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IHESIS

This is to certify that the

dissertation entitled

SPATIAL EQUILIBRIUM ANALYSIS OF CONIFER TIMBER MARKETS
IN SOUTHERN BRAZIL AND IN THE OTHER MERCOSUR COUNTRIES

presented by

MARCELO SERGIO SOUZA HIECHETECK

has been accepted towards fulﬁllment
of the requirements for

Ph.D. degree in Forestry

 

 

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Major professor

Date 05/11/2001

 

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6/01 cJCIRC/Dateouepes-p. 15

SPATIAL EQUILIBRIUM ANALYSIS OF CONIF ER TIMBER MARKETS

IN SOUTHERN BRAZIL AND IN THE OTHER MERCOSUR COUNTRIES

By

Marcelo Sérgio Souza Wiecheteck

A DISSERTATION
Submitted to
Michigan State University

In partial fulﬁllment of the requirement
For the degree of

DOCTOR OF PHILOSOPHY

Department of Forestry

2001

ABSTRACT

SPATIAL EQUILIBRIUM ANALYSIS OF TIMBER MARKETS IN SOUTHERN
BRAZIL AND IN THE OTHER MERCOSUR COUNTRIES

By

Marcelo Sergio Souza Wiecheteck

With an estimated 242 million people in 2000 and an aggregate GDP of USS 1.7
trillion in 1999 (CIA, 2000), Mercosur has become an important economic bloc in the
Southern Hemisphere. Its forest products market has gained growing importance from
large investments in plantations and industrial facilities notably in Brazil, Chile and
Argentina. In particular, the conifer sawlog and lumber markets face increasing demand,
limited supply of plantation industrial roundwood, and trade liberalization.

The purpose of this study was to investigate the market equilibrium for conifer
sawlogs and lumber in Mercosur, focusing on Southern Brazil. The mathematical
framework was based on Samuelson (1952), extended by Takayama and Judge (1964a.
1964b and 1971). Multiple regression analysis and price endogenous linear
programming system for economic modeling (PELPS III - Zhang, Buongiomo and Ince,
1993) were used to create a regional spatial equilibrium model. Price elasticities were
mostly estimated through systems of simultaneous equations.

A spatial equilibrium model was developed, which simulates the optimal trade
pattern, consumption, production, and capacity that satisfy the price and the

supply/demand quantity relationship. The study demonstrated that the Southern Cone

countries have increased their pattern of production, consumption and trade under more

economic efﬁciency. Limited production of pine sawlogs and lumber in Brazil and
relative expansion of the lumber market in Chile and Argentina are indicated. In
addition, limited trade within the bloc and signiﬁcant trade with partners outside the bloc
are also predicted. The sensitivity analyses indicated the possible changes in the
outcomes as a result of changing one key variable at a time, including changes in the
availability of pine sawlogs in Brazil, change in the growth of GDP in Brazil and change
in the price elasticity of demand for sawnwood in both Brazil and Chile. The future
pattern of production, consumption and trade of conifer lumber in Southern Brazil is
likely to be highly inﬂuenced by the magnitude of changes in the availability of pine
sawlogs from plantations. Southern Brazil could face a signiﬁcant reduction in its pine
lumber production and consequent exports, even becoming a net importer, if a drastic
decrease in the availability of pine sawlogs occurs. Such a scenario, however, will be
positively or negatively inﬂuenced by the interaction with other major macroeconomic
variables and the expansion of the resource base, both in Brazil and in some of the other
Mercosur countries. The model is useful as a policy instrument to analyze strategies for

further forest sector development in individual countries.

DEDICATION

This dissertation is dedicated to my wife, Rosane Braga Wiecheteck, for her
encouragement and support throughout this project. Her patience and love kept my
perspective on life and helped me to accomplish this goal. I also dedicate this work to my
mother and father, Nadazir and Arnoldo Wiecheteck, who taught me to pursue my

dreams and have always been a source of inspiration.

iv

ACKNOWLEDGEMENTS

This project is the result of the efforts of a great number of individuals at
Michigan State University, in Brazil, and in a few other countries. First and foremost, I
thank Dr. Karen Potter—Witter, my advisor and professor, for her continuous support,
expertise, and for the opportunity I had to pursue my graduate program under her
guidance. Her patience and generosity in terms of time and resources were invaluable and
helped me throughout the study. My sincere thanks also to my other committee members,
Dr. Scott Swinton and Dr. Larry Leefers, and Dr. Runsheng Yin for their helpﬁil
comments, encouragement, and positive criticism.

This work was ﬁinded by the scholarship received from the ‘Conselho Nacional
de Pesquisa Cientiﬁca e Tecnolégica’ (CNPq/Brazil). I am also thankful for the graduate
assistanship received from the Eastern Hardwood Utilization Program at the Department
of Forestry of Michigan State University, which helped me while part of this project was
can'ied out. I am deeply indebted for the support and contribution from these institutions.

Special thanks go to Dr. Luiz Roberto Graca, for his invaluable support,
friendship, and help on providing part of the data needed for this project. I extend my
sincere thanks to Dr. James Stevens, whose support contributed signiﬁcantly to the result
of this study. His encouragement, expertise, and guidance in the initial phase of the study
were determinant for me to pursue this study. Also thanks to Dr. Joseph Buongiomo
(UofW/Madison) and Dr. Peter Ince (U SFS/F PL) for their invaluable advises and
contributions. My gratitude to Patti Lebow (USFS-FPL) and James Turner (UofW) for
their continuous technical help with the use of PELPS HI. For many professionals in

Brazil, Chile, Argentina, Paraguay, Uruguay, and in the USA, who provided me with data

and useﬁil information for this work, my most sincere appreciation, in particular to
Marlus Wiecheteck, Jefferson Mendes, Luiz César Ribas, Sebastiao Kengen (Brazil),
Gonzalo Paredes (INFOR/Chile), Liliana Corinaldesi and Ricardo Larobla
(SAGPyA/Argentina), Mr. Robert Flynn (USA), and professionals from
CNPF/EMBRAPA, IBGE, STCP, IBAMA, ABIMCI, and CEDEFOR (Brazil), FAO-
Latin America and INFOR (Chile), INDEC and SAGPyA (Argentina). Many others
deserve my acknowledgements and thanks including Dr. Daniel E. Keathley, my
dedicated friends Gem Castillo, Demetris Gatziolis, and Joe Zelesnik, and other friends,
colleagues and staff members of the Department of Forestry at Michigan State
University.

Finally, I would like to take the opportunity to say my special thanks to my wife
(Rosane), my parents (Nadazir and Arnoldo), sister, brother, my family and friends that
have always been there for me. You are the best!

Hopefully, the results of this study will contribute to the design of forest policies,
programs, and projects that respond to the continuos advancement of the forest sector in

Brazil and in the other Mercosur countries, under a sustainable and responsible manner.

vi

TABLE OF CONTENTS

 

 

 

 

 

LIST OF TABLES - - __ - _- -- - - xi
LIST OF FIGURES - - - ..... -- - - xiii
LIST OF ABBREVIATIONS . ..... xv
CHAPTER 1 - - - - -- -- ....... 1
1.1 Statement of the Problem .......................................................................................... 3
1.2. Objectives of the Study ............................................................................................. 4
1.3. Study Outline and Organization of the Dissertation ................................................. 4
CHAPTER 2 - - - -- - .......... 39
Introduction ..................................................................................................................... 9
2.1 Solidwood Sector in Brazil ...................................................................................... 10
2.1.1 Forest Resources - Natural Forests and Plantations .......................... - ................ 10
2.1.2 Historical Developments of the Solidwood Sector ........................................... 12
2.1.3 The Forest Sector and Industrial Roundwood Production/Consumption ......... 16
2.1.4 The Solidwood Industry and the Sawmilling Sector ......................................... 19
2.1.4.1 Domestic Production of Conifer Sawnwood .............................................. 20
2.1.4.2 Domestic Consumption of Conifer Sawnwood .......................................... 22

2.1.5 Markets of Roundwood and Sawnwood ........................................................... 23
2.1.5.1 Roundwood Supply and Demand ................................................................... 23
2.1.5.2 Sawnwood ...................................................................................................... 28

2.2 Forest Products Trade and Related Issues ............................................................... 33
2.2.1 World Market of Solidwood Products .............................................................. 33
2.2.2 World Sawnwood Market ................................................................................. 33
2.2.3 Brazilian Conifer Sawnwood Exports ............................................................... 37
2.2.4 Trade Partners and Flow of Sawnwood ............................................................ 39
2.2.5 The Government Role and the Free Trade ........................................................ 40

2.3 Mercosur and Regional Integration ......................................................................... 41
2.3.1 Overview ........................................................................................................... 41
2.3.2 Conifer Sawnwood Exports and Directions of Trade ....................................... 43
2.3.3 Tariff and Non-tariff Barriers ............................................................................ 46

2.4 Quantity and Price Analysis .................................................................................... 47
2.4.1 Brazil ................................................................................................................. 47
2.4.1.1 Quantity Analysis ....................................................................................... 47
2.4.1.2 Price Analysis ............................................................................................. 53

2.4.2 Chile .................................................................................................................. 57
2.4.2.1 Quantity Analysis ....................................................................................... 57
2.4.2.2 Price Analysis ............................................................................................. 59

vii

2.4.3 Argentina ........................................................................................................... 60

 

 

2.4.3.1 Quantity Analysis ....................................................................................... 61
2.4.3.2 Price Analysis ............................................................................................. 64
2.4.4 Uruguay, Paraguay and Bolivia ........................................................................ 66
2.5 Conclusions ............................................................................................................. 68
CHAPTER 3 ............... _ - 71
Introduction ................................................................................................................... 71
3.1 Objectives ................................................................................................................ 71
3.2 Literature Review .................................................................................................... 72
3.2.1 Demand for and Supply of Forest Products ...................................................... 72
3.2.2 Econometric Studies of Solidwood Products .................................................... 74
3.2.3 Studies of Solidwood Products in Brazil .......................................................... 82
3.3 Theoretical F rarnework ............................................................................................ 83
3.4 Empirical Method .................................................................................................... 85
3.4.1 Model Structure ................................................................................................. 87

- Conifer Sawnwood Market ................................................................................... 89

- Conifer Sawlog Market ........................................................................................ 90

3.5 Data Sources ............................................................................................................ 92
3.6 Results and Discussion ............................................................................................ 95
3.6.1 Brazil ................................................................................................................. 95
3.6.2 Chile ................................................................................................................ 101
3.6.3 Argentina and other Mercosur countries (Uruguay, Paraguay and Bolivia)... 107

- Combined Estimation of Conifer Sawlog System — AUPB countries ............... 112
3.6.4. Pooled Data Estimation .................................................................................. 114
3.6.5. Own-Price Elasticities of Supply and Demand for All Regions .................... 115
3.6.6. Model Testing - Residuals ............................................................................. 122
3.7 Conclusions ........................................................................................................... 125
CHAPTER 4 - -- - 127
Introduction and Rationale for the Study .................................................................... 127
4.2 Objectives .............................................................................................................. 128
4.3 Literature Review .................................................................................................. 129
4.3.1 Forest Sector Models - Overview and Application to the Forest Sector ......... 129
4.3.2 Simple Sector Models ..................................................................................... 130
4.3.3 Market Equilibrium Models ............................................................................ 131
4.3.3.1 Spatial Equilibrium Models ...................................................................... 132
4.3.3.2 Spatial Equilibrium Models in Forestry .................................................... 134
4.3.4 The Conceptual Model .................................................................................... 141
4.3.5 Description of PELPS III and its latest versions ............................................. 148
4.4 Empirical Method .................................................................................................. 151
4.4.1 Boundaries of the Study .................................................................................. 152
4.4.2 Economic Actors and Major Elements ............................................................ 153
4.5 Data Requirements and Sources ............................................................................ 156
- Transportation cost estimates ............................................................................. 161

viii

4.6 Results and Discussion .......................................................................................... 161

 

 

 

4.6.1. Base Scenario ................................................................................................. 163
4.6.1.1. Base Period (Static Phase) ....................................................................... 163
- Conifer Sawlogs ................................................................................................. 163
- Conifer Sawnwood ............................................................................................. 166
- Directions of Trade — Conifer Sawlogs and Sawnwood .................................... 168
4.6.1.2. Dynamic Forecast .................................................................................... 172
4.6.2. Sensitivity Analysis ........................................................................................ 178
4.6.2.1. Change in the Growth of the Availability of Conifer Sawlogs in Brazil. 178
4.6.2.2. Change in the Growth of GDP in Brazil .................................................. 181
4.6.2.3. Change in the Price Elasticity of Demand for Conifer Sawnwood ......... 183
- Change in the Price Elasticity of Demand for Conifer Sawnwood in Brazil ..... 183
- Change in the Price Elasticity of Demand for Conifer Sawnwood in Chile ...... 185
4.6.2.4. Summary of the Sensitivity Analyses ...................................................... 187
4.7 Conclusions ........................................................................................................... 191
CONCLUSIONS - .......... 193
- Recommendations ..................................................................................................... 196
LITERATURE CITED - - ---198
APPENDICES. ..... -- - -- .......... -211
Appendix 1 — Conversion factors ............................................................................. 212
Appendix 2 — Conifer sawnwood data for the regression analysis in Brazil ........... 213
Appendix 3 — Conifer sawlog data for the regression analysis in Brazil ................. 214
Appendix 4 — Conifer sawnwood data for the regression analysis in Chile ............ 215
Appendix 5 — Conifer sawlog data for the regression analysis in Chile .................. 216
Appendix 6 — Conifer sawnwood data for the regression analysis in Argentina ..... 217
Appendix 7 — Conifer sawlog data for the regression analysis in Argentina ........... 218
Appendix 8 — Conifer sawlog data for the regression analysis in the AUPB countries
...................................................................................................................... 219
Appendix 9 - Price Elasticities of Demand and Supply of Lumber — Literature ..... 220
Appendix 10 - Price Elasticities of Demand and Supply of Sawlogs — Literature .. 221
Appendix 11 - Samuelson’s conceptual framework ................................................ 222
Appendix 12 — Kuhn-Tucker conditions for optimality ........................................... 225
Appendix 13 — Input data for the PELPS’s demand ................................................ 227
Appendix 14 — Input data for the PELPS’s supply .................................................. 228
Appendix 15 — Input data for PELPS’s manufacture activity (sawmilling) ............ 229
Appendix 16 - Input data for PELPS’s manufacturing costs and capacity
(sawmilling) ................................................................................................. 230
Appendix 17 - Input data for PELPS’s transportation costs .................................... 231
Appendix 18 — Input data for PELPS’s exchange rate ............................................. 233
Appendix 19 —— Distance between the main centers in each region (sawlogs) ......... 234
Appendix 20 — Distance between the main centers in each region (sawnwood) ..... 235
ix

Appendix 21 — Exogenous changes in PELPS ......................................................... 236
Appendix 22 — Summary statistics of Lindo® in the Base Scenario (1995) ............ 237

LIST OF TABLES

Table 2. 1. Evolution of the production of industrial roundwood in Brazil (1979-99) ..... 18

Table 2. 2. Evolution of the production of solidwood products in Brazil (1970-99) ....... 19
Table 2. 3. Estimated installed capacity of solidwood industries in Brazil (1993) .......... 20
Table 2. 4. Percentage of regional production of solidwood products by volume (1993) 22
Table 2. 5. Sawnwood consumption in Brazil (1990-2000) ............................................. 23
Table 2. 6. Conifer and non-conifer sawnwood production in Brazil (1970-95) ............. 32
Table 2. 7. International trade of sawnwood (1980-99) .................................................... 34
Table 2. 8. Major importers of conifer sawnwood (1980-99) ........................................... 35
Table 2. 9. Major exporters of conifer sawnwood (1980—99) ........................................... 37
Table 2. 10. Direction of trade of conifer sawnwood for Mercosur countries (1997) ...... 45
Table 3. 1. Coniferous species by region .......................................................................... 87
Table 3. 2. Estimated structural equations for conifer sawnwood in Southern Brazil
(1982-95) .................................................................................................................... 97
Table 3. 3. Estimated structural equations for conifer sawlogs in Southern Brazil (1983-
96) ............................................................................................................................ 100

Table 3. 4. Estimated structural equations for conifer sawnwood in Chile (1980-98) 103
Table 3. 5. Estimated structural equations for conifer sawlogs in Chile (1980-98) ....... 106
Table 3. 6. Estimated structural equations for conifer sawnwood in Argentina (1982-95)
.................................................................................................................................. 109
Table 3. 7. Estimated structural equations for conifer sawlogs in Argentina (1982-95) 111
Table 3. 8. Estimated structural equations for conifer sawlogs in the AUPB countries

(1982-95) .................................................................................................................. 1 13
Table 3. 9. Price elasticities of supply of and demand for conifer sawnwood and sawlogs
in Brazil .................................................................................................................... 116

Table 3. 10. Price elasticities of supply of and demand for conifer sawnwood and sawlogs
in Chile ..................................................................................................................... 116
Table 3. 11. Price elasticities of supply of and demand for conifer sawnwood and sawlogs

in Argentina and in the AUPB count1ies .................................................................. 116

xi

Table 4. 1. Conifer sawlogs and lumber supply and demand regions ............................ 153

Table 4. 2. Price elasticities in different regions ............................................................. 158
Table 4. 3. Transportation cost equations ....................................................................... 162
Table 4. 4. Estimates of conifer sawlog trade in Mercosur countries in 1995 ................ 168

Table 4. 5. Estimates of conifer sawnwood trade among Mercosur countries in 1995.. 170
Table 4. 6. Potential exports of conifer sawnwood from Mercosur countries (1995-2010)
.................................................................................................................................. 176
Table 4. 7. Potential directions of trade of conifer sawnwood for 2000 — base scenario 177
Table 4. 8. Potential directions of trade of conifer sawnwood for 2010 — base scenario 177
Table 4. 9. Percentage change in the minimum availability of conifer sawlogs in Brazil
.................................................................................................................................. 179
Table 4. 10. Potential directions of trade of conifer sawnwood for 2010 — Low-growth
scenario for the supply of conifer sawlogs ............................................................... 180
Table 4. 11. Potential directions of trade of conifer sawnwood for 2010 — Lower-growth
scenario for the supply of conifer sawlogs ............................................................... 181

Table 4. 12. Potential directions of trade of conifer sawnwood for 2010 — High-grth

scenario for the supply of conifer sawlogs ............................................................... 181
Table 4. 13. Change in the Growth of GDP in Brazil ..................................................... 182
Table 4. 14. Estimates of conifer sawnwood exports in the sensitivity analysis for change

in GDP growth rate between 2000-2010 .................................................................. 183

