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Finance and Competitiveness in Developing Countries

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Finance and Competitiveness in Developing Countries

Edited by José María Fanelli and Rohinton Medhora

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International Development Research Centre
PO Box 8500, Ottawa, ON, Canada K1G 3H9
www.idrc.ca

National Library of Canada cataloguing in publication data

Main entry under title :

Finance and competitiveness in developing countries

Includes bibliographical references and an index.
ISBN 0-88936-978-X

1. Finance — Developing countries.
2. Competition — Developing countries.
3. Finance — Developing countries — Case studies.
4. Developing countries — Commerce.
5. Developing countries — Commerce — Case studies.
I. Fanelli, José Maria.
II. Medhora, Rohinton, 1959– .
III. International Development Research Centre (Canada)

HG195.F527 2002    332.097174       C2002-980130-3

Contents

List of figures

vii

List of tables

ix

List of contributors

xiii

Acknowledgements

xvi

1 Finance and competitiveness: Framework and synthesis

1

JOSÉ MARÍA FANELLI AND ROHINTON MEDHORA

 

2 Finance and changing trade patterns in developing countries: The Argentine case

20

JOSÉ MARÍA FANELLI AND SAÚL KEIFMAN

 

3 Finance and changing trade patterns in Brazil

45

MARIA CRISTINA T. TERRA

 

4 International competitiveness, trade and finance: India

77

A. GANESH-KUMAR, KUNAL SEN AND RAJENDRA R. VAIDYA

 

5 International trade, productivity and competitiveness: The case of the Indonesian manufacturing sector

121

ARI KUNCORO

 

6 Trade, competitiveness and finance in the Philippine manufacturing sector, 1980–95

155

JOSEF T. YAP

 

7 Competitiveness, international trade and finance in a minerals-rich economy: The case of South Africa

181

TREVOR BELL, GREG FARRELL AND RASHAD CASSIM

 

8 Trade, finance and competitiveness in Tunisia

222

MUSTAPHA K. NABLI, MEJDA BAHLOUS, MOHAMED BECHRI, MAROUANE EL ABBASSI, RIADH EL FERKTAJI AND BECHIR TALBI

 

9 Trade openness, industrial change and economic development

257

PAOLO GUERRIERI

 

10 Trade specialization and economic growth

291

JAIME ROS

 

11 Two problems in bank lending for development

323

RODNEY SCHMIDT

 

12 Exchange rates, real–financial and micro–macro linkages

334

ROHINTON MEDHORA

 

Index

358

Figures

2.1

Evolution of money and credit

22

2.2

Trade flows, 1983–96

24

2.3

Evolution of total assets

30

2.4

Pattern of finance, manufacturing

33

2.5

Pattern of finance of winners

33

2.6

Pattern of finance of losers

33

2.7

Pattern of finance of the non-tradable sector

34

2.8

Evolution of long-run liabilities

35

2.9

Evolution of net-denominated debt

36

2.10

Evolution of debt items in the non-tradable sector

37

3.1

Real exchange rate: level and volatility

50

3.2

Current account

50

3.3

Labor productivity in industry

52

3.4

Labor productivity, wage and unit labor cost in industry

52

3.5

Unit labor cost, prices in USD and profitability

53

3.6

Contribution to trade balance

56

3.7

Industrial firms

60

3.8

Liabilities/assets and debts/assets ratios for large and small firms

61

3.9

Liabilities/assets and debts/assets ratios for more and less financially dependent firms

62

3.10

Liabilities/assets and debts/assets ratios for domestic and multinational firms

63

4.1

India's exports, imports, trade balance, current account balance and openness measure

81

4.2

Real effective exchange rate of the rupee

82

4.3

India's competitiveness and export growth – all commodities

84

4.4

India's competitiveness and export growth – manufacturing commodities

85

4.5

Labour productivity, real wages and unit labour costs (ULC)

93

4.6

Aggregate intra-industry trade

98

7.1

The relationship between the GDP growth rate and the current account deficit/GDP ratio, 1960–98

183

7.2

The relationship between the potential GDP growth rate and the current account deficit/potential GDP ratio, 1960–98

183

7.3

GDFI to GDP ratio

184

7.4

Gold output, exports and price

186

7.5

Exports by main economic sector, 1959–93

187

7.6

Monthly commodity price indices

188

7.7

Real exchange rates

191

7.8

Labour productivity, real wages and unit labour costs

199

7.9

Monthly conditional variance of the REER

206

8.1

GDP growth and current account deficit

223

8.2

Terms of trade, real exchange rate and current account deficit

233

8.3

Manufacturing exports and unit labour costs

234

10.1

Determinants of growth and macro-linkages between trade specialization and growth

292

10.2

The pattern of specialization in a neoclassical trade model

295

10.3

Multiple equilibria in a model with increasing returns

298

10.4

Growth and current account volatility

312

10.5

Growth and real exchange rate volatility

314

Tables

2.1

Evolution of selected macroeconomic variables

22

2.2

Contributions to the trade balance

25

2.3

Aggregate indices of intra-industry trade

25

2.4

Manufacturing: trade-balance-, exports- and imports-to-output ratios

26

2.5

Manufacturing: indices of output, labor productivity and unit labor costs

26

2.6

Export growth and investment

27

2.7

Credit and manufacturing activity level

28

2.8

Evolution of key real variables of loser sectors

30

2.9

Evolution of key real variables of winner sectors

30

2.10

The determinants of asset accumulation

31

2.11

The determinants of the long-run debt ratio

35

2.12

Trade specialization and external dependence ratio

38

2.13

Indices of concentration of exports

39

2.14

Volatility measures for liabilities and income: manufacturing industry

41

2.15

Volatility measures of balance-sheet items in services and manufacturing

41

3.1

Selected macroeconomic data

48

3.2

Contribution to trade balance

55

3.3

Revealed comparative advantage – Balassa's index

58

3.4

Grubel and Lloyd intra-industry index

58

3.5

Sample firms broken down by sector

59

3.6

Pattern of finance

60

3.7

Regression results

65

3.8

Regression results

67

3.9

Credit to private sector

68

3.10

Regression results

69

3.11

Contribution to the trade balance across periods

71

3.12

Correlation coefficient between external dependence and CTB

72

3.13

Regression results

73

4.1

Decomposition of India's exports

85

4.2

Real and nominal exchange rate, inflation differentials and competitiveness

87

4.3

Export shares of select commodities

89

4.4

t-Test on sample means of RCAs

91

4.5

Winner loser industries

92

4.6

Percentage change in unit labour costs (ULC), India, 1982–92

94

4.7

Distribution of manufactured exports by technological complexity

99

4.8

Sample size

102

4.9

Firm characteristics

103

4.10

Sources and uses of funds – domestic and exporting firms

104

4.11

Sources and uses of funds – winning and losing exporters

106

4.12

Variables and their definition

111

4.13

Investment function estimates – all firms

112

4.14

Investment function estimates – exporting firms versus domestic firms

112

4.15

Investment function estimates – small versus large firms

113

4.16

Investment function estimates – exporting firms versus domestic firms and small versus large firms

114

4.17

Investment function estimates – winning and losing exporters

114

4.18

Investment function estimates – winner/loser–industry/firms

116

5.1

Structure of Indonesian exports

123

5.2

Selected macroeconomic indicators, 1990–7

124

5.3

Trend of Indonesian exports

129

5.4

Index of labor productivity and productivity growth

130

5.5a

Net exports by industry (1990)

134

5.5b

Net exports by industry (1995)

136

5.6

Net exports according to ownership status

138

5.7

Net exports according to firm size

138

5.8

Nominal and real exchange rate

139

5.9

Indonesia manufacturing sector: several financial indicators

142

5.10

Indonesian manufacturing sector: firm financial aspects at the sectoral level

143

5.11

Factors affecting contribution to trade balance and probability to specialize in leading RCA sectors

146

5.12

Sources of finance

148

5.13

Factors affecting firm portfolio decisions

150

6.1

The Philippines, selected economic indicators

156

6.2

Indices of average labour productivity overall, agriculture and manufacturing

158

6.3

Selected indicators, East Asian economies

159

6.4

Revealed comparative advantage: Phillippine share/world share per industry

164

6.5

Contribution to trade balance, 1980–95

165

6.6

Share to total exports, 1980–95

166

6.7

Estimation of eqn (6.1)

169

6.8

Estimation of eqn (6.2)

171

6.9

Estimates of Spearman rank coefficient

174

7.1

Average annual exports growth rates in constant USD

186

7.2

Shares in total exports excluding services

188

7.3

Average annual export growth rates in constant USD

189

7.4

Shares in manufacturing exports

189

7.5

Average annual growth rates of natural resource-based and downstream manufactured exports in constant 1990 USD

190

7.6

Average annual growth of South African manufacturing exports in constant 1990 USD

195

7.7

Shares in manufacturing exports of SACU

196

7.8

Average annual growth rates of manufactured exports of SACU, constant 1990 USD

197

7.9

Average annual growth rates of South Africa's exports and imports in constant 1990 USD, 1958–98

201

7.10

Empirical results of investment function, 1990–7; all firms with dummy variables

208

7.11

Empirical results of investment function, 1990–7; using sectoral classifications

209

7.12

Average annual export growth rates in constant 1993 rands

217

8.1

Growth and the current account

223

8.2

Foreign trade indicators

224

8.3

Exportables

225

8.4

Categories of tradables and specialization

226

8.5

Revealed comparative advantage

228

8.6

GDP and TFP growth

229

8.7

Indicators of liberalization

231

8.8

Share of 'off-shore' in total exports

232

8.9

Financial sector development

235

8.10

Competitiveness and overall financial development

237

8.11

Long-term debt

238

8.12

Winners and losers in the credit market

240

8.13

Financial structure and liberalization

243

8.14

Debt allocation and efficiency, 1990–2

245

8.15

Firm characteristics and investment determinants

247

9.1

Shares in world exports of selected groups of countries

263

9.2

Standardized trade balances of selected groups of countries

264

9.3

Weights of the sectoral groups in world exports

265

9.4

Trade specialization patterns of selected groups of countries

266

9.5

Standardized trade balances of selected groups of countries

267

9.6

Trade specialization patterns of selected countries

268

9.7

Standardized trade balances of selected countries

269

9.8

Shares in world exports of selected groups of countries

272

9.9

Shares in world exports of selected groups of countries

273

9.10

Standardized trade balances of selected countries

274

9.11

Trade specialization patterns of selected countries

275

9.12

Shares in world exports of selected groups of countries

278

9.13

Standardized trade balances of selected countries

279

9.14

Trade specialization patterns of selected countries

280

9.15

Intra-industry trade: index of Gruber Lloyd

281

10.1

Trade orientation, investment and growth

306

10.2

Cross-country correlations (twenty-two countries)

307

10.3

Cross-country correlations (thirty-two countries)

307

10.4

Trade orientation, current account volatility and output volatility

311

10.5

Growth performance, output volatility and current account volatility

313

Contributors

Mejda Bahlous is Associate Professor of Finance at the Institut des Hautes Etudes Commerciales, University of Tunis. She obtained her Doctorate in Management Sciences in 1991 from the University of Rennes I, France.

Mohamed Bechri is Professor of Economics at the University of Sousse, Tunisia. He holds a Ph.D. in Economics from the University of Southern California and a Doctorat de Troisième Cycle in economics from the University of Paris I, Panthéon-Sarbonne.

Trevor Bell is Professor Emeritus of Economics and Economic History of Rhodes University, South Africa. He holds a Masters degree from Vanderbilt University, USA, and a Ph.D. from Rhodes. His research interests are in South African trade and industrialization, on which he has published extensively.

Rashad Cassim is the Executive Director of the Trade and Industrial Policy Secretariat (TIPS) in Johannesburg, South Africa. He holds a Ph.D. in Economics from the University of Cape Town. His research interests are in trade policy, regional integration and the economics of regulation.

Marouane El Abbassi is Assistant Professor of Economics at the Institut des Hautes Etudes Commerciales, University of Tunis. He holds a Doctorat de Troisième Cycle in economics from the University of Paris I, Panthéon-Sorbonne.

Riadh El Ferktaji is Assistant Professor of Economics at the Institut des Hautes Etudes Commerciales, University of Tunis. He obtained his Doctorate in economics in 1994 from the University of Paris I, Panthéon-Sorbonne.

José María Fanelli has a Ph.D. in Economics, specializing in macroeconomics and monetary economics. He is currently Senior Professor of Macroeconomics and former Director of the Economics Department at the University of Buenos Aires. Since 1984 he has been senior researcher at CEDES (the Center for the Study of State and Society) and Conicet (the National Research Council) in Buenos Aires.

Greg Farrell was Research Officer, Institute for Social and Economic Research, University of Durban-Westville, Durban, until October 1998; he is now an economist in the Research Department of the South African Research Bank, Pretoria.

A. Ganesh-Kumar is Assistant Professor at the Indira Gandhi Institute of Development Research, Mumbai, India. Research interests are in agricultural economics, international trade and applied general equilibrium modelling. Previous publications include 'Agricultural Trade Liberalization: Growth, Welfare and Large Country Effects' (with Kirit S, Parikh, N. S. S. Narayana and Major Panda), Agricultural Economics 17(1), 1997.

Paolo Guerrieri is Professor of Economics at the University of Rome, La Sapienza and the College of Europe, Bruges, and Director of the International Economic Unit of the Institute for Foreign Affairs of Rome. He is the author of several books and articles on international trade policy, technological change, international political economy, and European integration issues.

Saúl Keifman, majored at the University of Buenos Aires and obtained his Ph.D. in Economics at the University of California-Berkeley. He is currently Professor of Economics and Director of the Master Program in Economics at the University of Buenos Aires and Associate Researcher at CEDES.

Ari Kuncoro is currently Research Associate at the Institute of Economic and Social Research, Faculty of Economics, University of Indonesia. He obtained his Ph.D. in Economics from Brown University, Rhode Island. He is a participant in the Global Development Network sponsored by the World Bank and East Asian Development Network, and the Pacific Economic Council Structure Meeting, a research-oriented body supported by the Asian Pacific Economic Council.

Rohinton Medhora received his Ph.D. in Economics at the University of Toronto, where he was also a faculty member in economics. He has published principally in the areas of monetary integration and central banking. At Canada's International Development Research Centre, he has led programs on adjustment policies and poverty, and trade. He is currently responsible for the Centre's social and economic policy programming.

Mustapha K. Nabli is Regional Chief Economist within the World Bank's Middle East and North Africa Region. He was previously Minister of Planning and Economic Development in the government of Tunisia and Chairman of the Tunis Stock Exchange. He holds a Ph.D. in economics from the University of California at Los Angeles and was Professor of Economics at the University of Tunis and other universities in Europe, Canada and the United States.

Jaime Ros is Professor of Economics and Fellow of the Helen Kellogg Institute for International Studies at the University of Notre Dame. His main research interests include development theory and the macroeconomics of developing countries.

Rodney Schmidt is Program Advisor to the International Development Research Centre and Coordinator of the Vietnam Economic and Environmental Management (VEEM) program, based in Hanoi. Prior to joining IDRC, Dr. Schmidt worked as an economist with the Canadian Department of Finance and the Belize Ministry of Economic Development. He obtained his Ph.D. in economics from the University of Toronto where he also worked as a lecturer. Dr. Schmidt has written several papers on money and finance.

Kunal Sen is Lecturer in Economics at the School of Development Studies, University of East Anglia, UK. Research interests are in macroeconomics and international trade. Previous publications include The Process of Financial Liberalization in India published by Oxford University Press and Economic Restructuring in East Asia and India published by Macmillan.

Béchir Talbi is Professor of economics at the University of Sousse, Tunisia. He holds a Doctorat de Troisième Cycle in economics from the University of Paris X, Nanterre, France.

Maria Cristina T. Terra is Professor of Economics at the Graduate School of Economics, Getulio Vargas Foundation, Rio de Janeiro, and Associate Editor of The Brazilian Review of Economics.

Rajendra R. Vaidya is Associate Professor at the Indira Gandhi Institute of Development Research, Mumbai, India. Research interests are in macroeconomics, finance and industrial economics. Previous publications include The process of Financial Liberalization in India published by Oxford University Press.

Josef T. Yap is Senior Research Fellow at the Philippine Institute for Development Studies (PIDS). His areas of interest are in macroeconomic policy and econometrics. Dr Yap was recently a consultant at the Regional Economic Monitoring Unit of the Asian Development Bank. He is co-author of the book The Philippine Economy: East Asia's Stray Cat? Structure, Finance, and Adjustment (London: Macmillan Press Ltd, 1996).

Acknowledgements

This volume is genuinely a group effort and would not have been possible without the cooperation of a number of persons and organizations.

At IDRC, thanks are due to Rashad Cassim, Marie-Claude Martin, Caroline Pestieau and Andres Rius for their input, encouragement and participation from the very beginning. Lynne Richer at IDRC and Delia Carval at CEDES handled with patience and efficiency the administrative aspects of this project.

The first methodological meeting of the project was hosted by Centro de Estudios de Estado y Sociedad (CEDES) in Buenos Aires, Argentina, in April 1997. We thank in particular Mario Damill and Roberto Frenkel for their participation in this meeting, and CEDES for its management of the logistics. The mid-term meeting of this project was hosted by the Philippine Institute for Development Studies (PIDS), Manila, Philippines, in April 1998. The seminar disseminating the final results of this project was hosted by the Trade and Industrial Policy Secretariat, located at the Johannesburg office of Canada's International Development Research Centre. We thank PIDS and TIPS for their hospitality and organization of an excellent program during our stay with them.

Finally, thanks are due to Nilima Gulrajani at IDRC and Nicole Krull at Routledge for their yeoman work in bringing twelve chapters written by twenty-one economists from all over the world into a coherent and readable whole.

José María Fanelli and
Rohinton Medhora

1 Finance and competitiveness: Framework and synthesis

José María Fanelli and Rohinton Medhora

The issues of international competitiveness and integration in the global economy play a central role in the discussion on development and there is no indication that their importance will decline in the near future. At the analytical level, and often at the policy level as well, competitiveness is seen largely in real or trade terms, at the expense of the key roles that financial factors, and institutional and other micro-level features play in determining how successful a country's trade and competitiveness position is. Trade theories are for the most part silent on financial issues while policy makers cannot afford the luxury of seeing the two as separate or, worse, unconnected. Likewise, once financial issues are brought into the picture it is not possible to ignore the role of the macroeconomic factors and their interaction with the microeconomic structure of the economy.

The main goal of the research project whose results are presented in this book is, precisely, to contribute to filling the theoretical and empirical gap which exists in the development literature regarding the linkages of trade, finance and macroeconomic factors. The project comprised eight country studies (Argentina, Brazil, Uruguay, Indonesia, India, the Philippines, Tunisia and South Africa) which were elaborated according to the same framework and four thematic papers that analyzed the relationship between competitiveness, finance and trade at a more abstract level. In this chapter we present a framework for our analysis and an overview of the key results of the studies. This chapter has two sections. In the first section we discuss why 'achieving competitiveness' is an important policy goal in the developing world and why it is closely associated with the process of catching up. Then, we discuss four stylized facts characterizing developing countries that explain why it is necessary to analyze the relationship between trade, finance and macroeconomic issues. In the second section, we elaborate on our view of the micro/macro and trade/finance linkages. We conclude with the three most important questions that have been targeted in the project.

1. Competitiveness and the developing world

The challenges of economic policy

There are three facts that are crucial to understanding the ongoing debate on development strategies and on economic policy in developing countries. The first is that growth is the most important economic policy goal to these countries. It is true that it would be hard to find someone involved in economic policy-making or politics who would deny or ignore that development is a complex process that entails much more than mere increasing per capita GDP, and, consequently, that the results of a specific economic program should be assessed on the basis of an ample set of indicators including variables related to, say, income distribution and the environment. In practice, however, it is growth that is privileged as the measure of economic attainment. One important reason for privileging growth as an indicator of development is that there is a firm consensus among politicians, public opinion, advisors from multilateral agencies and policy makers that it is much easier to undertake the difficult and often painful economic and social changes required to ensure development in a context of sustained growth. Two necessary conditions for sustainable growth are the steady increase of overall productivity and macroeconomic stability. Hence, the preeminence given to growth naturally implies that policies for productivity enhancement and for preserving macroeconomic equilibrium take priority on the economic policy agenda.

The second fact is closely associated with the previous one. If growth is the measure of attainment, how do we know whether a given rate of growth is high or low? In other words, what is an acceptable rate of growth? For a developing country, 'development' means, in the first place, approaching the per capita GDP of industrialized countries and, hence, developmental success means reducing the income gap between the country under consideration and the wealthier ones. In this way, achieving a rate of growth in overall productivity which is higher than the average rate observed in the developed world becomes a key target for economic policy. Under these circumstances, Krugman's (1996) reasoning that the increase in national productivity is what matters to the improvement of the standard of living independently of what is happening with the productivity of the rest of the world may be correct at first sight, but is politically vacuous. The common consensus is that a sound development policy should set the country on a growth path which would help it to catch up with the industrialized economies. To be sure, the primary reason for using the industrialized countries' income as a benchmark is neither the search for national prestige nor the belief that international economic competition across countries resembles competition across firms, but, rather, the need to fix a standard to assess how well a country is doing. It seems only natural to set such a standard at the highest level of welfare observed at a specific moment.

The third fact is that, at present, integration in the global economy is conceived of as central to fueling productivity and fostering growth (Sachs and Warner 1997). One important reason that accounts for the perception of the international economy as a window of opportunity is the increasing interaction in recent decades between domestic economies and the world economy. The most important indicators of this 'globalization' process are the growing share of international trade in world output and an extraordinary rise in capital mobility, including foreign direct investment.1 In such a context, it is believed that developing countries could enlarge the size of their export markets and use the proceeds coming from higher exports to foster productivity gains via the acquisition of investment goods abroad and improvements in the quality and variety of imported intermediate inputs used in production. The international capital market is also viewed as a potential source of productivity enhancement. Specifically, it is assumed that a greater supply of foreign direct investment means both greater availability of savings and technology, while accessing international capital markets implies accessing not only more foreign savings but also better alternatives for the diversification of national risks. The most important piece of evidence flagged in the literature favoring these ideas is the experience of the Asian Tigers. Many studies have concluded that countries that have expanded most successfully in the postwar period heavily relied on external trade – or at least on an export-oriented strategy – as a source of dynamism for the domestic economy. An additional piece of evidence is that many countries which did not privilege external trade in their development strategy have faced enormous problems not only to sustain growth but also to maintain a reasonable level of macroeconomic stability. Latin American countries usually serve as a paradigm of the problems that the lack of 'outward orientation' can create. It was suggested that the domestic-market-oriented development strategy these countries followed in the postwar period led to the misallocation of investment and recurrent balance-of-payments crises, which were specially marked in the 1980s during the 'debt crisis'.

The lessons drawn from the analysis of concrete development experiences and the theoretical contributions of the 'liberalization' approach to development theory crystallized in the so-called Washington Consensus in the mid-1980s (Williamson 1990). The Washington Consensus was extremely effective at criticizing the inefficiencies of the older development paradigm based on import substitution and state intervention and at establishing new guidelines for the design of policies oriented to liberalizing repressed markets and reducing the size and functions of the state. A good number of developing countries put into practice the policy recommendations of the Consensus. In spite of its success as a framework for designing market-oriented structural reforms and for eliminating many of the inefficiencies of the old development model, the results of the reforms in terms of growth, productivity and macroeconomic stability, nonetheless, were mixed. In general, after a decade of deep reforms, we do not see a spectacular improvement in growth and catching up, not to mention social equity. Under these circumstances, it is not surprising that the Consensus is, at present, under scrutiny (Rodrik 1999).

The three aspects of economic policy that we highlighted – preeminence of growth indicators, the aim of catching up and outward orientation – suggest that an analytical approach which can tackle the policy issues developing countries are now facing should be able to integrate the interactions between the determinants of productivity growth, macroeconomic stability and integration in the global economy in a single analytical framework. The Washington Consensus attempted to make such an integration in the 1980s and it was one of its main virtues. In fact, one could argue that a good part of the popularity of the Consensus was due to its ability to present a guide for policy design in a wide range of areas within a consistent framework. At present, nonetheless, the approach shows difficulties in accounting for a set of highly relevant developments. Given the objectives of our research, there are two features of those developments that we would like to highlight. The first is that many countries that opened their capital markets and undertook trade liberalization did not grow as fast as had been expected. These are, for example, the cases of Turkey, Mexico, the Philippines and Brazil. Argentina, on the other hand, grew much faster than in the 1980s after deepening trade liberalization and privatization, but was severely affected by the Tequila effect and the Asian crisis. The reforms were unable to radically eliminate the tendency to generate 'excessive' current account deficits that had been a structural characteristic of the Argentine economy during the import substitution period. The experience of these countries suggests that high productivity and competitiveness are not automatically achieved as a result of market deregulation, financial and trade liberalization, and integration in the international capital markets. The second is that, somewhat unexpectedly, some of the most successful outward-oriented countries, like Korea and other 'new' tigers like Indonesia, experienced deep macroeconomic disequilibria which matched some of the characteristics of Latin American instability, such as currency attacks, financial fragility and deep falls in the activity level. These facts made it evident that imperfections exist in the functioning of international capital markets which can jeopardize even the most successful countries and that, under such circumstances, outward orientation per se is not enough to protect a given country from exposure to capital flows volatility. Likewise, the crises show that macroeconomic instability, financial fragility and the capital structure of the firms are closely associated.

In most cases, the economies facing problems of current account sustainability devalued their currencies in the 1990s. The diagnosis to support devaluation is that the external imbalance is basically a macroeconomic problem: a 'wrong' exchange rate erodes the competitiveness of the economy and prevents the country from competing successfully in international markets. From the static point of view this policy is correct since, if an equilibrium exists, there is always an exchange rate which leaves the balance of payments in equilibrium and permits the country to fully exploit its comparative advantage. When dynamic factors are taken into account however, the experience of developing countries shows that to ensure competitiveness countries need much more than just getting the real exchange rate right. The experience of Latin American countries in the 1980s is particularly relevant in this regard. The systematic use of devaluation as a means of improving competitiveness accelerated inflation and damaged financial intermediation, thereby increasing volatility in both prices and quantities. In this way, the upward correction of the real exchange rate was achieved only at the cost of a sharp worsening of the macroeconomic setting. The effects of increased volatility and the deterioration of financial conditions on investment were devastating. There was a generalized fall in the investment/GDP ratio in the entire region. This, in turn, resulted in the stagnation of productivity and the widening of the productivity gap between Latin America and other more successful developing countries such as the Asian NICs. The ultimate consequence of the attempt to get the real exchange 'right' in a context of high uncertainty and severe financial constraints was the opposite of the effect that was sought for: competitiveness was severely hindered by the lagging evolution of productivity.

To be sure, our reference to the Latin American experience during the debt crisis is not meant to imply that the real exchange rate is irrelevant for a country's competitiveness. The lesson that we do draw, instead, is that competitiveness is a complex issue which has price and non-price dimensions and that embraces micro- and macro-elements which interact with each other. At the micro-level, it is normal for a growing economy to experience rising and falling competitiveness in different industries, since productivity growth is not a uniform process across sectors. If the country is losing competitiveness in many industries simultaneously however, the loss of competitiveness can be caused either by a misalignment of domestic costs or by low average productivity growth vis-à-vis the rest of the world. If the problem is the misalignment of relative prices, devaluation may be the cure. But if the problem is lagging productivity, we should not take it for granted a priori that devaluation will be sufficient to enhance competitiveness. When we take into account the micro-dimension of the problem, it is clear that if a country specializes in the least dynamic industries (with flat learning curves, low returns to scale, small scope for innovation) it will enjoy little productivity growth and will lose competitiveness. If this is the case, it is plausible to think that devaluation is not the best response from a long-run perspective. The real problem lies in the specialization pattern and the lagging path of productivity. This makes it clear that trade specialization patterns, productivity growth and current account sustainability are not independent phenomena. In fact, the trade specialization pattern might be a source of macroeconomic instability per se. For example, a country which depends on the surplus generated by a narrow set of products with high price volatility to close the external gap can be more unstable than another with a more diversified surplus structure. Additionally, it must be taken into account that micro/macro-interactions are also relevant for the non-price dimensions of competitiveness. A highly unstable environment can hinder the evolution of productivity via its effect on non-price determinants of competitiveness, such as the level of financial deepening. For example, trade liberalization may eliminate an anti-export bias in the economy. In the new scenario one would expect that firms will be restructured to take advantage of the trade opportunities offered. It is very doubtful, however, that firms will in fact get the funds needed for restructuring in a context of credit rationing.

We think that the traditional approach has difficulties with the issue of competitiveness because it tends to ignore the kind of micro/macro-interactions that we are stressing here. In this regard, one particularly inadequate characteristic of the traditional approach is the excessive use of dichotomies as a methodological recourse to simplify the analysis. There are marked dichotomies between micro-and macro-problems, between the real and the financial side, and between open and closed economies. It is undeniable that these dichotomies are powerful simplifying devices, but it is also true that such dichotomies are often maintained even under circumstances in which real/financial and micro/macro-interactions are extremely relevant for a specific economic phenomenon as in the case of the turbulences in 'emerging' economies. In these economies, it is pretty obvious that the problems of macroeconomic sustainability, capital flows and trade specialization are closely interrelated.

Dichotomies play a particularly relevant role in the case of trade theory. There is a long tradition in the field of international economics of sharply separating trade and the 'micro'-question of optimum resource allocation from monetary and macroeconomic issues. For example, the typical international economics textbook is divided into two parts. The first analyzes the real economy with a 'micro'-perspective, omitting the existence of money, financial intermediation and current account disequilibria. The second part studies open economy macroeconomics. In this part the trade specialization pattern plays no role and output is highly aggregated. Some of the agents' financial decisions (domestic vs foreign bonds) come to the forefront and macroeconomic problems like the correct level of the real exchange rate, aggregate domestic absorption, portfolio decisions and balance-of-payments equilibrium take center stage. It is not clear what the relationship between the first and the second part is or how the results of each part are modified by the results obtained in the other.2 As a consequence of this dichotomy, in the literature on trade, the problem of achieving a sustainable macroeconomic equilibrium is conceived of as being largely independent of the question of competitiveness. Trade imbalances can always be corrected through fiscal, monetary or exchange rate policies because competitiveness problems (i.e. recurrent current account crises) have their roots in inadequate macroeconomic policies rather than in weak productive structures.

The dichotomy between the real and the financial side also plays a significant role in trade theory. It is implicitly assumed that the firms seeking to exploit a competitive advantage can always finance their productive projects and thereby 'real' decisions are isolated from financial ones. In fact, it is implicitly assumed that the latter are irrelevant, as it would be the case under perfect capital markets. In such a world, there is no chance that a firm which is potentially competitive at the international level will be forced to forego trade opportunities either because of liquidity constraints created by credit rationing or because interest rates are abnormally high as a consequence of an excessively volatile macroeconomic setting.

Another important weakness of the standard approach to the competitiveness problem is that it is not clear enough what the sources of a sustained increase in productivity are. This is apparent in the Washington Consensus' policy recommendations. It is generally explicitly assumed that market deregulation and outward orientation (i.e. 'undistorted' integration in the world economy) should be sufficient conditions to ensure an upward trend in productivity. The recent experience of many developing countries suggests, nonetheless, that market deregulation alone is not enough to take full advantage of the creativity of the private sector and to enhance productivity growth. Although it is true that many countries enjoyed important static efficiency gains after the liberalization and opening of the economy, the growth path of productivity and competitiveness has been far from satisfactory from the point of view of catching up and macro-stability. In this regard, a central weakness of the traditional theory is that it downplays the role of market failures. The assumption that product and factor markets will function well after liberalization may be unwarranted in the case of developing countries, where imperfections are pervasive in the markets for knowledge, human capital, infrastructure services and finance. These markets have a determinant influence on the evolution of productivity.

This criticism of the traditional approach, however, is not meant to imply that we should start from scratch to better understand the interactions of productivity, stability and openness. In the first place, in spite of its flaws, the Washington Consensus has clarified a variety of issues, particularly those related to static economic efficiency and macroeconomic stability originating in monetary and fiscal imbalances. Second, there is a series of more heterodox contributions, both analytical and empirical, which have shed light on key aspects of developing economies. On the analytical side, there were new advances in trade, finance and growth theory which take explicitly into account the existence of market failures and, therefore, are specially useful for the analysis of developing economies which have incomplete market structures. Likewise, researchers working in the technological and industrial organization area have studied the determinants of competitiveness and productivity in developing countries and have showed the limits of the neoclassical approach for analyzing the dynamics of technical change, trade and competitiveness in the developing world.3 This new intellectual environment has led many researchers to challenge the orthodox interpretation of the Asian NIEs' experience. They demonstrated that many highly relevant facts do not fit into and cannot be accounted for within the liberalization paradigm.4 From our point of view, these contributions are promising steps towards the construction of a post-Washington Consensus approach. This new approach to development problems will surely integrate many of these recent contributions as its building blocks.

Four stylized facts

In addition to the analytical advances in the study of economies with missing and imperfect markets, any attempt to improve our understanding of the problems of competitiveness and the challenges of economic policy should take into account some specific features that characterize the structure of developing countries. There are four of these features that we would like to highlight because of their relevance for the present investigation.

(a) Developing countries tend to be more unstable than developed countries from the macroeconomic point of view. There is a large amount of empirical evidence that documents this fact (IDB 1995). The central point that we would like to emphasize is that a higher degree of volatility in the stochastic processes generating key macroeconomic prices represents a deadweight cost for the economy as a whole which can severely hinder productivity (Fanelli and Frenkel 1995; Ramey and Ramey 1995). The basic reason is that volatility affects the investment rate. Greater volatility means greater risk and, consequently, higher discount rates. Under these circumstances, the minimum rate of return required for a project to be considered profitable is higher and the rate of investment, ceteris paribus, lower. In this way, macroeconomic instability has sizeable economic costs which affect the sources of productivity enhancement at the micro-level, to the extent that investment is correlated with learning, the adoption of new technologies and the acquisition of skills. These micro/macro-linkages could play an important role in explaining the existence of some vicious circles which are very frequently observed in the developing world: the obstacles to developing dynamic comparative advantages force the country to depend on a few export items to finance imports and to close the external gap; this makes the country highly vulnerable to terms of trade shocks and the evolution of key macroeconomic variables becomes more volatile; the volatility of the macro-environment, in turn, hinders the country's capacity to develop new comparative advantages to the extent that a weak investment rate creates an anti-innovation bias. This kind of vicious circle may be relevant in explaining the experience of countries showing chronic balance-of-payments problems and difficulties in strengthening their competitiveness.

(b) The external constraint is a key source of aggregate instability. This is closely associated with the existing imperfections in international capital markets and the lack of trade diversification. There are two factors that play a central role in explaining the relevance of the external constraint as a source of macro-instability. The first is the lack of export diversification. When the prices and/or the quantities sold of a good or service which account for a high share of exports fluctuate, large swings in the availability of foreign exchange are likely to result if exports are not diversified. Under circumstances of greater volatility in the supply of foreign exchange, it is reasonable to assume the hypothesis that there will be a higher volatility in key macroeconomic prices (particularly the real exchange rate) and quantities. We consider that the roots of this problem lie in the lack of competitiveness to the extent that the country is unable to diversify sufficiently its exports via the acquisition of comparative advantages in new sectors or specific products.

The second factor has to do with the imperfections in international capital markets. In the context of perfect foreign capital markets, the lack of export diversification should be less of a problem because it would be possible to diversify the risks implicit in the fluctuations of one country's export proceeds by resorting to capital markets. The market would make it possible to combine the financial instruments of countries whose export proceeds are negatively correlated in a well-diversified portfolio. Likewise, countries suffering temporal terms of trade shocks could tap international credit markets in order to stabilize the level of domestic absorption every year around its long-run national income. They would run, say, deficits in bad years and surpluses in good ones. Fluctuations in the terms of trade and, hence, in the availability of foreign exchange would not be an obstacle to exploit investment opportunities and to develop dynamic comparative advantages. Regrettably, international capital markets have proven to be unable to diversify national risks efficiently and to finance temporary current account deficits (Obstfeld and Rogoff 1996; Obstfeld 1998). The existence of a credit constraint means there will be a kind of accelerator mechanism working at the aggregate level. In this way, the imperfections of capital markets on the financial side of the economy become a problem on the real side. When financial constraints exist, a country needs to enhance its competitiveness to avoid a risky dependence on the availability of credit in foreign markets. This association, between the external balance of the economy and macroeconomic instability, is aggravated in the present context of openness and free capital mobility because it seems that herding behavior and other irrational phenomena in international capital markets could be an independent source of volatility for a country whose financial needs are too high or somewhat inelastic in the short run.

(c) The economic structure comprises sectors characterized by different productivity levels and growth potential. The economic structure of a country is a complex system that includes physical and human resources, markets, organizations and institutions. It is a well-known stylized fact of the development process that the economic structure experiences systematic changes as skills, technologies and capital accumulate. Chenery and Syrquin (1986), for example, studied a broad set of countries and showed that as economies become industrially mature, manufactured exports tend to move from simpler to more complex activities, use more advanced products and processes within activities, and increase local contents in physical inputs, services and technologies. In a situation of growth and structural change, productivity growth at the aggregate level can be decomposed into a shift in the production structure towards activities with higher levels of productivity and into the growth of productivity in all existing activities (Llal 1995). In such a context, it is very unrealistic to assume that firms in countries showing different stages of development use identical production functions to produce homogeneous products. It seems more plausible to adopt the hypothesis that there will be some technological differences between firms in the same sector in different countries because learning curves and the evolution of technological capabilities in general depend on the previous path of development as well as on the overall economic environment (infrastructure, financial deepening, the quality of government). Under this assumption, comparative advantages are determined not only by the natural endowment but also by dynamic factors associated with technological capabilities and the whole economic environment. A country will not show any tendency to catch up with developed countries if it specializes in low-productivity sectors with low growth potential.

We have seen that the convergence with the productivity level of developed countries is a privileged policy goal. A country, however, cannot freely choose which sectors to specialize in precisely because, in the long run, the path of specialization is determined by factors such as the previous evolution of technological capabilities and accumulation of physical and human capital. When the rate of growth of aggregate productivity (which determines the competitive strength) is very low in a given country, a phenomenon that is often observed is that the country recurrently runs into balance-of-payments problems. Normally it occurs because there is a pressure to maintain the standard of living when a negative shock hits the economy. In this situation, the authorities face a policy dilemma. To break the inertia behind the lagging path of productivity and induce a 'jump' in competitiveness in order to secure current account sustainability, it is necessary to invest in physical and human capital and technologies. In the short run, however, higher levels of investment could worsen the current account. It is precisely because of this sort of policy dilemma that it is interesting to investigate in detail the micro/macro-linkages between the determinants of the evolution of aggregate and sectoral productivity at the micro-level and competitiveness and current account sustainability at the macro-level.

(d) Factor markets show severe failures and such failures are much more important than those observed in developed countries. This has an impact on competitiveness and growth. It is a very well-known fact that there may be significant failures in the factor markets where developing country agents operate. In our research, however, we concentrate on financial markets. It is common to observe the following phenomena in the developing country financial structure: a high degree of segmentation in financial markets which acts against innovative and smaller enterprises; a marked scarcity of long-term credit for the financing of private investment; very low total capitalization of the stock exchange market as compared to the size of the economy; severe difficulties to diversify non-systematic risk because the range of activities quoted in the stock market is very narrow; an elevated degree of financial fragility in the system which – via systemic risk premia – results in excessively high interest rates (Fanelli and Medhora 1998).

When these imperfections exist in the credit markets, firms are not uniformly affected (Fazzari et al. 1988; Hubbard 1998). Smaller firms with less net worth or firms producing in more risky sectors or with less marketable assets to be used as collateral will be more affected (Harris et al. 1994). In a context of tighter overall financial conditions, these firms will face a disproportionate widening in the external finance premium or will be rationed out of credit markets. This means that when the interest rate rises as a consequence of an increase in volatility induced by a worsening in the macroeconomic situation, the financial conditions will worsen in a disproportionate way for some enterprises. If these enterprises are also those with the most profitable and innovative projects, macroeconomic instability will be extremely costly for productivity growth.

2. Competitiveness: Price and non-price dimensions

From our previous reasoning, it follows that to face the challenges of economic policy and understand the determinants of sustainable growth in a small economy that is open to international flows of goods and finance, we should be able to model the links between productivity growth, macroeconomic stability and financing of the firm. There are a growing number of researchers who are using the problems posed by competitiveness as the pivot of their analysis of these questions. Competitiveness is defined as 'an economy's ability to grow and to raise the general living standards of its population in a reasonably open trading environment without being constrained by balance of payments difficulties' (Haque 1995). Although some academic economists are reluctant to apply the concept of competitiveness because they consider it to be redundant, we believe that even if it is not strictly necessary as a 'primitive' concept in economic theory, it is still very useful. For one thing, it summarizes in a single concept the problems of growth, openness and productivity which, as we argued before, are at the heart of policy-makers' concerns.5

A country's competitiveness has two components: price competitiveness and non-price competitiveness. Price competitiveness measures a country's ability to increase its share in world markets by selling at a lower price than its competitors. If price competitiveness were all that mattered, a country's market share would rise (fall) as its real exchange rate or unit labor cost fell (rose) vis-à-vis the rest of the world. The limitations of price-competitiveness indicators, however, came to the forefront when Kaldor (1978) found that the industrialized countries which gained market share (West Germany and Japan) were also the ones that experienced a rise in unit labor costs vis-à-vis their competitors. Kaldor's 'paradox' suggests that factors other than price, such as product differentiation, technological innovation and capacity to deliver, must also be taken into account (Fagerberg 1988).6

The inclusion of non-price factors into the picture naturally calls for an approach more akin to the Schumpeterian view of development. Specifically, this requires recognizing the key role of technology and innovation, of financial constraints and of systemic elements in determining the evolution of competitiveness. In Schumpeterian models of growth in an open economy, the rate of technical progress and the pattern of international trade are jointly and endogenously determined and dynamic comparative advantages become a critical factor (Aghion and Howitt 1997). Finance matters because financial institutions contribute to fostering productivity and hence absolute advantage. Financial intermediaries can spur technological innovation by identifying and funding those entrepreneurs with the best chances of successfully implementing innovative products and production processes. In this way, the development of financial institutions and markets is a critical and inextricable part of the growth process (Levine 1997). Systemic factors need to be integrated into the analysis because technological development significantly depends on the existence of a suitable environment for learning, imitation and innovation. The most relevant systemic factors that are usually highlighted in the literature are the quality of the physical and institutional infrastructure, financial deepening, and the characteristics of the national system of innovation.

The literature stressing the role of non-price and systemic factors, however, has two weak points. The first is that there is no systematic research on the effects of macroeconomic disequilibrium on competitiveness. There are two facts that make this point important: (i) It is undeniable that the macroeconomic regime is a relevant component of the systemic environment in which the firm operates. (ii) As we have stressed, macroeconomic instability is much more significant in the specific case of developing countries. These two facts imply that it is very important to identify what are the channels through which macroeconomic disequilibria affect the microstructure. The second weak point is that the literature analyzing the links between growth and financial deepening has relied excessively on cross-country evidence. As a consequence, too many unanswered questions remain about the concrete features of the causal links between financial constraints, growth and the creation of comparative advantages. We believe that much more empirical research should be done. We need econometric analyses and case studies about both specific countries and industries. A deeper understanding of the micro/macro and real/financial interactions at the country, firm and sectoral levels would greatly contribute to our understanding of the determinants of competitiveness. The main objective of the project whose results we present in this book is, precisely, to contribute to filling this gap in the development literature.

Micro/macro-linkages

One important reason explaining the scarcity of studies analyzing micro/macro-linkages is that, until recently, economic theory made a sharp distinction between the economy's growth trend and cycles. Traditionally, business cycle theorists have analyzed de-trended data and considered the trend as exogenous to the cycle, and growth theorists have focused on characterizing a long-run growth path. One important weakness of this approach is that it cannot account for the existence of stochastic trends (Aghion and Howitt 1997). The view that, under certain circumstances, macroeconomic disequilibrium can have permanent effects on the microeconomic structure implies that temporary shocks are embedded into long-run paths and hence is a view consistent with the approach of some endogenous growth models. It is interesting to notice, in this regard, that although there is consensus on the fact that in developing countries (particularly in Latin America) excessive macroeconomic instability and sluggish growth have not been independent phenomena, there have been no attempts until recently to analyze the effects of volatility on the growth trend. It must be stressed, nonetheless, that temporary shocks can either hamper or benefit growth. We have already given an example in which volatility acts as an obstacle to growth. But we can also easily think of situations in which a temporary boom can have permanent positive effects. Suppose that there is a surge in capital inflows that reduce the interest rate and, as a consequence, credit-constrained firms experience a long period of excess liquidity. It is possible for the higher investment rate that will result from a softer liquidity constraint to have permanent favorable effects on productivity, if learning and skill accumulation are complementary to physical capital accumulation. Mechanisms of this kind have been present in the experience of countries like Argentina and Peru in the 1990s.

When trends are assumed to be stochastic, not only the temporary shocks that hit the economy but also the characteristics of the short-run macroeconomic adjustment path may have long-lasting effects. This fact is a primary source of concern for policy makers. The policy dilemmas associated with the choice between alternative exchange rate regimes is a good example. Policy makers care about the exchange rate regime because the adjustment paths under alternative exchange rate regimes are different. If the effects of the adjustment path on the long-run equilibrium are non-neutral, the adoption of a particular exchange rate regime matters for the evolution of competitiveness. Let us take the example of the recent crises in Asia and Latin America. The balance-of-payments disequilibria typically arose in the context of (approximately) fixed exchange rate regimes. Under such circumstances, there were two basic policy reactions. The most usual option was the devaluation of the currency and the change of the exchange rate regime. Other economies (notably Argentina and Hong Kong), on the contrary, did not resort to devaluation and privileged the maintenance of the regime over the need to correct the exchange rate parity to preserve competitiveness. Devaluation proved to be very effective to correct external imbalances in the short run. Why, then, have there been countries which did not devalue? The case of Argentina is very interesting in this regard. The diagnosis behind the decision to maintain convertibility was that changing the regime would be too costly. The most important costs were considered to be the higher volatility of key relative prices, inflation acceleration and, particularly, the increase in the fragility of the financial system. These costs could more than offset the benefits of correcting the misalignment of the exchange rate in the short run. Independently of whether or not the Argentine authorities were right in their assessment of the costs and benefits of a regime change, the Argentine case clearly shows that relative prices are not the only channel through which macroeconomic factors influence microeconomic decisions and competitiveness.

One important conclusion that our analysis of micro/macro-interactions suggests is that competitiveness means more than just getting the relative prices (and, particularly, the exchange rate) right. The presence of fragmented or missing markets and weak institutions in developing countries determines that externalities, spillover effects, financial constraints and strategic interactions between economic actors are pervasive. Under these circumstances, when, say, a negative macroeconomic shock occurs, the market forces (i.e. relative price changes driven by excess demand) which should automatically restore equilibrium are too weak. As a consequence, there will be a tendency for the disequilibrium to set in motion destabilizing forces such as deep recessions accompanied by high and persistent inflation and/or unemployment. We think that this is an important reason why it is frequently observed that macro-disequilibria in developing countries are deeper and more unstable than the disequilibria observed in economies with a more complete market structure.

When we adopt a more comprehensive view of the determinants of competitiveness, many other elements enter the picture. In the first place, a static equilibrium can be 'bad' from the point of view of both the level of welfare of the population and the incentives for productivity growth and the development of dynamic comparative advantages. It is undeniable that an upward correction in the real exchange rate can be, under certain circumstances, a strong incentive for the production of tradable goods and it is also true that getting the exchange rate right may be of great help in reducing macroeconomic volatility. We should not reduce the issue of the overall economy's competitiveness, however, to the profitability of the tradable sector. For one thing, in the long run human capital accumulation, the development of indigenous technological capabilities, and the quality of the physical, financial and institutional structures critically depend on the efficiency of the non-tradable sector. In the second place, as we have seen, the way in which the correction in the exchange rate is achieved is of no minor importance, since the path of relative prices will likely affect the long-run equilibrium. In sum, in an open economy there is no guarantee that the equilibrium which will result from getting the exchange rate 'right' will not be a sort of 'bottom of the well' equilibrium characterized by sluggish productivity growth and/or high macroeconomic volatility. Obviously, if we assume away market imperfections and dynamic considerations, relative prices can only be distorted if the government interferes in the functioning of markets. Under such circumstances, if the government does not intervene, and we still observe low productivity growth, we should accept that the 'bottom of the well' equilibrium is the best situation for the country under consideration. Such a dismal conclusion, though, is not warranted. Given the characteristics of developing economies, assuming away market imperfections and the role of the factors associated with resource accumulation, finance, and technologies may be an erroneous starting point.

Trade and finance

The traditional theory of international trade emphasizes efficient resource allocation and endowment as the main explanation for a country's specialization pattern. The pattern of specialization and trade does not depend on absolute but rather relative costs of production, which under certain conditions are determined by relative factor endowments. In this setting, the firm is not much more than a production function and does not have to bother with financial questions to carry out its production projects. If the project is good, it is undertaken. It can always be financed in a perfect market setting. Under these circumstances, there is no room for such financial phenomena as credit rationing or financial fragility to influence the firm's decisions regarding production, and hence competitiveness. The 'new' trade theory has clarified the role of economies of scale, externalities, learning by doing, technical progress, product differentiation, and oligopolistic and monopolistic market. The firm is much more complex in this new framework which incorporates some insights from industrial organization theory. But especial consideration of financial issues is yet to be made. There is no systematic treatment of how financial decisions affect competitiveness either at a global or firm level. Indeed, when we depart from traditional trade theory, financial issues become more relevant in determining competitiveness. Compare, for instance, the constant-returns-to-scale static comparative advantage case with the 'learning by doing' dynamic comparative advantage perspective. Finance is unlikely to make a difference in the former as firms of any size and experience will produce always at the same unit cost. However, in the latter case, as experience becomes a crucial determinant of unit costs, new entrants will initially produce at a loss that has to be financed somehow. If capital markets are reluctant to finance these initial losses, firms that have the potential to become competitive will never have a chance, unless government intervenes to correct this market failure.7

If we assume that it is possible to explain the competitive performance of a firm without making any explicit reference to financial issues, we are implicitly assuming that Modigliani–Miller's theorem and Tobins' and Fisher's separation theorems hold. The company's funding decisions are irrelevant to the choice of projects. The present value of each project is independent of the way in which it is financed. This assumption is too strong in the context of most developing countries because, as we noticed, there are highly significant failures in factor and capital markets.

If the idealized conditions that define a perfect capital market do not hold, and finance matters, then it will be easy to find two firms with the same potential levels of competitiveness but very different foreign trade performances. One might be able to finance projects very easily while the other might not because of extremely high interest rates or credit rationing. Often in developing countries, suppliers of raw materials and intermediate goods provide credit to their buyers, especially small- and medium-sized ones which do not have access to credit from financial institutions (Petersen and Rajan 1996). This phenomenon has implications for the development of strong and competitive trading firms as well as for the financial intermediation process in a country. Of course, this is a direct consequence of the existence of market segmentation.

Finance also matters for the allocation of resources for another reason. Financial institutions are supposed to screen for the best projects and monitor their development. Consequently, if key segments of the capital markets are missing it implies that there will be less screening and monitoring and, hence, a worse allocation of resources. A company that has no bank monitoring may incur an inefficient allocation of real resources and have a suboptimal risk management. In a modern capitalist economy, the financial institutions have the important task of collaborating in 'picking the winner' in a decentralized way.

