Industrial Planning and Research Institute, 1977; Columns 1 and 2, Association for Industrial Pollution Problem, 1974; columns 3 and 4, Osaka Prefectural Government, 1982; coefficient for residents, Hokkaido Government, 1984; Japan Sewerage Association, 1980.

An Environmental Profile The environmental profile for the central area of Hokkaido obtained in this study is summarized in Figure 16.4, where the annual rates of growth in total quantity of BOD, COD, NOX, and SOX to be emitted by all industries and residents during the coming twenty years are shown. Although until the first half of the 1980s output of these four materials had been decreasing, the rate of decline has become smaller with

232

The Analysis of Social and Environmental Problems

FIGURE 16.1 Movements of growth rates of real output by industry. Numbers on curves refer to industrial classification; see Table 16.1.

time, because of the increasing growth rate of industrial activities. This tendency is marked for COD, NOX, and SOX. It should be noted however, that if the assumption about technical progress reducing environmental pollution is correct, the area under study will be no problem in the future. Thus the outcome depends on whether this assumption is realistic. Finally, let us sum up the prospect for the Hokkaido economy as a whole. According to the prediction made using a macroeconometric model of Hokkaido, the rate of economic growth in this area will be slightly higher than that on the mainland. The most remarkable feature is the growth in housing investment, at a rate of about 10 percent annually. This growth can be thought to have brought about industrial development in Hokkaido, together with the growth rate of more than 5 percent in private fixed investment and exports.

Industrial and Environmental Aspects of the Hokkaido Economy

233

FIGURE 16.2 Movements of growth rates of demands for labor by industry. Numbers on curves refer to industrial classification; see Table 16.1.

ACKNOWLEDGMENTS I am grateful to S. Nakamura and K. Miyamoto, University of Tokyo, and J. Takahashi, Keio University, for their useful comments. I also thank A. Kubo and Y. Shinozaki of The Institute of Industrial Studies for their assistance in gathering data on the environmental variables and coefficients. Y. Maeda assisted in the data processing and estimation of the inverse matrix for the Hokkaido economy.

REFERENCES Association for Industrial Pollution Problem (Sangyo Kogai Boshi Kyokai). 1974. "General feature of pollution load factor on emitted smoke." (Haien ni Kansuri Osen Fukaryo Gentani Chosa Kekka Gaiyo). Tokyo, Japan.

234

The Analysis of Social and Environmental Problems

FIGURE 16.3 Movements of growth rates of demands for space (SP), water (WT), and fuel (FE).

FIGURE 16.4 Movements of growth rates of polluting materials. COD, carbon dioxide; SOX, sulfur oxides; NOX, Nitrogen oxides; BOD, biological oxygen demand.

Bank of Japan. (Monthly). Economic Statistics Monthly (Kleiziai Toukei Geppo). Tokyo: Bank of Japan. Economic Planning Agency. (Annual). Annual Report on National Accounts. Tokyo, Japan. Hamada, F. 1984. A Macroeconometric Analysis of Japan (Nihon-Keizai no Macro Bunskei). Tokyo: Nihon-hyoron Sha. Hamada. F'., and R. Chido. 1983. "Direct estimation of production function by industry." Keio Economic Studies 20: 33 66.

Industrial and Environmental Aspects of the Hokkaido Economy

235

Hokkaido Government. 1984. Input Output Tables of Hokkaido: 1980. Hokkaido, Japan. Japan Industrial Planning and Research Institute (Sangyo Kenkyu Sho). Tokutei Chiiki 0 Taisho To Sum Chiikikankyo Keizai Sogo Model. Tokyo, Japan. Japan Sewerage Association (Nihon Gesuido Kyokai). 1980. Report on Global Planning of Sewerage Construction by Basin (Ryuiki Betsu Gesuido Seibi Sogo-Keikaku Chosa). Tokyo, Japan. Japan Spacing Center (Nihon Ricchi Sentaa). 1984. Report on Manufacture Locational Unit Survey (Kogyo Ricchi Gentani Chosa Hohkoku). Tokyo, Japan. Leontief, W. 1941. The Structure of the American Economy, 1919-39. New York: Oxford University Press. Osaka Prefectural Government (Osaka Fu). 1982. A Brief Outline of Global Planning of Osaka Living Environment (Osaka Fu Kankyo Sogo Keikaku Gaian Kisoshiryo). Osaka, Japan.