Table 4. 15. Potential directions of trade of conifer sawnwood for 2010 with price
elasticity of domestic demand in Brazil of -0.05 ..................................................... 185
Table 4. 16. Potential directions of trade of conifer sawnwood for 2010 with price
elasticity of domestic demand in Brazil of -1 .00 ..................................................... 185
Table 4. 17. Estimates of conifer sawnwood exports from 1995-2010 in the sensitivity
analysis for change in the price elasticity of conifer sawnwood in Chile ................ 187
Table 4. 18. Summary of the results of the sensitivity analysis for a change in timber
availability in Brazil ................................................................................................. 189
Table 4. 19. Summary of the results for the sensitivity analysis of changes in the GDP
growth in Brazil and price elasticities of demand for conifer sawnwood in Brazil and
Chile ......................................................................................................................... 190

xii

LIST OF FIGURES

Figure 2. 1. Map of South America .................................................................................. 10
Figure 2. 2. Area approved for pine plantation using ﬁscal incentives in Brazil (1967-86)
.................................................................................................................................... 14
Figure 2. 3. Sawnwood production by group of species in Brazil (1980-2000) ............... 29
Figure 2. 4. Matrix of sawlog-sawnwood transformation ................................................. 30
Figure 2. 5. Plantation forest estimates in South American countries (1995) .................. 43
Figure 2. 6. Consumption and production of conifer sawlogs in Brazil (1980-99) .......... 48
Figure 2. 7. Consumption and production of conifer sawnwood in Brazil (1980-99) ...... 49
Figure 2. 8. Consumption estimates of pine and Araucaria sawlogs in Brazil (1980-95) 50
Figure 2. 9. Production estimates of pine and Araucaria sawnwood in Brazil (1980—95) 51
Figure 2. 10. Production and consumption of conifer sawnwood in Brazil (1989-98) 52
Figure 2. 11. Stumpage and market prices of pine sawlogs in Parana (1989-96) ............. 53
Figure 2. 12. Unit value of production for ‘other roundwoods’ in Southern Brazil (1982-
96) .............................................................................................................................. 55
Figure 2. 13. Price of pine sawlog in Southern Brazil (1981-95) ..................................... 56
Figure 2. 14. Consumption and production of conifer sawlog in Chile (1980-99) ........... 58
Figure 2. 15. Consumption and production of conifer sawnwood in Chile (1980-99) ..... 59
Figure 2. 16. Price of radiata pine sawlogs and sawnwood in Chile (1980—97) ............... 60
Figure 2. 17. Consumption and production of conifer sawlog in Argentina (1980-99) 62
Figure 2. 18. Consumption and production of conifer sawnwood in Argentina (1980-99)
.................................................................................................................................... 63
Figure 2. 19. Stumpage price of conifer sawlogs in Argentina (1983-95) ....................... 64
Figure 2. 20. Unit value of imports of conifer sawnwood in Argentina (1980—96) .......... 66
Figure 3. 1. Diagram of the regional domestic sawlog and sawnwood supply and demand
model for each region ................................................................................................. 88
Figure 3. 2. Standard errors of the regression for the conifer sawnwood linear demand
and supply equations (ZSLS) ................................................................................... 123
Figure 3. 3. Standard errors of the regression for the conifer sawlog linear demand and
supply equations (ZSLS) .......................................................................................... 124

xiii

Figure 4. 1. Product linkages in the conifer lumber market in Southern Brazil and in other

Mercosur countries ................................................................................................... 154
Figure 4. 2. Conifer sawlog supply by region for base period and published data ........ 164
Figure 4. 3. Conifer sawlog prices by region for base period and published data .......... 166

Figure 4. 4. Conifer sawnwood demand by region for base period and published data. 167
Figure 4. 5. Conifer sawnwood prices by region for base period and published data 167
Figure 4. 6. Estimates of the demand for conifer sawnwood in the dynamic forecast —
base scenario ............................................................................................................ 173
Figure 4. 7. Estimated conifer sawnwood prices in the dynamic forecast — base scenario
.................................................................................................................................. 174
Figure 4. 8. Estimates of conifer sawlog prices in the dynamic forecast — base scenario
.................................................................................................................................. 175
Figure 4. 9. Estimates of conifer sawnwood prices in the sensitivity analysis for change in
GDP grth rate between 2000-2010 (prices in 2010) ............................................ 182
Figure 4. 10. Estimates of conifer sawnwood prices in the sensitivity analysis for price
elasticity of demand (2010) ...................................................................................... 184
Figure 4. 11. Demand for conifer sawnwood in Chile in the sensitivity analysis for price

elasticity of demand ................................................................................................. 186

xiv

1,"

List of Abbreviations - National and International Agencies and Institutions

LIST OF ABBREVIATIONS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CODE AGENCY (Local Name) AGENCY (English Name) COUNTRY
ABIMCI Associacao Brasileira de Brazilian Association for Brazil
Industria de Madeira Mechanically Processed
Processada Mecanicarnente Timber
ABPM Associacao Brasileira de Brazilian Association of Brazil
Produtores de Madeira Wood Producers
ANF PC Associacao Nacional de National Association of Pulp Brazil
F abricantes de Papel e and Paper Producers
Celulose
BRACELPA Associacao Brasileira de Brazilian Pulp and Paper Brazil
(ex-ANF PC) Celulose e Papel Association
BNDES Banco Nacional de National Developement Bank Brazil
Desenvolvirnento
BRDE Banco Regional de Southern Regional Brazil
Desenvolvirnento do Development Bank
Extremo Sul
CEDEFOR Conselho de Forest Sustainable Brazil -
Desenvolvirnento Development Council of Mercosur
Sustentado Florestal do Mercosur
Mercosul
CIA - Central Intelligency Agency USA
CNPF/ Centro Nacional de Pesquisa National Forest Research Brazil
EMBRAPA de Florestas/Empresa Center/Brazilian Agriculture
Nacional de Pesquisa and Pasture Agency
Agropecuaria
DNER Departamento Nacional de National Road Department Brazil
Estradas e Rodagem
ECLAC Comisién Economica para a Economic Commission For International
América Latina y el Caribe Latin America and Caribbean
FAO Food and Agriculture International
- Organization
FF Fundacao Florestal Forest Foundation Brazil
FGV Fundacao Gett'rlio Vargas Gettilio Vargas Foundation Brazil
FPL/USFS Forest Products Laboratory / USA
- US Forest Service
LIADB Banco Inter-Americano Inter-American Development International
Bank

 

XV

 

.. . continued

List of Abbreviations - National and International Agencies and Institutions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CODE AGENCY (Local Name) AGENCY (English Name) COUNTRY
IBAMA Instituto Brasileiro do Meio Brazilian Institute of Brazil
(ex-IBDF) Ambiente e dos Recursos Environment and Renewable
Naturals Renovaveis Natural Resources
IBDF Instituto Brasileiro de Brazilian Institute for Forest Brazil
(currently Desenvolvirnento Florestal Development
IBAMA)
IBGE Instituto Nacional de Brazilian Institute of Brazil
Geograﬁa e Estatistica Geography and Statistics
INDEC Instituto Nacional de National Institute of Statistics Argentina
Estadistica y Censos and Censuses
INF OR Instituto F orestal de Chile Forest Institute - Chile Chile
INE Instituto Nacional de National Institute of Statistics Chile
Estadistica
INE Instituto Nacional de National Institute of Statistics Uruguay
Estadistica
IPEA Instituto de Pesquisa Research Institute of Applied Brazil
Economica Aplicada Economics
NIECON Ministério de Economia Ministryof Economy Argentina
MGAP Ministério de Ganaderia, Ministry of Farming, Argentina
Agricultura y Pesca Agriculture and Fisheries
MMA Ministério do Meio Ministry of Environment Brazil
__ Ambiente
RISI Resource Information Resource Information USA
. Systems, Inc. Systems, Inc.
‘ RM Revista da Madeira ‘Revista da Madeira’ Journal Brazil
SAGPyA Secretaria de Agricultura, Secretariat of Agriculture, Argentina
Ganaderia, Pesca y Livestock, Fisheries and Food
\ Alimentacion
SEAB Secretaria do Estado e do Bureau of State and Brazil
‘ Abastecimento Provisions (Parana)
SIFRECA Sistema de Informacées de ‘System for Information on Brazil
Fretes para Cargas Freight for Agricultural
Agricolas Cargo’
STCF STCF-Engenharia de STCF-Engenharia de Projetos Brazil
Projetos Ltda Ltda
USFS US Forest Service US Forest Service USA
WFI World Forest Institute World Forest Institute USA
WTO - World Trade Organization International

 

xvi

 

CHAPTER 1

INTRODUCTION

The forest products industry has become an important economic sector in Brazil
and in some of the other Southern cone countries, including Chile, Argentina, and
Uruguay. Among the important solidwood products markets are those for conifer sawlogs
and lumber, which have experienced a fast growth in supply, demand and trade in recent
years. This rapid expansion has been based on large-scale plantations with fast-growing
pine species, established primarily in Southern Brazil and in Chile since the mid-19605,
and more recently in Argentina and Uruguay. As a result, the proﬁle of the conifer-based
industries has been re-shaped by shifting from the consumption of a native conifer

(Araucaria) in the past to planted pines, developing a new market product, with

Signiﬁcant social and economic impacts.

Recent economic, political, social, and environmental developments have become
dI’iVing forces that may affect the future of the solidwood and sawmilling industries in
Various countries. A major event was the creation of Mercosur, the trade bloc agreement
in(:luding Brazil, Argentina, Uruguay, and Paraguay as member countries, with Chile and

Bolivia as associate members. With an estimated 240 million people in 2000 and an
aggregate GDP of USS 1.7 trillion in 1999 (CIA, 2000), Mercosur has become an
important economic bloc in the Southern Hemisphere. It brings opportunities for trade
Creation, and industrial expansion and diversiﬁcation. Its forest products market has
gained growing importance from large investments in plantations and industrial facilities

notably in Brazil, Chile and Argentina. In particular, the conifer sawlog and lumber

markets face increasing demand, limited supply of plantation industrial roundwood, and
trade liberalization.

In Brazil, the forest industry emerged when tax incentives were made available
for reforestation from 1966-86, resulting in the reforestation of over 5 million ha with
Firms and Eucalyptus (Suchek, 1991). Such species are highly integrated into the
industrial process, primarily in the South and Southeast regions, where a signiﬁcant
proportion of the country’s forest plantations and forest-based industries are located.
Since the end of the ﬁscal incentive program, however, and with few economic
incentives for new plantations (e.g.: BRDE, 1995; MMA, 2000) the annual planting rate
has declined signiﬁcantly. Both the South and Southeast Brazil will likely face a deﬁcit
of roundwood starting in the ﬁrst decade of the twenty-ﬁrst century (Azeredo, 1993;
Wiecheteck and Stevens, 1997; Leitel, 1998, cited by Kengen and Graca, 1998). On the
other hand, countries like Chile, and, in a smaller scale, Argentina and Uruguay, have
steadily expanded their forest base. Some studies indicate that the possible deﬁcit of
roundwood in Southern Brazil could be reduced through investments in new plantations
(Dos Santos, 1996) or through an effective re-planting program (Ramos, 1993). Both
studies, however, did not consider the classical price-quantity relationship, the inter-
dependencies of the forest based-industries, the stages of the transformation process, the
inﬂuence of demand and supply shift variables, the relationship with the other Mercosur
countries, the opportunity for intensifying intra and inter-Brazilian trade, nor the spatial
distribution of the plantations. Ramos (1993) suggested that the allocation pattern should
allow wood transportation under economically viable conditions. In 2000, the Brazilian

' Leite, N.B. 1998 Pesquisa Florestal 16 na Frente [Forest Research Ahead]. Speech at CNPF/Embrapa,
August 11, 1998

government launched a forest policy program aimed at sustainable deve10pment, and
among other measures, creating incentives for plantations of 600,000 ha per year by 2010
(MMA, 2000). However, the lengthy sawlog rotation for pines, of as low as 15 years,
makes this policy effective primarily for the middle- to long-run, with little impact in the
short-run.

Other important aspects are environmental issues, the increasing costs of
transportation from more remote supply regions, and changes in the domestic economic

policies effecting both domestic and international trade.

1.1 Statement of the Problem

With limited forest resources and capital, and an expected shortage of roundwood
in some industrial sectors and regions, combined with forest and industrial expansion in
some countries, it is important to determine the optimal use of the resources and the
pattern of production, consumption and trade of forest products, as well as the geographic
and timing pattern for establishing new plantations. The idea of importing forest products
rather than producing regionally is another alternative, taking advantage of more
globalized markets and the relatively stable economy of the Mercosur countries in recent
years. Therefore, the market forces, the government policies and regulations, the forest
industry proﬁle in each country, and the social-economic trends are important variables
to be considered in a study related to the supply, demand and trade of forest products.

The relevance of this study relies on the economic and social importance of the
species and commodities under investigation and in the lack of previous studies focusing

on the classical quantity-price relationship for the forest products market in the Mercosur.

Considering the existing gap in knowledge, this research investigates the market
equilibrium for prices and quantities and trade ﬂows for supply and demand of sawlogs
and sawnwood in Brazil and in the other Mercosur countries. The study is based on the
theory of spatial equilibrium of supply and demand among separate markets. A spatial
equilibrium model that represents the relationship among demand and supply of conifer
sawlogs and lumber and transportation is developed using the formulation proposed by
Samuelson (1952), extended by Takayama and Judge (1964a and 1964b), and adapted by

Zhang, Buongiomo and Ince (1993).

1.2. Objectives of the Study

The general objectives of this study are:

(a) to investigate qualitatively and quantitatively the forest resources and
solidwood industry in Mercosur, with focus on the Southern Brazilian market
for conifer sawlogs and sawnwood;

(b) to evaluate the relationship between demand, supply and prices of sawlogs
and sawnwood in the countries under investigation;

(c) to determine the optimal trade pattern, and the economic allocation of forest
plantations and industrial capacity and production that satisfy the price and the

supply/demand quantity relationship.

1.3. Study Outline and Organization of the Dissertation

To accomplish the objectives described above, the dissertation is divided in three

major chapters, which address different related research questions using speciﬁc
principles and tools.

Chapter 2 provides an overview of the dynamics of the plantation forests and the
solidwood industry in Brazil and in the other Mercosur countries pointing out the relative
importance of each country and the trends, challenges and opportunities faced by their
sawtimber and lumber industries. While focusing on the Southern Brazilian market for
conifer sawlogs and lumber, the chapter also includes recent developments in Chile, -
Argentina and Uruguay (the bloc’s other major forested countries). Qualitative and
quantitative approaches are combined to provide an overall picture of the regional forest
sector and the conifer sawlogs and lumber markets introduces the variables and data used
in the remaining chapters. A historical perspective of the development of the forest sector
in Brazil is presented, which to a certain extent coincides with the development of the
forest business in Southern Brazil. In discussing the importance of forestry in Brazil and
in the Southern region, statistics about the magnitude and location of the existing forests
and the forest-based industries, major products, exports and imports and social aspects
are presented.

Overall, Chapter 2 analyses the plantation forest industry in Brazil, by describing
the historical developments of the sawmilling sectors, the conifer sawlog and lumber
markets, the forest products trade, the Mercosur integration, and the opportunities and
limitations of each country. Trends in domestic consumption, production and prices of
conifer sawlogs and lumber in Brazil, Chile and Argentina, with brief references to the
other Mercosur countries (Uruguay, Paraguay, and Bolivia) are also presented.

Particularly for Brazil, Chapter 2 provides a new synthesis of the Brazilian forest sector

supported by literature and trend analysis. The analysis is intended to provide a better
understanding of the setting and the major actors/elements in the study.

While Chapter 2 focused on an overall description of the forest sector in each of
the Mercosur countries, Chapter 3 explores the simultaneous price-quantity relationship
between supply and demand for each forest product (sawlogs and sawnwood) and region
under investigation, as well as their relationship with other supply and demand shift
variables. The main objective is to provide estimates of the price elasticities of supply
and demand for each product and country to be used in the spatial equilibrium analysis of
Chapter 4. Such elasticities have not been investigated before in other research, which
makes this an important chapter by revealing the range of possible elasticities. A review
of the literature is presented, focusing on the major economic and market forces that
affect the demand for and supply of forest products, the approach and results from other
econometric studies related to solidwood products, and a review of the limited studies for
other solidwood products carried out in Brazil. While discussing the forces affecting
supply and demand for a given commodity, the fact that lumber is a derived demand from
the transformation of sawlogs is considered, bringing to attention the major demand and
supply shifter variables. The theorethical foundation and framework of the study, as well
as the simultaneous equation estimation procedure used in the chapter is discussed. Given
data limitations and the characteristics of speciﬁc countries, the econometric analysis was
carried out for Brazil, Chile and Argentina, with the other Mercosur countries (Uruguay,
Paraguay, and Bolivia) combined in a separate analysis with Argentina. Results for each
country are compared with ﬁndings from other studies. Price and GDP elasticities of

demand and supply to be used in the spatial equilibrium analysis of Chapter 4 were

estimated in Chapter 3. The major ﬁndings of the analysis are discussed, including
recommentadions for further studies.

Chapter 4 combines data and information from the previous chapters, as well as
additional data for each country and region, in order to create a functional price-quantity
spatial equilibrium model of supply of and demand for conifer sawlogs and sawnwood.
The model is a partial equlilibriurn model, where the domestic demand and supply
markets in Brazil, Chile, Argentina and the other Mercosur countries are combined with
the markets for imports from and exports to the ASIA-S countries and the rest of the
world. The main goal is to determine the optimum level of production, consumption, and
equilibrium price in each region and the interregional equilibrium trade ﬂows of
commodities between pairs of regions. The problem involves the estimation of a set of
prices that equate supply and demand among regions. In the literature review of Chapter
4, an overview of forest sector models and applications to different forest sectors are
presented and followed by a comparison of different sector and market equilibrium
models. In the review of the market equilibrium models, Sarnuelson’s (1952) conceptual
framework of spatial equilibrium among separated markets is presented and a description
of PELPS III, the price-endogenous linear programming system used in the study, is
detailed. The empirical method is formulated in terms of the stages of production, the
boundaries of the study and the economic actors and major elements involved. Results of
the base scenario (for the base year 1995 with a 15 year-horizon) and from the sensitivity
analysis are presented for each individual country. The study is also discussed with
reference to its major contributions, strengths and limitations. Major conclusions are

formulated and recommendations for further studies are suggested.

Chapter 5 is a conclusive chapter that outlines the major conclusions from the
previous chapters, also addressing policy-related issues and recommendations for future

research.

CHAPTER 2

THE PINE SOLIDWOOD SECTOR IN BRAZIL AND IN THE OTHER
MERCOSUR COUNTRIES

Introduction

This chapter presents an overview of the solidwood sector in Brazil and in the
other Mercosur2 countries (Figure 2.1), with focus on the pine markets in Southern
Brazil. Qualitative and quantitative approaches were combined to provide an overall
picture of the forest sector and the market of conifer sawlogs and lumber in the region.

Plantation forestry, particularly with the emergence of pines, has become an
integral part of the forest landscape of Southern Brazil and some of the other Mercosur
countries (such as Chile, Argentina, and Uruguay) due largely to past government
incentives, land use policies, and public and private investments. Plantations have also
added signiﬁcantly to the development of the regional forest-based industries in the past
decades.

In Brazil, pines have become an important part of the forest industry, being
primarily used by the pulp and paper and the lumber industries. However, the current lack
of incentives for forest investments, the proﬁle of the forest-based industries, and the
overall economic situation of the country in the recent past have raised the question of
whether or not the existing forests and forest companies will be able to fulﬁll the growing

demand for pine sawlogs and sawnwood.