Following the lines of thinking of Schumpeter, McKinnon and Shaw and the more recent literature on finance and endogenous growth, Rajan and Zingales (1998) state that capital markets make a contribution to growth by reallocating capital to the highest value use while limiting risks of loss through moral hazard, adverse selection or transaction costs. This implies that the lack of financial development should disproportionately affect those firms that are heavily dependent on external finance. From this hypothesis two testable facts follow: (i) Industries which are more dependent on external financing grow faster in more financially developed countries. (ii) Given that new firms depend more on external finance, financial development favors growth by disproportionately improving the prospects of young firms. The Rajan and Zingales (1998) paper uses the external dependency ratio of firms in different sectors to test these two hypotheses using a panel of developed and developing countries. Their main conclusion is that 'financial development has a substantial supportive influence on the rate of economic growth and this works, at least partly, by reducing the cost of external finance to financially dependent firms' (p. 584). On the basis of the evidence found, Rajan and Zingales advance two conjectures which deserve more research. The first is that 'the existence of a well-developed (capital) market in a certain country represents a source of comparative advantage for that country in industries that are more dependent on external finance'. The second is that 'the costs imposed by a lack of financial development will favor incumbent firms over new entrants. Therefore, the level of financial development can also be a factor in determining the size and composition of an industry as well as its concentration' (p. 584). These conjectures are in line with the two main hypotheses of our project: finance matters and, specifically, finance matters to comparative advantage. In fact, combining Rajan and Zingales' assumptions about external dependency ratios with the analyses of trade specialization pattern and firms' capital structure, we have found additional support for the hypothesis that the level of financial deepening has an impact on competitiveness and trade patterns.

To be sure, the studies presented in this volume portray a picture of finance 'mattering' crucially in determining competitiveness. The link operates through various channels. At the micro and sectoral level, there is almost universal evidence for credit constraints operating to determine the range and nature of trade specialization. The link between credit dependence and 'winning' and 'losing' firms and sectors is remarkably tight, and best illustrated in the cases of Argentina and Brazil. Indonesia and South Africa are illustrations of how the credit constraint has maintained specialization in traditional products. Indeed, an important lesson from the country case studies is how the exploitation of dynamic comparative advantage and genuine infant industry arguments in developing countries depends on the availability of credit, a point highlighted in the Philippines case study.

But not just any credit, as Schmidt argues in his paper, and a point also made in the India and Tunisia case studies. Depending on the regulatory framework and political imperatives within which they operate, banks in particular can lend too much or too little. It is clear from the India, Indonesia and Tunisia studies that the existence of a market failure in the financial sector need not automatically result in an improvement with government intervention. It is here that caution is called for in generalizing and presenting a 'set' of policy recommendations that may then be followed in cookie cutter fashion.

The story and policy lessons emerging from the micro-level are further complicated by the role that finance plays at the macro-level. The Asian crisis brought to the lexicon of economics a word borrowed from the epidemiology literature – contagion. To be sure, high and highly volatile levels of short-term capital played havoc in the frontline countries of any crisis. But with contagion, Argentina and Brazil were compelled to reassess their monetary and exchange rate arrangements as a result of events in East Asia a few months earlier. In addition to short-term volatility, financial flows can have more persistent effects which often go beyond conventional Dutch disease explanations. There is, in all of this, a question of what constitutes the equilibrium exchange rate. But the South Africa study and chapters by Ros and Medhora suggest that this is not just a technical issue. There are important consequences to having an imbalance in the trade and real sectors on the one hand, and the financial sector on the other.

It is at this point that Guerrieri's call for a more nuanced assessment of the link between trade openness and economic development becomes a powerful one. Still, the purpose of this set of studies is not to suggest that no answers exist to complicated situations. Rather, it is to suggest that answers do exist, but they have to be actively sought out, and not inferred from a preferred approach, much less derived from consensus. In large part, deep and well-run financial markets are indispensable to a successful strategy to link competitiveness in trade with economic growth and, ultimately, development. The benefits of having such markets and the costs of not having them are clear. Developing such markets often involves the active intervention of the state, to a point. Finding the balance between market creation and market liberalization is the ultimate challenge that faces all countries.

In sum, we believe that the concept of competitiveness represents a challenge to economic analysis because the level of competitiveness a country shows results from complex interactions of microeconomic, macroeconomic and financial factors and the analytical tools of the traditional approach are not especially useful for analyzing such interactions. Indeed, one important motivation for undertaking the country studies that we present here was, precisely, to investigate whether or not micro/macro and real/financial interactions are as relevant for competitiveness, the trade pattern and current account sustainability as the stylized facts that we have listed above seem to suggest. To be more specific, the hypotheses of our research work have been:

1 Both the degree of macroeconomic stability and the quality of financial markets are relevant in determining the evolution of competitiveness via their influence on non-price factors.

2 The sectoral mix of specialization at the 'micro'-level contributes to determining macroeconomic stability via its influence on the sustainability of the current account. The hypothesis of an imperfect access to international capital markets is crucial for this hypothesis.

3 Financial factors matter at the micro-level via their influence on the firms' capital structure and investment behavior, and they matter for competitiveness to the extent that there may be an anti-innovation or anti-trade bias in the allocation of financial resources.

The studies that follow elaborate on these points and, we trust, highlight their veracity while remaining true to the important distinctions and nuances that always exist across countries and development experiences and, indeed, change over time within countries.

Notes

1 On globalization, see Rodrik (1998).

2 For a paradigmatic example, see Krugman and Obstfeld (1991).

3 See for example Haque (1995) and Guerrieri (1994).

4 See World Bank (1993) and the references there.

5 The authors who propose leaving aside the concept of competitiveness should develop an alternative and more efficient view that can consistently integrate the problems implicit in this concept. Krugman, for example, after uncovering some mistakes which originated in an incorrect interpretation of competitiveness, calls for a counter–counter revolution which would supersede the Washington Consensus (Krugman 1992). But such a revolution is still wanting.

6 The inclusion of proxies of technological activity (R&D investments, patents granted) and productive capacity (capital stock growth, investment rates) in regressions explaining market shares or trade flows yielded the 'right' signs for the estimated coefficients of price-competitiveness indicators (Fagerberg 1988; Amendola et al. 1993; Agénor 1997). The analytical underpinnings of non-price competitiveness determinants are to be found in the new theories of trade and growth as explained, for instance, by Helpman and Krugman (1985) and Grossman and Helpman (1991); see also Ocampo (1991) and Dosi (1991). Other contributions stem from endogenous growth theory. For a comprehensive and consistent presentation of this issue, see Aghion and Howitt (1997) and Fagerberg (1994).

7 We owe this example to Saul Keifman.

References

Agénor, P. (1997) 'Competitiveness and External Trade Performance of the French Manufacturing Industry', Weltwirtschaftliches Archiv 133(1): 103–33.

Aghion, P. and Howitt, P. W. (1997) Endogenous Growth Theory. Cambridge, MA: MIT Press.

Amendola, G., Dosi, G. and Papagni, E. (1993) 'The Dynamics of International Competitiveness', Weltwirtschaftliches Archiv 129(3): 451–71.

Chenery, R. and Syrquin, M. (1986) Industrialization and Growth. Oxford: Oxford University Press.

Dosi, G. (1991) 'Una reconsideración de las condiciones y los modelos del desarrollo. Una perspectiva "evolucionista" de la innovación, el comercio y el crecimiento', Pensamiento Iberoamericano 20: 167–91.

Fagerberg, J. (1988) 'International Competitiveness', The Economic Journal 98: 355–74.

Fagerberg, J. (1994) 'Technology and International Differences in Growth Rates', Journal of Economic Literature XXXII: 1147–75.

Fanelli, J. M. and Frenkel, R. (1995) 'Micro–Macro Interaction in Economic Development', Unctad Review. New York: United Nations.

Fanelli, J. M. and Medhora, R. (eds) (1998) Financial Reform in Developing Countries. London: Macmillan.

Fazzari, S., Hubbard, G. and Petersen, B. (1988) 'Financing Constraints and Corporate Investment', Brookings Papers on Economic Activity, No. 1.

Grossman, G. M. and Helpman, E. (1991) Innovation and Growth in the Global Economy. Cambridge, MA: MIT Press.

Guerrieri, P. (1994) 'International Competitiveness, Trade Integration and Technological Interdependence', in OECD, The New Paradigm of Systemic Competitiveness: Toward More Integrated Policies in Latin America. Paris: OECD Development Centre.

Haque, I. (1995) Trade, Technology, and International Competitiveness. Washington, DC: World Bank.

Harris, J. R., Schiantarelli, F. and Siregar, M. G. (1994) 'The Effect of Financial Liberalization on the Capital Structure and Investment Decisions of Indonesian Manufacturing Establishments', The World Bank Economic Review 8(1): 17–47.

Helpman, E. and Krugman, P. (1985) Market Structure and Foreign Trade. Cambridge, MA: MIT Press.

Hubbard, R. G. (1998) 'Capital-Market Imperfections and Investment', Journal of Economic Literature XXXVI: 193–225.

Inter-American Development Bank (IDB) (1995) Economic and Social Progress in Latin America, 1995. Washington, DC: Inter-American Development Bank.

Kaldor, N. (1978) 'The Effect of Devaluation on Trade in Manufactures', in Further Essays on Applied Economics. London: Duckworth.

Krugman, P. R. (1992) 'Toward a Counter–Counter-Revolution in Development Economics', Mimeo, Harvard University.

Krugman, P. R. (1996) Pop Internationalism. Cambridge, MA: MIT Press.

Krugman, P. R. and Obstfeld, M. (1991) International Economics. Theory and Policy. Boston: Harper Collins.

Levine, R. (1997) 'Financial Development and Economic Growth: Views and Agenda', Journal of Economic Literature XXXV: 688–726.

Llal, A. (1995) 'The Creation of Comparative Advantage: The Role of Industrial Policy', in I. Haque (ed.) Trade, Technology, and International Competitiveness. Washington, DC: World Bank.

Obstfeld, M. (1998) 'The Global Capital Market: Benefactor or Menace?', Journal of Economic Perspectives 12(4): 9–30.

Obstfeld, M. and Rogoff, K. (1996) Foundations of International Macroeconomics. Cambridge, MA: MIT Press.

Ocampo, J. A. (1991) 'Las nuevas teorías del comercio internacional y los países en vías de desarrollo', Pensamiento Iberoamericano 20: 193–214.

Petersen, M. A. and Rajan, R. G. (1996) 'Trade Credit: Theories and Evidence', National Bureau of Economic Research Working Paper, No. 5602.

Rajan, R. G. and Zingales, L. (1998) 'Financial Dependence and Growth', American Economic Review 88(3): 559–86.

Ramey, G. and Ramey, V. (1995) 'Cross-Country Evidence on the Link between Volatility and Growth', American Economic Review 85(5): 1138–51.

Rodrik, D. (1998) 'Symposium on Globalization in Perspective: An Introduction', Journal of Economic Perspectives 12(4): 3–8.

Rodrik, D. (1999) 'Governing the Global Economy: Does One Architectural Style Fit All?', Mimeo, John F. Kennedy School of Government.

Sachs, J. and Warner, A. (1997) 'Fundamental Sources of Long-Run Growth', Recent Empirical Growth Research (Area Papers and Proceedings) 87(2): 184–8.

Temple, J. (1999) 'The New Growth Evidence', Journal of Economic Literature XXXVII: 112–56.

Williamson, J. (1990) 'What Washington Means by Policy Reform', in J. Williamson (ed.), Latin American Adjustment: How Much Has Happened? Washington, DC: Institute for International Economics.

World Bank (1993) The East Asian Miracle, Economic Growth and Public Policy. Washington, DC: Oxford University Press.

2 Finance and changing trade patterns in developing countries: The Argentine case1

José María Fanelli and Saúl Keifman

1. Introduction

Structural reforms, the creation of a customs union with Mercosur, and the renewal of capital inflows into Latin America profoundly changed the structure of incentives Argentine firms faced in the 1990s. One important consequence was a higher degree of heterogeneity in the performance of firms and sectors. The non-tradable sector was a privileged recipient of foreign funds as a result of the privatization process and the deregulation of foreign investment. Producers of tradable goods faced greater competition as well as new opportunities from Mercosur markets as well as the availability of inputs and investment goods at international prices. The changing environment affected profitability across and within industries in complex ways and obliged firms to restructure. Some firms adopted offensive strategies to restructure, taking advantage of new market opportunities, implementing organizational improvements and upgrading capital equipment. Other firms, however, followed purely defensive restructuring strategies, their principal objective being to ensure survival in a far more challenging environment. This chapter explores how the changes in the macroeconomic setting and the interactions between developments in trade and finance created winners and losers in Argentina's recent past.

We will examine two different periods of the Argentine experience: the 1983–90 period when economic policies were dominated by the adjustment of the economy to the financial constraints imposed by the international debt crisis, and the period which followed the launching of Convertibility in 1991, when structural reforms and liberalization were implemented. We will place more emphasis on the latter, the richest in transformation, but also the least understood. The second section analyzes the stylized facts related to the evolution of trade and specialization patterns. The third part studies the characteristics of firms' financial structure and portfolio decisions in the context of imperfect capital markets such as those existing in Argentina. The fourth section builds on the stylized facts presented in the previous two sections and advances some hypotheses on micro–macro and trade–finance interactions. In what follows in this introduction, we briefly review the most important features of the overall evolution of the Argentine economy in the period under analysis in order to set the context of our research.

Productivity, competitiveness and growth in postwar Argentina reveal clearly differentiated periods. Until the mid-1970s, the country followed a strategy of import substitution industrialization. Although the economy grew during this period, the average growth rate was much lower than that observed in Latin American countries like Brazil or Mexico that were following the same strategy. In particular, the rate of productivity increase in Argentina was low and the country systematically lost competitiveness. This discouraging evolution in competitiveness, in turn, resulted in recurrent balance-of-payments crises and stop-and-go cycles determined by the availability of foreign exchange. In 1975, the country suffered a huge macroeconomic crisis that set the economy on the brink of hyperinflation. This crisis made it clear that the import substitution strategy had been exhausted as a means of increasing productivity and competitiveness.

From the macroeconomic collapse of the mid-1970s to 1990, Argentina made several fruitless attempts to reform its economy. Two features that were prevalent until 1990 are highly relevant to the topic of this book: the recurrent balance-of-payments crises which resulted in major macroeconomic instability, and the strong drop in the demand for domestic financial assets which gave rise to an unprecedented tightening in the rationing of credit toward productive firms, even for larger ones. It is not surprising that investment activity collapsed during this period and that productivity stagnated. Under such circumstances, the need to close the current account deficit required a sharp trade-off between living standards and competitiveness since the only way to gain competitiveness in the short run was to reduce domestic costs via huge devaluations. The maxi-devaluations of the 1980s, however, affected not only human welfare and competitiveness but also macroeconomic stability. Consequently, the economy underwent two hyperinflationary episodes by the end of the period.

In the 1990s the situation changed radically. In the first place, structural reforms were deepened. The process to open the trade and capital accounts, as well as the liberalization of the financial system, was completed, and state-owned firms were privatized. In the second place, price stability was achieved via the implementation of a currency board scheme which pegged the Argentine peso to the US dollar (the so-called Convertibility Plan). Third, the greater availability of foreign finance and the fall in international interest rates relaxed the external financial constraint significantly, thus eliminating one of the causes of macroeconomic disequilibria during the debt crisis. This is perhaps the most important fact that distinguishes the 1980s from the 1990s. In the 1990s it was possible to finance the higher trade and current account deficits resulting from the structural reform and stabilization processes. One significant factor generating the current account deficit was the recovery in the private demand for capital goods. Another important factor was that the private agents also increased their demand for consumption goods. In this way, the recovery in investment was not accompanied by savings, leading to a widening in the private deficit.

These macro-developments have had very important consequences at the micro-level. While the increased macro-stability reduced uncertainty in decision making, the greater availability of credit dramatically softened the credit rationing that firms were facing. Both factors heavily contributed to reversing the stagnant path of productivity, particularly in the manufacturing industry. Exports have shown a greater dynamism during the 1990s as compared to the 1980s, in spite of the real appreciation of the peso, while the overall economy has been growing at around 6 per cent per annum. This might indicate that the loosening up of financial constraints made a difference, probably, via investment and productivity. Table 2.1 shows the evolution of some key macroeconomic variables.

In the 1980s there was a systematic fall in the domestic demand for financial instruments because of the extreme uncertainty. In the 1990s, the upsurge in capital inflows, together with the recovery in the demand for domestically issued financial assets in a context of increasing stability, led to an increase in financial deepening. This can be seen in Figure 2.1, which shows the evolution of M4 and total credit. This not only softened the tight credit rationing of the 1980s, but also opened up opportunities for firms to innovate in the form of financing capital projects. Despite these remarkable changes, however, the new situation of the 1990s presents an important weakness. The macroeconomic equilibrium is highly dependent on the stability of capital inflows, and the recent Mexican and Asian crises have made it clear that international flows into 'emerging' countries are

Table 2.1 Evolution of selected macroeconomic variables

Period average

Real GDP growth (%)

GDP deflator change (%)

Investment/GDP (%)

Current account (billion (USD)

Real exchange rate (1990 = 100)

Openness (X + M)/GDP (%)

1983–90

–0.2

911.6

17.1

–1.4

103.6

15.7

1991–7

  6.2

  23.1

21.0

–5.7

  78.4

27.6

Source: Elaborated on the basis of Ministry of Economy data.

Image

Figure 2.1 Evolution of money and credit (millions of pesos).

Source: Elaborated on the basis of Central Bank data.

far from stable. The figure also records the sharp reversal experienced by the financial deepening process in 1995 as a result of the Tequila effect. It took over one year for the economy to recover from the consequences of the instability triggered by the crisis. In fact, a more severe crisis was avoided only because of measures taken by both the Central Bank as lender of last resort, and by the IMF in providing external support.

2. Trade specialization and competitiveness: The stylized facts

This section is devoted to establishing the 'trade facts', that is, the evolution and structure of trade flows in the recent past, the evolution of productivity and domestic costs and their relationships with the current account.

After a poor performance in 1983–90, trade flows increased dramatically in 1991–6. However, as imports grew much faster than exports, the trade balance turned negative. An important factor behind the growth in exports was the launching of Mercosur (Southern Cone Common Market) in 1994. Import growth, in turn, was driven by GDP growth, a real currency appreciation and trade liberalization.

Regarding the pattern of inter-industry specialization, food items remained the main source of foreign exchange in the 1980s and the 1990s. This dependence on primary sectors was reinforced in the 1990s as fuels became a surplus sector while most manufactures increased their negative contribution to the trade balance. The rise in capital goods imports (machinery and transport equipment) was an important factor behind this development.

A more detailed examination of trade flows, however, shows that intra-industry trade experienced a significant and continuous increase throughout the 1980s and the 1990s. That is to say, all sectors exported and imported more, including deficit sectors (manufacturing) which managed to increase their exports, especially in the 1990s.

Real unit labor costs declined in the 1980s and rose in the 1990s, mainly as a result of the ups and downs of dollar wages. Labor productivity grew dramatically in the 1990s (after a slip in the 1980s) thanks to higher capacity utilization, technological change and the resumption of investment activity.

Overall evolution of trade flows

The performance of trade flows between 1983 and 1990, the years of financial restraint, was rather disappointing (see Figure 2.2). Imports remained stagnant, hardly surprising given the lack of GDP growth. Exports, in turn, grew at 6.7 per cent per annum, well below the 10.1 per cent annual growth rate recorded by world exports. Additionally, exports behaved countercyclically2 and were also strongly influenced by commodity prices. The annual average of the trade balance was 3.9 billion USD in 1983–90. In other words, the goal of generating trade surpluses to serve the foreign debt was accomplished at a high social cost.

From 1991, trade flows increased dramatically. Imports took the lead, growing at a 34 per cent yearly rate, mainly fueled by lower tariffs, real currency

Image

Figure 2.2 Trade flows, 1983–96.

Source: Elaborated on the basis of Ministry of Economy data.

appreciation and output growth. This brought about a trade deficit from 1992, but exports soon caught up, closing the gap in 1995 and 1996. Between 1990 and 1996, the annual rate of export growth was 11.6 per cent, which favorably compared to the annual growth rate in world exports which was 7.6 per cent. Most of Argentina's export growth took place in 1994–6, at a 22 per cent yearly rate. Something remarkable about the export surge initiated in 1994 is that it seems to have broken the aforementioned countercyclical pattern, occurring not only during the Tequila's recessionary year (1995) but also in expansionary years, such as 1994 and 1996. Trade balances in this period averaged a 1.2 billion USD annual deficit, though this figure masks a yearly 4 billion USD deficit in 1992–4 and a near balance situation in 1995–6.

As a result of the growth in trade flows, Argentina has recently become a much more open economy. Total real trade flows as a percentage of real GDP3 have increased from an average of 16 per cent in 1983–90 to an average of 28 per cent in 1991–6.4

Changes in the pattern of inter-industry trade specialization

Table 2.2 provides information on the sectoral contributions to the trade balance5 according to aggregate Standard International Trade Classification (SITC) commodity groupings. This indicator is adjusted by the overall trade balance. When the economy began to adjust to the debt crisis (1983–4), only food items made a positive contribution to the trade balance. By the end of the first period (1988–90) two more groups, Agricultural raw materials and Other manufactures, added positive contributions to the trade balance, while the remaining ones (Fuels, Ores and metals, Chemical products, Machinery and transport equipment) reduced their deficit shares.

Table 2.2 Contributions to the trade balance

 

1983–4

1985–7

1988–90

1991–3

1994–6

Food items

647.50

556.07

448.35

521.50

456.43

Agricultural raw materials

–20.40

–7.87

2.21

3.20

16.73

Fuels

–58.06

–67.59

–40.02

45.07

75.66

Ores and metals

–42.95

–38.69

–41.62

–17.63

–10.83

Chemical products

–173.69

–157.69

–155.79

–100.59

–103.41

Other manufactures

–97.26

–12.89

18.86

–90.89

–73.60

Machinery and transport equipment

–255.23

–271.35

–231.05

–359.71

–361.32

Unallocated

0.09

0.01

–0.93

–0.95

0.35

Cereals

301.39

175.97

91.32

110.93

97.99

Crude and manufactured fertilizers

–7.47

–7.28

–7.08

–4.42

–11.60

Crude petroleum

0.00

3.78

4.06

24.72

77.44

Medical and pharmaceutical products

–18.09

–20.48

–16.54

–14.80

–16.40

Textile fibers, yarn and clothing

5.56

23.71

31.07

–14.90

8.96

Metals and metal manufactures

–66.45

–16.96

7.82

–5.72

–11.31

Machinery

–206.89

–234.99

–201.73

–268.42

–279.84

Transport equipment

–48.34

–36.36

–29.32

–91.28

–81.49

Source: Elaborated on the basis of Ministry of Economy data.

 

Table 2.3 Aggregate indices of intra-industry trade (based on SITC three-digit groupings)

Indices

1983–4

1985–7

1988–90

1991–3

1994–6

Grubel and Lloyd

0.159

0.205

0.250

0.273

0.320

Aquino

0.151

0.196

0.253

0.276

0.326

Source: Elaborated on the basis of Ministry of Economy data.

Changes in the intra-industry pattern of trade

Indices of contributions to the trade balance are good indicators of the pattern of inter-industry specialization. When trade is driven by comparative advantage, that is all there is to know. Trade is also driven by economies of scale, however, which affects the volume of intra-industry trade. Therefore, the evolution of indices of intra-industry trade gives important information on the structure of the economy since they measure the exploitation of economies of scale and the degree of technological sophistication of an economy.

Table 2.3 shows the evolution of intra-industry trade as measured by the indices proposed by Grubel and Lloyd,6 and Aquino,7 based on SITC three-digit groupings. They both show a dramatic increase in the significance of intra-industry trade, which more than doubles between 1983–4 and 1994–6.

The evolution of manufacturing trade flows, output, productivity and domestic costs

To better understand this story, we now focus on the evolution of the manufacturing sector that underwent serious structural change. The ratio of the trade balance in manufacturing to gross output rises from a surplus position in 1986 (1.7 per cent), peaks in 1990 as a result of a deep recession and hyperinflation (8.5 per cent), and then falls dramatically reaching a 5.4 per cent deficit in 1996 for the overall manufacturing (Table 2.4). This might suggest that manufacturing is one of the big losers of the opening-up process and that Argentina is being deindustrialized. Furthermore, this ratio falls in most manufacturing branches. This ratio, however, conceals the fact that both imports and exports increased faster than total production (of course, the former more quickly than the latter). The rise in the imports to gross output ratio from 3.4 per cent in 1990 to 20 per cent in 1996 is hardly surprising given the mix of currency appreciation and drastic tariff reductions. It is more remarkable that export ratios also improved from 12 per cent in 1990 to 15 per cent in 1996. We have already mentioned that export growth in the mid-1990s seems to have broken the old countercyclical pattern. We now see that manufacturing is part of this change in behavior. Naturally, this parallel increase in both imports and exports is closely related to the important rise in intra-industry trade mentioned above.

Table 2.4 Manufacturing: trade-balance-, exports- and imports-to-output ratios (%)

 

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

(X–M)/Q

1.7

0.7

3.0

5.8

8.5

2.7

–3.9

–5.2

–8.1

–3.1

–5.4

X/Q

5.1

4.7

6.6

9.4

12.0

10.1

8.9

9.8

10.6

14.7

14.9

M/Q

3.4

4.0

3.6

3.6

3.5

7.4

12.8

15.0

18.8

17.8

20.3

Source: Elaborated on the basis of Ministry of Economy data.

(XM)/Q: trade-balance-to-output ratio; X/Q: exports-to-output ratio; M/Q: imports-to-output ratio.

 

Table 2.5 Manufacturing: indices of output, labor productivity and unit labor costs

 

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

Q

128.6

130.2

121.2

110.6

100

109.9

121.1

127.3

135.2

125.8

132.4

Q/L

108.2

111.4

100.8

102

100

110.7

119.9

127.2

133.7

138.2

146.6

WL/QE

  76.7

  70.2

  81.2

  45.3

100

112.3

133.2

136.6

137.7

136.9

136.0

WL/QEPx

  93.5

  78.6

  78.8

  44.8

100

107.9

122.7

126.4

124.6

114.6

112.3

WL/QEPm

  92.9

  80.0

  83.5

  48.0

100

107.6

126.4

133.9

132.4

124.5

120.9

Source: Elaborated on the basis of Ministry of Economy data.

Q: output index; Q/L: labor productivity index; WL/QE: US dollar unit labor cost index; WL/QEPx: US dollar unit labor cost index deflated by export prices; WL/QEPm: US dollar unit labor cost index deflated by import prices.

Table 2.6 Export growth and investment

Variable

Coefficient

t-statistic

Independent

6.18

2.34

Constant

1.35

5.47

Adj. R2 = 0.09; observations included = 65.

In order to measure the importance of trade developments in the 1990s, it should be noted that they took place in a context of a swift growth in manufacturing output (see Table 2.5). The driving force behind the expansion in manufacturing output was productivity growth since employment actually declined. Indeed, the record of labor productivity growth in manufacturing between 1990 and 1996 was remarkable. Total manufacturing labor productivity increased by 46.6 per cent, or a 6.6 per cent annual rate of growth between 1990 and 1996. According to Katz (1997), these developments made it possible to reduce the productivity gap between Argentina and the United States from 45 to 33 per cent during the same period.8 We are also certain that part of the measured growth in productivity is due to cyclical rather than structural factors as manufacturing output reached a trough in 1990. This is an important point to keep in mind when trying to ascertain and gauge its sources, something we do not attempt to do in this paper.

Despite higher labor productivity, real unit labor costs9 increased for the manufacturing sector and most of its branches as the increment in productivity only partially compensated for the growth in dollar wages caused by the currency appreciation that took place from 1991.

We suspect that there is a connection between the jump in productivity and the recovery of investment spending which increasingly satisfied its demand via capital goods imports. If it is true that investment has affected productivity significantly in the recent past, then it has also influenced competitiveness through its impact on unit labor costs. In this regard, learning how investment is financed becomes crucial to understanding the linkage between trade flows and finance. In a context of credit rationing, access to credit might become the binding constraint on investment decisions thereby hampering productivity change and the growth of trade flows. We found some preliminary evidence on the linkage between investment levels and export growth based on a regression run with data from sixty-five manufacturing branches in 1994–7, as shown in Table 2.6. The dependent variable is the growth rate of the export-to-output ratio between 1994 and 1997, and the independent variable is the average investment-to-value-added ratio in 1994–6. We are aware of the limitations of this piece of evidence but this is as far as we can go given the available data.

3. Competitiveness, finance and macroeconomic stability

The objective of this section is to assess the extent to which finance matters in explaining the degree of success or failure of firms and industries in Argentina. The question is relevant because changes on the financial side of the economy were as marked as those that occurred on the real side in the last two decades. Furthermore, since Argentina's capital markets are far from perfect, it seems plausible to assume that finance does matter in explaining not only the results of the restructuring process launched in the 1990s, but also the kinds of strategies specific firms chose.10 In what follows, we will try to shed some light on this issue based on available empirical evidence, using data from the financial system and the sample of firms listed on the Buenos Aires Stock Exchange.

We begin with some evidence on how the amount of credit generated by the formal system influences the activity level of manufacturing in order to set the context for the analysis that follows on the financial decisions of firms in manufactures.11 Our hypothesis is that, in a context of pervasive imperfections in financial markets (rationing), the availability of real credit in the banking system (crtot) has a strong influence on the activity level of manufacturing (gdp) in the short run, while there is a long-run relationship between the stock of real credit and the activity level of manufactures (eqns (2.1)–(2.3)).

Image

Image

Image

Table 2.7 details the results obtained using this error correction model (ECM).

The coefficient of credit in the long-run equation (δ2) is positive and significant at the 5 per cent level and so it seems that these two variables are co-integrated. As coefficients δ3 and δ5 are also significant while coefficients δ7 and δ8 are not, it seems that credit Granger-causes manufacturing GDP. In sum,

Table 2.7 Credit and manufacturing activity level

Coefficient

Estimated value

t-statistic

δ1

  0.87

δ2

  0.29

21.4

δ3

–1.43

–4.7

δ4

  0.53

–2.5

δ5

–0.27

–3.0

δ6

  0.01

  1.0

δ7

  0.75

  1.3

δ8

–0.15

– 0.4

δ9

–0.40

–2.4

δ10

  0.04

  1.7

Adj. R2 = 0.48; observations included = 32; sample period: 1989:3/1997:2

the evidence is consistent with the hypothesis of a relevant influence of credit on manufacturing output.

Competitiveness and asset accumulation: Winners and losers

In this part of the study, we focus on the micro-level and try to investigate whether, and in which way, financial factors were relevant in determining success or failure in an environment with higher external competition. To this end, we use the empirical evidence provided by the balance-sheet data of the sample of firms listed on the Buenos Aires Stock Exchange. We work with different aggregates of firms. First, we differentiate between firms which produce non-tradable goods and services (mostly newly privatized public utilities) and firms in the manufacturing industry which, in the new open-economy environment, belong to the tradable sector. Given our goals we concentrate on the tradable sector and use non-tradables as a benchmark. Second, we work with disaggregates within the tradable sector so as to identify interactions between real and financial factors at the sectoral level, and elaborate hypotheses on the financial variables determining the success or failure of firms. Third, in order to isolate the features associated with market imperfections we use other criteria to divide up the sample by, for example, the size of the firm.

In spite of the important recovery in manufacturing output achieved in the 1990s, the value of total assets of the industrial firms in our sample was only 20 per cent greater in the first half of 1997 than in 1986.12 This fact, however, conceals marked differences in the firms' dynamic paths in each manufacturing branch. In order to capture such differences, we have classified the manufacturing firms in the sample according to the branches they belong to, and then split them into two groups: 'winners' and 'losers'.13 A branch is defined as a winner if a representative firm's assets have grown by more than the average and as a loser if they have not. Figure 2.3 shows the evolution of the assets of winner and loser firms.

The contrast between the evolution of assets of the firms in the winner and loser sectors is striking. While the real value of the assets of the winners more than doubled, the aggregate assets of the losers were much lower in 1997 than in 1986. While winners increased the size of their assets, investment in the latter sectors became negative. Figure 2.3 clearly shows that there was a break in the dynamic path of the variables under study at the beginning of the 1990s, followed by the amplification of the differences in the economic behavior of losers and winners. The important questions from the point of view of our study are, on the one hand, whether financial factors matter in explaining these facts and, on the other, whether there are relevant linkages between finance and changes in international competitiveness. In answer to the second question, the stylized facts of the overall evolution of competitiveness in the branches in which loser and winner firms produce are highly relevant. These are summarized in Tables 2.8 and 2.9.

From the tables, it seems that both winner and loser firms faced important changes in factors affecting competitiveness. First, all cases showed a marked increase in the imports-to-output ratio (except tobacco). This can be interpreted as evidence that these firms were experiencing far tougher competition from abroad.

Image

Figure 2.3 Evolution of total assets (1986:1 = 100).

Table 2.8 Evolution of key real variables of loser sectors (1991/6; %)

Sector

Output growth

Labor productivity growth

Labor costs growth

Exports/output

Imports/output

Trade balance/output

 

 

 

 

1991

1996

1991

1996

1991

1996

Textiles

–7.6

23.1

22.9

  4.1

  7.6

  5.9

14.7

  –1.8

  –7.1

Paper-cell.

30.3

30.3

35.8

  4.0

  9.8

  9.4

24.7

  –5.4

–14.9

Metals

27.7

63.2

–6.2

20.1

17.1

  9.6

13.2

  10.5

    3.9

Chemicals

29.8

30.2

24.2

  9.1

10.4

19.1

24.8

–10.0

–14.7

Source: Elaborated on the basis of INDEC data.

 

Table 2.9 Evolution of key real variables of winner sectors (1991/6; %)

Sector

Output growth

Labor productivity growth

Labor costs growth

Exports/output

Imports/output

Trade balance/output

 

 

 

 

1991

1996

1991

1996

1991

1996

Food

23.9

  23.8

  36.4

26.0

32.0

  1.5

  2.9

24.5

  29.1

Wood

–4.3

  15.3

–11.6

  1.8

  7.9

  7.1

11.0

–6.1

  –3.9

Petroleum

  1.3

203.0

–34.8

  6.9

  8.1

  1.3

  3.5

–1.2

  –2.2

Electrical machinery

40.1

  71.6

–16.1

  3.2

  5.0

37.0

58.2

–1.8

–21.2

Tobacco

14.1

  89.0

–41.5

  1.1

  0.6

  0.1

  0.1

  1.0

    0.5

Non-metallic

  2.7

  28.5

  29.6

  4.0

  4.3

  4.0

11.0

  0.0

  –6.7

Transport equipment

85.6

  75.8

–32.9

  7.2

11.2

15.4

37.4

–4.0

–22.0

Source: Elaborated on the basis of INDEC data.

Second, all branches responded to the challenge by increasing productivity and most managed to increase output levels. Another positive feature is that the exports-to-output ratio also increased (except tobacco). Nonetheless, sectoral trade deficits widened (except food).

One important difference in the performance between loser and winner branches is that the former experienced increased labor costs while the latter managed to reduce them (with a few exceptions: metals among losers and food and non-metallic products among winners). The fact that labor productivity grew much faster in most winner branches probably explains that outcome. All this means, ceteris paribus, that winner firms were better positioned during the restructuring process. If we assume that the increase in total assets is a reasonable proxy for investment, it is clear that one specific and highly relevant difference between winners and losers is that the former were able to invest and adopt a more offensive strategy while the latter reduced their assets and increased their productivity implementing a defensive restructuring of the firm. Why were winners able to adopt a more offensive strategy based on increases in investment? In order to assess the role of market imperfections, we first examine the determinants of investment in assets and, second, study the differential patterns in which loser and winner firms fulfill their financial requirements.

One hypothesis we would like to investigate is whether the chosen strategy was either defensive or offensive because of firms' ability to access the necessary funds to finance their restructuring. We utilized panel data for the 1986–97 period to analyze the determinants of changes in firms' assets and to check for the presence of market imperfections. On theoretical grounds, the first candidate in the search for determinants of a firm's investment decision (Δ asset) is its rate of profit (profit). In a context of perfect capital markets the firm should invest in all projects that are profitable under existing market conditions. When imperfections in financial markets are present, however, cash flow and the ability to access credit markets also matter.14 We utilized the operational income of the firm (income) and leverage (leverage, measured as the total-debt to asset ratio) as proxies for cash flow and the ability to obtain funds from credit markets, respectively, and estimated the model represented in eqn (2.4).

Image

Using the fixed-effects approach we found that the three exogenous variables were significant at the 5 per cent level, as shown in Table 2.10. The empirical evidence,

Table 2.10 The determinants of asset accumulation

Variable

Coefficient

t-statistic

profit

18.70

4.24

income

  0.43

2.23

leverage

  3.30

3.00

Adj. R2 = 0.51; F = 65.61; D–W = 2.06; total panel observations: 140.

then, does not reject the hypothesis that there exist imperfections in capital markets that constrain asset accumulation. Given this evidence, the question that naturally arises is whether there are any systematic differences in the way in which aggregate agents finance their stocks. In the next part, we focus on the evolution of stocks (particularly on the liabilities side of the firms' balance sheets) and examine whether these reveal some clues that help answer this question.

Imperfect capital markets and patterns of finance

Firms' decisions about the proportion of debt and net worth on the balance sheet are much more complex when market failures exist. In a world where the supply of credit at the ongoing interest rate is not infinitely elastic and the cost of the funds raised from distinct sources can differ significantly, the managers in charge of the capital budgeting process face additional restrictions. In such a context, the decision over the mix of owned and borrowed capital is crucial. It is not only essential to maximize the present value of a firm's assets but also to minimize the probability of unexpected increases in financial fragility which could result in financial distress and even bankruptcy. Figure 2.4 shows the evolution of total assets, liabilities and net worth of the aggregate of firms in the industrial sector. Between 1986 and 1996, total assets of aggregated manufacturing firms grew. Between 1986 and 1990 large swings in the value of real debts and a sharp drop during the hyperinflationary period (1989–90) made it harder for firms to satisfy their financial requirements.

In the 1990s, financial deepening and capital inflows increased credit supply allowing firms to increase their leverage after a long period of tight rationing. This picture, however, conceals important differences between winners and losers. Figure 2.5 shows the evolution of the most important items that define the balance sheet of winners: net worth, assets and liabilities.

As can be seen, the substantial growth in the value of assets is accompanied by a still higher increment in the stock of total liabilities. As a consequence, there is a systematic elevation in the leverage ratio. In fact, the augmentation in the stock of debt held by winners in the 1990s is impressive, between 1990:4 and 1997:2 it grew by 229 per cent. The evolution of the stock of debt, however, exhibits a greater variance than net worth and assets. Furthermore, the fact that the higher level of uncertainty caused by the Tequila crisis in the 1995 to mid-1996 period induces a sharp but temporary reversal in the upward trend of the leverage ratio, and a fall in the real value of the outstanding stock of debt, suggests that the winners' ability to access credit markets did not suffice to isolate them from macro-shocks. Figure 2.6 showing the performance of the losers' balance-sheet items is very different from that of the winners.

There are two salient features in the evolution of financial variables in the case of losers. The first is that the three variables under study exhibit a clear downward trend. It seems that it has been very difficult for losers either to generate funds internally or raise them in capital markets. Under such circumstances, they were even unable to maintain the size of their firms. The second feature is that the

Image

Figure 2.4 Pattern of finance, manufacturing (1986:1 = 100).

Source: Elaborated on the basis of Sistema de Informacion Bursatil data.

Image

Figure 2.5 Pattern of finance of winners (1986:1 = 100).

Source: Elaborated on the basis of Sistema de Informacion Bursatil data.

Image

Figure 2.6 Pattern of finance of losers (1986:1 = 100).

Source: Elaborated on the basis of Sistema de Informacion Bursatil data.

Image

Figure 2.7 Pattern of finance of the non-tradable sector.

Source: Elaborated on the basis of Sistema de Informacion Bursatil data.

effects of macroeconomic shocks on the balance sheet of losers differ from the case of winners. Loser firms were able to increase their leverage in the context of the greater macroeconomic stability in the 1991–4 period much like winners. Unlike winners, however, losers never recovered from the fall in the stock of debt and leverage ratio caused by the Tequila crisis, forcing them to undertake a defensive restructuring of their balance sheets. This hypothesis is consistent with Minsky's view on the changes in financial fragility throughout the business cycle and with Bernanke's approach regarding the effects of credit crunches on the external premium that firms pay for external finance.15 In both cases, market imperfections play a crucial role in determining the change in the level of fragility of (financially) heterogeneous agents.

The balance sheets of non-tradable producers are shown in Figure 2.7. The aggregate of non-tradable firms in the sample is a good standard to check for market segmentation because it is basically composed of firms that are, on average, larger in size and produce in sectors where the variance of cash flow is much lower. A priori, these firms should suffer from the effects of market imperfections less than manufacturing firms.

The pattern in the relationship between assets, liabilities and net worth reproduces the one corresponding to winners in the 1990s. There is a fact, nonetheless, that sharply differentiates this group from both the winners and losers: the effects of the Tequila crisis are hardly discernible in the graph. It is clear that these larger firms with a low standard deviation in cash flow have much more stable access to financial markets and are, thus, better equipped to resist macroeconomic shocks.

Market segmentation and foreign credit

We have seen that larger firms show lower financial volatility and seem to access long-term finance more easily. On the other hand, it can be argued that the accumulation of abundant liquidity can be a good strategy to avoid a liquidity crunch and maintain creditworthiness for managers facing excessive instability in the long-run segment of capital markets. Ceteris paribus the size of the firm, a strong liquidity position can increase the firm's access to long-run finance. Based on this reasoning, we investigate whether the ability to obtain long-run finance (leverlp) is explained by the size of the firm measured by the value of total assets (assets) and the real value of liquidity (liquidity). We used quarterly observations from the panel of manufacturing firms to estimate eqn (2.5).16 Table 2.11 presents the results obtained using the fixed-effect approach.

Image

All variables are significant at the 5 per cent level. This means that the hypotheses that there exists a segmented market for long-run credit on the one hand, and that managers use liquidity to signal a strong financial position as a means to soften the rationing of long-term funds on the other are both plausible.

Additional evidence on the issues under consideration can be obtained by looking at the evolution of different types of debt instruments and their distribution across the aggregates of firms. In the 1990s, the greater availability of long-term finance permitted enterprises to augment the proportion of long-term assets in the portfolio without jeopardizing the soundness of their financial positions. At the macroeconomic level this took the form of a strong recovery in the investment/GDP ratio. At the microeconomic level, though, the distribution of the increase in long-term credit among firms was far from even. Figure 2.8 shows

Table 2.11 The determinants of the long-run debt ratio

Variable

Coefficient

t-statistic

log assets

0.11

3.6

log liquidity

0.03

2.1

Adj. R2 = 0.814; F = 1,288; total panel observations = 585.

Image

Figure 2.8 Evolution of long-run liabilities (1986:1 = 100).

Source: Elaborated on the basis of Sistema de Informacion Bursatil data.

Image

Figure 2.9 Evolution of net-denominated debt (millions of USD).

Source: Elaborated on the basis of Sistema de Informacion Bursatil data.

the evolution of real long-term liabilities of losers and winners within the manufacturing industry.

While the real value of long-run liabilities in the case of winners shows a pronounced upward trend reaching a 1997 value which is eight times its 1986 level, that of the losers fluctuates around a mean of zero growth. Hence, the low (and even negative in some firms) investment demand of losers may be correlated with difficulties in obtaining long-term finance.

One important characteristic of financial underdevelopment is the inability to generate long-run debt instruments. Argentina is no exception to this rule and, consequently, international capital markets are a crucial alternative source of long-term finance. Likewise, in the particular case of Argentina, the growth of the dollarized segment contributed to the prolongation of debt contracts. The consequence of all this is that there tends to be a positive correlation between the proportion of long-run finance and the proportion of dollar-denominated debt held in the portfolio. Figure 2.9 shows the evolution of the dollar-denominated stock of debt in our sample of manufacturing firms.

The upward trend in the dollar-denominated debt of the winners is very strong while the opposite is true in the case of losers. At the beginning of the 1990s, the upsurge in capital inflows increased the availability of finance for the entire manufacturing industry. However, after the Tequila crisis, while winners could recover creditworthiness in international markets, losers could not. Why were losers unable to recoup credibility? A reasonable hypothesis is that the losers were less able to adapt to the post-Tequila scenario and, consequently, lost credit-worthiness. The tight rationing triggered by the Tequila effect in financial markets may have become an insurmountable obstacle for some firms to adapt to the new export-oriented scenario without implementing a defensive strategy. The question is why they tried to increase efficiency by means of a defensive strategy.17 We cannot give a definitive answer to these questions because more research (and particularly more data) is necessary, but we think that we have enough evidence to show that the interactions between finance and competitiveness merit more attention.

Image

Figure 2.10 Evolution of debt items in the non-tradable sector (1993:3 = 100).

Source: Elaborated on the basis of Sistema de Informacion Bursatil data.

The data on the evolution of non-tradable firms is consistent with the previous approach regarding the influence of segmentation and rationing on the productive strategies of the firms.

As Figure 2.10 shows, corporations in the non-tradable sector have privileged access to international markets and to long-run funds. The supply of foreign finance, on the other hand, seems to be much more stable than in the case of the other aggregates under analysis. The Mexican crisis, for instance, had much softer consequences on the ability of these firms to tap international capital markets to fulfill their financial needs.

Patterns of specialization and finance

Following Schumpeter, McKinnon and Shaw and the more recent literature on finance and endogenous growth, Rajan and Zingales (1998) state that capital markets make a contribution to growth by reallocating capital to the highest value use without substantial risk of loss through moral hazard, adverse selection or transaction costs. This implies that the lack of financial development should disproportionately hinder firms who are typically dependent on external finance. From this hypothesis two testable facts follow: (i) Industries which are more dependent on external financing grow faster in more financially developed countries. (ii) Given that new firms depend more on external finance, financial development favors growth by disproportionately improving the prospects of young firms.

One problem in testing these hypotheses is that the true optimal capital structure of firms cannot be observed in financially underdeveloped countries. Rajan and Zingales make two assumptions in order to overcome this problem and identify the 'technological' demand for external financing that a firm operating in a specific industry would choose in a perfect capital market. The first is that capital markets in the United States, especially for the large firms listed on the stock exchange, are relatively frictionless and, therefore, it is reasonable to assume that the observed ratio of external finance reflects the technological demand for external financing of the industry. Second, such a technological demand carries over to the same industries in other countries. On the basis of these assumptions, they identify the industry's technological demand for external finance using US data. The variable used is the external dependency ratio or EDR (capital expenditures minus cash flow from operations divided by capital expenditures), shown for nineteen ISIC sectors in the fourth column of Table 2.12.

The Rajan and Zingales paper utilizes these EDRs to test the two aforementioned hypotheses using a panel of developed and developing countries. Their main conclusion is that 'financial development has a substantial supportive influence on the rate of economic growth and this works, at least partly, by reducing the cost of external finance to financially dependent firms' (p. 584).

On the basis of the evidence found, Rajan and Zingales advance two conjectures that deserve more explanation. The first is that 'the existence of a well-developed (capital) market in a certain country represents a source of comparative advantage for that country in industries that are more dependent on external finance' (p. 584). The second is that 'the costs imposed by a lack of financial development will favor incumbent firms over new entrants. Therefore, the level of financial development can also be a factor in determining the size composition of an industry as well as its concentration' (p. 584). These conjectures are in line with the two main hypotheses of our project: finance matters and, specifically, finance matters to comparative advantage. In fact, combining Rajan and Zingales' assumptions about EDRs with our previous analysis on the trade

Table 2.12 Trade specialization and external dependence ratio

Rank

ISIC sector

CTB

EDR (%)

1

Food products and beverages

42.5

14.0

2

Leather, fur products and footwear

5.8

–11.0

3

Petroleum refineries

4.0

18.5

4

Basic metal products

2.1

9.0

5

Tobacco

0.1

–45.0

6

Wearing apparel

0.0

3.0

7

Furniture

–0.1

24.0

8

Printing and publishing

–0.2

20.0

9

Non-metallic mineral products

–0.3

19.0

10

Wood and cork products

–0.4

28.0

11

Textiles

–0.9

40.0

12

Metal products

–1.5

24.0

13

Paper

–1.8

18.0

14

Rubber and plastic products

–2.0

68.5

15

Professional and scientific equipment

–2.6

96.0

16

Transport equipment

–8.0

31.0

17

Industrial chemicals and other chemicals

–9.5

19.0

18

Electrical machinery

–11.8

77.0

19

Non-electrical machinery

–15.4

45.0

Source: Elaborated on the basis of Table 2.2 and table I in Rajan and Zingales (1998: 566–7).

specialization pattern and firms' capital structure, we can find additional support for the hypothesis that the level of financial deepening has a bearing on competitiveness and trade patterns.

In order to test the hypothesis about the relationship between finance and comparative advantage in Argentina, we ranked the ISIC sectors according to sectoral contributions to the trade balance (CTB), an indicator of comparative advantage that is included in the third column of Table 2.12. If finance matters for comparative advantage, there should be a negative association between the order of the sectors ranked by CTB and their order ranked by the EDR. In a financially underdeveloped country like Argentina, one would expect that sectors which have a greater probability to develop their potential comparative advantage would be those with a lower dependency ratio. We computed the Spearman rank correlation coefficient between the external dependence ratios and the contributions to the trade balance and found a very strong association between the two rankings: 0.98.

A second piece of evidence regarding the role of finance in trade has to do with the general hypothesis that those economies with weak capital markets develop a bias against new firms and innovators. In this sense, the lack of access to external finance acts as a barrier to entry. If this is true, firms which show stronger competitive advantage and, hence, are successful in export markets should tend to be older (traditional firms with established reputations and/or access to international capital markets) and larger in size. Likewise, there should be an important degree of concentration with few firms exporting a high share of total exports. In order to evaluate whether these hypotheses are relevant in the case of Argentina, we used the ranking of the top 1,000 exporter firms. The sample explains around 90 per cent of total exports. On the basis of such data it is possible to construct concentration indices and analyze the characteristics of the most successful exporter firms.

Table 2.13 clearly shows that there is a strong concentration in export markets. The top five firms account for 20 per cent of exports while fifty out of 1,000 firms explain 63.2 per cent of total exports. Likewise, the characteristics of the firms which appear at the top are telling: the overwhelming majority are either large

Table 2.13 Indices of concentration of exports

Position in the ranking

Accumulated value of exports (million USD)

Share of total exports (%)

Top five

  4,977

20.0

Top ten

  7,535

30.3

Top twenty

11,004

44.4

Top fifty

15,686

63.2

Top 100

18,202

73.4

Top 500

23,509

94.8

Bottom 100

      45

  0.02

Bottom 500

  1,285

  5.2

Source: Elaborated on the basis of Revista Mercado 1998 data.

traditional exporter firms (many of them belonging to 'grupos' – national holdings) or multinational corporations.18 As we have seen in the previous section, the larger firms have better access to both international and domestic capital markets. Hence, it is plausible to assume that finance is much less of a binding constraint for decision making in the case of these firms. On the other hand, it is clear that firms operating in either non-traditional industrial sectors and/or innovative firms are not present. These facts are consistent with the predictions that follow from our approach: the level of a country's financial deepening should be considered a very important non-price determinant of competitiveness.

