Investment, Growth and Employment: Perspectives and Policy

Investment, Growth and Employment

Investment—in both facilities and know-how—is essential for growth. Economists try to understand the forces that determine investment, but its behaviour is unruly; often the term ‘animal spirits’ is used to explain the resulting volatility. This volume presents a new set of studies to explain international investment behaviour and assess its impact on growth and jobs. The authors also examine policy measures aimed at reversing the climate of low investment that has characterised recent decades. The contributors examine how well standard models of investment work, the role of finance constraints, the effect of risk and uncertainty, the impact of alternative forms of corporate governance, the forces shaping the adoption of new technology, the impact of foreign direct investment, the effect of investment on the NAIRU, and the causal structure of investment and growth. Editors’ introductions to the different sections of the book provide comprehensive overviews of the main theories of investment, the impact of investment on growth and employment; they also examine the main questions raised for policy makers. Investment, Growth and Employment brings together in a single volume the main strands of work on investment and surveys the existing frontiers of knowledge. It will be of value to all students of, and researchers in, political economy, macroeconomics, the UK and European economies, and business economics. Finally, its argument that governments need actively to promote a climate of growth in the new Europe will be of interest to policy makers. Ciaran Driver is a Reader in Economics at Imperial College Management School, University of London and is the author, with D.Moreton, of Investment, Expectations and Uncertainty (1992). Paul Temple is a Lecturer at the Department of Economics, University of Surrey and is, with T.Buxton and P.Chapman, an editor of, and contributor to, Britain’s Economic Performance (2nd edn, 1998).

Investment, Growth and Employment Perspectives for policy

Edited by Ciaran Driver and Paul Temple

London and New York

First published 1999 by Routledge 11 New Fetter Lane, London EC4P 4EE Simultaneously published in the USA and Canada by Routledge 29 West 35th Street, New York, NY 10001 Routledge is an imprint of the Taylor & Francis Group This edition published in the Taylor & Francis e-Library, 2005. “To purchase your own copy of this or any of Taylor & Francis or Routledge’s collection of thousands of eBooks please go to www.eBookstore.tandf.co.uk.” © 1999 Selection and editorial matter Ciaran Driver and Paul Temple; individual chapters © their authors All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage retrieval system, without permission in writing from the publishers. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging in Publication Data Investment, growth and employment: perspectives for policy/ edited by Ciaran Driver and Paul Temple. p. cm. 1. Investments, Foreign. 2. Economic development. 3. Job creation. I. Driver, Ciaran. II. Temple, Paul, 1952–. HG4538. I65457 1999 332.67'3–dc21 99–19536 CIP ISBN 0-203-98420-X Master e-book ISBN

ISBN 0-415-19779-1 (hbk) ISBN 0-415-19780-5 (pbk)

Contents

PART I

List of figures

vi

List of tables

vii

List of contributors

ix

Preface

xi

Acknowledgements

xii

The determinants of investment

1

1

Overview: a survey of recent issues in investment theory CIARAN DRIVER AND PAUL TEMPLE

2

Finance, profitability and investment in manufacturing BRIAN HENRY, ANDREW SENTANCE AND GIOVANNI URGA

17

3

Credit rationing versus consolidation of financial structure JEAN-BERNARD CHATELAIN

30

4

Investment, uncertainty and industry structure RINA BHATTACHARYA AND PAUL HOPE

46

5

Corporate governance, investment and economic performance SIMON PECK AND PAUL TEMPLE

64

6

Uncertainty, macroeconomic volatility and investment in new technology OTTO TOIVANEN, PAUL STONEMAN AND PAUL DIEDEREN

81

PART II

The consequences of investment

2

95

7

Overview: investment, feedback and spillover CIARAN DRIVER AND PAUL TEMPLE

8

Components of investment and growth JERRY COAKLEY AND ANDREW WOOD

106

9

Foreign direct investment, innovation and economic growth within Europe RAY BARRELL AND NIGEL PAIN

117

Investment, growth and unemployment: modelling the supply side of the UK economy JAMES NIXON AND GIOVANNI URGA

131

10

PART III The policy lessons

96

148

11

Overview: a survey of key policy issues CIARAN DRIVER AND PAUL TEMPLE

149

12

Supply constraints and inflation CIARAN DRIVER AND DAVID SHEPHERD

157

13

Long-run effects of investment incentives MICHAEL SUMNER

174

v

14

Investment policy and the employers’ perspective KATE BARKER

180

15

The capacity to tackle unemployment JONATHAN MICHIE

185

16

The UK’s investment problem IAN BRINKLEY ANDSOTERIOS SOTERI

189

17

Investment and capital productivity in Europe and the US JAEWOO LEE

197

Index

207

Figures

1.1 1.2 1.3 2.1 2.2 4.1 5.1 7.1 7.2 7.3 7.4 8.1 8.2 10.1 10.2 10.3 10.4 10.5 10.6 11.1 16.1 16.2 16.3 16.4 17.1 17.2 17.3 17.4 17.5

Investment articles counted Rates of return and the cost of capital in the UK The marginal product of capital in a stochastic model Total UK fixed investment (volume index: trough of recession=100) Manufacturing investment recoveries (trough of recession 1992Q1=100) The marginal revenue product of capital and uncertainty Return on capital employed Gross domestic fixed capital formation as a percentage of GDP Machinery and equipment as a proportion of gross capital stock (all industries) Machinery and equipment as a proportion of gross capital stock (manufacturing) The impact of capital deepening on employment UK GDP and investment in buildings UK GDP and investment in equipment UK unemployment rate UK gross domestic product and trend: constant prices (log scale) The determination of equilibrium unemployment Factor volumes: percentage deviation from base Employment: percentage deviation from base after a 10 per cent increase in the money stock Unemployment rate: percentage deviation from base after a 1 per cent increase in the real cost of capital Industrial support in the UK, 1946/7–1990/1 Private sector and business investment (percentage of GDP using 1990 constant prices) Gross capital stock (£bn at 1990 prices) Public investment as share of national income (net public sector investment as percentage of GDP) Investment over two recoveries, 1981–1992=100 (1990 prices) Gross fixed income Investment in machinery and equipment Non-equipment investment Share of investment: goods versus services Capital productivity

2 4 10 17 18 48 72 97 99 99 103 108 108 132 132 136 142 143 143 151 190 191 191 193 198 199 199 201 203

Tables

1.1 2.1 2.2 2.3 2.4 3.1 3.2 3.3 4.1 4.2 4.3 4.4 4.5 4.6 4.7 5.1 5.2 5.3 5.4 6.1 6.2 6.3 6.4 6.A.1 6.A.2 6.A.3 7.1 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 10.1a 10.1b 10.1c

Alternative investment regimes Orders of integration Cointegration tests on alternative models Hybrid model: likelihood ratio tests REH models for investment Different regimes for debt level Sample sizes Estimation results Basic equation Impact of uncertainty Impact of uncertainty Impact of uncertainty Decomposition of percentage change in the investment— output ratio, 1984–1992 average Distribution of concentration ratios, 1980–1992 average Industries in sample A taxonomy of governance structures Stock market capitalisations, 1997 Ownership of common stock in 1990 (percentage of outstanding shares owned) Empirical studies of short-termism Country-wise descriptive statistics Variable definitions Three sets of results Estimated derivatives and elasticities of ln (St/St–1) with respect to explanatory variables Correlation matrix of some explanatory variables Wald restriction tests Results from ARCH estimations Reasons for foreign investment Data coverage Pairwise regressions: trace and eigenvalue test statistics Pairwise regressions: tests for significance of loading parameters (t-ratios) Trace and maximum eigenvalue test statistics α and β matrices from cointegrating VARs Restrictions on the α matrix (x2(r)) Trace and maximum eigenvalue test statistics α and β matrices from cointegrating VARs Tests for restrictions on the β matrix Tests for restrictions on the α matrix Global foreign direct investment stocks FDI flows in selected OECD economies ($ billion, period totals) Cross-border mergers and acquisitions, 1991–1995 The importance of foreign-owned firms, manufacturing sectors Accounting for inward-investment growth in Europe Selected data for US non-bank foreign affiliates in Europe 3SLS estimates of a West German production function with endogenous technical progress Within-industry spillovers from foreign firms in manufacturing Cointegration tests on unrestricted levels system Cointegration tests when linear homogeneity with respect to output is imposed Cointegration tests when homotheicity is imposed

2 24 25 26 27 35 41 42 52 53 54 54 55 56 58 66 67 67 75 85 86 87 88 91 91 92 101 109 110 110 111 111 112 112 113 113 114 117 118 119 120 122 123 126 128 138 138 138

viii

10.1d 10.2 10.3a 10.3b 10.3c 10.3d 10.4 10.5 12.1 12.2 12.3

Cointegration tests when Harrod neutral technical progress is imposed Levels and dynamic estimates of main coefficients Long-run Allen elasticities of substitution Short-run Allen elasticities of substitution Long-run price elasticities Short-run price elasticities Cointegrating regression for wages with productivity Dynamic wage equations based on labour productivity Stationarity tests: sample 1976Q4–1997Q1 Granger causality tests on ΔP and ΔC Dependent variable: percentage responding ‘up’ to price question (ΔP), sample 1976Q4–1997Q1, IV(2SLS) estimation 12.4 Dependent variable: percentage responding ‘up’ to price question (ΔP), sample 1976Q4–1997Q1, IV(2SLS) estimation 12.5 Significance of the utilisation and constraint variables 12.6 Dependent variable: percentage responding ‘up’ to price question (ΔP), sample 1980Q1–1992Q4 12.A.1 Dependent variable: percentage responding ‘up’ to price question (ΔP), sample 1976Q4–1997Q1 12.A.2 Dependent variable: percentage responding ‘up’ to price question (ΔP), sample 1976Q4–1997Q1 13.1 Measures of fiscal policy 13.2 Order of integration 13.3 Cointegration tests 13.4 Error correction models 16.1 Investment shares compared, 1960–1997 16.2 Productivity and real wage growth (annual percentage change) in the 1990s 16.3 Wages, investment and profits, 1990–1997 17.1 Average growth rates in gross fixed investment 17.2 Sectoral investment shares

138 138 139 139 139 139 140 140 160 161 162 164 165 166 169 170 175 175 176 177 190 192 192 197 201

Contributors

Ciaran Driver is a Reader in economics at Imperial College College Management School, University of London. He holds degrees in electrical engineering and systems, with a Ph.D. in economics. He has worked in the UK government economic service and as consultant to public and private bodies. He has held visiting positions in the US and Australia and has published widely in leading international journals and has authored and edited several books. Paul Temple is a Lecturer in economics at the University of Surrey which he joined in 1997 after working as Research Fellow in the Centre for Business Strategy, London Business School. He has worked as an economic policy adviser and has contributed to a wide variety of issues relating to the competitiveness of the UK economy. He is an editor of Britain’s Economic Performance (Routledge). More details of publications etc. can be obtained from website: http:// www.econ.surrey.ac.uk Kate Barker is Chief Economic Adviser at the CBI. She joined the CBI in 1994 from Ford of Europe where she was Chief European Economist. She is a former member of the Treasury Panel of Independent Forecasters. Ray Barrell is a Senior Research Fellow at the National Institute, and he has led the World economy team there for a decade. Prior to that he was an economic adviser at HMT for three years and an academic for ten. His research interests include the determinants of growth and location, European integration and the analysis of policy. He has published widely in these areas in books and in journals such as European Economic Review, Economic Journal, the Journal of Policy Modelling, and the Review of Economics and Statistics. Rina Bhattacharya is currently working for the International Monetary Fund in Washington. She holds a Ph.D. in Economics from Yale University in New Haven, Connecticut, USA. Her main areas of interest are open-economy macroeconomics and international finance. She has worked as a lecturer at the University of Sussex, and also at the UK Treasury and the Bank of England prior to joining the International Monetary Fund. Ian Brinkley is Senior Policy Officer in the TUC’s Economic and Social Affairs Department, with particular responsibility for developing and presenting the TUC’s economic policies and developing the TUC’s work programme on the labour market. He previously worked as a researcher on labour market policy at the University of Kent at Canterbury and at the Centre for Environmental studies. Jean-Bernard Chatelain (Ph.D.) is a researcher at the Centre de Recherche, Banque de France. Jerry Coakley is Reader in Financial Economics at London Guildhall University. He publishes and does research on the Feldstein—Horioka puzzle and capital mobility, tests for PPP, and estimating and testing nonlinear (threshold autoregressive) models of financial and other markets. Paul Diederen studied econometrics at the University of Amsterdam. At MERIT in Maastricht he wrote his Ph.D. thesis on ‘Technological progress in enterprises and diffusion of innovations’. As a post-doc. researcher at Warwick University he worked on investment and diffusion theory and on technology policy. He is currently employed at the Agricultural Economics Research Institute in The Hague. Brian Henry is Director of Research at the Centre for Economic Forecasting, London Business School. Previously he has been senior economic adviser at the Bank of England and an economic adviser at the IMF, as well as holding teaching posts at the London School of Economics, University College London and Cambridge. His research interests are in macro and monetary economics and in econometric modelling, areas in which he has published widely. Paul Hope is currently a Senior Economist at the Office of Water Services in Birmingham, UK. He previously worked at the UK Treasury and at the Bank of England. He holds degrees in economics from the Universities of Leicester and Manchester, and his interests include industrial economics and European Monetary Union. Jaewoo Lee currently works at the International Monetary Fund, and was previously Professor at the University of California at Irvine. Jonathan Michie is Professor of Management at Birkbeck College, University of London. Recent books include Firms, Organizations and Contracts: A Reader in Industrial Organization (edited with Peter Buckley, Oxford University Press,

x

1996) and Contracts, Co-operation, and Competition: Studies in Economics, Management, and Law (edited with Simon Deakin, Oxford University Press, 1997). James Nixon is a Research Fellow at the Centre for Economic Forecasting, London Business School. His research interests include macroeconometric modelling and the analysis of the economic policy. Previously James has been Chief UK Economist at the LBS, producing regular forecasts and commentary of the UK economy. Before joining the LBS he was an Economic Adviser at HM Treasury, working on the Budget Forecast team. Nigel Pain is a Senior Research Fellow at the National Institute of Economic and Social Research (NIESR). Formerly at HM Treasury, he joined NIESR in 1988. He has published widely on the determinants of international trade and investment, and on the impact of multinational corporations on the structure and performance of national economies. Further details are available on the NIESR internet site at http://www.niesr.ac.uk Simon Peck is currently a Research Fellow at City University Business School. His research embraces various aspects of the links between corporate governance and economic performance. Andrew Sentance is Chief Economist, British Airways plc. He joined BA in 1998 from London Business School where he was Director of the Centre for Economic Forecasting. He is a former Director of Economic Affairs at the Confederation of British Industry and a former member of the Treasury Panel of Independent Forecasters (‘the wise men’). He is also a Visiting Professor at Royal Holloway, University of London, and Chairman of the Society of Business Economists. David Shepherd is Senior Lecturer in macroeconomics in Imperial College College Management School, University of London. He has published in a number of leading economics journals, and is co-author of a key work on British manufacturing investment overseas. Soterios Soteri is a Policy Officer at the Trades Union Congress where he works on macroeconomic policy related issues. He has previously worked as an economist at the National Institute of Economics and Social Research and as a research officer at the Civil and Public Services Association. Paul Stoneman (BA (Warwick), M.Sc. (Econ.), (London), Ph.D. (Cambridge)) is Research Professor in Warwick Business School with research interests centring on the Economics of Technological Change and Technology Policy. He has published many articles in these fields and a number of books including the Handbook of the Economics of Technological Change (Basil Blackwell, 1995). He has also been involved on a practical level with policy making in the UK and has undertaken various studies for national and international government bodies as well as other private sector organisations. Michael Sumner has been Professor of Economics at Sussex since 1983. His main research interests are in macroeconomics, public finance, and firm behaviour. Otto Toivanen researches at the Department of Economics, Helsinki School of Economics, Finland. He has published in leading economics journals. Giovanni Urga is a Research Fellow in the Centre for Business Strategy of the London Business School. He is also Visiting Professor in Econometrics at the Economics Department of Bergamo University (Italy) since 1992 and at New Economic School (Moscow) since 1996. He was formerly a Lecturer at Queen Mary and Westfield College and Research Officer at the Institute of Economics and Statistics (Oxford). His research centres on econometric modelling, the economics of investment decisions, and the econometrics of financial markets and panel data analysis. Andrew Wood is Senior Lecturer in Economics at London Guildhall University. He has previously worked for the National Economic Development Office, the Treasury and Civil Service Committee, and was research fellow at Birkbeck College and South Bank University.