2 Trade bloc formed by Brazil, Argentina, Uruguay and Paraguay as member countries and by Chile and
Bolivia as associate members.

 

 

 

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. a, A

 

ommmlemmcMMMAe

. '1. if South America
' .5 D
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Source: Magellan (1992)

Figure 2. 1. Map of South America

2.1 Solidwood Sector in Brazil

2.1.1 Forest Resources - Natural Forests and Plantations

Brazil is the world’s ﬁfth largest country with over 8.5 million square kilometers
(5.3 million square miles), representing 47% of South America. About 90% of the
country is located between the Equator and the Tropic of Capricorn. The long distance
from North (5°N) to South (35°S), stretching over 4,500 km (2,800 miles), combined

with diverse climates, geological formations and changes in elevation created varied

ecosystems across the country (Tomaselli, 1998; Kengen and Graca, 1998). Brazil is
subdivided into ﬁve political regions (South, Southeast, North, Northeast, and West
Central), each with diverse vegetation types. Over 60% of the country is covered with
natural forests, with nearly 394 million ha potentially productive forests, excluding
protected forests and Indian reservations.

The Amazon rain forest, in the North along the Equator (including its extensive
buffering zone in part of the Northeast and West-Central regions), is the largest
concentration of natural hardwood forests in the country, comprising 320 million ha of
forestland, or almost 50% of the country’s area (Tomaselli, 1998). The total wood
volume is estimated at 50 billion m3, with only 10% considered of commercial use,
represented by 1/6 of the 30,000 existing species (Higushi, 1985).

Another important natural ecosystem is the Parana pine (Araucaria angustifolia)
forest in the South. This forest type has been systematically depleted over most of the
twenty century as result of agricultural clearing, logging, and urban/industrial
development. Other important natural forest types include the Atlantic Rain Forest
(extending throughout the Coastal zones of the South, Southeast, and part of the
Northeast); the Cerrado (a Savannah-vegetation type in the West Central region); the
Pantanal (a wetland forest type also in the West Central), and the Caatinga (a dry-bush
vegetation type in the Northeast).

The Amazon and the Atlantic forests (hardwoods), and the Araucaria forests
(conifer) have played a major role as sources of roundwood for the solidwood industry
during most of this century. However, in the past few decades, extensive plantations with

pines and Eucalyptus throughout the country have emerged as important sources of

11

industrial roundwood. Pines (primarily Southern yellow pines) are concentrated in the
Southemmost states, in a sub-tropical zone, and Eucalyptus have been planted along the
Atlantic Coast from the South to the North, and inland in the West-Central region.
Nationwide, plantations are estimated at 4.6 million ha, respectively 1.7 million
with Pinus and 2.9 million ha with Eucalyptus (Sobrinho, 1996). The majority of the
pines are located in the Southern states of Parana (35.8%), Santa Catarina (20.6%), 830
Paulo (12%) and Rio Grande do Sul. The state of Minas Gerais has the largest area of
Eucalyptus (41 % of the country’s total), which has been closely linked to the steel
industries and the pulp and paper sector. $5.0 Paulo, Bahia and Mato Grosso do Sul have
also established large-scale Eucalyptus plantations, primarily for the pulp and paper

industry. Minor species account for the remaining 0.3 million ha (Tomaselli, 1998).

2.1.2 Historical Developments of the Solidwood Sector

The forest activity in Brazil started at the time the Portuguese ﬁrst arrived in
1500, with the harvesting of ﬁne hardwoods for the European market. During the
following four centuries harvesting was extractive and to a large extent, not linked to any
industrial process. However, during most of the twenty century, harvesting of natural
forests intensiﬁed signiﬁcantly as result of extraction and the clearing for agriculture,
cattle ranching, industrial use and urbanization (BNDES, 1995), primarily in the states
along the Atlantic coast. National development policies set by the government with
strong subsidies for agriculture as well as public and private investments lead to the

intensiﬁcation of deforestation and change of the land use in most of the country.

12

The solidwood industry emerged in the beginning of the twenty century in
Southern Brazil, based initially on the abundant Parana Pine forests and intimately related
to the sawtimber production. Argentina remained the main export market for many years,
but, as result of its high lumber quality, Parana pine gained markets in Europe and in the
US (Tomaselli, 1998). Intensiﬁcation of commercial harvesting and deforestation caused
by agricultural projects gradually reduced its supply. As result, the “Instituto Nacional do
Pinho” (National Institute of Parana Pine) was created in 1941, becoming the ﬁrst
national agency concerned with monitoring and providing incentives for plantations
(BNDES, 1995).

In the 1960’s, as a result of shortage of Parana pine supply and the government
strategy to develop the North region, the timber industry started to move towards the
Amazon to take use of its extensive hardwood resource. The vast area of land Opened to
agriculture and pasture made available large volumes of high quality logs at a relatively
low cost, although most of the resource did not ﬁnd a commercial use. In 1966, IBDF
(currently IBAMA) was created with the objective of developing national forest
programs and related policies. In the same year, the federal government launched a ﬁscal
incentive program (law 5106, 1966) allowing individuals and corporations to invest part
of their tax liabilities in reforestation projects. This model offered generous terms and
conditions to both domestic and foreign investors through a ﬁscal incentive scheme for
regional development programs (Kengen and Graca, 1998). During the greatest tax relief
period (1967-1974), over US$50 millions were set aside by the government for planting
programs (U SDC, 1991). The total area approved for reforestation from 1967-86

amounted to 6,252,483 ha, 52% (3.2 million ha) were Eucalyptus, 30% (1.9 million ha)

13

Pinus, and about 18% comprised of Parana Pine, other native species, fruit trees, palms
and others (WFI, 1995; BNDES, 1995; Kengen and Graca, 1998) (Figure 2.2).

Volume and rotation ages vary with location of the forest and species. Eucalyptus
yield mean annual increment (MAI) from 5-35 m3/ha.year in 7-21 year rotation. Pines’
MAI varies from 8-30 m3/ha.year in 20-25 year rotation (including intermediate
thinning). However, the MAI is improving considerably and rotation ages are shortening
through the use of genetically superior stock. (WFI, 1995), and more suitable

management practices.

 

180.0
160.0
140.0
120.0
100.0
80.0
60.0
40.0
20.0
0.0

’\ 9 '\ '5 (O ’\ 9 '\ ‘5
~60 195° ,é\ @ § '3’} '55\ '9?) 9% '92?

year

area (000 ha)

 

 

 

 

Source: IBDF (cited by Kengen, 1987 and Cottle & Schreuder, 1990)

Figure 2. 2. Area approved for pine plantation using ﬁscal incentives in Brazil (1967-86)

The forest plantations with ﬁscal incentives became a major source of industrial
roundwood as well as raw-material to some energy-using industries. By establishing
plantations, companies also fulﬁlled their ‘forest recovery’ programs (law 4771/1965)
which requires that wood-consuming ﬁrms invest on self-production of raw-material they

consume. With the incentives, ﬁrms carried out their ‘forest recovery’ programs and

14

generated capital for continuous investments on industrial plants (Ramos, 1993).

The ﬁscal incentive policy aimed primarily at private businesses (Garlipp, 1997).
Overall, private companies and large-landowners were the major beneﬁciaries of the
program, accounting for 90% of the plantations (Kengen and Graca, 1998). Pulp and
paper companies, as well as the pig-iron and steel industries, took advantage of this
opportunity and established plantations to attain raw-material self-sufﬁciency, and some
of them, primarily pine growers, became net suppliers of timber. In contrast, sawmills
and the ﬁuniture and plywood industries, with a lower degree of self-sufﬁciency and a
less organized structure, did not take part in the incentive scheme (Kengen and Graca,
1998). These authors also pointed out that the ﬁscal incentive program was not aimed at
farmers and small landowners, and indeed they did not participate in it.

Financial problems and misuse of the incentives caused the shrinkage of the
program in 1983. The program ended in 1986, with the withdrawal of the incentives
leading to signiﬁcant decrease in plantations. The average annual planting dropped from
400,000 ha between 1974 and 1982, to around 200,000 ha in 1983 and even less
thereafter. Since the end of the program, plantations has resulted from isolated public and
private actions, carried out primarily by large and usually more capitalized forest-based
companies (Kengen and Graca, 1998) and by some local and state government agencies.
Indeed, in recent years plantation forests have been established by more capitalized ﬁrms,
such as pulp and paper companies, to fulﬁll their own industrial needs, with little regard
to the future timber market and the sawmilling industries’ demand. In addition, large
forest-based ﬁrms have initiated stewardship planting programs with small landowners,

with the ﬁrst option of buying the stumpage by the harvesting time. It adds a burden to

15

the less structured solidwood industries, which over the years became dependent on raw-
material from those ﬁrms.

During the 19805, Brazil faced some political turmoil and periods with high
inﬂation rates that undermined investment, particularly in forestry. As result, only a
relatively small area was planted during the 19905, despite the growing demand for
industrial roundwood. From the average of 400,000 ha planted in the mid-19703,
reforestation has been reduced to an average of 150,000 ha per year, insufﬁcient to meet

the country’s future wood requirement (Leitel, 1998, cited by Kengen and Graca, 1998).

2.1.3 The Forest Sector and Industrial Roundwood Production/Consumption

The Brazilian forest sector is formed primarily by the lumber, veneer, plywood,
particle board, ﬁber board, cellulose and paper segments. Overall, it accounts for annual
revenue of US$ 11 billion (representing 4% of the GNP), generates USS 2.3 billion a year
of foreign exchange credits, and provides 600,000 direct jobs, beneﬁting approximately
3.5 million people indirectly (WFI, 1995).

Wood processing in Brazil may be evaluated by grouping species used as input in
the industrial process. Tomaselli (1998) indicated three major groups of industries as: (a)
using native hardwood species from the Amazon basis; (b) using planted pines; and (c)
using planted Eucalyptus. The ﬁrst group is located primarily in the North region and
consumes ﬁne native wood species (e. g. mahogany, cedar, and ‘virola’), producing
lumber, wood panels and plywood for domestic consumption and exports. This group is

formed by a large number of small mills, many originally from the South, and

16

characterized by low productivity, technology, and poor managerial standards. Their
harvesting practices have resulted in excessive waste both in the forest and in the mills,
since most of the ﬁrms have not implemented strict sustainable forest management
practices. The lack of a clear government policy for the Amazon, associated with the
existence of Brazilian and, more recently, large Southeastern Asian sawmills make the
social, economic and environmental impacts still uncertain for the region. Industries
utilizing pines, are located primarily in the South, and formed by sawmills, plymills,
pulpmills, and wood panel ﬁrms (particle and ﬁberboard). Their products are used
domestically on housing, civil construction, and by the fumiture industry, or exported.
The group using Eucalyptus is formed primarily by pulp and papermills spread across the
South, Southeast and Northeast and some steel companies in the Southeast. Eucalyptus is
also used in hardboard production, primarily for export (BNDES, 1995). In recent years,
Eucalyptus lumber started to be produced on a modest scale since problems with the
product quality and market acceptance must be still overcome.

By the mid-19705, plantations became an important source of raw-material to the
growing forest industries. In particular, pulp and papermills, and some wood panel
industries beneﬁted from this new resource and expanded considerably (BNDES, 1995).
Therefore, both natural forests and plantations played a major role on the development of
the Brazilian forest sector during the second half of the last century. Plantations are
mostly owned by the private sector, primarily by large forest-based companies although
varied small businesses also own some shares of planted forests. Non-private owners are
a minor category of plantation owners.

The increasing production in all categories of industrial roundwood since the

17

19705 is notable and indicates investments and diversiﬁcation of the Brazilian forest
sector (Table 2.1). Given an existing ban on roundwood exports until 1993, and
negligible imports during the period (IBAMA, 1996), the production also represents the

industrial roundwood consumption by forest-based industries.

Table 2. 1. Evolution of the production of industrial roundwood in Brazil (1979-99)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

. Pul wood Other
Industrial Sawlogs and and Particle Industrial
Roundwood Veneer Logs

Year (000 m3) (000 m3) Board Roundwood

(000 m3) (000 m’)

C NC C NC C NC C NC
1970 11113 12825 9320 7470 1400 2110 393 3245
1980 25814 35908 19916 16296 5400 15500 498 4112
1990 31291 42986 20085 17883 10600 20101 606 5002
1995 33032 51486 21779 26000 10600 20101 653 5385
1999i” 22458m 30605?27 21779 25000 n.r. n.r. 679 5605
1170-80 132% 180% 114% 118% 286% 635% 27% 27%
1580-90 21% 20% 1% 10% 96% 30% 22% 22%
7190-99 - - 8% 40% - - 12% 12%

 

 

 

 

 

 

Source: F AO Forest Products Yearbook (2000)

Notes: C — conifer; NC - non-conifer; n.r. — not reported
(1) last year reported by FAQ

(2) not including totals for pulpwood and particleboard.

The highest annual growth for all categories occurred during the 19705, which
coincides with the increasing availability of planted roundwood and a strong growing
economy. For the period 1980-99 a slower annual grth was observed, still with
impressive increase in pulpwood consumption (96% and 30% a year respectively for
conifer and non-conifer pulpwood and particleboard). With respect to conifer sawlogs
and veneer logs the impressive grth during the 19705 (114%) may be attributed to
higher Araucaria supply, since pines were not available in commercial quantities till the

early 1980’s (Table 2.1).

18

2.1.4 The Solidwood Industry and the Sawmilling Sector

Domestic production of solidwood products has increased in the past decades,

mostly during the 19705 (Table 2.2). This may be an indirect result of economic

expansion, population increase, and increasing demand for building and packaging

materials and furniture. During the 19805 and the 19905 the average annual growth rate

was variable, but still positive for all the product categories, except ﬁberboard.

Table 2. 2. Evolution of the production of solidwood products in Brazil (1970-99)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Sawnwood Wood Panel
Year (000 m’) 0 . (000 m’)

C NC Fiber Particle Plywood Veneer Total

Board Board Sheets

1970 4535 3500 269 1 12 342 96 819
1980 7143 7738 780 660 826 216 2482
1990 7923 9256 698 660 1300 234 2892
1995 8591 10500 698 660 1900 300 3558
1997 8591 10500 1150“) 1150(1) 1900 300 3558
1999‘” 8591 10000 698 660 1500 240 3098
A70-80 58% 121% 190% 489% 142% 125% 203%
A80-90 1 1% 20% -1 1% 0% 57% 8% 17%
1190-99 8% 8% 0% 0% 15% 3% 7%

 

Source: F AO Forest Products Yearbook (2000)

Notes: C — conifer; NC - non-conifer; n.r. — not reported
(1) last year reported by F A0
(2) not including totals for pulpwood and particleboard.

For sawnwood, the average annual growth in production was 1.1% and 0.8% a

year, respectively for the 19803 and 19905. However, since the late 19905 FAO estimates

have been reported with the same value as for 1995 for most of the products (Table 2.2).

19

 

2.1.4.1 Domestic Production of Conifer Sawnwood

Sawmilling is the leading solidwood sector both in number of ﬁrms and
production capacity (Table 2.3). In contrast to the pulp and paper sector, the sawmilling
industry is formed by thousands of small, portable, undercapitalized independent mills
(Kengen and Graca, 1998), and characterized by low product quality and lack of
modernization. Over 80% of the sawmills have low productive capacity with production
of up to 6,000 m3 of lumber per year (BNDES, 1995). However, in recent years, some
technological developments have been noticed in both the sawmill and fumiture
industries (Sobrinho, 1996). On the other hand, wood panel producing companies have
high productivity levels, industrial modemization and automation, good product quality

and more professional management structures (BNDES, 1995).

Table 2. 3. Estimated installed capacity of solidwood industries in Brazil (1993)

 

 

 

 

 

 

 

Solidwood Firms Production Capacity
P_roducts (number) (000 m3/year)
Sawnwood "1 8,000 e 10,000 18,000 - 22,000
Plywood ‘21 around 400 2,200
Veneer ‘21 40 - 50 400
Fiberboard 9’ 2 700
Particleboard (I) 6 1,285

 

 

 

 

Source: (1) ABPM; (2) ABIMCI; (3) BNDES; (4) ABIPA; (cited by BNDES, 1995)

Sawmills are concentrated in the North and South regions (Revista da Madeira,
1995a). There are 8,000-10,000 sawmills nationwide in relatively small units (Table 2.4).
Over 50% of these are located in the southernmost states of Parana (2,350), Santa
Catarina (1,900), and Rio Grande do Sul (1,000), and consume mostly pine sawlogs. In

the South, sawlog consumption increased 163% between 1986-95, from 5 million m3 to

20

13.2 million m3, the great majority being pines (Sobrinho, 1996).

Over the 1990-2000 period, the average grth of sawnwood production in Brazil
was 3.6% per year, with a clear differentiation between production of hardwood and
softwood sawnwood. Over the period the average growth of softwood sawnwood was
5.5% per year and only 3.1% for hardwood sawnwood. Signiﬁcant growth of Eucalyptus
sawnwood production over the last years have been noticed, with is estimates for 2000
reaching near 700 thousand m3. (ABIMCI, 1999).

Production of value-added products (blocks and blanks, EGP, and moldings) grew
from 515 thousand m3 in 1995 to estimated 859 thousand m3 in 2000. These volumes are
signiﬁcant considering the recent development of this activity in the country. Growth
rates over the period are high, mainly for blocks/blanks and moldings. The production of
blocks/blanks, engineered glued products (EGP) and moldings are mostly based on pines,
with the main producers located in the Southern region (ABIMCI, 1999).

Conﬂicting estimates of sawnwood production are reported. FAO estimated 19
million m3 of sawnwood produced in 1995 (FAO, 2000), while Revista da Madeira
(1995a) reported 13 million m3, with the South and North regions producing 5 and 8
million m3 of lumber respectively. For the year 2000, Revista da Madeira (1995b)
estimates a total sawnwood production of 20 million m3, 8.5 million 1113 in the South and
12 million m3 in the North. On the other hand, BNDES (1995) indicates a different
scenario, with the combined South and Southeast regions accounting for 80% of the
sawnwood production for 1993 (Table 2.4).

Table 2.4 reveals the leadership of both South and Southeast regions in the

production of all major product categories. That is coherent with the historical pattern of

21

industrial development and location of the major consumer markets. Although the North
is responsible for only 20% of the sawnwood production it has been an important supplier
of hardwood sawlogs to Southern sawmills. Even though transportation is relatively
costly, the low production cost and the high price of manufactured products still make the

North-South supply ﬂow proﬁtable.

Table 2. 4. Percentage of regional production of solidwood products by volume (1993)

 

 

 

 

 

 

 

 

 

Region Sawnwood Plywood Fiberboard Particleboard
and Veneer
South/Southeast 80 75 100 100
Others 20 25 0 0

 

Source: ABPM, ABIMCI, ABIPA (cited by BNDES, 1995)

2.1.4.2 Domestic Consumption of Conifer Sawnwood

Sawnwood represents a large proportion of the forest resource utilization for
intermediate industrial use in Brazil (excluding ﬁrelwood), consumed in a diversiﬁed
domestic market as well as exported. The national sawnwood consumption grew 39%
during 1990-2000, resulting in an average 3,3% annual growth rate (Table 2.5). The
consumption of hardwoods grew 38% (3.2% per year), while softwoods presented a
growth of 41.4% (3.5% per year).