From macro to micro: Credit fluctuations and financial volatility

In highly unstable countries like Argentina in the 1980s, the most important sources of instability are usually inflation and macroeconomic disequilibria. Fanelli and González Rozada (1998) show that volatility has not been constant in Argentina and has sharply declined under the Convertibility Plan. Here, we check whether volatility is relevant to firms' financial decisions at the micro-level and explore differences in winners and losers and the tradable and non-tradable sectors' financial behavior. In a capital market where segmentation and market failures are pervasive it seems reasonable to expect a priori that financial volatility differs if capital structure and access to capital markets differ among firms.

We have shown that more fragile firms face wider variation in their access to credit markets when shocks occur. Under such circumstances, we should observe a greater volatility in the evolution of the stock of debt in the case of losers. However, if macro-volatility has a bearing on the microstructure, we should also observe a reduction in overall volatility under Convertibility to the extent that the standard deviation of expectations will be lower. We will present some evidence of these facts below.

In Table 2.14 we have estimated the trend of the debt series and taken the unexplained portion of the total variance of the dependent variable (1 – R2) as a proxy for the level of volatility built into the time series.

Since the unexplained portion of the variance of the liabilities series is significantly lower in the case of winners, these results are consistent with the hypothesis that the financial position of losers is more volatile than that of winners. The table also presents the volatility of income. The objective is to show that there is practically no difference in the amount of volatility in the income of winners and losers. In other words, the higher volatility in the evolution of the stock of debt cannot be attributed to a higher volatility in the flow of income.19 Imperfections in capital markets do matter when explaining the facts under study.

Table 2.15 can be used to check for differences in financial volatility in tradable and non-tradable sectors. The methodology is the same as before. The table shows that all volatility indicators classify the group of firms in a way which is coherent with our previous arguments: the losers are first in the volatility ranking and the services sector is last. This occurs independently of the item on the balance sheet. The volatility of liabilities is higher than the volatility of assets in the

Table 2.14 Volatility measures for liabilities and income: manufacturing industry (1986:2–1997:2)

Dependent variable

Trend coefficient

Volatility(1– R2) (%)

log liabilities winners

  2.5

19.8

log liabilities losers

–0.6

69.6

log income winners

  0.6

93.2

log income losers

–0.3

93.8

 

Table 2.15 Volatility measures of balance-sheet items in services and manufacturing (1993:3–1997:2)

Group of firms

Liabilities volatility

Income volatility

Assets volatility

Net worth volatility

Services

  6.9

47.6

  2.6

  6.3

Winners

23.9

70.8

  9.3

11.3

Losers

47.0

95.8

33.4

43.8

three groups. This means that the short-run fluctuations in the stock of debt around its long-run trend are more frequent and greater in size than the same kinds of changes in the case of assets held by agents. This can be interpreted as evidence that when a disequilibrium occurs in agents' portfolios, the velocity of asset adjustment toward equilibrium values is lower than the velocity with which liabilities adjust. This is why firms are vulnerable to sudden and unexpected changes in credit conditions when a shock of a certain magnitude occurs. Debtor firms are 'tied' to assets more than creditors are 'tied' to the debt instruments that firms issue. This implies that, ceteris paribus, the more unstable and volatile the conditions are to access credit markets, the greater is the preference of the firm for flexible assets. Under conditions of increasing uncertainty, the managers will develop a strong preference for flexibility. The data suggest that this reasoning is not misleading. If we rank the firms by the volatility of their assets using Table 2.15, losers rank first, followed by winners while non-tradable firms come last. This coincides with the previous findings regarding the ability of different groups to access capital markets in a context of segmentation, and also suggests that tradable sectors suffer from a competitive disadvantage vis-à-vis the non-tradable sector from the financial point of view.

4. Conclusions: Trade specialization and micro–macro and trade–finance interactions

After a long period of high inflation, economic stagnation and recurrent balance-of-payments crises, Argentina has achieved macro-stabilization in the 1990s. In addition, structural reforms such as trade liberalization and privatization of state-owned enterprises were implemented. In turn, these developments were accompanied by the rebound of economic growth and investment, a rapid rise in trade flows, the resumption of capital inflows, an important increase in the level of financial deepening, as well as a real currency appreciation and higher current account deficits. The doubling of exports and the boom of imports, particularly manufactures, was the flip side of a process that created both winners and losers.

In spite of higher trade flows and greater intra-industry trade, the inter-industry pattern of specialization has not been upgraded and the country's balance of payments still remains vulnerable to foreign shocks, as proven by the Tequila effect, the Southeast Asian crisis and Brazil's devaluation of the 'real'. We believe that both micro–macro-interactions and finance mattered in all these cases.

An important question that we tried to answer is whether finance has played any role in the winner/loser game. We found evidence that supports the existence of important imperfections in Argentine capital markets that constrain asset accumulation, as cash flow and leverage ratios are significant determinants of asset changes, besides profits. In this regard, the fact that loser firms, that is to say, firms that contracted, seemed to have been less successful in increasing productivity suggests that financial market imperfections have forced these firms to pursue defensive restructuring strategies.

Furthermore, we found that asset size and liquidity also affect access to long-run finance, and verified that loser firms have had very little access to long-term funding and foreign sources of credit.

We collected evidence for the hypothesis that finance also plays an important role in determining the pattern of trade specialization. More specifically, we found an almost perfect (0.98) negative correlation between the pattern of inter-industry trade of Argentina and the EDRs computed by Rajan and Zingales (1998), which suggests that the lack of financial development is distorting the pattern of trade against sectors that are more dependent on external finance.

Finally, we examined the effects of financial volatility stemming from macro-instability on winners and losers. While the difference in income volatility between winners and losers is minor, the volatility of liabilities has been much higher for loser firms. In addition, the fact that firms in the non-tradable sector suffered from much lower levels of volatility in all categories of balance-sheet items than manufacturing firms (whether winner or loser) shows how financial market imperfections hinder firms in the tradable sector.

We conclude that the main lesson from the Argentine case study is that non-price determinants of competitiveness such as the level of financial development and macroeconomic stability are highly relevant to the degree of success of trade liberalization and financial opening.

Notes

1 We are grateful to Martín González Rozada for his superb econometric advice and Sebastian Katz for his helpful research assistance.

2 All jumps match recessionary years: 1985, 1988, 1989 and 1990.

3 See Table 2.1, last column.

4 Measuring the same variables in nominal terms yields no increase because of the fall in the relative price of tradables vis-à-vis non-tradables, caused by the currency real appreciation.

5 The contribution of sector (or industry) i to the trade balance (CTBi)is defined as

Image

where Xi and Mi are exports and imports of good i, respectively, and X and M are total exports and imports. Summation of CTBi over i equals 0. See Guerrieri (1994).

6 The Grubel and Lloyd index of intra-industry trade for country j and industry i is defined by

Image

The Grubel and Lloyd indices of intra-industry trade can be aggregated according to the following:

Image

7 Grubel and Lloyd indices have been criticized for being sensitive to the overall trade imbalance. Aquino suggested another index of intra-industry trade which corrects for aggregate trade imbalances in the following way:

Image

The aggregate Aquino index of intra-industry trade for country j is, in turn,

Image

8 These figures merit a word of caution. Official statistics on recent expansion in manufacturing value added and, therefore, productivity, might be somewhat overestimated as they do not adjust for the fall in the ratio of value added to gross output that took place in the 1990s, most likely, as a result of the opening up of the economy. Until better estimates are available, we can only conjecture on the order of magnitude of the bias. Our guess is that actual productivity growth has been, nevertheless, outstanding in the 1990s.

9 Unit labor costs (ULC) are computed according to the following definition:

Image

where W is the hourly wage rate, Q/L is output per hour worked, and P is either one of three prices: export price, import price or the peso/US dollar exchange rate.

10 Levine (1997) discusses this issue in the context of endogenous growth theory.

11 For an overall assessment of Argentina's financial deepening process with an emphasis on the role of credit factors in the business cycle, see Fanelli et al. (1998).

12 The activity level in 1986–7 represents a peak in industrial production for the 1980s during the debt crisis.

13 The branches represented are food, fuels and petroleum, metals, textiles, transport equipment, electric machinery, tobacco, chemicals, non-metallic products, paper and cellulose, wood products and others.

14 See on this issue, Fazzari et al. (1988) and Harris et al. (1994).

15 See Minsky (1977) and Bernanke et al. (1993).

16 The variable leverlp is defined as the long-run debt/total assets ratio, hit is the error term. We corrected for the presence of first-order autocorrelation.

17 Unfortunately, firms that did not survive the Tequila effect are not included in our sample and are, therefore, outside of this discussion.

18 The top twenty exporting firms are YPF, Cargill, Ford, Aceitera Gral Deheza, Grupo Fiat, Volkswagen, Vicentin, Nidera, Louis Dreyfus, Aerolineas Argentinas, Molinos Río de la Plata, Siderca, La Plata Cereal, Oleaginosas Moreno, Continental, Prod. Sudamericanos, Perez Companc, Guipeba, Toepler and Renault.

19 We will see below, though, that income volatility was higher for the losers in the 1990s.

References

Bernanke, B., Gertler, M. and Gilchrist, S. (1993) 'The Financial Accelerator and the Flight to Quality', National Bureau of Economic Research Working Paper, No. 4789.

Fanelli, J. M. and Frenkel, R. (1995) 'Micro–Macro Interaction in Economic Development', UNCTAD Review: 129–55.

Fanelli, J. M. and González Rozada, M. (1998) 'Convertibilidad, Volatilidad y Estabilidad Macroeconómica en Argentina', Mimeo.

Fanelli, J. M., Rozenwurcel, G. and Simpson, L. (1998) 'Argentina', in J. Fanelli and R. Medhora (eds), Financial Reform in Developing Countries. London: Macmillan.

Fazzari, S., Hubbard, G. and Petersen, B. (1988) 'Financing Constraints and Corporate Investment', Brookings Papers on Economic Activity 1: 141–95.

Guerrieri, P. (1994) 'International Competitiveness, Trade Integration and Technological Interdependence', in The New Paradigm of Systemic Competitiveness: Toward More Integrated Policies in Latin America. Paris: OECD Development Centre.

Harris, J. R., Schiantarelli, F. and Siregar, M. G. (1994) 'The Effect of Financial Liberalization on the Capital Structure and Investment Decisions of Indonesian Manufacturing Establishments', The World Bank Economic Review 8(1): 17–47.

Katz, J. (1997) 'The Dynamics of Technological Learning during the ISI Period and Recent Structural Changes in the Industrial Sector of Argentina, Brazil and Mexico', Mimeo.

Levine, R. (1997) 'Financial Development and Economic Growth: Views and Agenda', Journal of Economic Literature XXXV: 688–726.

Minsky, H. P. (1977) 'A Theory of Systemic Fragility', in E. I. Altman and A. W. Sametz (eds), Financial Crises. New York: Wiley.

Rajan, R. and Zingales, L. (1998) 'Financial Dependence and Growth', American Economic Review 88(3): 559–86.

3 Finance and changing trade patterns in Brazil1

Maria Cristina T. Terra

1. Introduction

Balance-of-payments crises have been recurrent throughout Brazilian history. The depth and length of these crises depend basically on the country's vulnerability to external shocks and its capability to generate the necessary trade surplus after an adverse external shock. These, in turn, depend on trade diversification and competitiveness. With more diversified exports and imports, the country becomes less vulnerable to specific sector shocks. Increased competitiveness, furthermore, should facilitate trade balance reversals. This chapter focuses on one possible determinant of competitiveness, which is the existence of credit constraints. In a world with no missing markets, no informational asymmetries and no transaction costs, credit supply and demand should be equalized by an appropriate interest rate level, with no need for a financial sector. A vast literature, both theoretical and empirical, studies the effects on the economy when these conditions do not hold. In the real world, information asymmetries and transaction costs for acquiring information create the need for a financial system. The role of the financial sector is then, in summary, to allocate savings to the best investment projects, to monitor managers and to diversify risk (see Levine (1997) for a discussion on the roles of the financial system). In such an environment, financial system imperfections create credit restrictions, which in turn may affect firms' investment decisions. Hence, financial sector underdevelopment may be harmful for growth.

Moreover, it is plausible to presume that firms in different sectors have different financial needs. Rajan and Zingales (1998) compute the external financing pattern for different industries in the United States, and they arrive at large disparities among them. If that is the case, the effect of credit restrictions should not be equal among industries. In this chapter, I try to identify whether Brazilian firms are credit constrained, and the relation between industries' financial needs and their competitiveness.

The period from 1974 to 1997 is studied, comprising four different situations with respect to macroeconomic environment, trade policy and balance-of-payments conditions. They are briefly described below.

Period 1

From 1974 to 1982 the country was suffering from the adverse trade balance effects of the two oil shocks. Despite implementation of import restrictions and export promotion policies, a large current account deficit mounted up over the period. The economy was nevertheless growing rapidly (7 per cent per year on average), thanks to large capital inflows.

Period 2

From 1982 to 1990 the economy experienced the effects touched off by the Mexican moratorium declared in 1982. Even stronger trade barriers were imposed, and capital flows were very timid. The current account suffered a drastic reduction, from a 6 per cent deficit in 1982 to zero balance in 1984. The economy faced major macroeconomic instability, with two-digit monthly inflation rates in the late 1980s, and several unsuccessful price stabilization programs were attempted.

Period 3

From 1990 to 1994 the macro-instability scenario did not change, but drastic trade liberalization was carried out. There was an upsurge of capital inflows to Brazil, following the Latin American trend.

Period 4

From 1994 to 1997 macro-stability finally was achieved, together with even stronger capital inflows. Increasing current account deficits gave rise to concerns about sustainability.

This chapter is divided into six sections. Section 2 describes the economic environment over the time period studied, emphasizing the four distinct periods outlined above. Section 3 analyzes trade pattern evolution in Brazil over time. Section 4 studies the extent to which Brazilian firms have been credit constrained. Section 5 synthesizes the results from Sections 3 and 4, analyzing possible influences of finance on the evolution of trade specialization. Section 6 concludes.

2. Economic environment

Trade policy

Brazil has a long history of external trade intervention. After the Second World War, it engaged in an import substitution strategy that lasted for decades, following the trend in most Latin American countries. Import substitution meant a gradual process of industrialization based on domestic market protection and subsidies for investments in specific industrial sectors. From the mid-1960s to 1973, the country carried out slow import liberalization, combined with export promotion policies, which included frequent exchange-rate devaluations, subsidized credit and tax and tariff exemptions for export activities. This combination of policies resulted in an important shift in the composition of exports, favoring industrial goods to the detriment of traditional coffee exports. Coffee as a share of total exports was around 40 per cent in 1964, dropping to only 20 per cent in 1973. The degree of diversification of imports did not achieve that of exports. Although some import substitution occurred in the intermediate and capital goods sectors, there was no substantial expansion of domestic oil production. Imports continued to be concentrated on oil and intermediate and capital goods. These are known as the 'miracle' years in Brazil. Gross national product grew at an astonishing average yearly rate of 11.1 per cent, and annual industrial growth averaged 13.1 per cent over the period.2

Period 1

Oil prices quadrupled at the end of 1973, and they would increase again in 1979. Since oil was an important part of Brazilian imports (20 per cent in 1974), there was a severe impact on the trade balance, which changed from a modest surplus to a 4.7 billion USD deficit in 1974. The current account deficit deteriorated substantially, increasing from 1.7 billion USD in 1973 to 7.1 billion USD in 1974. The government chose not to depreciate the real exchange rate. Non-essential imports were discouraged, and the country borrowed internationally to level its balance of payments and ensure the country's fast growth path. A dynamic export promotion policy was then implemented to compensate for the anti-export bias created by the import restraints.3 Average growth from 1974 to 1982 was indeed high – 6.6 per cent average GDP growth for the period (Table 3.1).4

Period 2

A new shock hit the economy in 1980 – the increase in international interest rates. From 1975 to 1979, the LIBOR averaged 7.8 per cent, and world inflation 8.9 per cent.5 Thus, over the period real interest rates were negative on average. From 1980 to 1984, however, the LIBOR averaged 13.0 per cent and world inflation only 1.2 per cent. As most of Brazilian external debt was at floating interest rates, debt service increased substantially. The 1982 Mexican moratorium induced a capital flow reversal from Latin America. The increasing current account deficits could no longer be financed by capital inflows, so that a large trade surplus would have to be generated to provide foreign reserves to pay the debt service, and thereby equilibrate the current accounts (Table 3.1). A rapid trade surplus was achieved by further import repression and active export promotion policies.6 An industrial policy was also conducted, granting fiscal incentives and subsidized credit from the state development bank to selected firms.

Although a restrictive trade policy had been in place in Brazil for decades, its justification changed over time, in three distinct phases. First, from the First World War to the early 1970s it was part of an active import substitution program. Then,

Table 3.1 Selected macroeconomic data

 

Inflation (%)

GDP growth (%)

Current account (% GDP)

Exports (% GDP)

Imports (% GDP)

Oila (% imports)

Coffee (% exports)

Terms of trade

International reservesb (million USD)

Real exchange ratec

1974–82

    63.6

 6.6

–4.7

  7.3

8.4

33.6

13.0

125.2

   7011.9

  91.2

1983–90

   699.0

 3.2

–0.7

10.3

5.8

30.9

  7.4

103.2

  8,756.3

175.4

1991–4

1,261.4

–0.2

  0.2

  8.6

5.7

11.6

  3.7

112.5

26,044.3

155.4

1995–7

    11.9

 4.2

–3.3

  6.4

7.2

  2.9

  4.3

115.2

56,521.7

123.7

1990

1,794.8

 3.2

–0.8

  7.0

4.6

21.1

  3.5

114.8

  9,973

130.3

1991

   478.1

–4.6

–0.4

  8.2

5.4

16.0

  4.4

117.6

  9,406

151.2

1992

1,149.1

 0.3

  1.6

  9.6

5.5

14.9

  2.7

109.3

23,754

163.4

1993

2,489.1

–0.8

–0.1

  9.0

5.9

  8.5

  2.8

109.1

32,211

160.2

1994

  929.3

 4.2

–0.3

  7.8

5.9

  7.1

  5.1

114.0

38,806

146.6

1995

    22.0

 5.7

–2.5

  6.5

6.9

  5.2

  4.2

115.3

51,840

126.4

1996

      9.3

 4.2

–3.2

  6.4

7.1

  0.7

  3.6

115.0

60,110

121.4

1997d

      4.3

 2.7

–4.0

  6.4

7.6

 

  5.2

 

57,615

123.3

Sources: Boletim do Banco Central do Brasil, FUNCEX, International Financial Statistics (IMF).

Notes

a Oil and natural gas.

b International liquidity.

c e (WPI/CPI): nominal exchange rate, multiplied by US wholesale price index, divided by Brazilian consumer price index.

d Current account, export and import data up to July.

from the early 1970s to the early 1980s, the intent was to improve the deteriorating trade balance due to the oil shocks. Finally, from the early 1980s to 1990, it served as a drastic measure to deal with the debt crisis. Trade policy during the first phase was designed as an incentive to selected sectors, whereas in the other two phases, and especially in the third, trade policies in the form of both tariff and non-tariff barriers were created due to the macroeconomic instability.7

The effect of these policies on relative prices distorted microeconomic incentives. By the end of the 1980s, a maze of incentives and disincentives was in place. It is important to emphasize the harm of such a distorted and arbitrary system. It was prone to stimulate rent-seeking activities, drawing resources to the unproductive activity of seeking special treatment. It also displaced entrepreneurial effort from productive activities to seeking the best path through the maze of policy incentives.

Periods 3 and 4

A much-needed trade liberalization process was initiated by a new government in 1990. The BEFIEX program was immediately terminated (no new contracts were to be signed). Trade liberalization was to be carried out in three steps:

1 the abolition of all 'special regimes' for imports;

2 the abolition of all quantitative restrictions and their replacement by tariffs; and

3 the lowering of tariffs, according to a preannounced schedule to be over four years. By the end of the liberalization process in 1995, all tariffs would be in the range 0 per cent to 40 per cent, averaging 20 per cent.

Trade liberalization was carried out as planned. Import levels did not increase during the 1990–3 period, despite the lowering of tariffs and elimination of quantitative import restrictions. Two factors contributed to this: the real exchange-rate devaluation during 1990–1 (between January 1990 and December 1992 the real devaluation amounted to 36 per cent), and the low economic activity during the period (the average GDP growth rate was negative 2 per cent).

Brazil, as other Latin American countries, experienced a capital inflow upsurge in the 1990s. There was a substantial capital inflow increase after the implementation of the Real Plan, a successful price stabilization program introduced in July 1994. Real exchange-rate appreciation resulted, producing a mounting current account deficit, now led by a steep increase in imports. The reliance on capital inflows was severely questioned after the Mexican crisis in December 1994. This led to a partial reversal in trade liberalization. Some quantitative restrictions were temporarily reintroduced, and tariffs were increased for those products most responsible for increased imports.

Real exchange rate, trade flows and the current account

Figure 3.1 shows the evolution of the real exchange rate and its volatility,8 and Figure 3.2 shows the current account, imports and exports as a percentage of GDP from 1974 to 1996. The charts are divided into the four subperiods described above.

Image

Figure 3.1 Real exchange rate: level and volatility.

Image

Figure 3.2 Current account.

Period 1

During the first period, the real exchange rate (RER) appreciated more than in the other periods. The crawling-peg exchange-rate regime maintained low RER volatility, except for the maxi-devaluation episode in 1979. Imports, which had increased substantially after the oil price increase, decreased steadily until 1979 due to a restrictive trade policy. The second oil price increase caused the trade balance to deteriorate, leading to the currency maxi-devaluation in 1979. Figure 3.2 shows the jump in export share after the devaluation (it increased from an average of 6.8 per cent of GDP during 1973–9 to 8.5 per cent in 1980). The current account was negative throughout the period, reaching negative 6 per cent of GDP in 1982, despite the trade balance surplus in that year. The current account deficit was caused by the high debt service cost, due to the increase in international interest rates. The country nevertheless experienced high GDP growth rates over the period (Table 3.1).

Period 2

There was a sharp RER devaluation during the debt crisis, accompanied by higher volatility. The RER volatility increased over the period, reaching its peak in 1990. The period was characterized by deep macroeconomic instability. As shown in Table 3.1, inflation reached extremely high rates. Several heterodox price stabilization attempts managed to reduce inflation from two-digit monthly figures to zero in a very short period of time, only for it to take off again after failure of the plans. Not even the crawling-peg regime pursued was capable of preventing high RER volatility.

The more devalued RER was accompanied by a substantial trade balance improvement, led mainly by increasing exports, as shown in Figure 3.2. The current account moved from negative 6 per cent of GDP in 1982 to a near-zero balance over the whole period. The investment rate decreased during the period. The sharp balance-of-payments adjustment was accompanied by bitter recession: GDP decreased 4.2 per cent and 2.9 per cent in 1982 and 1984, respectively. High growth rates were experienced from 1985 to 1987, but they decreased again towards the end of the decade.

Period 3

The third period started with very high RER volatility, during a short period of RER appreciation. The RER depreciated again, but to a lower level compared with the second period, while RER volatility decreased substantially. The current account maintained its near-zero balance, while imports started an upward trend, following the 1990s trade liberalization. GDP growth rates were near zero or negative over the period.

Period 4

The nominal exchange rate was allowed to float during the first months after the Real Plan's implementation in June 1994, causing a RER volatility increase. The capital inflow during the period caused the exchange rate to rise, leading to higher imports. The current account moved from a zero balance at the beginning of the period to a 3.2 per cent deficit in 1996. Annual growth rates were between 4 and 6 per cent during 1994–6, but in 1997 growth declined to 2.7 per cent.

Labor productivity and unit labor cost evolution

Labor productivity was stationary between the mid-1980s and 1990.9 As shown in Figure 3.3, it then increased continuously between 1990 and 1996 at an average annual rate of approximately 7 per cent. Over the whole period it increased

Image

Figure 3.3 Labor productivity in industry (seasonally adjusted data – Jan-85: 100).

Image

Figure 3.4 Labor productivity, wage and unit labor cost in industry (seasonally adjusted data – Jan-94: 100).

by more than 50 per cent. To a large extent, this productivity increase reflects the impact of greater international competition encountered by domestic producers.

Labor productivity is a basic factor in explaining competitiveness. Hence, one would expect that the significant productivity increase in Brazil has been translated into greater competitiveness. At least since 1994, however, this has not been the case, since wages for most of the period have grown faster than labor productivity.

Image

Figure 3.5 Unit labor cost, prices in USD and profitability (seasonally adjusted data – Jan-94: 100).

Figure 3.4 shows labor productivity evolution, wages in US dollars and the unit labor cost (ULC is the ratio of wages in dollars to productivity) for the industrial sector as a whole. The ULC provides an indication of labor costs measured in the relevant foreign currency per unit of output. Wages in dollars grew continuously, totaling a 70 per cent increase between 1994 and 1997. Labor productivity, in turn, increased by 32 per cent. ULC increased by 40 per cent until mid-1996 and has been falling somewhat since then. The ULC increase in the initial period resulted from the fact that wages grew faster than productivity, and vice versa in the later period.

Hence, for the period as a whole, to the extent that the ULC is a good measure of competitiveness, the latter has not increased since 1994. Based on this criterion, overall competitiveness fell until mid-1996 and it has recovered slightly since then.

The price of tradable goods in dollars and 'profitability' as measured by firms' profit margin may be more refined measures of competitiveness. Figure 3.5 shows the evolution of the industrial goods producer price index (PPI), and the ratio between PPI and ULC as a measure of aggregate profit margin. PPI increased around 20 per cent between January 1994 and mid-1995 and then decreased approximately 10 per cent. Profitability fell 20 per cent and then recovered. Both the lower price index in dollars and the profitability increase in the more recent period resulted from the ULC reduction.

Summarizing the main findings in this section, three aspects may be highlighted. First, external shocks, such as the oil price hikes and external debt crisis, had crucial roles in determining both the macroeconomic environment and trade policy choices. Second, trade liberalization had a positive effect on productivity during the 1990s. Third, the macroeconomic environment played a decisive role in ULC evolution, and hence competitiveness.

3. Trade pattern evolution

This section analyzes the pattern of trade since 1974, using several indexes that characterize trade patterns.

Contribution to trade balance

The first index used to characterize trade patterns in Brazil is the contribution to trade balance (CTB) index. Table 3.2 presents the performance of this index from 1974 to 1997, in a ten-sector aggregation.10 The index of contribution of industry i to trade balance was calculated using eqn (3.1):

Image

where Xi and Mi are exports and imports of industry i, respectively, and X and M are total Brazilian exports and imports, respectively.

The first term of the index represents net exports (by sector), whereas the second represents 'neutral' net exports, that is, net exports (by sector) that would be observed if the share of each product in overall net exports were equal to its contribution to total trade. Thus, the index value equals zero for a given period when the ratio of the net sector exports to overall net exports is equal to the sector's contribution to total trade. Its value will be positive or negative depending on whether net exports are larger or smaller relative to the 'neutral' value. Note that if a country is running a trade deficit, a product may show a positive sign even if its imports are larger than its exports. The opposite is true for a trade surplus.

Figures 3.6a and b show the index evolution for each of the ten industries. Note that the industries in Figure 3.6a have a much stronger contribution to trade balance than those of Figure 3.6b: the scale in Figure 3.6a ranges from –0.6 to +0.6, whereas in Figure 3.6b it ranges from only –0.1 to +0.1. Lines have been drawn on the years corresponding to the periodicity used above.

There were important changes in the composition of imports and exports over the period. The food and beverages sector presented the largest CTB decrease in absolute terms over the period. Its CTB fell from 0.50 in 1974 to 0.22 in 1997, representing a 54 per cent decrease. This shows the increasing importance of other industries in Brazilian exports over the period. The strongest change occurred between the first and the second periods (the oil shock and debt crisis periods), when the food and beverages CTB decreased from an average of 0.43 to 0.22. After the Real Stabilization Plan, it increased slightly. Food and beverages remains the sector with highest CTB.

Energy material, on the other hand, exhibited the largest swing in CTB. Over the first period (oil crisis), energy material CTB decreased from –0.26 in 1974

Table 3.2 Contribution to trade balance (ten-sector aggregation)

 

Food products and beverages

Textiles, apparel and footwear

Transport equipment

Metal products

Chemical products

Wood, paper and products

Construction material

Machinery

Energy material

Other industries

1974–82

0.4288

0.0702

  0.0229

 0.0371

–0.0740

0.0175

–0.0011

–0.1244

–0.3660

–0.0110

1983–90

0.2223

0.0601

  0.0312

0.1366

–0.0793

0.0266

  0.0012

–0.0903

–0.2953

–0.0133

1991–4

0.1532

0.0467

  0.0158

0.1805

–0.0945

0.0437

  0.0019

–0.1337

–0.1918

–0.0217

1995–7

0.1980

0.0271

–0.0199

0.1540

–0.0774

0.0478

  0.0004

–0.1776

–0.1282

–0.0243

1990

0.1750

0.0515

  0.0428

0.1856

–0.0947

0.0320

  0.0003

–0.1417

–0.2322

–0.0186

1991

0.1327

0.0537

  0.0330

0.2135

–0.0986

0.0326

  0.0009

–0.1286

–0.2191

–0.0201

1992

0.1485

0.0562

  0.0360

0.1749

–0.0903

0.0406

  0.0010

–0.1273

–0.2161

–0.0235

1993

0.1513

0.0429

  0.0133

0.1736

–0.0910

0.0481

  0.0034

–0.1273

–0.1924

–0.0219

1994

0.1803

0.0340

–0.0191

0.1599

–0.0982

0.0534

  0.0021

–0.1515

–0.1395

–0.0214

1995

0.1807

0.0263

–0.0433

0.1640

–0.0773

0.0602

  0.0018

–0.1649

–0.1225

–0.0249

1996

0.1940

0.0303

–0.0120

0.1646

–0.0740

0.0428

  0.0001

–0.1747

–0.1386

–0.0324

1997

0.2193

0.0248

–0.0045

0.1336

–0.0809

0.0403

–0.0007

–0.1932

–0.1233

–0.0154

Sources: IBGE and FUNCEX.

Image

Figure 3.6 Contribution to trade balance. Industries in (a) make a stronger contribution compared to those in (b).

to –0.46 in 1983. From 1983, on the other hand, it increased to –0.12 in 1997. This movement reflects the decreasing importance of oil in Brazilian imports. In 1974 this sector had the lowest CTB, and in 1997 it had given up its last position to machinery (which was second to last in 1974).

In percentage terms, the sectors that underwent the largest changes in CTB were wood, paper and products, and metal products, increasing 285 per cent and 291 per cent, respectively.

Transport equipment CTB decreased steeply over the last period. Its value increased during the first period, from –0.0191 in 1974 to +0.0593 in 1982. During the second period its value averaged 0.0312, thereupon falling from 1992 to its lowest value of –0.433 in 1995.

Both exports and imports became more diversified over the period. The index variance across sectors ranged from 0.038 to 0.052 in the 1970s and early 1980s. It started decreasing steadily in 1983, and its value has been around 0.013 in the past few years.11 This means that the index has become more uniformly distributed across sectors over time. Hence, exports and imports have become less concentrated in specific sectors.

Revealed comparative advantage

The other index used to assess each sector's role in inter-industry trade is Balassa's index of revealed comparative advantage (RCA). This index measures the relative importance of a sector in a country's total exports with respect to the relative importance of that sector in world total exports. It is given by eqn (3.2):

Image

where Xij is country j's exports by industry i, Xj is country j's total exports, XiW is world exports by industry i, and XW is total world exports.

The evolution of RCA indexes is shown in Table 3.3. The index has been calculated for the period from 1986 to 1997, for ten aggregated sectors. The largest movement occurred in the wood, paper and products sector. Its RCA increased 85 per cent over the period (from 1.16 to 2.15). The CTB of that industry increased 42 per cent over the same period, indicating that its increased importance in Brazilian exports surpassed the increase of the sector's importance in world exports. Other significant changes are a 22 per cent decrease in the RCA for textiles, apparel and footwear, also following its decrease in CTB, and a 22 per cent and 29 per cent increase in transport equipment and construction material, despite those sectors' decrease in CTB.

The sectors presenting the highest RCA in 1997 are food and beverages (3.15), metal products (2.82), and wood, paper and products (2.15). The lowest ones are machinery (0.31) and chemical products (0.43).

Intra-industry trade

The Grubel and Lloyd intra-industry trade measure, presented in Table 3.4, is calculated with eqn (3.3):

Image

where its aggregate values, shown in the last column of Table 3.4, are calculated with eqn (3.4):

Image

The aggregate index shows a steady increase over time. As for the index's evolution for the different industries, it increases for most of them. The most spectacular increase was in the textiles, apparel and footwear industry. The Grubel and Lloyd index started at 29 per cent in 1974, decreased until reaching its lowest level of 12 per cent in 1981, and then increased until reaching 82 per cent in 1995. For metal products, on the other hand, the index decreased substantially.12 As the performance of the index indicates, the advent of Mercosur had a positive effect on intra-industry trade levels.

Table 3.3 Revealed comparative advantage – Balassa's index (ten-sector aggregation)

 

Food products and beverages

Textiles, apparel and footwear

Transport equipment

Metal products

Chemical products

Wood, paper and products

Construction material

Machinery

Energy material

Other industries

1986

3.1364

1.1520

0.7125

2.4402

0.4022

1.1629

0.6125

0.3245

0.5530

0.2955

1987

3.1567

1.1759

0.9625

2.3162

0.3107

1.0862

0.5708

0.3070

0.6740

0.3179

1988

2.9034

1.0938

0.8728

2.7100

0.2979

1.2804

0.5841

0.2993

0.8320

0.2173

1989

2.7652

1.0613

0.9287

2.8408

0.3457

1.1800

0.6370

0.3268

0.6682

0.2672

1990

2.9444

1.0293

0.8708

3.1946

0.3788

1.3065

0.5866

0.3103

0.5170

0.3176

1991

2.5808

1.0515

0.8072

3.5885

0.3857

1.3874

0.6111

0.3198

0.4882

0.3471

1992

2.6849

1.0564

0.9717

3.4264

0.3357

1.5768

0.6735

0.3314

0.5301

0.3291

1993

2.8163

1.1443

0.9949

3.4320

0.4525

0.8081

0.8957

0.3526

0.6067

0.2902

1994

3.1015

0.9062

0.9535

2.9531

0.3896

1.8603

0.7600

0.3186

0.6384

0.3221

1995

3.1510

0.8935

0.8725

2.8121

0.4389

2.1495

0.7896

0.3123

0.6017

0.3382

 

Table 3.4 Grubel and Lloyd intra-industry index (ten-sector aggregation)

 

Food products and beverages

Textiles, apparel and footwear

Transport equipment

Metal products

Chemical products

Wood, paper and products

Construction material

Machinery

Energy material

Other industries

Aggregate

1974–82

0.3246

0.1861

0.7243

0.7583

0.3304

0.7272

0.7983

0.4835

0.1408

0.7427

0.3979

1983–90

0.2802

0.2426

0.5522

0.2889

0.6499

0.3438

0.6177

0.8725

0.5049

0.8904

0.4547

1991–4

0.4139

0.4578

0.7170

0.2704

0.5842

0.3265

0.6929

0.7544

0.4381

0.9340

0.5128

1995–7

0.5141

0.7746

0.8747

0.4238

0.5318

0.5545

0.9487

0.5149

0.3624

0.7341

0.5657

1990

0.3699

0.3811

0.5646

0.2933

0.5601

0.4016

0.7762

0.7057

0.4298

0.9710

0.4731

1991

0.4593

0.3992

0.6223

0.2639

0.5545

0.4121

0.7380

0.7440

0.3977

0.9581

0.4912

1992

0.3557

0.3206

0.5731

0.2502

0.6464

0.2762

0.6701

0.8176

0.4277

0.9837

0.4791

1993

0.3998

0.5137

0.7352

0.2620

0.6156

0.2975

0.6177

0.7853

0.4375

0.9164

0.5213

1994

0.4410

0.5977

0.9374

0.3054

0.5205

0.3203

0.7458

0.6709

0.4896

0.8776

0.5597

1995

0.5707

0.8184

0.7929

0.4304

0.4931

0.5238

0.9418

0.5122

0.3750

0.6969

0.5640

1996

0.5694

0.8017

0.8884

0.4227

0.5142

0.6204

0.9430

0.4770

0.3118

0.6165

0.5601

1997

0.4021

0.7038

0.9428

0.4183

0.5879

0.5194

0.9612

0.5555

0.4005

0.8888

0.5731

4. Liquidity constraints, finance pattern and corporate investment

This section studies Brazilian companies' financing decisions, and the extent to which they have been financially constrained. The empirical investigation uses balance sheet data for firms that are required by law to publish them. The data was collected by IBRE (Instituto Brasileiro de Economia, Getúlio Vargas Foundation) from the Gazeta Mercantil and Diário Oficial, from 1986 to 1997, with the number of firms each year ranging from 2,091 to 4,198. From the original sample, I selected those firms which had data published for all years considered13 – from 1986 to 1997 – a total of 550 firms. Non-industrial firms were excluded, as well as those with missing data. The sample used is composed of 468 firms, broken down by sector as in Table 3.5.

The data has two breaks over time, one in 1990 and the other in 1994, due to changes in balance sheet reporting criteria after the implementation of inflation stabilization plans (the Collor Plan in 1990 and the Real Plan in 1994). All the analyses are carried out taking into account those two breaks in the time series.

The time frame for which we have firms' level data is shorter, including only three of the four periods described earlier. The analysis in this section will therefore divide the period from 1986 to 1997 into three subperiods: the

Table 3.5 Sample firms broken down by sector

Sector

Number of firms

Average value of assetsa (1994–7)

Apparel and footwear

16

151,226,212

Beverages

15

599,900,102

Chemical products

71

814,435,409

Drugs

11

137,409,771

Electric equipment

28

338,754,575

Food products

70

170,390,346

Furniture

  4

  35,992,189

Leather

  3

  19,429,112

Machinery

38

176,597,031

Metal products

60

606,166,766

Non-metal products

30

371,050,142

Other industries

  9

  92,482,669

Paper and products

19

857,736,540

Perfumery and soap

  3

  24,547,019

Plastic products

  8

  74,543,359

Printing and publishing

11

  97,283,390

Rubber products

  1

  17,969,577

Textiles

40

121,148,883

Tobacco

  1

409,941,742

Transport equipment

23

256,891,434

Wood products

  7

252,427,396

Note

a In 1996 constant Reals.

macro-instability and balance-of-payments crisis period (1986–90), the macro-instability and trade liberalization period (1990–4), and the macro-stability and capital inflow period (1994–7).

Pattern of finance

The analysis starts with a description of the firms' patterns of finance. The set of firms is divided into subcategories, trying to identify possible differences in finance patterns across different groupings, or across different time periods. Two leverage measures are calculated: the ratio between liabilities and assets, and the ratio between debts14 and assets. Figure 3.7 presents both measures' evolution for the whole sample of firm averages, and Table 3.6 presents the averages across periods. Over the first period, liabilities and debts were stable in relation to total asset ratios, averaging 35 per cent and 11 per cent, respectively. There was a slight increase in both measures during the second period. Firms were clearly becoming more leveraged over the last period, 1994–7, when liabilities averaged 47 per cent and debts 16 per cent of total assets.

First, the sample of firms is divided into subgroups based on an a priori hypothesis with respect to firms' credit accessibility. It is reasonable to assume

Image

Figure 3.7 Industrial firms.

Table 3.6 Pattern of finance

 

Debts/assets

Liabilities/assets

 

Mean

Median

Standard deviation

Mean

Median

Standard deviation

1986–9

0.11

0.08

0.12

0.35

0.32

0.17

1990–3

0.13

0.09

0.13

0.37

0.35

0.18

1994–7

0.16

0.13

0.18

0.47

0.41

0.41

1986–7

0.14

0.11

0.12

0.40

0.36

0.21

that larger firms would have more access to credit markets than smaller ones. As Gertler and Gilchrist argue:

[W]hile size per se may not be a direct determinant [of capital market access], it is strongly correlated with the primitive factors that do matter. The informational frictions that add to the costs of external finance apply mainly to younger firms, firms with a high degree of idiosyncratic risk, and firms that are not well collateralized. [T]hese are, on average, smaller firms.

(1994: 313)

The finance pattern evolution for those two groups of firms is indeed interesting, as shown in Figures 3.8a and b. Although leverage measured as liabilities as a share of total assets does not differ between the two groups of firms, the debts to assets ratio is quite different between them. Large firms have a higher debts to assets ratio throughout the whole time frame compared to small firms.

There are two possible explanations for the higher indebtedness of large firms compared to that of small firms. Low debt for small firms may be either the result of pure financial decisions or an indication of the credit restrictions they face. If the first alternative is true, some firms simply chose to use fewer external loans, and those are coincidentally the small ones. If the latter is true, a group of firms was credit restricted, and therefore it was not possible for them to be more leveraged. The empirical exercise performed in the next subsection tries to identify which explanation is more consistent with the data.

Rajan and Zingales (1998) construct a measure of external dependence for different industries, using data on external finance for US industries. They assume

Image

Figure 3.8 Liabilities/assets (a) and debts/asssets (b) ratios for large (–––) and small (----) firms.

Image

Figure 3.9 Liabilities/assets (a) and debts/assets (b) ratios for more (–––) and less (----) financially dependent firms.

that there is a technological reason for some industries to depend more on external finance than others. They argue that

... to the extent that the initial project scale, the gestation period, the cash harvest period, and the requirement for continuing investment differ substantially between industries, this is indeed plausible. Furthermore, we assume that these technological differences persist across countries, so that we can use an industry's dependence on external funds as identified in the United States as a measure of its dependence in other countries.

(Rajan and Zingales 1998: 563)

By using the measure constructed in that paper, firms also have been divided according to their external dependence: firms in the sectors exhibiting more external dependence have been separated from those firms in sectors presenting less finance dependence.15 It is interesting to note that in Brazil, as Figure 3.9a shows, more financially dependent firms are on average more leveraged than less financially dependent firms, looking at the ratio of liabilities to assets.16 That is, firms more in need of external finance according to the external dependence measure exhibit greater use of external finance. With respect to the debts to assets ratio, there is no pattern for the difference between these two groups: in some periods firms in less dependent sectors have a higher debts to assets ratio, compared to more dependent ones, whereas in other periods they have a lower measure (Figure 3.9b).

Image

Figure 3.10 Liabilities/assets (a) and debt/assets (b) ratios for domestic (–––) and multinational (----) firms.

Finally, the sample of firms is divided between multinational and domestic firms. The motivation for this division is that multinational firms may have more access to international credit markets, and therefore be less credit constrained. In both leverage measures, Figures 3.10a and b show higher leverage for multinational firms until 1993, and higher leverage for domestic firms since then.

Credit constraints

There is ample literature that seeks empirical evidence of credit constraints by looking at the firm's investment decision. Fazzari et al. (1988) were the first of several to estimate models of investment demand, including cash flow, as an independent variable. The reasoning is that if firms are not credit constrained, their cash-flow variations should not affect investment decisions, after investment opportunities are controlled for. Equation (3.5) is the general form for the investment equations they estimate:

Image

where Iit and Kit represent investment and capital stock of firm i at time t, X represents a vector of variables affecting firms' investment decisions, according to theoretical considerations, and uit is an error term. In some specifications, the Q investment model is estimated by using Tobin's q as the vector X, and including the cash-flow variable in the equation. In other specifications, the accelerator model of investment is used, and the X vector is replaced by contemporaneous and lagged sales to capital ratios.

Using a different method, Whited (1992) estimates Euler equations for an optimizing investment model under two different assumptions: when firms are credit constrained, and when they are not. Gertler and Gilchrist (1994), on the other hand, study whether small and large firms respond differently to monetary policy. They find that smaller firms have a much stronger response to monetary tightening than larger firms, indicating they are more credit constrained. All these studies use data for US firms.

The accelerator model specification from Fazzari et al. (1988) will be reproduced for Brazilian data to identify the existence (or not) of credit constraints.17 The empirical exercises performed here are based on the sales accelerator investment demand model, where investment is explained by current and past sales. Cash flow is included as an explanatory variable for investment, as shown in eqn (3.6):

Image

where Sit represents the sales of firm i at time t. Cash flow should not be a significant explanatory variable for investment, except when firms are credit constrained. That is, the parameter α should not be significant for firms that are not creditconstrained, and it should be positive and significant for credit-constrained firms.

Table 3.7 presents the initial results. All regressions include firm-specific effects and two dummies: one for 1990 and another for 1994 to account for the breaks in the data.18 First, the investment accelerator model is estimated without including cash flow as an explanatory variable. The best specification for our data is the one including two lags of the sales variable. As column 1 of Table 3.7 shows, variations in the sales variables explain 52 per cent of investment changes. When cash flow is included in the regression, independent variables explain 81 per cent of investment variations, and cash flow has a positive and significant coefficient (with t-statistics of 10.1). According to our conjecture, this is an indication that firms were credit constrained over the time period studied.

One should note, however, that the period under study encompasses two distinct situations with respect to capital inflows. From 1986 to 1994 there was very little external capital inflow into Brazil, and from 1994 to 1997 current account deficits increased substantially, reaching 4 per cent in 1997, as shown in Table 3.1. It is possible that the higher capital inflow increased the credit supply, therefore lessening firms' credit constraints. A slope dummy for cash flow for the period 1994–7 has been included in the regression. This variable equals cash flow and capital ratio for the years 1994–7, and is zero for the rest of the period. If firms were less credit constrained over 1994–7, this slope dummy should not be positive. That is not the case though. The slope dummy coefficient is positive, with a t-statistic of 1.93. Thus, there is no evidence that firms became less credit constrained with the external capital inflow.

The next step is to investigate possible differences in credit constraints across groups of firms. First, as argued in the previous subsection, it is reasonable to expect that small firms are more credit constrained than large ones. The sample is then split according to firms' size, and the regression results are presented in columns 4–7.19 Cash-flow coefficients are also positive and significant for both

Table 3.7 Regression results (dependent variable: investment)

Independent variable and summary statistics

Whole sample

Large firms

Small firms

 

1

2

3

4

5

6

7

(CF/K)it

 

    1.339

   0.882

  1.985

   1.860

  1.313

   0.852

 

 

 (10.111)

  (3.708)

 (4.793)

  (7.220)

 (8.405)

  (3.662)

CF/K slope dummy

 

 

   0.553

 

   0.138

 

   0.604

1994–7

 

 

  (1.933)

 

 (0.407)

 

  (2.005)

(S/K)it

  –0.284

  –0.127

  –0.118

   0.233

  0.239

  –0.137

 –0.134

 

(–3.975)

(–5.462)

(–5.115)

  (1.299)

 (1.267)

(–6.695)

(–6.764)

(S/K)i, t–1

   0.284

   0.101

   0.084

 –0.319

 –0.328

   0.112

   0.100

 

  (3.467)

  (3.397)

  (3.106)

(–1.632)

(–1.553)

  (3.926)

  (3.755)

(S/K)i, t–2

  –0.034

 –0.001

   0.004

   0.034

   0.038

  –0.004

  –0.001

 

(–0.034)

(–0.069)

  (0.341)

  (0.882)

  (0.902)

(–0.309)

(–0.121)

R2

   0.515

   0.807

   0.816

   0.863

   0.863

   0.792

   0.805

Number of firms

    468

    468

    468

     75

     75

    393

    393

Number of observations

 4,680

 4,680

 4,680

   750

   750

 3,930

 3,930

Notes: The dependent variable is investment–capital ratio. The CF/K slope dummy is a variable that has value equal to CF/K for the years 1994–7, and zero in all other years. All regressions have been estimated using firms' fixed effects and dummies for the years 1990 and 1994, but the coefficients are not reported. The t-statistics in parentheses are based on White heteroskedasticity-consistent standard errors.

groups of firms (columns 4 and 6). Hence, there is no evidence that larger firms are less credit constrained than smaller firms.20

Second, international credit markets may be more accessible for multinational firms, compared to domestic ones. Columns 1–4 in Table 3.8 present the results for the regressions estimated for multinational and domestic firms separately. Again, all cash-flow coefficients are positive and significant, indicating credit constraints for both groups. There is an important difference in the cash-flow slope dummy in the period 1994–7 for the two groups, though for domestic firms this coefficient is positive and significant, and for multinationals it is negative, with a t-statistic of –1.238. This can be interpreted as an indication that multinational firms were less credit constrained over the period 1994–7, when there was a large capital inflow. Hence, the capital inflow seems to have lessened only multinational firms' credit constraint.21

The sample has also been divided according to external dependence, using Rajan and Zingales' (1998) measure, and the estimated regressions are presented in Table 3.8, columns 5–8. The cash-flow coefficients are significant in all regressions, but the coefficient is higher for less dependent firms. One interpretation is that less dependent firms would use less external finance; therefore their investment would be more cash flow sensitive. The cash-flow slope dummy is positive, but not significant for both subsamples.

The results so far indicate credit restrictions across the whole sample of firms, and also across subgroups formed by larger and smaller, more and less externally dependent, multinational and domestic firms. The only instance of credit-constraint reduction was among multinational firms, from 1994 to 1997.22

Further results

Kaplan and Zingales (1997) argue that investment-cash-flow sensitivities do not provide a useful measure of finance constraints, introducing controversy regarding the validity of this methodology. An alternative empirical exercise is then performed, without the use of cash flows. It was motivated by Rajan and Zingales (1998).

Rajan and Zingales (1998) investigate the effect of financial sector development on industrial growth. Their main hypothesis is that 'industries that are more dependent on external financing will have relatively higher growth rates in countries that have more developed financial markets' (p. 562). They use industry-level data for several countries to estimate an equation where industry growth is explained by the interaction between an industry's external dependence and the country's financial development, controlling for country indicators, industry indicators, and that industry's share in the country's economy. That is, they have an equation that tries to capture possible variables that explain differences in industry growth rates in different countries, and they include a new variable in the equation, namely external dependence times financial development. Their conjecture is that if financial development is indeed important for growth, the coefficient of this

Table 3.8 Regression results (dependent variable: investment)

Independent variable and summary statistics

Multinational

Domestic firms

More dependent

Less dependent

 

1

2

3

4

5

6

7

8

(CF/K)it

   1.098

   1.367

   1.437

   0.878

   1.022

   0.644

   1.573

   1.304

 

  (5.939)

  (6.738)

  (8.036)

  (3.737)

  (6.040)

  (2.883)

  (9.014)

  (7.613)

CF/K slope dummy

 

 –0.284

 

   0.737

 

   0.520

 

   0.302

1994–7

 

(–1.238)

 

  (2.278)

 

  (1.669)

 

  (1.175)

(S/K)it

 –0.152

 –0.151

 –0.148

 –0.136

 –0.128

 –0.121

 –0.091

 –0.086

 

(–1.762)

 (1.794)

(–6.346)

(–6.413)

(–5.466)

(–5.516)

(–1.904)

(–1.811)

(S/K)i, t–1

   0.040

  0.052

   0.115

   0.098

   0.128

   0.108

   0.037

   0.033

 

  (0.591)

 (0.714)

  (3.752)

  (3.516)

  (2.879)

  (2.644)

 (1.087)

 (0.993)

(S/K)i, t–2

  0.040

  0.037

 –0.006

 –0.002

 –0.016

 –0.011

  0.031

  0.031

 

  (1.130)

 (1.040)

(–0.469)

(–0.193)

(–1.139)

(–0.796)

 (2.001)

 (2.155)

R2

  0.918

  0.918

   0.785

   0.802

   0.765

   0.780

  0.844

  0.846

Number of firms

    46

   46

    413

    413

    179

    179

    289

    289

Number of observations

  460

  460

 4,130

  4,130

  1,790

  1,790

  2,890

  2,890

Notes: The dependent variable is investment–capital ratio. The CF/K slope dummy is a variable that has value equal to CF/K for the years 1994 –7, and zero in all other years. All regressions have been estimated using firms' fixed effects and dummies for the years 1990 and 1994, but the coefficients are not reported. The t-statistics in parentheses are based on White heteroskedasticity-consistent standard errors.