17 An Application of Input-Output Techniques to Labor Force Allocation in the Health and Medical and the Social Welfare Service Sectors YOSHIKO KIDO

The ageing of Japanese society had been and will continue to be very rapid compared with other developed countries. The proportion of the elderly (65 years old and over) to the total population in 1970, 1980, and 1985 was 7.1, 9.0, and 10.1 percent, respectively. The Ministry of Health and Welfare forecasts that this proportion will be 14 percent in 1996. This implies that the time needed for the proportion to change from 7 to 14 percent is only twenty-six years in Japan. In contrast, in West Germany and the United Kingdom forty-five years are needed; in Sweden eighty-five years; and in France (the slowest case) 115 years are needed for such a change in the age structure of the population. The same estimates show that the proportion of elderly people in the year 2000 will be 15.6 percent and that the number will peak in 2020 at 21.8 percent. This will be the highest proportion among the developed countries. The increase in number of elderly, especially those seventy five years old and over, causes an increase of demand for health, medical, and social welfare services. The increase in demand for these services usually leads to an increase in demand for labor in these sectors. At the same time Japan will face a decrease in the population of working age (fifteen to sixty four years old), as well as ageing of the working population, since the proportion of those aged forty and over in the work force will increase. Therefore the allocation of the labor force across industries, and the application of technological innovation in all industries, especially in the health and medical and the social welfare sectors, are now becoming very important problems. In general it is very difficult to make a quantitative analysis of manpower needs in the social policy field (the health and medical and social welfare service sectors). However in this chapter we treat the requirement for workers in these sectors in the same way as the demand for labor in ordinary industries, and we analyze the industrial characteristics of these sectors, consider their interrelations with other industries, and try as far as possible to set out a quantitative analysis. For this purpose we apply input -output techniques to the problem of labor force allocation.

237

Application of Input-Output Techniques to Labor Force Allocation

LABOR INPUT COEFFICIENTS IN THE SOCIAL POLICY SECTOR The application of input -output techniques to labor force allocation is based on the usual principles of I-O analysis for goods and services. It aims at measuring how much additional labor is needed in a specific industry, elsewhere in the economy, and in total in response to a unit increase in the demand for the good and service produced by that specific industry, or a unit increase in one of the final demand components (e.g., private consumption expenditure or government expenditure). This technique of analysis takes into consideration, therefore, the direct and indirect effects on the demand for labor generated in one of the industries. As a preparatory step for such an analysis of the close interrelations between industries, we consider the coefficient showing the amount of labor input needed for the production of a unit of output in each industry, and we discuss the characteristics of industrial activity in the health and medical and the social welfare sectors. The labor input coefficient considered here is the amount of labor input per unit of output, defined as the total number of workers in each industry divided by the corresponding total volume of output. Table 17.1 shows the labor input coefficient in both sectors, calculated at 1980 constant prices, for the years 1970, 1975, and 1980. These years are chosen because we will use the 1970-1975-1980 Link Input-Output Table, recently published by the TABLE 17.1 Labor input coefficients (persons/billion yen) and labor productivity (index 1970 = 100.0) Health and Medical