Although statistics about the role of pines in different structural wood markets is
limited, the furniture industry represents an important segment with growing pine
consumption. In the early 19905, the ﬁrmiture industry started to consume massive

volumes of pine, representing over 80% of the furniture exports by 1996, ﬁve times more

22

than during the 19805. There were 13,500 furniture mills in the country in 1996, with
pines representing 80% of the wooden raw-material for the RTA (‘ready to assemble’)
furniture, primarily in Santa Catarina and Rio Grande do Sul states (Donnelly and
Suchek, 1996). Increasing pine lumber consumption has also been noticed in the

construction industry (Revista da Madeira, 1996).

Table 2. 5. Sawnwood consumption in Brazil (1990-2000)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Sawnwood Consumption
Year Hardwood Softwood Total
000 m3 % 000 m3 %
1990 10,360 78 2,850 22 13,210
1991 1 1,510 77 3,440 23 14,950
1992 12,157 78 3,407 22 15,564
1993 12,404 77 3,670 23 16,074
1994 12,179 78 3,451 22 15,630
1995 13,022 78 3,570 22 16,592
1996 13,291 78 3,653 22 16,944
1997 13,752 79 3,648 21 17,400
1998 13,450 79 3,660 21 17,110
1999 13,860 78 3,840 22 17,700
2000 * 14,300 78 4,030 22 18,330

 

 

 

Source: ABIMCI and STCP (ABIMCI, 1999)

 

2.1.5 Markets of Roundwood and Sawnwood

2.1.5.1 Roundwood Supply and Demand

In contrast to common thought, Southern Brazil has been the major producing
region of wood products nationwide with 54.1% of the total market in the early 19905,
followed by the North with 30.3%. In 1988, the Southern region produced approximately

2 million m3, 61% from Pinus plantation. The remaining production came from Parana

23

pine and hardwood forests, whose depletion has changed the proﬁle of the regional
industry (Revista da Madeira, 1993), toward the consumption of pine species.
Demand for industrial roundwood in Southern Brazil reached 17 million 1113 in
1990, an increase of 11% since 1980. Sawmills represented the major roundwood-
consuming industry (8.8 million m3, or 52%), followed by the pulp and paper mills (7.0
million m3, or 41%), with only 3% and 4% allocated respectively for veneer and
particleboard. It reﬂects the importance of both sawmills and pulpmills in the region
(Siqueira, 1995). Intra- and inter-regional transportation of industrial roundwood and
processed and serrri-processed forest products is a common practice in the Southern
states. The regional industrial roundwood equivalent consumed in 1990 reached 20.5
million m3, 6% coming from other states and 94% being regionally produced and traded.
Roundwood imports in 1990 accounted for 1.2 million m3 (a decrease of 50% from 1980)
while exports were negligible. For the total sawtimber production in the South, 94% was
consumed domestically and 6% was exported (Siqueira, 1995).

Nationwide, roundwood is supplied from both plantations (Pinus and Eucalyptus)
and natural forests (a small proportion from regional hardwoods, Parana pine, and
hardwoods ﬁ'om the North). The roundwood from plantations showed impressive grth
of 863%, between 1975-95 from 2.5 million m3 to 24.5 million m3 (Siqueira, 1995). Most
of the supply in 1990 was industrial logs (17.3 million m3), while 6.9 million m3 and
227.4 thousand m3 respectively were fuelwood and charcoal/ﬁrewood.

In recent years, excess supply of pine roundwood has entered the domestic market

as result of a large proportion of plantations reaching harvestable ages. The supply

increase has driven prices down, reducing incentives for continuing forest investment.

24

Fast-growing plantations are usually owned by forest subsidiaries, which are committed
to supply raw-material for their associated industry (e.g., forest branches of pulp and
paper mills). For such companies, prices reﬂect more a transfer value from the subsidiary
rather than the true market price. On the other hand, sawmills to a large extent have been
dependent on the excess supply of pine sawlogs from other producers such as papermills’
subsidiaries. In this case, sawlog prices tend to express a competitive market price, since
both buyers and sellers have the expected behavior of the demand and supply agents.

Since the end of the ﬁscal incentives in 1987, only the more capital intensive
companies (pulp and paper mills’ forest subsidiaries) have invested on plantations. Given
the low price of roundwood (notably pulpwood), self-generated capital has been limited
and, as result, plantations have been linked to the companies’s future pulpwood
requirement, with not much left as potential excess supply for sawmills. This scenario has
perpetuated a situation in which capital intensive companies have established enough
plantations to fulﬁll their needs while sawmills have not generated enough capital to
invest in their own forest assets to supply their growing demand for sawlogs.

With limited forest resources and lack of policies and programs for new
plantations, there is an overall concern that Southern Brazil may face a deﬁcit of
roundwood in the coming decades. Given a growing demand for roundwood in the South,
Siqueira (1995) estimated a regional deﬁcit of Pine roundwood by the year 2015, and
from Eucalyptus by 2007. Ramos (1993), in his study about the supply and demand of
roundwood in the state of Parana, predicted that scarcity of industrial roundwood will
reach the Southern state between 2003 to 2007 if the 1993 trend of consumption and

planting continues. A study carried out by BRDE bank for the Southern states estimates a

25

deﬁcit of industrial roundwood for by 2007 (Kengen and Graca, 1998). Wiecheteck and
Stevens (1996), in a gap analysis study of the pulpwood market in Brazil, identiﬁed
shortages of Pinus and Eucalyptus pulpwood, respectively by 2004 and 2008 if reduced
plantation area and increasing growth in domestic demand for pulpwood and sawnwood
continue. Revista da Madeira (1995a and 1995b) suggests a sharp shortage of roundwood
after 2005, in Southern Brazil as result of the non-continuity of forest plantations.
Depending on changes in the economy, this trend could be expected (Revista da Madeira,
1995b). Bacha (2000) also suggests the potential for shortage of certain types of
roundwood from plantations (mostly large logs and fuelwood) considering the balance
past supply of and demand for those products. However, such forecasts are
generalizations and more detailed studies are needed to effectively account for spatial and
industrial aspects. Although forest resources are available to a certain extent, the forest
location, the age of the stands, the lack of investments, and the growing environmental
concerns are major factors limiting the area available for harvesting (Dos Santos, 1995).
Bacha (2000) recommended the need for new incentive policies for reforestation.
Although this author indicated that several states already have their public and private
incentive programs for plantations, he stressed that these such programs should address
the small and medium-size land-owners.

Dos Santos (1995) and Siqueira (1995) suggested that if continuous reforestation
is not targeted and carried out, Brazil might import timber and wood products in the near
future. This situation has already been noticed in some regions. Due to the limited
availability of native hardwoods in the South, sawmills have “imported” sawlogs from

the far North, although this situation is not likely to remain as prices of hardwoods and

26

transportation costs increase. BNDE (1995) for instance estimated that Brazil will need
around 15 million ha of industrial forests by 2010.

Few studies have reported the pattern of future plantations required to supply the
increasing demand for industrial roundwood in the South. Ramos (1993), using a gap
analysis of supply and demand, estimated a deﬁcit of 40 to 80 million In3 starting in 2000
or 2003, and the minimum planting program to be implemented to ﬁll the future gap of
roundwood in the state of Parana. Siqueira (1995) suggested investments in new
plantations in the Southern states without referring to the total area needed or the annual
planting area. However, the pattern of allocation of forest plantation in order to ﬁll this
gap was not investigated in their studies.

Although exports of most of the solidwood products have increased, the domestic
market has been, and is expected to continue to be, more important than the international
market. Besides the expectation of continuous economic and population growth, which
alone would contribute to an increase in domestic demand for products in general, there
are still niches to be occupied by the lumber sector, such as the construction market.

In order to fulﬁll the demand for forest products, some suggestions have been
proposed. Among them is the production of high value-added products, and the search for
markets suitable in terms of costs and product quality to the company and region’s
condition (Dos Santos, 1995). In addition, companies should also search for continuous
performance gains, increasing their global competitiveness. Shift in sawnwood
production from the South to other regions has been suggested, where smaller demand for
land would favor the establishment of plantations and the management of natural forests

(Revista da Madeira, 1995a). However, considering the pattern of land use by large

27

landowners in the Amazon and the incipient government control, it may represent a threat

to the sustainable development in the region.

2.1.5.2 Sawnwood

On the demand side, FAO estimated a 12% annual growth in sawnwood
consumption from 1990 (17 million m3) to 2010 (over 59 million m3) as result of
increasing population and purchasing power. Dos Santos (1995) suggested consumption
of 25-30 million in3 by 2010, a more modest annual growth rate of 3-5% and close to the
expected GDP growth for the period. FAO’s growth projection seems to be overestimated
given the 1999 Brazilian economic turmoil and the expected low population growth.

Large-scale pine sawnwood production started in the early 19805 with a dramatic
increase in volume during the 19905 (Figure 2.3). Pines substituted almost completely for
Araucaria and hardwoods from the South and Southeast. In 1980, 98.6% of the total 9.5
million m3 of lumber produced nationwide came from native species (24.8% Southern
hardwoods, 52.9% Northern hardwoods and 20.9% Parana pine) with only 1.4% as
planted pines. After natural forests became scarce and plantations reached commercial
age, an inverse trend was observed. In 1995, 63.7% of the total lumber production (9.2
million m3) was pine, only 2.5% and 0.1% respectively Parana pine and Southern
hardwoods, but still 33.1% Northern hardwoods. This trend reﬂects the importance of
pine to the regional lumber industry (Azeredo, 1993). In 2000, over 75% of sawnwood
was estimated to come from the South and Southeast, pines accounting for over 98% of

the total (Revista da Madeira, 1996).

28

 

 

 

000 m3

 

 

 

 

1980 1987 1991 1995 2000
Year
5 Araucaria I Southern Hardwood
Pines [1 Northern Hardwood

 

 

 

Source: Revista da Madeira (1996); estimates for 2000

Figure 2. 3. Sawnwood production by group of species in Brazil (1980-2000)

Sawnwood production in Brazil is integrated with other related industries (Figure
2.4). The sawnwood market is estimated at 8 million in3 of pine sawnwood a year out of
15 million m3 (Dos Santos, 1995). Production of Eucalyptus sawnwood had been
negligible until the recent past, with only 80,000 m3 produced in 1994, or less than 0.5%
of the national total (Revista da Madeira, 1996). Pine sawnwood has shown better wood
properties than Eucalyptus for structural uses (Revista da Madeira, 1996) and Eucalyptus
had been considered less well understood in terms of market potential and processing
technology (Flynn et al., 1998). However, there is an overall perception that Eucalyptus
may become a substitute for some Pinus sawnwood in the next decade (Revista da
Madeira, 1995a), given the resource availability and technological advances in recent
years. Massive investments in sawmilling capacity by major Eucalyptus pulp and paper

companies have been made in the past years both in the South and Southeast.

29

Current demand for roundwood from plantations is already high and likely to
surpass the supply, especially after 2000. This trend has already been observed in

Southern Brazil, primarily for high-grade sawnwood (Dos Santos, 1995).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MARKET OF CONIFER
SAWLOGS AND LUMBER
PRIMARY CONIFER 7 ‘ NON-CONIFER
PRODUCT SAWLO'CS ' 1 1 ' SAWLOGS
E
.LABOR X M l
.ENERGY T A NON-CONIFER
.OTHERS E R SAWMILLS
R K 1
SECONDARY . '~ 1 E N E E
PRODUCT CONIFER ; A T ‘ NON-CONIFER
. LUMBER . 1 L 1 ' LUMBER
LEGEND 1 V V 3
E'EXPWS DOMESTIC LUMBER MARKET i

 

I - Imports 1

 

 

 

 

 

Figure 2. 4. Matrix of sawlog-sawnwood transformation

Although statistics on consumption and use of pines by structural-wood
consuming industries are not available, the fumiture industry is a growing market for pine
sawnwood. Until the end of the 19805, the production of furniture had steady growth,
with a large utilization of native species. In the early 19905, pine was introduced into this
new market, and by 1996 it accounted for over 80% of the exported furniture (Revista da

Madeira, 1996).

30

The domestic market has traditionally been the main market for solidwood
products, given the country’s large population. Most of the sawnwood is consruned in the
South and Southeast, where the higher-income population is concentrated. $50 Paulo is
the main market for wood products, although other emerging regions include the South
(with the expanding market for higher quality wood, primarily pine clearwood) and the
Northeast (with a growing per capita income in the past years) (Tomaselli, 1998).

A growth rate of 4-5% domestic a year in demand for solidwood products is
projected for the next years, above the international average, representing an opportunity
for the industrial expansion. Tomaselli (1998) pointed out that in 1998 over 90% of the
sawnwood and 70% of the wood panel production was traded in the domestic market.

The sawnwood industry in Brazil is characterized by a high level of wood
residues as a result of incompatibility of the speciﬁcation of the product produced and the
product demanded, outdated technology and the use of unskilled labor.

A close look at sawlog and sawnwood production by species group indicates a
dramatic increase in the past decades. The highest annual growth was noticed during the
19705 with signiﬁcant decrease thereafter, except for non-conifer sawlogs and veneer
logs, which expanded during the 19905. For conifer sawnwood, a modest, but still
impressive, annual grth is observed for the 19705 (5.2% a year), followed by a steady
growth of about 1% since 1980. However, conifer sawnwood disaggregated into pines
and Araucaria shows a rather different picture (Table 2.6). Data from ABPM (1994),
referring to Southern Brazil only, shows an impressive grth of 165% a year in pine
sawnwood production during the 19805, followed by a modest, but still signiﬁcant,

annual growth of over 16% for the 19905.

31

Table 2. 6. Conifer and non-conifer sawnwood production in Brazil (1970-95)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Sawnwood (000m3)
Non-Conifers Conifers
Year Total (1) Total (1) Firms 0’ Araucaria (2
Brazil Brazil Southern Southern
Brazrl Brazrl

1970 3,500 4,535 n.a. n.a.

1980 7,738 7,143 130 1,990

1990 9,256 7,923 2,500 380

1 995 10,500 8,591 5, 000 200

2000 9’ n.a. n.a. 7,000 180
%A 70-80 11.0 5.2 n.a. n.a.
%A 80-90 1.8 1.0 165.7 - 7.4
%A 90-95 1.7 1.1 16. 7 -7.9
%A 90-00 n.a. n.a. 16.4 -4.8
Source/Note:

(l) FAO Forest Products Yearbook, 1999

(2) Conifer sawnwood production in Southern Brazil (ABPM, 1994)
(3) ABPM estimate (1994)

n.a. — not available

Although the South is a major producing region of forest products in Brazil, with
traditions in both plantation and wood products manufacturing, a number of problems
may affect its future leadership and overall performance. A major inhibiting factor is the
limited capital resources of some forest sector segments and the consequent lack of
investments in plantations. Other important factors are the lack of competitive technology
(primarily in the solidwood industry), the existence of low productivity and unregulated

forests, and the non- participation of non-industrial private owners in the forest sector.

32

2.2 Forest Products Trade and Related Issues

2.2.1 World Market of Solidwood Products

The world market of solidwood products has traded over US$40 billion a year
from 1995-99 (FAO, 2000b), with a total of US$43 billion in 1999. The participation of
Brazil has been modest, with approximately 2% of the exports for all forest types and
4.5% for tropical hardwoods in 1997. Overall, FAO estimates of ﬁrture forest products
production and consumption suggest unbalanced supply and demand in the international
market in the ﬁrst decade of the twenty-ﬁrst century. That is a function of reduced
production in some Southern Asian countries such as Malaysia and Indonesia, and
expected stagnation of the major producers in the Northern hemisphere as a result of
environmental and social pressures (Macedo et al., 1996).

In 1996, solidwood products exports for Brazil reached US$1.1 billion in addition
to exports of US$266 million worth of wooden fumiture (Macedo et al., 1996). In terms
of sawnwood, the Brazilian exports from 1990-96 increased 16%, reaching US$ 345

million. Exports of sawnwood have been modest, below 10% of the domestic production.

2.2.2 World Sawnwood Market

International trade of sawnwood, the strongest solidwood product, reached an
average of US$25 billion a year over the 1995-99 period, with US$278 million in 1999
representing 119 million m3. During 1980-99 the annual grth in conifer and non-

conifer sawnwood exports was respectively 2.6% and 1.9% (Table 2.7).

33

Table 2. 7. International trade of sawnwood (1980-99)

 

 

 

 

 

 

 

 

 

 

 

Product Year Annual Growth
1980 I 1990 ] 1999 1980/99 (%)

Exports — Volume (000 m3)

. Conifer Sawnwood 66,442 73,818 100,956 2.6

. Non-Conifer Sawnwood 13,186 15,185 18,278 1.9

Total 79,628 89,003 1 19,234 2.5
Exports - Value (000 USS)

. Conifer Sawnwood 138 172 176 1.4

. Non-Conifers Sawnwood 241 303 547 6.4

Total 155 195 233 2.5

 

 

 

 

 

 

 

Source: F AO Yearbook — Forest Products (2000) adapted from Macedo et al. (1996)

Most of the sawnwood trade in 1999 was conifer, over 100 million m3 or 85% of
the total traded. However, for unit value of exports a rather different picture emerges. A
much higher annual growth of 6.4% was noticed for non-conifer sawnwood in
comparison with 1.4% for conifers over the period 1980-99 (Table 2.7).

International trade of conifer sawnwood has been attractive in recent years,
reaching US$ 18 billion in 1999. Combined, the USA, Canada, and the EU account for
over 80% of the world’s imports and exports of conifer sawnwood (Tables 2.8 and 2.9).

In terms of imports, the US alone accounted for 45% of the world total of 100
million m3 in 1999, followed by the EU with 32%, and Japan with 8.3%, all totaling 85%
of the world’s imports. In the EU, major importing countries were the United Kingdom,
Italy, Germany, Denmark, Netherlands and France accounting for 85% of the region’s
total (Table 2.8). Egypt and China have increased imports since 1980, accounting for
3.2% of the worldwide trade in 1999. Mercosur countries are primarily exporters, with
the exception of Argentina which has actually decreased imports since the 19705 as result

of increasing domestic production using mature pine stands.

34

Table 2. 8. Major importers of conifer sawnwood (1980-99)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Countries/ Volume Value
Regions 1980 1990 1999 1999

(000 m3) (000 m3) (000 m3) "/6 (million USS) %
WORLD 64499 75435 100349 100.0 18324 100.0
USA 22206 28670 44807 44.7 7374 40.2
EU (+15) 9’ 25881 29003 31757 31.6 5791 31.6
. United Kingdom 5990 9826 6604 6.6 1356 7.4
. Italy 4388 4426 5550 5.5 994 5.4
. Germany 5817 5222 5319 5.3 976 5.3
. Denmark 1233 1483 3500 3.5 370 2.0
. Netherlands 2471 2547 3352 3.3 603 3.3
. France 2651 1742 2539 2.5 507 2.8
Japan 4955 7369 8372 8.3 2402 13.1
Egypt 1428 1400 2237 2.2 300 1.6
China 31 294 988 1.0 206 1.1
Norway 363 401 775 0.8 189 1.0
Canada 768 998 742 0.7 151 0.8
Saudi Arabia 919 480 721 0.7 1 10 0.6
USSR - Europe ‘1’ 124 100 651 0.6 54 0.3
ROW ‘3) 7824 6721 9298 9.3 1748 9.5

 

 

 

 

 

 

 

 

Note: Source: FAO (2000b)

(1) European Union: Austria, Belgium, Denmark, Finland, France, Germany, Greece,
Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and UK;

(2) Former-USSR in Europe (1999): Belarus, Estonia, Latvia, Lithuania, Moldova,
Russian Federation, and Ukraine; numbers for 1980 and 1990 represent former USSR.