Preface

This book has its origins in a realisation that interest in capital investment, particularly in Europe and the UK, is growing rapidly. Moreover, it is an area where there is still a wide gulf between academic debate and the concerns of economic policy. Nowhere is this more readily apparent than in the contentious issue of whether, in the current context, greater investment would contribute to employment growth. To stimulate a debate aimed at bridging this gap, the editors called a conference in May 1997 at Imperial College London organised in cooperation with the London Business School. Key contributers to the investment literature, together with more policy oriented economists were invited to participate. Some of the contributions that day, updated and revised appear here. Although the book grew out of the conference it is, we hope, more than just a conference volume. Indeed we are pleased to include several pieces not presented at the conference and we have also added (by way of a guide to the literature) three overviews on the themes of investment theory; investment consequences; and on the implications of both for economic policy. The book is international in scope and contains several chapters with an international dimension. There is also however, a major focus on UK investment behaviour and the policy chapters reflect this. Indeed, many of the lessons in the UK may be becoming increasingly relevant for Europe more generally, where a major slowdown in rates of capital formation has attracted widespread attention. We certainly hope that the book will be of interest to more than the narrow spectrum of academic economists.

Acknowledgements

The authors express their gratitude to participants at a conference at Imperial College, May 1997, and all those especially at Imperial, Surrey, and London Business School, whose discussions have made this work possible. We would particularly like to thank the editorial staff at Routledge, whose encouragement made this book possible. We would like to remember our families, whose forbearance was an essential ingredient.

Part I The determinants of investment

Figure 1.1 Investment articles counted. Source: Social Science Citation Index.

1 Overview A survey of recent issues in investment theory Ciaran Driver and Paul Temple

Introduction Interest in capital investment has risen sharply in the 1990s after stagnating at a low level during the 1980s as may be seen from the international count of relevant journal articles in Figure 1.1.1 The preceding low level of interest may have something to do with the difficulty of breaking out of the narrowly defined ‘modern’ approach to investment, originating in Abel (1980). By contrast in the 1990s we have seen a divergence in approach, which has widened the scope of enquiry not only in respect of the causes of capital investment but also its consequences. In this first overview we deal with the determinants of investment. The overview for Part II addresses the consequences, while that for Part III considers some key issues for economic policy. Understanding investment Despite the rise in research activity shown in Figure 1.1, there has been no breakthrough on the empirical front; the ability to forecast investment expenditures seems as elusive as ever. The average root mean squared errors one year ahead for seven forecasting models is four times larger for non-residential real fixed investment as it is for GDP growth (Granger 1994). In some ways this should not surprise given the volatility of investment expenditure which entails a stock adjustment. Nevertheless, the forecasting performance of consumer durables, which has an even higher volatility than investment is rather better. This discrepancy may perhaps be due to the long-lived nature of capital equipment and structures or to the complex interactions between firms which characterise the investment decision. These complexities may be illustrated by listing some of the information requirements of an investment appraisal. These include: 1 Construction of forecasts and sensitivities in respect of macroeconomic variables, industry demand and market share, including conjectures of rival responses, public policy, effect of investment on existing activity and availability of complementary inputs. 2 Estimation of option values associated with acting (or not), including the technological and market positioning of the firm.

RECENT ISSUES IN INVESTMENT THEORY

3

3 Construction of a financial view including forecast cost of capital and long-run hurdle rates; conceptualisation of a market-based or managerialist philosophy on risk and liquidity constraints. In the absence of a social context there would almost certainly be huge variation in the way in which firms assess the future. In practice, although there is variation, it tends to be tightly contained. One explanation of this is the way in which political and social influences generate a robust consensus on the external environment facing firms. These long-run expectations characterise the regime that firms face in carrying out investment decisions. When these expectations change there is unusually high uncertainty due to the possibility of a regime change. We may illustrate this idea by adapting the theory of Marglin and Bhaduri (1989) to construct the matrix of investment regimes shown in Table 1.1. Table 1.1 Alternative investment regimes Public policy focus Investment

Demand

Profit

Less sensitive to current profit than to demand More sensitive to current profit than to demand

Co-operative 1950s Conflictual 1960s/early 1970s

Potentially co-operative? Post- 1980s Conflictual Late 1970s/1980s

The table illustrates a near-orthodox model of investment where the main determinants are demand and profitability with the role of demand crucial in translating current profit margins into expected profitability. Cooperative outcomes between labour and capital are possible where the likely influence of demand growth is to increase confidence in sustained profitability. In Kaleckian fashion, cooperative outcomes are unstable since they lead to a profit squeeze. The restoration of profitability tends to occur through a focus on profit-enhancing policies and accompanying destruction of demand. The resultant rise in profitability does not automatically entail cooperative growth as confidence may only be restored by a timely switch to a demand-led policy focus and a reduction in uncertainty. However, many economic variables—capital intensity, wage adjustment and expectations of inflation—adjust very slowly (Bean 1989; Wardlow 1994; Blanchard 1997). Public policy is also slow to adjust because credibility is related to policy consistency. These considerations limit the power of equilibrium analysis. If such forces do in fact underlie the longer-term cycles in advanced economies it can readily be understood why the formal models of investment, despite their sophistication, tend to predict poorly in the medium term.2 Most theories of investment are, at bottom, simply dynamic versions of an equilibration between the expected marginal revenue product of capital and its expected marginal cost. But neither of these schedules are likely to evolve in easily predictable ways. In forming expectations about the marginal revenue product, the corporate players have to weigh up, as in the Marglin and Bhaduri model, the relative influence of current profitability and demand conditions—no easy task. Certainly, the ex post realisations do not always bear out the simple story of equilibration between return and cost even in the medium run. In the UK, for example, as Figure 1.2 shows, the series for average profitability and average cost of investment have been diverging for over twenty years, suggesting the need for a disequilibrium analysis and the presence of constraints (Schultze 1987). At face value it might appear that the rising gap between the marginal revenue product of capital and the cost of funds should have produced an investment boom. The fact that this has most definitely not occurred needs to be explained. Possible candidates are measurement errors, the existence of constraints on investment or the occurrence of a regime shift. One important idea is that as capital flows have been liberalised, the opportunity cost of funds needs to be calculated as the rate of return on foreign assets (Young 1994; Cowling and Sugden 1996).3 If domestic investment has to meet a hurdle rate equal to this return, it could explain the coincidence of rising profitability and stagnant investment. Other explanations such as financial constraints or uncertainty are discussed in some of the chapters below. In this overview we review some of the main aspects of modern investment theory before commenting on the individual chapters in the book. Among the most influential (and relatively recent) developments in orthodox theory have been: 1 the development of dynamics and the testing of q-theories; 2 the incorporation of constraints; 3 models of investment under uncertainty. In reviewing these topics we will use their simplest representative form and offer a flavour of intuition along with the formal structure of a model.

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Figure 1.2 Rates of return and the cost of capital in the UK. Source: Bank of England.

Dynamics Much recent empirical work on investment follows the ‘adjustment cost’ approach which sees the investment decision as the optimal forward-looking tracking of a stochastic target. Earlier work—the flexible accelerator— stressed that there was a tradeoff between immediate adjustment to a target and the cost of rapid adjustment. The more modern version originating with Abel (1980) is in the same mould and can be simply described by a brace of equations obtained by differentiating the value of the firm (Vt) conditional on the previous period capital stock (Kt–1). The value of the firm may be written in dynamic programming form as the current value of profits and the continuation value representing the expected value from time t+1. To save on notation we ignore depreciation and discounting and consider only the fixed factor K. Thus: (1) given the assumption of zero depreciation. Differentiating Vt with respect to the ‘state variable’ Kt–1 gives the Euler equation: (2) The term on the left is the shadow value of one unit of capital. Repeated substitution gives the familiar condition that the shadow value of capital is the sum of the future marginal values of that capital. Differenting Vt with respect to the ‘control variable’ It gives: (3) Combining (2) and (3) gives (4) This states that the shadow value of capital is the marginal effect of investment (rate of change of capital) on current profits; this is the cost of investment. In a static framework that cost of investment is just the purchase price. In a dynamic framework, the right-hand side is the sum of the purchase price and an adjustment cost that is some convex function of investment, C(I). Equations (1) to (4) offer a variety of approaches to estimating investment equations. Three basic models have been used (a) First, equation (4) has been estimated directly by using stock market data to represent the left-hand term which is the shadow value of capital. The average valuation of the capital stock as a ratio of its replacement value (Tobin's average q) has been used to proxy the marginal value of q. If adjustment costs C(I) are assumed quadratic in the investment rate (I/K) so that marginal adjustment costs are linear, it is easy to obtain an equation from (4) which relates (I/K) log-linearly to q and to the real cost of capital.4

RECENT ISSUES IN INVESTMENT THEORY

5

(b) An alternative method is to dispense with observed stock market data and to attempt to proxy marginal q by an auxiliary equation which sums future marginal values of capital. This can be obtained by forecasting future marginal revenue products using an assumed representation for the production function and an estimated vector autoregression which links successive time periods (Abel and Blanchard 1986). (c) A third method is to eliminate the shadow value of capital from the investment equation by using a differenced version of (4). Using (2), the left side can be written as δπ/δKt. Given a representation for marginal revenue product and the adjustment cost function, and using rational expectations to eliminate the expectation, this equation can be estimated without recourse to data on marginal q. However, it may pertinently be asked how account is taken of the under-utilisation of capital in this framework. With constant returns, perfect competition and stable prices for capital goods, the equation reduces to a condition relating adjustment cost now to that in the next period, i.e. investment is simply driven by the expected path of adjustment costs. There has been some disagreement in the literature over the most promising approach. It seems generally agreed that the qtheory approach has disappointed empirically, either because of a failure of stocks to mirror fundamentals or for broader reasons of investment strategy (Blanchard et al. 1993; Chirinko 1993).5 As for the other approaches, Blundell et al. (1992) favour the Euler equation approach partly because it avoids the auxiliary assumptions that have been used to measure the shadow value of capital. It appears, however, that most estimated Euler equations do not satisfy their own theoretical restrictions and may be ‘mongrel’ relationships (Nickell and Nicolitsas 1996; see also Chirinko 1993, note 40). A serious problem with much of the above theory is the reliance on restrictive functional forms, in particular the assumed convex quadratic form for the adjustment cost. While this makes the solution of the differential equation system tractable, deviations from it can render the solution unstable. Furthermore the a priori basis for it is thin (Rothschild 1971; Maccini 1987). Convex costs may be an element of firm behaviour, e.g. in the sense that rapid expansion induces managerial overload (Penrose 1959) or as in R&D projects where parallel rather than serial expenditures might reduce learning effects (Scherer 1986). More generally the model is explained in terms of disruption to production flow. But if these aspects of cost were binding constraints on investment at firm or business level we might expect there to be an extensive managerial discourse on the problem. This appears not to be the case. Recent empirical work has confirmed the lack of evidence for convexity (Hamermesh and Pfann 1996). It has also been known for some time that using quadratic adjustment costs in macro equations implies an implausibly large adjustment cost (Bosworth 1981). It seems therefore that convex costs of adjustment can be rationalised only to the extent that they mimic aggregate investment response.6 An alternative approach to explaining lagged adjustment is external adjustment costs, representing, for example, a rising supply price for capital goods in a monopsonistic setting or where competitive firms face similar demand variation. Chirinko (1994) shows that in this case the q-theory formulation is modified to include future investment expenditure rates and supply elasticity of capital goods. However, for this theory to be a serious contender in explaining general investment lags, there would have to be marked differences between the investment lag structures of small open capital importing economies and those relatively self-sufficient in capital. This possibility does not seem to be observed, though neither has it been extensively studied. Yet another approach explains lagged adjustment by time to build (Taylor 1982; Pindyck 1993). However, while this may be important, it cannot be the full story; delivery lags seem similar for different industries but the lagged adjustment shows considerable cross-industry variation (Abel and Blanchard 1983). In any event, the delivery lag model cannot be used to explain the lag structure of investment authorisations. While the lags here are shorter than for expenditures, there is still a lag structure to be explained. Some of the more recent work on investment stresses the role of nonconvexities such as irreversibility and also the role of lumpiness of capital expenditure.7 Research at individual plant level has confirmed that most firms are characterised by ‘zeros and ones’, i.e that investment tends to be a discrete activity, at any rate for large projects (Nilsen and Schiantarelli 1996). The probability of an investment burst for a plant increases with the time since the previous burst. Firms also invest disproportionately heavily when capital shortage is high than when it is low or negative. The aggregation of such lumpy investments across heterogeneous firms can be shown to create a smoothed adjustment that appears to explain aggregate dynamics better than existing theories. Simulations also suggests that the longer-term effect of tax policy may be much stronger than conventionally measured because of the problems in aggregating dissimilar responses (Caballero et al. 1995).