Social Insurance and Welfare

Subtotal Social Policy Sector

Aggregate Economy

230.1 243.0 208.8

214.0 173.4 137.3 40.4 27.3 14.4 179.4 148.5 123.8

152.5 130.5 107.8 53.1 39.7 29.2 94.3 85.2 74.3

100.0 105.7 90.0 — — — 100.0 105.6 90.7

100.0 81.0 64.2 100.0 67.6 35.6 100.0 82.8 69.0

100.0 85.6 70.7 100.0 74.8 55.0 100.0 90.3 78.8

Input

Total Self-employed

Employees

1970 1975 1980 1970 1975 1980 1970 1975 1980

208.9 160.7 126.6 40.4 27.3 14.4 166.8 132.1 111.5

234.7 248.1 211.2

— — —

Productivity Total

Self-employed

Employees

1970 1975 1980 1970 1975 1980 1970 1975 1980

100.0 76.9 60.6 100.0 67.6 35.6 100.0 79.2 66.8

238

Analysis of Social and Environmental Problemsems

Japanese government, later in this chapter. The labor input coefficient in this table is expressed in terms of labor requirements per billion yen of output for the health and medical and the social insurance and social welfare sectors, for a subtotal relating to the social policy sector as a whole, and for an aggregate of 158 industries that is added for the purpose of comparison. Separate coefficients are shown for all workers, for the employed, and for the self-employed; the latter defined as entrepreneurs and their family members working together. By definition, the labor input coefficient is the inverse of the productivity of labor measured in terms of output, so that the size of the labor input coefficient indicates the degree of labor productivity. In the case of manufacturing industry, when the labor input coefficient is small, labor productivity is high. In the case of the health and medical and the social insurance and social welfare sectors, however, the validity of this interpretation is restricted. This is because output for these sectors is measured only in terms of expenditures and, especially in the case of the social insurance and social welfare sectors, an important part of expenditure consists of general government consumption. It is therefore impossible to evaluate the volume of output, and labor productivity, in this sector in the same manner as in manufacturing industry. In spite of this restriction we can point out some findings from the table. First, the labor input coefficient in the social policy sector is larger than that for aggregate industry, except in the case of the self-employed, which implies that the social policy sector is more labor intensive than ordinary industry, with the social welfare and social insurance sector being an extreme case. This finding is in accordance with our daily experience and with common sense: In both of the social policy subsectors, close person-to-person services are still expected, as is the help provided by new technologies. Second, the difference in labor input coefficient between the social policy sector and aggregate industry has decreased during the last ten years. This is a result of the large reduction of the labor input coefficient in the former, due principally to a remarkable decline in the health and medical sector. The labor input coefficient in the social insurance and social welfare sector increased at first and then fell by a small proportion. As a result this coefficient remains much higher in 1980 than in the health and medical sector and than in aggregate industry. The reasons this input coefficient increased were institutional factors, such as the increase of the statutory staff/beneficiary ratio in various social welfare facilities and the growth of nursing homes for the elderly and home helpers in the early 1970s. The increase in statutory TABU; 17.2

1970 1975 1980

Labor force inducement effects (persons/billion yen)

Health and medical Social ins. and welfare Health and medical Social ins. and welfare Health and medical Social ins. and welfare

Inducement Direct Total

Ratio

326.2 285.1 243.5 308.7 201.8 276.4

0.6274 0.7641 0.6600 0.8037 0.6404 0.8232

208.9 234.7 160.7 248.1 126.6 211.2

Sensitivity 214.5 234.7 170.8 248.1 139.4 211.2

Labor Input Coefficient 0.7291 0.7978 0.7000 1.0168 0.7062 1.0699

Application of Input-Output Techniques to Labor Force Allocation

239

staff/beneficiary ratios is intended to provide better services to the users of social welfare facilities, so that the quality of output in this sector should have improved at the same time that, the labor input coefficient increased. This discussion indicates the need to be very careful in interpreting the magnitude of labor input coefficients in this sector. In addition, we would expect the labor input coefficient in this sector to be reduced by the usual factors, such as mechanization, the use of better facilities, and improvements to buildings and the environment. These factors are likely to be still more important in the future. In the health and medical sector as well, personal service is the basis of the activity, but increases in labor productivity through new technologies are important as well. TOTAL AND DIRECT INDUCEMENT EFFECTS FOR LABOR The labor input coefficient measures the additional number of workers directly employed for a unit increase in the demand for output (measured in billion yen in this case). However, a unit increase in output demand for a specific industry induces demand for labor in other sectors as well, because of the interdependence of industries. Similarly a unit increase in output demand for all industries induces additional demand for labor in a specific industry such as the health and medical sector. Table 17.2 summarizes these inducement effects caused by industrial interdependence, using the 1970-1975-1980 Link Input-Output Table with 158 industries. The total inducement indicates the total of each column (i.e., those corresponding to the health and medical and the social insurance and social welfare sectors) in the inverse matrix multiplied by the labor input coefficient for each of the 158 industries. Each component of the Leontief inverse of an input-output table is a technical coefficient indicating the amount of additional output in an industry induced by a unit increase in final demand, so the number of workers required to produce the additional output can easily be calculated by multiplying the labor input coefficient by the amount of output induced in each industry. If we compare the total inducement effect of the two subsectors in the social policy field, that of the health and medical sector was larger in 1970. But since the reduction in the labor input coefficient in the health and medical sector has been remarkable, especially between 1970 and 1975, the inducement effect in the health and medical sector became much smaller than that in the social insurance and social welfare sector in 1980. On the other hand, the total inducement effect in the social insurance and social welfare sector has fallen by only a little during the ten-year period, and indeed it increased during the first half of the 1970s. As far as this sector is concerned, a major part of the total inducement effect is a direct effect, and the demand for labor in this sector is derived almost entirely from the increase in output of the sector itself. The direct inducement effect is merely another name for the labor input coefficient; the direct inducement ratio denotes the ratio of the labor input coefficient to the total labor inducement. It indicates, therefore, the degree of influence of a unit increase in the demand for output in a specific sector on the increased demand for labor in other industries, and the subsequent repercussions on the demand for labor in the original sector. The larger the ratio, the smaller the influence on other industries and the repercussions for the original sector. In this respect the health and medical sector has more influence on the demand for labor in other industries, and more