(3) Rest of the world

With respect to the value of imports, countries tend to maintain their world share
as for volume, although the USA showed a smaller proportion (40.2% as compared to
45% in volume) and Japan an impressive 13% (as compared to 8.3% in volume) (Table
2.8). This may be the result of interacting factors, including differences in import tariffs,
' price differentials, and/or non-homogeneous products. Price differential could be caused
by demand-supply-price interaction in individual countries with transportation cost
playing a major role. For instance, US imports come primarily from Canada, with a

signiﬁcantly smaller cost of transportation when compared to Japan imports, from

35

various and more distant countries.

In terms of exports of conifer sawnwood, Canada accounted for 48% of the
world’s total of 101 million m3 in 1999, followed by the EU with 29%, the former USSR
in Europe with 10%, and the USA with 4.5%. These countries combined accounted for
over 90% of the world’s exports. Latin American countries were minor exporters with
3% of the world’s total (FAO, 2000). Among them Brazil, Chile, and Argentina,
contributed only 1%, 1%, and less than 0.1% of the world’s exports respectively (Table
2.8). In the EU Sweden, Finland and Austria were the major exporting countries,
accounting for 85% of the region’s total. Other important exporting countries were the
Russian federation and the USA with 6% and 3% of the world’ share respectively.
Emerging exporting countries with large-scale pine plantations are New Zealand, Chile
and Brazil, with 3% of the worldwide share. Brazil, despite its area and the large-scale
pine plantations, still has a modest performance with only 678 thousand m3 exported in
1999, although this represents a signiﬁcant increase since 1990. The in Brazilian exports
of conifer sawnwood decrease from 1980-90 may be the result of a shift from pines to
Araucaria use. The increase thereafter is possibly related to the maturing pine plantations
and consequently larger production aimed at the international market. Overall, for value
of exports, countries tend to maintain their ranking as for volume exported (Table 2.9).

Roundwood from natural forests has been gradually substituted for engineered
products and planted roundwood. An annual 2% growth in worldwide sawnwood
consumption is estimated for the next years, conifers still accounting for most of the trade
(Macedo et al., 1996). Assuming that non-conifers will be supplied primarily by native

species, there are opportunities for companies managing homogeneous forest plantations.

36

Table 2. 9. Major exporters of conifer sawnwood (1980-99)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Countries/ Volume Value
Regions 1980 1990 1999 1999
(000 m3) (000 m3) (000 m3) % (million USS) %
WORLD 66442 73818 100956 100.0 17786 100.0
Canada 28993 37465 48336 47.9 8518 47.9
EU (+15) “1 19239 18258 29227 28.9 5529 31.1
.Sweden 5894 6235 11040 10.9 2140 12.0
.Finland 6902 4156 8269 8.2 1499 8.4
.Austria 4254 4070 5627 5.6 1046 5.9
. Germany 490 910 1969 1.9 310 1.7
. Belgium-Lux 128 347 533 0.5 115. 0.6
.France 229 459 525 0.5 91 0.5
USSR-Europe“) 7187 6200 10155 10.1 1162 6.5
. Russian Fed. * * 6105 6.0 613 3.4
.Latvia * * 2447 2.4 333 1.9
. Estonia * "‘ 812 0.8 121 0.7
USA 4798 7010 3225 3.2 792 4.5
Czech Republic 1048 838 1480 1.5 212 1.2
Romania 572 58 1210 1.2 158 0.9
New Zealand 614 580 1185 1.2 280 1.6
Chile 1259 1196 1143 1.1 163 0.9
Poland 690 295 760 0.8 105 0.6
Brazil 187 80 677 0.7 161 0.9
Row“) 1425 1205 2124 2.1 498 2.8

 

 

 

 

 

 

 

 

Note: Source: FAO (2000)

(1) European Union: Austria, Belgium, Denmark, Finland, France, Germany, Greece,
Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and UK;

(2) Former-USSR in Europe: Belarus, Estonia, Latvia, Lithuania, Moldova, Russian
Federation, and Ukraine; numbers for 1980 and 1990 represent former USSR.

(3) Rest of the world

(*) not reported

2.2.3 Brazilian Conifer Sawnwood Exports

The Brazilian participation in the global market of forest products has been

modest, even more modest for conifer sawnwood. Although Brazil was the ﬁfth largest

37

roundwood producer in 1997, the country produced with less than 2% of the world
exports of high value-added forest products such as lumber, wood panels, and paper;
except wood pulp with 6.3% (F A0, 2000).

A number of factors has prevented it from increasing its forest products exports.
This has been the result of high and growing domestic demand for forest products, the
low level of industrial technology in the solidwood sector, unregulated forests resulting in
unsuitable roundwood for some high value-added uses, underdeveloped marketing of
new products and species, and underinvestrnent in the solidwood sector until the recent
past. In addition, macroeconomic factors, including government policies for resource
uses (social, physical and capital), incentives and subsidies to exports, forest and
environmental laws, and economic plans aiming at boosting the economy have to varying
extents played a role on shaping the position of the country in the international scene.
Despite its modest participation in the international market, Brazil has the potential to
increase its production and exports given some competitive advantages. They include low
plantation cost, scientiﬁc technology for fast-growing species, land availability at
relatively low costs, shorter rotations, infrastructure to support export efforts, and a
developed international market for some Brazilian forest products. However, a limiting
factor for increasing forest products exports is the growing domestic consumption as
result of population and per capita income increase.

Although its participation is modest in the international market, Brazil increased
its exports during the 19905. In the early 19905 exports of forest products reached record
levels, as the result of growing demand from Mercosur countries and the economic

recovery of the USA. For conifer sawnwood exports grew from 46 thousand in3 in 1988

38

to 102 thousand or3 in 1993, respectively US$17 million and U55 37 million (IBAMA,
1996). The value of sawnwood exports grew from US$141.5 million in 1990 to
US$344.7 million in 1996, a 16% annual increase. In 1996, 54% of the total sawnwood
exports from Brazil were native hardwoods. Major importing countries from Brazil have
been the USA, France and UK, accounting for over 50% of the Brazilian exports of
sawnwood. However, France and UK re-export part of the Brazilian sawnwood to other
European countries (Revista da Madeira, 1995).

The search for new markets has created opportunities for Brazil in the world
market, however the Brazilian forest sector has faced limitations to compete in the global
economy. Particularly for conifer sawnwood, exports are conducted by small to medium-
size companies in Brazil, with the exception of some branches of multinational
companies with more powerful marketing channels. This condition shows the
vulnerability of the sawnwood exports, given that a company’s size and its revenue
usually do not allow signiﬁcant investments in technology and marketing resources,

factors needed for international competitiveness (Macedo et al., 1995).

2.2.4 Trade Partners and Flow of Sawnwood

The large domestic market, associated with lack of capital investment and
technological innovation, has been one of the reasons for the poor performance of the
Brazilian timber industry in the international market. However, the main export markets
for Brazilian solidwood products have been the EU and the USA (Tomaselli, 1998).

Brazil is by far the largest source of wood products from Latin America for the

US. In 1997, Brazil supplied nearly one-half of the solidwood products imported from

39

Latin America, although hardwood products are still an important component of forest
products exports. Chile was the next most important source with 29%. The remaining
was divided among many countries, with none holding more than a 4% share. US imports
of softwood lumber from non-Canadian sources, although representing only a small
portion of total imports, have been growing rapidly, Brazil being the most important
source. In 1997, Brazil accounted for 30% of these non-Canadian lumber imports in the
US, followed by Mexico with 22% and Chile with 20%. US imports of Brazilian
softwood lumber have increased at impressive rate during the last several years, from

minimal in the early 19905 to nearly 400,000 m3 in 1997 (Flynn, 1999b).

2.2.5 The Government Role and the Free Trade

The Brazilian government has played an important role on developing and
shaping the country’s forest sector in the past decades. Constraints on growth in the
Brazilian forest products sector over the past years have centered around the country’s
ﬁnancial difﬁculties which include foreign debts, high inﬂation periods, sharply reduced
federal spending and other industrial incentives, and reduction in foreign and domestic
capital investment in market activity (U SDC, 1991). Although several forest industries
have dramatically expanded in the mid-19805 a number of macroeconomic problems
have signiﬁcantly decreased the capacity of expansion and new projects (U SDC, 1991).

On the private industry side, Brazilian labor costs for forestry-related activities are
signiﬁcantly lower than for some developed nations. In the past , however, Brazil’s high
inﬂation, high energy and transport costs and high interest rates have reduced

competitiveness in world markets for wood products (U SDC, 1991).

40

While the government directly participates in timber management operations, the
private sector handles most of the industrial and commercial activities relating to the
production and sale of timber and other forest products. The government had provided
the industry with a range of incentives for exports as well as import licenses for selected
products. In addition, in order to increase the country’s foreign exchange earnings and
protect its wood processing industry from international competition the government has
encouraged production and exports of high value-added manufactured products through
the ban on log exports of native sources (U SDC, 1991). However, in 1992 the
government lifted the prohibition by allowing roundwood exports ﬁ'om plantations,
opening new opportunities for exporting the excess supply of wood products (chips,
pulpwood and high grade sawlog) from pines and Eucalyptus although maintaining the

prohibition for species coming from native areas (Tomaselli, 1998).

2.3 Mercosur and Regional Integration

2.3.1 Overview

With a combined market of 242 million consumers estimated for 1999, an
aggregate GDP of approximately US$ 1.7 trillion, and a land area of 13.7 million square
kilometers (4.7 million square miles) (CIA, 2000), the Southern Cone Common Market
(Mercosur) has become an important economic regional bloc and a signiﬁcant free trade
pact, with remarkable initial success since its inception in 1991. The successful
integration of Mercosur is expressed by the increased intra-regional trade, which has

more than tripled from US$4 billion in 1990 to US$145 billion in 1995. Such growth can

41

be attributed to trade liberalization, and gradual elimination of tariff and non-tariff
barriers, as well as an encouraging scenario for foreign investments.

Although Mercosur has gained political commitment and, to some extent,
economic success, its path towards achieving a perfect free-trade area is restricted by
some important issues. Major issues include the possible trade diversion or creation for
some forest products, implications of direct foreign investment, uncertain economic
stability, and the recent admission of Chile and Bolivia as associate members. These
factors make the future unclear for forest products trade.

In terms of plantations, the total estimated area in South America in 1995 was 8.2
million ha with 82 % of the resource distributed in three countries: Brazil (4.2 million ha;
2.7 million ha with Eucalyptus and 1.1 million ha with pines); Chile (1.7 million ha,
mostly pines); and Argentina (0.8 million ha) (Brown, 2000). Most of the forests are used
for pulpwood and sawlog production, with a maximum rotation of 25 years for pine
sawlogs. Plantations in Brazil are mostly in age classes over 11 years old, mature for
commercial use, with a signiﬁcantly smaller proportion in below 10 year age class
(Figure 2.5). Chile’s plantations are in a relatively younger stage (mostly in age class 6-
10, with a signiﬁcant area also in classes 0-6 and 11-15). Argentina has a relatively
smaller area, more evenly distributed among all age classes up to class 21-25. Such ﬁgure
indicates that Brazil may face an unbalanced supply of some roundwood products, while
Chile and Argentina will have a more regulated supply in the coming decades. Annual
planting rates in these countries were reported as 100,000 year in Brazil and Chile and

about 30,000 ha in Argentina in 1995 (Brown, 2000).

42

 

 

l ,400

 

 
  
 

 

 

.Brazll
Cl .

1.200 ‘ 7 7 7 Chlle .
DArgentina
IVenezuela

1.000 ‘ uOthercountries F

 

  

Area (in thousand ha)

400‘

200‘

......

 

 

0-5 6-10 1 1-15 16-20 21—25 26-30 31—35 36—40 41-45 46-50 >50

 

 

Age-class (in years)

 

Source: Brown, 2000
Figure 2. 5. Plantation forest estimates in South American countries (1995)

2.3.2 Conifer Sawnwood Exports and Directions of Trade

A preliminary study investigated the main features of the market for forest
products within Mercosur (Wiecheteck and Tella, 1997). Trends in sawnwood exports
have been unstable for most of Mercosur, although Brazil has signiﬁcantly increased its
exports since 1991 (both coniferous and non-coniferous species). Although Chile has
been successful in exporting softwood lumber to the US, Brazil has been even more
successful. US imports from Brazil had been less than 100,000 m3 in 1993, but reached
495,000 m3 in 1998 (Flynn, 1999b).

Trade of veneer, plywood, particleboard and other forest products has shown an

erratic but increasing trend since 1991. These categories are likely to remain unstable

43

with spurts of increases and decreases in trade as a possible response to the construction
industry and forest investments in each country.

All major importing countries in the bloc have shown a decreased trend in
sawnwood imports since the early 19805, that can be reversed in case a country increases
its domestic demand (as result of increasing per capita income as result of overall
improvement of the economy) and/or face constrained roundwood supply (as result of
environmental/scarcity constraints).

For conifer sawnwood trade, Chile was the leading exporter in 1997 with over 1.5
million m3, followed by Brazil (650 thousand) and Argentina (50 thousand), representing
96% of Mercosur trade (Table 2.10). Total trade from non-Mercosur countries to each of
the Mercosur ones was almost negligible for 1997. Of the total 2.3 million m3 trade,
98.3% represented exports to outside Mercosur countries, only 1,7% being trade within
the bloc (Table 2.10). All Mercosur members trade primarily with non-Asian countries,
except Chile that trades equally with the Rest of the World region (ROW) and the Asian-
selected ones (Japan, China, Hong Kong, and South Korea). This is partially explained by
Chile’s location, targeting the Paciﬁc Rim market, led by imports from Japan and China.
Distances between regions, and consequent transportation costs, traditional trade in the
past, characteristic of products, and other non economic variables can be inﬂuential in
determining the actual trade ﬂow between pairs of countries and regions.

For lumber exports, the US. and Argentina are the most important markets for
Brazil. For Chile, Japan is a major market, but primarily for the lower grades such as
packaging lumber. The Middle East is also important as a market for low-grade lumber

for some of the Mercosur countries. For higher value products, such as moulding,

44

components for doors and windows, furniture, edge-glued panels, etc., the US. is the
most important market for Chile. A total of 75 % of the value-added lumber products

exported from Chile (percentage based on value) go to the US. (Flynn, 1996).

Table 2. 10. Direction of trade of conifer sawnwood for Mercosur countries (1997)

From :> REGIONS
Exports
To U

0
0
0
ru 12 0
IA-S 3 9 2
OW 41 1 612 733 14
otal 51 18 649 1565 1 16
Note: Source: adapted from FAO - Directions of Trade (2000)
volumes in thousand m3
(1) ASIA-S countries: Japan, China (+ Hong Kong) and South Korea.
(2) ROW is Rest of the World: all non-Mercosur and non-ASIA-S countries.
(3) Trade between ASIA-S and ROW is not included.

 

One outcome of free trade areas is that traditional trade ﬂows from lowest cost
producers are sometimes disrupted as free trade blocks are formed, i.e., trade is diverted
to new paths. Within Mercosur the potential for trade diversion is limited since imports of
coniferous sawnwood are relatively low among members, with Brazil, Argentina and
Uruguay being the major importers. However, the growing demand for conifer
sawnwood in Brazil along with limited availability of future high-grade roundwood may

shift this trend, creating an opportunity for other countries increase exports to Brazil.

45

2.3.3 Tariff and Non-tariff Barriers

Imports and exports by Mercosur countries can also be affected by tariff and non-
tariff barriers. By the end of 1998, Brazil, the largest consumer market with low average
tariffs for forest products, and an overvalued currency was an attractive importer; while
Argentina, with the highest average tariff was the least attractive. Early in 1999 two
major events in the bloc created an unlikely scenario, changing such trend. Brazil faced
an economic turmoil, devaluating its currency and creating a direct impact on neighbor
countries. In addition, starting on January 1St 1999, all member countries were to reduce
to zero the tariff level within the bloc, phasing out their tariff levels for most products.
Although Brazil has recovered economically; other events such as economic stagnation
and political uncertainty in a few member countries, a recent economic turmoil in
Argentina, and the possibility of Chile joining NAFTA instead of Mercosur may play a
major role on reshaping the pattern of trade within and outside the bloc. The Free Trade
Agreement of the Americas (FTAA), still being discussed, and with a proposed calendar
to phase out tariffs in all signatary countries by 2005 could also change future scenarios
for trade of conifer sawnwood among the Mercosur countries. Parallel bilateral
agreements between Mercosur and other trade blocs, such as the EU, may inﬂuence
ﬁlture trade patterns among the Mercosur members.

Trade is also affected by non-tariff barriers, although they seem to be minimum
among Mercosur countries. A ban on exporting native species as roundwood from Brazil
has caused a boom in sawnwood production and exports since the 1960’s. Subsidies
provided to encourage pine plantations in Chile, Argentina, Paraguay and Uruguay

(Garlipp, 1997) and pine/Eucalyptus planting incentives in Brazil have had an impact on

46

the increase in exports of forest products in most of the members. Phytosanitary measures

are possible non-tariff barrier that has had so far a minor impact in the region’s trade.

2.4 Quantity and Price Analysis

The following section focuses on descriptive statistics of sawlog and sawnwood
prices and quantities supplied and demanded in Brazil, Chile and Argentina. It introduces
the data used in Chapter 3, their range and trends in each country since the early 19805.

For conifer sawlogs and sawnwood in Mercosur, Brazil has had a prominent role
in the past decades accounting with an average of 75 % of the total volume produced and
consumed since the early 19805. Chile follows with about 20% of the total volume,
although it has increased its contribution to over 25% in recent years. Argentina,
Uruguay, Paraguay, and Bolivia combined have accounted for less that 5 % in average

over the period 1970-96, increasing its contribution to around 6 % since 1995.

2.4.1 Brazil

2.4.1.1 Quantity Analysis

Brazil has the most extensive area with plantations in South America (mostly with
Eucalyptus and pines), and about the same area in pine plantations as Chile. As discussed
in previous sections, Brazil has gone through an intensive process of species substitution,
with pines taking the place of Araucaria since the early 19805. Such substitutionhas
proved to be successful in terms of industrial production and market acceptance, both

domestically and for exports.

47

StatiStics of quantities produced and, consequently, consumed of conifer
sawnwood in Brazil is highly variable and conﬂicting depending on the sources
reporting. FAO reports around 2.5 times more volume produced during the period 1989-
98 than ABIMCI/STCP does. ABIMCI/STCP’S report may be a more accurate estimate,
since it represents a sector study using domestically reported data, rather than FAO ones.