Table 3.9 Credit to private sector (% GDP)

 

Argentina

Brazil

Philippines

France

Germany

India

Mexico

South Africa

Tunisia

Uruguay

United States

1991

12.5

33.1

17.8

96.6

  89.7

25.6

20.2

 

53.8

28.5

66.7

1992

15.2

54.3

20.6

96.7

  90.7

26.6

27.2

62.7

54.0

27.5

63.3

1993

16.5

82.2

26.4

92.0

  96.5

25.7

30.4

62.4

53.9

27.0

62.5

1994

18.2

45.5

29.1

86.1

  98.8

25.2

36.6

65.5

53.8

25.6

62.7

1995

18.1

30.8

37.5

84.9

  99.8

24.3

26.7

67.5

54.5

28.3

65.0

1996

18.1

26.3

49.0

81.8

104.9

25.6

16.4

70.5

49.1

28.8

65.6

1997

19.3

26.0

56.5

80.7

108.2

 

12.7

73.5

50.2

31.1

67.1

Source: International Financial Statistics, IMF.

Table 3.10 Regression results (dependent variable: investment)

Independent variable and summary statistics

Whole sample
1

Winners
2

Losers
3

Interaction (external dependence × firm size)

308.985

206.645

959.942

 

  (2.601)

  (2.276)

  (3.508)

(S/K)it

 –0.284

  –0.325

  –0.272

 

(–3.996)

(–4.831)

(–3.092)

(S/K)i, t–1

   0.283

   0.277

   0.286

 

  (3.482)

  (4.375)

   2.608

(S/K)i, t–2

  –0.035

  –0.032

 –0.036

 

(–1.235)

(–1.515)

(–0.793)

R2

   0.516

   0.502

   0.533

Number of firms

    468

    235

    233

Number of observations

  4,680

  2,350

  2,330

Notes: All regressions were estimated using firms' fixed effects, but the coefficients are not reported. The t-statistics in parentheses are based on White heteroskedasticity-consistent standard errors.

interaction variable should be positive: more dependent industries would tend to grow faster in a more financially developed environment.

I borrow this idea from Rajan and Zingales (1998) in the following way. It seems plausible to take Brazil as a financially constrained economy. As shown in Table 3.9, Brazil has low domestic credit as a proportion of GDP compared to developed countries. In this financially constrained environment, more dependent firms that have access to credit should be relatively better off. Less dependent firms, on the other hand, should not be much affected by credit access. Hence, when explaining cross-firm investment levels, more dependent firms would tend to invest more when they have more access to credit, in a credit-constrained environment.

The empirical implementation is carried out by estimating the investment accelerator model, including the interaction between external dependence and credit access. Firm size is used as a proxy for credit access. If Brazil has a credit constrained economy, and if firm size is a good proxy for credit access, the coefficient for the dependence and firm size interaction term should be positive. Table 3.10 presents the results. The estimated regression for the whole sample of firms is presented in column 1. The coefficient for the interaction term is indeed positive and statistically significant; more dependent and larger firms do invest more.

In this empirical specification, it makes no sense to divide the sample of firms into large and small, or more and less dependent, because the criteria used for such divisions are already contained in the new independent variable used. An alternative grouping of firms is used, based on asset growth. One group, denoted 'winners', is composed of those firms that presented an above-average asset growth rate over the period, and the other group, 'losers', is composed of firms with asset growth rate below average. The interaction term (external dependence times firm size) is positive and significant in both subgroups, as shown in columns 2 and 3. It is interesting to note, though, that the coefficient is more than four times larger for the group of loser firms.23

5. Credit constraints and trade patterns

Section 3 describes industrial trade pattern evolution in Brazil, and Section 4 investigates the extent of credit constraint faced by Brazilian firms. In this section, I try to interact these two analyses in order to extract some evidence of the working of credit constraint as one of the sources of comparative advantage.

I divide time into the four subintervals used in previous sections: oil crisis (1974–82), debt crisis (1982–90), trade liberalization (1990–4) and capital inflow (1994–7). Trade barriers increased over the first two periods, and started being removed in the late 1980s. Trade pattern evolution over the first two periods would not necessarily represent the response to comparative advantages, but rather to distorted incentives. During the last two periods, on the other hand, trade distortion diminished substantially, and trade pattern evolution can be taken as an expression of the countries' comparative advantages.

CTB averages are calculated for each subinterval, identifying whether they are significantly different across subintervals, at a 5 per cent significance level. The results are presented in Table 3.11. Winners are sectors which significantly increased their CTB from one period to the next; losers are those significantly decreasing their CTB; and stagnant are the ones that showed no significant change. The most interesting features of the pattern observed in Table 3.11 are the following:

• Machinery is a winner from the first to the second periods only, that is, under trade distortions. It is a loser from the second to the third, and stagnant from the third to fourth.

• Drugs, plastic products and electric equipment follow a similar pattern. They are stagnant from the first to second periods, and losers over the other periods.

• The opposite is true for the sectors wood products, furniture, leather and tobacco. These sectors are losers from the first to the second periods (except tobacco, which was stagnant), and they are winners through the other periods.

• Food products, metal products and rubber products are also interesting cases: the first is a losing sector across all periods, except from third to fourth, and the opposite is true for the other two sectors.

• Textiles is a losing sector in all periods.

It is reasonable to conclude that Brazil shows no comparative advantage in machinery, drugs, electric equipment and textiles, and shows comparative advantage in wood products, furniture, leather and tobacco. Our main question is whether finance was one of the sources of this comparative advantage. Looking at the external dependence measure, it is interesting to note that the four sectors with lack of comparative advantage are among the seven most externally dependent

Table 3.11 Contribution to the trade balance across periods

 

1982–90 over 1974–82

1990–4 over 1982–90

1994–7 over 1990–4

Metal products

Rubber products

Paper and products

=

Apparel and footwear

=

Non-metal products

=

=

Machinery

=

Tobacco

=

Beverages

=

=

=

Transport equipment

=

=

Perfumery and soap

=

=

Chemical products

=

=

Other industries

=

=

Drugs

=

Electric equipment

=

Plastic products

=

Furniture

Wood products

Leather

Printing and publishing

=

Food products

Textiles

Notes: ↑: winners; ↓: losers; =: stagnant.

ones. Tobacco and leather, on the other hand, are the two least externally dependent – actually, they have negative measures of external dependence.

For a statistical comparison of the two measures – CTB and external dependence – the correlation between the two is calculated for each year, and the results presented in Table 3.12. Not only is the correlation between CTB and external dependence negative, it also decreases over time. This means that, on average, more externally dependent industries have lower CTB measures, and this negative relation is stronger over time.

The nature of the relation between CTB and external dependence is investigated in panel data regression, where external dependence by sector is used as an explanatory variable of CTB. The results are presented in column 1 in Table 3.13. The coefficient of external dependence is not significantly different from zero, and solely fixed effects explain 78 per cent of cross-sector variation in CTB. The correlation between the two measures, presented in Table 3.12, indicates that the relation between them changes over time. An interaction term between external dependence and time is then used as explanatory variable of CTB instead. As shown in column 2 in Table 3.13, this interaction term has a negative and significant coefficient. These results do not change when both the interaction term and external dependence are used as explanatory variables (see column 3 in Table 3.13).

Table 3.12 Correlation coefficient between external dependence and CTB

1974

–0.14

1989

–0.29

1975

–0.18

1990

–0.32

1976

–0.20

1991

–0.29

1977

–0.18

1992

–0.31

1978

–0.22

1993

–0.30

1979

–0.24

1994

–0.35

1980

–0.18

1995

–0.35

1981

–0.19

1996

–0.39

1982

–0.22

1997

–0.39

1983

–0.23

Averages

1984

–0.22

1974–82

–0.20

1985

–0.25

1982–6

–0.25

1986

–0.28

1986–90

–0.29

1987

–0.27

1990–4

–0.32

1988

0.27

1994–7

–0.37

As I argued in the beginning of this section, the pattern of trade evolution between 1974 and 1990 was responding to distorted trade incentives, whereas after trade liberalization in the 1990s it could have become an expression of the country's comparative advantages. In order to capture possible differences of the effect of external dependence over the two periods, I have run a regression including two variables as explanatory variables for CTB: external dependence, and a slope dummy for external dependence for 1990–7. The slope dummy equals external dependence for the years 1990–7, and equals zero in all other periods. As presented in column 4 in Table 3.13, the coefficient for external dependence is again not significantly different from zero, and the slope dummy coefficient is negative and significant. This indicates that external dependence explains cross-sector variations in CTB for the period from 1990 to 1997, but not from 1974 to 1990. When all three variables are included in the regression (column 5 in Table 3.13), the only significant coefficient is the negative coefficient for the slope dummy.

The results indicate that external dependence has a negative effect on cross-sector CTB for the period from 1990 to 1997, and no relation in the previous period studied. That is, sectors less external dependent are the ones with higher CTB during the period under more liberalized trade in Brazil.

6. Conclusion

This chapter has sought to investigate whether credit constraints may have influenced trade pattern evolution in Brazil. The analysis started with a description of economic development over the time period studied – 1974–97. The Brazilian economy suffered several large external shocks over the period, leading, along with other factors, to major macroeconomic disturbances. Macroeconomic volatility was extremely high over the 1980s and early 1990s. Since the implementation of the Real Plan in mid-1994, the country has experienced relative macroeconomic

Table 3.13 Regression results (dependent variable: contribution to trade balance index)

Independent variable and summary statistics

1

2

3

4

5

External dependence

 –3.50E–04

 

   5.41E–04

   1.42E–04

 –1.50E–03

 

 

(–3.79E–16)

 

  (5.05E–16)

  (1.03E–16)

(–1.02E–15)

Interaction (external dependence X time)

 

 –3.40E–04

–3.40E–04

 

 –2.21E–05

 

 

 

(–7.01)

(–7.00)

 

(–0.18)

External dependence slope dummy 1990–7

 

 

 

  –7.21E–03

  –6.85E–03

 

 

 

 

(–7.13)

(–3.64)

 

R2

  0.780

  0.820

  0.82

   0.828

   0.826

Notes: The dependent variable is contribution to trade balance index. The external dependence slope dummy is a variable that has value equal to external dependence for the years 1990–7, and zero in all other years. All regressions were estimated using sectors' fixed effects, but the coefficients are not reported. All regressions include twenty-one sectors, with observations from 1974 to 1997.

stability. Trade policy was characterized by two main situations: severely restrictive trade policy until the late 1980s, and more liberalized trade in the 1990s.

Trade pattern evolution was described through a series of indexes, and they identified a clear trade diversification in the course of the time period studied. Notably, the sector food and beverages represented a very important export sector at the beginning of the period, and its importance thereupon decreased substantially. An interesting feature of trade pattern evolution is the reversal in some sectors' CTB after trade liberalization in the 1990s. Some sectors, such as machinery, drugs, plastic products and electric equipment, presented increasing (or non-decreasing) trade balance contributions over the restricted trade period, and decreasing contributions after liberalization. The opposite is true for some other sectors, such as wood products, furniture, leather and tobacco.

Credit constraints were investigated using a firm's balance sheet data. The empirical exercise tried to answer two questions: whether firms are credit constrained, and whether credit constraints differ among different groups of firms. Following an influential trend in the empirical literature in this area, an investment accelerator model was estimated, including cash flow as an explanatory variable. If firms are not credit constrained, the cash-flow coefficient should not be significant, once investment determinants are controlled for. Estimated results indicated that Brazilian firms are indeed credit constrained. The only instance in which credit constraints seemed softer was among multinational firms, during the period 1994–7.

After describing trade pattern evolution, and establishing that Brazilian firms are credit constrained, the concluding question is: is there a link between credit constraint and trade pattern? The link is investigated by comparing the contribution to trade balance index to sectoral external dependence. External dependence is a measure constructed by Rajan and Zingales (1998) that indicates the amount of external finance an industry would use in an environment with no credit restrictions. Contributions to trade balance and external dependence are negatively correlated, that is, in any given year, sectors with higher CTB are the ones with lower external dependence on average. It is interesting to note that the negative correlation becomes stronger over time, especially after trade liberalization, when trade started to reveal the economy's comparative advantages with less artificial distortions. This is an indication that sectors less in need of external financing would be relatively better off in Brazil, which we identified as a credit-restricted economy. Thus, credit restrictions may be a source of comparative advantage.

Notes

1 I am grateful for helpful comments and suggestions from José Fanelli, Saul Keifman, Naércio Menezes and seminar participants at the IDRC workshop on Finance and Changing Patterns in Developing Countries, FEA – Universidade de São Paulo, and the PRONEX seminar held at Getulio Vargas Foundation. I am especially grateful to Edward Amadeo, with whom this project started, for many insightful conversations, and the elaboration of the section on labor productivity and unit labor cost evolution. I thank Patrícia Gonçalves, from IBRE, Getulio Vargas Foundation, for kindly furnishing data on Brazilian firms' balance sheets, Carla Bernardes and particularly Cristiana Vidigal for superb research assistance. Financial support from IDRC is gratefully acknowledged. I also thank CNPq for a research fellowship.

2 For an overview of the period from 1964 to 1973, see Bonomo and Terra (1999).

3 BEFIEX (Comissão para a Concessão de Benefícios Fiscais a Programas Especiais de Exportação) coordinated export incentives. Long-term (usually ten-year) contracts were signed between BEFIEX and the exporting firm, in which the firm would commit to a certain amount of exports over the period, and in exchange it would have reduced import duties and taxes. The program was effective – during the 1975 to 1990 period exports grew over 7 per cent a year on average, accompanied by impressive diversification.

4 For further details, see Simonsen (1988).

5 World inflation is measured here as the rate of increase in world export prices in US dollars.

6 Among other measures, the government created the 'Law of Similar National Products', determining that a product could not be imported if there existed a similar good being produced domestically.

7 See Bonelli et al. (1993).

8 Real exchange-rate volatility is measured as the monthly real exchange-rate standard deviation for a twelve month period. The measure is centered, i.e. the measure for June corresponds to the standard deviation from January to December. The real exchange rate was measured as RER = e.(WPI)/CPI, where e is the nominal exchange rate published by the Brazilian Central Bank, WPI is the US wholesale price index and CPI is the Brazilian consumer price index (INPC series from IBGE).

9 In this section, the time period analyzed is shorter due to lack of data.

10 FUNCEX provided by monthly sector data for imports and exports.

11 Looking at the CTB index for a twenty-three-industry aggregation, its variance ranged from 0.0061 to 0.0115 in the 1970s and early 1980s, going down to around 0.002 for the past few years.

12 The Aquino intra-industry trade index has also been calculated, and the results are similar to the ones reported here for the Grubel and Lloyd index.

13 This procedure may bias the sample of firms used, but I argue that the bias should not favor the result I am investigating. We are trying to identify whether firms are credit constrained. It is plausible to believe that firms which survived throughout the period studied should not be the more credit-constrained ones. Hence, if this (possibly) biased sample presents credit constraints, the unbiased sample should also be credit constrained.

14 The measure for debt is the long- and short-term loans on the firm's balance sheet. Liabilities include all other accounts under liabilities, such as dividends and taxes to be paid.

15 Firms which are less dependent on external finance are those in the following sectors: furniture, chemical products, wood products, transport equipment, textiles, machinery, perfumery and soap, electric equipment, plastic products, drugs, and other industries.

16 Note that Rajan and Zingales' external dependence measure refers to all sorts of external financing, not only loans.

17 It is very difficult to replicate Whited (1992) for Brazilian data, due to a lack of data on some crucial variables. For the same reason, it is also not possible to replicate the Q model of investment used in Fazzari et al. (1988).

18 All regressions were also estimated in first differences, and the results were qualitatively similar to the ones reported here.

19 Instead of splitting the sample into subgroups, another specification is also used, which will be denoted here as 'slope dummy specification'. In this specification, slope dummies are included in the regression with the whole sample of firms, which was equal to cash flow for the alternative groupings of firms, and zero otherwise. These slope dummies should capture differences in the cash-flow coefficient for the different groups of firms. The same qualitative results were obtained.

20 As a further result, in the slope dummy specification, the slope dummy for large firms is not significantly different from zero. Therefore, the null hypothesis that the cash-flow coefficient is equal for the two groups of firms cannot be rejected.

21 In both other specifications – slope dummies and first differences – the slope dummy coefficient for 1994–7 is negative and significant for the group of multinational firms.

22 All regressions were also run including only one and three lags for sales, and the results were unchanged.

23 All cash-flow regressions were also estimated for the groups of winner and loser firms separately, but no difference between them was identified in those regressions.

References

Bonelli, R., Fritsch, W. and Franco, G. H. B. (1993) 'Macroeconomic Instability and Trade Instability in Brazil: Lessons from the 1980s and 1990s', in A. Canitrot and S. Junco (eds), Macroeconomic Conditions and Trade Liberalization. Washington, DC: Inter-American Development Bank.

Bonomo, M. and Terra, C. (1999) 'The Political Economy of Exchange Rate Policy in Brazil: 1964–1997', Inter-American Development Bank Working Paper Series, R-367.

Fazzari, S. M., Hubbard, R. G. and Petersen, B. C. (1988) 'Financing Constraints and Corporate Investment', Brookings Papers on Economic Activity 1: 141–206.

Gertler, M. and Gilchrist, G. (1994) 'Monetary Policy, Business Cycle, and the Behavior of Small Manufacturing Firms', Quarterly Journal of Economics 109(2): 309–40.

Kaplan, S. and Zingales, L. (1997) 'Do Investment Cash Flow Sensitivities Provide Useful Measures of Financing Constraints?', Quarterly Journal of Economics 88(3): 169–215.

Levine, R. (1997) 'Financial Development and Economic Growth: Views and Agenda', Journal of Economic Literature 25: 688–726.

Rajan, R. G. and Zingales, L. (1998) 'Financial Dependence and Growth', The American Economic Review 88(3): 559–86.

Simonsen, M. H. (1988) 'Brazil', in R. Dornbusch and F. Helmes (eds), The Open Economy: Tools for Policymakers in Developing Countries. New York: Oxford University Press.

Whited, T. M. (1992) 'Debt, Liquidity Constraints, and Corporate Investment: Evidence from Panel Data', The Journal of Finance XLVII(4): 1425–57.

4 International competitiveness, trade and finance: India1

A. Ganesh-Kumar, Kunal Sen and Rajendra R. Vaidya

1. Introduction

In 1990–1, the Indian economy underwent a severe balance-of-payments crisis. By the summer of 1991, India's foreign exchange reserves covered less than two weeks of imports. The immediate cause of the crisis was the increase in world oil prices and the drop in the remittances of migrant workers from the Gulf following the annexation of Kuwait in September 1990. There was a realisation among Indian policy-makers, however, that 'the roots of the crisis were more structural in nature and lay in the import-substituting industrialisation (ISI) strategy followed by successive Indian governments since independence' (Agrawal et al. 1995: 161). While the ISI regime had enabled India to develop a large and diversified manufacturing sector, the net result of the protectionist policies was 'the growth of a high-cost, capital-intensive domestic industry that was by and large incapable of withstanding international competition' (p. 175). Not only did these policies severely inhibit India's export performance, they also served to limit the possibility of growth based on domestic demand.2 In spite of four decades of import-substitution policies, production in the Indian manufacturing sector remained greatly import intensive. As a consequence, with India's trade regime providing little incentive to export, growth based on domestic demand would lead to balance-of-payments problems sooner or later.3

In June 1991, the new government that assumed office (led by P. V. Narasimha Rao) embarked on an economic reform programme along with several macro-stabilisation measures. One of the major long-term objectives of the reforms was to increase India's international competitiveness, both in relation to its past and to the fast-growing economies of East Asia. While the 1991 reforms could be seen as a continuation of the deregulation measures that were initiated in the mid-1980s by the Rajiv Gandhi government, they were far more comprehensive in scope and radical in substance. The macroeconomic stabilisation programme initiated in 1991 yielded immediate benefits, with foreign exchange reserves recovering from just over 1 billion USD at the time of the crisis to over 6.4 billion USD at the end of 1992–3. The inflation rate, which had peaked at 17 per cent in 1991, came down steadily to 7 per cent in 1992–3. Real output growth, which had dipped to 1.2 per cent in 1991–2, recovered to 4 per cent in 1992–3. It is far from clear, however, whether the long-term goal of the 1991 reforms with regard to international competitiveness has been achieved.

International competitiveness refers to the ability of a country to expand its share in world markets. The standard view on competitiveness is that it is essentially determined by factor endowments and comparative advantage. Thus, to exploit comparative advantage, it is necessary to minimize various distortions in the economy and 'to get the prices right'. In recent years, two more views have emerged: One argues that technological differences across firms, industries and countries are an important determinant of competitiveness. The other view locates competitiveness in the firm's investment decisions and hence in its ability to obtain investible funds. Both these views stress the importance of non-price factors and provide an important role for the government to build technological capabilities and to ensure a financial environment that is able to identify and allocate resources to the best investment projects.

In this study, we examine the international competitiveness of India's manufacturing sector. We take the view that competitiveness is a multifaceted issue and that no single theory (and the associated measures) adequately captures its complexity. We thus use several measures of competitiveness to examine the relative importance of various factors that influence it. Section 2 deals with competitiveness at the aggregate and sectoral level. Here we assess the relative importance of the real exchange rate and trade specialisation patterns in explaining India's trade flows. We also examine the link between labour costs and competitiveness. Further, we explore the technological intensity of India's exports. In Section 3, we analyse one important determinant of competitiveness, i.e. the financial environment. Specifically, we ask the question: to what extent has the Indian financial sector provided an enabling environment for successful export performance by manufacturing firms in the post-1991 period? We attempt to answer this question in two steps. First, we examine firms' sources and uses of funds to discern whether there is any systematic relationship between export performance and financing patterns. Next, we estimate investment functions for a sample of firms in the Indian manufacturing sector to see whether finance constraints are less severe for the exporters as compared to firms whose sales are primarily to the domestic market.

We begin with a more explicit consideration of trade, industrial and financial sector policies in India and the periodisation of the policy regime that we have used in our study.

Policy regimes and periodization

The Indian policy regime can be categorised into three distinct phases. The first phase was the era of planning from 1951 to 1984 when the state had strict control over resource allocation. The second period was a period of partial deregulation from 1985 to 1991 when the state retained a major role in resource allocation even as private agents were given greater freedom in investment decisions. Finally, in the post-1991 period, resource allocation was primarily market driven.4 In what follows, we provide a brief overview of the economic policies followed in each period.5

1951–84

During this period, India had a highly restrictive trade and industrial policy regime. Nearly all imports were subject to discretionary import licensing or were 'canalised' by government monopoly trading organisations. The only exceptions were commodities listed in the Open General License (OGL) category. Capital goods were divided into a restricted category and the OGL category. While import licenses were required for restricted capital goods, those in the OGL could be imported without a license subject to several conditions. Intermediate goods were also classified into the banned, restricted and limited permissible categories plus an OGL category. As these names suggest, the first three lists were in order of import licensing stringency. The import of consumer goods was, however, banned. Like imports, exports were also subject to an elaborate licensing regime. To counteract the anti-export bias of the trade regime however, there were a large number of export incentives for manufactured goods.

The principal instrument of industrial policy was an elaborate industrial licensing framework under the Industries Development and Regulation Act of 1951. The Act stipulated that no new units (above a certain size) could be set up nor substantial expansion be made to existing units without a license from the government. The Monopolies and Restrictive Trade Practices Act (MRTP) became effective in 1970 to ensure against concentration of economic power and check restrictive trade practices. Foreign investment in India was regulated by the Foreign Exchange Regulation Act (FERA) of 1974.

With respect to financial sector policy, there was a period of increasing financial repression from the early 1970s. In 1969, fourteen of the largest commercial banks were nationalised followed by six more in 1980. Moreover, commercial banks were increasingly pressured to lend to the 'priority sector', comprising agriculture, small-scale industry, retail trade, transport operators, professionals and craftsmen. While the commercial banks essentially provided short-term credit to the manufacturing sector, long-term loans were provided by All India Development Banks like Industrial Development Bank of India and Industrial Credit and Investment Corporation of India. These term-lending institutions depended a lot on the government for resources (usually subsidised heavily), and their allocation of long-term loans to firms was strictly monitored by the government according to plan priorities. Interest rates both of commercial banks and term-lending institutions were controlled by the government. The stock markets too were controlled by the government with respect to pricing, quantum and timing of new issues.

Finally, with respect to exchange rate policy, the rupee was pegged to the pound sterling till 1975 (except for a brief period when the rupee was pegged to the US dollar). In September 1975, the peg was altered to a basket of currencies with undisclosed weights. For much of the period, the peg was 'passive', with the sole intention of keeping the real exchange rate constant.

1985–91

With the advent of the Rajiv Gandhi government in 1985, piecemeal reforms were initiated in trade and industrial policy. Several initiatives were taken to limit the role of licensing, expanding the scope for contribution by large business houses to growth, encouraging modernisation and allowing existing firms in certain industries to achieve minimum economic level of operations. The shift from quantitative import controls to a protective system based on tariffs initiated in the mid-1970s was considerably quickened from 1985 onwards. Also, beginning in the mid-1980s, there was a renewed emphasis by the new administration on export promotion. The number and value of incentives offered to exporters were increased and their administration streamlined. The allotment of REP licenses – tradable import entitlements awarded to exporters on a product-specific basis – became increasingly generous. There was also a steady devaluation of the Indian rupee during this period. Effectively, India operated an 'active' crawling peg from 1986 onwards to produce a sharp real depreciation of the rupee in the period 1986–90.

Post-1991

As noted earlier, the year 1991 marked a watershed in Indian economic policy. As a part of the structural adjustment programme, quotas on the imports of most machinery and equipment and manufactured intermediate goods were removed. REP licenses were abolished and a large part of the import licensing system was replaced by tradable import entitlements linked to export earnings. There was also a significant cut in tariff rates, with the peak tariff rate reduced from 300 per cent to 150 per cent and the peak duty on capital goods cut to 80 per cent. There was, however, little change in trade policy with respect to consumer goods which remained banned. With respect to industrial policy, industrial licensing was abolished altogether except for a select list of environmentally sensitive industries. MRTP was substantially revised so that regulations restricting the growth and merger of large business houses were eliminated. FERA was altered in 1993 so that the earlier policy of restricting foreign investment became one of actively promoting it.

From 1991 to 1993, India moved gradually to full current account convertibility of the exchange rate, first in March 1992, with the replacement of the tradable import entitlements, with a dual-exchange rate system, and then in March 1993, moving to a unified 'market-determined' exchange rate system (i.e. a managed float). Nonetheless, strict controls over the capital account, especially capital out-flows, remain.

In the financial sector, from the point of view of the financing decisions of firms, the two most important changes were the deregulation of interest rates (both of commercial banks and term-lending institutions) and the freeing of pricing restrictions on new issues of shares through the stock markets.

Our study is mostly confined to the 1985–91 and post-1991 policy regimes. In the next section, we attempt to trace the effects of these policy changes on export competitiveness of the Indian manufacturing sector. Specifically, we look for breaks in the trend in competitiveness across these two periods. In Section 3, where we analyse financial factors at the firm level, we confine ourselves to the post-1991 period for obvious reasons.

2. Productivity and the price determinants of competitiveness

In this section, we assess the relative importance of the real exchange rate and labour productivity (and domestic costs) in explaining India's trade performance in the recent past. We begin with overviews of India's trade performance and the evolution of the current account. We then attempt to determine the importance of the real exchange rate in explaining India's competitiveness in both total and manufacturing exports. Next we examine in detail India's trade specialisation patterns. We compute export shares and indices of revealed comparative advantage to assess competitiveness at a sectoral level. 'Winner' and 'loser' industries are then identified and the links between competitiveness, labour productivity and domestic costs are explored. We end by examining alternate measures of trade specialisation such as intra-industry trade and the technological complexity of exports.

Overview of trade flows

India had a persistent deficit in the trade account during the period 1971–96 (Figure 4.1). The trade deficit as a percentage of GDP was smaller in magnitude in the 1990s as compared to the 1980s. This, in spite of a rapid increase in imports as a ratio of GDP, was due to a strong performance by the export sector. It is clear that due to the sharp increase in both the ratios of exports to GDP and imports to

Image

Figure 4.1 India's exports, imports, trade balance, current account balance and openness measure.

GDP since the mid-1980s, the economy has been increasingly 'open' during this period (Figure 4.1).6 There has also been a steady increase in manufacturing exports as a proportion of India's total exports since the 1980s, from less than 60 per cent in 1979–80 to about 75 per cent in 1995–6. Nonetheless, market shares of India's total and manufacturing exports in world exports have not improved substantially and continue to remain at less than 1 per cent. There does not seem to be any perceptible increase in the annual growth rates of both total and manufacturing exports in the post-1991 period. For the period 1981–90, the average annual growth rates for total and manufacturing exports were 9.4 and 11.8 per cent, respectively, while for the period 1991–6 the average annual growth rates for total and manufacturing exports were 8.9 and 9.9 per cent, respectively. Therefore, the 1991 reforms do not seem to have had any perceptible positive effect on India's export performance.

Evolution of the current account and the real exchange rate

It is evident from Figure 4.1 that it is only in the early 1980s that India had large deficits in the current account. In the 1990s, while India still had a deficit in its current account, the current account deficit to GDP ratio was considerably lower than in the 1980s. We have already observed earlier that India had a rapidly falling deficit in its trade balance from the early 1980s as exports grew rapidly during this period (see Figure 4.1). Moreover, the real effective exchange rate (REER) had been steadily depreciating since the mid-1980s (Figure 4.2). During this period, India followed a policy of steadily devaluing the rupee in combination with other export promotion measures to boost exports. Clearly then, the worsening current account deficit in the 1980s cannot be attributed to a weakly performing export sector.

Image

Figure 4.2 Real effective exchange rate of the rupee (1979 = 100).

Joshi and Little (1994) argue that the increase in the current account deficit to GDP ratio in the 1980s could be linked to an increase in the investment–savings gap. Underlying this was the widening fiscal deficits of the central government, with the public investment–savings gap increasing from 7.1 per cent of GDP in 1982–4 to 8.4 per cent of GDP in 1985–9. With the fiscal retrenchment initiated in 1991, there was a narrowing of the investment–savings gap in the 1990s and a consequent decrease in the current account deficit to GDP ratio. Thus, the large current account deficits of the 1980s could be attributed to a macroeconomic imbalance (related to a widening fiscal deficit) rather than a stagnant export sector or an inappropriate real exchange rate. The structural adjustment programme of 1991 led to some correction in this imbalance and, hence, a more sustainable current account deficit. It should be noted, however, that in contrast to its behaviour in the mid-to-late 1980s, the real exchange rate (RER) has shown a slight appreciation in the very recent past.

We have observed earlier that India followed a discretionary crawling peg in the 1970s and 1980s to maintain an 'appropriate' level of the RER. Yet there were periods, particularly in the early 1980s, when the nominal exchange rate was kept fixed in spite of a high inflation rate prevailing in the domestic economy. It is commonly agreed that sustained RER misalignment may contribute to severe macroeconomic disequilibria and a balance-of-payments crisis. Moreover, there is evidence to suggest that more 'successful' countries owe much of their success to having been able to maintain the RER at its 'appropriate' level (Edwards 1994). To what extent can it be argued that India had 'misaligned' RERs during the period under consideration? Elbadawi (1994) estimates the degree of misalignment in India's real exchange rate for the period 1965–88. The degree of mis-alignment is defined as the deviation of the actual RER from the equilibrium RER. The latter is itself the level of the RER which allows the economy to simultaneously attain internal equilibrium (i.e. the non-tradable market clears, the budget is balanced and portfolio equilibrium holds) and external equilibrium (the current account is in balance). Elbadawi has developed a model of the equilibrium RER where the equilibrium RER is determined by domestic absorption and government expenditures (both as ratios of GDP), terms of trade and a measure of the degree of 'openness' of the economy. Elbadawi finds that except for 1965 and 1986, which witnessed episodes of overvaluation of 16.3 per cent and 10.6 per cent, respectively, the period is characterised by single-digit RER misalignments, most of which are actually quite small. This, according to the author, supports the view that 'India, while maintaining an elaborate ensemble of economic controls, has nonetheless adopted a rather conservative macroeconomic policy' (Elbadawi 1994: 126).

Aggregate competitiveness

To measure competitiveness at the aggregate level, we use the constant market share (CMS) analysis. According to the CMS method, the proportionate increase in exports over time comprises a number of effects: (a) standard growth effect,

Image

Figure 4.3 India's competitiveness and export growth – all commodities (SITC two-digit level).

(b) commodity composition effect, (c) market distribution effect, and (d) a residual effect which may be termed 'competitiveness'. In other words, the increase in exports can be 'explained' in terms of four factors: the general growth of world exports to the focus destination; the commodity mix of exports and differential growth in import demands; the extent to which the particular market represents growing centres of demand; and finally, a residual term which captures the net gain or loss in the market shares presumably due to changes in the relative price and/or quality of the product, not to mention the marketing effort and skill of the exporters.7

The estimates of each of the above-mentioned effects depend on the 'standard' against which the focus country's exports to the focus destination is to be compared. This study has used the world standard, assuming that the commodity composition of world exports bears a reasonably good relationship to that of the focus exporter.

The CMS methodology is used to decompose the annual change in India's total exports, all commodities and manufacturing commodities separately, over the period 1970–92.8 The data set used is the World Trade Database from Statistics Canada made available through the NBER (Feenstra et al. 1997). Based on the trade data from the United Nations Statistical Office, this database provides on a consistent basis the annual bilateral trade values for all countries of the world over 1970–92.9

In Figure 4.3, we plot the competitiveness measure for all commodities as obtained from the CMS methodology along with the annual growth of total exports. Similarly, in Figure 4.4, we plot the competitiveness measure only for manufacturing commodities along with the annual growth of manufacturing exports. In both cases, the change in competitiveness is correlated with export growth. However, there is a closer correlation between the growth rate and the

Image

Figure 4.4 India's competitiveness and export growth – manufacturing commodities (SITC two-digit level).

Table 4.1 Decomposition of India's exports (%)

 

World trade effects

Commodity effects

Market effects

Competitive effects

Export growthactual (USD 000)

All commodities

 

 

 

 

 

1971–5

     –66.4

         28.3

        50.7

         87.5

2,415,207.2

1976–80

     168.7

       –14.2

        14.8

       –69.3

3,583,957.3

1981–5

        6.6

         12.9

      –22.9

       103.5

1,473,356.8

1986–90

      87.9

          7.3

      –20.5

         25.3

8,965,485.2

1991–2

   –140.3

          1.7

       78.5

       160.2

2,598,590.4

Manufacturing

 

 

 

 

 

1971–5

32,964.5

–17,278.3

18,974.5

–34,560.7

   933,390.1

1976–80

    128.3

        –1.5

      17.9

      –44.6

2,740,358.5

1981–5

      52.0

      –11.2

        6.1

        53.0

   810,632.4

1986–90

  –604.6

      –35.0

   –277.6

   1,017.2

7,394,295.2

1991–2

    182.3

        –3.3

   –111.1

       32.1

2,531,672.7

change in competitiveness of manufacturing commodities than there is between the growth rate and the change in competitiveness for all commodities (the correlation in the former case is 0.861 as compared to 0.796 for the latter case). This indicates that competitiveness may play a greater role in determining the export performance of the manufacturing sector than it does for all other sectors.

The CMS methodology decomposes the change in a country's exports into four components – the world trade effect, the commodity composition effect, the market effect and the competitiveness effect. In Table 4.1, we decompose exports into these four components for all commodities and for manufacturing commodities. We find that the relative importance and the direction of change of the four components for all commodities is quite different from the relative importance of these components for manufacturing commodities for most subperiods. For example, in 1971–5 and in 1986–90, the competitiveness effect is large in magnitude (and opposite in direction, for the period 1971–5) for manufacturing exports as compared to all exports. This may indicate that the factors explaining competitiveness for manufacturing exports may be different from those explaining competitiveness for all exports. The periods 1971–5 and 1986–90 are striking in that we find that for manufacturing exports, the competitiveness effect is negative and large in magnitude in the first period and positive and, again, large in magnitude for the second period. What explains these large variations in the aggregate competitiveness of both total exports and total manufacturing exports? We examine this below.

The real exchange rate and aggregate competitiveness

The real exchange rate is often viewed as the most important determinant of the overall competitiveness of an economy. We examine this relationship for aggregate competitiveness measured over all commodities (CMSA) and over manufacturing commodities (CMSM) as estimated earlier using the CMS methodology (Table 4.2). Towards this, we regress CMSA on the change in the real exchange rate (RER) (Model 1a), and on the change in the nominal exchange rate (NER) and the inflation differential between India and the US (INF) (Model 1b). Similarly, we regress CMSM on the change in the real exchange rate (RER) (Model 2a), on the change in the nominal exchange rate (NER) and the inflation differential between India and the US (INF) (Model 2b), on the change in the sector-specific real exchange rate (RERM) (Model 2c), and, finally, on NER and on the sector-specific inflation differential between India and the US (INFM) (Model 2d). A linear functional form was specified and estimated using ordinary least squares (OLS) over the period 1971–92.

For CMSA, the change in RER is positive and significant at the 5 per cent level, albeit with a lag (Model 1a). The current change in RER was found to be insignificant. Decomposing the RER into its components, we find that it is the change in NER with a lag that explains the variations in CMSA (Model 1b). Similarly, for CMSM, the change in RER is positive and significant with a lag (Model 2a), with the decomposition again indicating that it is the change in NER that matters (Model 2b). Further, sector-specific RER does not have as much explanatory power as the economy-wide RER (Models 2c and 2d).

As noted earlier, India has followed an active exchange rate policy to boost exports since the mid-1980s. The evidence above shows that such a policy has indeed been effective. With a shift towards a more market-determined exchange rate since 1991 however, such a policy option may no longer be available.

Table 4.2 Real and nominal exchange rate, inflation differentials and competitiveness

Dependent variable

Explanatory variables

Image

D–W

 

Constant

ΔRER(–1)

ΔRERM(–1)

ΔNER(–1)

ΔINF(–1)

ΔINFM(–1)

 

 

ΔCMSA

–58,864.2

439,952.2

 

 

 

 

0.287

1.846

(1a)

     (–0.32)

  (3.01)*

 

 

 

 

 

 

ΔCMSA

–216,538.0

 

 

7,520,050.0

–2,184,505.0

 

0.309

2.007

(1b)

     (–0.99)

 

 

  (3.28)*

  (–0.59)

 

 

 

ΔCMSM

 –17,959.2

316,787.7

 

 

 

 

0.285

2.797

(2a)

     (–0.13)

  (2.99)*

 

 

 

 

 

 

ΔCMSM

 –97,534.9

 

 

5,047,609.0

–2,036,369.0

 

0.247

3.006

(2b)

     (–0.59)

 

 

  (2.92)*

  (–0.74)

 

 

 

ΔCMSM

–6,400.6

 

476,982.1

 

 

 

0.185

2.795

(2c)

    (–0.04)

 

 (2.36)*

 

 

 

 

 

ΔCMSM

–133,039.6

 

 

4,640,995.0

 

2,650.5

0.225

3.073

(2d)

     (–0.81)

 

 

  (2.67)*

 

(0.001)

 

 

Notes: t-tests are reported in brackets; * indicates significance at the 5 per cent level.

ΔCMSA: change in competitiveness – all commodities; ΔCMSM: change in competitiveness – manufacturing; ΔRER(–1): 1 period lag in change in real exchange rate – all commodities; ΔRERM(–1): 1 period lag in change in real exchange rate – manufacturing; ΔNER(–1): 1 period lag in change in nominal exchange rate; ΔINF(–1): 1 period lag in change in inflation differential between India and USA – all commodities; ΔINFM(–1): 1 period lag in change in inflation differential between India and USA – manufacturing.

Trade specialisation patterns

Data

The database used is obtained from the International Economic Data Bank (IEDB) at the Australian National University and provides trade and industry data at the ISIC four-digit level. The source of the industry data is UNIDO's Industrial Statistics databank, which in turn is compiled from the Annual Survey of Industries published by the Central Statistical Organisation, India. The export data is obtained from the United Nations Trade Database and uses a commodity concordance developed by the United Nations and further refined by the IEDB. The commodity concordance involves a mapping from the SITC classification system used by the Trade Database of the United Nations in reporting export data to the ISIC classification system used by the UNIDO in reporting industry data. While all the commodities that are usually included in the SITC definition of manufacturing exports (SITC 5 to 8 less 68) have been reclassified according to their industry of origin at the ISIC four-digit level, the ISIC classification contains some additional commodities not included in the SITC classification. As is well known, one limitation of the SITC classification of manufacturing exports is that it excludes processed food items and tobacco products (which are included in SITC 0 and 1). In contrast, the ISIC (i.e. industry-based) classification of manufacturing includes all such commodities in ISIC 311 (food products), 313 (beverages) and 314 (tobacco products). Furthermore, the ISIC classification of manufacturing also includes non-ferrous metals (ISIC 372), which are usually excluded from the SITC-based classification of manufacturing. Therefore, the coverage of manufacturing exports using the ISIC-based definition (i.e. the definition used in this chapter) may be considered to be more comprehensive than the more commonly used SITC-based definition.

Export shares and indices of revealed comparative advantage

In Table 4.3, we present the top two dozen commodities (at the ISIC four-digit level) in terms of export shares in India's total manufacturing exports over the period 1971–96. It is evident from the table that the shares of ISIC 3211 (spinning, weaving and finishing of textiles) and ISIC 3231 (tanneries and leather finishing) have declined significantly in the period under consideration from a total of around 34 per cent in 1971–5 to less than 14 per cent in 1991–6. On the other hand, the shares of ISIC 3220 (wearing apparel excluding footwear) and ISIC 3901 (jewellery and related articles) have increased in this period from a total of less than 8 per cent in 1971–5 to around 32 per cent in 1991–6. Basic industrial chemicals (excluding fertilisers, ISIC 3511) also seem to be increasingly important in India's manufactured export basket over time. The shares of most other commodities do not show any significant change in trend over the period 1971–96. It is also evident from Table 4.3 that these twenty-four commodities have consistently accounted for more than 85 per cent of the manufacturing

Table 4.3 Export shares of select commodities

ISIC Industry code and name

1971–5

1976–80

1981–5

1986–90

1991–6

3111-SLGHTRG, PREP, PRESERV MEAT

  1.2

  1.6

  1.7

  1.0

  0.8

3115-MANUF VEG, ANL OILS + FATS

  7.3

  4.6

  3.1

  2.6

  3.8

3116-GRAIN MILL PRODUCTS

  3.1

  6.4

  7.8

  4.6

  4.1

3118-SUGAR FACTORIES REFINERS

  7.0

  3.5

  1.3

  0.1

  0.5

3121-MANUF OF FOOD PRODS NEC

  1.1

  1.3

  1.3

  0.9

  0.6

3211-SPINNG, WEAVG, FINSHG TEXTS

24.6

14.8

12.3

10.5

11.9

3212-MAN MDUP TXT GDS EX WEARG APP

  7.5

  3.4

  2.7

  1.3

  0.8

3214-CARPETS

  2.0

  3.0

  4.3

  3.9

  3.3

3220-MANUF WEARG APP EX FTWR

  4.9

10.1

13.7

18.0

19.7

3231-TANNERIES, LTHER FINISHNG

  9.2

  7.9

  5.7

  4.4

  1.7

3233-MAN PRODS LTER EXC FWR, APP

  0.3

  0.4

  0.9

  1.4

  1.7

3240-MAN FTWR EX RUBBR, PLASTC

  0.9

  1.3

  2.9

  3.7

  2.8

3511-BASIC IND CHEMS EXC FERT

  1.5

  1.7

  1.8

  3.8

  5.3

3522-DRUGS + MEDICINES

  0.8

  1.3

  2.5

  2.6

  2.6

3523-SOAP, CLNS PRPS, PERF, COSM

  0.4

  0.7

  1.4

  1.0

  0.7

3530-PETROLEUM REFINERIES

  0.8

  0.5

  4.2

  6.9

  2.5

3710-IRON + STEEL BAS INDS

  4.2

  6.5

  1.2

  1.7

  3.8

3720-NON-FER METAL BASIC IND

  2.5

  2.5

  0.4

  0.5

  0.9

3819-FAB MET PRD EX MACH EQP NEC

  1.2

  2.1

  1.6

  1.1

  1.5

3824-SPEC IND MACH + EQP EX 3823

  0.9

  1.0

  1.3

  1.4

  0.9

3829-MACH, EQUIP EX ELECT NEC

  0.9

  1.2

  1.2

  0.9

  1.1

3839-ELEC APPAR + SUPPLIES NEC

  0.9

  1.0

  1.4

  1.1

  0.5

3843-MOTOR VEHICLES

  1.8

  2.6

  2.6

  1.9

  2.4

3901-JEWELRY + RELATED ARTICLES

  3.0

  6.4

  9.4

13.1

12.1

Cumulative share of the above 24 commodities

88.0

85.8

86.7

88.4

86.0

Note: Export share of industry i = (export of industry i/India's total manufacturing exports) * 100.

exports during this period. This seems to suggest that India's manufacturing exports have not diversified over the past twenty-five years.

We computed the revealed comparative advantage (RCA)10 of India's manufacturing exports for each year over the period 1971–96.11 The RCA computations showed that for a vast majority of industries, India is just not competitive in export markets as indicated by RCAs that are less than 1 over the entire period. In the post-1991 period, India was most competitive in ISIC 3901 (jewellery), followed by ISIC 3214 (carpets). In the case of jewellery, in particular, the increase in RCA has been dramatic, from 2.4 in 1971–5 to 12.8 in 1991–6. Other commodities whose export competitiveness has been increasing over the period 1971–96 are ISIC 3116 (grain mill products), ISIC 3220 (manufacture of wearing apparel excluding footwear), ISIC 3233 (manufacture of leather excluding footwear, apparel) and ISIC 3551 (tire and tube industries). Commodities with declining competitiveness are ISIC 3212 (manufacture of made-up textile goods excluding wearing apparel) and ISIC 3231 (tanneries and leather finishing).

Winner and loser sectors

In order to determine which industries 'gained' and which industries 'lost' in competitiveness, we adopt a non-parametric approach involving essentially a t-test (and an associated F-test) on the sample mean of RCAs across different subperiods of interest. The theme of the t-test is to split the whole time series of RCAs into two subsamples (say, Period I and Period II), compute the means of the series over the subsamples and test for equality or inequality of these two subsample means. At a given level of significance, a significant positive (negative) t-statistic would indicate a significant increase (decrease) in the mean level of the RCAs in Period II compared to Period I. An insignificant t-statistic would indicate equality of the mean level of the RCAs between the two subperiods, i.e. the RCAs are more or less constant over the full sample. The mathematical expression for the test statistic can be found in Brockett and Levine (1984) and Kanji (1993).

As we have noted in Section 1, the Indian economy has undergone two sets of reforms in the recent past, once in 1985, and the second in 1991. To see whether these two rounds of reforms have had any discernible effect on external competitiveness of the Indian manufacturing sector, we conduct the t-test on the sample means of RCAs once between the periods 1970–84 and 1985–91 and a second time between the periods 1985–91 and 1992–6. An industry whose RCA showed a significant increase (decrease) is considered to be a 'winner' ('loser') industry over the relevant period. Industries whose RCA did not show a significant change are considered to be 'stagnant'. We confine the t-tests to those industries which had RCAs greater than one for at least one of the subperiods. The results are tabulated in Table 4.4. A summary of these results is reported in Table 4.5.

From these results it is clear that some industries have gained in competitiveness while others have lost following the two rounds of reforms. Furthermore, there have been more winners than losers after the 1991 reforms as compared to the earlier reforms of 1985. Only one industry, namely leather products (excluding

Table 4.4 t-Test on sample means of RCAs

ISIC Industry code and name

Sample mean

t-statistic

Significance level (%)

Sample mean

t-statistic

Significance level (%)

 

1970–84

1985–91

 

 

1985–91

1992–6

 

 

3115-MANUF VEG, ANL OILS+FATS

  3.8139

  3.0220

–1.58

12.89

  3.0220

  4.8482

  2.33

  4.20

3116-GRAIN MILL PRODUCTS

  3.8752

  5.9993

  3.41

  0.34

  5.9993

  7.6897

  1.44

21.04

3118-SUGAR FACTORIES REFINERS

  3.3591

  0.5569

–4.09

  0.08

  0.5569

  1.8683

  2.31

  6.01

3121-MANUF OF FOOD PRODS NEC

  2.7737

  1.8439

–3.44

  0.88

  1.8439

  1.0759

–2.81

  2.29

3211-SPINNG, WEAVG, FINSHG TEXTS

  5.0223

  3.6114

–4.15

  0.05

  3.6114

  4.4774

  2.14

  5.78

3212-MAN MDUP TXT GDS EX WEARG APP

17.4034

  6.5477

–5.24

  0.01

  6.5477

  3.8569

–3.56

  0.92

3213-KNITTING MILLS

  0.8682

  1.0096

  0.45

66.15

3214-CARPETS

  8.3973

12.8104

  4.75

  0.02

12.8104

12.3311

–0.48

64.17

3215-CORDAGE ROPE, TWINE INDS

  0.5291

  1.7030

  6.62

  0.01

3220-MANUF WEARG APP EX FTWR

  3.1098

  4.9518

  5.24

  0.00

  4.9518

  5.0752

  0.33

75.05

3231-TANNERIES, LTHER FINISHNG

25.5586

13.4599

–2.89

  0.91

13.4599

  5.0011

–5.09

  0.14

3233-MAN PRODS LTER EXC FWR, APP

  2.0642

  4.9482

  2.84

  1.01

  4.9482

  5.9971

  2.28

  4.58

3240-MAN FTWR EX RUBBR, PLASTC

  1.9479

  3.9214

  8.61

    0.00

  3.9214

  3.1289

–8.39

  0.00

3511-BASIC IND CHEMS EXC FERT

  0.7722

  1.2161

  3.13

  1.21

3521-PAINTS, VARNISH LACQUERS

  1.2697

  1.4107

  0.36

  72.37

  1.4107

  0.1447

–7.22

  0.02

3522-DRUGS+MEDICINES

  1.3252

  2.1755

  1.18

  25.03

  2.1755

  1.5513

–1.20

25.65

3523-SOAP, CLNS PRPS, PERF, COSM

  1.8687

  1.8269

–0.09

  92.98

  1.8269

  0.7205

–3.98

  0.26

3530-PETROLEUM REFINERIES

  0.2373

  1.7062

  4.90

  0.17

  1.7062

  0.9406

–2.54

  3.88

3551-TIRE+TUBE INDUSTRIES

  0.8314

  1.1584

  1.11

28.11

  1.1584

  1.7966

  2.50

  4.66

3692-CEMENT, LIME AND PLASTER

  1.1028

  0.4034

–2.45

  2.47

  0.4034

  2.0359

  2.77

  3.96

3699-NON-MET MINL PRODS NEC

  1.2433

  0.8894

–1.17

25.72

  0.8894

  2.0929

  5.32

  0.11

3710-IRON+STEEL BAS INDS

  0.4119

  1.1236

  4.07

  0.23

3811-CUTLY, HAND TLS, GEN HDWRE

  1.3135

  1.0637

–2.41

  2.81

  1.1584

  1.7966

  2.50

  4.66

3813-STRUCTURAL METAL PRODUCTS

  1.0962

  0.6143

–5.05

  0.01

3819-FAB MET PRD EX MACH EQP NEC

  1.1295

  0.8542

–2.63

  1.57

  0.8542

  1.0363

  1.15

27.74

3844-MOTOR CYCLES+BICYCLES

  2.1411

  2.2228

  0.16

  87.62

  2.2228

  2.5623

  0.72

48.61

3901-JEWELRY+RELATED ARTICLES

  5.8760

13.3393

  9.55

  0.00

13.3393

12.7182

–0.57

57.87

3903-SPORTING+ATHLETIC GOODS

  1.9433

  1.1650

–3.37

  0.29

  1.1650

0.9652

–1.54

16.19

Note: t-Tests were done only for those industries for which the average RCA is greater than one in at least one of the subperiods.