240

The Analysis of Social and Environmental Problems

repercussions on the demand for labor in the health and medical sector itself, than the social insurance and social welfare sector. Furthermore this ratio has tended to increase during the ten-year period for both social policy subsectors, which implies that their influences on other industries, and their repercussions for their own sectors, have weakened during the period. The sensitivity indicates the total effect summing along each row (i.e., those corresponding to the health and medical and the social insurance and social welfare sectors in the inverse matrix) multiplied by the labor input coefficient for each of the two sectors. Thus the figures indicate the effects on the demand for labor in both sectors arising from the change in output demand for the industries themselves and other industries. At first sight, the social insurance and social welfare sector seems to be more sensitive than the health and medical sector to a uniform increase in output demand for all industries, but it is sensitive only to the level of output demand for the sector itself. Finally, on the basis of this discussion we can say that the influence of the two sectors on labor demand in other industries can to some extent be analyzed using I-O techniques. However, since the effects from other industries on the demand for labor in the two sectors are very small, this application of I-O techniques is subject to limitations. This results from a peculiarity of these sectors, which is that their output consists almost entirely of final demand and not of intermediate input.

DEPENDENCE OF LABOR DEMAND ON COMPONENTS OF FINAL DEMAND

We have looked at the industrial characteristics of each of the two subsectors in the social policy field, and at their interrelations with or interdependence on other industries. Next we consider which component of final demand causes the largest increase in the demand for labor in these two sectors. This is shown by the so-called dependency ratio of labor force demand on the components of final demand. This ratio measures the relative magnitudes of the labor force increase in each industry induced by a unit increase (here of one billion yen) in each of the components of final demand (e.g., private consumption expenditure, general government consumption, etc). Table 17.3 shows the dependency ratio of the demand for labor for the two subsectors in the social policy field. Increases in stocks, gross domestic private fixed capital formation, exports, and imports have few effects on the demand for labor in the social policy field, nor does gross domestic government fixed capital formation. Economic activities in the social policy field are really types of consumption. Summarizing, a unit increase in private consumption expenditure causes almost all of the increase in the demand for labor in the health and medical sector, whereas a unit increase in general government consumption causes most of the increase in the demand for labor in the social insurance and social welfare sector in 1980. The consumption of private nonprofit organizations also plays an important role in increasing the demand for labor in the latter sector in the same year. A unit increase in private consumption expenditure causes only 20 percent of the total increase in the demand for labor in this sector in 1980, whereas in 1975 a unit increase in private consumption expenditure caused 61.1 percent of the total increase, and in 1970 the

Application of Input-Output Techniques to Labor Force Allocation

241

TABLE 17.3 Dependency ratio of labor-force demand on final demand (%) Consumption