Aggregate conifer sawlog production and consumption (Pinus and Araucaria)
increased between 1980-99, mostly during the early 19805. Production data from FAQ
for the period 1992-99 is reported with the same value for each year, a highly unlikely

situation, indicating possible data problems (Figure 2.6).

 

22500
22000
21500
21000
20500
20000
19500
19000
18500
18000

000 m3

 

o (1. ta to <15 o q, is "o to
to co Q: to ‘b 0.. o o o o
,9 ,9 .9 .9 .9 .31 .9 .9 .9 ,9

year

 

El Production Consunption

 

 

 

 

Source: FAO (2000b) (*)

(*) Consumption equals to production + imports — exports. Although sawlog imports and exports
are not reported by FAO (2000b) during 1990-99, consumption assumes no imports or
exponsdudngthepenod.

Figure 2. 6. Consumption and production of conifer sawlogs in Brazil (1980-99)

48

 

An average annual growth of 1.7% for both production and consumption occurred
For conifer sawnwood, an increasing trend in production and consumption

 

       

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for the period 1980-85, with 0.5 % for 1980-99. Consumption follows production closely
analysis and sawlog imports were negligible. With the allowance for exotic (planted)
occurred for most of the period 1980-99 with an average annual growth of 1.0 and 0.7 %

given the fact that roundwood exports were banned in Brazil during most of the period of
species exports by the early 19905, modest volumes of pine sawlogs started to be

exported after 1994 (Figure 2.6).

respectively (Figure 2.7).

year

49

 

 

I Production Consumption I

During 1980-85, both sawnwood production and consumption increased

 

(respectively 2.9 and 3.2 % a year), and stabilized thereafter. This increase coincides with
the substantial market supply of pines, which eventually substituted for Araucaria in the

Figure 2. 7. Consumption and production of conifer sawnwood in Brazil (1980-99) .

Source: FAO (2000b)

processing industries. However, unlike that observed for sawlogs, sawnwood production
exceeded domestic consumption for most of the period, the difference being exported,
since imports were negligible.

A more interesting comparison arises when the aggregate volume is broken down
into the two conifer groups. Pine and Araucaria sawlogs (Figure 2.9) were estimated
from sawnwood production data (Revista da Madeira, various years) by using roundwood
equivalent ratios (from FAO data) for each year. Figures 2.8 and 2.9 illustrate opposite
trends in sawlog consumption and sawnwood production of pines and Araucaria and are
therefore not comparable to Figures 2.6 and 2.7. For pine sawlogs, consumption
increased considerably, from 352 thousand m3 in 1980 to over 12.2 million In3 in 1995,
with average annual increase of 211% per year (Figure 2.8). On the other hand,
consumption of Araucaria sawlogs decreased during the period, from almost 5.4 million

In3 in 1980 to only 490 thousand m3 in 1995 (average annual decrease of -5.7% per year).

 

20000
1 5000
1 0000

000 m3

5000
0

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,8)

 

u
g
8

9:”??6’Q3’999q’
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year

 

 

 

EConsumption - Araucaria Consumption - Pinus

 

 

Source: ABPM and Revista da Madeira (various years) (*)
(‘) based on actual consumption for 1980, 1983, 1986, 1987, 1990, 1991, 1994, and 1995. Values
for other years are interpolation between actual values.

Figure 2. 8. Consumption estimates of pine and Araucaria sawlogs in Brazil (1980-95)

50

As expected, production of pine and Araucaria sawnwood (Figure 2.10) showed a
similar trend for consumption of sawlog. Production of pine sawnwood increased
signiﬁcantly from 130 thousand m3 in the early 19805 to 6 million m3 in 1994, decreasing
slightly to estimated 5 million in 1995 (annual increase of 234% per year for 1980-95).
On the other hand, production of Araucaria sawnwood decreased sharply between 1980-
95, from almost 2 million m3 in 1980 to only 100 thousand m3 in 1995 (annual decrease
of 5.6% a year). Pines have dominated the market of conifer sawlogs and sawnwood in
Brazil since the early 1980s, and quickly became the major lumber species (Figure 2.9).
Araucaria became a minor species as result of forest depletion and strict environmental
legislation, with very limited availability to increase its contribution in the short to

middle-term.

 

 

 

 

 

 

  
  

 

 

 
  

 

 

 

 

 

 

 

 

 

 

 

 

 

t\\\\\\\\\\\\‘3

. PH
§§§§§§§§§§§§§§§y

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In Ptoduction -Finus Reduction - Araucaria ]

 

 

Source: ABPM — Revista da Madeira (several years) (*)
(*) based on actual consumption for 1980, 1983, 1986, 1987, 1990, 1991, 1994, and 1995.
Values for other years are interpolation between actual values.

Figure 2. 9. Production estimates of pine and Araucaria sawnwood in Brazil (1980-95)

Demand for pine sawlogs is expected to increase but wood supply may not be

available to support it, considering that the peak of pine planting was in the late 19705.

51

As demand increases during the next decade, assuming a 20-year rotation, there comes a
point around mid-decade when timber supply is likely to decline (Flynn, 1999b).
Statistics from a ABIMCl/STCP study (ABIMCI, 1999) indicates a steady
increase on production and consumption of conifer sawnwood during the 19905, with the
maximum production of 4,25 million m3 in 1997 and maximum consumption of 3.7
million m3 in 1993. Domestic production and consumption increased respectively 7%and

5% annually between 1989-98 (Figure 2.10).

 

 

     
   

    

       
     
   

 

     
   
   
   
 
  
  
 
  

    
   
   

   

 
      
     

  
   
   
 

 

 

     

4500

woo 1 11.;
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1000‘ II" % ‘ 1% “II 1.111% 1%
500- IIII 11g- IIIII%-%II1ZI%
0, .111 12.1. Il/III/
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998

year
In Ptoduction Consunption

 

 

 

Source: ABIMCI/STCP (ABIMCI, 1999)

Figure 2. 10. Production and consumption of conifer sawnwood in Brazil (1989-98)

The domestic consumption of sawnwood (conifer and non-conifer) is linked
basically by the furniture industry, packaging, and civil construction. Civil construction
and the fumiture industry are the major individual groups with respectively 21% and 15%
of the nationwide total, and followed by wholesale, industries in general, and others,

respectively 36%, 15% and 13% (ABIMCI, 1999).

52

2.4.1.2 Price Analysis

Sawlog and sawnwood prices are from different sources as they refer to stumpage
and market prices in Southern Brazil, also including the unit value of production of
higher value roundwood, a possible proxy for price (SEAB, 1998; IBGE, 1999; Revista
da Madeira, 1995). Although consumption and production of pine sawlog and sawnwood
increased significantly during the period of the analysis, prices tended to show an

expected stationary behavior (Figures 2.1 1-2. 13).

 

 

 

 

 

 

50.0
g 40.0 '\
O9
5 30.0 \A/\H
M
E 20.0 N m
a W
a 10.0
0.0 I I I I I I I

 

 

 

1989 1990 1991 1992 1993 1994 1995 1996

year

 

+ Pine-stumpage —I-— Pine-market

 

 

 

 

 

 

Note: (1) Real prices in US$/m3 (1995)
Source: Data originally from SEAB (1998), and modiﬁed from Anadalvo’s price analysis (Graca,
1998, personal communication).

Figure 2. 11. Stumpage and market prices of pine sawlogs in Parana (1989-96)

Stumpage and market prices of pine sawlogs in the state of Parana remained
relatively stable between 1989-96, with a decrease noticed in the beginning of the period
and an ascending trend in 1993-94, with stabilization thereafter (Figure 2.12). The

average annual sawlog stumpage and market prices for the period were respectively

53

US$18/m3 (range from US$14-22/m3) and USS 36/m2 (range from US$29-44/m3), with
standard deviations respectively of 3.4 and 5.1. The difference between stumpage and
market prices refers to transportation costs, taxes, and proﬁt, accounting for 49% of the
average market price.

IBGE’s unit value of production of ‘other roundwoods’ (excluding ﬁielwood,
ﬁrewood and pulpwood) from plantations show regional differences among the three
Southern states (Figure 2.12). Pine sawlogs are the main representative in this category.
To a certain extent, such unitary values could represent a proxy for the commodity price.

Trends in unit value of production of ‘other roundwoods’ in Parana and Santa
Catarina, where most of the pines were planted, follow a similar pattern with the
increases and decreases culminating in the same periods. For Rio Grande do Sul the unit
values show an opposite trend for the years 1986 and 1991. This can be related to the fact
that other species besides pines formed the mix of species planted in that state, indicating
a possible weighted price. IBGE’s unit values of ‘other roundwoods’ from plantation in
Parana and Santa Catarina follow the trend observed for the SEAB’s market price of
pines in Parana for 1989-95 (Figure 2.12).

As for prices of other conifers in Southern Brazil, Siqueira (1995) reported
IBGE’s unit values of industrial roundwood from plantations (pulpwood/higher grade
logs) as proxies for prices, aggregating them into ﬁve year intervals between 1975-90.

3 to about

From 1975-80 prices decreased signiﬁcantly in Parana (from US$33/m
US$l6/m3), remaining stable in Santa Catarina and Rio Grande do Sul (respectively
US$13/m3 and US$l8/m3). Between 1980-85 prices decreased slightly in Parana and Rio

Grande do Sul, and sharply in Santa Catarina (from US$18/m3 to US$10/m3). From

54

1985-90 prices increased slightly in Santa Catarina and Rio Grande do Sul, decreased

slightly in Parana, with average of US$14/m3 in all three states (Siqueira, 1995).

 

100
g 80
O,
:1 60
1")
g 40
(a;
3 20

 

0

99’ a?" <85 <2?) <2,Q <5" 9" 9%
1\°-’ '9 \°-’ '8’ '9 ’9 \°-’ '3’

year

 

+ Pine-PR market -l—~ Pinus-PR
+ Pinus-SC —X— Pinus-RS

 

 

 

 

 

 

Note: (1) Real prices in US$/m3 (1995)

(2) PR, SC, and RS stand for Parana, Santa Catarina, and Rio Grande do Sul.

Sources: Pine-PR market (SEAB, 1995) and Pinus- PR, Pinus-SC, and Pinus-RS (IBGE, 1998).
Prices for 1985 and 1989 were estimated as arithmetic average between the prior and
following years (respectively 1984 and 1986, and 1988 and 1990), given the lack of
reported prices for 1985 and an extreme outlier for 1989.

Figure 2. 12. Unit value of production for ‘other roundwoods’ in Southern Brazil (1982-
96)

Prices of conifer solidwood products in Brazil have been lower than worldwide
prices. This might be the result of high availability of stumpage and excess supply
ﬂowing into the domestic market. Considering the reduction in pine plantation in the past

decades, sawlog prices are expected to increase in the next years assuming there is no

direct substitute species. Increased prices could divert some of the pine pulpwood to the

55

sawnwood market, stimulate a boom of new plantations and promote more regional trade
of pine-derived products.

Prices for pine sawnwood vary over the period 1981-95 and show an expected
stationary behavior, except for the occurrence of an outlier in 1986 (Revista da Madeira,
1995) (Figure 2.13). Although the nature of the change is not revealed, such a high price
(U S$191/m3) could have been the result of the economic boom after an economic plan
was launched by the government in that year. The plan resulted in higher per capita
income, that may have stimulated more expenditure, in consuming goods and housing
construction, two important lumber demanding sectors. The average price over the period

was US$84.5/m3 (range of US$42 to US$191/m3) with a standard deviation of 36. 1.

 

250

 

 

200

 

US$/m3

 

 

.50 A

100 V/ \ f4
50 m

0 r r r r r

3%“ 19935 '99:" (58‘

 

 

 

year

 

 

 

Source: Revista da Madeira (1995)

Figure 2. 13. Price of pine sawlog in Southern Brazil (1981-95)

It is important to consider that average sawlog prices of Pinus vary according to
the log diameter class. Real 1995 prices of pine sawlogs (at the sawmill) ranging from
10-20 cm, 20-30 cm, 30-40 cm, and over 40 cm in diameter were respectively US$80,

US$11.4, US$158, and US$197 per m3 in Parana in September/1997 (SEAB, 1998).

56

2.4.2 Chile

Commercial plantations are the source of most of Chilean forest products.
Government policies have promoted the growth of large-scale plantations, and some 2
million ha are under management (Trade and Investment Guide — Chile, 1999). Pinus
radiata is the most widely used species, accounting for 87% of the plantations. The
establishment of radiata pine started in the early 1940s with average annual plantings
increasing signiﬁcantly after 1974. Most of the plantations are located in Southern Chile,
from Concepcion to Valdivia (Jélvez et a1, 1989). Over the years the government has also
successfully encouraged the growth of substantial value-added wood processing
industries.

Overall, exports of forest products were expected to reach US$ 2.6 billion in
1996. Chile exports about three-quarters of its forest production and about 80 % of
exports are in the form of white cellulose, although newsprint and manufactured wood
products (sawlogs and lumber) are also growing commodities (Trade and Investment

Guide - Chile, 1999).

2.4.2.1 Quantity Analysis

Production and consumption of conifer sawlogs in Chile has increased steadily
since 1980 (Figure 2.14). The production followed cycles during 1982-87 and 1989-95,
with a respective average annual growth of 12.2 and 13.0 %. Over the entire period an
annual increase of 6.4 % was observed, the result of signiﬁcant investments in the sector,
plantations, infrastructure and installed capacity. Sawlog consumption increased 4.5 %

per year during 1980-89, apparently following the production cycles. The difference

57

between production and consumption accounts entirely for exports since no imports were
observed. Commercial radiata pine sawlogs have been available since the mid-19705,

reaching over 30 % of the total production in the early 19805.

 

12000
10000
8000
6000
4000
2000
0

o (L Q? e <29 9 '1, v1 e Q)
Q) Q) Q) Q) Q Q Q Q) Q
\9 ’9 '9 \9 '3’ '3’ '3’ '9 '3’ '3’

000 m3

 

year

 

IE] Production Consumption

 

 

 

 

Note: Consumption = production + imports — exports, not shown after 1989 because FAO does
not report total exports and imports of sawlogs after 1989.
Source: FAO (2000b)

Figure 2. 14. Consumption and production of conifer sawlog in Chile (1980-99)

With respect to sawnwood, production and consumption increase increased
beginning in early 19805 (Figure 2.15). Production has far exceeded consumption by an
average of 41% a year over 1980-99 (64% in 1980 to 27% in 1999). The difference is
accounted for sawlog exports, since imports are negligible. Production and consumption
followed an erratic path, without well-deﬁned cycles, although with an annual increase

respectively of 5.4% and 17.1% over 1980-99.

58

 

4500
4000
3500
3000
2500
2000
1500
1000
500
0

000 m3

 

o q, .91 e ‘b o q. r. 6 <2:
‘6 Q) ‘b ‘b 9 Q: Q Q Q
Q5 (9 dB 4% §3 8% db <5 §3 §9

year

 

1:] Production Consumption

 

 

 

 

Source: FAO (2000b)

Figure 2. 15. Consumption and production of conifer sawnwood in Chile (1980-99)

2.4.2.2 Price Analysis

Prices of radiata pine sawlog increased from 1984-94, and stabilized thereaﬁer
(Figure 2.16; INFOR, 2000). The average price over the period was US$30.6/m3 (range
of US$13.9/m3 to US$44.2/m3), with 11.1 stande deviation. According to Cerda (1996),
domestic sawlog prices have followed the same path of international sawlogs prices and
domestic sawnwood prices.

Prices of radiata pine sawnwood increased over the period 1984-94, with short-
term decreases observed in the yearly 19805 and between 1994-97. This pattern coincides
with the consumption and production pattern of pine sawlogs and sawnwood (Figures
2.14 and 2.15). The average price over the period 1980-97 was US$80.6/m3 (range of

US$45.7/m3 and US$113.4/m3), and 22.2 standard deviation.

59

 

120.0

100.0 MN

80.0 \

60.0 \ M

40.0 J N ' cw
20-0 W 3 ‘

0.0 T I I I I T I T r I I I I

e x
‘b 9:
>9 .9

 

 

 

 

 

US$/m3 (1995)

 

 

 

 

e‘l'ébe‘eﬁebéePege°<b~e¢€°eF<§°e°éI
199'9'999199'93’919'9'9319

year

 

[-o—Pine Sawlogs + Pine Sawnwood l

 

 

 

 

Source: INFOR (1998)

Figure 2. 16. Price of radiata pine sawlogs and sawnwood in Chile (1980-97)

2.4.3 Argentina

Production and consumption of conifer sawlogs and sawnwood in the other
Mercosur countries is led by Argentina and followed by Uruguay. Paraguay and Bolivia
have negligible volumes. Over the period 1980-99, Argentina accounted for an average of
74.1 % of the total production and consumption of conifer sawlogs among those
countries, with Uruguay accounting for the remaining 25.9 % (F A0, 2000).

Forest plantation in Argentina started in the 19403, currently with approximately
770,000 ha of fast-growing species (Eucalyptus, pines and willows) concentrated in the
Northeastern region (bordering Brazil, Paraguay and Uruguay). Argentina has
approximately 400,000 of coniferous plantations and almost the same in non-coniferous

species (Flynn, 1999b). Provinces of Misiones and Corrientes in the Northeast have

60

between 327,000-370,000 ha of southern yellow pine (Pinus taeda and Pinus elliottii),
Patagonia has 38,000 ha of Ponderosa pine and Pinus “Oregon” and Cordoba has
between 32,000-37,000 ha, depending on the source of information (SAGPyA, 1998;
MacDonagh, 1997, Corinaldesi et al., 1999). Misiones accounts for 55% of the nation’s
pine plantation (SAGPyA, 1998). The industrial plantations supply pulpmills and
sawmills, which combined consume 5.2 million tons per year (MacDonagh, 1997,
personnal communication).

Planting was relatively steady during the early 19903, but after the arrival of some
major Chilean companies and government incentives for plantations, the planting rate
increased. The resource base is expected to expand during the next decade (Flynn,
1999b), although it will take a decade or two to create an impact on the forest products
market. Growth rate of pine plantations are similar to growth rates for radiata pine in
Chile and southern yellow pines in Brazil, varying from as high as 25-35 m3/ha.year in
Missiones and Corrientes (20 year rotation) to 18-20 m3/ha.year in Cordoba (23 year
rotation) (Corinaldesi, 1999). The major consuming region is the Province of Buenos

Aires and some products have also been exported. As an alternative form of
transportation, railroad has been used for sawlog export, and some forest companies in

Misiones are investing in port facilities along the Parana river.

2.4.3.1 Quantity Analysis

Production and consumption of conifer sawlogs in Argentina follow the same
Path given that exports and imports have been negligible. They show two distinct cycles

between 1980-99. An increase was noted in the early 19805, followed by a decrease until

61

1987. Between 1987-96 a signiﬁcant increase is observed (average growth of 8.8 % a
year for production and consumption), with a peak of over 1.4 million in 1995 (Figure
2.17). However, no change in production and consumption was reported by FAQ for
1996-99, a highly unlikely scenario. Pines accounted for the majority of the conifer

production: 3.5 million m3 of pines (94%) and 220,450 m3 of Araucaria (6%) in 1996.