Table 4.5 Winner and loser industries

1985–91 over 1970–84

1992–6 over 1985–91

 

Winners

Losers

Stagnant

Winners

Man Prods Lter Exc Fwr, App

Petroleum Refineries Man Footwear Ex Rubber, Plastics

Grain Mill Products Carpets Manuf Wearing App Ex Footwear Jewelry + Related Articles

Losers

Spinning, Weaving, Finishing Texts Cutly, Hand Tls, General Hardware Sugar Factories Refiners Fab Met Prd Ex Mach Eqp Nec

Manuf of Food Prods Nec Man Mdup Txt Gds Ex Wearing App Tanneries, Leather Finishing

Sporting + Athletic Goods

Stagnant

Manuf Veg, Anl Oils + Fats Tire + Tube Industries

Paints, Varnish Lacquers Soap, Clns Prps, Perfumes, Cosm

Drugs + Medicines Motor Cycles + Bicycles

footwear and apparel), has been winning over both rounds of reforms. In contrast, three industries have lost in both rounds of reforms. These are food products (NEC), textile goods (excluding wearing apparel) and product of tanneries and leather finishing. There have been some industries which gained in one round of reforms but lost in another round, such as footwear (excluding rubber and plastics) and sugar factories. One possible explanation for this could be that these industries may have gained/lost (as the case may be) due to inter-industry effects of the reform measures that dominated the direct effects of reforms.

Evolution of labour productivity and unit labour costs

It is well recognised in the literature that a key determinant of external competitiveness is unit labour costs (see Fagerberg 1988). To what extent this hypothesis is relevant in the Indian context is of great significance given that India is perceived to be a labour-surplus economy. There has been a significant increase in labour productivity in the manufacturing sector since the early 1980s, with a levelling off in the 1990s (Figure 4.5). Real wages followed labour productivity for much of the 1970s and 1980s, leading to no perceptible change in unit labour costs during this period. In the late 1980s however, there was a slight decline in unit labour costs in the manufacturing sector, as labour productivity growth overtook growth in real wage per worker. In the early 1990s, with stagnation in labour productivity, unit labour costs began to increase. We observe that the movements in unit labour costs during the 1980s and early 1990s seem to have a fairly strong negative correlation with India's market share in world manufacturing exports.

Image

Figure 4.5 Labour productivity, real wages and unit labour costs (ULC).

During the early 1980s, with little change in unit labour costs, there was no significant change in India's market share. With the decline in unit labour costs in the late 1980s, India's market share improved. Finally, in the early 1990s, with a slight increase in unit labour costs, there was a fall in India's market share. There is preliminary evidence, then, that at the aggregate level, the behaviour of unit labour costs may have played an important role in determining India's international competitiveness in the period under consideration.

Data on changes in unit labour costs by industry show that there is no consistent pattern on unit labour cost growth across industries (Table 4.6). In keeping with the trend in unit labour costs at the aggregate level however, a larger proportion of industries witnessed declining unit labour costs in the period 1986–90 as compared to the periods 1982–5 and 1991–2. The correlation coefficients between growth in unit labour costs and the change in RCAs across industries indicate that for the period 1982–5, growth in unit labour costs in a particular industry may be negatively correlated with the change in the international competitiveness of that industry (the correlation coefficient between the two is –0.25). On the other hand, there is little correlation between growth in unit labour costs and the change in RCAs for the other two subperiods. Moreover, when we attempted to relate changes in unit labour costs with the classification of industries into winners and losers, no discernible pattern emerged at the sectoral level on the linkage between domestic costs and export competitiveness (see Table 4.6). It should be noted nonetheless that such an analysis is incomplete until we can compare the evolution of unit labour costs at the sectoral level in India with a world norm. Clearly, what is of relevance for export competitiveness of a particular sector is the relative movement of its domestic costs with respect to the

Table 4.6 Percentage change in unit labour costs (ULC), India, 1982–92

ISIC Industry code and name

1982–5

1986–90

1991–2

Winner/loser

 

 

 

 

1985–91 over 1970–84

1992–6 over 1985–91

3111-SLGHTRG, PREP, PRESERV MEAT

  0.23

–0.03

–0.23

 

3112-MANUF OF DAIRY PRODUCTS

  0.27

–0.03

  0.34

 

 

3113-CANNG, PRES FRUITS VEGS

–0.03

  0.15

–0.04

 

 

3114-CAN, PRES, PRS OF FISH, CRUS

  0.06

  0.04

  0.16

 

 

3115-MANUF VEG, ANL OILS + FATS

  0.00

  0.16

  0.37

Stagnant

Winner

3116-GRAIN MILL PRODUCTS

  0.08

  0.14

  0.32

Winner

Stagnant

3117-MANUF OF BAKERY PRODUCTS

  0.15

  0.07

  0.21

3118-SUGAR FACTORIES REFINERS

–0.10

  0.13

  0.15

Loser

Winner

3119-MANUF COCOA, CHOC + SUG CONF

  0.01

–0.57

  0.34

 

 

3121-MANUF OF FOOD PRODS NEC

–0.07

  0.16

  0.40

Loser

Loser

3122-MANUF OF PREPD ANL FEEDS

  0.12

  0.10

  0.37

 

 

3131-DISTG, RECTG, BLENG SPIRITS

  0.16

  0.09

  0.28

 

 

3132-WINE INDUSTRIES

  0.10

  0.25

–0.50

 

 

3133-MALT LIQUORS AND MALT

  0.14

  0.04

  0.44

 

 

3134-SFT DRNKS + CARB WTRS IND

  0.12

–0.02

  0.08

 

 

3140-TOBACCO MANUFACTURES

–0.01

  0.06

  0.23

 

 

3211-SPINNG, WEAVG, FINSHG TEXTS

  0.07

  0.02

  0.27

Loser

Winner

3212-MAN MDUP TXT GDS EX WEARG APP

  N/A

  0.38a

  0.22

Loser

Loser

3213-KNITTING MILLS

  0.17

  0.15

  0.20

 

 

3214-CARPETS

  N/A

–0.73a

–0.21

Winner

Stagnant

3215-CORDAGE ROPE, TWINE INDS

  N/A

  0.30a

–0.05

 

 

3219-MANUF OF TEXTILES, NEC

  N/A

–0.07a

  0.27

 

 

3220-MANUF WEARG APP EX FTWR

  0.12

  0.08

  0.35

Winner

Stagnant

3231-TANNERIES, LTHER FINISHNG

  0.12

–0.02

  0.28

Loser

Loser

3240-MAN FTWR EX RUBBR, PLASTC

  0.10

  0.11

  0.18

Winner

Loser

3311-SAWMLS, PLNG OTH WD MILLS

  0.10

  0.00

  0.28

 

 

3312-MAN WD, CNE CNTS, SML CNWR

–0.08

  0.02

  0.33

 

 

3319-MAN WOOD CORK PRODS NEC

  0.05

  0.04

  0.35

 

 

3320-MAN FURN, FIXT EX PRIM MTL

  0.16

–0.01

  0.29

 

 

3411-MAN PULP, PAPER, PAPERBOARD

  0.14

–0.02

  0.30

 

 

3412-MAN CONTS, BXES PPR, P/BRD

  0.17

–0.03

  0.47

 

 

3419-MAN ART PULP, PPR, P/BRD NEC

–0.09

  0.33

  0.19

 

 

3420-PRNTNG, PUBLNG ALLIED IND

  0.07

  0.07

  0.07

 

 

3511-BASIC IND CHEMS EXC FERT

  0.20

  0.00

  0.39

 

 

3512-FERTILISERS PESTICIDES

  0.10

  0.11

  0.04

 

 

3513-SYN RESINS ETC EXC GLASS

  0.01

  0.12

–0.01

 

 

3521-PAINTS, VARNISH LACQUERS

  0.11

  0.02

  0.22

Stagnant

Loser

3522-DRUGS + MEDICINES

  0.07

  0.07

  0.24

Stagnant

Stagnant

3523-SOAP, CLNS PRPS, PERF, COSM

  0.08

  0.07

  0.10

Stagnant

Loser

3529-CHEMICAL PRODUCTS NEC

  0.10

–0.01

  0.25

 

 

3530-PETROLEUM REFINERIES

  0.09

–0.02

  0.20

Winner

Loser

3540-MISC PRODS OF PETR, COAL

  0.15

  0.14

  0.13

 

 

3551-TIRE + TUBE INDUSTRIES

–0.10

  0.20

  0.13

Stagnant

Winner

3559-MANUF OF RUBBER PRODS NEC

  0.05

  0.03

  0.42

 

 

3560-PLASTICS PRODUCTS NEC

  0.05

  0.14

  0.31

 

 

3610-POTTERY, CHINA, EARTHWARE

  0.24

–0.02

  0.22

 

 

3620-GLASS + GLASS PRODUCTS

  0.00

  0.08

  0.24

 

 

3691-STRUCTURAL CLAY PRODUCTS

  0.20

  0.06

  0.13

 

 

3692-CEMENT, LIME AND PLASTER

–0.06

  0.04

  0.45

 

 

3699-NON-MET MINL PRODS NEC

  0.09

  0.08

  0.22

 

 

3710-IRON STEEL BAS INDS

  0.11

–0.01

  0.32

 

 

3720-NON-FER METAL BASIC IND

  0.10

  0.13

  0.21

 

 

3811-CUTLY, HAND TLS, GEN HDWRE

  0.08

  0.04

  0.14

Loser

Winner

3812-FURN + FIXT PRIM OF METAL

  0.05

  0.04

  0.71

 

 

3813-STRUCTURAL METAL PRODUCTS

  0.09

  0.30

  0.10

 

 

3819-FAB MET PRD EX MACH EQP NEC

  0.09

  0.07

  0.28

Loser

Winner

3821-ENGINES + TURBINES

  0.07

  0.07

  0.08

 

 

3822-AGRIC MACHINERY AND EQUIP

  0.14

  0.04

  0.32

 

 

3823-METAL + WOODWORKING EQUIP

  0.14

  0.03

  0.29

 

 

3824-SPEC IND MACH+EQP EX 3823

  0.04

  0.08

  0.20

 

 

3825-OFF, COMPUTG, ACCOUNTG MACH

  0.06

  0.25

  0.16

 

 

3829-MACH, EQUIP EX ELECT NEC

  0.12

  0.10

  0.13

 

 

3831-ELEC IND MACH + APPARATUS

  0.11

  0.06

  0.24

 

 

3832-RADIO, TELE, COMM EQP, APPAR

  0.13

  0.11

  0.08

 

 

3833-ELEC APPLNCS + HOUSEWARES

  0.11

–0.09

  0.28

 

 

3839-ELEC APPAR + SUPPLIES NEC

  0.16

  0.02

  0.14

 

 

3841-SHIPBUILDING + REPAIRING

  0.11

–0.14

  0.25

 

 

3842-RAILROAD EQUIPMENT

  0.08

  0.01

  0.33

 

 

3843-MOTOR VEHICLES

  0.10

  0.17

  0.31

 

 

3844-MOTOR CYCLES + BICYCLES

  0.14

  0.10

  0.22

Stagnant

Stagnant

3845-AIRCRAFT

  0.02

  0.10

  0.24

 

 

3849-TRANSPORT EQUIPMENT NEC

  0.24

  0.25

  0.01

 

 

3851-PROF, SCIEN, MSRG, CNTRL EQU

  0.05

  0.08

  0.20

 

 

3852-PROF, SC, MSRG, CONT EQU NEC

  0.02

  0.26

  0.46

 

 

3853-WATCHES + CLOCKS

  0.21

  0.13

  0.36

 

 

3901-JEWELRY + RELATED ARTICLES

–0.19

  0.53

–0.30

Winner

Stagnant

3902-MUSICAL INSTRUMENTS

–0.39

  0.35

  0.02

 

 

3903-SPORTING + ATHLETIC GOODS

  0.04

  0.04

  0.20

Loser

Stagnant

3909-MANUF INDUSTRIES NEC

–0.03

  0.12

  0.21

 

 

Correlation with change in RCA

–0.253

  0.072

  0.006

 

 

Notes

a Some years are not available and the average has been adjusted for missing data.

N/A: data not available.

domestic costs of the destination country and that of other competitors in the same sector.

Alternate measures of trade patterns

Hitherto, our analysis has been based on an implicit assumption that trade specialisation is based on comparative advantage emanating from perfectly competitive domestic and international markets. In reality however, markets, both in India and abroad, would generally be characterised by product differentiation and economies of scale. We look at two measures of competitiveness that incorporate such assumptions.

Measures of intra-industry trade12

If a significant proportion of the industrial sector is characterised by imperfect competition, measures of intra-industry trade may indicate the extent of product differentiation and the presence of economies of scale in a particular industry. Furthermore, with trade reforms, one would expect an increase in the share of intra-industry trade in total industry trade as firms specialise in the production of certain products and not in others within an industry group (Helpman and Krugman 1989). As is clear from Figure 4.6, there has been a significant increase in aggregate intra-industry trade in the Indian manufacturing sector since the mid-1980s. Interestingly, one notes a slight downturn in total intra-industry trade in the mid-1990s. Measures of intra-industry trade by industry13 show that the industries with the highest share of intra-industry trade in total trade (0.8 and above) are ISIC 3215 (cordage, rope and twine industries), 3311 (sawmills, plying mills), 3312 (wooden and cane containers), 3521 (paints, varnishes, lacquers), 3620 (glass and

Image

Figure 4.6 Aggregate intra-industry trade.

glass products), 3691 (structural clay products), 3710 (iron and steel basic industries), 3819 (fabricated metal products), 3831 (electrical industrial machinery), 3833 (electrical apparatus and supplies) and 3843 (motor vehicles).14 It is an open issue, however, to what extent the high volumes of intra-industry trade evident in these industries are due to the existence of scale economies and differentiated products or due to industry classifications that are not comprehensive enough (see Loertscher and Wolter 1980).

Technological complexity of exports

A classification of India's manufactured exports by technological complexity indicates that India's manufactured exports are very much at the low end of the 'technology spectrum' (Table 4.7).15 Labour-intensive and resource-based products are the two dominant categories in India's manufacturing export basket. There has been some increase in the total share of scale-intensive, differentiated and science-based products in India's manufacturing exports from 18.1 per cent in 1980 to 23.2 in 1995. Nonetheless, it is far below that of China (38.4 per cent), Malaysia (79 per cent) and Thailand (53.6 per cent).16 A closer look at the 'winners' in either of the two subperiods, 1985–91 and 1992–6, shows that these are either labour-intensive, resource-intensive or scale-intensive products.

The comparison with China is particularly revealing. As of 1995, 9.7 per cent and 16.3 per cent of China's manufactured exports were in science-based goods and differentiated products, respectively, as compared to 5 per cent and 4.1 per cent for India. Differentiated products are technology-intensive engineering products while science-based products use leading-edge technologies (Lall 1998). Both these types of goods could be classified as 'high technology'. While China and India are both large labour-surplus economies with comparative advantage in labour-intensive manufactures, China is also diversifying into the low-medium technology end of export-oriented activity, with India doing poorly in this area. Clearly, the relatively slow progress in 'climbing up the technology ladder' with respect to exports may act as a constraint on India's long-term export performance and growth potential.

The evidence presented in this section does not allow for an unequivocal interpretation of the role of price factors in determining India's external competitiveness.

Table 4.7 Distribution of manufactured exports by technological complexity (%)

Category

1980

1995

Resource based

26.5

31.4

Labour intensive

55.4

45.3

Scale intensive

11.2

13.5

Differentiated

  4.1

  4.7

Science based

  2.8

  5.0

Source: Lall (1998).

While at the aggregate level, the real exchange rate and unit labour costs seem to have a definite link with external competitiveness in the Indian context, the picture is far less clear at the sectoral level. This may indicate the importance of firm-level and industry-level non-price factors that may impinge on export performance. We explore in the next section one important determinant of competitiveness at the firm level, namely the availability of external finance. This factor acquires greater significance in the context of the wide-ranging reforms in the Indian financial sector since 1991.

3. Competitiveness and finance

There is widespread agreement in the literature that price competitiveness is a necessary but not a sufficient condition for export success. Among the non-price factors, the ones most commonly identified in Indian policy discussions are technology upgradation, product quality and infrastructural bottlenecks. One non-price factor that has received less attention, however, is the financial environment, i.e. the extent to which the financial sector provides an enabling environment for successful export performance. In the context of this study, an important question that arises is whether there has been any relationship between export performance of firms and financial factors in the Indian context. This question can be framed in two parts. First, is there a systematic relationship between export performance and the financing patterns? Here, we classify firms in certain selected industries into three categories, namely 'domestic firms', 'winning exporters' and 'losing exporters'. For each of these categories, we study the Sources and Uses of Fund Statements as well as a few other financial performance indicators to look for differences in their financing patterns and financial performance.

Second, do successful exporters face less information-based capital market imperfections than the not-so-successful exporters? Modern theories of finance which attempt to explain differences in financing patterns across firms emphasise differences in costs associated with different providers of funds. It stresses the lack of substitutability between internal sources (retained profits and depreciation) and external sources (different types of debt and new equity) of funds. This imperfect substitutability arises primarily due to asymmetric information between the suppliers and users of funds and incentive problems between managers and owners of the firm. It has generally been argued that these information asymmetries and incentive problems make external funds more costly than internal funds. In the new equity markets, this manifests as a 'lemons premia' (as pointed out by Myers and Majluf 1984) and in credit markets as credit rationing or loan mis-pricing (as pointed out by Stiglitz and Weiss 1981, and others). Further, this view contends that the cost differential between internal and external funds would vary across firms depending upon the extent of the information asymmetry. Besides, this view also suggests that simple transaction costs might also vary across firms. The implication of a higher cost of external funds is that internal funds would be more important than external funds in financing investments. Clearly, to the extent that a firm is forced to depend on internal sources for investment, its growth is said to be finance constrained.

If exporting firms are finance constrained then this would be a major impediment to sustaining their competitiveness in international markets.

In what follows, we explore this hypothesis by estimating investment functions which explicitly allow for the presence of finance constraints, i.e. models that allow the costs of internal and external sources of finance to be different (see Hubbard (1997) and, in the Indian context, see Athey and Laumas (1994)).

Classification of firms

Classification of firms into the above three categories proceeds as follows: firms are first categorised as 'domestic firms' and 'exporting firms' based on the share of exports in their total sales. If the exports to sales ratio of a firm exceeds 5 per cent over more than half the number of years in the sample period, then the firm is considered to be an 'exporting firm' whereas it is a 'domestic firm' otherwise. The reasoning behind this first level of categorisation is that there exist a large number of firms even in the tradable sector (be they winner or loser industries) who primarily sell only in the domestic market.17 Issues such as export competitiveness obviously are of little relevance to these 'domestic firms'. 'Exporting firms' are then further classified into 'winning exporters' and 'losing exporters' based on a comparison of the annual growth rates of their exports vis-à-vis the annual growth rate of exports for the industry to which they belong. If the growth rate of exports of a firm exceeds the industry export growth rate for more than half the number of years in the sample period, then the firm is classified as a 'winning exporter'; otherwise it is considered a 'losing exporter'. It may be noted here that this way of classifying exporting firms into winners and losers is largely consistent with the procedure adopted earlier for classifying industries.18

Furthermore, this procedure allows for the possible existence of winning exporters within a losing industry and vice versa. Consider, for example, a losing industry (the analogy runs similarly for winning industries also), i.e. an Indian industry whose exports are growing but whose RCA is falling over time.19 The growth rate of exports of some firms in this industry may be higher than the industry average. We consider such a firm to be a winner firm as it has outperformed the industry. This indicates that there could be some firm-specific characteristics (unobserved as yet) that enable such firms to outperform the industry. Similarly, a losing firm is one which has not been able to match the industry performance in terms of export growth, again perhaps due to certain firm-specific characteristics. We feel that it may be important to distinguish these two types of firms.

This analysis is done for firms belonging to five industries. Earlier, we have identified 'winner' and 'loser' industries based on whether their RCAs have been increasing or falling over time, respectively. Three winner industries from that classification, namely ISIC 3220 (manufactured wearing apparels excluding footwear), ISIC 3511 (basic industrial chemicals excluding fertiliser) and ISIC 3901 (jewellery and related articles), and two loser industries, ISIC 3211 (spinning, weaving, finished textiles) and ISIC 3839 (electrical apparatus and supplies NEC), have been selected for this analysis. The average share of these industries in total exports over the period 1991–6 was 19.7 per cent, 5.3 per cent, 12.1 per cent, 11.9 per cent and 0.5 per cent, respectively (see Table 4.3).

The database used is PROWESS provided by CMIE, Mumbai. The PROWESS names for the above industries are readymade garments, industrial chemicals, gems and jewellery, cotton textiles and electrical machinery, respectively. It must be noted that the mapping from ISIC to PROWESS may not be perfect. Only those firms for which data are available for all the years of the sample period are considered for the analysis here. Table 4.8 reports the number of firms in the balanced panel for the three categories for the sample period, 1993–7.

Descriptive statistics on financial variables

The indicators of the financial performance of firms used here are assets, export–sales ratio and profitability ratio (profit before interest, depreciation and taxes to sales). Movements in these indicators over the time period 1993–7 are studied by pooling firms across industries within each category. We present in Table 4.9 the average values of these variables. It may be noted that these summary statistics for the 'exporters' reported in these tables are estimated over winning and losing exporters combined. The following broad conclusions emerge:

1 The average size and the rate of growth in assets are found to be the lowest for domestic firms. Among exporters, winning exporters outperform the losing exporters in both average asset size and growth.

2 The export to sales ratio, which was similar for both winning exporters and losing exporters at the beginning of the period, grew for the former while it fell for the latter.

3 Winners and domestic firms, on an average, have been more profitable than the losers. Moreover, this ratio has been more or less stable over the years for all three groups.

4 We also found that the correlations between assets (i.e. firm size), profitability and the export to sales ratio were all insignificant.20

The above patterns must, however, be interpreted with caution as all these three variables show substantial variation across firms within the groups for each year.

Table 4.8 Sample size (sample period: 1993–7)

 

Domestic

Winner

Loser

Total

Ready-made garments

    0

  4

    3

    7

Cotton textiles (cloth)

  13

12

  10

  35

Electrical machinery

  58

19

    9

  86

Industrial chemicals

  44

15

  11

  70

Gems and jewellery

    0

  2

    4

    6

Total

115

52

  37

204

Note: The number of exporting firms equals the sum of winners and losers.

Table 4.9 Firm characteristics

Average over firms

Firm type

1993

1994

1995

1996

1997

Assets

Domestic

  70.56

  93.64

121.54

151.38

183.38

 

Winners

100.68

132.79

186.08

235.81

264.39

 

Losers

  73.78

  99.22

131.38

156.97

169.08

 

Exporters

  89.50

118.84

163.34

203.04

224.77

Export to sales ratio

Domestic

   0.01

   0.01

   0.01

   0.01

   0.01

 

Winners

   0.25

   0.27

   0.29

   0.31

   0.32

 

Losers

   0.26

   0.28

   0.26

   0.24

   0.23

 

Exporters

   0.26

   0.28

   0.28

   0.28

   0.28

Profitability ratio

Domestic

   0.16

   0.16

   0.17

   0.17

   0.06

 

Winners

   0.16

   0.17

   0.13

   0.17

   0.15

 

Losers

   0.15

   0.14

   0.15

   0.15

   0.12

 

Exporters

   0.15

   0.16

   0.14

   0.16

   0.14

Source: Firm-level data are from PROWESS, CMIE, Mumbai. The aggregates reported are based on the authors' calculations.

With the exception of profitability, the other two variables have coefficients of variation greater than 1.0.

We now examine the sources and uses of funds to see if there are differences in the financing pattern of firms in these three categories. The sources and uses of funds statements for the domestic firms, and all exporters (winners and losers) are reported in Table 4.10. These statements for winning exporters and losing exporters are reported in Table 4.11. The following broad patterns emerge:

1 In 1993, the average amount of funds raised and used was more or less identical across the three groups. Over time, however, winning exporters on an average have been able to raise more funds from various sources than losing exporters and domestic firms.

2 Across all the three groups and over the entire period, external sources are the most important, accounting for more than 60 per cent of the funds raised.21 For domestic firms, the importance of internal sources has risen by over 10 percentage points. For winning exporters, the importance of internal sources has fallen over time by around 5 percentage points. For losing exporters, no clear pattern is found in the share of internal sources.

3 Within external sources, funds raised through capital markets have been most significant for the winning exporters.

4 On an average, borrowings have been more important for domestic firms and losing exporters than for winning exporters.

5 The share of gross fixed assets in the uses of funds has risen substantially for the winning exporters. There seems to be no discernible trend for losing exporters and for domestic firms.

The broad conclusions above seem to suggest that exporters as a group are likely to be less financially constrained than domestic firms. Further, it is likely that winning

Table 4.10 Sources and uses of funds – domestic and exporting firms

 

Domestic firms

Exporting firms

 

1993

1994

1995

1996

1997

1993

1994

1995

1996

1997

Sources of funds

 

 

 

 

 

 

 

 

 

 

Internal sources

     23.9

     26.1

     34.7

     39.1

     32.0

     29.9

     25.1

     22.7

     27.1

     33.1

Retained profits

     10.2

     20.5

     22.2

     27.1

     15.8

     14.8

     15.2

     14.8

     16.9

       7.7

Depreciation

     13.7

       5.6

     12.6

     12.0

     16.2

     15.1

       9.9

       7.9

     10.2

     25.4

External sources

     76.1

     73.9

     65.3

     60.9

     68.0

     70.1

     74.9

     77.3

     72.9

     67.0

Capital markets

     34.4

     40.9

     30.4

       7.0

     14.0

     39.0

     47.3

     41.1

     12.7

     27.9

Fresh capital (excl. Bonus issue)

       5.4

       9.4

       4.3

       2.9

       3.1

       6.7

       7.0

       6.2

       2.4

       1.9

Share premium

       6.1

     16.9

     31.8

       8.4

       3.2

     20.5

     33.1

     27.9

       8.7

     10.3

Debentures/bonds

     23.1

     14.0

     –5.8

     –4.2

       7.2

     11.4

       5.3

       5.1

       1.6

     15.3

Fixed deposits

     –0.2

       0.6

       0.2

     –0.2

       0.5

       0.4

       2.0

       1.9

       0.0

       0.5

Borrowings

     29.9

     16.7

     23.3

     32.8

     42.7

     27.4

     11.2

     18.5

     43.3

     20.9

Bank borrowings

       9.5

     –0.8

     17.2

     13.1

     18.0

     15.1

       2.4

     11.1

     31.0

     –4.4

Financial institutions

     16.7

     13.1

       5.2

       5.2

       8.5

     10.9

       2.4

       7.0

       8.4

     16.5

Loans from corporate bodies

       0.5

     –0.3

       2.6

     –0.9

       3.4

       2.1

     –0.3

       0.9

     –0.2

       2.6

Other borrowings

       3.2

       4.7

     –1.6

     15.4

     12.8

     –0.7

       6.7

     –0.6

       4.1

       6.3

Current liabilities and provisions

     11.8

     16.4

     11.6

     21.2

     11.3

       3.7

     16.4

     17.7

     16.8

     18.1

Sundry creditors

       6.0

     12.0

       6.3

     15.3

       9.1

       8.7

     11.8

     11.8

     12.1

     13.7

Uses of funds

 

 

 

 

 

 

 

 

 

 

Gross fixed assets

     62.4

     55.0

     40.1

     56.2

     50.7

     45.8

     38.0

     37.9

     53.6

     74.6

Work in progress

     36.4

     17.0

   –20.0

     12.4

     16.2

       2.0

       1.9

     10.4

       8.9

     13.5

Investments

       3.8

     15.2

       8.2

       0.0

       0.5

       4.1

     17.5

     17.2

       1.4

       1.6

Current assets

     33.8

     29.8

     51.7

     43.9

     48.8

     50.1

     44.6

     44.9

     45.1

     23.8

Inventories

       7.4

       2.3

     14.0

     11.2

       9.2

     18.4

     11.6

     13.0

     13.1

       4.4

Debtors

     17.7

     13.9

     23.7

     25.8

     21.2

     23.0

     16.2

     13.7

     20.7

       5.7

Cash and bank balances

      0.9

       1.1

       1.0

       2.1

       6.8

     –6.7

       2.8

       5.9

     –1.1

       7.2

Total sources/uses of funds

2,137.3

2,779.7

3,343.4

3,810.4

4,053.1

1,710.5

2,875.9

4,300.6

3,892.6

2,337.3

Total sources/uses of funds-average

     18.6

     24.2

     29.1

     33.1

     35.2

     19.2

     32.3

     48.3

     43.7

     26.3

Total sources/uses of funds-standard deviation

     66.2

     96.0

     66.8

     77.7

     110.0

     52.2

     83.0

     100.0

     86.0

     61.0

Total sources/uses of funds-minimum

   –23.6

     –5.5

     –8.5

     –7.7

     –29.7

   –79.2

     –7.0

     –6.3

     –2.1

   –86.4

Total sources/uses of funds-maximum

   591.3

   745.2

   451.1

   538.5

     844.8

   378.4

   550.2

   611.1

   480.9

   429.8

No. of companies in panel

   115

   115

   115

   115

   115

    89

    89

    89

    89

    89

Source: Firm-level data are from PROWESS, CMIE, Mumbai. The aggregates reported are based on authors' calculations.

Note: Individual items of sources and uses of funds are reported as percentages of the total while the rest are in Rs Crores.

Table 4.11 Sources and uses of funds – winning and losing exporters

 

Winner exporting firms

Loser exporting firms

 

1993

1994

1995

1996

1997

1993

1994

1995

1996

1997

Source of funds

 

 

 

 

 

 

 

 

 

 

Internal sources

   29.6

     27.3

     21.5

     24.9

     23.0

     30.3

     21.2

     25.5

     32.8

   70.6

Retained profits

   15.1

     18.6

     14.0

     15.8

      5.8

     14.5

      9.3

     16.7

     19.6

   14.9

Depreciation

   14.6

      8.7

      7.5

      9.1

     17.3

     15.8

     12.0

      8.8

     13.2

   55.7

External sources

   70.4

     72.7

     78.5

     75.1

     77.0

     69.7

     78.8

     74.5

     67.2

   29.4

Capital markets

   58.0

     47.2

     45.0

     13.6

     36.3

     12.8

     47.5

     32.4

     10.4

   –3.4

Fresh capital (excl. bonus issue)

     9.1

       6.3

       6.5

       3.0

       2.8

       3.3

       8.1

       5.6

       1.1

   –1.3

Share premium

   31.5

     37.3

     31.4

      8.0

     14.8

       5.3

     25.8

     20.0

     10.6

   –6.7

Debentures/bonds

   17.1

       2.1

       4.4

       2.7

     17.8

       3.5

     10.9

       6.6

     –1.3

    5.9

Fixed deposits

     0.2

       1.5

       2.7

       0.0

       1.0

       0.6

       2.8

       0.2

       0.0

   –1.3

Borrowings

   21.4

       7.9

     15.1

     43.6

     29.1

     35.8

     16.9

     26.1

     42.6

   –9.9

Bank borrowings

   13.1

       0.3

     12.6

     29.7

       5.2

     17.9

       6.2

       7.9

     34.3

 –40.5

Financial institutions

     8.7

       3.1

       3.6

     12.1

     23.8

     14.0

       1.0

     14.8

     –0.9

 –10.8

Loans from corporate bodies

     2.0

     –1.3

       0.6

     –0.1

       0.3

       2.3

       1.4

       1.6

     –0.4

   11.0

Other borrowings

   –2.4

       5.8

     –1.6

       2.0

     –0.2

       1.6

       8.3

       1.7

       9.6

   30.4

Current liabilities and provisions

   –9.0

     17.5

     18.4

     17.8

     11.6

     21.2

     14.3

     16.0

     14.2

   42.6

Sundry creditors

     2.5

     11.2

     12.1

     13.0

       5.9

     17.2

     12.8

     11.0

     10.0

   43.1

Uses of funds

 

 

 

 

 

 

 

 

 

 

Gross fixed assets

  42.3

     36.9

     40.9

     60.3

     75.1

     50.7

     39.8

     31.1

     36.1

  72.7

Work in progress

  –6.7

       7.6

     15.7

     10.7

     12.5

     13.8

     –8.0

     –1.9

       4.3

  16.9

Investments

    6.1

     13.6

       9.8

     –1.9

       4.6

       1.3

     24.2

     34.0

       9.8

  –9.6

Current assets

  51.6

     49.5

     49.3

     41.6

     20.3

     48.1

     36.1

     34.9

     54.0

  36.9

Inventories

  19.2

     12.5

     11.3

     14.2

       6.0

     17.3

     10.2

     16.8

     10.1

  –1.5

Debtors

  25.2

     17.5

     15.9

     18.5

      6.8

     20.0

     13.9

      8.6

     26.2

    1.4

Cash and bank balances

–13.5

      3.5

      7.6

   –2.1

      4.7

       2.7

      1.7

      1.9

      1.5

  16.2

Total sources/uses of funds

991.3

1,823.4

2,988.8

2,804.9

1,844.5

    719.1

1,052.5

1,311.8

1,087.7

492.8

Total sources/uses of

  19.1

    35.1

    57.5

    53.9

    35.5

    19.4

    28.5

    35.5

    29.4

  13.3

funds – average

 

 

 

 

 

 

 

 

 

 

Total sources/uses of

  57.0

    81.1

  104.1

    96.4

    72.2

    45.6

    86.7

    93.8

    67.5

  37.7

funds – standard deviation

 

 

 

 

 

 

 

 

 

 

Total sources/uses of

–79.2

    –0.2

    –0.6

    –0.1

  –81.6

    –9.5

    –7.0

    –6.3

    –2.1

–86.4

funds – minimum

 

 

 

 

 

 

 

 

 

 

Total sources/uses of

378.4

  550.2

  611.1

  480.9

  429.8

  184.9

  393.8

  534.4

  369.1

175.9

funds – maximum

 

 

 

 

 

 

 

 

 

 

No. of companies in panel

  52

   52

   52

   52

   52

   37

   37

   37

   37

  37

Source: Firm-level data are from PROWESS, CMIE, Mumbai. The aggregates reported are based on the authors' calculations.

Note: Individual items of sources and uses of funds are reported as percentages of the total while the rest are in Rs crores.

exporters are less financially constrained than losing exporters. In the next section we attempt to estimate investment functions separately for these categories to test the extent of financial constraints that firms in various categories face.

Investment function

Specification22

In the empirical literature on firms' investment behaviour, two sets of hypotheses relating to finance constraints are usually tested. First, the presence of a finance constraint is explored using the specification for a panel of firms in eqn (4.1):

Image

where I is investment, K is capital stock, Q is an estimate of Tobin's q, IS is internal sources of funds, ε is the error term, i is the firm subscript and t is the time subscript. If in the above specification the estimated coefficient c turned out to be positive and significant, it is taken as evidence in favour of the finance constraint hypothesis. Sometimes, besides IS, a variable measuring leverage is also added to the above specification.

The second hypothesis explored in this literature is the so-called 'excess sensitivity hypothesis', which states that the degree of finance constraints varies across firms of different characteristics representing inter-firm differences in information costs. In order to test the excess sensitivity hypothesis, firms are grouped into 'high information cost' and 'low information cost' categories based on some a priori criteria (such as firm size). A higher value for the estimated coefficient c for the 'high information cost' group points to the excess sensitivity of this group to financing constraints.

Empirical work within this framework in the developing country context is sparse. In the few studies available, Tobin's q is replaced by a traditional sales-accelerator model of investment. In this approach, fluctuations in output/sales motivate capital spending. To such a model, cash flow and leverage ratios are added to capture finance constraints. A typical specification is as follows in eqn (4.2) (see Harris et al. 1994):

Image

I is investment, K is capital stock, S is sales, IS is internal sources of funds, D is debt, ν is error term, λ is the time-invariant firm-specific effect, η is a common time effect, ε is the idiosyncratic component of the error term, i is firm subscript and t is time subscript. Positive and significant estimates of α2 indicate the presence of finance constraints. Tests of the excess sensitivity hypothesis can be done as described earlier. The coefficient α3 reflects the premium above the safe rate that must be paid as the debt to capital ratio increases and it may vary across groups of firms.

In this study, we intend to estimate an investment function such as eqn (4.2) to test for the presence of finance constraints. Earlier, we had seen that the share of external finance in the total sources of funds is larger for exporters than for domestic firms. Moreover, external funds raised through capital markets as a percentage of total external funds are higher for exporting firms. The relative success of exporters in raising funds through capital markets possibly suggests that these firms might belong to the low information cost category while the domestic firms might belong to the high information cost category. From the perspective of the suppliers of funds, the quality of investment projects is likely to be superior for exporters who have a proven record in international markets – given that international markets are perceived to be highly competitive. The flip side to this is that success (or mere continued presence) in domestic markets, which were largely protected in the pre-1991 regime, is not sufficient assurance that the firm will remain successful in the increasingly competitive environment that is evolving in the domestic product markets since 1991. This suggests that finance constraints are likely to be more severe for domestic firms than for exporters (excess sensitivity hypothesis). In an exactly analogous way, even among exporters, losers are likely to face more severe finance constraints than winners (winners and losers as we defined above). In what follows, we attempt to empirically test the above propositions.

It may be noted here that the criteria we have used to classify firms into high and low information cost categories are, to our knowledge, unlike any used hitherto in the literature. Traditionally, a common criterion used to distinguish firms into high and low information cost has been firm size (usually measured in terms of net fixed assets). We also attempt to evaluate if the above-mentioned excess sensitivity hypothesis (exporters versus domestic firms) holds after controlling for firm size.

Prior to 1991, the Indian Government strictly controlled the creation of new firms and the expansion of existing firms through a rigid licensing regime in accordance with plan priorities. The plans had both industry-specific real capacity targets and a financial plan to ensure the realisation of these targets. Control was exercised on the financial side by public ownership of financial institutions providing long-term loans to the private corporate sector. The government provided subsidised credit to these financial institutions, which were in turn directed to the private corporate sector at a fixed rate of interest implying that these institutions had a limited screening role to perform. Private corporate firms faced severe restrictions on the pricing, quantum and timing of new issues and the government also forced certain industry-specific debt/equity ratio norms on firms, leaving little leeway for firms to choose their capital structure.

In such a scenario, de facto, finance did not matter for investment and the traditional finance literature that focuses on informational asymmetries and agency costs faced by suppliers of funds can be argued to be of little relevance for the pre-1991 period. Furthermore, during the period 1991–3, rapid changes were taking place in the financial sector so we chose to exclude these years from our analysis. We, therefore, estimate the investment function for the period 1993–7.

Empirical results

The investment functions are estimated using the pooled data, namely for five years (1993–7) across 204 firms (total 1,020 observations). For the construction of the dependent variable (I/K) and explanatory variables (ΔS/K, IS/K and D/K), we need a measure of the real capital stock. We estimate the beginning of the period capital stock from book value using a method similar to that of Athey and Laumas (1994). The following assumptions are made:

1 All the firm's capital has an identical useful life Li.

2 The firm's initial end of period capital stock equals the book value of net fixed assets in current rupees.

3 Firms use the straight line method of depreciation and actual depreciation is exponential with depreciation 1/Li.

4 All investments are made at the beginning of the year and all depreciation is subtracted at the end of the year.

We estimate the beginning of the period's capital stock by eqn (4.3):

Image

where P is the wholesale price index of capital goods.

Besides the above three explanatory variables, we also construct various dummy variables to represent firms according to different categories such as domestics, winners, losers, etc. Table 4.12 lists the variable notations used and also their definitions.

Finance constraints – Overall sample

The investment function – eqn (4.2) – for the entire sample (i.e. no distinction is made between domestics, exporters, etc.) is estimated using panel data techniques (a) in levels allowing for both firm and time effects, and (b) in first differences allowing only for time effects. Time effects were found to be insignificant in both cases whereas firm-specific effects were found to be significant in the levels. Table 4.13 reports the GLS estimates for the levels regression and OLS estimates for estimation in first differences. The positive and significant coefficient for IS/K shows that for the entire sample financial constraints are important in explaining investment behaviour.

Table 4.12 Variables and their definition

Notation

Definition

ΔS/K

Change in sales as a ratio of real capital stock

IS/K

Internal sources as a ratio of real capital stock

D/K

Long-term debt as a ratio of real capital stock

EDUMMY

Dummy variable: one for exporting firm, zero otherwise

EIS/K

EDUMMY * IS/K

ED/K

EDUMMY * D/K

WDUMMY

Dummy variable: one for winning exporter firm, zero otherwise

WIS/K

WDUMMY * IS/K

WD/K

WDUMMY * D/K

LDUMMY

Dummy variable; one for losing exporter firm, zero otherwise

LIS/K

LDUMMY * IS/K

LD/K

LDUMMY * D/K

SF

Dummy variable: one for small firms (NFA < Rs 25 crore)

LF

Dummy variable: one for large firms (NFA ≥ Rs 25 crore)

SFIS/K

SF * IS/K

SFD/K

SF * D/K

SFD

SF * DDUMMY

SFE

SF * EDUMMY

LFE

LF * EDUMMY

SFDIS/K

SFD * IS/K

SFEIS/K

SFE * IS/K

LFEIS/K

LFE * IS/K

SFDD/K

SFD * D/K

SFED/K

SFE * D/K

LFED/K

LFE * D/K

WW

Dummy variable: one for winner firm in winning industry, zero otherwise

LW

Dummy variable: one for loser firm in winning industry, zero otherwise

WL

Dummy variable: one for winner firm in losing industry, zero otherwise

LL

Dummy variable: one for loser firm in losing industry, zero otherwise

WWIS/K

WW * IS/K

LWIS/K

LW * IS/K

WLIS/K

WL * IS/K

LLIS/K

LL * IS/K

WWD/K

WW * D/K

LWD/K

LW * D/K

WLD/K

WL * D/K

LLD/K

LL * D/K

Exporting firms versus domestic firms

To test the excess sensitivity hypothesis between exporting and domestic firms, dummy variables are introduced into the regression for both the intercept and the slope coefficients attached to IS/K and D/K. The dummy variable takes the value one for exporting firms and zero for domestic firms. The estimation is carried out using OLS without allowing for any firm-specific effect or time effect.23

The regression results in both levels and in first differences are reported in Table 4.14. It is seen that the slope dummy attached to IS/K is negative and significant in both the levels and first-difference regressions. This suggests that finance the constraint is less binding for the exporting firms than for domestic firms upholding the excess sensitivity hypothesis. We, therefore, re-estimate the investment function separately for the domestic firms and for exporting firms (Table 4.14). These show that the finance constraint is unambiguously binding for domestic firms. For exporting firms the coefficient of IS/K is clearly lower than that for the domestic firms in both the levels and first-difference regressions. There is, however, some ambiguity as to the significance of the coefficient between the levels and first-difference regressions.

Table 4.13 Investment function estimates – all firms

 

Levelsa

First differenceb

Constant

0.0843

  (2.82)c

–0.0113

(–0.85)

ΔS/K

0.0133

  (2.90)

  0.0100

  (2.19)

IS/K

0.5406

(37.85)

  0.6417

 (40.20)

D/K

0.0973

  (3.66)

–0.2106

(–6.33)

D-o-F

813

608

Notes

a In levels, estimation is using GLS allowing for firm-specific effects.

b In first-differences, estimation is using OLS.

c t-values are reported in parentheses.

 

Table 4.14 Investment function estimates – exporting firms versus domestic firms

 

Levels

First difference

 

All firms

Domestic firms

Exporting firms

All firms

Domestic firms

Exporting firms

Constant

  0.0902

  0.0898

  0.1943

 –0.0235

 –0.0218

 –0.0310

 

(8.17)

(7.05)

(17.80)

(–1.49)

(–1.19)

(–2.34)

EDUMMY

0.1029

 

 

–0.0065

 

 

 

 

(5.55)

 

 

(–0.27)

 

 

ΔS/K

0.0143

0.0361

–0.0002

   0.0095

   0.0232

   0.0016

 

(3.18)

(4.39)

(–0.04)

   (2.31)

   (2.92)

   (0.43)

IS/K

  0.5169

  0.4577

   0.0718

  0.6686

  0.6299

 –0.0345

 

(36.15)

(19.15)

(3.03)

(45.15)

(25.41)

(–0.78)

EIS/K

–0.4609

 

 

–0.7280

 

 

 

(–13.8)

 

 

(–12.2)

 

 

D/K

0.1723

0.2241

0.0490

–0.2929

–0.2556

0.1024

 

(6.65)

(6.68)

(1.13)

(–9.34)

(–6.33)

(1.37)

ED/K

0.1253

 

 

0.3910

 

 

 

(–1.94)

 

 

(3.60)

 

 

D-o-F

1,013

571

441

605

341

263

Note: Estimation is using OLS. t-values are reported in parentheses.

Small versus large firms

As indicated earlier, firm size has often been used as a criterion to classify firms into high information cost and low information cost categories. From our point of view, it is important to ensure that the excess sensitivity hypothesis between domestic firms and exporters continues to hold after controlling for firm size. Towards this end, we first define a firm to be a small firm if its net fixed assets are less than Rs 25 crore (i.e. Rs 250 million).24 Dummy variables for small firms and large firms are accordingly defined. Investment functions incorporating size effects are estimated first without distinguishing exporters and domestic firms (Table 4.15) and next by making this distinction (Table 4.16).

From Table 4.15 we find that the slope dummy for small firms attached to IS/K is negative and significant (in both levels and first differences), indicating that finance constraints are less important for small firms than for large firms – a somewhat counter-intuitive result. A similar conclusion was arrived at by Athey and Laumas (1994), who attribute it to the government's policies that favoured small firms.

We now turn to the question of whether the excess sensitivity hypothesis between exporters and domestic firms holds after controlling for size. We concentrate on the slope dummies attached to IS/K which correspond to small domestic firms, small exporting firms and large exporting firms. From the results reported in Table 4.16, we see that these slope dummies are negative and significant (in both levels and in first differences). Moreover, the results suggest that the finance constraint is less severe for (a) small exporters than for small domestic firms, and (b) large exporters than for large domestic firms.25 Thus, the excess sensitivity of domestic firms to finance constraints over exporting firms holds well across firms of similar size.

Winning and losing exporters

A similar approach as above is adopted to test the excess sensitivity hypothesis between winning exporters and losing exporters. Two sets of dummy variables are used to distinguish the winners and losers vis-à-vis the domestic firms. As above, the dummy variables are used in both the intercept and slope terms. Table 4.17

Table 4.15 Investment function estimates – small versus large firms

 

Levels

First difference

Constant

  0.1260

   (10.16)

–0.0204

  (–1.22)

SF

  0.0439

    (2.55)

–0.0044

  (–0.19)

ΔS/K

  0.0060

    (1.38)

  0.0053

    (1.35)

IS/K

  0.5466

  (39.21)

  0.6864

  (48.48)

SFIS/K

–0.4728

(–17.07)

–0.7383

(–15.15)

D/K

  0.1303

   (5.21)

–0.3235

(–10.79)

SFD/K

–0.1181

 (–1.90)

  0.4145

   (3.91)

D-o-F

1,013

605

Note: Estimation is using OLS. t-values are reported in parentheses.

Table 4.16 Investment function estimates – exporting firms versus domestic firms and small versus large firms

 

Levels

First difference

Constant

  0.1021

    (5.98)

–0.0302

  (–1.31)

SFD

  0.0714

    (3.03)

  0.0013

    (0.04)

SFE

  0.0701

    (2.77)

  0.0062

    (0.18)

LFE

  0.0690

    (2.13)

  0.0035

    (0.11)

ΔS/K

  0.0060

    (1.39)

  0.0052

    (1.32)

IS/K

  0.5465

   (38.69)

  0.6887

  (48.18)

SFDIS/K

–0.4286

  (–9.23)

–0.6815

  (–8.37)

SFEIS/K

–0.4888

(–14.77)

–0.7717

(–12.8)

LFEIS/K

–0.0850

  (–0.62)

–0.4311

  (–2.60)

D/K

  0.1298

    (5.10)

–0.3304

(–10.95)

SFDD/K

–0.4606

   (–2.73)

–0.0128

  (–0.04)

SFED/K

–0.0751

   (–1.14)

  0.4638

    (4.19)

LFED/K

–0.1130

   (–0.76)

  0.2101

    (0.80)

D-o-F

1,007

599

Note: Estimation is using OLS. t-values are reported in parentheses.

 

Table 4.17 Investment function estimates – winning and losing exporters

 

Levels

 

 

First difference

 

All firms

Winners

Losers

All firms

Winners

Losers

Constant

    0.0902

    0.2181

  0.1575

   –0.0235

  –0.414

  –0.0170

 

  (8.19)

  (14.94)

(9.67)

  (–1.49)

 (–2.36)

(–0.86)

WDUMMY

    0.1267

 

 

    –0.0133

 

 

 

  (5.50)

 

 

  (–0.46)

 

 

LDUMMY

    0.0667

 

 

    0.0064

 

 

 

  (2.69)

 

 

   (0.19)

 

 

ΔS/K

    0.0137

  –0.0051

  –0.0003

   0.0095

  –0.0203

   0.0042

 

  (3.06)

(–0.54)

(–0.05)

   (2.31)

(–1.76)

  (1.10)

IS/K

    0.5184

   0.1087

   0.0492

    0.00684

   0.0812

  –0.0557

 

   (36.27)

   (2.88)

  (1.60)

   (45.05)

  (0.74)

(–1.17)

WIS/K

   –0.4391

 

 

   –0.6498

 

 

 

  (–8.98)

 

 

  (–4.43)

 

 

LIS/K

  –0.4797

 

 

   –0.7419

 

 

 

(–11.0)

 

 

(–11.3)

 

 

D/K

    0.1710

0.0016

   0.1122

   –0.2928

   0.1104

   0.1738

 

   (6.61)

(0.03)

  (1.75)

  (–9.30)

  (1.41)

  (0.58)

WD/K

   –0.1727

 

 

    0.3875

 

 

 

   (2.06)

 

 

   (3.46)

 

 

LD/K

   –0.0608

 

 

    0.4629

 

 

 

  (–0.66)

 

 

   (1.09)

 

 

D-o-F

  1,010

  256

181

602

152

107

Note: Estimation is using OLS. t-values are reported in parentheses.

reports the estimation results. The slope dummies attached to IS/K for both winners and losers turn out to be negative and significant as expected. Nonetheless, when the investment function is estimated separately for winners and losers, the coefficient of IS/K turns out to be substantially lower for both these types of firms than for the domestic firms (Table 4.14). In fact, in first differences the coefficient is insignificant for both these categories.