Private Total industry Health and medical Social insurance and welfare

1970 1975 1980 1970 1975 1980 1970 1975 1980

58.7 60.9 60.2 93.0 95.7 96.2 67.4 61.1 20.0

Nonprofit Bodies

0.9 0.6 0.7 1.3 -0.3 -0.1 15.6 18.7 36.8

General Government

9.3 9.7 9.5 5.6 4.6 0.1 17.0 20.2 43.2

Government Capital Formation

Total Final Demand

10.1

100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

9.4 9.3 0.0 0.0 0.0 — — —

figure was even larger. Thus the dependence of labor force demand in this sector on general government consumption, and on the consumption of private nonprofit organizations, has tended to rise over the same period, and has undergone a remarkable increase since 1975, implying a complete change in the consumption pattern during the period observed. The importance of consumption by private nonprofit organizations in the social insurance and social welfare sector in recent years is based on the fact that many of the social welfare facilities in Japan are owned and managed by private nonprofit organizations. The importance of private consumption expenditure in the health and medical sector has not changed much throughout the period, although there has been a small increase. Private consumption expenditure is important in this sector because of the existence of compulsory private medical insurance schemes, which provide a system of private doctors covering the whole nation, and because many hospitals also belong to private doctors. Thus most health and medical expenditures take the form of private consumption, financed by general government current transfers to households.

FACTORS AFFECTING THE NUMBER OF WORKERS IN THE SOCIAL POLICY FIELD As we have already stated, Japan has been ageing very rapidly during the past ten or fifteen years. The increase in the number of elderly persons has increased the demand for health and medical and for social welfare services during the period. According to the 1970-1975-1980 Link Input-Output Table, final demand for health and medical services, measured at 1980 constant prices, increased by 68.7 percent from 1970 to 1975 and by 50.4 percent from 1975 to 1980 (figures that imply a 154.1% increase from 1970 to 1980). At the same time the proportion of the elderly increased from 7.1 percent in 1970 through 7.9 percent in 1975 to 9.0 percent in 1980. Similarly, final demand for social insurance and social welfare services increased by 17.5 percent from 1970 to 1975 and by 25.8 percent from 1975 to 1980 (implying an increase of 47.8 percent from 1970 to 1980). There was a much smaller increase in the number of workers in these sectors over the same period: 29.4 percent from 1970 to 1975 and 18.5 percent from 1975 to 1980

242

The Analysis of Social and Environmental Problems

(53.4 percent for 1970 to 1980) in the health and medical sector, and 24.2 percent from 1970 to 1975 and 7.0 percent from 1975 to 1980 (33.0% from 1970 to 1980) in the social insurance and social welfare sector. The main reason why the increase in the number of workers in these sectors is smaller than the increase in final demand for these services is the impact of labor-saving measures in these sectors. Table 17.4 presents an analysis of the factors causing changes in the number of workers in both sectors. These changes are decomposed into the demand effect, the labor-saving effect, and the technological change effect (change in the technological relationships between raw materials and output). The table also gives the total effect and the number of workers, expressed as an index number. The demand effect is defined to be the change in the number of workers that can be attributed to the change in final demand, that is l 0 B 0 AF, where l0 denotes the labor input coefficient in the base year, B0 denotes the Leontief inverse matrix in the base year, and AF denotes the change in final demand between the two time points. The labor-saving effect is defined as the estimated change in the number of workers attributable to change in the labor input coefficient, written as AIB0F1, where A/ denotes the change in labor input coefficient and F1 the level of final demand in the year of comparison. Similarly, the technical change effect is defined as the estimated change in the number of workers caused by change in the technological structure, that is l}ABFlt where /j denotes the labor input coefficient in the comparison year and AB denotes the change in the technological relationship between raw materials and output. The total effect is the sum of these three effects. The numbers in the first four columns of Table 17.4 are expressed as percentages of the total number of workers in the base year. As can be seen from the table, the technological effect has been very small during the period. In contrast, the demand effect has been remarkable, as we would expect from our earlier discussion, particularly in the health and medical sector. The demand effect in the social insurance and social welfare sector has not been as large, especially during the first five years. The larger demand effect in this sector in the latter half of TABLE 17.4

Factors affecting number of workers in social policy-related sectors Demand Effect t