 

1 600
1 400
1 200
1 000
800
600
400
200

 

O

o tr e ‘b ‘L e 'b
’99; ’83: ’99 '99: .99 39° ’99 '99“ ’99 '99

 

El Reduction Consun'ption

 

 

 

 

Source: FAO (2000b)
Note: Consumption = production + imports — exports, not shown aﬁer 1989 because FAO does
not report total exports and imports of sawlogs after 1989.

Figure 2. 17. Consumption and production of conifer sawlog in Argentina (1980-99)

For conifer sawnwood the picture is quite different, with consumption far
exceeding production over most of the period 1980-99 (Figure 2.18). This is a result of
limited production and relatively higher domestic consumption, balanced by imports that

exceeded 80,000 m3 in 1980. However, in recent years, considering the maturity of pines

62

and investments in sawmilling capacity, production overcame consumption, with exports

growing from 5,700 m3 in 1994 to 34,300 m3 in 1996 (SAGPyA, 1998).

 

 

   

 

 

_%

...... ./...I.. ../I/II/...

III/. .////II II.... .II/II/II. .III... ..I.. II. I .II/I III/I III .II./..

.1."3777/7/41“..IIﬂIV/Zﬁ...//./.ﬂ//.AI..0........7..7/.I/.ﬁ......./I//IV.. .... .

VIII/$77!... 9 /6.//I/I/I//I//I..II/I///I. .I/I/ III/IIIII/I. .I/

ﬁ/II7/7717/77/7In7. ..//////... .IVIII/lI/I/wz/II/I.

7.1... II.
VII/7157.0“ .VI//.I/I./.WI...II07I....J/W~III
II

wkI/I/ﬂﬂﬁ/ﬂ/I/V/IIu/C/IJI /9././//I.

«II/

 

 

 

Ill
.U./I.. ...//n/II... .../I./.../.. .I. ../.I

      

 

 

///./////// //////// 44777727471777?

llrl

///////7I///////////..////1///7/// ///1///././/.///\/// 11..1/1/./71I

77174.7.7771/71/7427/77/7/7/6w77.

Irll

///I//////7/////7/IVl////n. nV/gllllV/[VVIMM .71/l/I711//..1.I

                
               

   

                               

III/II III. .I... II .I/IVII I./.IJI.. «9.11/le/474.
II.///I11/| Ina/WI

97W/II1/I/dvI7/I.

  

././II//// ////.///.71././

7A7//I'7.7//ﬂ¢/I17/../7 ////I7/.

//////////l////////I" .I/lr /1//////1/|I/I//.I////I

          
   

2744/77 T/I. 1777/7474

//u//////////l..// I/uIl/IY/IVK/l/INM/IZ

III1

 

   

   

 

00
00
76

0
0
5

l

 

I Reduction ConsunptionJ

 

 

 

Source: FAO (2000b)

Figure 2. 18. Consumption and production of conifer sawnwood in Argentina (1980-99)

Sawmills produce mainly for the construction sector, located near the harvesting

and consumption center (SAGPyA, 1998). Sawmilling is the ﬁrst link in wood processing

industries and its great variety of products has caused the development of low-technology

sawmills in the past. This proﬁle has improved in recent years, however, as there were

1995).

2200 sawmills with approximately 12,000 employees in 1995 (SAGPyA,

Backward technology is typical of small to medium size sawmills and investment is

necessary to compete both domestically and for exports as forests become mature.

63

2.4.3.2 Price Analysis

Prices of southern yellow pine sawlogs with diameters between 20-30 cm were
reported in the range of US$19-23/ton in Misiones and Corrientes, US$22.5-26.5/ton in
Entre Rios and up to USSSO/m3 in the Southern province of Patagonia (SAGPyA, 1996).
Sawlog market prices for the bulk of pine species (over 12 cm in diameter) were in the
range of US$29-30/ton in Buenos Aires. These prices are lower than in Chile (about
US$40/m3) and for the range of prices in Southern Brazil in 1996.

For conifer sawnwood, prices of US$lO/ton and between US$20-30/ton were
reported respectively for Misiones and Patagonia as the price perceived by the producer
(MECON, 1996). Prices of two other conifer species are reported for 1996. Araucaria
sawlogs in Misiones were in the range of US$30-55/ton (log diameter between 20-30 cm)
and ‘Pino Oregon’ in Patagonia as US$7O/m3. Figure 2.19 shows the average real gross

stumpage price of conifer sawlogs between 1983-95.

 

 

 

 

 

 

 

 

 

25.00
20.00 -'+4\/\
210.00
5.00
0.00--..........
§§$$§§§§§§§§ﬁ
year

 

 

 

Source — SAGPyA — LaRobla (personal communication, 1999)

Figure 2. 19. Stumpage price of conifer sawlogs in Argentina (1983-95)

Assuming that during the 1980’s imports accounted for over 50 % of the domestic
consumption of sawnwood in Argentina and in the other minor Mercosur countries, the
deﬂated unit value of imports from FAO (2000) could provide a rough indication of what
the trend in domestic prices may look like (Figure 2.20).

Between 1980-89, the average imports of conifer sawnwood represented 50.1% of
the consumption, with the unit value of imports decreasing until 1987, and increasing
thereaﬁer. The average unit value of imports was US$222.6/m3 between 1980-1996. Such
value far exceeds domestic prices as they include transportation costs and tariffs. Figures
2.19 and 2.20 show opposite trends, particularly during the 19905, indicating that unit
value of imports may not be a good proxy for domestic sawnwood prices in Argentina or
that other variables could be playing a major role on explaining this difference. Another
rough proxy for domestic sawlogs and sawnwood prices in Argentina could be prices in
Southern Brazil. However, Graca (personal communication, 1999) suggested that prices
in the Argentina would be expected to be higher than prices in Brazil.

Freight costs provide a better idea about the price of different products in different
domestic markets. MECON (1996) reports that trucking cost from the Misiones to
Buenos Aires (700 miles) was US$ 35/ton and train cost from Misiones to Ibicuy port
(600 miles) was US$ 19/ton. The Parana river is an important transportation route ﬁ’om
Misiones, Corrientes and Entre Rios. As result of becoming a free economy, freight costs
from Conientes or Misiones to Buenos Aires were expected to go to US$ 7/ton by 2000.
International freight costs have decreased in recent years, and by 1996 they were

approximately US$1,300/container (a 40 feet container with 1,600 cubic feet of wood)

65

from Buenos Aires to Hong Kong, US$1,300-1,600 to Taiwan, and US$1,600 to the

Eastern USA (MECON, 1996).

 

 

400
350

300 /
250 -

200 ﬁ‘ﬂj/

150
100

 

 

 

 

US$/m3 (1995)

 

 

 

 

 

 

 

 

Source: FAO (2000b)

Figure 2. 20. Unit value of imports of conifer sawnwood in Argentina (1980-96)

2.4.4 Uruguay, Paraguay and Bolivia

The other Mercosur countries, Uruguay, Paraguay and the associate member
Bolivia have had relatively minor developments in consumption and production of
conifer sawlogs and lumber. Their relatively small population results in a smaller demand
for such products when compared with Brazil, Chile and Argentina, besides the fact that
other species could be substitutes for conifers in those countries.

Uruguay leads the group in area of pine plantation and production of both

products. The area of plantations in Uruguay has more than doubled in the past ﬁve

66

years, due to an attractive government incentive program, and participation by major
international companies (Flynn, 1999b). In Uruguay, total plantations reached 221,616
ha, 88% planted under the new National Forest Management Plan (Law 15,939) ﬁ'om the
late 1980’s. Eucalyptus is the most important species group (MGAP, 1997), with about
80% of the plantations (Flynn, 1999b). For conifer resources, the author indicated that
Uruguay has only 50,000 ha or so of pine plantations (mostly loblolly pine). Pine planting
is starting to accelerate, but represents modest ﬁgures when comparing with other major
Mercosur countries. Pines are primarily located in the northern part of the country near
the border with Brazil and Argentina. Paraguay is just beginning to establish a plantation
forest base, primarily with Eucalyptus (Flynn, 1999b). Bolivia has no reported

commercial plantation of pine species.

67

2.5 Conclusions

The descriptive and quantitative analysis in this chapter has shown the trend in
forest and industrial development followed by Brazil and the other Mercosur countries
over the past decades. The overall picture indicates the search for self-sufﬁciency in
roundwood for solidwood uses and industrial forest products, which created a tremendous
opportunity for exporting high value-added products. The forest sector succeeded in
establishing large-scale plantations with pines and Eucalyptus, which form the forest base
for the ﬁber and solidwood industries, primarily in Brazil and Chile, followed more
recently and in a smaller scale by Argentina and Uruguay.

The mix of solidwood products has changed signiﬁcantly in the past decades in
terms of resource use (from native conifer to planted pines) and industrial diversiﬁcation.
It has also changed to a certain extent due to technological advancements. Re-engineered
products or raw-material from fast-growing plantations have been gradually substituted
for roundwood from natural forests. However, the lack of investments in forest
plantations in the recent past in Brazil has raised questions about the ﬁrture supply of
roundwood for industrial processing. Studies have suggested a shortage of roundwood,
primarily for sawmilling in the next decade. Such a scenario can change the relative
position of Brazil, moving it from a net exporter of most forest products to a net importer
of primary products (e.g., sawlogs) or intermediate products (e.g., lumber).

International trade projections indicate modest participation by Brazil in the trade
of the major commodities, including solidwood products such as conifer lumber. Among
other reasons, this has been the result of high domestic demand. Worldwide forecasts

indicate annual grth of sawnwood consumption of 2% a year for the next years

68

(Revista da Madeira, 1995), conifer sawnwood being a major contributor. Assuming new
investments in forest plantations and industrial capacity are made, there are opportunities
for Brazil and other major conifer producing countries in Mercosur to expand their
participation. However, if investments in plantations for solidwood use are not re-started
in Brazil, its industry may face signiﬁcant challenges and consequent changes. Among
them, one can consider a possible migration of sawmills to other regions where forest
resources are available at a competitive costs, species substitution (e.g. Eucalyptus
sawlogs), and even technical and administrative re-structure. Investments on new
plantations is mandatory to guarantee the future supply of planted species, although it is a
time demanding task that could take up to 20 years before the forests can be harvested for
industrial use. On the other hand, Chile and Argentina may beneﬁt ﬁom an increasing
worldwide demand for forest products and from the comparative advantages they may
have.

The solutions to the future challenges that the Brazilian solidwood sector may
look complex given the existence of interrelated markets and the proﬁle of the forest
sector, which was traditionally extractivist. The demand for roundwood combined with
its limited availability in given time and location requires awareness of and action toward
the complete productive process: identifying competitors, partnerships, and
complementary goals, ﬁ'om both the government and the private sector.

If Brazil is to increase its trade participation in the international arena in products
for which it has comparative advantage, while meeting the growing domestic demand in a
sustainable manner, immediate investments and changes in forest policies must be

undertaken. On the government side, more aggressive and dynamic forest policies should

69

be addressed, ultimately stimulating plantation forestry. In fact, a National Program for
Forests was launched in 2000, aimed at creating opportunities for sustainable forest
development (including plantations) across the country. On the industrial side, primarily
the solidwood industries, a change in the companies’ proﬁles toward better management
of resources, managerial innovations, improved competitiveness, and economic strategies
that guarantee the continuous ﬂow of raw-material, and even its expansion, for industrial
use are all issues to be addressed. In addition, the comparative advantage of Mercosur as
a trade agreement and their countries in terms of climate, land availability, and labor are
additional factors contributing to expansion of the regional forest sector.

Massive investment in both forestry and forest industries in Brazil, Chile,
Argentina, and Uruguay can increase their production and consequent exports of forest
products, generating more capital for further investments, as well as job opportunities or
reduce imports. Increasing population and improvement of per capita income can play an
opposite role in increasing domestic demand for speciﬁc forest products.

It is still unclear how each country will address the main forest issues affecting it,
and how the free trade agreement may contribute to the overall economic development
collectively or individually. However, given the path of forest—industry developments
since a few decades ago, there are ﬁrture opportunities for continuous forest advances in

most of the Mercosur countries.

70

CHAPTER 3

ECONOMETRIC ANALYSIS OF SAWNWOOD AND SAWLOG SUPPLY AND
DEMAND IN BRAZIL AND OTHER MERCOSUR COUNTRIES

Introduction

This chapter explores the simultaneous price-quantity relationship between supply
and demand for each forest product (sawnwood and sawlogs) and region, as well as their
relationship with other supply and demand shiﬁ variables. Its objective is to provide
estimates of the price elasticities of supply and demand to be used in the spatial
equilibrium analysis of Chapter 4. For the purpose of the analysis, the terms lumber and
sawnwood are treated interchangeably. As a description, sawnwood includes sleepers,
unplaned, planed, grooved, tongued, etc., sawn lengthwise or produced by a proﬁle-
chipping process and planed wood, which may also be ﬁnger-jointed, tongued or
grooved, chamfered, rabbeted, v-jointed, beaded, etc.; excluding flooring (F A0, 1996).

Sawnwood exceeds 5 millimeter in thickness (F A0, 1996)

3.1 Objectives

This chapter’s main'objectives are:

(a) to estimate the simultaneous system of price-endogenous supply and demand
equations for sawnwood and sawlogs for Brazil, Chile and Argentina; and

(b) to estimate the price elasticities of supply and demand for each product and region

to be used in the spatial equilibrium analysis.

71

3.2 Literature Review

3.2.1 Demand for and Supply of Forest Products

Decision-makers in the public and private sectors are continuously interested in
models that provide equilibrium prices and quantities of wood products in national and
regional markets. Foremost, sector models need to address the relevant questions as
simply as possible and in an intuitively understandable manner, to contain relevant policy
variables or criteria, and to be developed on a geographical scale that is relevant to the
users and decision makers (Haynes and Harou, 1993).

Market equilibrium models usually require the estimation of supply and demand
for speciﬁc commodities and markets under investigation. Therefore, demand and supply
of a given commodity have been integral parts of either simple models (e. g. gap analysis
in which prices are exogenous) or more complex and detailed models (e.g. spatial
equilibrium models).

For most wood products, demand is derived from the sale of all items into which
lumber, plywood, and paper (Gregory, 1987) as well as other wood composites are made.
Product demand can be treated through econometric analysis, derived from input
consumption or output production, and subjective assumptions and/or projections from
different sources. Assuming that ﬁrms choose the set of inputs that minimizes production
costs, the derived demand for each input depends upon a number of explanatory shiﬁers.
In fact, product demand in an economic context is considered a function of prices and
other shifters, and often these relationships are based on major consuming segments.
Demand for lumber, as with most forest products, is a derived demand from the products

into which lumber is manufactured or fashioned (Gregory, 1987, and Duerr, 1993).

72

Hence, factors affecting the consumption of these products such as population and
income, and actions of the construction industry, particularly residential housing,
indirectly inﬂuence the lumber consumption (Gregory, 1987). For the US, both authors
suggested that the interest rate, and especially mortgage rates, are major factors in
determining the demand for housing, and consequently the demand for lumber. Duerr
(1993) pointed out that demand for lumber is usually inelastic, in most of cases the
elasticity falls between -0.5 and 0.

On the other hand, demand for stumpage is derived from the demand for
manufactured wood products (intermediate or ﬁnal consumer goods). A typical chain
stretches from consumers’ demand for housing to lumber to sawlogs to stumpage
(Gregory, 1987). Given that there is virtually no substitute for sawlogs in manufacturing
lumber, several authors have concluded that the demand for sawlogs must be inelastic
(Mead3, 1966, cited by Gregory, 1987). Duerr (1993) pointed out that derived demand
tends to be less elastic than the ﬁnal consumer price-demand from which is derived.

For the supply side, theorists assume that each ﬁrm’s production objective is
proﬁt maximization (Gregory, 1987), and the theory states that the quantity supplied is
determined by the marginal cost of production and the price of the product (Stynnan and
Wibe, 1986). In an economic context, product supply is the quantity produced as a
function of some prices and costs and can be estimated using econometric methods. In
roundwood form, this is the derived consumption of roundwood or a derived demand
function if it includes price terms (Haynes and Harou, 1993). Haynes (1993) pointed out
that stumpage supply is usually treated as a function of timber availability and can be

3 Mead. J.W. 1966. Competition and Oligopsony in the Douglas Fir Industry. Berkeley: University of
California Press.

73

determined from timber inventory or timberland areas, harvest regulations, government

ﬁat, industrial demand, past trends, prices and other factors.

3.2.2 Econometric Studies of Solidwood Products

There have been few studies about the demand and supply of conifer products in
Brazil, and most of them did not use an econometric approach, in which prices and
quantities are simultaneously estimated. A close look at the studies reveals that the price
elasticities of supply and demand for most of the solidwood products have not been
estimated, nor were the most relevant econometric parameters affecting them
determined. Important studies on the domestic demand and supply of forest products in
Brazil include Graca et al. (1988), Sperandio4 (1989) (cited by Ochoa, 1996), Azeredo
(1993), Ramos (1993), Siqueira (1995), Tomaselli (1998) and various articles published
in applied journals such as the “Revista da Madeira”. However, the studies are basically
descriptive, focusing primarily on the historical development of the forest sector and the
potential of the forest products markets rather than on econometric modeling. The study
by Sperandio (1989) for plywood is an exception.

Studies from other countries, although not related to the Brazilian and the
Mercosur conifer industry, may contribute to a better understanding of the nature of the
price-quantity relationship of forest products, and to the choice of the major variables
affecting supply and demand for econometric modeling. Major studies focusing on the
supply and demand of stumpage and solidwood products include Adams and Haynes

‘ Sperandio. J. Demand and Supply of Plywood in the State of Parana: An Econometric Analysis. Federal
University of Parana, Curitiba, Brazil. Ph.D dissertation 147 p. 1989.

74

(1980), Buongiomo et al. (1981), Rockel and Buongiomo (1982), Luppold (1984), Shim
(1985), Brannlund et al. (1985), Buongiomo and Chang (1986), Newman (1987), Haeri
(1987), Nautiyal (1988), McKillop and Liu (1990), Hetemaki and Kuuluvainen (1992),
Bigsby (1993), Buongiomo et al. (1994), Kant et al. (1996), Buongiomo (1996), and
Manurung and Buongiomo (1997).

The general formulation for a derived demand for any input expresses the quantity
demanded as function of demand shitting variables which may include the price of the
input, the price of other inputs, and the price of the output (Mosaks, 1938, and Ferguson",
1971; cited by Luppold, 1984). In Luppold’s study of the US hardwood lumber market, a
recursive econometric model with the causal ﬂow originating from the demand was
expressed as ﬁrnction of hardwood lumber price, price of substitute material, wage and
interest rates, price of the demanders’ output, and a time trend variable (associated with
input inefﬁciencies). In particular, the demand for hardwood lumber was dependent on
past rather than current input prices, resulting ﬁ'om the inability of furniture and pallet
manufacturers to change their input mix in the short run, making the hardwood lumber
market recursive. The price of wooden furniture was used as a proxy for the price of the
output, and a price index of hardboard was used as a proxy for the price of substitute
materials. In the supply equation, the quantity of lumber supplied was expressed as
ﬁrnction of lagged supply, hardwood lumber prices, wage rates, stumpage costs, interest
rates and time. A price equation and an equilibrium identity were also added to the model

and a two-step, full information method was used to account for autocorrelation.