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Investment under constraints Micro-constraints The starting point for many theories of investment is some version of a neoclassical model. Confronted with the data these models perform poorly. Data for the main OECD countries suggest that the three main variables, capital stock, output and relative factor price, are not cointegrated.8 However, there is evidence that an increment of output affects the growth rate of the capital stock permanently (Ford and Poret 1991). Other research has found conflicting evidence as to whether gross investment (regarded perhaps as a proxy for replacement investment in long-run equilibrium) is cointegrated with output and the cost of capital. (Lomax 1990; Henry et al. in this book, Chapter 2; and Sumner, Chapter 13). In response to these findings, theorists have argued that the adjustment process is in some way supply-constrained, since the adjustment of capital is so slow. This has naturally led to the incorporation of constraints in the investment specification. Both the management and method of financing investment projects offer possible lines of development on the basis that such factors cannot as easily be ‘bought in’ as other factors such as physical plant and equipment.9 Both managerial and financial constraints are of interest because they may be both highly non-linear and firm specific in nature. While therefore of great potential importance in resolving some of the paradoxes of investment behaviour, this has led to considerable problems when it comes to empirical testing. In basic models of finance, the provision of finance plays a role orthogonal to the investment decision. According to the Modigliani and Miller theorem, the firm’s capital structure is irrelevant for its market value. This striking result can be thought of in terms of an arbitrage condition in a situation of zero transactions costs: if firms are maximising profits subject to a production function, then any change in value resulting from a different financial structure would give rise to arbitrage opportunities. While the list of violations to the assumptions of the Modigliani-Miller result is a substantial one, the literature has focused on the idea that capital structures reflect information asymmetries between management and owners and/or lenders. Typically, management may be expected to possess more information about the value of the firm’s investments (and of their own efforts) than either debt or equity holders. Schianterelli (1996) has pointed to two major results from this literature. The first is that uncollateralised loans will be more costly than internal finance because lenders will demand a ‘lemons premium’. The second is that the size of this premium will be inversely related to the borrower’s net worth—defined to include cash, liquid assets, and the portion of illiquid assets (such as the future profits of the firm) deemed suitable as collateral. The latter aspect will tend to amplify any financial shock to production and investment, since these impact on net worth as well as through the cost of capital via the conventional market rate of interest. This is the so-called ‘financial accelerator’ effect. Given the fact that at any point in time individual firms may or may not be suffering from a financing constraint poses empirical estimation problems in the testing of either of the two hypotheses above. In practice much of the methodology has stemmed from the contribution of Fazzari et al. (1988) who used an a priori initial classification of firms into constrained and unconstrained regimes on the basis of dividend pay-out ratios. Arguably, high dividend firms are likely to fall into the latter regime. Other means of proxying for the probability of a particular firm falling into one or other regimes include firm size and age (Devereux and Schianterelli 1990), bond rating (Whited 1992), and affiliation to a Japanese industrial group (Hoshi et al. 1990). Many of the studies assume that any given firm is permanently allocated to one regime or another over the sample period; this runs counter to the idea of important financial accelerator effects. An exception here is Hu and Schianterelli (1994) who employ a ‘switching’ regression which is dependent upon both firm characteristics and macroeconomic conditions. In general, empirical implementation has followed the approach outlined above using either Q or an Euler equation. Typically, convex adjustment costs are assumed. A fundamental problem with the Q formulation is in devising a suitable control for investment opportunities, especially if it is given that stock market valuations may not reflect all that is known about future profitability. In that event, measures such as cash flow may be proxying for this missing information. Gilchrist and Himmelberg (1994) have developed the Abel and Blanchard (1986) approach described above (page 7) in an attempt to control more tightly for investment opportunities. The other alternative is of course the Euler equation itself. For firms which are financially constrained, the standard Euler equation (2) is mis-specified; in theory, as Hubbard (1998) points out, an augmented Euler equation framework allows for the impact of changes in cash flow or net worth to vary systematically. For example, in Whited’s 1992 model, the shadow cost of external financing varies depends upon the firm’s coverage ratio. This is broadly the approach developed by Chatelain in Chapter 3 below. The research paradigm on financing constraints described above is now a considerable one, as the surveys of Schianterelli (1996) and Hubbard (1998) make clear. Results are probably robust enough for the impact of information problems in capital markets to be taken seriously, although a dissenting note was recently expressed by Kaplan and Zingales (1998). However, thus far implementation has been restricted to the basic neo-classical model described above and have not begun to explore the implications of irreversibility and options based investment models outlined below.

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Managerial constraints of the sort introduced by Penrose (1959) are discussed in Peck and Temple below (Chapter 5) but have not featured strongly in the empirical literature, although they have informed a debate centred on the motivation of modern management which remains important. Indeed the positive relationship between cash-flow found in the asymmetric information approach may also be a consequence of discretionary managerial behaviour favouring sales or growth (which are linked to salaries and promotion opportunities). As Schianterelli (1996) observes, costly actions taken to control management may be difficult to distinguish from the costs attached to adverse selection. Saving as a macro-economic constraint Financial constraints clearly operate at the micro-level. That individual firms may find themselves financially constrained does not imply that profitable investment opportunities run to waste, since other firms may take them. On the other hand, such an optimistic scenario cannot be assumed since such opportunities may be highly firm specific. Whatever, financial constraints need to be clearly distinguished from a constraint imposed by saving—the resources created for investment— because the incomes created by current sales are not immediately consumed. In contrast to the idea of a financial constraint on individual firms, the idea of a savings constraint is primarily a macro-phenomenon, impacting on firms via the cost of capital. However, the level of aggregation at which such a constraint may operate—be it nationally or globally—is a matter of considerable interest. In the standard economic model of a closed economy with full employment, subsidies to investment or a reduction in taxes on saving lead to similar results. This conclusion needs to be severely modified, of course, under Keynesian conditions of less than full employment where attempts to stimulate savings are likely to prove self-defeating because the deflationary impact of the rise in saving more than offsets any tendency for rates of interest to fall. Policies directed at investment directly contribute to effective demand and employment, and must under these conditions be favoured. As noted graphically by William Vickery (1993), savings are not like a sack of potatoes—if they are not used they disappear in reduced income. This point of departure is of course well known; it implies that interest rates are not solely determined by the interplay of demand and supply for loans and that an independent investment equation is needed to obtain model closure.10 The orthodox view, by contrast, argues that savings have an important effect on investment at the global level and that the reduction in savings rates, especially by loose fiscal policy since the late 1960s, has contributed to lower investment in OECD economies (Jenkinson 1996). This orthodoxy might not apply to a small open economy in the presence of a global capital market. In such a case, attempts to subsidise domestic saving in order to raise investment merely result in a substitution of domestic saving for capital imports; domestic investment would again be independent of levels of domestic savings. However, capital markets may not yet be fully global— international portfolio investment is not greatly diversified internationally partly because of exchange rate risk (Feldstein 1994).11 It is difficult to distinguish empirically between these views because they each comprise a complex of hypotheses. However, the failure of a standard model of investment to perform adequately suggests that there is a need for an independent investment equation. Furthermore, the weak elasticities of investment with respect to interest rates also undermines the orthodox view. Finally work on public dis-saving in the US suggests that fiscal deficits have actually contributed to gross private domestic investment and to national saving (Eisner 1994). Uncertainty The work on dynamics has to some extent been superseded by work on irreversibility and uncertainty. Some two decades after Nickell first argued that irreversibility combined with risk could offer an alternative explanation for adjustment lags, the idea has become relatively commonplace and forms the basis for some recent models (Dixit and Pindyck 1994; Abel et al. 1996). The traditional literature on investment under uncertainty consisted of a myriad of models in which there could be a positive or negative bias or no bias at all to investment depending on the assumptions chosen (Aiginger 1987; Driver and Moreton 1992). To simplify, but not unduly, the three main ways in which demand uncertainty matters are: first, when there is risk aversion, second, where marginal profit with respect to capital depends non-linearly on demand and, third, when the capacity utilisation rather than price is the equilibrating variable in the event of a shock. In the first case, demand uncertainty usually results in a lower optimal capital stock. Survey-based evidence also suggests that this may be the primary method by which uncertainty has an effect on investment, at any rate for large projects (Aiginger 1987). In the second case, a mean-preserving spread will cause a bias, but the direction and magnitude will depend on technology, demand and the form of the stochastic error; some examples of bias are given below.12 The most quoted effect is the result that uncertainty raises investment for price-taking firms, but more generally the effect is ambiguous. To see this, consider first the case where, under risk neutrality uncertainty will not cause the optimal decision to differ from that under certainty. In the case of uncertainty over the output price (p), with capacity or output given by q we may write expected profit as:

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where the integral is over the full range of p. Differentiating this w.r.t. q Equating to zero gives E[p]=c′(q). This output rule is the same as under certainty if E(p) is the same as the certainty price. However, changing the context slightly, e.g. introducing a tax rebate which makes the downside different from the upside, would introduce a bias. A bias would also be introduced if we relax the assumption that p is exogenous: If we make q=upε, with mean of u=1: The derivative of this with respect to q will contain a term in u, not present in the certainty case, where u=1. For these kinds of models, standard results with q the decision variable and p the stochastic variable may be derived (Rothschild and Stiglitz 1971; Aiginger 1987)

The intuition behind this can be seen by assuming that the marginal impact on profit of an extra unit of q is convex in the price. This means that the expected value of the marginal profit with respect to q is greater than the marginal profit under certainty. Under certainty the point of zero marginal profit will be met sooner, i.e. with lower q and so, q(opt) > q(cert). For example, consider a constant elasticity demand curve shifted by a random term u. The marginal revenue function is: This is concave in u for elastic demand and so the optimal q is smaller than under certainty. Extension to a two-factor setting with perfect competition and price uncertainty is sometimes known as the Hartman-Abel model (Hartman 1972; Abel 1983). Then optimal level of K will be determined by: where K is capital fixed in advance of the price but where L/K could be varied ex post. The function in the square brackets is linear in p if L does not vary with p, e.g. as in a fixed coefficients model. If L rises in response to higher P—as would be indicated by the usual marginal productivity conditions if there is flexibility—FK will rise too and the function in square brackets will be convex in p, imparting an upward bias to capital input.13 The intuitive reason for the result is that as the price rises and labour input has to be increased to suboptimal levels, the value of a unit of capital is increasing non-linearly in p.14 The third and perhaps most interesting case is the Newsboy inventory model applied to capital input. The previous models have assumed that there is no rationing. Firms can always meet demand and price adjusts upward so that demand is met. This would not appear to be always sensible at least at the level of the individual firm, where a forecasting error could cause a firm to run out of capacity. Because of this we must consider a new set of models where sales are distinct from production. This turns out to have the radical implication that we cannot use the usual convexity/concavity formulation of the RothschildStiglitz condition because we now have three distinct variables: the decision variable, e.g. output; the stochastic variable, e.g. price; and a new variable distinct from output-demand. This context is sometimes known as a stochastic rationing context and the usual condition imposed is that Sales=min[Production, Demand] Outlined below is the simple Newsboy model, adapted for the case of capital input, where the following notation applies: Output price (p); capacity (y); capacity cost per unit of production (c); demand under certainty (D0(p)); stochastic shift parameter (a). Setting marginal cost with respect to y equal to expected marginal revenue, with an absolute capacity constraint at y=aD0,

where Z—the ratio of capacity to mean demand—is an inverse indicator of expected utilisation. Note that Z>1 implies that the firm plans to hold excess capacity. For a symmetrical distribution of a, F(1)=1/2. It then follows that:

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Not surprisingly, a distribution with larger tails will for any given c/p ratio have an optimal Z that is further away from unity. For example, if p>2c, there will be planned excess capacity.15 With fixed price margins, the effect of uncertainty is to encourage investment only if the unit profit at full capacity is above a threshold; otherwise the bias is negative. Real options In the Newsboy model above, irreversibility of capital is assumed. But the model is limited to two stages: investment followed by a realisation of demand. In recent years a new literature based on real options has added a new element in the context of a model with at least three stages: investment possibility one; investment possibility two; realisation of demand. (Dixit and Pindyck 1994). The basic idea of real options is that firms possess valuable opportunities which can be exploited with some discretion as to time but which once exercised are at least partially irreversible. The exercise of the option (investment) thus implies a cost which should be added to the usual user cost of capital when deciding on the timing of investment. There is a value to waiting where the uncertainty is partly resolved by waiting. Waiting makes costly (irreversible) mistakes less likely and this explains lagged adjustment.16 The basic Dixit and Pindyck model is set out in Appendix 4.2 to the chapter by Bhattacharya and Hope, Chapter 4 in this volume. The derivation of an investment equation from this perspective is not straightforward. Risk will induce caution by raising the threshold or investment trigger. But increased risk will also raise the variance of demand (and value of the firm) thus increasing the chance that the threshold will be exceeded in any period. Typically investment is delayed by risk (Pindyck 1991). However, the firm also has to assess the cost of waiting in terms of the erosion of market opportunities whether by time or by competitors’ actions. This measures the cost of not exercising the option to invest which is difficult to measure. One strategy is to relate the threat of new entry to the realisation of a favourable profit outcome.17 The effect of risk on the size of incremental capacity choice is also negative in imperfect competition (Pindyck (1988, 1991, 1993), though the effect on the capital output ratio is less clear cut. The Hartman-Abel convexity effect which tends to increase investment under risk dominates only under perfect competition and constant returns. In that case the marginal revenue product of capital is independent of the capital stock and thus irreversibility can never be problematic for the firm. More recently, real option theory has been subjected to an important extension in that the early version of it assumed no constraint on expansion. A more general approach can take into account the possibility of both irreversibility and constraints on expansion (Abel et al. 1996). The firm then has to bear in mind, when making its investment decision, not only irreversibility but the prospect of being short of capital if the realisation of demand turns out to be high. Abel et al. argue that this requires a consideration of an option to expand. To put it somewhat differently, earlier discussions such as Pindyck (1991) focused mainly on the decision of when to exercise existing options.18 The advance on this is to suggest that the decision to obtain an option is itself part of each investment process. Abel et al. explain that in the context of a two-period investment model, the ex ante investment may be no longer appropriate in the light of the realisation of the stochastic variable e. In the second period, one might prefer to sell part of the capital invested or exercise a right to buy more at a prearranged price. This complication results in the following expression (Abel et al., expression 17) in place of the Jorgenson user cost term (c): where b, bL, bH are the first period purchase price of a unit of capital and the corresponding selling and buying prices respectively; F(e) is the distribution function of the underlying stochastic variable; and RK is the marginal return on capital installed which may have to be evaluated at a nonoptimal level of the capital stock. The terms eL and eH are the critical values of the stochastic variable at which the original capital is no longer optimal ex post: either because the return is no longer greater than the return from selling the capital or because the return has risen to the rental on new capital purchased at the option price. Capital should then be bought or sold until the marginal return equals the lower or upper rentals bL or bH. This is represented by Abel et al. in their figure 1 reproduced below as Figure 1.3. There are some hints here as to how risk might affect investment. The firm has to bear in mind the cost of exercising the option and the cost of failing to obtain options.19 Abel et al. (1996) remark that ‘although the NPV rule is theoretically correct it is very difficult to implement in practice’ (p. 761). The reason is that the option values are difficult to compute. The theory here reflects, in a dynamic context, the same dilemma illustrated in the Newsboy model: how to balance the likelihood and net gains of excess capacity on the one hand and insufficient capacity on the other. So far we have seen little empirical work that takes these points fully into account.20 And the theory so far developed, despite its increased realism in dealing with options, is limited to market clearing behaviour. The models of uncertainty reviewed above are essentially models of risk in that the probability distribution or stochastic process is well determined. This ignores the problem of fundamental uncertainty stressed in post-Keynesian writing, where it is argued that the properties of the stochastic system are not time invariant. Some empirical evidence for both positions is available in that business decision-makers offered a choice between a vision of calculable probabilities and fundamental

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Figure 1.3 The marginal product of capital in a stochastic model (Abel et al. 1996).

uncertainty seem to split roughly two-thirds in favour of the former (Aiginger 1987). One implication of fundamental uncertainty is that investment decisions may need to be modelled in terms of bounded rationality or in terms of conventional behaviour (Heiner 1983; Driver 1992; Conlisk 1996). A useful discussion of these issues may be found in the debate between Crotty (1996) and Fazzari and Variato (1996). Authors’ contributions In this part of the book we take forward some of the themes discussed above, in particular those of constraints and uncertainty. We add a further emerging theme—that of investment as an aspect of technology adoption. The chapter by Brian Henry, Andrew Sentance and Giovanni Urga (Chapter 2) is a splendid example of the opportunities and challenges in estimating stable investment relationships using the standard cost-of-adjustment approach. Their models offer considerable flexibility, allowing for imperfect competition, financial constraints both internal and external, represented by terms in undistributed profits, a liquidity ratio and a gearing ratio. They are catholic in their use of proxies for or alternatives to the Tobin-q variable using, for example, profitability. Finally they consider whether direct survey-based expectations offer additional explanatory power and whether firms make investment decisions on the basis of rational expectations of the explanatory variables. The results of this study may be divided into the tests on long-run cointegrating relationships and the findings from the dynamic estimation. The authors seek a cointegrating relationship between gross UK manufacturing investment (which is treated as an I(1) variable) and various real and financial indicators. One result is that the neo-classical model and the averageq model (with gearing) do not cointegrate. The best-performing long-run models are those involving output and profits and (with an incorrectly signed relative price term) output and liquidity.21 What are we to make of the finding that profitability and output are possibly the main long-run determinants of investment? On the one hand these variables hardly surprise in empirical investment functions—they have been offered as competing or complementary determinants at least since the 1950s when serious econometric work on investment began. But on the other hand it is instructive that these variables are only loosely related to the economic theory with which the chapter began—the standard cost-of-adjustment approach. In the chapter the authors introduce profitability as an empirical counterpart to Tobin’s q. This seems defensible enough in a disequilibrium context where profitability is diverging from the cost of capital. The theoretical relationship between profitability and firm investment behaviour is, however, not well understood. There may well be a variety of responses depending on whether a firm is undertaking defensive investment in response to low profitability or reacting with expansionary investment to higher profitability. It seems likely also that some firms will operate in submissive mode where investment is curtailed in response to rising profitability elsewhere. The balance of these effects is picked up in this chapter as a positive relation between investment and profitability; it would be of interest to investigate this in a panel context. As for the real output variable, the authors give four separate reasons for its inclusion. On the one hand there is the standard argument that firms may be output constrained in which case the desired capital stock is directly determined by (exogenous) output. Second, under imperfect competition, output affects the marginal revenue product of capital. Third, a similar effect follows in a union-based model. Finally, anticipated output constraints may alter the optimal path of adjustment. It would be good to be able to discriminate between these explanations of the oldest and most robust empirical fact in investment studies; in the current chapter it seems that only the union-based interpretation is rejected.