Labor-Saving Effect ct

Technology Effect ct

Total Effect

ct

1970- 1975

1970

Health and medical Social ins. and welfare Subtotal % elderly

68.7 17.5 57.4

-38.8 6.7 -28.8

-0.5 0.0 -0.4

29.4 24.2 28.2

Total industries

27.2

-18.4

-6.3

2.6

8

8

4

No. of Workers

5

4

3

1975 -1980

1975

100.0 129 .4 100.0 124.2 100.0 128.2 7.1 7.9 100.0

126.2

1975

1980

Health and medical Social ins. and welfare Subtotal % elderly

50.4 25.8 45.2

-31.8 -18.8 -29.0

-0.1 0.0 -0.1

18.5 7.0 16.1

100.0 118.5 100.0 107.0 100.0 116.1 7.9 9.0

Total industries

24.8

-21.7

1.4

4.5

100.0 104.5

Application of input-Output Techniques to Labor Force Allocation

243

the period seems to reflect the more rapid ageing of the population from 1975 to 1980 relative to the earlier years. This development has important implications for the problem of labor force allocation across industry, since it is clear that a much larger labor force will be needed in this sector in the future. The demand effect in the social policy field as a whole during the late 1970s has been much larger than the effect for aggregate industry. In this sense the labor-saving effect in these sectors is much more important than in other industries. The labor-saving effect in the health and medical sector has been very large throughout the period, and much larger than the corresponding effect for aggregate industry. If we consider the social insurance and social welfare sector, the labor-saving effect has been small, and was positive (indicating increased labor requirements from this factor) during the first five years of the period. Since the laborsaving effect is related to changes in the labor input coefficient, and we have already discussed the meaning of these changes in the social policy field, we shall not repeat the discussion here. But it is necessary to stress the importance of applying new technologies to these fields and of improving the environment of social welfare services in the future. Because of the magnitude of the labor-saving effect, the total effect and the actual increase in the number of workers in these two sectors have not been as substantial as we feared, in spite of the huge contribution of the demand effect during the period. But this large increase in demand also has meant that the increase in number of workers has been proportionately greater in these sectors than in aggregate industry, in spite of the significant labor-saving effect.

CONCLUSION In this chapter we have discussed the problem of labor force allocation in the social policy field using input-output techniques. Although there are some limitations that affect the application of I-O techniques to this problem, we can draw two policy implications from this discussion. First, since we are sure of a large increase in the demand for labor in the social policy sector from now on, we should try to maintain a large labor-saving effect in these sectors, as has been done in the health and medical sector with the help of new technologies. We should make much more effort to increase labor productivity, particularly in the case of social welfare services, as well as trying to offer an improved and more humane service. Second, as far as health and medical services are concerned, there has been an excessive tendency to depend on the private sector. From now on, particularly in the fields of prevention and primary care, we need more direct government intervention and government supply of a basic minimum level of services.

Index

Agriculture in Japan, 9, 149-57 Allocation coefficients, 26- 28. Aluminum industry, in Taiwan, 29-32 Australia, 101-3 Autarchy, 166 Balance of trade, 10, 177, 203 Banks, 124-25, 128, 130 Battelle Institute, 142 Biological oxygen demand, 223, 228, 231-32 Business cycles, 127, 137 Juglar, 137 Kitchin, 137 Kuznets, 137 Kondratieff. See Long wave Business forecasting, 3 By-products, 44, 46 48, 50. See also Joint production Capital coefficients, 22-23, 122, 204. See also Investment output ratio Carbon dioxide, 223, 228, 231-32 Cobb-Douglas production function. See Production function Commodity technology, 44 Comparative advantage, 11, 203-4, 208 indices of, 204-5 Comparative cost differences, 8, 113-19, 203 Competitive industry, 121, 123, 131-32 Computable general equilibrium models, 10 Concentration ratio, 9, 133-35 Conglomerate firms, 7 Consumer-demand systems, 10, 143, 174-75 Consumption function, 143 Consumption table. See Final demand tables Core, 167, 171, 171n.4 Cost function, 108-11, 142 Denmark, 58, 63 Developing countries. See Less-developed countries Disequilibrium, in consumer markets, 10, 174, 182 Dual prices, 10, 167, 176. See also Equilibrium prices EC. See European Community