These results should, however, be interpreted with caution. While the excess sensitivity of domestic firms versus the exporters is clearly established, the same cannot be said between winners and losers. Possibly this is due to the fact that sample size is rather small for the winners and losers. It may be pointed out here that while the criteria used to categorise firms into domestics and exporters are rather straightforward, the same cannot be said about the criteria used to categorise exporters into winners and losers. This is because we have essentially compared the annual growth rate of a firm's export with a benchmark growth rate of the relevant industry's exports. One may expect that the growth rate in exports is more volatile for individual firms than for the industry as a whole. Considering that we have only five years of data this could lead to misclassification of some firms as either winners or losers, thus affecting our results.

Another possible reason for not obtaining clear results at the level of winner and loser firms could be that we have not controlled for the fact that a winner/loser firm belongs to a winner/loser industry. We attempt to control for this factor below.

Winner/loser–industry/firms

A priori we would expect that the information cost would be the least for winner firms in winner industries, followed by loser firms in winner industries, winner firms in loser industries, loser firms in loser industries, and finally domestic firms in that order. Accordingly, the severity of finance constraints would increase in the above order.

We define dummy variables that distinguish firms into four categories, namely winner firms in winner industries, winner firms in loser industries, loser firms in winner industries and loser firms in loser industries. Distinction of domestic firms by winner/loser industries is not made here since the focus is on the severity of finance constraints within different categories of exporting firms. Table 4.18 reports the results of this dummy variable regression.

These results indicate that:

1 The severity of finance constraints is highest for domestic firms followed by exporting firms in loser industries and is the least for exporting firms in winner industries (compare the coefficients of WWIS/K and LWIS/K on the one hand with those of WLIS/K and LLIS/K on the other).

2 The above expected progression in the severity of finance constraints is found to hold true for exporting firms within loser industries but not for exporting firms within winner industries (compare the coefficients of WWIS/K with LWIS/K and that of WLIS/K with LLIS/K).

Table 4.18 Investment function estimates – winner/loser–industry/firms

 

Levels

First difference

Constant

  0.0903

    (8.20)

–0.0234

  (–1.49)

WW

  0.1147

    (3.50)

–0.0141

  (–0.34)

LW

  0.0530

    (1.54)

–0.0027

  (–0.06)

WL

  0.1307

    (3.71)

–0.0181

  (–0.53)

LL

  0.0583

    (1.62)

  0.0106

    (0.25)

ΔS/K

  0.0130

    (2.88)

  0.0107

    (2.59)

IS/K

  0.5205

   (36.30)

  0.6652

  (44.99)

WWIS/K

–0.4411

  (–8.35)

–0.6810

  (–3.29)

LWIS/K

–0.5069

(–10.34)

–0.8731

(–11.26)

WLIS/K

–0.4089

  (–2.95)

–0.5808

  (–2.80)

LLIS/K

–0.3125

  (–3.11)

–0.4234

  (–3.52)

D/K

  0.1692

    (6.53)

–0.2897

  (–9.27)

WWD/K

–0.1073

  (–0.94)

  0.5439

    (3.74)

LWD/K

–0.0566

    (0.59)

  0.2794

    (0.54)

WLD/K

–0.2328

  (–1.99)

  0.1614

    (0.97)

LLD/K

  0.0846

    (0.24)

  0.7865

    (1.09)

D-o-F

1004

596

Note: Estimation is using OLS. t-values are reported in parentheses.

Coefficients of ΔS/K and D/K

The coefficient of the accelerator (ΔS/K) is in most of the cases positive and significant in both levels and first-difference regressions as expected. The exceptions are the regressions in first differences for exporters, winners and losers, and in levels for winners and losers. With respect to the coefficient of the leverage term (D/K), however, no clear pattern emerges either on the sign of the coefficient or its significance. This could perhaps be due to the small size of our sample. We may note here that some other studies have also reported similar results (see Hall 1992; Harris et al. 1994).

Examining the financing patterns of firms, we have found that winning exporters have been able to raise more funds from various sources than losing exporters and domestic firms. Furthermore, there is evidence to suggest that while financial constraints are important in explaining investment behaviour for all firms in our sample, they are less binding for exporting firms, particularly those in the winner industries.

4. Stylised facts on micro–macro and trade–finance interactions

Trade specialisation and sustainability of current account

From the policy-maker's perspective, issues such as patterns of trade specialisation and competitiveness are of interest primarily because of their implications for the link between economic growth and current account sustainability. It is well known that the current account balance is influenced both by micro-factors (such as trade specialisation and competitiveness) and macro-factors (mainly the savings–investment gap). In India, in the 1980s and 1990s, macro-factors played a dominant role in determining the current account balance (as has been noted in Section 2). Our findings suggest that with greater amounts of resources flowing into the exporting sectors since the 1991 reforms, poor export performance due to resource constraints is unlikely to be a source of concern for current account sustainability. Other factors, however, such as the lack of adequate infrastructural facilities (roads, ports, power, etc.) may prove to be a generalised constraint on the supply side, which could affect export performance and thus ultimately the trade balance.

Labour costs and trade competitiveness

The non-dynamism of the export sector has long been an issue of policy concern in India. Given India is a labour-surplus country, it has been suggested that trade patterns should follow comparative advantage and that India should specialise in the export of labour-intensive commodities. The 1991 reforms were an attempt to provide an impetus to India's manufacturing exports, especially of the labour-intensive type. There is little evidence, however, of a significant increase in manufacturing exports (labour intensive or otherwise) in the post-1991 period. Moreover, we do not observe any correlation at the sectoral level between unit labour costs and export competitiveness. The lack of importance of price factors in determining competitiveness at the sectoral level could be taken to provide support for the view that non-price factors (such as finance constraints) may be important determinants of competitiveness at the firm and industry levels. Equally, it could also be due to the possibility that, notwithstanding the economic reforms of 1991, there remain severe distortions in the Indian economy, which are far too deep and in extent to enable the country to exploit its labour resources.26

Financial environment and finance constraints

In the restrictive policy environment prior to 1991, financial intermediaries were passive conduits of funds from the government and the banking sector to manufacturing firms. The 1991 reforms have empowered financial intermediaries to play an active role in resource allocation. We have argued that in a regime where resources are allocated according to government directives, the very concept of finance constraints is of little consequence (as all real plans are backed by a financial plan). Finance constraints (caused by informational and agency costs) are relevant when financial intermediaries screen projects. In our empirical work, we have demonstrated that in the new environment financial intermediaries seem to take export performance as an indicator of a firm's competitive strength. Thus, investments by exporters in general and, in particular, among exporting firms in winning industries are not restricted by the availability of internal funds. On the other hand, financial intermediaries seem to view firms that operate primarily in domestic markets as lacking in competitive strength and, consequently, investments by these firms are constrained by the availability of internal funds.

Notes

1 We gratefully acknowledge comments by Mustapha Nabli, José María Fanelli, Ari Kuncoro, Paolo Guerrieri and other participants in the Interim Workshop held at the Philippines Institute for Development Studies, Manila, during 21–23 April 1998, and in the Final Workshop held at the Trade and Industrial Policy Secretariat, Johannesburg, during 30 November–2 December 1998. Usual disclaimers apply.

2 As we shall see in Section 2, there was, in fact, an improvement in India's export performance since the mid-1980s, due in great part to the export-promotion policies followed during this period.

3 There was another mechanism by which the foreign exchange constraint would prove to be binding on domestic demand-driven growth in the Indian context. An increase in aggregate demand would lead to higher food prices and, hence, inflation. Given the aversion of Indian policy-makers to high inflation, this would invariably trigger off deflationary fiscal and monetary policies (as the lack of adequate foreign exchange reserves precluded the possibility of large-scale imports of food).

4 It should be noted that such a periodisation is widely accepted in the literature. For example, see Ahluwalia (1991) and Joshi and Little (1994).

5 Further details of these policies can be found in Ganesh-Kumar et al. (1998).

6 Openness as conventionally defined is the sum of exports and imports as a ratio of GDP.

7 A full discussion of the CMS methodology is available in Kumar, Sen and Vaidya (1999).

8 The definition of manufacturing used here is the SITC-based one and includes all commodities in the SITC categories 5 to 8 excluding 68. It should be pointed out that this definition differs from the definition of manufacturing exports used in Section 2 beginning with 'The real exchange rate and aggregate competitiveness'.

9 It should be noted that the CMS analysis ends in 1992 while the rest of the empirical results in this section are until 1996. The CMS analysis requires data on bilateral trade flows for all commodities and all countries. Such detailed data for the post-1992 period were not readily available at the time of the study.

10 The RCA measure expresses the share of country i's export of product j in total world exports of product j, as a ratio to the share of country i's total exports of manufactures in world total exports of manufactures. An RCA of unity would imply 'normal' export performance of product j relative to the size of country i, as an exporter, while a ratio of 2 would suggest that the product j's share in country i's exports is twice the corresponding world share, and so on. An RCA of more than unity is usually taken as an indicator of competitiveness, while an increase in the RCA supposedly suggests a strengthening of the competitiveness so revealed (see Balassa (1965) for further details).

11 Commodity-wise time series of RCAs are not presented here but will be made available upon request.

12 We use the Grubel and Lloyd (1975) measure of intra-industry trade (IIT), defined as

Image

where Xit is India's exports of commodity i at time t, and Mit is India's imports of commodity i at time t. The variable IITit can be between 0 and 1, with higher values indicating greater intra-industry trade.

13 Details available from the authors upon request.

14 We have excluded ISIC 3909 from the above list as it includes manufacturing industries not elsewhere classified. Therefore, by definition, ISIC 3909 would have a high volume of intra-industry trade.

15 We have used Lall's (1998) classification of the technological complexity of various exports, which is a refinement of OECD (1998).

16 See Lall (1998) for further details.

17 The presence of such domestic firms can be explained primarily in terms of the extremely restrictive trade and industrial policy regime that prevailed prior to 1985.

18 Recall that an Indian industry was classified as winner/loser by comparing its export performance with a world norm for that industry: winner if it outperformed the world norm; loser otherwise.

19 Note that RCA of an industry can rise/fall over time even when the world trade in that industry is falling over time. None of the industries (both winners and losers) chosen below in our analysis fall in such a category. Hence this case is not discussed further.

20 We have not reported the correlations matrix for brevity.

21 The figures for 1997 for both winners and losers seem to be influenced by some extreme values and thus need to be interpreted with caution.

22 We draw upon Hubbard (1997), Athey and Laumas (1994) and Harris et al. (1994) in this discussion.

23 Note that introducing a dummy variable for exporting firms and simultaneously allowing for firm-specific or time effects through relevant dummy variables would result in the equation being collinear. Hence, we ignore firm and time effects.

24 Note that the Government of India defines a small firm as one whose gross fixed asset is less than Rs 1 crore for its priority lending policies. Historically, this limit was much lower.

25 The small size of our sample in each of these four categories prevented us from estimating the investment function separately.

26 Two such distortions that may be relevant here are the lack of an exit policy in labour market and the widespread reservation of products for the small-scale sector.

References

Agrawal, P., Gokarn, S., Mishra, V., Parikh, K. S. and Sen, K. (1995) Economic Restructuring in East Asia and India: Perspectives on Policy Reform. Basingstoke: Macmillan.

Ahluwalia, I. J. (1991) Productivity Growth in Indian Manufacturing. New Delhi: Oxford University Press.

Athey, M. J. and Laumas, P. S. (1994) 'Internal Funds and Corporate Investment in India', Journal of Development Economics 45: 287–303.

Balassa, B. (1965) 'Trade Liberalisation and Revealed Comparative Advantage', Manchester School 33(2): 99–123.

Brockett, P. and Levine, A. (1984) Statistics and Probability and Their Applications. New York: CBS College Publishing/Saunders College Publishing.

Edwards, S. (1994) 'Real and Monetary Determinants of Real Exchange Rate Behaviour: Theory and Evidence from Developing Countries', in J. Williamson (ed.), Estimating Equilibrium Exchange Rates. Washington, DC: Institute for International Economics.

Elbadawi, I. A. (1994) 'Estimating Long-Run Equilibrium Real Exchange Rates', in J. Williamson (ed.), Estimating Equilibrium Exchange Rates. Washington, DC: Institute for International Economics.

Fagerberg, J. (1988) 'International Competitiveness', The Economic Journal 98: 355–74.

Feenstra, R. C., Lipsey, R. E. and Bowen, H. P. (1997) 'World Trade Flows, 1970–1992 with Production and Tariff Data', National Bureau of Economic Research Working Paper, No. 5910.

Ganesh-Kumar, A., Sen, A. K. and Vaidya, R. R. (1998) 'Finance and Changing Trade Patterns in Developing Countries: A Case-Study of India', Indira Gandhi Institute of Development Research, Mumbai PP Series, No. 38.

Grubel, H. and Lloyd, P. J. (1975) Intra-Industry Trade. London: Macmillan.

Hall, B. H. (1992) 'Investment and Research and Development at the Firm Level: Does the Source of Finance Matter?', National Bureau of Economic Research Working Paper, No. 4096.

Harris, J. R., Schiantarelli, F. and Siregar, M. G. (1994) 'The Effect of Financial Liberalization on the Capital Structure and Investment Decisions of Indonesian Manufacturing Establishments', The World Bank Economic Review 8(1): 17–47.

Helpman, E. and Krugman, P. (1989) Trade Policy and Market Structure. Cambridge, MA: MIT Press.

Hubbard, R. G. (1997) 'Capital Market Imperfections and Investment', National Bureau of Economic Research Working Paper, No. 5996.

Joshi, V. and Little, I. M. D. (1994) India: Macroeconomics and Political Economy, 1964–1991. New Delhi: Oxford University Press.

Kanji, G. K. (1993) 100 Statistical Tests. New Delhi: Sage Publications (India) Private Ltd.

Lall, S. (1998) 'Technological Capabilities in Emerging Asia', Oxford Development Studies 26(2): 213–43.

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5 International trade, productivity and competitiveness: The case of the Indonesian manufacturing sector

Ari Kuncoro

1. Introduction: Competitiveness, macroeconomic and financial problems

The growth path of the Indonesian economy from the late 1960s to the early 1990s was not always smooth. Indonesia experienced several economic crises, usually dictated by the development of external events, which resulted in the deterioration of the current account. The problem stemmed from the fact that throughout the 1970s, Indonesian exports depended heavily on oil, gas and primary products. From 1973 to 1980, the value of Indonesian exports was dominated by oil, gas and timber, which made up approximately 60 per cent of the total exports. As more and more processing plants developed domestically, the share of semi-processed goods in total exports rose steadily, and from the mid-1980s to the early 1990s became one of the most important foreign exchange earners.

According to Haque (1995), competitiveness is defined as an economy's ability to grow and to raise the general living standard of its population without being constrained by balance-of-payment difficulties. In other words, competitiveness is defined as the capacity to increase productivity without generating a balance-of-payment crisis. We use this definition of competitiveness to examine the evolution of economic growth and current account sustainability in Indonesia.

The structure of Indonesian exports (heavily dependent on oil and gas and natural resource products) made the current account very vulnerable to international price fluctuations. The increase in oil prices in 1973 helped the government finance its economic development plan.1 On the negative side, the heavy dependence of the government on oil export revenues made the economy very vulnerable to the fluctuations in oil and other primary commodity prices on the international market. It is not too surprising that in an economy where government expenditures constitute most of the domestic purchasing power, any balance-of-payment crisis immediately translates into low or even negative economic growth. In the 1982–3 period, Indonesia was hit by the economic crisis that resulted from the drop in oil prices. The crisis worsened because of the fall in primary commodity prices in international markets as industrialized countries entered economic recession. The current account deficit suddenly soared from a mere 0.67 per cent of GDP in 1980 to 6.2 per cent of GDP in 1982 and 8.1 per cent of GDP in 1983.

This event brought strong impetus for a change in development strategy. It was acknowledged that export revenues from oil and primary commodities were unreliable and it was seen as urgent to develop and diversify non-oil sectors in the economy, particularly manufacturing and agriculture. As a first step, the government announced the Banking Deregulation in 1983, followed by an overhaul of the taxation system.

Another economic crisis taking place in 1986, once again caused by the sharp fall in oil price, caused the current account deficit to jump from 2.2 per cent of GDP in 1985 to 6.2 per cent in 1986. This crisis increased the need to speed up economic reforms. The government launched broad-based economic reforms in 1986, which covered all aspects of the economy: labor markets, the goods and services market and financial markets. The reforms also encompassed the improvement of market infrastructures, including administrative procedures, licensing systems and the legal system. The economic reforms marked changes in trade and industrial policies, from the import substitution industrial policy with emphasis on the development of capital-intensive manufacturing in the upstream and resource-based industries to labor-intensive export-oriented industries.

Foreign and domestic investors responded favorably to improvements in investment climate and the impact was felt in the structure of Indonesian exports. Foreign direct investment made a significant contribution to the diversification of exports, especially manufactured exports. Exports of manufactured goods produced by cheap labor such as textiles, processed woods, electronics and shoes started to rise. In 1991, the share of non-oil exports in the total exports surpassed oil and gas exports. The share of non-oil exports in 1985 was 32 per cent, of which 8 per cent consisted of manufactured exports. In 1991, the share of non-oil exports reached 62 per cent, of which 32 per cent was manufactured goods (Table 5.1). In the 1990s, Indonesia was no longer as dependent on oil exports. Oil exports are still important, however, since without them, the trade balance remains negative.

The emergence of unskilled labor-intensive industries as foreign exchange earners creates a new problem for the current account. These industries are very dependent on imported inputs, and this, as in the current economic crisis, makes them very vulnerable to exchange rate fluctuations. Their contribution to the trade balance in relation to their contribution to employment creation is still relatively small.2 The problem of labor-intensive export-oriented industries' dependency on imported inputs comes, to a large extent, from a government policy to provide incentives to both export-oriented and import substitution industries. To offset negative incentives created by the structure of protection to export activities, the government has designed a so-called duty drawback scheme in which producers are eligible for reimbursement on the taxes paid on imported inputs, provided that the final products are destined for export markets. Obviously, with this scheme there is no incentive for export-oriented producers to use inputs from domestic suppliers since it will be more expensive and also it cannot qualify for the duty drawback scheme. As a result, there is minimal linkage between export-oriented producers and domestic suppliers of inputs.

Besides export-oriented producers, there are also domestically oriented industries. From a macroeconomic standpoint, the presence of domestic industries with heavy

Table 5.1 Structure of Indonesian exports (selected commodities, % of total exports)

 

1985

1991

1993

1995

1997

Total exports

Oil and gas

68.42

37.39

26.47

23.04

21.75

Non-oil exports

31.58

62.61

73.53

76.96

78.25

Unskilled labor intensive

Shrimp

  0.17

  0.32

  0.26

  0.21

  0.17

Fish

  0.14

  0.75

  0.91

  0.70

  0.69

Coffee

  1.52

  1.28

  0.89

  0.50

  0.58

Textiles

  1.18

  6.13

  7.26

  6.20

  6.84

Garments

  1.83

  7.86

  9.53

  7.46

  5.38

Resource intensive

Rubber

  3.68

  3.29

  2.89

  4.82

  3.72

Plywood

  4.44

  9.85

11.56

  7.62

  6.38

Sandwood

  1.65

  0.61

  1.06

  1.00

  0.71

Palm oil

  0.89

  1.15

  1.28

  1.65

  2.71

Paper and paper goods

  0.11

  0.92

  1.36

  2.23

  1.76

Technology intensive

Tin

  0.11

  0.10

  0.05

  0.09

  0.12

Aluminum

  1.21

  2.35

  1.42

  0.58

  0.41

Nickel

  0.29

  0.15

  0.13

  0.12

  0.08

Fertilizer

  0.43

  1.02

  0.42

  0.61

  0.58

Chemicals

  0.31

  0.51

  0.71

  1.14

  1.35

Cement

  0.12

  0.15

  0.18

  0.02

  0.07

Electronic

  0.77

  2.29

  4.45

  2.03

  2.56

Source: Central Bureau of Statistics, Economic Indicator.

dependence on imported inputs and low exporting activity has burdened the current account. Why do these industries exist at all? The issue has something to do with the effective protection rate (EPR). The combination of an overvalued exchange rate, a high degree of protection given to final products and low tariffs imposed on industrial inputs has prevented the development of linkages between producers of final goods and domestic suppliers of inputs. Given the low backward linkages of these industries with domestic suppliers of inputs, the expansion of aggregate demand very soon resulted in the deterioration of the current account, with little multiplier effects in the Keynesian sense. As shown in Table 5.2, in 1995 and 1996, a new wave of deregulation measures spurred economic growth to around 8 per cent, leading to the worsening of the current account deficit to around 4 per cent of GDP.

High dependency on imported inputs is the main weakness of the Indonesian manufacturing industry. The sharp depreciation of the rupiah in the current economic crisis affects the manufacturing sector negatively across the board, including segments that are highly export orientated. Under normal conditions, exports would be expected to rise when the exchange rate depreciates. This does not seem to be the case, however, for Indonesia's labor-intensive export-oriented manufacturing

Table 5.2 Selected macroeconomic indicators, 1990–7

 

1990

1991

1992

1993

1994

1995

1996

1997

Real GDP growth

  9.0

  8.9

  7.2

  7.3

  7.5

  8.1

  7.8

  4.7

Agriculture

  2.3

  2.9

  6.3

  1.7

  0.6

  4.2

  1.9

  0.6

Industry

13.2

11.6

  8.2

  9.8

11.1

10.2

10.4

  6.2

Services

  7.6

  9.3

  6.8

  7.5

  7.2

  7.9

  7.6

  3.8

Inflation (CPI)

  9.5

  9.5

  4.9

  9.8

  9.2

  8.6

  6.5

11.1

Fiscal balance (% of GDP)

  0.4

  0.4

–0.4

–0.6

  0.1

  0.8

  0.2

 

Current account balance

–2.8

–3.7

–2.2

–1.6

–1.7

–3.7

–4.0

–2.3

Net capital inflows

  4.9

  5.0

  3.8

  1.9

  2.4

  4.6

  5.0

 

Net portfolio investment

–0.1

  0.0

–0.1

  1.1

  2.2

  2.0

 

 

Net direct investment

  1.0

  1.3

  1.4

  1.3

  1.2

  2.2

 

 

Other capital

  3.3

  3.6

  3.5

  1.4

–0.9

  1.3

 

 

Net error and omissions

  0.7

  0.1

–1.0

–1.9

–0.1

–0.9

 

 

Total external debt (% of exports)

222.0

236.9

221.8

211.9

195.8

205.0

194.0

 

Short-term debt (% of total external debt)

15.9

17.9

20.5

20.1

17.7

20.9

24.8

 

Short-term debt (billions of USD)

11.1

14.3

18.1

18.0

17.1

24.3

29.3

 

Debt-service ratio (% of exports)

30.9

32.0

31.6

33.8

30.0

33.7

33.0

 

Exports (% of GDP)

26.6

27.4

29.4

25.9

26.0

26.0

26.2

 

Exports (% growth rate)

15.9

13.5

16.6

  8.4

  8.8

13.4

  9.7

 

Exchange rate (Rp/USD)

1,901

1,992

2,062

2,110

2,200

2,308

2,383

5,700

Sources: International Financial Statistics, various issues.

industry. Export-oriented industries are not able to capitalize on the depreciation of the rupiah, not only because of their high dependency on imported inputs, but also because they have difficulty obtaining trade finance due to the deterioration of the Indonesian banking system's creditworthiness. In addition, foreign importers are reluctant to import from Indonesia due to the general perception that Indonesian exporters are unable to guarantee continuity in supply.

In 1991, the good economic performance that marked the 1986–91 period began to show signs of leveling off.3 Several macroeconomic indicators illustrated this slackening off. After managing to grow around 21 per cent a year during 1985–91, export growth fell to 12.4 per cent in 1993 and 8 per cent in 1994. This performance could be attributed to the disappointing performance of manufactured exports, which recorded 15 and 12 per cent growth in 1992 and 1993 respectively, in comparison with an average growth rate of 30 per cent per annum during 1985–91.4 In the case of foreign investment, the economy also seemed to lose its attractiveness to foreign investors. Post-1992, the value of new foreign investment continued to fall. In the first half of 1994, for example, the number of approved foreign investments declined by 43 per cent when compared to 1992. In 1992 and 1993, the total value of approved foreign investments amounted to 10.32 billion USD and 8.4 billion, respectively.

In response to this situation, the government in 1994 announced a bold economic reform, including the abolition of the limitation on foreign ownership, a reduction in trade barriers in the form of tariff cuts, and the opening up of ten previously closed sectors to foreign investment. Foreign investors were allowed to have full ownership (i.e. a 100 per cent stake) of business entities in Indonesia. Investors responded favorably to these measures, reflected in the influx of foreign investment during the second half of 1994 such that, by the end of the year, the value of foreign investment projects reached an all time high of 23.7 billion USD.5 The resurgence of flows of foreign investment continued well until the middle of 1997 when the currency crises hit Indonesia. In 1994, for the first time the value of approved new domestic investment projects exceeded approved foreign investment.

Various deregulatory measures announced in the middle of 1994 changed many aspects of economic incentives, including consumption, investment and exports. The government itself seemed to underestimate the impact of deregulation on the expansion of aggregate demand. Economic growth rebounded to 7 per cent, and even reached 8 per cent during the 1994–6 period. At first glance, the figures of economic growth are impressive. Looking more deeply however, the growth was hardly sustainable. Economic growth actually took place in non-tradable sectors like residential and non-residential construction and infrastructure (e.g. roads and telecommunications), contributing very little to foreign exchange generation as it was intended to meet domestic demand. To complicate matters, most of the expansion of the non-tradable sector was financed by short-term foreign commercial loans.6 So Indonesia experienced not only a problem of currency mismatch, but also a maturity mismatch.

It soon became apparent that the domestic economy was overheating. The expansion of domestic aggregate demand was reflected in the ballooning of the current account deficit. The current account deficit rose from 2.9 billion USD in 1994 to 7.9 billion USD in 1995, approximately a two and a half times increase. This increase could be attributed to rising imports of intermediate goods due to the expansion of domestic demand. The flood of foreign direct investment also contributed to a soaring current account deficit as a result of a rise of capital good imports and rising demand for foreign consultants, especially for setting up plants.

To contain the economy from overheating, the monetary authority continued to pursue a tight monetary policy. At the same time, to maintain the competitiveness of non-oil exports the central bank adopted a policy to depreciate the currency at the rate of 5–6 per cent per annum. Within the targeted depreciation rate, the currency was allowed to fluctuate within a band. In the long run, however, these policies proved to be inconsistent. High interest rates attracted huge capital inflows that had to be bought or sterilized by the central bank if the currency depreciation was to be maintained. This operation injected new liquidity in the economy, which in turn had to be absorbed by a high interest rate. Thus, the central bank has been burdened by two conflicting tasks, namely to maintain a low inflation rate and to maintain a competitive exchange rate. As the purchasing power parity theorem tells us, the combination of a high domestic interest rate and a low expected currency depreciation produced an overvalued rupiah, making import activities and borrowing from abroad artificially cheaper in the domestic currency. Moreover, the high interest rate policy that led to the currency appreciation, as well as the policy to depreciate the rupiah, required timely interventions which were themselves susceptible to currency speculation.

The current account deficit reached approximately 4 per cent of GDP in 1996 and 1997, reflecting Indonesia's vulnerability to external events. In the past, soft loans and foreign direct investment mainly financed the deficit. Starting in 1991, portfolio investment that was lured by high domestic interest rates and the perception of Indonesia as a stable and booming economy was increasingly playing an important part in financing the deficit. Meanwhile, high domestic interest rates and an overvalued rupiah encouraged the private sector to borrow heavily from overseas to finance their domestic ventures, many of which were intended for the domestic market. For this reason, the deficit was also increasingly financed by short-term private loans. To make matters worse, the predictability of exchange rate movement with a steady depreciation of around 4–5 per cent provides very little incentive for borrowers to hedge their foreign debt.

Various macroeconomic conditions such as a huge current account deficit, a mounting foreign debt and a weak banking system with large non-performing loans put Indonesia in the same category of countries like Thailand and Korea, and to a lesser extent with Malaysia and the Philippines. The 1994–6 economic boom did not last very long, coming to an abrupt end in August 1997 when Indonesia sunk into the worst economic crisis in its history. The Indonesian crisis started as a currency crisis triggered by the economic crisis in Thailand. After the Thai crisis, suddenly there was doubt about Indonesia's economic stability that brought about a reversal in expectations. As the direction of capital inflows started to reverse, the external value of the rupiah plummeted – between June and November 1997, the external value of the rupiah depreciated by 35 per cent. It became apparent then that the monetary authority did not have sufficient reserves to defend the rupiah; instead after increasing interest rates it opted first to enlarge the band and finally to move to a free float system. Despite a high interest rate, capital outflows continued to accelerate and as a result the currency continued to weaken. The move towards a free float created a panic among domestic corporations with large exposure to overseas loans. Due to the stability of the rupiah in the past, these debts were largely unhedged. As they scrambled to buy US dollars, it put further pressure on the domestic currency. Worse still, as the crisis continued to deteriorate, the international banking community cut its credit line to Indonesian banks (including trade financing). The lack of credit financing, particularly for export-oriented sectors, was partially blamed for the failure of Indonesian exports to benefit from the currency depreciation.

In the short run, Indonesia's recovery will depend on its ability to regain market confidence. The economic stabilization program sponsored by the IMF is only the first step in this direction, and by itself does not guarantee a speedy recovery. The return of the private sector's money – foreign or domestic – is what Indonesia needs in the short run. In the long run, there is a need to change the strategy of economic development away from the current import substitution approach to a more export-oriented strategy, including in agro-business-related industries.

Based on the Indonesian experience, many researchers have largely accepted the notion that both competitiveness and external trade were key to its rapid growth and stability, and that to achieve this requires liberalizing the economy. It is true that important improvements in resource allocation were observed after the 1986 deregulation. Nonetheless, the last economic deregulation in 1994 seemed less successful than the 1986 economic deregulation, also because it culminates in the 1997 currency crisis. Some cite an overvalued exchange rate and a weakly supervised banking system as the main weaknesses of the Indonesian economy. Some claim that other factors such as a reversal of international expectation and corruption were the main causes behind the crisis. The 1994 economic deregulation, although at first appearing to spur economic growth between 1994 and 1996, also produced unsustainable trade account deficit in the following years, which was followed by the 1997 currency crisis.

The apparent failures of the 1994 deregulation measures, however, are not interpreted as flaws of traditional theory. Rather, the failures stem from the problem of sequencing market liberalization. It is hypothesized that the comparative static of the model is correct and that more research is necessary regarding the dynamic of the liberalization model.

At present, it seems researchers have reached some consensus on two issues. First, there is little disagreement over the crucial role of competitiveness and trade in fostering growth and avoiding recurrent balance-of-payment crises. Second, there is agreement that more research needs to be done to settle the question regarding the relationship between trade, competitiveness and macroeconomic stability.

Accordingly, this study will examine the linkages between international trade, productivity and competitiveness in Indonesia. Since comprehensive data is only readily available for the Indonesian manufacturing sector, the study will focus on this area. Two specific linkages will be the focus of this study: the relationship between trade specialization and productivity growth and, second, the linkages between productivity and current account sustainability. In order to achieve these ends, the research strategy will proceed in two steps. The first step is to analyze the country's trade specialization by examining the Indonesian export commodity base available from the Central Bureau of Statistics (CBS). Data from 1980 to 1994 is used to construct an index of revealed comparative advantage (RCA). The pattern of trade specialization emerging from the RCA analysis will then be used to examine the relationship between trade specialization, the evolution of competitiveness and the sustainability of the current account.

2. Trade specialization, productivity growth and current account sustainability

The effort to examine the linkage between trade and competitiveness can only be accomplished by examining the annual survey on manufacturing firms, which contains important information regarding export–import activities and production processes at the firm level. Based on the definition of competitiveness, we try to identify winner and loser sectors in the Indonesian economy. Competitiveness at the sectoral level is defined as the capacity to increase productivity as well as to contribute to the closing of the current account deficit. Indonesian manufacturing surveys are well suited for this purpose since they provide information on export strategy and imported inputs at the firm level which can easily be aggregated to the industry level. It is quite possible that a highly productive firm or sector does not meet the above definition of competitiveness due to a heavy dependence on imported inputs such that its net contribution to the current account is negative. The effort to assess the evolution of sectoral competitiveness necessitates the measurement of productivity. The manufacturing surveys provide information on the values of output and inputs, such that by assuming a specific functional form for production process, it is possible to construct the level of labor productivity and labor productivity growth at the sectoral level.

Trade specialization

RCA is one method to measure comparative advantage in a geographic area. The argument behind this approach is that the flow of goods between countries is the reflection of the comparative advantage of the nation. The pattern that emerges from this measure does not only reflect the cost to produce such commodities, but also the difference in other non-price factors.

Applying the RCA method to the Indonesian non-oil export data, twenty commodities (three-digit SITC) that have relatively constant comparative advantage during the period 1990–4 can be identified, and thus are supposed to be leading export commodities. Most of these consist of primary products, while the rest are manufactured goods. In the category of primary products, agricultural products such as coffee, coconut, tea, spices, rubber and vegetable oil (SITC 071–5, 121, 231, 422 and 431) dominate. Other primary products include minerals such as iron ore, non-ferrous metal, coal and tin (SITC 283, 284, 321 and 087). One primary product that dropped out from the leading commodities in the period 1990–4 was forestry goods (SITC 024), which in the 1980s still dominated primary product exports. Apparently, the government decree that banned the export of unprocessed timber as well as other products such as rattan was responsible for the disappearance of forestry products from the list of leading commodities.

Looking at manufactured goods, products that possessed comparative advantages were textiles (SITC 651, 652, 653 and 656), household appliances from metal (SITC 697), furniture (821), garments (841) and footwear (851). If we look at the growth rates of exports from 1985 to 1997, the conclusion drawn from the RCA exercise seems to be confirmed (Table 5.3). Textiles, garments, paper, chemicals and electronics showed high rates of growth. Although their shares in total exports are still relatively small, products that have good export potential are paper and electronic products. It would be interesting to know whether these products derived their competitiveness from productivity. To achieve this purpose, the manufacturing database is needed since the export database does not possess information on production processes and related items such as value added, output, number of workers, etc.

Table 5.3 Trend of Indonesian exports (selected commodities, million USD)

 

1985

1991

1997

GR 85–91

GR 91–7

GR 85–97

Total exports

18,586.7

29,142.4

53,443

  7.78

10.64

  9.20

Oil and gas

12,717.9

10,894.9

11,622

–2.55

  1.08

–0.75

Non-oil exports

  5,868.8

18,247.5

41,821

20.81

14.82

17.78

Unskilled labor intensive

 

 

 

 

 

 

Shrimp

  30.8

    93.6

    92.1

20.35

–0.27

  9.56

Fish

  26.3

   217.2

   369.3

42.17

  9.25

24.63

Coffee

282.7

   371.7

   307.9

  4.67

–3.09

  0.71

Textiles

219.7

1,785.1

3,658

41.79

12.70

26.41

Garments

339.6

2,289.9

2,875.6

37.45

  3.87

19.49

Resource intensive

 

 

 

 

 

Rubber

683.3

  959.9

  1,988

  5.83

12.90

  9.31

Plywood

824.7

2,871

  3,410

23.11

  2.91

12.56

Sandwood

307.2

  177.3

     380

–8.75

13.55

  1.79

Palm oil

166.2

  335.4

   1,446

12.41

27.58

19.76

Paper and paper goods

  20.9

  267.6

      938.5

52.95

23.26

37.31

Technology intensive

 

 

 

 

 

Tin

  21.1

  29.2

    64.1

  5.56

  14.00

    9.70

Aluminum

225.2

683.7

  221.4

20.33

–17.13

  –0.14

Nickel

  54

  42.3

    41.8

–3.99

  –0.20

  –2.11

Fertilizer

  80

297.6

  312.4

24.48

    0.81

  12.02

Chemicals

  56.7

147.2

  721.2

17.23

  30.32

  23.61

Cement

  21.5

  43.2

    39.5

12.33

  –1.48

    5.20

Electronic

144

668.7

1,370.6

29.16

  12.71

  20.66

Sources: Economic Indicator, various issues.

The evolution of productivity

After looking at the figures of labor productivity (LP) at the three-digit level, one thing is certain: it is hard to make any generalizations (Table 5.4). There are three distinct high value-added industries: high physical capital intensity such as basic metal (371–2), basic chemicals (351) and cement (363); high-skill intensity sectors

Table 5.4 Index of labor productivity and productivity growth

ISIC

Industry

1986

1991

1995

GR 86–91

GR 91–5

311

Food

  50.12

109.34

  78.79

  16.88

  –6.34

312

Food

  47.43

  47.43

  62.09

    0.00

    5.53

313

Beverages

152.59

161.68

224.95

    1.16

    6.83

314

Tobacco

113.30

  56.23

214.09

–13.08

  30.66

321

Textiles

  95.54

  60.41

115.25

  –8.76

  13.79

322

Garment

  40.43

  44.61

  61.49

    1.99

    6.63

323

Leather products

111.45

  73.29

  88.80

  –8.04

    3.91

324

Footwear

112.27

  30.46

  65.06

–22.96

  16.39

331

Wood products

  87.79

  83.93

107.56

  –0.89

    5.09

332

Furniture

  30.17

  64.93

  42.12

  16.56

  –8.29

341

Paper and paper products

  78.04

132.37

189.27

  11.15

    7.41

342

Printing and publishing

  68.97

109.72

130.52

    9.73

    3.53

351

Industrial chemical

172.63

242.13

329.05

    7.00

    6.33

352

Other chemical

129.04

110.62

110.24

  –3.03

  –0.07

353

Petroleum refinery

   N/A

   N/A

627.54

    N/A

    N/A

354

Oil and gas processing

   N/A

  70.26

124.73

    N/A

  12.17

355

Rubber products

  47.74

  65.25

  49.09

    6.45

  –5.53

356

Plastics

  37.47

  35.12

  55.84

  –1.29

    9.72

361

Ceramics

  40.22

  62.04

  69.22

    9.05

    2.21

362

Glass products

161.29

  70.61

145.07

–15.23

  15.49

363

Cement

171.74

229.53

173.30

    5.97

  –5.47

364

Structural clay

  13.87

  96.27

  16.39

  47.33

–29.82

369

Other non-metallic mineral

  47.49

175.70

  79.55

  29.91

–14.66

371

Iron and steel

1,008.75

287.29

896.99

–22.21

  25.57

372

Basic metal exc. iron and steel

   N/A

721.94

1,021.92

   N/A

    7.20

381

Metal products

  73.34

  67.65

100.78

  –1.60

    8.30

382

Non-electrical machinery

  46.82

140.49

186.62

  24.58

    5.84

383

Electrical equipment

108.88

106.83

192.52

  –0.38

  12.50

384

Transportation equipment

  87.27

133.65

323.34

    8.90

  19.33

385

Professional equipment

  22.00

  55.43

  98.19

  20.30

  12.11

390

Miscellaneous

  41.97

  38.65

  57.86

  –1.63

    8.40

 

Total

  91.66

  82.88

125.46

  –1.99

    8.65

Sources: Calculated from Industrial Surveys.

such as machinery (ISIC 381–4); and highly differentiated products such as beverages (313) and tobacco (314). Caution is needed when interpreting the high figures of value added for electronics (383), transportation equipment (384) and chemicals (ISIC 35) since they are influenced, to a certain extent, by the degree of protection given to these industries.

On the other hand, there are also labor-intensive industries with low productivity. Food (311–12), textiles (321), garments (322), leather (323), footwear (324), rubber products (355), plastics (356) and several non-metallic mineral industries (such as bricks, tiles and ceramics), professional equipment and miscellaneous industries can be considered as labor-intensive sectors with LP less than half of highly productive industries. Resource-based industries cover a wide range of factor intensities from relatively more labor-intensive wood and rubber products to relatively more capital-intensive ones such as basic metal and cement. In this category, LP is usually lower in the more capital-intensive ones.

An examination of the figures of LP growth at the three-digit level reveals that manufacturing productivity growth is very erratic (Table 5.4). The range of total factor productivity growth is very broad. The manufacturing sector as a whole experienced negative LP growth during the 1986–91 period. Manufacturing is one sector that benefited the most from a series of deregulation measures since 1986. Previously, we have seen that manufacturing export growth accelerated immediately once economic liberalization started. The impact on productivity growth, however, was only apparent almost five years later. Obviously there are many factors behind this fact. It is possible that a better investment climate did not produce an instant improvement in the productivity of capital and labor. For example, it takes time to produce better quality labor. Another explanation is also possible: the 1991–6 period were years of prosperity for Indonesia. In this period, flows of capital, foreign direct investment (FDI) and portfolio investment accelerated. Outward orientation created by the 1986 economic liberalization gave firms the opportunity to gain better access to the global market, which allowed the acquisition of technology, the import of capital goods, management and professional services and new products and process. This eventually led to improvements in firms' productivity.

Another interesting observation is that in the period 1991–5, the productivity gap among industries seemed to decrease. If we use the whole manufacturing LP growth as a benchmark, there is a huge gap between industries with high positive total factor productivity (TFP) growth and industries with negative ones. Between 1986 and 1991, for example, structural clay (364) recorded the highest positive TFP growth around 47 per cent, followed by other non-metallic minerals (369) in second place. The worst case is footwear (324) with almost 23 per cent negative LP growth. This is in contrast with the 1991–5 period where the productivity disparity narrowed. The highest LP growth is recorded by tobacco (314), while structural clay posted the lowest LP growth.

Even after the era of economic liberalization, there remains some of the legacy of the era of import substitution industrial policy. High valued-added industries usually enjoyed higher effective rates of protection compared to other industries. In the period 1986–95, the performance of these industries varied. Cement (363), for example, showed better performance in 1986–91 with positive TFP growth. In the period 1991–5, the impact of the 1986 economic liberalization in terms of reduced trade barriers started to have a harmful impact on the cement industry as LP growth turned negative. The steel industry (371), on the other hand, recorded negative LP growth during the first half of the 1986–95 period, and then showed a dramatic improvement in the later period.

Machinery industries (ISIC 381–4) also enjoyed a high degree of protection during the era of import substitution policy. Judging from the figures of LP growth, however, these industries appeared to benefit from a better investment climate after the 1986 economic deregulation. Good performance of LP growth was also reflected in the strong growth of domestic demand and greater penetration of global markets. In the 1986–95 period, electrical machinery showed dramatic improvements in LP growth, from near zero initially to around 16 per cent in 1991–5. This proved that greater inflows of foreign investment had a favorable impact on this industry. The only shortcoming was that there is almost no linkage between local firms producing parts and export-oriented producers. This can be attributed, in part, to the present structure of protection along with the duty drawback scheme.

Garments (322) and footwear (324) are among the most important foreign exchange earners for the country. In the period 1986–91, footwear in particular showed negative LP growth that confirmed that these industries based their competitiveness on low labor costs rather than productivity gains. The pattern of productivity improvement observed earlier in the case of machinery industry seems to appear again – economic liberalization tended to have positive impact on LP growth later in the 1986–95 period rather than earlier. Aside from the improved investment climate discussed above, another explanation involved the expansion of domestic aggregate demand after 1991 as a result of the injection of capital inflows into the economy. These industries were able to capitalize on apparent increase in per capita income brought about by a booming domestic economy. In other words, the resurgence of LP growth might have come from an aggregate demand shock.

After comparing the RCA analysis with productivity in the manufacturing industry, it is clear that Indonesia is still specializing in products with low productivity. The base of Indonesian manufactured exports is still narrow and mainly consists of wood products, textiles, garments and footwear. The good thing is that these main export products continue to record at least positive LP growth. Performance in terms of LP growth, however, is not sufficient to determine competitiveness since we have to look at industry's contribution to the trade account.

Contribution to the trade balance and sectoral competitiveness

Using information from the manufacturing surveys, it is possible to distinguish export-oriented producers from domestically oriented ones. It is possible that highly productive firms or sectors do not meet the definition of competitiveness, which involves both the capacity to increase productivity and to contribute to the closure of the current account deficit. Production activity may require a lot of imported inputs, such that an attempt to increase productivity will put a heavy burden on the current account gap. It seems that the contribution of the manufacturing sector in Indonesia to foreign exchange generation is much less than its neighbors in Southeast Asia. For this reason, examining trends in export orientation is incomplete without examining firms' input structure (Tables 5.5–5.7).

The Indonesian manufacturing surveys provide information on imported inputs in terms of their dollar value and as a percentage of total inputs. Using the most recent data (1990–5), it can be observed that the entire manufacturing sector's contribution to the trade balance is positive, meaning it generates more foreign exchange through exports than it uses to purchase imported inputs, excluding capital goods. The net contribution of the manufacturing sector rose from merely 1.8 billion USD in 1990 to 7.7 billion USD in 1995. In general, multinational corporations (MNCs) are more dependent on imported inputs than their local counterparts. The portions of imported inputs for the manufacturing sector were between 29 per cent in 1990 and 27 per cent in 1995, while the figures for MNCs were almost twice as high (57 per cent in 1990 and 56 per cent in 1995). For this reason, in 1990 local industries made a net positive contribution of 590 million USD to the trade balance, while the deficit of MNCs amounted to 839 million USD (Table 5.6).

The same observation can be extended further in more detailed fashion to the three-digit ISIC code. First, we look at industries characterized by a very high export orientation (i.e. exported more than 50 per cent of total output). These industries include garments (322), footwear (324), wood products (331), furniture (332) and rubber products (355). Some of these industries have substantial import content (for 1995), notably textiles (31 per cent), garments (33.12 per cent) and footwear (58 per cent). Despite their high dependency on imported inputs, these industries are the top three foreign exchange contributors to the trade balance, with footwear the highest contributor, followed by garments and textiles. Industries with high or moderate export orientation made a positive contribution to the trade balance in 1995. These industries include food (311), beverages (313), tobacco (314), leather (323), furniture (332), paper products (342), basic chemicals (351), petrochemicals (354), rubber products (355), ceramics (361), glass products (362), structural clay products (364), non-ferrous basic metal (372), professional equipment (385) and miscellaneous industry (390). Electrical equipment is one exception since it has high export orientation (43 per cent) and thus has the ability to generate a substantial amount of foreign exchange. However, it is not high enough to offset its high dependency on imported inputs such that the contribution of electronic industry to the trade balance is still negative.7 High dependence on imported inputs can also be found in low export-oriented industries. In general, these industries export less than 10 per cent of total output, although imported inputs are more than 30 per cent of output, and in some cases like machinery (ISIC 38) exceed 50 per cent.

Referring back to the definition of competitiveness, i.e. the ability to improve productivity without worsening balance of payments, it seems that few industries

Table 5.5a Net exports by industry (1990)

ISIC

Industry

Exports (%)

Exports ($000)

Imports (%)

Imports ($000)

Net exports ($000)

311

Food

11.39

  487,046.90

  9.39

225,932.00

261,114.90

312

Food

18.19

  197,846.10

17.53

101,329.00

96,517.10

313

Beverages

  1.94

     4,595.61

25.38

  17,617.00

–13,021.39

314

Tobacco

  1.29

   39,085.31

10.94

126,524.00

–87,438.69

321

Textiles

15.84

 628,259.20

30.09

722,401.00

–94,141.80

322

Garment

40.85

 485,568.50

32.04

224,513.00

261,055.50

323

Leather products

46.44

   57,361.22

16.15

  12,720.00

44,641.22

324

Footwear

53.08

  188,261.60

44.16

  67,114.00

121,147.60

331

Wood products

47.35

1,776,936.00

  3.32

  68,468.00

1,708,468.00

332

Furniture

49.19

  143,633.10

  2.29

    3,606.00

140,027.10

341

Paper and paper products

10.03

  128,458.80

38.88

231,608.00

–103,149.20

342

Printing and publishing

  1.82

     7,866.46

27.88

  77,642.00

–69,775.54

351

Industrial chemical

10.50

  220,156.20

54.22

640,495.00

–420,338.80

352

Other chemical

  4.50

    70,232.18

51.85

388,396.00

–318,163.82

353

Petroleum refinery

  N/A

        N/A

  N/A

      N/A

N/A

354

Oil and gas processing

  0.00

          0.00

27.75

    3,801.00

–3,801.00

355

Rubber products

42.48

690,879.40

19.24

203,349.00

487,530.40

356

Plastics

  9.31

  68,743.32

44.71

209,288.00

–140,544.68

361

Ceramics

10.44

  17,734.37

67.09

  34,952.00

–17,217.63

362

Glass products

  6.14

  12,802.84

18.08

  17,812.00

–5,009.16

363

Cement

10.00

  74,727.53

17.67

  33,281.00

41,446.53

364

Structural clay

  0.60

      226.44

54.66

   6,781.00

–6,554.56

369

Other non-metallic mineral

  8.67

    8,871.50

45.32

  17,004.00

–8,132.50

371

Iron and steel

  8.90

201,966.70

43.77

447,529.00

–245,562.30

372

Basic metal exc. iron and steel

33.83

202,214.90

64.63

262,857.00

–60,642.10

381

Metal products

  8.29

101,367.00

36.30

304,756.00

–203,389.00

382

Non-electrical machinery

  1.32

    5,868.33

65.52

180,325.00

–174,456.67

383

Electrical equipment

13.52

173,873.70

61.91

515,828.00

–341,954.30

384

Transportation equipment

  1.36

  31,191.91

54.92

684,560.00

–653,368.09

385

Professional equipment

  8.16

    2,089.64

61.92

    8,164

–6,074.36

390

Miscellaneous

15.88

   25,754.41

32.92

  31,472

–5,717.59

Sources: Calculated from Industrial Surveys, CBS.

Table 5.5b Net exports by industry (1995)

ISIC

Industry

Exports (%)

Exports ($000)

Imports (%)

Imports ($000)

Net exports ($000)

311

Food

19.96

1,662,922.02

  4.84

   277,421.00

1,385,501.02

312

Food

16.12

   384,797.01

26.63

   401,383.00

   –16,585.99

313

Beverages

10.77

     63,201.17

22.97

     40,651.00

     22,550.17

314

Tobacco

36.81

1,949,429.78

  8.33

     97,592.00

1,851,837.78

321

Textiles

24.90

2,337,334.09

21.16

1,712,733.00

   624,601.09

322

Garment

54.05

1,304,675.93

33.12

   487,313.00

   817,362.93

323

Leather products

33.08

     91,782.22

24.53

    42,592.00

     49,190.22

324

Footwear

70.68

1,587,613.60

58.54

  728,945.00

   858,668.60

331

Wood products

65.78

4,007,056.24

  2.79

    96,207.00

3,910,849.24

332

Furniture

53.33

   453,448.87

  3.39

    16,966.00

   436,482.87

341

Paper and paper products

  7.33

   219,968.56

33.37

  551,498.00

 –331,529.44

342

Printing and publishing

23.81

   242,206.51

16.65

    87,013.00

   155,193.51

351

Industrial chemical

24.58

   868,285.04

44.91

  730,133.00

   138,152.04

352

Other chemical

  5.74

   187,737.54

48.34

  776,398.00

 –588,660.46

353

Petroleum refinery

  0.00

            0.00

  6.51

        357.00

       –357.00

354

Oil and gas processing

24.03

      8,377.36

17.18

     2,380.00

      5,997.36

355

Rubber products

61.23

1,688,168.55

  8.91

  184,592.00

1,503,576.55

356

Plastics

14.88

   343,230.42

37.58

  505,779.00

 –162,548.58

361

Ceramics

13.57

     59,387.41

44.63

    49,658.00

      9,729.41

362

Glass products

22.90

     97,477.31

41.81

    54,489.00

    42,988.31

363

Cement

  1.75

     25,715.60

  9.17

    35,896.00

  –10,180.40

364

Structural clay

11.21

       9,713.36

34.38

      8,191.00

     1,522.36

369

Other non-metallic mineral

14.01

     40,917.51

39.06

    44,998.00

    –4,080.49

371

Iron and steel

  8.99

   374,954.85

51.02

  958,696.00

 –583,741.15

372

Basic metal exc. iron and steel

45.53

   498,959.15

83.62

  494,500.00

      4,459.15

381

Metal products

13.06

   368,429.90

38.89

  604,045.00

 –235,615.10

382

Non-electrical machinery

  8.53

   106,445.69

58.59

  469,722.00

 –363,276.31

383

Electrical equipment

43.02

2,404,342.94

72.29

2,523,430.00

 –119,087.06

384

Transportation equipment

  2.41

   155,459.53

52.32

1,990,615.00

–1,835,155.47

385

Professional equipment

47.65

     97,847.13

75.84

    84,389

    13,458.13

390

Miscellaneous

44.59

   230,799.61

31.76

   102,229

   128,570.61

Sources: Calculated from Industrial Surveys, CBS.