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In some ways we may interpret this chapter as a first-stage clearing of the ground in an attempt to find a set of variables which are data admissible. The reader must judge to what extent the results are compatible with standard theory. Certainly the difficulty in finding robust cointegrating relationships—and the presence of a time trend in those identified—suggests that it is not only the dynamics of investment behaviour that remain poorly understood. The dynamic results that the author present are important and interesting. They find evidence that forward-looking behaviour matters, but that the forward-planning horizon is limited to three quarters.22It would be interesting to see whether the VAR relationship for the determinants of investment imply forecastability beyond a three quarter horizon. If this were the case it is not clear that the authors’ findings can be said to confirm rational expectations behaviour. In any event the use of a three quarter ahead horizon poses a difficulty for the cost-of-adjustment model which generally assumes an infinite horizon. If investment is based on short-term forecasts only it is easy to see why the omission of terms in animal spirits and long-run uncertainty could render the investment equation unstable. Such omissions may account for the model’s failure to explain adequately the behaviour of investment in the 1992–1996 period.23 Jean-Bernard Chatelain (Chapter 3) also addresses the issue of financial constraints. He distinguishes between a rationing ‘debt ceiling regime’ (where the debt-equity ratio is endogenised as a function of expected profits acting as collateral) and a ‘consolidation regime’ where there is some incentive for firms to repay debt rather than invest because their average cost of capital is thereby reduced. With firms in the consolidation regime, tax policy will not be very effective, as the provision of funds will simply allow firms to repay debt faster. This contrasts with the case of finance rationing where tax policy should have a strong impact. Certainly firms in France and elsewhere have been retiring debt and decreasing the debt-equity ratio at the same time as showing considerable restraint in regard to investment. The question is to what extent this can be rationalised as a response to the differential movement in interest rates and profit rates such that debt is retired as the former rises relative to the latter. For empirical implementation Chatelain considers a sample split based on the extent of debt repayment over the latter years of the sample; high debt repayment suggests consolidation. Another split is based on whether the interest rate and profit rates are such that the consolidation would be indicated. From a panel of 292 French firms, Chatelain seeks evidence of an endogenous debt ceiling for the non-consolidation regime. The results are clearly preliminary, but at this stage the significance of the important ζ parameters are not as clear-cut as might have been hoped. This may reflect the assumptions made in respect of how collateral values are taken into account. Chatelain’s chapter is largely concerned with the possibility of debt repayment as an alternative to investment in a scenario of high interest rates. It is, however, of interest to note that firms have increasingly taken to buying back their own equity as well. While the latter phenomenon can clearly not be explained in the Chatelain model, it perhaps indicates that there are nonfinancial factors driving the slowdown in investment which in turn get reflected in the retirement of both debt and equity. Information related problems are of course at the root of the short-termism debate which surfaced in the UK and the US during the 1980s, and this is also discussed by Simon Peck and Paul Temple in Chapter 5. These economic systems differ substantially from those found elsewhere in Europe or Japan in that ownership is not so concentrated and hence incentives to monitor management tend to be weaker. That these type of systems may accentuate information related problems is at the heart of the short-termism debate. The result is a tendency for financial markets to systematically under value more distant profit flows. The role of uncertainty in investment is addressed in Chapter 4 by Rina Bhattacharya and Paul Hope. They use panel data on 103 manufacturing industries in the UK, using the survey data from the main UK Business database, that of the Confederation of British Industries (CBI). Their theoretical section brings out clearly the many ways in which uncertainty can influence investment and the ambiguity of the direction of its influence. Although they do not consider the disequilibrium or rationing framework (see above) they discuss how risk aversion, non-linearities, and option theory can influence investment. The uncertainty variable used is a direct survey question in the CBI database. Although there is some debate as to whether this variable represents uncertainty or lack of confidence, the authors condition the results on a real variable (capacity utilisation) to take account of a possible variety of responses. Liquidity constraints and cost of capital effects are also entered with significant coefficients and expected signs. An interesting aspect of this chapter concerns testing the sensitivity of investment to uncertainty according to the degree of imperfect competition. Caballero (1991) has argued that a negative irreversibility effect can only be obtained for the case of imperfect competition and the strength of this should depend on the degree of monopoly power proxied by the elasticity of demand. Ghosal and Loungani (1996) and Guiso and Parigi (1996) have investigated this with contradictory results.24 In this chapter the finding is that the influence of uncertainty on investment is negative for less concentrated industries. The authors note that this is the reverse of that predicted by Caballero and suggest that the discrepancy may be due to increasing returns to scale in the highly concentrated industries.25 Another possibility is that demand shocks are accommodated by quantity adjustments, i.e. variation in capacity utilisation rather than price. In that case, firms with market power will be more likely to hold more reserve capacity, as in the Newsboy model, than firms with lower profit margins.

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The relationship between technology and investment has often been mooted in the literature but rarely developed. Paul Stoneman has long argued for an approach that sees the duality of gross investment and technological diffusion. Indeed it can be argued that diffusion theory anticipated some recent concerns in investment theory in that the Mansfield or David models emphasise the roles of risk and firm-specific factors in explaining adoption (Stoneman, ed. 1995). In Chapter 6, Otto Tovanen, Paul Stoneman and Paul Diederen conclude Part I by investigating the effect of uncertainty and volatility on a more narrowly defined investment category—that of industrial robots. These authors use unbalanced panel data on 16 countries’ stock of robots over the period 1981 to 1993 to estimate an epidemic diffusion model in which volatility variables affect the adjustment speed of adoption to close the gap between the actual and target stock. Conditional volatility variables, i.e. estimates of the forecast variance (ARCH) are constructed, but at this aggregate level most of the forecast variances turned out to be time invariant and accordingly unconditional variance was employed instead. The results suggest that taking account of volatility doubles the mean adjustment lag from about 5 years to 10. This result is driven by volatility in the price of robots which also includes exchange rate volatility. Other indicators of volatility such as GDP are not significant although negatively signed. It is possible that this reflects the correlation between volatility and structural change with new technology investment such as robots being favoured by a loosening of the industrial structure (Sentance and Urga 1997). Apart from volatility, the authors allowed the rate of inflation to affect the adjustment speed to test the prevailing orthodoxy of the virtuous effect of low inflation. The result here indicated a perverse effect with high inflation encouraging faster adoption of robots. This is perhaps not all that surprising. It has been noted elsewhere that high inflation might encourage quality enhancing investment in a context where inflation might facilitate a corresponding widening of the profit margin (Wardlow 1994). Overall these contributions take the debate on investment forward in a number of respects and certainly provide a snapshot of available knowledge. More detailed empirical work will be needed at all levels of aggregation, but particularly at the micro level, to fully resolve the issues raised in this introduction and in the chapters which follow. There is space for both formal econometric work, simulations, decision theory, and case studies to help resolve the many puzzles that remain. We hope that the gathering interest in the area revealed in Figure 1.1 will prove sustained. Notes 1 Figure 1.1 shows the number of articles identified in the Social Science Citation Index using the keywords ‘capital investment, fixed investment, capital expenditure’. Although this fails to capture some important contributions, it excludes articles purely concerned with financial investment. 2 See Chick (1998) for a discussion of formalism and ‘mock’ precision: Chick, V. (1998) ‘On knowing one’s place: the role of formalism in economics’. 3 The overseas operations of UK companies have tended to generate profits not only larger than the domestic profit rate but significantly higher than other countries overseas return on foreign assets, especially Europe and Japan. It is quite possible that this has led to a submissive response by UK based business in the manner described for US industries in Scherer (1991). 4 Chirinko (1987) derives the relationship between marginal and average q under non-constant returns and imperfect competition. 5 Investment and q may also be jointly determined as in real option models (see below) or where merger activity is affecting both. 6 Quite apart from the convexity assumption, it is unclear why cost of adjustment involved in building or modernising a plant should always depend on the firm’s existing capital stock. Nor is it clear that a rational manager would always decide to overbuild in order to reduce future adjustment costs, though that is implicit in the models. 7 With non-convexities alone, it is possible to reformulate the q-model to incorporate irreversibility (Abel and Eberly 1994; Eberly 1997). 8 The cost-minimising alternative specification where output is determined exogenously by demand suggests that capital, output and the ratio of the price of capital to output price should be cointegrated; this also appears not to be the case. 9 Another possible constraint on adjustment is an overhang of excess capital which might constrain new capital formation (Whittaker 1998). It is not clear even in theory how excess capital will be eliminated (Ghemawat and Nalebuff 1985; Grant 1990; Pindyck 1991). It seems unlikely, however, that this situation characterises more than a few industries in the UK given the depth of recent recessions, but it may apply to some extent in Europe. 10 More complex models have wealth effects which depend on savings parameters (Skott 1988). 11 There is a large literature on the so-called Feldstein-Horioka paradox—a close and apparently robust statistical correlation between rates of domestic saving and rates of domestic investment (Feldstein and Horioka 1980; for a review see Devereux 1996). One approach to the puzzle is simply to see the relationship as the converse of a tendency for the balance of payments on current account (i.e. the amount that needs to be borrowed on capital account) to be stable in the medium to long term. One reason for this might be an exchange rate risk that tends to increase as capital imports (or exports) mount; an alternative might be endogenous government policy responses. In any event, this approach suggests that the relationship is symptomatic of a binding balance of payments constraint, driven by Harrod’s foreign trade multiplier (for a discussion see Fagerberg 1996). An implication of this is that new investment if it is to be successful in stimulating growth should ultimately relieve this balance of payments constraint, though that does not imply that investment must be directly concentrated in the tradable sector.

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12 The bias arises because of Jensen’s inequality. 13 Where there are sharply decreasing economies of scale it is possible that the rise in FK could be reversed: the exact condition is that σ>1/(1–η) where σ is the elasticity of substitution and η is the economies of scale parameter. However this is considered unlikely. 14 Note, however, this model does not consider the case of capital being unused, i.e. it makes no distinction between the marginal return on capital in use and the marginal return return on capital purchased. (The K in the equation for πK should relate to capital purchased, whereas the K in the equation for πL relates to capital in use.) 15 Where variable cost w is included replace p by p-w. For a flexible-price version of the Newsboy model see Driver et al. (1996). 16 In some models, information can only be obtained by commitment. This may be the case with R&D investments. In such cases the use of option values suggests that investments should be judged at a lower hurdle rate than the user cost because an option is being acquired. A similar reversal can take place when there are long delivery lags. Here higher uncertainty may tend to speed up uncertainty (Bar-Ilan and Strange 1996). 17 Some industries may have coordination mechanisms to facilitate high capacity utilisation (Gilbert and Lieberman 1987). In this case the timing of expansion depends on a conjecture in respect of new entry. 18 The acquisition of options is treated more briefly. ‘How do firms obtain investment opportunities?…they arise from a firm’s managerial resources, technological knowledge, reputation, market position, and possible scale, all of which may have been built up over time’ (Pindyck 1991, p. 1111). Some have argued that the dependence of investment on these previously acquired options is a main issue in investment theory (Gordon 1994). 19 This clarification explains why there has traditionally been a split between the finance function and the strategy function in the planning of capital investment— traditionally financial analysis (NPV) did not include option values (Kaplan 1986). 20 Guiso and Parigi (1996) look at the effect of irreversibility using Italian firm-level data on investment intentions, output expectations and measures of subjective uncertainty as well as three separately defined measures of irreversibility including direct perception, cyclicality of the industry and extent of second-hand sales. In panel data estimation they show that the more irreversible the investment context, the greater the negative effect of uncertainty. See also Driver (1999). 21 The failure of the relative price terms in the equations is not unusual. It is often argued that the cost of capital is an endogenous cyclical variable which makes it difficult to observe causal effects. In a study of Irish investment, where the cost of capital varies considerably due to variation in capital grants and where the interest rate may be less correlated with the domestic cycle, a strong cost of capital effect is observed in a similar cointegrating relationship (Driver and NESC Secretariat 1998). 22 The finding that forward looking behaviour matters is also supported by the finding that direct survey-based expectations add to the explanatory power of the equations. This finding mirrors a similar result for Australian manufacturing investment (Driver and Dowrick 1997). 23 Whitaker (1998) also considers other possible reasons for the slow recovery of investment in the post-1992 period, including measurement error in real investment, the role of capacity utilisation and financial variables. 24 Guiso and Parigi find evidence that the negative sensitivity of investment to uncertainty increases with market power. However, they proxy demand elasticity by the price margin which may be correlated with capital intensity. 25 It may also be noted that the data employed is industry data. This raises complications because as Pindyck (1993) has noted in response to Caballero’s argument, industry-level uncertainty can have a negative effect on investment even in perfect competition. This arises because industry price uncertainty is accompanied by uncertainty over entry which biases down the upside.

References A.B.Abel (1980) ‘Empirical investment equations: an integrative framework’, 12th Carnegie Rochester Conference on Public Policy, Vol. 12, pp. 39–91. A.B.Abel (1983) ‘Optimal investment under uncertainty’, American Economic Review, March, 73, 1, 228–33. A.B.Abel and O.J.Blanchard (1983) ‘Investment and sales’, working paper No. 2050, National Bureau of Economic Research. A.B.Abel and O.J.Blanchard (1986) ‘The present value of profits and cyclical movements in investment’, Econometrica, 54, 2, 249–73. A.B.Abel and J.Eberly (1994) ‘A unified model of investment under uncertainty’, American Economic Review, 84, 5, 1369–84. A.B.Abel, A.K.Dixit, J.C.Eberly and R.S.Pindyck (1996) ‘Options, the value of capital and investment’, Quarterly Journal of Economics, 111, 3, 753–77. K.Aiginger (1987) Production and Decision Theory under Uncertainty, Oxford: Basil Blackwell. P.Arestis and M.Sawyer (1998) ‘The macroeconomics of New Labour’, mimeo, University of East London. A.Bar-Ilan and W.C.Strange (1996) ‘Investment lags’, American Economic Review, 86, 3, 610–22. C.R.Bean (1989) ‘Capital shortage and permanent unemployment’, Economic Policy, April, 11–54. C.R.Bean (1991) ‘An econometric model of manufacturing investment in the UK’, Economic Journal, March, 91–121. O.Blanchard (1997) ‘The medium run’, Brookings Papers on Economic Activity, 2, 89–158. O.Blanchard, C.Rhee and L.Summers (1993) ‘The stockmarket, profits and investment’, Quarterly Journal of Economics, 108, 1, 115–36. M.Blomstrom, R.E.Lipsey and M.Zejan (1996) ‘Is fixed investment the key to economic growth?’, Quarterly Journal of Economics, 111, 3, 269–76. R.Blundell, S.Bond and C.Meghir (1992) ‘Econometric models of company investment’, in L.Mayyas and P.Severstre (eds), The Econometrics of Panel Data: handbook of theory and applications, Dordrecht: Kluwer Academic Publishers.