Economic development, 11, 149. See also Lessdeveloped countries and "long waves," 9, 137-38 Economic policy, 3 Education, 152-53 Effective demand, 4 Effective protection, 11 Elasticity of substitution, 71, 81, 153 ELES. See Extended Linear Expenditure System Employment linkages, 200-203 Environment, 12, 138, 223-24, 228-32 Equilibrium prices, 171, 174-75 ESA. See European System of Integrated Economic Accounts European Community, 11, 199-200, 205-8 European System of Integrated Economic Accounts (ESA), 6, 39, 44-48, 50-51 Exchange rates, 112 Extended Linear Expenditure System, 174-75, 178 Factor intensities, indices of, 203-4, 209 Fixed capital formation, 10 Final demand tables, 141—43 Foreign debt, 187-88 Foreign trade, 187-88. See also International trade France, 9, 101-3, 145-47 General equilibrium, 10 Germany. See West Germany Gibson paradox, 127 Harrod-Domar model, 194 Health care, 12, 236-43 High-technology industry, 7, 69, 72-74, 88, 90, 101-3 Hokkaido, 12, 223-33 Households, 124 Hungary, 10, 187-88, 194-95 Imports coefficient, 211, 212-13 Import substitution, 11, 190, 211-19 Imports, and concentration ratio, 135 Income distribution, 11, 127-28, 138, 191, 194

Index Index numbers, exact, 109 Laspeyres, 53 Paasche, 54 Tornqvist, 111 India, 11,215-19 Indonesia, 199-200, 201, 205-8 Industrial organization, 5 Industrial structure, 3, 10, 12 Industrialization, 194 Industry technology, 44 Inflation, 128, 174, 179 Innovation, clustering of, 138-39, 142 Input-output coefficients changes in, 22, 26, 32, 140, 169-70, 204-5 extrapolation of, 5, 70-72, 83, 103 interregional comparisons, 5, 24 stability of, 25-33 Input-output matrix and intersectoral linkages, 11, 25, 139, 199-203 and similarity transforms, 5, 17-24 balancing of, 53, 55, 58-59, 62-64 compilation of, 6-7, 43-44, 4>-51, 53-65 productivity of, 166 Input-output system environmental models, 12, 226-28 integration with national accounts, 40 regional models, 10, 25, 161-69 supply-driven, 6, 25-26, 200 Interest rate, 122, 127-28, 130 International trade, 8, 11, 199, 203-8 Investment cycle, 193 Investment output ratio, 163. See also Capital coefficients Investment table. See Final demand tables Ireland, 175 Irrigation, 155-56 Italy, 175 Japan, 7, 8, 12, 128, 224-26, 229 comparative costs, 108-9, 113-19 high-technology industries, 69-70, 72, 83, 8890, 101-3 technical change in agriculture, 149-57 welfare services in, 236-43 Joint production, 7 Joint stability, 27-29, 33-35 Kazakhstan, 168 Keynes, J. M., 126, 130 Korea. See South Korea Labor coefficients, in social policy sector, 237-39 Labor supply, 174 Land, 154-55 Latvia, 169 LDCs. See Less-developed countries Leontief inverse, 17-18, 200, 214-15, 239, 242 Leontief paradox, 11 Less-developed countries (LDCs), 149-50, 199200, 206-8 Linear programming, 10, 34n.6-8, 174, 176 Linkages, indices of, 200, 202-3, 208-9 Long wave (Kondratieff cycle), 137-38, 142-43, 146-47