Table 5.6 Net exports according to ownership status

 

Exports (%)

Exports ($000)

Imports (%)

Imports ($000)

Net exports ($000)

1990

 

 

 

 

 

Local

19.03

3,601,927.08

29.94

3,011,151.00

  590,776.08

MNC

16.91

1,210,319.25

57.58

2,049,369.00

–839,049.75

Other

13.02

1,241,373.12

15.36

   809,602.00

  431,771.12

1991

 

 

 

 

 

Local

24.58

5,459,593.44

29.66

3,597,036.00

1,862,557.44

MNC

20.28

1,530,312.61

55.31

2,311,197.00

 –780,884.39

Other

14.88

1,687,624.90

23.97

1,540,316.00

  147,308.90

1993

 

 

 

 

 

Local

22.96

5,530,395.91

24.28

3,185,139.00

2,345,256.91

MNC

29.52

3,455,223.21

54.74

3,309,260.00

   145,963.21

Other

18.73

4,199,743.03

19.18

2,580,721.00

1,619,022.03

1995

 

 

 

 

 

Local

28.96

9,676,576.78

27.18

4,818,921.00

4,857,655.78

MNC

33.70

7,019,409.90

56.12

6,310,679.00

   708,730.90

Other

21.22

5,175,678.23

21.46

3,031,219.00

2,144,459.23

Sources: Manufacturing Surveys.

Table 5.7 Net exports according to firm size

 

Exports (%)

Exports ($000)

Imports (%)

Imports ($000)

Net exports ($000)

1990

 

 

 

 

 

Large

18.29

   4,090,604.30

33.48

  3,748,327.00

   342,277.30

Medium

16.52

   1,704,142.46

29.21

  1,790,515.00

   –86,372.54

Small

  8.75

     255,086.48

17.34

     325,898.00

   –70,811.52

Total

 

  6,049,833.24

 

  5,864,740.00

   185,093.24

1995

 

 

 

 

 

Large

31.81

16,508,543.07

38.59

10,228,534.00

6,280,009.07

Medium

22.79

  4,774,463.38

25.78

  3,417,420.00

1,357,043.38

Small

10.27

     587,398.39

15.55

     513,940.00

     73,458.39

Total

 

21,870,404.84

 

14,159,894.00

7,710,510.84

Sources: Manufacturing Surveys.

can satisfy such a definition. According to 1991 data, some industries like paper (341), industrial chemicals (351), cement (364), non-electrical machinery (382) and transportation equipment (384) possess above-average LP growth but are not very export oriented and often negatively contribute to the trade balance. These industries generally enjoy a high degree of protection; therefore their value-added figures might be inflated by the degree of protection. The picture is not quite so different in 1995; although most industries have positive LP growth, many domestic-oriented industries such as paper products (341), plastic (356), iron and steel (371), metal products (381), non-electrical machinery (382) and transportation equipment (384) are negative contributors to the trade balance. Some industries such as textiles, garments, footwear and wood products emerge from the RCA analysis of the 1995 sample as the most competitive sectors since they recorded positive productivity as well as made a positive contribution to trade balance.

Overall competitiveness and the current account sustainability

The overall competitiveness of the Indonesian economy is measured by the evolution of real exchange rate (RER) (Table 5.8). RER is obtained by combining a measure of domestic inflation, as a proxy for the costs of domestic producers, with foreign inflation, as a measure of the change in world prices, and the nominal exchange rate. RER is calculated as nominal exchange rates times a measure of tradable prices (the attractiveness of export versus import substitutes) divided by a domestic price index, which serves as a proxy for domestic costs of production. RER can serve as an indicator of the domestic producer's competitiveness vis-à-vis the rest of the world. Operationally, RER is defined as a weighted average of the bilateral exchange rate of trading partners. It is common to use the value of trade (exports plus imports) as a weight. To measure the prices of tradables, three indexes are often used, industrial countries' consumer price index (CPI), industrial countries' wholesale price index (WPI) and import/export unit value indices, where CPI is used to measure domestic costs. In this study the United States, Japan, United Kingdom, Germany and France are chosen as trading partners because of the size of their bilateral trade with Indonesia.

Table 5.8 Nominal and real exchange rate

Year

IRER

IRERD

INER

INERD

INFL*

INFLDOM

CA/GDP

1988

  80.19

 

  88.30

 

 

 

–1.7

1989

  86.80

    8.24

  90.50

    2.49

  3.43

   6.42

–1.2

1990

100.00

  15.21

100.00

  10.50

  3.97

   7.76

–2.8

1991

  99.18

  –0.82

105.10

    5.10

  3.70

   9.40

–3.7

1992

  86.27

–13.02

103.56

  –1.46

  2.56

   7.59

–2.2

1993

  79.74

  –7.57

104.89

    1.28

  2.07

   9.60

–1.6

1994

  76.60

  –3.94

111.36

    6.16

  1.52

   8.53

–1.7

1995

  79.36

    3.61

118.43

    6.35

  1.16

   9.43

–3.7

1996

  92.03

  15.97

122.45

    3.39

  1.28

   8.03

–4.0

1997

137.98

  49.93

284.51

132.35

  2.08

   4.71

–2.3

1997:Q1

  95.33

 

121.90

 

–1.31

 –0.98

 

1997:Q2

  92.09

 

123.34

 

  1.33

   0.87

 

1997:Q3

102.68

 

163.27

 

  0.25

   1.62

 

1997:Q4

122.35

 

232.42

 

  0.04

   5.91

 

1998:Q1

150.62

 

415.08

 

  0.25

 15.24

 

Source: Calculated from Economic Indicator, Central Bureau of Statistics.

IRER: index of trade-weighted real exchange rate; IRERD: trade-weighted real exchange rate depreciation; INER: index of trade-weighted nominal exchange rate; INERD: trade-weighted nominal exchange rate depreciation; INFL*: inflation rate in six biggest trading partners; INFLDOM: domestic inflation rate based on CPI.

It is quite obvious that the appreciation of the RER in recent years has been significant. There was an indication that the Indonesian economy was experiencing gradual erosion of competitiveness. This phenomenon was a part of the wider trend taking place in Southeast and East Asian countries like Thailand, Malaysia, Philippines and Singapore (Wallace 1997). The reason behind this appreciation was higher inflation in Indonesia compared to its trading partners. The domestic inflation itself is the outcome of interaction between the domestic goods and services market, the labor market, and some function of domestic monetary and fiscal policies.

In the labor market, there was upward pressure on wages as the government continued to enforce the increase of minimum wages (i.e. since 1991). There was also an indication of a liquidity increase; the ratio of M2 to reserves which in the 1990–3 period was very close to five, rose to a little over six in the 1994–6 period. The source of this liquidity increase was mostly short-term capital inflows that were channeled to finance investment, mainly in non-traded goods sectors such as property and infrastructure. The worsening of the current account deficit also reflected the erosion of fiscal discipline that created excessive aggregate demand. In the goods and services market, the concentration of market power in the hands of a very few firms, particularly those producing essential commodities, resulted in upward price adjustments as they exercised their market power.

The slowdown of export growth in the 1991–6 period seems to support the assertion that Indonesia is losing competitiveness in its main manufactured exports such as footwear and garments which exploit the relative abundance of unskilled labor.8 As shown before, these industries recorded positive LP growth, although apparently their productivity increase could not match the appreciation of the real exchange rate. There was also evidence that successive increases in minimum wages were not matched by increases in productivity (Kuncoro 1995).9 The export growth in 1993 and in 1994 slowed to 8.4 and 8.6 percent, respectively, in contrast to an average of 15 per cent in the period 1986–91.

In the period 1991–6, the persistence of an interest rate differential between Indonesia and the rest of the world attracted huge capital inflows, most of which were sterilized by the central bank. Without such intervention, such inflows lead to the nominal appreciation of the rupiah, which would eventually hurt exporters. This would be in conflict with the central bank's policy to depreciate the rupiah at roughly 5 per cent per annum. Over the years, Indonesia has pursued a policy to maintain the competitiveness of non-oil exports. In fact, given the inability of the government to combat a high-cost economy resulting from bureaucratic red tape, corruption and inefficient regulation, currency depreciation remains the only workable alternative to maintain the competitiveness of the Indonesian economy. Substantial portions of the inflows were channeled directly and indirectly through the banking system to finance investments, including those in non-tradable sectors such as construction, real estate and financial services. As a result, overall investment jumped from approximately 28 to 32 per cent of GDP.

The combination of export slowdown and investment boom resulted in the deterioration of the current account, a deficit around 2 per cent of annual GDP in 1993 and 1994 increasing to 3.7 per cent in 1995 and 4 per cent in 1996.

The phenomena of real exchange rate appreciation and worsening current account deficits were also observed before the 1994 Mexican crisis, although for Indonesia these two factors do not sufficiently explain the spillover of the Thai currency crisis to Indonesia. In the Indonesian case, currency appreciation was also the result of capital inflows that were directed to finance excessive investment in non-traded good sectors such as property and infrastructure. The most important factor was the huge amount of short-term foreign borrowing, which due to the predictability of rupiah depreciation in the past was largely unhedged. The movement of the exchange rate from 2,500 to 4,000 between July and September was perhaps dictated by the amount of the current account deficit. Movement beyond 4,000 however, probably reflected attempts by foreign investors to avoid short-term capital losses and by domestic firms with large exposure to foreign debt to buy foreign exchange to service their future debt repayment.

3. The financial environment and portfolio decisions of firms

Financial characteristics: Winner and loser firms10

In this study, several variables are constructed to analyze the financial aspect of manufacturing firms. First, to measure firms' profitability or rates of return, we use the ratio of gross operating surplus before interest payment in relation to capital, which reflects the total returns to capital independent of financial structure. Second, the degree of leverage is measured by the ratio of debts to capital. Finally, to measure the average cost of borrowing funds, we use the ratio of total interest payments to total debt. The key summary statistics for manufacturing firms are given in Tables 5.9 and 5.10. These tables provide information on all manufacturing firms as well as on specific samples according to different categorization: (i) according to firm size – large, medium and small establishments; (ii) according to ownership status – MNC versus local firms; (iii) according to export orientation – exporter versus non-exporter. The observations are taken between 1990 and 1995.

A general observation suggests that in the last period of analysis (i.e. 1995), firms are more leveraged than in the first period (i.e. 1990). The ratio of total debt to capital stock for winner industries (e.g. garments, footwear and wood products) is higher in comparison to loser sectors (e.g. industrial chemicals and transportation equipment).11 Interestingly, the domestically oriented cement industry (363), which is controlled by conglomerates, had the highest leverage in 1995. The data also shows that the average cost of borrowing (interest payment over the stock of debt) was generally higher than in 1991. In this respect however, winner industries generally paid lower interest rates.

High interest rates were the consequence of the tight monetary policy pursued by the government. Initially, this policy was originally intended to stop large capital flight in response to the rumor of devaluation in 1991. In subsequent years, the policy was maintained to sterilize large capital inflows in the form of commercial loans and portfolio investment which were coming to Indonesia in response to a booming domestic economy and more openness created by deregulation measures.

Table 5.9 Indonesia manufacturing sector: several financial indicators

 

INV-STOCK

PROFIT

LEVERAGE

AVCOB

FLOAN

1987

 

 

 

 

 

Large

0.3125

0.5313

0.4422

0.1070

0.0632

Medium

0.2250

0.3906

0.3534

0.1066

0.0161

Small

0.1187

0.1603

0.2884

0.0399

0.0025

1991

 

 

 

 

 

Large

0.1055

0.1222

0.2648

0.0489

0.0253

Medium

0.6869

0.3348

0.0460

0.0767

0.0565

Small

0.3360

0.3384

0.0129

0.2200

0.3300

Exporters

0.1231

0.1762

0.2459

0.0815

0.0336

Non-exp.

0.3340

0.1342

0.0828

0.1566

0.0578

1995

 

 

 

 

 

Large

0.2577

0.6703

0.3035

0.1554

0.0908

Medium

0.2837

0.4824

0.2810

0.1479

0.0738

Small

0.5210

0.4300

0.1124

0.3175

0.0603

Exporters

0.2954

0.6647

0.3159

0.1609

0.0997

Non-exp.

0.2670

0.5423

0.2256

0.1713

0.0169

Sources: Manufacturing Surveys, CBS.

INV-STOCK: ratio of investment to capital stock; PROFIT: ratio of gross operating surplus to capital stock; LEVERAGE: ratio of debt to capital stock; AVCOB: average cost of borrowing; FLOAN: ratio of foreign debt to total debt.

In this case, instead of letting the exchange rate appreciate, the monetary authority opted to sterilize the flows since an undervalued domestic currency was needed to keep exports competitive.

High domestic interest rates in 1991 caused a substantial rise in the interest rate burden (as illustrated in the 1995 figures) for all sizes of firms (Table 5.9). This high interest rate environment appears to have had an impact: comparing the 1987 data across all categories shows lower leverage than in 1991. After 1991, the interest rate falls slowly, although it has not yet come back to the 1990 level.12 Predictably, this new development had some impact on firms' behavior; between 1991 and 1995 all categories show some increase in leverage. The environment faced by small establishments, however, is characterized by higher average costs of borrowing when compared to large and medium ones. In this period, we also started to observe substitution between domestic credit and foreign credit. Particularly in the category of large firms, there was indication of an increase in the portion of foreign debt. It appears that the booming domestic economy and easy access to offshore borrowing made it possible for firms to thrive in a high interest rate environment.

According to Table 5.9, exporters tend to be more leveraged. The average cost of borrowing is lower for exporters, not too surprising since exporters have better access to foreign loans. The only exception was in 1991, when domestic interest rates reached their highest level. Various deregulatory measures since the mid-1980s have positively impacted exporters. This situation was documented in profit

Table 5.10 Indonesian manufacturing sector: firm financial aspects at the sectoral level

ISIC

Industry

1987

 

 

 

 

1990

 

 

 

 

 

 

INV-ST

PROFIT

LEVER

AVCOB

FLOAN

INV-ST

PROFIT

LEVER

AVCOB

FLOAN

311

Food

0.2115

0.4821

0.1717

0.1472

0.0125

0.1574

0.6938

0.0144

0.2252

0.5523

321

Textiles

0.1436

0.3938

0.3844

0.1042

0.0464

0.1055

0.0225

0.0716

0.0478

0.0392

322

Garment

0.1016

0.2193

0.3069

0.0312

0.0007

0.6480

0.5459

0.0063

0.0926

0.2314

324

Footwear

0.0240

0.0760

0.1088

0.1680

0.0000

0.5405

0.8929

0.0369

0.1146

0.3832

351

Industrial chemical

0.6313

0.2738

0.5317

0.1023

0.1090

0.1755

0.3502

0.1015

0.0613

0.1983

352

Other chemical

0.6253

1.4179

0.3328

0.4049

0.1191

0.9244

1.3597

0.0106

0.1149

0.2303

362

Glass products

0.4218

1.5738

0.5548

0.0860

0.0000

0.2815

1.1398

0.0017

0.0479

0.1698

363

Cement

0.0467

0.3294

0.1807

0.0997

0.0000

0.1813

0.5415

0.0104

0.3314

0.1520

383

Electrical equipment

0.3351

0.6445

0.2393

0.2649

0.0107

0.0593

0.0115

0.0305

0.0437

0.0160

384

Transportation equipment

0.3958

0.3142

0.8030

0.0264

0.1474

0.9853

0.3617

0.0104

0.1314

0.2632

385

Professional equipment

0.3918

0.4525

0.2073

0.0867

0.0000

0.1572

0.6122

0.0722

0.0729

0.0000

390

Miscellaneous

0.4308

0.7027

0.3244

0.2221

0.0142

0.1875

0.8028

0.0023

0.5135

0.0000

 

 

1991

 

 

 

 

1995

 

 

 

 

311

Food

0.0451

0.0607

0.0316

0.1213

0.0061

0.1436

0.3881

0.2621

0.0885

0.0217

321

Textiles

0.1654

0.1228

0.4110

0.0531

0.0363

0.2666

0.3573

0.4595

0.1001

0.0650

322

Garment

0.1914

0.0701

0.0650

0.1369

0.0214

0.4885

0.9095

0.4242

0.1813

0.0368

324

Footwear

0.9498

0.6840

0.0722

0.9143

0.2231

0.2820

0.6289

0.3967

0.1088

0.1912

351

Industrial chemical

0.8172

0.4860

0.1079

0.7287

0.7177

0.1701

0.5066

0.2707

0.2356

0.0970

352

Other chemical

0.6526

0.3335

0.0171

0.1449

0.0810

0.2361

0.9573

0.2970

0.1722

0.0780

362

Glass products

0.2280

0.8572

0.1694

0.4076

0.0461

0.4504

0.6138

0.1027

0.8300

0.0001

363

Cement

0.5348

0.4614

0.0096

0.1417

0.1237

0.1133

0.3981

0.5883

0.0348

0.0640

383

Electrical equipment

1.4194

0.5348

0.0572

0.1207

0.6380

0.4369

1.1696

0.4460

0.1282

0.3118

384

Transportation equipment

0.1171

0.0999

0.0580

0.1387

0.0247

0.6559

1.0919

0.0691

0.8008

0.1559

385

Professional equipment

0.0777

0.4476

0.3930

0.0542

0.0000

0.4971

0.4678

0.3698

0.0663

0.0038

390

Miscellaneous

0.4431

0.7391

0.0117

0.7750

0.4600

0.1681

0.2940

0.3361

0.0912

0.0242

Sources: Manufacturing Surveys, CBS.

INV-ST: ratio of investment to capital stock; PROFIT: ratio of gross operating surplus to capital stock; LEVER: ratio of total debt to capital stock; AVCOB: average cost of borrowing.

figures, where the rates of return were consistently higher for exporters. The rates of return gap between exporters and non-exporters was somewhat narrowed in 1995 in the context of a booming domestic economy. The profitability of non-exporters increased substantially, although it was still lower than for exporters.

Looking at the industry level, winning sectors (garments, footwear and wood products) are more leveraged than others (Table 5.10). Interestingly, some domestically oriented industries have higher leverage rates than winners, for example cement (363). Cement, which is controlled by politically well-connected conglomerates, in fact is the most leveraged industry at the three-digit level. With their political ties to the government, it is possible for conglomerates to have preferential access to credit from state banks at subsidized rates. This practice is a legacy of the past, although it is still prevalent for industries with very strong connections to the government. Conglomerates also have another advantage, namely their high profiles, which have enabled them to get better access to offshore credit markets.

If one looks at the data classified by firm size, large enterprises tend to have higher leverage than medium or small establishments. Also, large establishments are more likely to use offshore borrowing as a source of capital. Better access to foreign credit markets is also evident for export-oriented firms, particularly in recent years. Interestingly, in the past (1990) domestically oriented firms have been more likely to resort to foreign credit. This picture is rather difficult to interpret since the domestic interest rates climbed sharply in 1991, but the portion of foreign credit did not increase.

After observing the financial aspects of establishments, it can be concluded that the ability to obtain external funds in the domestic market differs among small and large firms, and between exporters and non-exporters. The credit market usually favors large firms, outward orientation and a good reputation. Firms not possessing such characteristics may have to assume higher costs of borrowing. This situation is nonetheless much better than during times of financial repression when credit is rationed and allocated according to a set of rules defined by the government. A liberal foreign exchange regime also provides additional advantages for certain firms to borrow funds from overseas markets. Unfortunately, there is a lack of information on the ethnicity of the owner of firms in general. It is common knowledge, however, that conglomerates and large Chinese firms with connections to financial markets in Singapore and Hong Kong, foreign firms and exporters all have better access to foreign loans. Also, even after the 1983 Banking Deregulation, state bank credit at below market rates is still available for people and large firms with strong political connections to the government. So, even after deregulation, credit market segmentation remains. The only difference with the past is that access is higher now for previously disadvantaged firms, albeit at a higher rate.

Trade specialization, productivity and finance: Econometric evidence

In this section we try to answer a fundamental question regarding the interactions between financial factors, trade specialization and international competitiveness. The question asks what the important factors are in determining whether a firm specializes in winning or losing sectors. Perhaps financial factors such as the availability of credit are very important in determining the pattern of specialization. The pattern of specialization itself is very important in determining the country's competitiveness since a country might be locked into activities that contribute very little to the sustainability of the current account. To be able to answer the above question, we model the firm's specialization decision, that is, to specialize in one particular sector or another. After a firm chooses a sector, it will then choose its market orientation (i.e. whether to concentrate on export market or domestic market). Firms' choices will ultimately determine the pattern of specialization and current account sustainability.

Consider a manufacturing firm making a decision to choose a sector or activities. A firm will choose a particular sector when it offers the highest profit. Formally, from M activities firm t chooses sector k according to eqn (5.1):

Image

For simplicity, let N=2, so there are only two sectors in the economy: winning sectors (textiles, garments, footwear and wood products) and losers (other sectors). The observed variable πtk is defined as πtk=1 if a firm chooses a winning sector, and πtk=0 otherwise. The probability of specializing in the winning or losing sectors is determined by several factors, including firm characteristics and financial characteristics. Specifically, the profit function is specified as a function of output price (p), input price (w) and 'financial' price (f) reflecting financial access or availability. This is illustrated in eqn (5.2):

Image

where c represents various firm's characteristics and a refers to a productivity shock or productivity growth.

After examining the relationship between finance and patterns of specialization, the next task is to investigate the consequences of finance and pattern of specialization on the current account. For this, we need to model a firm's output decision. In standard microeconomics textbooks, profit maximization will result in an output supply function and input demand function. The firm's net exports, or contribution to the trade balance, are treated as the output supply function net of imported raw materials. Therefore, following eqn (5.1), the firm's exported output net of imported raw materials or net exports (nx) is specified in eqn (5.3):

Image

where px is the price of exported output.

For the firm's specialization function, probit regressions are performed on eqn (5.1), while for the contribution to the trade balance we use ordinary least square (OLS) estimation. For financial availability, we use two proxies: the ratio of domestic credit to total capital and the ratio of overseas borrowing to total capital. The ratio of value added to intermediate inputs is used as a proxy for output price. This variable is also intended to capture the differences in effective rates of protection among industries. The basic result of firm-level regression for the entire 1991 and 1995 samples can be seen in Table 5.11.

In the first exercise, we examine the determinants of a firm's contribution to the trade balance or exported output net of imported raw materials (numerical columns 1 and 2 of Table 5.11). For the complete sample of manufacturing establishments

Table 5.11 Factors affecting contribution to trade balance and probability to specialize in leading RCA sectors

 

Contribution to TB
Whole sample

 

Leading RCA sector
Whole sample

 

 

1991

1995

1991

1995

C

3.42E+04
[0.051]

4.61E+04
[–0.696E+07]

–9.31E–01
[–9.487]

–1.02E+00
[–15.742]

PRICES

5.72E+06
[2.312]

–6.96E+06
[–1.125]

27.379
[70.53]

23.023
[67.183]

WAGES

–8.554
[–0.606]

–1.60E+02
[–2.977]

1.77E–06
[–1.121]

–2.57E–05
[–4.361]

LPG

–5,591
[–1.577]

–17,621
[–1.472]

–0.021
[–27.279]

–0.046
[–36.898]

AGE

3,052.8
[0.399]

36,241
[2.117]

–0.015
[–12.363]

–0.015
[–13.147]

DMNC

–2.47E+05
[–0.547]

–5.84E+05
[–0.639]

–0.401
[–6.278]

–0.543
[–9.275]

DLARGE

1.58E+06
[2.149]

9.87E+06
[8.148]

7.28E–01
[6.898]

5.51E–01
[7.333]

DMED

4.13E+05
[0.603]

1.05E+06
[0.988]

3.11E–01
[3.103]

1.73E–01
[2.577]

DSMALL

81,965
[0.123]

1.20E+05
[0.120]

0.171
[1.755]

1.49E–01
[2.325]

DMLOAN

–0.004
[–1.431]

0.081
[4.264]

8.06E+08
[2.075]

1.36E–10
[0.114]

FRLOAN

–0.038
[–5.233]

–0.162
[–7.471]

9.74E–11
[–0.087]

–1.67E–09
[–0.960]

N

16,494

21,559

16,494

211,551

F

6.11

26.29

Rest-Log-L

–10,141.1

–1,318,980

Log-L

 

 

–6,466.66

–7,559.26

PRICES: price of exported output; LPG: labor productivity growth; DMNC: dummy for FDI firms; DLARGE: dummy for large firms; DMED: dummy for medium-sized firms; DSMALL: dummy for small firms; DMLOAN: domestic banks' credit; FRLOAN: overseas credit.

in the 1995 sample, we find a significant negative relationship between wages and contribution to the trade balance (TB) or net export. Thus in 1995, labor cost is still an important factor in determining the ability to compete in export markets. The later economic deregulation measures seem to make this pattern stronger, and thus confirm the result of the probit regressions.

We also find a negative relationship between TFP growth and contribution to TB, which means firms in industries with high TFP growth tend to not be export oriented. However, the coefficient is not significant at the 5 per cent confidence level. In the case of the age variable, there is a suggestion that older firms tend to generate surplus for the trade account. This result seems in conflict with the probit regressions, although if we take into account the possibility that older firms may have stronger linkages with domestic suppliers compared to younger ones, this result is plausible.

The impact of differences in firm type on foreign exchange contributions also present interesting results. In the 1991 sample, there is no significant difference between large, medium and small firms with regard to contribution to the TB. In the 1995 sample, however, large firms are more likely to contribute to the TB compared to medium and small ones. In another categorization, we test the behavior of MNC or FDI firms versus non-FDI firms. We find that in both samples, there is no significant difference between FDI firms and local firms in terms of contribution to the TB.

In the case of the impact of the availability of credit financing on contribution to the TB, we observe the phenomenon of currency mismatch. The availability of foreign credit does not guarantee that firms will be sufficiently export oriented. This is especially apparent in the 1995 sample, as more and more domestically oriented firms with negative contributions to the TB use foreign credit to finance their domestic activities. A booming domestic economy, an overvalued exchange rate and high domestic interest rates have made borrowing from abroad an attractive option. Obviously, this behavior puts pressure on the current account and is also one of the many factors that contributed to the crisis. Interestingly, firms with a positive contribution to the TB are more likely to use domestic credit as a source of finance.

In the second regression (numerical columns 3 and 4 of Table 5.11), the coefficient of exported output price is positive as expected. Meanwhile the coefficient of wages is negative as expected, although it is only significant in the 1995 sample. This means that low wages are still important in influencing Indonesia's pattern of specialization, which confirms an earlier assertion that Indonesia is still specializing in low-end products which base their competitiveness on cheap labor. The sign of the LP growth coefficient also supports this conclusion. The coefficients of LP growth are negative and significant in both samples. It becomes more negative and significant in the later period. Indonesia is locked into the pattern of specialization in the production of low-productivity goods with high dependency on imported raw materials. There was some indication that the economic liberalization that started in 1986 reinforced this pattern of specialization.

There is a sign that finance does affect the pattern of specialization, particularly if we look at the coefficient of domestic credit in the 1991 sample. The coefficient of domestic credit is positive and significant, indicating the role of domestic banking in financing activities in export-oriented sectors. The coefficient becomes insignificant in the 1995 sample, so it is possible that the booming activity in the non-tradable sectors such as property, infrastructure and other less export-oriented activities has shifted the banking sector's attention away from the tradable sector.

All the dummy variables for capturing differences in a firm's size are significant in both samples. The most significant variable, and at the same time the largest positive coefficient, is the dummy for large firms, followed by medium and small establishments. Therefore, larger-size firms have a higher probability of specializing in the leading RCA sectors. With regard to age difference, the regression results also reveal that young firms tend to specialize in the leading RCA sector. Perhaps young firms with newer technologies are more suited to export markets.

The MNC dummy variable capturing the differences between MNC and local firms is negative and significant, meaning MNC firms do not tend to specialize in the leading RCA sectors. The negative coefficient is stronger and more negative in the 1995 sample, which suggests that FDI firms in the later period tend to see Indonesia as a potential market to be exploited, rather than as a base for export expansion. This does not mean that the later deregulation policies seeking to open up the economy are misplaced. Rather, inconsistency in the government's trade and industrial policies in the form of non-trade barriers makes the rates of returns on domestically oriented economies artificially high, while at the same time lower trade barriers in input markets and an overvalued currency make the cost of imported inputs artificially cheaper.

Firms' portfolio decisions

The Indonesian Manufacturing Surveys provide information regarding the sources of capital. We use this information to address the question about firms' portfolio decisions. An overview of the sources of firm finance is presented in Table 5.12. In general, firms in winning sectors tend to have a higher proportion

Table 5.12 Sources of finance

1991

 

1995

 

 

All

Winner

All

Winner

Own capital

31.46

34.81

23.15

17.37

Retained earnings

12.89

10.19

10.68

  9.02

Stock

  8.89

11.52

11.04

  8.52

Domestic credit

28.47

29.43

33.14

36.43

Foreign credit

  5.79

10.87

14.54

18.62

Foreign placement

  9.47

20.07

  4.07

  4.60

Government equity

  4.03

  1.11

  3.38

  5.44

Sources: Calculated from the Industrial Surveys database.

of foreign loans when compared to the average manufacturing firm. In 1991, for example, the figure for foreign loans in the winning sectors was 10.87 per cent, while the corresponding figure for all manufacturing firms was 5.79 per cent. In 1991, on average a winner's other important financial sources were own capital (34.81 per cent), domestic banking credit (29.43 per cent), retained earnings (10.19 per cent), stocks (11.52 per cent) and foreign direct capital placements (20.07 per cent). Thus, winners are more leveraged and more likely to obtain foreign loans than average manufacturing firms.

In 1991, for winning sectors the portions of retained earnings, foreign direct capital placement and government equity participation were lower than the average manufacturing firm. So it could be concluded that winning sectors tend to have higher portions of domestic bank credits, foreign credits, their own capital and the stock market as sources of finance. In 1995, some portfolio shifts took place; for both winners and the average manufacturing firms a big decline of the share of own capital could be observed. Thus, all firms tended to raise capital externally, in particular in the domestic credit market and through overseas borrowing.

To model the portfolio decisions of firms, we employ a multinomial logit model in which the choice of sources of finance is specified as a function of firm characteristics including age, labor productivity growth, size differences, and MNC versus local firms. As in the above discussion, there are seven choices for financing: own-capital injection, retained earnings, stock market, domestic banking, overseas borrowing, foreign direct capital placement and government equity participation. As required by the multinomial logit procedure, the parameters of one choice need to be normalized to zero. In this case, we chose own-capital injection to be normalized. The reason behind this choice is that we are more interested in other sources of financing. The results of multinomial logit regressions are presented in Table 5.13. First, we look at the 1991 sample. At first glance, it can be seen that one variable which is always significant in all cases is labor productivity growth (LPG). Thus, productivity growth is always important in all choices of source of finance. Firm age has a negative coefficient in three choices: stock market, domestic bank credit and foreign credit. This means older firms are less likely to use these three sources of financing. For foreign direct capital placement, the sign of the coefficient is positive and significant, indicating that older firms are more likely to choose this source of finance. Another interesting variable is the dummy variable for large firms. The sign of this variable indicates that large firms have a high probability of using the stock market, bank credit, foreign borrowing and foreign direct capital placement to raise capital. Medium firms, with foreign direct capital placement as an exception, also showed similar preferences.

In the 1991 sample, the case of MNC firms is also interesting. The only positive and significant variable is foreign credit, indicating better access to overseas credit markets. MNC firms are also less likely to use domestic bank credit as a source of funds. Better access to overseas borrowing and higher domestic interest rates make it unnecessary to secure domestic credit. In the case of winner firms, retained earnings, foreign credit and foreign direct capital placement are chosen as sources of finance. Interestingly, they avoid government equity participation.

Table 5.13 Factors affecting firm portfolio decisions (multinomial logic estimation)

Retained earnings

 

Stock

 

Domestic bank credit

 

 

1991

1995

1991

1995

1991

1995

C

–1.30857
(–21.1683)

–0.864072
(–16.6740)

–2.44869
(24.0431)

–3.29912
(–24.2278)

–0.861983
(–15.4582)

–0.669374
(–13.3598)

AGE

4.19E–03
(1.67229)

0.013078
(5.01024)

–0.14078
(3.03601)

–5.70E–03
(–0.873041)

–9.94E–03
(–3.94927)

–4.19E–03
(–1.54099)

PG

3.58E–06
(4.97147)

9.55E–06
(4.67062)

4.71E–06
(6.54513)

9.99E–06
(4.00521)

4.28E–06
(6.15864)

8.25E–06
(4.07938)

DMNC

0.136403
(0.918419)

0.798009
(3.94947)

0.27653
(1.46656)

0.604485
(1.90015)

–0.347212
(–2.40387)

0.50547
(2.63725)

DWIN

0.215943
(3.08950)

–0.151825
(–2.18725)

–0.204891
(–1.82397)

0.074109
(0.506668)

0.114795
(1.84872)

0.205713
(3.34649)

DLARGE

–0.61389
(–0.495102)

0.692944
(5.51038)

1.26361
(8.54642)

2.08893
(9.89206)

0.707507
(7.40571)

1.38599
(12.4313)

DMED

0.13216
(1.77613)

0.327876
(4.21228)

0.969101
(8.91211)

1.64711
(10.8018)

0.46621
(7.19578)

0.873283
(12.7565)

Foreign credit

 

Foreign direct capital

 

Government equity

 

 

1991

1995

1991

1995

1991

1995

C

–3.42145
(–21.5331)

–4.36467
(–20.6467)

–3.36586
(–26.0583)

–4.74043
(17.1974)

–4.62034
(–13.9996)

–4.74043
(–17.1974)

AGE

–0.024471
(–3.39453)

–0.032728
(–2.97558)

0.022923
(5.94416)

–0.087641
(5.36597)

9.86E–03
0.7288226

–0.087641
(5.36597)

LPG

5.20E–06
(6.77664)

1.31E–05
(5.92772)

4.39E–06
(5.53726)

9.39E–06
(3.38700)

4.97E–06
(4.13023)

9.39E–06
(3.38700)

DMNC

1.54151
(8.55219)

3.57384
(15.4204)

–0.35744
(–1.04789)

5.76058
(18.6109)

–25.7244
(–0.799E–04)

5.76058
(18.6109)

DWIN

0.551413
(3.78128)

0.424417
(2.38965)

0.416206
(2.96919)

–0.451513
(–2.17890)

–1.54404
(–2.51481)

–0.451513
(–2.17890)

DLARGE

1.41212
(7.36451)

2.40685
(9.34578)

0.881273
(4.85236)

1.28005
(4.28290)

0.553575
(0.878425)

1.28005
(4.28290)

DMED

0.769345
(4.73044)

1.55227
(6.95577)

–0.46763
(–0.279974)

0.923312
(–25.4382)

0.438528
(1.02922)

0.923312
(3.74099)

Note: All t-statistics significant at the 5 per cent level.

The results for the 1995 sample differ from the 1991 sample, with some exceptions. Labor productivity is still important in all choices. With regard to age, older firms tend to choose retained earnings. The positive and significant coefficient for government equity participation must come from the presence of the substantial number of state-owned enterprises. Older firms are also less likely to use foreign credit and foreign direct capital placement.

The size factor is still important in the 1995 sample, as indicated by the coefficient of the dummy variable for large firms which is significant in all choices of financing. The same picture also applies to medium firms. MNC firms show different behavior. It is true that they still prefer to use foreign credit, and in 1995 also foreign direct capital placement. Unlike in the 1991 sample, MNC firms now are more supportive of the use of retained earnings and domestic bank credit. Winner firms also show a more favorable attitude towards the use of domestic bank credit, although the choice of foreign credit is still in favor. There is a shift in behavior, however, with regard to the use of retained earnings and foreign direct capital placement as they are now less likely to use these sources.

4. Conclusion

The structure of Indonesian exports in the 1970s and the early 1980s, with a heavy dependence on oil/gas and natural resource products, made the current account very vulnerable to international price fluctuations. The economic reforms initiated in response to the plummeting of oil prices marked a change in trade and industrial policy from import substitution, with its emphasis on the development of capital-intensive manufacturing in the upstream and resource-based industries, to labor-intensive export-oriented industries.

The impact of economic reforms after 1986 is very obvious in the structure of Indonesian exports. Exports of manufactured goods like textiles, processed woods, electronics and shoes started to rise. In 1991, the share of non-oil exports in total exports exceeded oil and gas exports. Until the mid-1990s however, Indonesia's manufactured export base was still very narrow, and mainly consisted of wood products, textiles, garments and footwear, industries exploiting low-cost labor rather than productivity as a source of competitiveness. There was an indication that the economic liberalization of 1986 seemed to reinforce this pattern of specialization. Indonesia is locked into a pattern of specialization that emphasizes the production of low-productivity goods with high dependency on imported raw materials. The emergence of unskilled labor-intensive industries as foreign exchange earners creates a new problem for the current account. As these industries are very dependent on imported inputs, this makes them very vulnerable to exchange rate fluctuations, as happened in the recent economic crisis.

There is an indication that finance does affect the pattern of specialization. In a 1991 sample, the domestic banking system had a very important role in financing activities in export-oriented sectors. In later periods, however, the booming activity of the non-tradable sectors (such as property, infrastructure and other less export-oriented activities) shifted the attention of the banking sector away from tradable sectors. The influx of capital inflows is also responsible for the expansion of the non-tradable sector during the 1991–5 period. As activities in non-tradables continued to expand, high growth was maintained until 1996, although the current account deficit continued to soar. This eventually triggered a reversal in expectations.

Notes

1 In terms of development strategy, the oil boom also produced a shift in the strategy of economic development. The availability of money and the expansion of domestic aggregate demand persuaded the government to pursue an import substitution policy. Many ambitious infrastructure and industrial projects were launched. The shift towards more inward-looking policies was also reflected through more protectionist industrial and trade policies.

2 See Section 2 on contribution to the trade balance and sectoral competitiveness.

3 Some (e.g. Iqbal 1995) argued that the reason behind the economic slowdown was a slowdown in pace of deregulation. For example, although the nominal tariff showed a decreasing trend in the pre-1991, it hardly changed during the 1991–4 period. The same pattern could also be observed for products subject to import licensing.

4 Although some argued that the dismal performance of several economic indicators was caused by the slowdown in the pace of economic deregulation, others suggested that this downward trend was part of a global phenomenon. This latter argument was based on the observation that other Asian tigers like Malaysia, Thailand and South Korea were also exhibiting a similar trend. Another reason behind the slowdown of manufactured exports was the nature of the products produced – basically destined for low end consumption and relying on low-cost labor. With the successive increases of national minimum wages, the competitiveness of Indonesian labor-intensive exports seemed to erode.

5 Unlike the previous investment boom in 1988–92 where textiles, garments and footwear made the bulk of total investments, the second investment boom was more diversified, ranging from electronic components, automotive parts, chemicals, to food and beverages. Also, most of the projects in the latter boom were destined for the Indonesian domestic market.

6 The reason for this is that an overvalued rupiah and high domestic interest rates made borrowing from abroad cheaper.

7 It is worthwhile to highlight the Indonesian electronics industry since it might become the great foreign exchange earner for the country in the future provided that the government alters the structure of protection. At present, the electronic industry has not moved much beyond assembly operations.

8 There are several explanations regarding Indonesia's loss of competitiveness in its main manufacturing exports. One explanation is that the competitiveness of Indonesia's labor-intensive industries had been eroded by the successive increases of minimum wages. Other factors, such as the boom in the domestic market, might have played a role as well. Finally, Indonesian producers also recently faced stiff competition from several emerging markets such as China, Vietnam and India that produce products of comparable quality at competitive prices.

9 A study by the World Bank (1996) found that productivity growth in Indonesia kept pace with the large increases in minimum wages untill 1993. Thereafter, the minimum wage became more binding.

10 Winners and losers have been defined from the point of view of international trade. Based on the previous analyses, winner industries include garments, footwear and wood products. All of these industries are characterized by a huge contribution to the balance of trade. Meanwhile, loser industries include industrial chemicals and transportation equipment.

11 This phenomenon is not limited to the chosen winner and loser industries. In general, industries with a net positive contribution to the trade account are more highly leveraged than industries with negative contributions.

12 Nominal interest rates in Indonesia are the highest among Southeast Asian countries, though inflation in Indonesia is also the highest in the region.

References

Chapman, R. (1992) 'Indonesian Trade Reform in Close-Up: The Steel and Footwear Experiences', Bulletin of Indonesian Economic Studies 28(1): 67–84.

Dowling, M. (1997) 'Industrialization, International Trade and Structural Change in Indonesia during the Suharto Era', Paper presented at the conference 'Sustaining Economic Growth of Indonesia: A Framework for the Twenty-First Century', organized by USAID, ACAES, and Institute of Economic and Social Research University of Indonesia.

Fane, G. and Philips, C. (1991) 'Effective Protection in Indonesia in 1987', Bulletin of Indonesian Economic Studies 27(1): 105–26.

Haque, I. (1995) Trade, Technology, and International Competitiveness. Washington, DC: World Bank.

Harris, J. R., Schiantarelli, F. and Siregar, M. G. (1994) 'The Effect of Financial Liberalization on the Capital Structure', World Bank Economic Review 8(1): 1–47.

Henderson, J. V. and Kuncoro, A. (1996) 'Industrial Centralization in Indonesia', World Bank Economic Review 10(3): 223–39.

Hill, H. (1995) 'Indonesia's Industrial Policy and Performance: Orthodoxy Vindicated', Paper presented at the conference 'Building on Success: Maximizing the Gains from Deregulation', organized by the Association of Indonesian Economists and the World Bank.

Iqbal, F. (1995) 'Deregulation and Development in Indonesia', Paper presented at the conference 'Building on Success: Maximizing the Gains from Deregulation', organized by the Association of Indonesian Economists and the World Bank.

Kuncoro, A. (1995) 'The Impact of Seller Concentration on Industrial Location', Paper presented at the conference 'Building on Success: Maximizing the Gains from Deregulation', organized by the Association of Indonesian Economists and the World Bank.

Kuncoro, A. (1997) 'Export Orientation, Productivity and Finance', Paper presented at the conference 'Sustaining Economic Growth of Indonesia: A Framework for the Twenty-First Century', organized by USAID, ACAES, and Institute of Economic and Social Research University of Indonesia.

Lall, S. (1998) 'Technology Policies in Indonesia', in H. Hill and T. Kian Wie (eds), Indonesia's Technological Challenge. Singapore: ANU and Institute of Southeast Asian Studies.

Nasution, A. and James, W. E. (1995) 'Future April 12, Direction for Economic Policy Reform: Where Are We and Where Do We Go from Here', Paper presented at the conference 'Building on Success: Maximizing the Gains from Deregulation', organized by the Association of Indonesian Economists and the World Bank.

Wallace, W. (1997) 'Prospect for the Indonesian Current Account Deficit', Paper presented at the conference 'Sustaining Economic Growth of Indonesia: A Framework for the Twenty-First Century', organized by USAID, ACAES, and Institute of Economic and Social Research University of Indonesia.

6 Trade, competitiveness and finance in the Philippine manufacturing sector, 1980–951

Josef T. Yap

1. Introduction: The Philippine development experience

The East Asian miracle of the 1960s up to the mid-1990s and the East Asian debacle in 1997 put in perspective two crucial factors that affect sustainable economic growth and development. The first factor is outward orientation, which is a necessary ingredient for increasing the competitiveness of an economy, and the second is a sound financial structure that is required for efficient resource allocation and macroeconomic stability. The primary objective of this chapter, is to analyse how these two factors interact with each other, i.e. how the level of financial development affected the evolution of the Philippine current account. Of particular concern is the trade sector, with emphasis on the dynamics of competitiveness and the pattern of exports in the Philippine manufacturing sector.

The Philippines was pointedly left out of the list of High Powered Asian Economies (HPAEs) identified by the World Bank (1993) in its study of the East Asian miracle. This is due primarily to her erratic economic performance that has been characterized by boom-bust cycles. During the period 1970–97, for which data is presented in Table 6.1, the Philippines experienced three balance-of-payments (BOP) crises. The first and most acute was in 1983–5 following the onset of the international debt crisis, the second was in 1990–2 in the aftermath of Gulf War; and the last was in the second half of 1997 as the Philippines was drawn into the financial crisis. Even when the economy's performance was being considered exceptional by the international community, the peak GDP growth rate was only 5.7 per cent, which was recorded in 1996. Not surprisingly, this growth was the second lowest in Southeast Asia in that year.

The Orthodox view

The performance of the Philippine economy during the postwar period has been directly linked to the fortunes of its industrial sector. The various studies on this sector came up with the following major conclusions (Medalla et al. 1995):

1 That the more than three decades of protection had been very costly in terms of its inherent penalty on exports, its serious adverse impact on resource allocation, and dynamic efficiency losses arising from lack of competition.

Table 6.1 The Philippines, selected economic indicators

 

1970–4

1975–9

1980–2

1983–5

1986–9

1990–2

1993–7

Income (growth rates)

 

 

 

 

 

 

 

Real GDP

  5.4

  6.2

  4.1

 –4.3

  5.2

  0.9

  4.4

Agriculture

  2.8

  4.5

  2.8

 –2.1

  3.3

  0.2

  2.5

Industry

  8.0

  7.9

  4.0

 –8.9

  5.8

 –0.5

  5.3

Manufacturing

  7.9

  5.2

  2.6

 –6.1

  5.7

  0.2

  4.5

Services

  5.0

  5.4

  4.9

 –1.1

  5.6

  1.8

  4.7

Real GDP (% share)

 

 

 

 

 

 

 

Agriculture

27.4

24.5

23.3

23.2

23.9

22.6

21.6

Industry

35.3

39.6

40.7

38.0

35.2

34.9

35.2

Manufacturing

28.0

27.9

27.2

25.5

25.2

25.4

25.0

Services

37.4

35.9

36.0

38.8

40.9

42.5

43.2

External sector

 

 

 

 

 

 

 

Degree of openness (% of GDP)a

40.5

41.6

53.7

48.7

58.7

70.3

97.2

Value of exports (USD)

1,583

3,209

5,510

5,008

6,364

8,950

17,615

Share of manufactured exports

  8.6

  24.4

  41.0

  51.1

  60.7

  72.1

  80.7

Current balance/GDP (%)

  0.7

 –5.3

 –6.8

 –4.1

 –0.6

 –3.3

 –4.9

BOP/GDP (%)

  1.8

 –1.2

 –2.4

  0.6

  1.9

  1.8

  0.8

Real Effective Exchange Rate Indexb

98.9

96.8

102.5

86.4

68.8

71.2

83.6

Public Sector

 

 

 

 

 

 

 

Public sector deficit/GDPc

 

 –8.4

 –13.6

 –5.4

 –3.9

 –2.9

 –0.6

Monetary Sector

 

 

 

 

 

 

 

Money supply-M3 (growth rate)

23.2

18.9

18.6

11.8

14.6

15.0

22.6

M3/GNP

24.3

29.1

29.0

25.9

24.9

27.7

36.9

Labour Sector

 

 

 

 

 

 

 

Unemployment rate (%)

  5.6

  7.5

  8.9

  11.2

  10.4

  9.5

  9.1

Underemployment rate (%)d

13.4

11.6

26.3

30.8

24.6

21.8

21.2

Real wage (non-agricultural, pesos)

93.2

63.0

58.0

68.7

72.1

80.8

82.8

Sectoral employment (% share)

 

 

 

 

 

 

 

Agriculture

52

52.1

51.6

50.0

47.6

45.3

43.3

Industry

15.8

15.3

14.7

14.5

14.7

15.8

16.0

Services

32.2

32.5

33.8

35.5

37.7

39.2

40.6

Prices

 

 

 

 

 

 

 

Inflation rate (%)

18.8

  9.9

13.4

26.8

  5.9

13.1

  9.4

Internal terms of trade (% change)

  5.4

 –1.4

 –5.4

 –0.7

 –0.2

 –1.4

 –1.4

Population

 

 

 

 

 

 

 

Population growth rate (%)

  2.8

  2.7

  2.6

  2.5

  2.4

  2.6

  1.8

GNP per capita (USD)

     336

     587

     723

     547

     700

     831

  1,070

Real pesos of 1985

10,507

11,642

12,762

11,641

10,885

11,559

11,923

Sources: NSO, National Income Accounts; NSO, Philippine Statistical Yearbook; Central Bank, Annual Report.

Notes

a Defined as the ratio of the sum of imports and exports of goods and services to GDP; both terms at constant prices.

b Trade-weighted real exchange rate.

c Includes general government, state-monitored corporations and the Central Bank.

d Defined as workers working less than 40 hours per week.

e Ratio of implicit GDP deflator of agriculture to that of non-agriculture.

2 That a reform toward a more liberal and neutral trade policy is necessary to propel the economy to a higher level of industrialization.

This is the basic neoclassical view that revolves around the issue of comparative advantage. Economic protection in the past meant that the resources of the country flowed into sectors where the Philippines did not possess a comparative advantage. Hence, production, particularly in the industry sector, became highly inefficient. Moreover, such policies prevented export-led industrialization from taking root in the Philippine economy. Filipino entrepreneurs simply made profits behind the protective cover of tariff walls and non-tariff barriers to trade and did not aggressively seek to manufacture products where the Philippines had a distinct comparative advantage in the world market.

That the Philippine economy is largely inefficient is without question. This trend can be gleaned by comparing labour productivity across time and across countries in East Asia. Table 6.2 shows that labour productivity in the Philippines largely stagnated between the period 1975 and 1996. The overall labour productivity of Malaysia, Indonesia, Singapore and Thailand more than doubled in this period while the index for the Philippines even declined by one point. The agriculture and manufacturing sectors exhibited the same pattern.

Apart from reference to the neoclassical argument, the poor performance in terms of labour productivity can also be attributed to the low saving and investment rates in the Philippines (Table 6.3). A low rate of capital accumulation leads to a low marginal product of labour and low average labour productivity. The variance in the investment rate between the Philippines and the more developed Southeast Asian economies can be explained partly by the ability to attract

Table 6.2 Indices of average labour productivity overall, agriculture and manufacturing

 

 

1975

1980

1985

1990

1996

China

Overall

100

122

131

140

 —

Indonesia (1993 prices)

Overall

100a

126

131

148

204b

 

Agri

100a

104

121

114

160b

 

Mftg

100a

155

194

242

310b

Malaysia (1978 prices)

Overall

100

125

138

161

216

 

Agri

100

133

158

201

281

 

Mftg

100

104

118

143

181

Philippines (1985 prices)

Overall

100

119

92

102

99

 

Agri

100

117

100

109

108

 

Mftg

100

119

96

108

100

Singapore (1985 prices)

Overall

100

116

137

171

233

 

Agri

100

114

194

177

288

 

Mftg

100

115

128

171

272

Thailand (1988 prices)

Overall

100

116

132

181

297

 

Agri

100

101

113

118

234

 

Mftg

100

121

133

178

210

Sources: Intal and Basilio, 'The International Economic Environment and the Philippine Economy', PIDS Discussion Paper (1998) and ADB Key Indicators, 1988 and 1997.