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G.Bombach (1985) ‘Post-war economic growth revisited’, Professor Dr.de Vries Lectures in Economics; theory; institutions; policy, Vol. 6, Amsterdam: North Holland. S.Bond and T.Jenkinson, (1996) ‘The assessment: investment performance and policy’, Oxford Review of Economic Policy, 12, 2, 1–29. B.Bosworth (1981) ‘Comment’ on L.H.Summers, ‘Taxation and Corporate Investment: a q-theory approach’, Brookings Papers on Economic Activity, 61, 128–32. R.J.Caballero (1991) ‘On the sign of the investment-uncertainty relationship’, American Economic Review, March, 81, 279–88. R.J.Caballero, E.M.Engel and J.C.Haltiwanger (1995) ‘Plant-level adjustment and aggregate investment dynamics’, Brookings Papers on Economic Activity, 2, 1–54. M.Catinat, R.Cawley, F.Ilzkovitz, A.Italianer and M.Mons (1987) ‘The determinants of investment’, European Economy, 31. V.Chick (1998) ‘On knowing one’s place: the role of formalism in economics’, Economic Joumal, 108, 451, 1859–69. R.S.Chirinko (1987) ‘Intertemporal constraints, shadow prices and financial asset values’, NBER working paper No. 2247, May. R.S.Chirinko (1993) ‘Business fixed investment spending modeling strategies, empirical results, and policy implications’, Journal of Economic Literature, 31, 1875–1911. R.S.Chirinko (1994) ‘On the Keynesian investment function and the investment function(s) of Keynes’, in P.Davidson (ed.), Can the Free Market Pick Winners?: what determines investment, Armonk, NY: M.E.Sharpe. P.Clark (1979) ‘Investment in the 1970s: theory, performance and prediction’, Brookings Papers on Economic Activity, 1, 73–113. P.Clark (1993) ‘Tax incentives and equipment investment’, Brookings Papers on Economic Activity, 1, 317–47. J.Conlisk (1996) ‘Why bounded rationality?’, Journal of Economic Literature, 34, 2, 669–700. K.Cowling and R.Sugden (1996) ‘Capacity, transnationals and industrial strategy’, in J.Michie and J.Grieve Smith (eds), Creating Industrial Capacity, Oxford: Oxford University Press. J.Crotty (1996) ‘Is new Keynesian investment theory really “Keynesian”? Reflections on Fazzari and Variato, Journal of Post Keynesian Economics, 18, 3, 333–57. J.B.DeLong and L.H.Summers (1992) ‘Equipment investment and economic growth: how strong is the nexus? ’, Brookings Papers on Economic Activity, 2, 157–99. M.P.Devereux (1996) ‘Investment, saving, and taxation in an open economy’, Oxford Review of Economic Policy, 12, 2, 90–108. M.P.Devereux, and S.Schianterelli (1990) ‘Investment, financial factors and cashflow: UK panel data’, in G.Hubbard (ed.), Asymmetric Information, Corporate Finance and Investment, Chicago: University of Chicago Press. A.Dixit and R.Pindyck (1994) Investment Under Uncertainty, Princeton: Princeton University Press. S.Dowrick (1994) ‘Fiscal policy and investment: the new supply side economics’, mimeo, Australian National University, Canberra. C.Driver (1992) ‘The origin of predictable behaviour: comment’, American Economic Review, 82, 1, 368. C.Driver (1999) ‘Contrasting effects of risk on investment in two sectors: evidence from Ireland on real options’, Economic Letters, forthcoming. C.Driver and D.Moreton (1991) ‘The influence of uncertainty on UK manufacturing investment’, The Economic Journal, 101, 409, 1452–59. C.Driver and D.Moreton (1992) Investment, Expectations and Uncertainty, Oxford: Basil Blackwell. C.Driver and S.Dowrick (1997) ‘Investment intentions and investment realisations in Australian manufacturing’, Australian Economic Papers, 36, 68, 90–105. C.Driver and NESC Secretariat (1998) ‘Econometric analysis of investment and risk’, Chapter 4 of NESC Private Sector Investment in Ireland, pp. 49–56, Dublin: National Economicand Social Council. C.Driver, S.Abubacker and G.Argiris (1996) ‘Capacity choice under monopoly, flexible price and demand uncertainty’, Southern Economic Journal, 63, 2, 526–32. J.Eberly, (1997) ‘International evidence on investment and fundamentals’, European Economic Review, 41, 6, 1055–78. R.Eisner (1994) ‘National savings and budget deficits’, Review of Economics and Statistics, February, 181–5. J.Fagerberg (1996) ‘Technology and competitiveness’, Oxford Review of Economic Policy, 12, 39–51. S.M.Fazzari and A.M.Variato (1994) ‘Asymmetric information and Keynesian theories of investment’, Journal of Post Keynesian Economics, 16, 3, 351–70. S.M.Fazzari and A.M.Variato (1996) ‘Varieties of Keynesian investment theories: further reflections’, Journal of Post Keynesian Economics, 18, 3, 359–68. S.M.Fazzari, R.G.Hubbard and B.C.Petersen (1988) ‘Financing constraints and corporate investment’, Brookings Papers on Economic Activity, 1, 141–95. M.Feldstein (1994) ‘Tax policy and international capital flows’, NBER working paper No. 4851. M.Feldstein and C.Horioka (1980) ‘Domestic saving and international capital flows’, Economic Journal, 90, 314–29. R.Ford and P.Poret (1991) ‘Business investment: recent performance and some implications for policy’, OECD Economic Studies, 16, Spring, 79–131. P.A.Geroski and S.Machin (1994) ‘Innovation, profitability, and growth over the business cycle’, in K.Aiginger and J.Finsinger (eds), Applied Industrial Organisation, Dordrecht: Kluwer Academic Publishers. P.Ghemawat and B.Nalebuff (1985) ‘The devolution of declining industries’, The Quarterly Journal of Economics, 105, 1, 167–86. V.Ghosal and P.Loungani (1996) ‘Product market competition and the impact of price uncertainty on investment: some evidence from US manufacturing industries’, Journal of Industrial Economics, 44, 217–28. R.J.Gilbert and M.Lieberman (1987) ‘Investment and coordination in oligopolistic industries’, R Journal of Economics, 18, 17–33.

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S.Gilchrist, and C.P.Himmelberg (1994) ‘Evidence on the role of cash flow for Investment’, mimeo, Board of Governors, Federal Reserve System. M.J.Gordon (1994) ‘The neoclassical and a post-Keynesian theory of investment’, in P.Davidson (ed.), Can the Free Market Pick Winners?: what determines investment, Armonk, NY: ME Sharpe. C.W.J.Granger (1994) ‘Forecasting in economics’, in A.S.Weigend and N.A. Gershenfeld (eds), Time Series Prediction Wokingham: Addison-Wesley. R.M.Grant (1990) ‘Exit and rationalisation in the British cutlery industry 1974–1984’, in C.Baden-Fuller (ed.), Managing Excess Capacity, Oxford: Basil Blackwell. L.Guiso and G.Parigi (1996) ‘Investment and demand uncertainty’, discussion paper No. 1497, CEPR London. D.S.Hamermesh and G.A.Pfann (1996) ‘Adjustment costs in factor demand’, Journal of Economic Literature, 34, September, 1264–92. R.Hartman (1972) ‘The effects of price and cost uncertainty on investment’, Journal of Economic Theory, October, 5, 258–66. R.Heiner (1983) ‘The origin of predictable behavior’, American Economic Review, September, 560–95. T.Hoshi, A.K.Kashyap, and D.Scharfstein (1990) ‘Corporate structure and investment: evidence from Japanese panel data’, Quarterly Joumal of Economics, 106, 1, 33–60. X.Hu, and F.Schianterelli (1994) ‘Investment and financing constraints: a switching regression using US firm panel data’, Boston College working paper. R.G.Hubbard (1998) ‘Capital market imperfections and investment’, Journal of Economic Literature, 36, March, 193–225. N.Jenkinson (1996) ‘Savings, investment and real interest rates’, Bank of England Quarterly Bulletin, February, 51–60. S.N.Junankar (1988) ‘The CBI Industrial Trends Survey: 30 years of interpretation and analysis’, CBI Economic Situation Report. S.N.Junankar (1990) ‘How do companies respond to the Industrial Trends Survey?’, CBI Economic Situation Report. R.S.Kaplan (1986) ‘Must CIM be justified by faith alone?’, Harvard Business Review, March-April 87–95. S.N.Kaplan, and L.Zingales, (1998) ‘Do investment-cash flow sensitivities provide useful measures of financing constraints’, Quarterly Journal of Economics, 113, 1, 169–215. M.Kitson and J. Michie (1996) ‘Britain's industrial performance since 1960: under-investment and relative decline, The Economic Journal, 106, 434, 196–212. M.Lieberman (1985) ‘Capacity utilisation in the chemical processing industries: theoretical models and empirical tests’, Research Paper No. 817, Stanford University Graduate School of Business. J.W.Lomax, (1990) ‘A model of ICC's dividend payments’, Bank of England Discussion Paper No. 52, London UK. L.J.Maccini (1987) ‘Adjustment costs’, in J.Eatwell, M.Millgate and P.Newman, The New Palgrave: a dictionary of economics, Vol. 1, 23–5. S.A.Marglin and A.Bhaduri (1989) ‘Profit squeeze and Keynesian theory’, in S.A.Marglin and J.Schor (eds), The Golden Age of Capital Accumulation, Oxford, Clarendon. D.Mayes and G.Young (1993) ‘Industrial investment and economic policy’, discussion paper No. 56, National Institute of Economicand Social Research, London. S.J.Nickell (1978) The Investment Decisions of Firms, Cambridge: Cambridge University Press. S.J.Nickell and D.Nicolitsas (1996) ‘Does innovation encourage investment in fixed capital?’, Institute of Economics and Statistics, Oxford. O.A.Nilsen and F.Schiantarelli (1996) ‘Zeros and lumps in investment’, mimeo, Boston College and World Bank. E.Penrose (1959) The Theory of the Growth of the Firm, Oxford: Basil Blackwell. R.S.Pindyck (1988) ‘Irreversibility, investment and the value of the firm’, American Economic Review, December, 969–85. R.S.Pindyck (1991) ‘Irreversibility, uncertainty and investment’, Journal of Economic Literature, 29, 1110–48. R.S.Pindyck (1993) ‘A note on competitive investment under uncertainty’, American Economic Review, March, 273–7. R.S.Pindyck and A.Solimano (1993) ‘Economic instability and aggregate investment’, in O.J.Blanchard and S.Fischer (eds), NBER Macroeconomics Annual 1993, Cambridge, MA: MIT Press. S.Price (1995) ‘Aggregate uncertainty, capacity utilisation and UK manufacturing investment’, Applied Economics, 27, 147–54. P.Robinson (1996) ‘Skill shortages and full employment’ in J.Michie and J.Grieve Smith, Creating Industrial Capacity, Oxford: Oxford University Press. D.Rodrik (1994) ‘King Kong meets Godzilla: the World Bank and the East Asian miracle’, CEPR discussion paper No. 944, London, April. S.Roper et al. (1996) Product Innovation and Development in UK, German and Irish Manufacturing, Northern Ireland Economic Research Centre, University of Strathclyde, and IFO Institute, Munich. M.Rothschild (1971) ‘On the cost of adjustment’, Quarterly Journal of Economics, 85, 4, 605–22. M.Rothschild and J.Stiglitz (1971) ‘Increasing risk: its economic consequences’, Journal of Economic Theory, 3, 1, 66–82. R.E.Rowthorn (1995) ‘Capital formation and unemployment’, Oxford Review of Economic Policy, 1,1, 26–39. R.E.Rowthorn (1996) ‘Unemployment, wage bargaining and capital-labour substitution’, mimeo, University of Cambridge. F.M.Scherer (1986) ‘Time-cost trade off in uncertain empirical research projects’, Chapter 4 of Innovation and Growth, Cambridge, MA: MIT Press. F.M.Scherer (1991) ‘International R&D races: theory and evidence’, in L.G. Matteson and B.Stymne (eds), Corporate Industry Strategies for Europe, Barking: Elsevier Science Publishers. F.Schianterelli (1996) ‘Financial constraints and investment: methodological issues and international evidence’, Oxford Review of Economic Policy, 12, 2, 70-87.

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C.L.Schultze (1987) ‘Saving, investment and profitability in Europe’, in R.Lawrence and C.Schultze (eds), Barriers to European Growth: a transatlantic view, Washington: Brookings, 509–39. A.Sentance and G.Urga (1997) ‘Profitability, structural change and business investment in the UK’, London Business School, October. D.J.Sharp (1991) ‘Uncovering the hidden value in high-risk investments’, Sloan Management Review, Summer, 69–74. P.Skott (1988) ‘Finance, saving and accumulation’, Cambridge Journal of Economics, 12, 339–54. P.Stoneman, ed. (1995) Handbook of the Economics of Innovation and Technological Change, Oxford: Basil Blackwell. J.B.Taylor (1982) ‘The Swedish investment funds system as a stabilisation policy rule’, Brookings Papers on Economic Activity, 1, 57–105. W.Vickery (1993) ‘Today’s tasks for economists’, American Economic Review, 83, 1, 1–11. A.Wardlow (1994) ‘Investment appraisal criteria and the impact of low inflation’, Bank of England Quarterly Bulletin, August, 250–4. T.M.Whited (1992) ‘Debt liquidity constraints and corporate investment: evidence from panel data’, Journal of Finance, 47, 1425–6. S.Whittaker (1998) ‘Investment in this recovery: an assessment’, Bank of England Quarterly Bulletin, February, 38–47. G.Young (1994) ‘The influence of foreign factor prices and industrial taxation on fixed investment in the UK’, National Institute Discussion Paper, 66.