245 Macroeconomic models, 3-4, 9, 10, 83, 88-89, 223-26 Macroeconomic policy, 194 Macroeconomic theory, 3 Make matrix, 41, 45-46, 50, 55, 58, 59-65 Malaysia, 206 Marginal cost, 123, 125 Marginal propensity to consume, 175 Maryland, University of, 142 Material Product System, 39 Medical services, 236-43 Mexico, 199-200, 205-8 Mitchell, W. C, 127, 130 Mixed estimation, 153-55 Moses-Isard model, 164-65 MPS. See Material Product System Monopoly, 121, 123, 130-32 Monopoly power, in U.S., 132-36 Moving equilibrium. See Temporary equilibrium Multiplier, 129 Multiunit firm, 44, 49 National accounts, 6-7, 39-51, 56-57, 64-65 Netherlands, 53, 55, 61, 63-64 New Economic Mechanism, 11, 187 Nitrogen oxides, 223, 228, 231-32 Nonmarket production, 43 Nonsubstitution theorem, 4, 6 Nontariff barriers, 206 North-South trade, 206 Norway, 63 Objective function, 161, 164, 178 Obsolescence, 122 OECD. See Organization for Economic Cooperation and Development Okun's Law, 126-27 Oligopoly, 9 OPEC. See Organization of Petroleum Exporting Countries Optimizing behavior, 4 Organization for Economic Cooperation and Development (OECD), 103 Organization of Petroleum Exporting Countries (OPEC), 101, 103 Overproduction, by industrial sectors, 191-95 Overvaluation, of industrial sectors, 188-91 Pakistan, 199-200, 205-8 Pareto optimality, 167 Pennsylvania, University of, 83, 103 Planned economies, 10, 25, 174, 187 Planning, 6, 9-11, 26, 161, 174 Poland, 10, 174-75, 182 Pollution. See Environment Pontryagin's Maximum Principle, 151 Price behavior, J23, 130 Pricing of commodities, 123 Primary production, 202-3, 208 Prime cost, 122, 125-26 Production, vintage model of, 8, 121-22 Production coefficients. See Input-output coefficients

246 Production function, 108, 138, 140, 151 52 estimation of, 9, 151, 153-57 Productivity-increasing activities, 9, 150-55 Profit expectations, 122 Profit margins, 5 Profitability, 8 Profits, 121-22, 124, 130-32 Purchasing power parity, 8, 108, 112 Quasi-equilibrium solutions, 167 R & D expenditure, 71, 143, 152-53 RAS technique, 5, 8, 69-72. See also V-RAS technique Rationing, 6, 26, 29-32 Real wage rate, 122, 127-28, 130 Regional balances, 164-67, 171 Regional development, 223 Regional models. See Input-output system, regional models Regression, and similarity transforms, 18-21 Residual income. See Profits Russian Republic, 168, 171 Saving propensity, 128 Savings, 122, 153 Scenario analysis, 169 Separation theorem, 166 Shadow prices, 166 Shephard s lemma, 109 Siberia, 168-71 SNA. See United Nations System of National Accounts Social services, 12, 236-43 Socialist economies. See Planned economies South Korea, 199-200, 202, 205-8 Soviet Union, 10, 168 71 Spain, 142 Statistical unit, choice of, 39, 41-43, 48, 49 Stockholders, 125-26, 129-30 Stocks, valuation of, 57 Structural change, 69, 214 Structural efficiency, index of, 187, 194-95 Structural interdependence, 212, 239-40 Sulfur oxides, 223, 228, 231-32 Supply restrictions. See Rationing Supply side, 174

Index Taiwan, 29-32 Tariffs, 206 Technical change, 7-9, 69-103, 121 22, 131-32, 150-53, 191, 228, 232, 242-43 Technical progress. See Technical change Technological substitution, 6, 26-27, 71-72, 140 Technology, dissemination of, 9, 71, 138-39, 14243, 149-50 Temporary equilibrium, 123, 126-27 Total factor productivity, 7-8, 141-42, 143-45 in France, 145-47 in Japan, 69-71, 73-76, 83, 89-90 in Japanese agriculture, 149-57 international comparisons, 108-11 Trade coefficients, 165 Trade margins, 58, 59-61 Tsukuba, University of, 72 Ukraine, 168 Underproduction, by industrial sectors, 191-95 Undervaluation, of industrial sectors, 188-91 Unemployment, 130 United Nations Industrial Development Organization (UNIDO), 3, 11 United Nations Statistical Office, 207 United Nations System of National Accounts (SNA), 6-7, 39, 41-44, 49-50, 55 United Kingdom, 101-3, 128 United States, 8, 101-3, 137 comparative costs, 108-9, 113 19, 128 competitive and noncompetitive industries, 132-36 Use matrix, 41, 55, 58, 59-65 Utility function, 124 V-RAS technique, 69 72, 75-83, 103 Wage bill. See Wage fund Wage fund, 174, 176, 178, 188 Washington, state of, 34n.l2 West Germany, 8, 101-3, 175, 206 comparative costs, 108-9, 113-19, 128 Working capital, 128-30 World market prices, in Hungary, 188-91