Notes

 

 

 

 

 

 

a 1976.

 

 

 

 

 

 

b 1995.

 

 

 

 

 

 

foreign direct investment (FDI). In turn, both FDI and domestic investment are largely affected by the degree of macroeconomic stability in an economy.

The financial sector and macroeconomic stability

The dismal record of the Philippines in terms of macroeconomic stability is reflected in her higher inflation rate (Table 6.3). Econometric studies cite import costs and the money supply as the explanatory variables with the highest impact on Philippine inflation. Rapid monetary growth is usually related to a large public deficit, but a closer analysis of the Philippine financial system will reveal that the instability of the banking sector during the postwar period contributed heavily to macroeconomic imbalances.

The development of the financial system of the Philippines does not provide an exemplary case of smoothly operating financial markets fuelling investment and growth. On the contrary, structural features of the process of financial intermediation have been at the root of the recurring liquidity and solvency crises in various parts of the Philippine banking system and capital markets. Rather than providing channels to alleviate financial constraints, the malfunctioning of the financial system

Table 6.3 Selected indicators, East Asian economies

 

1980

1985

1990

1995

1997

Indonesia

 

 

 

 

 

M2/GNP

13.7

24.8

45.5

52.8

61.1

Inflation

18.0

4.7

7.4

9.5

6.1

Savings/GNP

30.5

31.1

33.8

31.5

32.0

Investment/GNP

21.8

29.2

32.2

32.9

32.6

FDI (million USD)

 

 

746a

4,348

5,350

Malaysia

 

 

 

 

 

M2/GNP

53.4

67.9

69.3

95.1

111.8

Inflation

6.7

0.3

2.6

5.3

2.7

Savings/GNP

34.2

35.2

34.9

41.5

46.7

Investment/GNP

31.6

29.7

32.7

45.7

45.1

FDI (million USD)

 

 

1,605a

4,132

3,754

Philippines

 

 

 

 

 

M2/GNP

22.8

26.8

34.2

49.0

59.0

Inflation

18.3

23.2

14.1

8.1

5.0

Savings/GNP

26.8

19.5

18.8

14.2

14.8

Investment/GNP

29.3

14.9

24.3

21.6

23.9

FDI (million USD)

 

 

501a

1,459

1,253

Thailand

 

 

 

 

 

M2/GNP

38.5

59.6

70.7

80.6

92.7

Inflation

19.8

2.5

6.0

5.8

5.6

Savings/GNP

23.2

25.2

34.7

37.8

37.0

Investment/GNP

29.4

28.7

41.9

42.5

36.1

FDI (million USD)

 

 

1,325a

2,002

3,600

China

 

 

 

 

 

M2/GNP

37.4

58.5

78.9

104.0

120.8

Inflation

7.5

11.9

3.1

16.9

2.8

Savings/GNP

35.2

37.7

34.6

41.5

 

Investment/GNP

34.1

35.5

38.6

41.7

 

FDI (million USD)

 

 

3,105a

35,849

45,300

Korea

 

 

 

 

 

M2/GNP

34.1

36.6

38.5

44.1

48.9

Inflation

28.7

2.5

8.6

4.5

4.5

Savings/GNP

24.8

34.7

36.4

37.1

35.7

Investment/GNP

33.0

30.6

37.2

37.4

35.4

FDI (million USD)

 

 

863a

1,776

2,341

Singapore

 

 

 

 

 

M2/GNP

66.4

69.8

90.5

83.7

84.0

Inflation

8.5

4.1

3.5

1.7

2.0

Savings/GNP

40.2

39.2

45.3

49.9

48.7

Investment/GNP

48.1

41.0

35.7

33.4

36.4

FDI (million USD)

 

 

3,592a

8,210

10,000

Sources: International Finance Statistics, IMF, World Investment Report 1998

Note

a Average of 1986–1991.

has been a source of macroeconomic problems. The structural problems relate to the segmented nature of the Philippine financial markets, the lack of competition among financial institutions, wide-ranging interlocking directorates and ownership patterns across the banking industry and other economic sectors, the shallowness of financial markets and the unresolved external debt overhang (Vos and Yap 1996).

The structure of the financial sector, specifically the banking industry, reflects the patrimonial nature of the Philippine state and the dominance of a predatory oligarchy which leads to an ineffective and inefficient bureaucracy.2 Banks in the Philippines are largely familial in nature wherein family conglomerates milked the loan portfolios of their own banks, causing liquidity problems. The situation was exacerbated by the inability of the Philippine Central Bank to regulate and supervise banks effectively, creating instability in the banking system. The existence of a patrimonial oligarchic state (as opposed to a patrimonial administrative state as in Thailand and Indonesia) could also explain why the protectionist policies in the Philippines deteriorated into rent-seeking activity while similar measures were a means of capital accumulation in other countries.

As a result, the Philippine financial system has had a strong dualistic nature, in which an important informal financial market segment coexists with the formal banking system. Informal moneylenders fund, at relatively high cost, small businesses and household firms which have little or no access to the formal banking system. Large private corporations are the preferred borrowers of the highly concentrated formally banking system. The interlocking interests of banks and corporate enterprises strongly direct the allocation of funds, often overriding normal financial risk assessment. Over-leveraged firms and bad loans have been systemic problems which have required Central Bank (now known as the Bangko Sentral ng Pilipinas or BSP) and government intervention to bail out ailing financial institutions, often with substantial macroeconomic costs. At the same time, financial markets have remained rather thin. While financial deepening has proceeded at an accelerated pace in neighbouring Asian countries, the mobilization of savings through the financial system has stagnated in the Philippines. This is reflected in a lower M2/GNP ratio up to the 1980s (Tables 6.3 and 6.1 for M3/GNP).

Various attempts at financial reform and liberalization during the 1970s and 1980s succeeded in reducing some of the structural problems of the Philippine financial system (cf. Intal and Llanto 1998). Adjustment policies in the early 1990s, particularly the liberalization of the capital account, sought to resolve the economy's fiscal and foreign exchange constraints. This included the rehabilitation of the BSP wherein the national government took over its bad loans. The M2/GNP ratio of the Philippines increased sharply after 1992 although this is largely a result of the liberalization of the capital account. Some reforms, however, exacerbated weaknesses, such as the increased concentration of the banking sector after the financial liberalization measures of 1981. Moreover, emphasis has been placed on increasing competition in the financial sector – mainly by allowing the entry of more foreign banks – rather than strengthening the supervisory and regulatory role of the BSP.

Framework and objective

An objective of this chapter is to examine the linkage between trade patterns and competitiveness – or the lack thereof – in the Philippine manufacturing sector, using data between 1980 and 1995. The most popular and influential standard for competitiveness is related to unit labour costs whereby a country attempts to keep wage increases in line with productivity changes. By keeping wage costs under control, a country can make its exports competitive – a higher market share for exports invariably reflects greater competitiveness. Recent evidence, however, has shown that unit labour cost is a weak indicator of a country's competitiveness (Fagerberg 1988). A more reliable measure would be productivity performance associated with technological development. Hence, competitiveness will be directly associated with measures of productivity.

Even with improvements in the technological capability of an economy, however, its trade performance may not show a commensurate response, or else the trade specialization of an economy diverges from the pattern dictated by its technological capability. If there is a weak relationship between these two variables, the next step is to determine to what extent this can be explained by an unstable macroeconomic environment, particularly in terms of exchange rate volatility and inflation. These variables usually work their way through the investment rate. Related to this is an inappropriate level of the real effective exchange rate which reflects an overvalued currency.

Meanwhile, a poorly functioning financial system can contribute to macroeconomic instability or hamper the flow of resources to sectors with high productivity growth thus failing to take advantage of export opportunities. Another major objective of this chapter is to determine how the level of financial development has affected the trade pattern.

2. Productivity, competitiveness and trade patterns

Theoretical developments

International competitiveness in a macroeconomic sense is defined as the 'ability of a country to produce goods and services that meet the test of international markets and simultaneously to maintain and expand the real income of its citizens (Haque 1995). The concern with international competitiveness stems primarily from the view that the growth of the HPAEs was export oriented. While it is still debated whether exports were the engine or merely a handmaiden of growth, increasing the competitiveness of the economy is definitely associated with greater efficiency and hence greater opportunities for economic growth.

Two advances in economic theory have brought non-price competitiveness – referring mainly to technological capability – to the forefront. The development of the New Trade Theory represents attempts to relax the restrictive assumptions of the neoclassical framework which assumes the existence of competitive markets, factor substitutability and mobility, and profit maximization. The new theory seeks to extend and develop the traditional framework by incorporating in its analysis such issues as the treatment of economies of scale, externalities, technical progress, product differentiation, and monopolistic and oligopolistic situations (Haque 1995). In this framework, a link between international technological competition and international trade is established, showing that strategic R&D rivalry between countries can be crucial for explaining the evolution of trade flows (Magnier and Toujas-Bernate 1994).

A parallel development occurred in the theory of economic growth that likewise stressed the importance of human resource development and technological accumulation: the development of endogenous growth models which suggest the hypothesis that investment (either in physical capital, human capital, or R&D activities) generates externalities that offset the decreasing returns to inputs. The offshoot of the new trade theory and endogenous growth theory was to shift the focus on technology capability as the primary determinant of an economy's competitiveness.

Analytical framework: Determinants of export share

We use the framework of Fagerberg (1988) to show the interrelation among the variables under consideration. Both technological competitiveness and price competitiveness should play a key role in determining the export market share of an economy. Even if a country is very competitive in terms of technology and prices however, it is not always able to meet the demand for its products because of a capacity constraint.

The market share of exports S(X) is expressed in multiplicative form in eqn 6.1 as

Image

where A, v, e and a are positive constants. T/Tw represents the technological competitiveness of a country, P/Pw is its price competitiveness, and C is its capacity to deliver. In this framework, export performance is affected by competitiveness and is not an indicator of competitiveness per se. Competitiveness is associated more with the concept of efficiency.

Fagerberg assumes that C depends on three factors: (a) the growth in technological capability and know-how that is made possible by the diffusion of technology from the countries on the world innovation frontier to the rest of the world (Q); (b) the growth in physical production equipment, buildings, equipment and infrastructure (K); and (c) the rate of growth of world demand (W). The latter could actually influence S(X) in both directions. Without a capacity constraint growth in W would lead to an increase in S(X). If demand outstrips the given level of capacity, exports will remain constant, but the market share of exports will decrease, because other countries will increase their exports.

Evolution of the Philippine manufacturing sector

The anti-protectionist neoclassical view became dominant among government technocrats starting in the late 1970s, and as a result a major trade reform programme was implemented in 1980. The objective was to make the Philippines more outwardly oriented by opening up its economy. After the trade reform process was disrupted because of the external debt crisis in 1984–5, major import liberalization programmes were implemented from 1986 to 1988. During this period, imports for more than 1,400 items were liberalized, bringing down the percentage of import-restricted items to less than 10 per cent.

This was followed by the second phase of the Tariff Reform Program that narrowed down the tariff range to mostly within 30 per cent. This was implemented by the Aquino administration under Executive Order (EO) 470 that covered the period 1991–5. Tariff reform was accelerated during the third phase of the programme this time under the Ramos administration. EO 264 called for a tariff range from 3 to 10 per cent by the year 2000 and a uniform 5 per cent tariff by the year 2004.

Partly because of the reforms in the trade sector, the overall efficiency of the manufacturing sector as measured by the effective protection rate (EPR) and the domestic resource cost (DRC) increased (Medalla 1998). In addition, total exports and the share of manufactured exports increased sharply. From only 4.8 billion USD in 1986, total exports surged to 20.5 billion USD in 1996. This represents an increase in the share of the Philippine exports in the world market from 0.24 per cent in 1986 to 0.40 per cent, although it is lower than the share of the developing HPAEs. The share of manufactured exports increased from 55 to 83 per cent (Table 6.1). Exports, however, are still concentrated in electronics and garments (at least up to 1993 for the latter), revealing a slow pace of change in the structure of the trade sector.

A more detailed exposition of the trade sector will show the evolution of the current account and the nature of structural problems of the Philippine economy. Table 6.4 presents data on revealed comparative advantage (RCA) for exports in the manufacturing sector.3 During the period 1980–95, the economy lost comparative advantage in tobacco manufactures, wood and cork products, and basic metal industries. The Philippines gained comparative advantage in electrical machinery during this same period, mainly through the semiconductor industry. It maintained a comparative advantage in food manufactures, footwear and wearing apparel, and furniture and fixtures. The RCA index for these industries, however, declined between 1980 and 1995.

The index of a sector's contribution to the trade balance (ICTB) is generally consistent with the trend in RCA (Table 6.5). The value of the ICTB for tobacco and basic metals fell during the period 1980–95. In the case of the food sector, there was a sharp drop in its ICTB while the values of footwear, wearing apparel and furniture remained fairly constant. The ICTB of electrical machinery turned from negative to positive in this period.

The distribution of exports across the different categories using data from the International Trade Statistics also reveals a disturbing trend (Table 6.6). Electrical machinery and miscellaneous manufactures have been the sectors with the fastest growing shares. Despite this development, gross value added of electrical machinery was only 2 per cent of GDP in 1997. Meanwhile special transactions,

Table 6.4 Revealed comparative advantage: Philippine share/world share per industry

 

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

Food manufactures

4.74

4.52

5.36

3.97

4.05

3.80

3.48

3.05

2.91

2.79

2.64

2.50

2.41

2.23

1.66

1.40

Beverage industries

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Tobacco manufactures

1.39

2.05

1.88

1.58

1.34

1.29

1.07

0.72

0.62

0.57

1.14

1.41

0.60

0.42

0.31

0.25

Textile manufactures

0.49

0.47

0.47

0.32

0.37

0.33

0.35

0.32

0.29

0.22

0.14

0.24

0.23

0.21

0.28

0.27

Footwear and wearing app.

2.80

3.33

3.11

3.16

2.11

2.42

2.08

1.67

1.70

2.12

2.42

5.41

2.22

1.96

1.84

1.77

Wood and cork prod.

6.11

6.60

6.38

6.98

5.72

5.27

4.89

3.69

3.21

2.67

1.84

2.09

1.26

0.81

0.81

0.73

Furniture and fixtures

2.67

3.09

2.87

3.30

3.28

3.35

2.89

2.67

2.98

3.24

2.80

2.33

2.07

2.01

1.97

1.79

Paper and paper prod.

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.11

0.12

0.14

0.12

0.11

0.08

Publishing and printing

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Leather and leather prod.

0.00

0.00

0.00

0.00

0.00

0.51

0.52

0.47

0.50

0.76

0.91

1.06

0.85

0.70

0.79

0.93

Rubber products

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Chemical and chemical prod.

0.14

0.19

0.19

0.20

0.25

0.56

0.86

0.63

0.47

0.46

0.42

0.46

0.31

0.26

0.24

0.20

Petroleum and coal prod.

0.03

0.03

0.03

0.13

0.09

0.05

0.14

0.14

0.22

0.23

0.22

0.29

0.30

0.27

0.23

0.24

Non-metallic mineral prod.

0.36

0.28

0.34

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.17

0.20

0.22

0.22

0.20

0.17

Basic metal industries

3.21

2.62

2.05

1.95

1.44

2.01

1.96

1.28

1.53

1.35

1.45

1.23

1.10

0.86

0.77

0.74

Metal industries

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.04

0.05

0.03

0.36

0.04

0.04

Machinery exc. electrical

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Electrical machinery

0.17

0.22

0.27

0.51

0.79

0.63

0.73

0.73

0.68

0.76

0.92

2.07

1.17

1.55

1.25

1.24

Transport equipment

0.07

0.08

0.05

0.05

0.06

0.05

0.06

0.09

0.02

0.02

0.06

0.03

0.05

0.08

0.12

0.13

Misc. manufactures

3.63

4.74

5.77

5.44

6.21

6.07

5.13

4.71

4.43

4.51

4.53

0.70

4.69

4.92

5.27

5.51

Source of basic data: UN International Trade Statistics, 1980–8, 1990–5. Figures for 1989 were obtained by taking the average of 1988 and 1990.

Table 6.5 Contribution to trade balance, 1980–95

 

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

Food

  0.294

  0.272

  0.307

  0.231

  0.232

  0.184

  0.186

  0.180

  0.141

  0.118

  0.097

  0.118

  0.101

  0.083

  0.057

  0.070

Tobacco

  0.001

  0.003

  0.002

–0.001

  0.001

–0.007

–0.009

–0.012

–0.007

–0.004

  0.001

  0.001

–0.004

–0.003

–0.007

–0.003

Textile

–0.014

–0.018

–0.017

–0.029

–0.028

–0.035

–0.051

–0.051

–0.051

–0.052

–0.052

–0.089

–0.047

–0.040

–0.036

–0.033

Wearing apparel

  0.048

  0.061

  0.055

  0.063

  0.046

  0.057

  0.060

  0.066

  0.063

  0.074

  0.085

  0.226

  0.088

  0.073

  0.069

  0.062

Leather

  0.000

  0.000

  0.000

  0.000

  0.000

  0.002

  0.003

  0.003

  0.003

  0.001

  0.003

  0.000

  0.001

  0.001

  0.003

  0.004

Footwear

  0.012

  0.013

  0.011

  0.011

  0.009

  0.009

  0.007

  0.006

  0.007

  0.009

  0.010

  0.016

  0.012

  0.012

  0.013

  0.009

Wood

  0.082

  0.072

  0.062

  0.078

  0.062

  0.055

  0.055

  0.056

  0.051

  0.037

  0.021

  0.025

  0.012

  0.006

  0.009

  0.006

Furniture

  0.014

  0.016

  0.013

  0.017

  0.017

  0.019

  0.020

  0.024

  0.028

  0.026

  0.024

  0.021

  0.019

  0.017

  0.018

  0.016

Paper

–0.014

–0.013

–0.014

–0.014

–0.018

–0.019

–0.024

–0.022

–0.021

–0.018

–0.013

–0.015

–0.011

–0.012

–0.012

–0.015

Printing

–0.004

–0.003

–0.003

  0.000

  0.000

–0.002

–0.003

–0.004

–0.003

–0.003

–0.002

–0.005

–0.003

–0.003

–0.003

–0.003

Chemicals

–0.077

–0.075

–0.075

–0.075

–0.079

–0.080

–0.088

–0.083

–0.082

–0.069

–0.062

–0.064

–0.057

–0.054

–0.050

–0.050

Petroleum

–0.299

–0.319

–0.276

–0.268

–0.265

–0.289

–0.171

–0.184

–0.124

–0.132

–0.130

–0.131

–0.117

–0.097

–0.079

–0.079

Rubber

–0.003

–0.002

–0.004

  0.000

  0.000

  0.000

  0.000

  0.000

  0.000

–0.004

–0.003

–0.002

–0.004

–0.004

–0.004

–0.004

Plastics

–0.013

–0.013

–0.016

–0.022

–0.016

–0.013

–0.023

–0.024

–0.022

–0.022

–0.025

–0.030

–0.023

–0.024

–0.026

–0.026

Non-metals

  0.001

  0.003

  0.003

  0.000

  0.000

  0.000

  0.000

  0.000

  0.000

–0.006

–0.001

  0.003

–0.001

  0.002

  0.002

  0.000

Basic metals

  0.142

  0.109

  0.042

  0.055

  0.046

  0.082

  0.056

  0.014

  0.022

  0.018

  0.009

–0.017

–0.021

–0.034

–0.031

–0.043

Fabricated

–0.013

–0.015

–0.018

–0.016

–0.007

–0.011

–0.007

–0.005

–0.006

–0.006

–0.006

–0.008

–0.007

–0.013

–0.013

–0.006

Machinery

–0.125

–0.104

–0.123

–0.112

–0.065

–0.067

–0.073

–0.075

–0.082

–0.096

–0.102

–0.070

–0.101

–0.113

–0.109

–0.100

Electrical

–0.032

–0.035

–0.034

–0.014

–0.001

  0.003

  0.010

  0.018

  0.015

  0.018

  0.024

  0.041

  0.035

  0.086

  0.061

  0.052

Transport

–0.061

–0.051

–0.034

–0.033

–0.035

–0.006

–0.006

–0.008

–0.039

–0.048

–0.062

–0.055

–0.058

–0.084

–0.074

–0.067

Prof. Scientific

–0.007

–0.006

–0.009

–0.010

–0.007

–0.009

–0.009

–0.008

–0.008

–0.008

–0.008

–0.001

–0.008

–0.010

–0.008

–0.009

Miscellaneous manufactures

  0.067

  0.105

  0.127

  0.137

  0.106

  0.127

  0.067

  0.109

  0.116

  0.167

  0.193

  0.036

  0.194

  0.211

  0.221

  0.220

Source of basic data: UN International Trade Statistics, 1980–8, 1990–5. Figures for 1989 were obtained by taking the average of 1988 and 1990.

Table 6.6 Share to total exports, 1980–95

 

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

Food

34.78

33.23

41.96

29.16

29.30

25.64

25.24

22.73

21.10

19.07

17.32

16.90

15.94

14.40

10.65

12.14

Tobacco

0.50

0.84

0.93

0.67

0.53

0.52

0.43

0.32

0.27

0.26

0.60

0.79

0.34

0.23

0.17

0.12

Textile

1.30

1.22

1.24

0.84

0.98

0.85

0.97

1.11

0.95

0.67

0.43

0.78

0.74

0.65

0.87

0.82

Wearing apparel

4.68

5.90

5.89

6.14

4.36

5.39

5.68

6.21

5.92

6.93

8.32

21.25

8.59

7.50

6.66

6.06

Leather

0.00

0.00

0.00

0.00

0.00

0.21

0.24

0.28

0.30

0.42

0.53

0.62

0.54

0.47

0.56

0.63

Footwear

1.16

1.28

1.24

1.10

0.86

0.84

0.64

0.55

0.64

0.81

0.95

1.52

1.19

1.25

1.30

0.88

Wood

7.94

6.96

6.68

7.69

5.95

5.24

5.17

5.32

4.81

3.49

2.36

2.61

1.55

1.10

1.08

0.88

Furniture

1.33

1.53

1.43

1.67

1.64

1.81

1.85

2.28

2.60

2.45

2.31

2.02

1.84

1.79

1.78

1.58

Paper

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.26

0.29

0.31

0.25

0.23

0.19

Printing

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Chemicals

0.42

0.50

0.60

0.72

1.00

2.33

4.12

3.35

2.64

2.45

2.28

2.56

1.59

1.32

1.28

1.13

Petroleum

0.63

0.55

0.64

2.21

1.53

0.74

1.27

1.65

2.04

2.13

2.21

2.63

2.42

2.01

1.59

1.50

Rubber

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Plastics

0.19

0.32

0.19

0.19

0.17

0.59

0.63

0.69

0.71

0.63

0.57

0.67

0.71

0.63

0.58

0.62

Non-metals

0.65

0.41

0.50

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.31

0.37

0.41

0.42

0.38

0.32

Basic metals

20.99

15.37

11.87

10.80

7.82

10.99

10.13

7.32

10.18

9.04

8.06

6.72

5.37

4.03

3.63

3.82

Fabricated

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.04

0.06

0.04

0.43

0.05

0.04

Machinery

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.84

3.73

1.97

1.89

1.73

2.53

Electrical

0.92

1.44

1.83

3.90

6.85

5.62

7.30

8.38

8.39

8.89

10.08

22.41

13.35

20.29

17.42

17.39

Transport

0.52

0.57

0.38

0.46

0.49

0.46

0.63

0.99

0.20

0.23

0.59

0.31

0.55

0.85

1.23

1.20

Prof. Scientific

0.34

0.36

0.23

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.71

0.00

0.00

0.00

0.00

Misc. Manufactures

18.00

23.63

28.65

28.17

33.26

32.87

29.91

32.78

32.85

33.96

34.91

4.11

35.61

39.21

39.65

41.80

Source of basic data: UN International Trade Statistics, 1980–8, 1990–5. Figures for 1989 were obtained by taking the average of 1988 and 1990.

Note: Breakdown of misc. manufactures: toys, sporting goods, etc.; gold, silver ware, jewelry; musical instruments, pts.; other manufactured goods; special transactions; gold, non-monetary nes.

consisting mainly of re-exports, are the main component of miscellaneous manufactures.

The deceptive export configuration explains why despite the increasing share of manufactured exports, the share of value added of the manufacturing sector in total output has remained stagnant for the past twenty years and is even lower than the value in 1980 (Table 6.1). Estimates of total factor productivity (TFP) for the Philippine manufacturing sector show a steady decline in the period 1956–75 which became sharper from 1975 to 1980 (Hooley 1985). The trend continued into the 1980s up to 1992 (Cororaton et al. 1995). The year-on-year growth of value added in the manufacturing sector in real terms has actually declined for thirteen consecutive quarters – from 1995Q4 to 1998Q4.

Medalla (1998) attributes the conflicting trends – a rise in efficiency measures in the manufacturing sector and continuing structural problems – to three factors: (1) adjustment, often times painful, to a more open trade regime; (2) a persistently overvalued currency; and (3) the switch in relative protection between agriculture and manufacturing, this time in favour of the former. One could add to this list a relatively low investment rate in the Philippines and poor infrastructure.

An inevitable outcome of a more open trade regime is that inefficient local firms are weeded out almost immediately because of the deluge of imports. It will take some time before the resources are reinvested in more efficient sectors that are usually export oriented. The restructuring process is akin to the 'J-curve' effect of a currency devaluation. In this case, the manufacturing sector contracts because of the closure of non-competitive firms but it should start to grow rapidly once resources are used more efficiently. This explanation, however, conveniently ignores the fact that the bulk of trade liberalization took place in the late 1980s but the marked slowdown of the manufacturing sector occurred between 1995 and 1997 in spite of accelerated economic growth up to 1996.

The restructuring process would have been smoother if the currency had been allowed to depreciate in real terms following the increase in demand for imports. The lower value of the peso would have acted as a cover for import-competing industries. Because of the overvaluation of the peso, import-competing firms were hit with a double whammy: lower tariffs and an artificially strong peso, both of which made imports cheap. An overvalued currency could also explain why exports are heavily concentrated in commodities that are import dependent. Because it is relatively cheap to import, exporters focus on products whose inputs can be sourced from abroad, making labour the primary source of value added.

Overall, the Philippines has taken great strides to enhance its outward orientation and is bordering on being a completely open economy by the year 2004. This progression has dovetailed with the process of globalization. Despite the policy reforms, however, manufacturing growth has not performed up to expectations. Apart from the factors discussed in this section, the reasons may deal with the structural aspects related to macroeconomic stability and weaknesses in financial institutions.

Empirical results

Based on the analytical framework, the following general functions were estimated using data from 1980 to 1995:

Image

Image

The index i refers to a particular manufacturing sector while t is an index for time.4

The RCA index for the various manufacturing sectors was computed and was used as the measure of trade performance and an indicator of the trade pattern in the Philippines. Competitiveness for each sector was determined using a productivity measure, ρ. The simplest would be growth in labour productivity.5 A more complicated procedure would be to estimate the TFP for each sector.

TFP is a concept of efficiency where the economy's productive inputs like labour and capital are jointly used in production. It can be measured in two ways: (1) the deterministic approach, and (2) the stochastic approach. The deterministic approach is further divided into two categories: (a) index number approach, and (b) growth accounting approach. The latter two methodologies are simple and TFP estimates can be easily computed. A weakness of these approaches, however, is the residual treatment of TFP that could render biased estimates.

The stochastic approach, on the other hand, assumes the existence of an unobservable production frontier function and from this, the actual production frontier is compared. In doing so, the residual treatment is eliminated and all factors contributing to production are accounted for. This approach can be used both for time series and cross-section data. Cororaton (1998) applied both the growth accounting and stochastic approach to Philippine manufacturing sector data.

The implicit price index Pit for each sector i was used as an indicator of price competitiveness since unit labour costs are not available for the given sectoral breakdown. The price index was scaled by an import price index for non-fuel products, P*, to get a measure of relative prices. The capability of an economy to deliver or its capacity is related to existing capital stock, K.

Estimates of K for each sector were obtained by Cororaton using the perpetual inventory method. These values of K, which were also used to obtain the productivity figures, were used for the econometric estimation.

Since K is generated by investment, it is through the latter variable that the link between trade and financial development can be established. The investment rate per sector (I/GVA), defined to be sectoral investment divided by sectoral gross value added, is modelled to be determined by volatility in the real effective exchange rate, σ, and the level of financial sector development which is captured by the ratio of broad money M3 to GNP. The technique employed by Schwert (1989) was used to estimate volatility of the real effective exchange rate.

The amount of FDI scaled by GDP should also affect investment. The experience of the developing HPAEs shows that the entry of foreign investment spurred an increase in domestic investment that was put in place to support the requirements of MNCs. The real effective exchange rate, e, is added to incorporate the effects of an overvalued currency.

In the various estimates of the first equation (see Table 6.7), the coefficients for the growth in labour productivity, TFP using the growth accounting approach and TFP using the stochastic are all insignificant.6 As a matter of fact, the coefficient of labour productivity growth is negative and significant at the 10 per cent level. The results show unambiguously that there is no empirical support for a link between the productivity measures and export performance. Changes in technology and productivity in the domestic manufacturing sector did not influence the pattern of Philippine exports during the period 1980–95.

The variable representing relative prices carries the correct negative sign but the coefficient is not significant. What is troubling though is the consistent negative sign of the coefficient for capital stock, which is significant at the 10 per cent level. It seems that increased investment activity that augments the capital stock does not contribute to better export performance and may even hamper it. This result, combined with the earlier observation that technological competitiveness and export performance are not related, is a clear indication that the export sector has its own dynamics, independent of the developments in the local manufacturing sector. A dichotomy exists between the domestic manufacturing sector and the export sector.

Estimates on eqn (6.2) were also run with and FDI/GDP as explanatory variables. Real exchange rate volatility may affect export performance directly since it affects the rate of return of exporters and hence their profit risk (Medhora 1998). FDI affects export performance in two ways. First, it relaxes the capacity constraint by providing more capital inputs for production. And second, to the extent that the FDI is export oriented, it directly contributes to the level of exports, and hence a higher market share. The results, however, did not improve with the inclusion of these two variables in eqn (6.2). Perhaps the results would differ if FDI by sector were used. Unfortunately, such data is not readily available.

Table 6.7 Estimation of eqn (6.1)

Estimation of eqn (6.1) using growth rate of labour productivity
Dependent variable: RCA?; Method: GLS (cross-section weights); Sample: 1981, 1995; Included observations: 15; Total panel (balanced) observations: 165; Cross sections without valid observations dropped.

Variable

Coefficient

Std. error

t-statistic

Prob.

C

  0.169304

  0.073901

  2.290947

0.0233

GLP?

–0.001219

  0.000559

–2.180934

0.0306

RELP?

–3.826360

  2.255614

–1.696372

0.0918

K?

–3.44E–05

  1.77E–05

–1.947312

0.0532

RCA?(–1)

  0.929035

  0.016572

56.06052

0.0000

Weighted statistics

 

 

 

 

R2:

  0.909251;

Mean dependent var.:              1.795564;

Adjusted R2:

  0.906983;

S.D. dependent var.:               1.396951;

S.E. of regression:

  0.426053;

Sum squared resid.:              29.04335;

Log likelihood:

 83.07333;

F-statistic:                          400.7776;

Durbin–Watson stat.:

   2.332512;

Prob(F-statistic):                    0.000000.

Estimation of eqn (6.1) using growth rate of TFP (growth accounting approach)
Dependent variable: RCA?; Method: GLS (cross-section weights); Sample: 1981, 1995; Included observations: 15; Total panel (balanced) observations: 180.

Variable

Coefficient

Std. error

t-statistic

Prob.

  C

  0.131304

  0.084445

  1.554896

0.1218

TG?

  0.144839

  0.095450

  1.517425

0.1310

RELP?

–2.746532

  2.570912

–1.068310

0.2869

K?

–3.64E–05

  1.94E–05

–1.877880

0.0621

RCA?(–1)

  0.931458

  0.015758

59.11175

0.0000

Weighted statistics

 

 

 

 

R2:

  0.915674;

Mean dependent var.:              1.541699;

Adjusted R2:

  0.913746;

S.D. dependent var.:               1.272487;

S.E. of regression:

  0.373717;

Sum squared resid.:              24.44122;

Log likelihood:

91.13248;

F-statistic:                         475.0681;

Durbin–Watson stat.:

  2.308555;

Prob(F-statistic)                    0.000000.

Estimation of eqn (6.1) using growth rate of TFP (stochastic approach)
Dependent variable: RCA?; Method: GLS (cross-section weights); Date: 11/19/98; Time: 11:36; Sample: 1981, 1995; Included observations: 15; Total panel (balanced) observations: 180.

Variable

Coefficient

Std. error

t-statistic

Prob.

C

–0.060769

  0.195637

–0.310623

0.7565

TS?

20.13391

17.27408

  1.165556

0.2454

RELP?

–2.237510

  2.451574

–0.912683

0.3627

K?

–3.31E–05

  1.79E–05

–1.844362

0.0668

RCA?(–1)

  0.927263

  0.016447

56.38029

0.0000

Weighted statistics

 

 

 

 

R2:

   0.903338;

Mean dependent var.:              1.707719;

Adjusted R2:

   0.901128;

S.D. dependent var.:               1.330213;

S.E. of regression:

   0.418271;

Sum squared resid.:              30.61633;

Log likelihood:

107.3332;

F-statistic                          408.8559;

Durbin–Watson stat

   2.285064;

Prob(F-statistic):                   0.000000.

Variable definitions: RCA?: revealed comparative advantage by sector (indexed by?); GLP?: growth rate of labour productivity by sector (indexed by?); TG?: growth rate of total factor productivity by sector (indexed by?) using growth accounting; TS?: growth rate of total factor productivity by sector (indexed by?) using stochastic approach; RELP?: relative price per sector; defined as Pi/P*, where Pi is the implicit price index of sector i and P* is the price index of non-oil imports. P* is not available on a sectoral basis; K?: capital stock by sector.

Table 6.8 Estimation of eqn (6.2)

Estimate of eqn (6.2) using REER volatility Dependent variable: INVA; Method: GLS (cross-section weights); Date: 11/19/98; Time: 12:48; Sample: 1981, 1995; Included observations: 15; Total panel (balanced) observations: 180.

Variable

Coefficient

Std. error

t-statistic

Prob.

C

  0.006326

0.003867

  1.635895

0.1037

SIGMA

–1.84E–05

0.000693

–0.026515

0.9789

M3GNP

  0.000306

0.000121

  2.524540

0.0125

FDIGDP

–0.002243

0.000979

–2.291524

0.0231

REER

–0.000137

5.01E–05

–2.729968

0.0070

INVA?(–1)

  0.730557

0.053361

13.69080

0.0000

Weighted statistics

 

 

 

 

R2:

0.534363;

Mean dependent var.:            0.016618;

Adjusted R2:

0.520983;

S.D. dependent var.:             0.020799;

S.E. of regression:

0.014396;

Sum squared resid.:              0.036058;

Log likelihood:

816.4368;

F-statistic:                           39.93638;

Durbin–Watson stat.:

2.509469

Prob(F-statistic):                  0.000000.

Estimate of eqn (6.2) using inflation as volatility measure Dependent variable: INVA?; Method: GLS (cross-section weights); Date: 04/29/99; Time: 11:49; Sample: 1981, 1995; Included observations: 15; Total panel (balanced) observations: 180.

Variable

Coefficient

Std. error

t-statistic

Prob.

C

  0.007200

0.003515

  2.048365

0.0420

INFL

–3.76E–05

3.63E–05

–1.034215

0.3025

M3GNP

  0.000267

0.000126

  2.120790

0.0354

FDIGDP

–0.002290

0.000914

–2.505885

0.0131

REER

–0.000127

4.97E–05

–2.558213

0.0114

INVA?(–1)

  0.718011

0.054607

13.14881

0.0000

Weighted statistics

 

 

 

 

R2:

0.490294;

Mean dependent var.:            0.016001;

Adjusted R2:

0.475647;

S.D. dependent var.:             0.019529;

S.E. of regression:

0.014141;

Sum squared resid.:              0.034796;

Log likelihood:

816.6738;

F-statistic:                           33.47463;

Durbin–Watson stat.:

2.512686;

Prob(F-statistic)                   0.000000.

Variable definitions: INVA?: investment per sector as a ratio to sectoral value added (indexed by?); SIGMA: measure of exchange rate volatility; M3GNP: ratio of total domestic liquidity to GNP; FDIGDP: ratio of foreign direct investment to GDP; REER: real effective exchange rate (1980 = 100), an increase in REER implies an appreciation; INFL: inflation rate.

Estimates of eqn (6.3) (Table 6.8) show a significant positive relationship between the investment rate and the measure of financial development. Because of the adverse relationship between capital stock and RCA obtained in the first equation, a conclusive statement on the impact of financial development on export structure cannot be made. A different line of analysis will be adopted and discussed in the latter part of the chapter.

Another variable that is significant is FDI although it carries a negative coefficient. Apparently the entry of FDI displaces some local investment or else it leads to complacency among domestic entrepreneurs. This result, however, must be studied more carefully. Certainly, it does not imply that policies discouraging FDI should be implemented.

The measure of exchange rate volatility is not significant although the level of REER carries a significant negative coefficient. A higher REER implies an appreciating peso in real terms which hurts import-competing industries and exporters. This would of course discourage investment in these two important sectors. Other measures of exchange rate volatility could also be used to model more closely the extent of macroeconomic instability. If the inflation rate is used instead of exchange rate volatility, there is a minor improvement in the equation but the variable for macroeconomic instability remains insignificant.

3. Competitiveness, finance and macroeconomic stability

Major hypothesis

The dichotomy between the domestic manufacturing sector and the export sector is the reason why the share of manufacturing value added to GDP has been stagnant despite the dramatic rise in the share of manufactured exports. One possible reason for the dichotomy is that the more efficient sectors are not allocated enough credit. This section aims to provide empirical evidence to test this hypothesis.

In a world of perfect capital markets where the Modigliani and Miller and the Fisher separation theorems would be valid, the performance of firms and economic sectors could be explained without reference to the developments in the financial sector. But at the onset, it is observed that the financial sector of the Philippines is far from perfect. Apart from the usual problems of asymmetric information in financial markets, the Philippine financial system has been hampered by structural problems related mainly to the oligarchic banking system. Access to credit, thus, is a key determinant of economic performance.

The role of export finance

Export finance is another area that may offer an explanation for the weak link between productivity growth and export performance. A survey of exporters revealed that only a minority were covered by the BSP's rediscount window, which was the most important export financing scheme in the Philippines, at least in the 1980s. Only about 500 out of about 6,000 direct exporters had access to the export loan discount scheme. As a result, export loans outstanding declined from 14 per cent of export value in 1982 to just 1 per cent in 1986–8 (Rhee et al. 1990).

Indirect exporters were not eligible for the CB's pre-shipment export finance window even though they are several times more numerous than direct exporters. This failure to assure equal access to working capital financing for indirect exporters hindered the development of backward linkages as well as the development of trading companies (Rhee et al. 1990). One mechanism suggested to expand the coverage to indirect exporters is the introduction of the domestic letter of credit.

The underdevelopment of the export financial system was generally a product of the underdevelopment of the entire financial system. For example, heavy collateral requirements by commercial banks have been cited as the major impediment to wider access to export financing. A pre-shipment export finance guarantee could have been designed to overcome this constraint. Such a scheme existed in the Philippines, but had only a limited role, at least in the 1980s. This could be explained by a shallow financial base that prevented effective risk sharing among the various parties involved.

Framework and empirical results

In the absence of robust financial data at the firm level, the methodology of Rajan and Zingales (1998) will be adopted. In their study, the growth of a particular industry is linked to the external financial dependence of that industry and the degree of financial development of the economy. Their hypothesis is that industries that are more dependent on external finance grow faster in economies that are more financially developed.

To test this hypothesis, Rajan and Zingales estimate the technological demand for external finance that a firm operating in a specific industry would choose in a perfect capital market. Since the US comes closest to the criteria for a well-functioning capital market, the observed ratio of external finance (defined to be the difference between investment and cash generated from operations) in the US for a particular industry is used as a benchmark.

To test the relationship between the level of financial development on the one hand and competitiveness and trade pattern on the other, the EDR is compared with the growth rate of productivity – the measure of competitiveness – and RCA. In both cases, the sectors are ranked, first, by labour productivity growth and, then, by RCA. A rank correlation coefficient using the EDR ranking as a basis for comparison is then computed for both cases.

In the context of a financially underdeveloped economy like the Philippines, there should be a negative correlation between the ranking obtained from EDR and the ranking obtained from the growth rate of labour productivity. This implies that inadequate access to credit prevents firms with a high EDR from reaching their potential growth, leading to low productivity performance. A similar explanation could be made in the case of the RCA measure. A negative correlation would imply that the economy is unable to develop a comparative advantage in particular sectors because of lack of access to credit.

The estimates of the rank correlation coefficients are shown in Table 6.9. There is no general pattern for the sample period 1980–95 for both RCA and growth of labour productivity. Moreover, the values are closer to zero than to one. It would seem that access to credit plays no major role in determining competitiveness or the trade pattern.

Table 6.9 Estimates of Spearman rank coefficient

Year

EDR, RCA

EDR, GLP

1981

  0.121

  0.046

1982

  0.121

–0.380

1983

  0.288

  0.204

1984

  0.288

–0.165

1985

  0.099

–0.301

1986

  0.099

  0.200

1987

  0.099

  0.327

1988

  0.110

  0.429

1989

  0.143

  0.512

1990

–0.058

  0.222

1991

–0.162

–0.301

1992

–0.102

  0.442

1993

–0.052

–0.235

1994

  0.080

  0.077

1995

–0.190

  0.209

EDR: external dependence ratio; RCA: revealed comparative advantage; GLP: growth rate of labour productivity.

Based on this evidence, the dichotomy between the export sector and the domestic manufacturing sector could be attributed more directly to real factors rather than financial constraints. What could be emphasized though is that the financial sector was a major source of macroeconomic instability leading to high inflation rates, an overvalued currency and a low investment rate.

4. Micro–macro and real–financial interactions

General analysis

The dichotomy between the export sector and domestic manufacturing sector transcends the usual dualistic structure that exists between the traditional and modern sectors. A possible explanation for this structure in the manufacturing sector is provided by Dohner and Intal (1989). Philippine export promotion measures allowed producers to obtain imported inputs at world market prices, leading to the development of export processing based on imported materials and the low wages of Philippine labour. The retention and augmentation of the system of protection for manufacturing firms producing for the domestic market meant that value-added margins of these export producers would remain very thin; the higher cost and lower quality of domestic materials precluded the growth of domestic sourcing. The high degree of protection of the domestic markets also tended to limit export products to industries where the transport cost of materials was low and labour input requirements high. Garments and electronic components, which have been the top two export categories since 1982, fit these requirements perfectly. Dohner and Intal describe export growth as intensive rather than extensive.

This explanation – citing the highly protectionist system as the main factor behind the narrow export base – is largely consistent with the orthodox or neoclassical economic view. The natural policy recommendation would be a more open trade regime. A corollary to the orthodox position is the problem of an over-valued currency. An artificially cheap peso encouraged exports that are import intensive. Exporters offset the penalty of an uncompetitive exchange rate by relying heavily on higher quality imports of raw materials and intermediate goods made relatively inexpensive by the overvalued peso.

The experience of the developed HPAEs provides a striking contrast to the neoclassical blueprint. Instead of working to get prices right, the economies of Japan, Korea and Taiwan implemented policies to get the fundamentals right. Among the major thrusts was to enhance their technology capability through the judicious use of policy interventions (Lall 1995). The developed HPAEs relied heavily on licensing agreements and reverse engineering and were selective with, even sometimes hostile to, FDI (Lall 1994).

Meanwhile, developing HPAEs and Singapore sourced the technological development of their export sector primarily from FDI. In this situation, the link between productivity growth in the manufacturing sector and export performance would depend on the level of FDI and degree of technology transfer. The evolution of the Philippine export sector since 1975, and its contrast to the experience of the developing HPAEs, can largely be explained by the nature and extent of FDI flows into the economy.

Table 6.3 shows that the Philippines was a laggard in terms of attracting FDI mainly because of the adverse macroeconomic and political environment. The pattern of export growth in the Philippines in the last two decades was simply a response to the trend towards the internationalization of the division of labour where the industries which lost their comparative advantage in the more developed countries found their way into economies characterized by a relatively low wage scale (Broad 1988). The inability of the export sector to effectively diversify into other commodities indicates that the Philippines was simply riding on the worldwide trend towards industry relocation rather than seriously implementing an industrial policy, particularly an export programme. Unlike Singapore and Malaysia, there was no coherent strategy implemented to ensure effective technology transfer.

A key finding of Cororaton et al. (1995) is that FDI has not generally been contributing to the technical progress of the manufacturing sector. This conclusion is consistent with the survey results of Lindsey (1989) from the manufacturing sector where he finds that: (1) most of the equipment brought in by investors are already in use in the Philippines; (2) R&D activities are limited to quality control instead of basic research; (3) there is minimal diffusion of technology to local firms; and (4) the processes used are very simple, leaving little room for skills development.

Implications for policy

To bring about a more integrated economy, economic managers in the Philippines followed the standard response, adopting a programme akin to the Washington consensus. Several analysts have cautioned against strict adherence to this framework (Rodrik 1992; Guerrieri 1994; French-Davis 1994). Structural transformation has a major influence on the acquisition of comparative advantage and is a cause of economic growth. Guerrieri argues that the economic metamorphosis should not be considered as an automatic by-product of an outward-oriented strategy and sound macroeconomic policies, as free trade orthodoxy regards it. Neoliberal economics largely disregards the key role played by technology in changing trade patterns and hence misses the structural dimension of a country's competitiveness.

Echoing this sentiment, Lall (1995) argues that the more important and pervasive source of market failure is likely to be learning processes in production rather than scale economies or externalities. This fact is particularly important for developing countries, which are latecomers to industrialization and thus face established competitors that have already undergone the learning process.

Depending on the extent of the learning costs involved, as well as the efficiency of the relevant factor markets and supporting institutions, there may be a valid case for selective and variable infant industry protection, and for the gradual exposure of existing activities to import competition. Since protection itself reduces the incentive to invest in capability building, however, it has to be carefully designed, sparingly granted, strictly monitored, and offset by measures to force firms to aim for world standards of efficiency. The most effective way to offset the disincentives to develop capabilities that arise from protection seems to be strong pressures to enter export markets, as a commitment to exporting disciplines not only firms but also those who design and administer policy. In Lall's view, the true contribution of export orientation to industrialization is to provide the right framework for selective interventions.

The emerging external environment, however, constrains the available policy options. As Lall (1994) points out: '... the international scene, the GATT, and the pressures exerted by the developed Western countries, are inimical to selective intervention ... Many instruments of industrial policy are increasingly constrained in the name of liberalization.' (p. 652) He correctly asserts, however, that if there is a valid case for intervention, then a review of the international rules of the game is warranted.

The recent performance of the Philippine manufacturing sector supports the aforementioned concerns. Despite the reforms implemented in the late 1980s and accelerated in the early 1990s, the manufacturing sector experienced a deceleration even prior to the 1997 financial crisis.

Meanwhile, the liberalization of the financial system and the capital account in order to spur financial development also has its downside, as painfully revealed by the 1997 East Asian financial crisis. These twin liberalizations could fuel what is termed 'financierism', characterized by the growing supremacy of financial activity over productive activity (French-Davis 1994). The adverse effects of financierism could be attributed to the inadequate regulatory structure in place at the time of liberalizing the financial system. Some analysts put the blame squarely on the corrupt practices in some of the East Asian countries, citing behest loans in Korea and crony capitalism in Indonesia.

What is certain is that the situation is more complicated than this. Many of the East Asian economies that were buffeted by the crisis had relatively strong macro-economic fundamentals and were dragged into crisis by the financial panic of foreign investors. Krugman (1999), for instance, does not agree that Asian economies are being punished for crony capitalism since the 'the scale of punishment seems wholly disproportionate to the crime' (p. 22). He has joined the bandwagon of those calling for the reform of the international financial architecture.

The ideology of liberalization should not cloud the objective of policy reforms: the improvement of the technological capability of the manufacturing sector, the establishment of a dynamic link between the manufacturing and export sectors, and the development of a stable financial system. Given that globalization is an irreversible process, the Philippines must strive to attract FDI and achieve the success of the developing HPAEs in this regard. Simultaneously, economic managers must apply strategic interventions to facilitate the transfer of technology. These would include:

1 Encourage the practice of 'mirroring' similar to the case of Korea. An expatriate engineer would be assigned a local counterpart whom he should train. The local engineer would eventually assume the responsibility of the foreign engineer.

2 Encourage multinational corporations to link up with a domestic firm and develop the latter as a source of intermediate inputs. Such subcontracting was practised extensively in Singapore and Malaysia.

3 The government must set clear strategies and policies on technology development – whether adoption, modification, or generation – by industry.

4 Develop in parallel the human resource capital to cope with the requirements of technology transfer.

These recommendations are consistent with the findings of a recent PIDS study (Yap 1998) showing that the Philippines has many weak links at the microeconomic level preventing the benefits from macroeconomic reforms from being realized. This includes a low level of technological capability that hampers backward and forward linkages in industries; a poor record in human resource development that contributes to low labour productivity; extremely slow alleviation of poverty and income inequality that gnaws at the basic fabric of social cohesion; and inadequate infrastructure that discourages domestic and foreign investment. These shortcomings are at the root of coordination failures that threaten macroeconomic stability.

Policy recommendations for the financial sector have to be studied more carefully given the recent experience in East Asia. The study by Rhee et al. (1990) recommended the establishment of a foreign currency loan scheme for exporters to take advantage of the lower international interest rates. Presumably, this need was addressed when the capital market was liberalized. Unfortunately, the dollar-denominated loans were not limited to exporters and borrowers without a natural exchange rate hedge also availed of these loans. This situation was one of the primary causes of the downward economic spiral when the crisis struck.

There are, of course, the standardized proposals for reform of the banking sector. It has been recommended that prudential regulation and supervision be strengthened by implementing comprehensive risk-based assessment and supervision instead of focusing primarily on credit risk. In addition, there is a need for more stringent information disclosure requirements, adequate accounting and auditing standards, as well as clearer rules and greater transparency in asset classification and provisioning (Intal and Llanto 1998).

These reforms, however, must take into consideration political and institutional factors which are at the core of the problems in the banking sector. For example, no matter how comprehensive the risk assessment that is required, it is ineffective if bank supervisors fall prey to the pressures of special interests. While making reforms more difficult to implement, these factors are fundamental in nature and, if tackled, would definitely bring about a beneficial transformation of Philippine society.

Notes

1 Funding from IDRC and CEDES and the organizational support of the Policy and Development Foundation, Inc. (PDFI) are gratefully acknowledged. This chapter would not have been possible without the excellent research assistance of Ma. Teresa DueñasCaparas. The author would also like to gratefully acknowledge the vast contribution of Dr Caesar B. Cororaton to this chapter in terms of estimates of capital stock and productivity. The usual disclaimer applies.

2 Hutchcroft (