2 Finance, profitability and investment in manufacturing Brian Henry, Andrew Sentance and Gioυanni Urga1

Introduction The sluggishness of capital investment is one of the more puzzling features of the UK economic recovery from the 1990s recession. Investment is normally much more volatile than the economy as a whole, increasing strongly in recoveries and falling sharply in recessions. Yet between 1992 and 1996, total fixed capital formation increased by just 4.4 per cent—an average rate of increase of 1.1 per cent. In the same period, GDP growth averaged over 2.5 per cent. Investment actually fell in 1995 and rose by just over 1 per cent in 1996 according to the current official estimates. This situation is very different from the early years of the 1980s recovery. In the first three years of that recovery alone— between the first half of 1981 and the first half of 1984—fixed investment rose by 20 per cent, accounting for over a third of the 8.5 per cent rise in GDP seen over that period. The 1980s appear unusual; as Figure 2.1 shows, the current investment recovery is more in line with the experience of the 1970s. Then, however, investment was held back by weak profitability and industrial relations problems in British industry. The industrial relations scene has since been transformed. But the weakness of investment does not appear to be due to any shortage of funds for business to invest, as company profits have risen by over 50 per cent. In manufacturing, a similar story can be told except that this recovery now appears to be the outlier. Figure 2.2 shows that since 1992 investment first grew at roughly the same rate as during the 1980s recovery but, from mid-1995, fell away. So far, the 1990s recovery in manufacturing investment has not matched that of the two previous episodes. Possible explanations for this unusual behaviour are investigated in this chapter. To evaluate possible explanations of sluggish investment, we concentrate on the case of manufacturing. The approach is to estimate models of manufacturing investment, which take into account demand effects and financial constraints in different ways. In short, we seek to establish an econometric specification which appears to fit the data well, is consistent with conventional economic theories of investment behaviour, and then test its implications for investment during the recovery.

Figure 2.1 Total UK fixed investment (volume index: trough of recession=100).

The plan of the chapter is to review the relevant theory next. We go on to describe the estimation techniques used. We then present data analysis and econometric results before offering conclusions.

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Figure 2.2 Manufacturing investment recoveries (trough of recession 1992Q1=100).

Theory The background Both the influence of financial factors and the effects of expectations of future profitability and demand upon investment have been continuing concerns in the empirical literature on investment determination. Concerning the success of this work, the view expressed by two of the more distinguished economists in the field is salutary. The explanation of aggregate and sectoral investment spending has been one of the less successful endeavours in empirical economies. The problem is not just that these models have been unable to explain or predict more than a small proportion of the movements of investment. In addition, constructed quantities that in theory should have strong explanatory power—such as Tobin’s q, or various measures of the cost of capital—in practice do not. (Dixit and Pindyck 1994, p. 419) As the quote from Dixit and Pindyck makes clear, the present state of research on investment, and on the empirical role of financial effects on investment in particular, is highly unsatisfactory. There is little evidence which supports neo-classical or ‘q’ variants of cost of capital and/or aggregate valuation effects on investment. (For similar conclusions see also Chirinko 1993.) Hence, one of the emphases which this chapter has is in how financial effects may work, including the nature of financial constraints (meaning broadly where firms may not be able to borrow at prevailing lending rates). It needs to be made clear at the outset that there is no attempt to rigorously derive an encompassing framework within which the alternative hypothesis— of financial effects or otherwise—can be tested against each other. The models are too diverse, and underlying theoretical assumptions vary too much for that. Among the most obvious of these differences are assumptions about imperfections in product and financial markets, the detail with which the tax structure is modelled, the alternative hypothesis about expectations formation, and finally different assumptions about technology. There are also differences which arise in how firms’ behaviour is analysed. In most models of investment, the underlying model is that of optimal factor demands, but in others a bargaining framework is used, determining both investment and wages (e.g. Denny and Nickell 1992). Indeed, given this diversity, some of these issues are not even reviewed in this chapter. We do not for example investigate the effect of different assumptions about technology, or the effects of joint modelling of factor demands. Nor, in spite of quoting from Dixit and Pindyck does uncertainty figure in what we do from here on. A recent paper providing empirical results for capital accumulation in a joint factor demand framework based on a generalised cost function is provided in Nixon and Urga (1997). For an approach to the issue of technology, see Young (1996) who discusses shortcomings in vintage production function models and contrasts these with more orthodox technologies. Econometric studies with some support for effects from uncertainty are reviewed in Driver et al. (1996). Although we do not intend to provide a review of the many theories, and different empirical results in the investment literature, none the less it is our intention to provide an assessment of the empirical merits of a selection of models which are presently in existence. For this we need to describe the alternatives we aim to cover in a unified framework. We proceed to this now, starting with a basic model, and amending this to incorporate what, in our judgement at least, are the most important additions to this basic model.

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The basic model We start with a few basic assumptions about the behaviour of the firm. In the models discussed here and later, the assumption underlying the analysis is that of a representative firm, which is intertemporally optimising subject to the standard assumptions of quadratic adjustment cost. We do this because it is then convenient to marshall the different models of investment which follow based on differing assumptions about product market competition, financial effects, and bargaining behaviour, in terms of their general implications for the future expected rental stream accruing to a unit increase in capital (net of adjustment costs). As will be seen, different models can be described in terms of what they say about this. So to start with a basic competitive model (the extensions produced by assumptions of imperfect competition, financing constraints, and expectations formation are described below), the firm's expected cashflow in period t(CF)t can be expressed. (1) where the output price is treated as numeraire, E refers to expectations formed in period t, F is the production function dependent on labour and capital (L and K respectively), and G is an adjustment cost function, arising because capital is quasi-fixed, hence we assume G(.) depends on K and investment (I). Lastly, W and PI are the real prices of labour and investment respectively (we make the assumption of perfect factor markets for the present). Constrained maximising proceeds along familiar lines. That is, the firm maximises (2) where δ is the exogenous rate of decay in the capital stock, and (1+r) is the discount factor. To obtain an equation which may be estimated, the optimal solution for (2) needs to incorporate a specific functional form for G(.), the adjustment cost function. Most often this assumes costs are quadratic in gross investment, linear homogenous in I and K, and affected by a technology shock (τ) (see Summers 1981). Hence, (3) where α is the adjustment cost parameter (the larger is α, the more slowly does investment respond), and time subscripts are suppressed for convenience. Assuming goods, factor and financial markets are perfect, substituting (3) into (1), optimising (2) w.r.t. K and solving subject to a transversality condition gives the investment equation (4) where ^ is defined as . In turn, λ is the shadow value of a change in the return to capital—the marginal revenue product of capital net of adjustment costs—and ρ equals (1−δ)/(1+r). Hence (4) shows that investment depends on the discounted sum of ‘spot’ marginal revenue products, and investment (relative to the capital stock) increases when the present value of benefits exceeds the present costs of investment, at a rate dictated by the adjustment cost parameter (α). This basic relationship is fundamental, and in the models which follow, alternative assumptions regarding product market competition, and financial constraints are summarised in terms of their implications for this net rental flow. Before proceeding to consider alternatives to (4) note that it can be cast as a q model, which typically sets the investment ratio as a function of the ratio of the financial value of the firm to the replacement value of its capital stock. According to this interpretation, Equation (4) is (5) where qA is average q, equal in turn to the ratio of the financial value of the firm to capital stock at replacement cost ((V/P1K) where V is the financial value of the firm). Although there are many problems in (5), one of its attractions is that it gives a straightforward way of relating the unobservable forward looking variable E(^) in (4) to observables, which in principle incorporate expectations about the future. None the less, this is not the only way to deal with expectations, and below other ways to include expectations in these equations, including direct measures of expectations, are illustrated. Before that, we describe how this simplified model can be extended, and it will be these extensions which will form the basis of the empirical models tested later. These extensions will deal with the introduction of imperfectly competitive product markets, the role of internal financing, alternative ways to include financial constraints, effects on investment arising from the presence of unions with bargaining power, and lastly the possible effects of expectations. Extensions to the basic model To confine the alternatives considered here to manageable proportions, we will make a few assumptions which will be common to each. Apart from the assumptions of quadratic adjustment costs, the models estimated later will be based on the

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assumption of monopolistic competition. This assumption is one of the possible rationales for incorporating output in the investment equation, something for which there is empirical support. Thus we suppose that technology is given by the linear homogeneous production function, depending upon capital (K) and labour (L), (6) and we further suppose that there are internal adjustment costs G (I, K) which are assumed to be quadratic. We assume the representative firm also faces a demand function (7) where is the general price level, η the elasticity of demand and a a shift parameter. Hence, the implications of imperfect competition means that the static marginal conditions for capital are amended from, e.g. pFi=pi, where p is the product price, and pi the factor price of the ith input, to be p(1−η−1)Fi=pi, where η is the elasticity of product demand. In this model, it is via this last term that output can be introduced. Below we comment on a further possible justification, arising from an expected output constraint (see below) Allowing for retentions and dividends Building further on the baseline model, to allow for retentions and dividends, as well as providing a rudimentary account of the company tax system, suppose the firm chooses K, L, Vn and I to maximise wealth of its shareholders, i.e. (8) Vn

where D are dividends, the value of new share issues, and Bj the discount factor. The γ term measures the relative tax advantage of dividends versus retained earnings (see Schiantarelli and Georgoutsos 1990). The first order condition for maximising Vt w.r.t. I, subject to the usual non negativity constraints on dividends, shares, and the capital accumulation equation Kt=(1−γ) Kt−1+It, where γ is the rate of depreciation, is that the cost of a marginal unit of capital must equal its shadow value λk (the non negative multiplier on the capital accumulation constraint). This, however, now has different implications as compared with the baseline model, (4). Now, the marginal shadow value of capital λk becomes (suppressing future time subscripts for simplicity) (9) where β is the discount factor. Other terms in equation (9) are as follows. • A is the discounted present value (DPV) of tax savings due to investment allowances. • H is the DPV of the cash flows which are due on the firm's debt, including all interest payments. • C is the DPV of the decrease in revenues produced by an additional unit of capital, since—given (7)—product price is lowered to sell additional output. The C terms capture the capitalised value of this effect. This way of introducing output into the investment decision rule may not be particularly appealing from an a priori viewpoint. Assuming firms are output constrained on the other hand, implies a cost minimising model, where output and relative factor prices determine investment. A more promising approach is described in Precious (1987). Here the forward looking firm may experience regimes where output (sales) is constrained. Even if the firm is currently unconstrained, it may none the less expect at a future date to encounter such a constraint. Expected output will therefore enter its investment decision; but the investment equation is not just the familiar cost minimising equation dependent on output and relative factor prices only. • γ is a parameter which determines whether the firm finances itself through new issues or retentions at the margin. • By simplifying somewhat the equations (8) and (9) above, the investment equation in this case depends upon average q (qA), output (or more precisely, expected output) and a set of other variables. (10) Schiantarelli and Georgoutsos (1990). Xt includes any variables which drive a wedge between investment, output and qA. In the present example this is current cash flow relative to debt. But there are other contenders for inclusion in X, and in what follows, we group these into financial and real variables. Financial effects on investment Equation (10) includes a further financial variable in cash flow. But other variables have been proposed to proxy financial imperfections. The approach we take to this is a pragmatic one, starting from the proposition that with capital market imperfections, internal and external sources of finance are not perfect substitutes. This is not the place to review the relevant arguments in detail. Suffice to note that general arguments suggest that it may be an optimal strategy for banks to set interest

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rates where an excess demand for credit exists due to adverse selection since banks cannot fully monitor the net worth of firms (Stiglitz and Weiss 1992). Firms may also face a rising supply price of debt depending upon, e.g. firm size and risk category. Also there may be agency costs in issuing and maintaining debt. Finally, on the debt side, there may be moral hazard involved, where with a high debt/ income ratio, the incentives of the firm (managers acting on behalf of shareholders) and creditors diverge, so managers have an incentive to undertake higher risk and return projects. In similar vein there are related imperfections in raising funds via equity; adverse selection and transactions cost (especially for smaller firms) being problems. Financial effects which enter Xt in (10) are: • Cash flow. This assumes that firms may either use retained earnings or new issues (but in which model the decision is exogenous), so the investment equation includes a cash flow term. where B=the market value of debt, ζ the corporate tax rate, and i the interest rate on debentures. • Capital gearing. The investment model also can be extended to include debt. In this case, agency costs are assumed as well as the usual interest costs on debt. These lead to an additional term in capital gearing, defined as the stock of debt as a proportion of the capital base (see Cuthbertson and Gasparro 1995). • Liquidity. A further argument is that firms may face a rising supply price of capital when borrowing and the effects of this may be proxied by liquidity (defined as liquid assets to total liabilities) (Kelly and Owen 1985). Real factors in investment Here, the main additional elements in X are real wages and measures of union strength. This results in a ‘hold-up’ model, where part of the rental stream accruing to the new investment is captured by unions in the form of higher real wages. Rather than take the firm’s factor demand decision, this alternative is a two-stage solution, where the firm is party to a conventional Nash wage bargain, the solutions to which determines wages, based on which, the firm then sets prices, investment and employment to maximise profit (see Denny and Nickell 1992). In summary, the solution to the wage bargain takes the form where is the outsider wage. We suppose (∂ ln w)/(∂ ln k)=γ(UN) , where UN is an index of union power, (γ1>0), that employment is set to maximise profit, and that prices can then be inferred from (7). The expected profit stream is then (11) where ε=1−1/η, and where the expected value in (11) depends on the solution (W, P, L, Y) from the wage bargain, and profit maximising above. The marginal benefit of extra investment is then the DPV of ∂П/∂k over the planning horizon of firms, which is equated to cost (p∂c/∂I). The model then yields an investment equation conditioned upon aggregate demand (output), and the real wage as well as indices of union power (proxied by UN here), i.e. (12) In this model, and I3>0 are expected signs. Explicit expectations In the above models expected values of the determinants of investment enter the dynamic investment equation. Here, we note how this can be developed into a dynamic equation for estimation, using equation (10) as an example. As quadratic adjustment costs are assumed, write the Euler equation above as, in unrestricted form (13)

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Upon factorising in conventional fashion (13) may be written (14) , Xt and (Y/K)t and λ is the stable root of equation where A is the vector of variables determining I/K. In the present case (13). Equation (14) now has an expected forward convolution of the variables determining the investment capital ratio. As is well known, the expectational terms in (14) can be introduced in several ways. In the empirical section we will use two: model based which we refer to as rational expectations hypothesis (REH) expectations, and, where future output is concerned, survey based (CBI) measures. Later, we describe how both of these may be estimated. Summary of alternative specifications To sum up, in what follows we consider dynamic models of investment behaviour based on the standard quadratic adjustment cost framework, where firms have informed, forward looking expectations. The variables included in the investment models purposely reflect a wide range of alternatives which have figured in the literature. On financial influences, we consider • Cost of capital. • qA variables, including proxy variable such as profitability (Sentance and Urga 1995). • Variables proxying financial constraints. These include undistributed profits, and liquidity (Woods 1995), and capital gearing (Cuthbertson and Gasparro 1995). • Additional variables are suggested by enlarging the model to include bargaining behaviour (Denny and Nickell 1992). This extension introduces indices of union power, the real wage and real price of investment goods as possible additional determinants of investment. Finally, as expectations play a central part in the empirical work, two broad approaches are employed: rational or informed expectations mechanisms, and direct survey based measures. Estimation methods Before moving to describe the estimation results, we briefly note some of the technical matters which crop up in estimating models where future expectations of demand, factor prices, etc. occur. Where we use direct measures of output expectations based on CBI surveys of business expectations the main problem which arises is the conversion of the survey responses to a quantified series. This is an extensive topic, and we do not intend even to try to outline it here (Pesaran 1984 provides an introduction to this subject). Of the two most favoured methods—the regression and the probability distribution based methods, we have opted for the regression based approach. Pesaran (1984) discusses the rival merits of these methods, and recommends the regression method as preferable. The rest of this section then discusses some of the basic estimation problems which arise when applying a rational expectations assumption, and how these may be addressed. We also show how cointegration results can be captured with a forward looking model of this sort, using a transformation of the model first described in Callen, Hall and Henry (1990). Estimating the forward looking model under the REH The optimal dynamic equation for net investment (14) can be written (15) (ignoring expectational and other errors) where A is a linear function of the long run determinants of investment (i.e. the cost of capital, output, etc.), and R is the discount factor. As background to what is used in the next section, recall that two approaches to estimating rational expectations models have proved popular in the applied literature; the errors in variables model (EVM), and the extrapolative model. In the former, where the model in (15) is simplified to an unlagged, two period model for expositional purposes only, we would have (16) where is a weakly exogeneous expectations variable, and μt is white noise. Then to apply the EVM method, we note that by the REH, (17) i.e. the actual future variable is equal to its rationally expected value plus a random error (the ‘expectations’ error). Substituting (17) into (16) we get

FINANCE, PROFITABILITY AND INVESTMENT

23

(18) where εt=μt−δ1wt+1–δ2wt+2. Clearly, at+j (j=1,2) and εt are correlated. Furthermore, the composite error term is serially correlated, having moving average error. Methods normally applied to deal with these problems involve the use of instrumental variables (IV), giving consistent parameter estimates, together with a correction to the variance—covariance matrix to ensure valid inference (see Cuthbertson, Hall and Taylor 1992, for a short review). The alternative assumes that there is a model determining the expected variables, which may be estimated jointly with the structural model (15). Thus suppose this model for can At+j be represented by the VAR (19) 1 where δ (L) is a polynomial in the lag operator. Then by substituting (19) into (15) we may write the investment equation as (20) (21) A variety of routes are open when applying this, including using predictions from (19) as instruments in estimating (15). (So introducing similar estimation problems to those noted above), or in joint full information maximum likelihood (FIML) estimation of (15) and (19). Estimating (15) with extrapolated predictions from (19), will further need to apply the restrictions embodied in (21) for rationality to apply. In this way it can be seen that an unrestricted version of (20) would give a lagged model for optimal investment, but applying the restrictions in (21) gives the REH version. In principle, conditional tests of the application of the REH can be based on this. In the applications below, the techniques of using an IV version of the extrapolative model is employed, and we also propose tests of the role of forward looking expectations formation following on from our earlier remarks. Before moving on to that, we indicate how cointegration methods can be brought to bear on the estimation of dynamic models with forward looking terms. Cointegration and forward looking behaviour For this, we need to extend (15) to allow it to have longer lags. This is easily justified, and can arise from non-quadratic adjustment costs, or a respecification of the firms’ objective function to penalise deviations of growth in investment away from its planned path. Specifically if It follows an AR(2) process, then by supposing that the long run equilibrium value for investment is I*, the investment equation can be reparameterised into an Error Correction Model (ECM) form. (22) where λ2 is the stable root, and where the di are dependent upon λl, λ2 and R. I* now refers to the long run (cointegrating) value of investment, which depends on the long run cost of capital, long run profitability, etc. In other words, it corresponds to the long run version of the linear function A in equation (15). The ECM (17) incorporates the feedforward–feedback restrictions of the REH into this reparameterised model (see Callen, Hall and Henry 1990). Indeed, as in the case of extrapolated predictions, reviewed earlier, this enables tests of forward looking versus backward looking versions of the model to be performed, a point we take up below. Finally, as is evident from (22), the dynamic equation can be estimated by a two step procedure, even where there are rational expectations. The first step involves estimating an equation for the long run determinants of investment (I*). We use cointegration techniques for this. The second step introduces these estimates into (22). This enables us to concentrate in the first step upon evidence for cointegration in the alternative models reviewed here. Hence, in the next section, to estimate the model we use a Johansen procedure, for identifying long run, cointegrating relations for investment, before moving to dynamic equations, where these may be backward or forward looking. Data analysis and estimation results The plan of this section on empirical results is as follows. It aims at an evaluation of the investment model using data on the manufacturing sector. Although increasingly less important in determining aggregate investment behaviour, manufacturing is none the less important both in its own right, and because the availability of sector specific variables (such as the CBI data we

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use later) means a rich set of tests of expectations formation and financial effects can be made. First, we describe the time series properties of the data, before moving to cointegration tests. Time series and other preliminary analysis of the data Time series analysis of the data Univariate tests for the orders of integration for the variables used in this chapter are given in Table 2.1. Among these tests some give ambiguous, and in some cases, counter-intuitive results. The first four, the cost of capital (CC), the Tobin’s ‘q’ variable (Qa), output in manufacturing (YM) and expected output ( ) reject stationarity in levels. Of the next five, only capital gearing (CG) is unambiguously non-stationary. Investment in manufacturing (MI), has a suggestion of stationarity in the ADF(4). In what follows we treat this as I(1), given the ambiguity of these tests. Relative factor prices in manufacturing (FPM), and liquidity (L) are I(0) according to both DF and ADP tests. In the cointegration tests below, we have experimented with treating them as I(0) or I(1) in the cointegration vector: with little variation in the results. Finally union density (UN) is evidently not I(0), and although there is some evidence that it could be I(2), in what follows we treat it as I(1). The remaining three variables—the real wage (RW), capacity utilisation (CU) and profitability—are taken to be I(1). Capacity utilisation might be expected to be I(0), but for this sample appears not to be. Cointegration tests for manufacturing investment We provide first a set of results for range of specifications for the investment equation, using output as the scaling variable, with alternative cost variables and variables proxying financial constraints. The purpose of this part of the exercise is twofold. First, to assess the empirical grounds for Table 2.1 Orders of integration Levels DF

ADF (4)

Differences DF

ADF (4)

CC Qa YM

−2.58 −2.82 −10.2 −6.2 −1.3 −1.5 −11.58 −4.9 −1.38 −1.81 −10.0 −4.3 −1.58 −1.74 −7.2 −4.09 MI −2.20 −3.68 −12.0 −4.6 CG −1.09 −1.26 −10.4 −5.3 FPM −2.99 −3.56 −9.4 −5.87 L −3.01 −3.81 −9.7 −4.1 UN 2.6 0.17 −3.88 −2.66 RW −1.9 −1.4 −11.9 −3.9 CU −2.3 −2.9 −10.5 −4.16 Π −2.17 −1.9 −14.0 −5.1 Notes: Variables, except the qA, liquidity and cost of capital variables, are in logs. All tests are run with a deterministic time trend. The relevant critical values are −2.88 (DF) and −3.44 (ADF (4)).

financial influences on investment, as well as familiar cost of capital and q variables. Second, to derive a well specified levels equation which will be used in the next section to test between forward and backward formed expectations models. The strategy used here is to set up several alternative models, and use the cointegration results as a test of their basic features. We consider a ‘union’ model familiar from the hold-up literature, where we concentrate on the evidence for real wage and strength of unions effects. Next, we formulate a basic ‘neo-classical’ model based on the cost of capital. We also experiment with a cost minimising model together with direct liquidity effects. Next, we investigate the role of profitability in an investment model which some people have suggested as an empirical counterpart to a ‘q’ model. Finally, we consider a ‘q’ model, incorporating gearing in this as an additional explanatory variable. After the cointegration results we move to evaluating a hybrid model based on what appear to be the most promising of the results, and this version of the model is the one we use to explore how the recent behaviour of investment may be accounted for. The results of the cointegration tests on the alternative models are shown in Table 2.2.

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25

Model 1 is the best result we were able to obtain for the union model. Output is restricted to unity in this model, although it actually fails this restriction (x2(1)=7.07). So the implied parameter restrictions accord with theory when, and only when, this restriction is applied, and we conclude that overall this model is not generally supported by the data. Model 2 is a pragmatic ‘neo-classical’ model, and again does not fare well. The cost of capital term is incorrectly signed. Moreover, the model does not cointegrate at the 5 per cent level. Table 2.2 Cointegration tests on alternative models (1) (2) (3) (4)

MI MI MI MI

YM

RW

UN

1.0 1.2 1.0 1.0

0.39

−0.01

CC

CU

0.05

−0.008

FPM

L

Π

LR

LT

20.1* 27.4* 37.3** 29.5**

40.9** 44.4 63.9** 45.7**

0.01 0.73 – – 0.42 a Q CG (5) MI 2.1 0.56 −0.46 18.1 37.2 Notes: Accepts hypothesis of cointegration at the 10 per cent (*) or the 5 per cent (**) level. All models were estimated using a VAR of length 2. Equations (3)–(5) included restricted deterministic time trends, as these gave the best version of the model. The remaining equations used restricted intercepts only. Equation (1) was estimated to the end of 1992 due to limited availability of data on unionisation. All other equations used data ending in 1996Q4. LR is the eigenvalue test, LT the trace test for the presence of cointegration.

Model 3 is based on the cost minimising model reported by Woods (ibid.). It has an important effect from liquidity (L) but the relative price term (FPM) is incorrectly signed. Further variants of this are discussed below. Model 4 is a model of the form which Sentance and Urga used in the business sector case (Sentance and Urga (1998)). The profitability parameter (Π) is correctly signed and the model cointegrates. The imposed unit coefficient on output is satisfactory (The Wald test gave x2(1)=2.6). Model 5 is the qA model coupled with gearing (G) model described in Cuthbertson and Gasparro (1995). Again this is an example of a model which does not appear to cointegrate, but the parameter values accord with prior theory. In the sample period used originally by the authors (1968Q1–1988Q4), the model does cointegrate. But overall, the conclusion from our limited exercise is that it appears that the model is not robust when the original data set is extended. A hybrid model Although this exercise has been quite informal, it is instructive, in that some models appear to be ruled out. Models (1)–(2), and model (5) appear not to have cointegration and/or broad confirmation of their a priori assumptions. So next, more formal testing is conducted between models (3) and (4). These both introduce additional financial influences on investment: liquidity and profitability respectively. Can we choose between these two variables? For this we use the hybrid model (23) To test between these alternatives we use overidentification tests on the cointegration vector (which is just identified), restricting the hybrid model (23) above to conform to one or other of the models on which it is based. The tests we consider are first that a1=1, a4=0. This would reject a separate influence from relative factor prices. Next, we consider evidence that a2=0. If upheld, this implies profitability and relative factor prices can be excluded. Finally, we consider the other alternative; that it is liquidity and relative factor prices which can be excluded Table 2.3 shows the results of likelihood ratio tests conducted on a hybrid model which uses all the variables from (3) and (4) in the model. It also shows that the first model with the relative factor price excluded and a unit co-efficient on output is accepted. Dropping profitability (Π) is strongly rejected. The alternative, keeping Π in the model, but dropping liquidity (L) is also rejected, but less strongly than the previous case. So the tests are not conclusive as between the choice between liquidity and profitability. They clearly reject the use of relative factor prices. So where does that leave us? We conclude that a parsimonious model for the level of investment, based on output, profitability and liquidity is the preferred version according to our results. We will use this long run result in the dynamic model which follows. Finally, in this section, a dynamic investment equation is estimated. Given the results from Table 3 the first equation is used, which is a hybrid model, incorporating financial variables both as suggested by Woods (ibid.) and Sentance and Urga (ibid.). A dynamic ECM, estimated for the period 1968Q3–1992Q1, with the first equation from Table 2.3 used as the ECM term is shown next

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(24)

This equation passes the usual misspecification tests: there is no evidence of significant serial correlation inappropriate functional form non-normal errors or heteroscedastic errors Interestingly, it suggests that there are short run negative effects from the financial variables, although these effects are positive in the long run. Table 2.3 Hybrid model: likelihood ratio tests a1

a2

α3

a4

1.0 1.0

0 .53 —

1.12 1.14

— —

Restrictions

a1=1, a4=0 a1=1, a4=0 a2=0 1.0 0 .6 — — a1=1, a4=0 a3=0 Note: Equations included restricted time trends, which are not shown for convenience.

LR X2(2)=1.8 X2 (3)=20.4 X2 (3)=10.3

Having obtained what appears to be the most successful of the models (without explicitly incorporating expectations), the final exercise is to consider how well it copes with the expansionary phase post-1992. Standard predictive failure tests can enlighten us on this score. For this we use a further misspecification test on evidence for parameter instability in the model. For the period 1992Q2– (18) of 31.88, which does not reject 1996Q3, the predictive failure test for the model is marginally upheld (i.e. it gives a the null of no change at the 5 per cent level). The role of expectations in manufacturing This final section considers the potential role for expectations. As anticipated earlier, both direct, survey based, measures of expectations, and full REH expectations of the determinants of investment are used. We review the results in turn. CBI output expectations Initially, the dynamic error correction model just discussed is re-estimated using an expected output series generated from the CBI output expectations survey ( ). This uses the expected quarterly change given by quantifying the survey, together with the actual level of manufacturing output to obtain an expected level of output for the (end of) the next quarter. Thus The next step replaces the actual output level in the first equation of Table 2.3 with expected output. This led to a cointegrating vector for investment (LR=45.1*, LT=74.4*). The vector with a unit elasticity for output imposed then gave A result very similar to that obtained when using actual output. Proceeding to the dynamic model also includes an output ‘surprise’ term ( ) as an additional dynamic (I(0)) term. According to this, if output is above expected, than this will encourage investment, although we might expect this to be a slow process given the typical lags in adjusting investment. The empirical result below in equation (25) shows there is support for including such a term, and this version of the model also has a somewhat better predictive performance compared with its predecessor, i.e. it now does not reject the null of no change at 5 per cent level. Although this is not powerful evidence, it is suggestive that indicating introducing explicit expectations improves certain aspects of the model.

(25)

FINANCE, PROFITABILITY AND INVESTMENT

27

Explicit forward looking (REH) models The previous results were suggestive that output expectations might be useful in helping to account for recent investment behaviour. The next set of results takes this further, and tests whether explicit rational forward looking behaviour improves its behaviour even more. To do this, the reparameterised version of the forward looking model described earlier is used, and equation (22) which is the form in which the model is estimated, is repeated here for convenience. (26) There are considerable advantages in using this procedure of reparameterising the dynamic REH model. It enables the earlier cointegration results to be carried over into an REH model for one thing. Also, by comparing the results from the restricted (REH) model with an unrestricted version, a test of the REH restrictions can be made. According to the latter, if these restrictions are upheld, then, for this model., there is evidence that forward looking behaviour matters. Taking the results from Table 2.3 again, with the cointegration equation for investment dependent upon output, profitability and liquidity, a forward looking version of this model is that it is expected values of these variables which determine investment. As (22) above shows, these forward expectations hold over a finite future period. In what follows, up to six quarters is used. The REH model takes forward expected values for investment determinants, but as actual future values are used in estimation, IV methods are employed for reasons described earlier. The REH restrictions are those imposing symmetry between the backward looking and forward looking parts of (15) (the restrictions are between λl, λ2 and the dj in the equation). Hence, to impose the restrictions non linear estimation is also necessary, yielding estimation for the key parameters λ1 and λ2. Results are shown in Table 2.4 below. (Using non-linear least squares). Of these, the second equation, which restricts the forward planning horizon to three quarters, appears preferred. Its serial correlation properties are much improved compared with (1). The models in Table 2.4 are estimated subject to REH restrictions between the lagged part and the future, Table 2.4 REH models for investment λ1 (1)

0.97 (6.9) (2) 1.599 (4.9) Instruments lagged: L, YM

λ2

SE

DW

Horizon (quarters)

−0.35 (3.9) −0.89 (3.87)

0.113

0.8

6

0.14

2.3

3

led, part of the equation. Tests of the validity of these restrictions can be made to judge the applicability of the restrictions, and hence the plausibility of informed forward looking expectations. These tests use quasi-likelihood ratio (QLR) tests, which are more appropriate to non-linear IV equations such as those used here. For model 2 in the table, the QLR gave X2(4)=3.0, which does not reject the REH restrictions. To sum up this section, we have shown that some broadly defined financial variables appear to help in the explanation of manufacturing investment. But, even when these are included, the best version of our lagged model does not appear to account for the slow recovery in investment during the most recent recovery. Including survey based output expectations improves the predictive power of the model though, but not hugely, it needs to be said. Finally we test whether the restrictions necessary to support rational expectations formation are upheld. For the most plausible version of the model we use, the answer appears to be in the affirmative. Conclusions Our conclusions can be divided into two parts: first, we have investigated a number of different models of investment behaviour. We have focused on two issues which have been at the centre of empirical research in this area: the role of financial effects and of expectations upon investment. On the former, our work has tended to confirm that the familiar cost of capital and Tobin’s q variables do not have much explanatory power. However in tests between other financial variables which have figured in recent discussions the results are more encouraging. In particular, by discriminating between a range of variables using cointegration techniques, models using profitability and liquidity appear to be capable of accounting for much of investment behaviour. Building on these results, tests of the probable importance of expectation effects suggest that versions of the model using expected future values of these variables are preferred and we believe this is a fruitful area for further research—particularly using direct measures of expectations from business surveys. This modelling work does shed some light on the second issue, which is the apparently puzzling behaviour of investment over the recovery. Our first conclusion in this area is that there may be much less of a puzzle than is apparent at first sight. We

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have found it possible to establish satisfactory equations for manufacturing investment based on a long-run relationship between investment, output and profitability (or the rate of return). We also find a significant role for liquidity constraints, and in all equations there is support for a unit elasticity for output in the long-run, so we would expect investment to rise broadly in line with GDP, in the absence of major shifts in profitability. Admittedly recent investment performance in the manufacturing sector is more of a problem. Here, the puzzling phenomenon is the sharp drop in manufacturing investment in the first half of 1996. The model including expected output and other variables are only suggestive at this stage. They hint that expected demand and other factors did not improve sufficiently quickly in the recovery for investment to increase. However mismeasurement may be another explanation. The CBI Industrial Trends Survey has continued to show strong investment intentions while the data has been showing that capital spending has been collapsing. It is too early then to conclude from this study that expectations hold the key. Further work is needed to clarify this point. Data appendix

CC

Cost of capital in manufacturing

δ – depreciation rate r – (nominal) interest rate Pk – price of capital goods YM

GDP manufacturing

MI

Fixed capital formation—manufacturing

MK

Gross capital stock at 1990 replacement cost—total manufacturing

RW

Real labour cost=Average earnings manufacturing/GDP deflator

FPM

Relative factor prices (Manufacturing)=(CC * GDP deflator market prices)/average earnings manufacturing

CG

Gearing=ICC’s net indebtedness/(net capital stock at replacement cost+value of stocks)

L

ICS’s liquidity ratio: liquid assets as a proportion of liabilities

UN

Union density

qA

Average Tobins q: market value of equity and debt as a ratio of the replacement value of assets Expected output: quantified series obtained from the CBI output expectations survey

CU

Capacity utilisation (measured by responses to CBI question 4)

Π

Profitability: non oil profits as a percent of capital at replacement cost

Notes 1 Grateful thanks for help in collecting data used in this paper are due to James Clarke, David Upton, and Paul Robson for assistance in applying CBI survey based measures of expectations. Thanks are also due to Rod Whittkaer and Robert Woods HMT, and Nigel Jenkinson and Simon Whittaker, Bank of England for providing data. Michael Summer provided useful comments for which we would like to express our thanks. None of the above share in the responsibility for contents of the paper which rests entirely with the authors. Financial support from ESRC grant number L116251013, Macroeconomic Modelling and Policy Analysis in a Changing World, is gratefully acknowledged by Andrew Sentance.

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29

References Callen, T., Hall, S. and Henry, S., (1990), ‘Manufacturing Shocks; Expectations, Risk and Cointegration’, Economic Journal, 100, 756–772. Chirinko, R.S., (1993), ‘Business Fixed Investment Spending: Modelling Strategies, Empirical Results and Policy Implications’, Journal of Economic Literature, 31, 1875– 1991. Cuthbertson, K. and Gasparro, D., (1995), ‘Fixed Investment Decisions in UK Manufacturing: The Importance of Tobin’s Q Output and Debt’, European Economic Review, 39, 919–941. Cuthbertson, K., Hall, S. and Taylor, M., (1992), ‘Applied Econometric Techniques’, London: Harvester Wheatsheaf. Denny, K. and Nickell, S., (1992), ‘Unions and Investment in Britain Industry’, Economic Journal, 192, 874–887. Dixit, A. and Pindyck, R., (1994), ‘Investment under Uncertainty’, Princeton University Press. Driver, C., Abubaker, S., and Argiris, G., (1996), ‘Capacity Choice under Monopoly, Flexible Prices and Demand Uncertainty’, Southern Economic Journal, 63, 2, 526–532. Hall, S.G. and Henry, S.G.B., (1988), ‘Macro Economic Modelling’, Contributions to Economic Analysis, North-Holland. Kelly, C. and Owen, D., (1985), ‘Factor Prices in the Treasury Model’, GES working paper, 83. Nixon, J. and Urga, G., (1997), ‘Unemployment and the Capital Stock: Modelling the Supply Side of the UK Economy’, DP 18–97, Centre for Economic Forecasting, London Business School. Pesaran, M.H., (1984), ‘Expectations Formation and Macro-economic Modelling’, in P.Malgrange, and P.A.Muet (eds.), Contemporary Macroeconomic Modelling, Basil Blackwell. Precious, M., (1987), Rational Expectations, Non Market Clearing and Investment Theory, Clarendon Press. Schiantarelli, F., Georgoutsos, D., (1990), ‘Imperfect Competition Tobin’s Q and Investment’, European Economic Review, 34, 1061–1078. Sentance, A. and Urga, G., (1995), ‘Boom Time for British Manufacturing Investment’, Economic Outlook, 19, 3, London Business School. Stiglitz, J. and Weiss, (1992), ‘Credit Rationing in Markets with Imperfect Information’, American Economic Review, 71, 393–410. Summers, L., (1981), ‘Taxation and Corporate Investment: A q-Theory Approach’, Brookings Papers, 1, 67–127. Urga, G., (1995), Firms in Investment Decisions: Cash Flow, Unions and Ownership Structure. Evidence from Italian Firm Level Data, Centre for Economic Forecasting, London Business School. Woods, R., (1995), ‘Econometric Models of Business Investment: The Role of Factor Prices, and Financial Constraints’, HM Treasury. GES working papers 127, Treasury working papers 69. Young, G., (1996), ‘A New System of Factor Demand Equations for the NIESR Domestic Model’, paper presented to the Macroeconomic Modelling Bureau Seminar, University of Warwick, July.

3 Credit rationing versus consolidation of financial structure Jean-Bernard Chatelain1

Introduction A high sensitivity of investment with respect to cash-flows is quite often related to financial constraints affecting firms. But recently, Kaplan et Zingales [1997] found that low dividend companies in the widely quoted Fazzari Hubbard and Petersen [1988] study can be split in several groups when taking into account additional and mostly qualitative information on the extent to which these firms were effectively facing a financial constraint. It turned out that their ‘financially constrained companies according to their new information’ were exhibiting a very low investment—cash-flow sensitivity with respect to the other low-dividends companies. Recent tests of financial constraints used the Euler equation (the marginal condition for capital) of the neoclassical model of investment. Bond and Meghir [1994] state that it is negative in the unconstrained model, and rising, eventually up to positive values, for financially constrained models. This point of view is similar to Hubbard, Kashyap and Whited [1995] when they make the value of relaxing the debt to capital ratio ceiling a decreasing function of cash-flows. The Lagrange multiplier is also parameterized as an increasing function of the ‘coverage ratio’, which itself is a decreasing function of cashflows, as in Whited [1992]. Finally, when this Lagrange multiplier is substituted in the Euler equation, as done by Jaramillo, Schiantarelli and Weiss [1996], both kinds of financial constraints (debt ceiling and an increasing cost of leverage) have no effect on the investment—cash-flow sensitivity, because the Lagrange multiplier related to a debt ceiling is a common multiplicative factor of both current investment and current cash-flows in the Euler equation. These contradictory pictures covering a central feature of investment behaviour are based on similar neoclassical models. The insights provided by the theoretical model have not been totally investigated, as acknowledged by Chirinko ([1993] pp. 1902–1904) in his research agenda described in the end of his Journal of Economic Literature survey for the economics of investment: ‘further work relating investment and financing decisions to explicitly specified capital market Frictions is clearly needed’. More precisely, Fazzari, Hubbard and Petersen ([1996], p. 26) put forward the argument that Kaplan and Zingales’ firms— tracked as low dividends, low investment—cash-flow sensitivities—needed mostly to use their cashflows to repay their debt instead of financing investment (they labelled this behaviour as relevant for ‘financially distressed’ firms). But in the regime with ‘only an increasing cost of leverage’, the entrepreneur faces a marginal return on investment which is lower than the marginal cost of its current debt (with this one being higher than the opportunity cost of retained earnings). Then, he has the incentive to use his cash-flows to repay part of his debt, so that the lower marginal cost of his new level of debt just matches the marginal productivity of the new level of capital (see Hubbard [1998]). This implies a negative relationship between debt and cash-flows and that investment should be more or less sensitive to cash flows, according to the slopes of investment demand and credit supply. By contrast, in an (endogenous) debt ceiling regime, investment is very sensitive to cash-flows (and the debt ceiling increases with profitability), as all of them should be used to fill the gap between the notional capital stock and effective credit rationed investment as soon as possible. The implications for fiscal policy as well as for monetary policy of taking into account these two different regimes are of major importance. For example, subsidies to investment or decrease on corporate taxation may have a much lower impact on investment, as these funds will be diverted to decrease of the amount of debt of firms facing regime 1, whereas these tax changes will have a strong impact on credit rationed firms. Moreover, a rise in real interest rate could, for example, lead firms to shift from regime 2 to regime 1, and thereby affecting aggregate investment through this composition channel. The ‘consolidation’ behaviour described above is not only the one of ‘financially distressed’ firms as described by Fazzari et al. [1996]. It can be adopted by financially very ‘healthy’ firms (e.g. with low leverage with respect to the sectoral average), when they have considerable cash-flow with respect to their investment demand. These firms may still have an incentive to provide low or zero dividends: the quicker they decrease their debt ratio, the quicker the average cost of capital decreases. In this respect, they are still ‘financially constrained’. Examples are provided by a lot of French firms following the rise of real interest rate in the early 1980s and presumably fostered by the gradual increase of the share of profit in value added and the development of the equity market: they used retained earnings to repay their debt instead of investing. It is

CREDIT RATIONING VERSUS CONSOLIDATION

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reflected in both national accounts and individual accounting data. At the aggregate level, the debt-equity ratio fell from 140 to 40 in six years (from 1983 to 1989) followed by relative stability. For most years of the 1990s, the aggregate level of retained earnings was more than 100 per cent of aggregate investment. On individual data, the fall is observed on a longer period, from 1983 to 1994. Most of the literature is based on an exogenous debt ceiling (Gertler, Hubbard and Kashyap [1990], Hubbard and Kashyap [1992], Whited [1992] and Hubbard, Kashyap and Whited [1995], henceforth GHKW). But if one assumes that the debt—capital ceiling depends on expected profits (i.e. future cash-flow net of bankruptcy costs are considered as collateral), the Lagrange multiplier affects future investment and cash-flows differently, even though this multiplier does not depend on cash-flows. Kaplan and Zingales variations of investment-cash-flow sensitivities for low dividend firms can be accounted for by the Euler equation, as a difference between firms facing credit rationing and an increasing cost of leverage versus firms facing only an increasing cost of leverage. Although the effects of the endogenous increase of the debt ceiling may be marginal, assuming an endogenous debt ceiling extends the range of structural parameters to be estimated, such as expected bankruptcy costs. This chapter first of all builds a theoretical model which relates investment and financing decisions to capital market frictions as specified by Kiyotaki and Moore [1997], and defined by an endogenous upper limit to the debt to capital ratio. This novelty is introduced into a standard model of investment behaviour for an infinitely long lived firm (Barran and Peeters [1998]). The empirical work which follows in this chapter develops the approach opened by Jaramillo, Schiantarelli and Weiss [1996] by testing the specificity of credit rationing with respect to an increasing cost of borrowing. A more structural test is proposed which simultaneously takes into account the debt and investment behaviour. The Lagrange multiplier on the debt ceiling constraint on dividends is usually parameterised in an ad hoc manner by empirical researchers (GHKW). But this Lagrange multiplier is related to the gap between the standard neoclassical investment behaviour and the (explicit) credit constrained investment. I derive its explicit expression which suggests specifications closer to the structural one for Euler equation tests of financial constraints. A second step towards a more structural modelling is to test simultaneously the Euler equation for capital and the flow of funds equation which determines both debt and investment behaviour for zero dividend firms. The remaining part of the chapter is as follows. The next section presents the intertemporal model of the firm facing a debt ceiling and an increasing cost of borrowing, as the degree of leverage increases. The following section describes the investment regimes. We then parametrize agency costs and adjustment costs for testing. This is followed by an empirical test and the conclusion. Intertemporal behaviour of the firm The model The model allows for an increasing cost of borrowing, as the degree of leverage increases, and for a ceiling on the latter as additional constraints with respect to the neo-classical model of investment. It is identical to the models of Jaramillo, Schiantarelli and Weiss [1996] or Barran and Peeters [1998] except on the point that the leverage ceiling is endogenous. Analysing investment begins with an expression for the value of the firm, which in turn stems from the arbitrage condition governing the valuation of shares. The after-tax return to the owners of the firm at time t reflects capital appreciation and current dividends. In equilibrium, if the owners are to be content holding their shares, this return must equal the after tax nominal return on riskless (government) bonds between period t and period t+1 ( represents the nominal return before income tax and mt is the personal income tax on dividends and interest income in period t).2 (1) where Vit is the value of the firm i at time t, Sit denotes the value of new shares issued at time t+1, ct is the accrual-equivalent capital gains tax rate, θt is the divided received by the shareholder when the firm distributes one unit of post-corporate tax earnings.3 Therefore, the tax rate on dividends is (1−mt)θt. Et is the expectation operator conditional on information known at time t. The after-tax capital gain of the current shareholders thus consists of the change in the market value of the firm less the component of this change due to new share issues. The dividends of the firm at time t+1 are di,t+1. In the absence of bubbles, solving the capital market arbitrage condition yields the following expression for the firm’s market value at time zero: (2) where the firm’s one period nominal discount factor is:

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(3) We define γt as the tax discrimination parameter that determines the relative tax advantage of dividend income against capital gains: (4) This is the first source of discrimination between retained earnings and new share issues, which implies that in most tax systems, retained earnings are cheaper than new share issues. The entrepreneur in firm i chooses dividends, investment, labour and price of output in maximizing the present value of dividends di,t on date t in an infinite horizon, with the discounted rate of the opportunity cost of internal funds, subject to several constraints. Initial conditions for internal equity Ai, 0 and for debt Bi,0 are given (Ki,0=Ai,0+Bi,0). The first constraint is the capital stock accounting identity, which defines the law of accumulation of a first state variable capital Ki,t. (5) where Iit is its investment at time t, and δ is the constant rate of economic depreciation. The second ‘flow of funds’ constraint defines firm dividends. Cash inflows include sales, new share issues, and net borrowing, while cash outflows consists of dividends, factor and interest payments, and investment expenditures: (6) Where: Nit=a vector of variable factors of production for firm i at time t, F(Ki,t−1,Nit)=the firm’s revenue function (FK>0, FKK0, ΨII >0, ΨK