5 Mosak, LL. 1938. Interrelationships of production, price and derived demand. J. Polit Econ. 46(6): 761-
788.

6 Fergusson, 1971 The Neoclassical Theory of Production and Distribution. Cambridge Univ. Press,
London 384. p.

75

In a study of spatial equilibrium of southern pine lumber in the US, Shim (1985)
estimated regional linear demand and supply functions using panel data with regional
dummy variables accounting for both intercept and slope (related to price) interactions.
The two-stage least squares procedure was used. For the demand equations, explanatory
variables included southern pine lumber prices, regional housing starts, an index of
industrial production, and an index of Douglas-ﬁr lumber prices. On the supply side,
explanatory variables included southern pine lumber prices, an index of electric power
prices, an index of petroleum products prices, regional average stumpage prices of
southern pine lumber, average hourly earnings (in regional sawmills) and a trend
variable.

Brannlund et a1. (1985) carried out an econometric analysis of the sawtimber and
pulpwood markets in Sweden, which were considered as two interrelated markets. The
sawtimber market was assumed to be competitive, with price being determined by the
equality of supply and demand, while for the pulpwood market the price was exogenous.
A simultaneous system of supply and demand equations for sawtimber was estimated.
The demand for sawtimber was function of the price of sawtimber, the ability to pay for
sawtimber (as the difference between the world market prices of sawn products and the
wage rate in the sawmill industry), and the lagged sawnwood supply. On the other hand,
the supply of sawtimber was function of the sawtimber price, pulpwood price, and the
harvesting cost. Linear and log-linear variations of the simultaneous model were
estimated using OLS (Ordinary Least Squares), 2SLS (Two-Stage Least Squares), and
3SLS (Three-Stage Least Squares). The 3SLS estimates showed minor differences from

the OLS estimates.

76

In a study of the structural change in demand for various forest products in OECD
countries, Buongiomo and Chang (1986) estimated demand ﬁrnctions using distributed
lags on ﬁrst logarithm differences of income and price for each country and year. In the
absence of consistent price data for the commodity groups for all countries of interest,
unit values of imports and exports were used as proxies. The price of a commodity in a
given country was estimated as a weighted average of unit values of imports and exports,
the weights being the quantities imported and exported. Pooled data for ten countries and
the ﬁrst difference was used to eliminate country effects.

Hetemaki and Kuuluvainen (1992), using simultaneous equations for modeling
the Finnish pulpwood market, found long- and short-term effects of stumpage price on
demand. However, Youn (1988) argued that since roundwood prices and consumption by
intermediate sectors are sometimes not available, demand for roundwood is difﬁcult to
model using production theory, explaining why most of earlier studies have been based
on the consumption theory. Given data limitations, Buongiomo et a1. (1994) modeled
demand for forest products in Nigeria as function of per capita income only, using cross
sectional and time series data combined from 37 other countries.

In an econometric analysis of the southern softwood stumpage market in the US,
Newman (1987) estimated a simultaneous system of linear equations for pulpwood and
solidwood. Demand for both pulpwood and sawtimber were. regressed as a function of
pulpwood and sawtimber prices, and the softwood inventory (as a proxy for harvesting
costs). The supply of each product was treated as dependent on its own stumpage prices,
the respective prices of the ﬁnal goods/output (solidwood or pulpwood products), the

respective prices of labor and capital and respective one-period lagged production. Since

77

production decisions in the model were assumed to affect both the solidwood and
pulpwood markets contemporaneously, the two markets’ structural equations could be
disturbance-related. For this reason, the three-stage least squares (3SLS) procedure was
used to give more efﬁcient, unbiased estimates of the model’s coefﬁcients. Linear and
logarithmic formulations were tested, although the linear form showed better ﬁt.

Adams and Haynes (1980) developed the Timber Assessment Market Model
(TAMM), an extensive spatial modeling of the North American softwood lumber,
plywood and stumpage markets designed to provide projections of price, consumption
and production trends. TAMM was disaggregated into ‘ﬁnal products’ (including lumber,
plywood, and pulp) and ‘stumpage’. Nine supply regions, for both product groups, and
six demand regions for ﬁnal product were identiﬁed. Demand region boundaries were
based on the authors’ estimates of areas of homogeneous end-use characteristics, while
supply region boundaries were dictated by availability of aggregated forest inventory
data. All equations were simultaneously estimated by using two-stage least-squares
(2SLS). Product demand (in particular lumber demand) was derived by non—econometric
means, in which regional demand elasticities were used to estimate the slope of the
demand function, while plywood demand was estimated by econometric methods.
Product supply was represented as a combination of supply equations and capacity
adjustment processes, being modeled as a function of the proﬁt margin for the producer
and one period lagged supply. In the stumpage sector, the authors considered that
aggregate derived demand and supply interact in each region to determine the level of
timber harvest and the stumpage price. As a simpliﬁcation of their model, sawtimber and

pulpwood stumpage were assumed to be perfectly substitutable. The derived stumpage

78

demand was a function of total recovery factors (for lumber and plywood), the product
output (lumber and plywood), the roundwood requirement for regional pulp output, and
other product outputs (including miscellaneous products, fuelwood, and log exports).
Stumpage supply was modeled for public and private owners, with the private supply
regressed as a simple function of the regional stumpage price and harvesting at the start
of the period inventory (one period lagged supply).

Sarkar (1991), working in a long-run timber model for Bangladesh, speciﬁed a
system of supply-demand equations, with equilibrium solutions obtained for price and
quantity of timber on the stump. The 2SLS procedure was applied. Timber demand was a
function of the weighted average real timber price, population, real gross national product
and time trend (representing changes in tastes and technology in timber using sectors),
using data from 1960-84.

Nautiyal (198 8) listed that the main factors determining the demand for lumber in
Canada and in the US were the house-building activity in North America and the per
capita income, although housing starts itself is dependent on the current income in a
society. A natural factor appearing to affect lumber demand is the price of lumber,
however empirical work in Canada failed to indicate price as signiﬁcant in this respect.
The apparent paradox from the demand theory may be a problem of measurement, since
lumber demand was actually signiﬁcantly affected by the price of all building materials
together. The estimated elasticity of demand for lumber within Canada was 0.19 with
respect to housing starts, 1.34 with respect to per capita income, and 10.25 with respect to
the price of construction material. The author also suggested that the US lumber market,

the country’s demography, prices of alternative products and interest rates should affect

79

the demand for lumber in Canada. On the other hand, lumber prices faced by American
consumers are perhaps the most important factors affecting the demand for Canadian
lumber in the US. This price is determined to a large extent by the exchange rate between
both currencies. Other factors such as per capita income, prices of materials and
substitutes in the US may have similar effects to those in Canada on demand for lumber.

Manurung and Buongiomo (1997) constructed a non-spatial equilibrium model to
simulate a ban on tropical log exports in Indonesia. The Indonesian econometric timber
market model was based on Vincent (1992) and included 12 supply and demand
equations and 3 equilibrium conditions. Elasticities were obtained by estimating the
export supply and import demand equations and the Indonesian demand and supply
equations for tropical logs, sawnwood and plywood. Price elasticities of domestic tropical
sawnwood demand and supply were respectively ~0.51 and 0.59, and for domestic
tropical log supply it was 0.45. Domestic demand was modeled using recovery rates. For
export supply and import demand price elasticities were respectively 0.49 and —0.80. The
model was calibrated with actual data for 1981 and 1989.

Brooks at al. (1995) modeling forest products demand, supply and trade for 18
European countries obtained short-run (domestic and import) demand and (export) supply
elasticities for different commodities by estimating system of simultaneous equations.
For conifer sawnwood, price elasticities of domestic demand ranged from as low as —0.04
for Italy up to -0.52 for France, and for import demand price elasticities varied between
0.02 and 1.26, respectively for Italy and Germany. With respect to export supply, short-
run price elasticities for conifer sawnwood were quite heterogeneous, with positive (0.62

for Germany, although non-signiﬁcant) or negative (-3.42 and -—l .30, respectively for

80

France and Austria, both signiﬁcant) values.

A review of literature on the supply of stumpage revealed that supply has not
often been related to prices and other shifting variables through econometric methods. In
a spatial equilibrium study of forest products in Nigeria, Buongiomo et al. (1994)
estimated future timber supply as a function of previous timber inventory and the opinion
of local forest production and management experts, assuming sustainable yield in
perpetuity. In another study for Indonesia, Buongiomo et al. (1981) considered the
maximum regional allowable cut as the log output (supply) constraint to his spatial
equilibrium model. Adams (1985), in a spatial equilibrium study of African-European
trade in tropical logs and sawnwood, formulated log production as a simple function of
the average tropical log price in each Aﬁican region. Brooks (1987) pointed out that log
supply relationships are generally difﬁcult to model because of the strong inﬂuences from
policy and institutional factors.

Econometric studies have estimated short-run price elasticities of stumpage
supply and demand in the US between 0.06 to 0.99, with most being inelastic and below
0.50 (at mean price in the sampling period) (Klemperer, 1996). Short-run stumpage
demand also tends to be inelastic with estimates ranging from —0.14 to —0.57 (Cubbage
and Haynes7, 1988; Hyde and Newmans, 1991, both cited by Klemperer, 1996). The
author points out that low timber supply elasticities have led some analysts to worry
about the market’s ability to provide timber as prices increase, although that is not

necessarily a problem in the long run. In an economic framework, the shortage never

7 Cubbage, F.W., and R.W. Haynes. 1988. Evaluation of the effectiveness of market responses totimber
scarcity problems. USDA. Forest Service, Marketing Research Report # 1149. Washington DC. 87 p.

8 Hyde, W.F. and DH. Newman. 1991. Forest economics and policy analysis: an overview. Discussion
Paper 134. The World Bank, Washington, Dc. 92 p.

81

occurs since shortages at one price lead demanders to bid up the price until supply equals
demand. The question is not whether the market will supply enough timber since supply
will equal demand in a well-functioning economy. Instead it is at which price supply will

equal demand (Klemperer, 1996).

3.2.3 Studies of Solidwood Products in Brazil

In Brazil, Ramos (1996) developed a gap model for analyzing demand and supply
of pine roundwood for the state of Parana, with price being treated as exogenous. The
analysis was non-econometric and based on the use of conversion factors and material
balances. Supply modeling was based on timber inventory and area with plantation
forests and estimates of future demand based on assumption of a constant annual growth.
The author simulated different scenarios for demand, which to some extent suggested a
shortage of future industrial roundwood if current plantation rates did not increase.

Sperandio (1989), cited by Ochoa (1996), in an econometric study of supply and
demand of plywood for the state of Parana, estimated price elasticities through a system
of equations. Demand was regressed as function of plywood price, costs of the
construction sector, and value of exports, while supply was a function of plywood price,
the index of wage in the manufacturing industry, energy price, and sawnwood price.
Results ﬁom ZSLS estimation showed adjusted st of 0.62 and 0.93, respectively, for
supply and demand. Estimates of the price elasticity of demand and supply were
respectively -0.83 and 0.92. Overall, the plywood market was inelastic with respect to all
variables.

Limitations on accurate and systematic statistics of supply, demand and prices of

82

forest products may be a major reason for the lack of other econometric studies in Brazil
and in other Mercosur countries. In Brazil and Chile, studies on supply and demand of
forest products have been more descriptive and related primarily to the historical
development of plantation forests, the industrial production over the decades, and future
perspectives. For Brazil, a major concern regarding the supply and demand for pine
roundwood, in particular for sawmills, is the ﬁrture capacity of the forest sector to fulﬁll
the existing and growing demand for sawlogs. This is particularly important given the
close integration of the pulpwood market (a major supplier of pine sawlogs) with the pulp
and papermills, as well as the fact that any adjustment to produce commercial volumes of

sawlog will take a considerable period of time.

3.3 Theoretical Framework

In an economic context, market prices and quantities traded are arrived at
simultaneously and endogenously as the result of interactions between consumers and
producers. In case of primary products, such as stumpage, consumers are ﬁrms, which
produce intermediate products for other end-uses.

With demand, supply, and prices being endogenously determined, an appropriate
modeling approach using time-series data is through a simultaneous system of equations
(Bigsby, 1993). A classical linear representation of a simultaneous two equation model of
market equilibrium for one region consists of three (endogenous) variables, supply,
demand, and price, as well as other shifter variables (here quantities are represented as

the dependent variable):

83

d it
Q, =ao+a.-P,+za,-X,+u, demand function(1)
i=1

Q: =b0+bl'Pt+Zlbi'Yr+Vr supply funaion (2)

d s
Q: = Q: market clearing condition (3)
where:

th and Qst are quantities of the product demanded and supplied respectively at
time t, a0 and b0 are intercepts of the demand and supply ﬁrnctions respectively, Pt is the
product price at time t, a1 and b1 are the slopes of demand and supply functions
respectively, Xt and Yt are the vectors of demand and supply input prices and/or shifters
respectively, aj and bi are the vectors of coefﬁcients of the n demand and m supply
shifters respectively, and ut and vt are the error terms. The expression in aj.Xt can
represent the price of substitutes, and the measure of intermediate or end use demand
activity. The expression 2m bi.Yt can represent the price of other inputs and a measure of
the technology employed in the production.

In addition to the analysis of the effect that shifting demands (say lumber
demand) might have on the prices (say stumpage prices), one should also consider the
change in expectations and how they are formed. On the supply side, for instance,
sawtimber owners could increase expectations and anticipate a decided rise in lumber and
stumpage prices by observing past increases in both lumber and stumpage prices and

reading about a growing consumption of lumber. This change in expectations would raise

the so-called reservation price (price necessary to persuade the owner to sell a particular

84

volume or area) of the owner’s timber, making sawlogs more costly by shifting the
sawlog supply curve upward and, if the increase is widespread in the industry, cutting
back production. On the other hand, if the demand curve for lumber shifted inward and
lumber price declined, then the opposite forces are set in motion. However, Gregory
(1987) has suggested that a decline in lumber price, unless very prolonged, is unlikely to
have a seriously depressing effect on price expectations of timber owners based on
historical lumber and stumpage prices. On the demand side, the demand for logs is
derived from an anticipated demand for lumber, and this is consequently inﬂuenced by
the mill owners’ expectations of consumption of products in which lumber is used, such
as furniture or housing construction. Gregory (1972) indicates that since expectations are
difﬁcult to quantify, this inﬂuence will reduce the accuracy of the estimated demand, and
because expectations are generally more volatile than actual occurrences one should
anticipate substantial ﬂuctuations in log demand, perhaps even more than would occur in
the lumber market itself.

Unbiased parameter estimates can be obtained by estimating the reduced form of
the system of equations using the OLS, or by estimating directly the system of equations
by 2SLS, instrumental variables (IV) techniques (Bigsby, 1993), or 3SLS.

The model (formulas above) can be expanded to a spatial equilibrium model and
formulated by econometric or operations research techniques. The equilibrium prices and

quantities among separate geographic regions will be the solution for the problem.

3.4 Empirical Method

The timber species groups and products in this study were based on their end use

85

characteristics, importance and data availability. Similar decisions were taken in deﬁning
regions of interest and the boundaries of the study.

Products were aggregated, unless otherwise noted, into two broad categories:
conifer sawlogs and sawnwood as deﬁned by F A0 (1996). Regions were grouped as
individual countries for Argentina, Brazil and Chile. Estimation of demand and supply
equations for Brazil, however, was made for the Southern region where most of the pine
plantations and sawmills are located. Given data limitations, an attempt to estimate
elasticities for the other Mercosur countries (Uruguay, Paraguay, and Bolivia) was made
by grouping them with Argentina. The systems of simultaneous domestic supply and
demand equations for each product (conifer sawlogs and sawnwood) were regressed for
each region.

Although the spatial equilibrium model is intended to estimate the optimum
equilibrium for pine products (sawlog and sawnwood), conifers in general were used in
this chapter for most of the regions as a proxy for pines given the lack of consistent time-
series prices and quantities across the selected regions. In the Southern Brazilian states,
plantations with conifers are composed of southern yellow pines (Pinus taeda and Pinus
elliottii) and Parana pine (Araucaria angustifolia). Pines have dominated the market for
conifer sawlogs and secondary products since the 1980s (Table 3.1). In Chile, radiata
pine (Pinus radiata) is the sole large-scale commercial conifer species, and therefore
FAO data on coniferous species already reﬂects pine. In Argentina and Uruguay southern

yellow pines are the primary conifers traded.

86

Table 3. l. Coniferous species by region

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CONIFER SPECIES
REGIONS PLANTATION FORESTS Natural
Large-scale Small-scale Forests

Southern . 1 , . 2 , . 2
Brazil Southern yellow pines Parana pine Parana prne
Rest of . . 3 . 1
Brazil Tropical prnes Southern yellow prnes None
Argentina and . 1 Radiata pine4 Parana pine?
Uruguay Southern yellow prnes (Uruguay) (Argentina)
Paraguay and None None None
Bolrvra
Chile Radiata Pine‘[ None Chilean pineS
Note:

I - Pinus taeda and Pinus elliottii,

2 - Araucaria angustifolia,

3 - Pinus caribaea (different varieties) and other minor tropical pine species,
4 - Pinus radiata,

5 - Araucaria araucana.

3.4.1 Model Structure

Consumers and producers in each region were assumed to participate in a single
aggregate market, subdivided in conifer sawnwood and conifer sawlog markets (Figure
3.1). Equilibrium prices in the stumpage market affect the domestic sawnwood market
(relationship 1) and vice versa (relationship 2). Such mutual effects are reﬂected in the
system of equations by the explanatory variables, primarily prices, indices (wage and
construction indices, interest rates) and quantities. The econometric models in this study
extend the models reviewed from the literature by incorporating speciﬁc and important
variables for each country under investigation and by comparing different speciﬁcations
and estimation procedures. The models are also in accordance with the economic theory

of simultaneously estimating quantities (demand/supply) and prices.

87

 

 

Market Equilibrium

 

Price and Quantity

 

 

 

 

 

- Sawlogs -

 

 

 

 

 

 

I
1:1 Domestic
I DEVI—AND
: ' for Conifer
T Sawlogs
S
Sawmills

 

 

 

 

 

PRIMARY
PRODUCT MARKET

 

M
sugar
of Conifer
Sawlogs

 

Wood
Producers

  

   
   
 

 

 

'JJ-JNO'UNFJ

 

 

 

  

 

 

 

 

SECONDARY
PRODUCT MARKET

I TRANSFORMATION I

----w-----

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  
 
    
 

 

 

 

 

 

 

p1,,

 

 

 

 

 

 

I Other Industries 7 Sawmills E
M Domestic Domestic x
f, mun s_UrrL_Y 1’
—> . . 0
R for Conifer of Conifer R
T Sawnwood Sawnwood T
S S
Market Equilibrium 2
> Price and Quantity
- Sawnwood -

 

 

 

 

 

Figure 3. 1. Diagram of the regional domestic sawlog and sawnwood supply and demand
model for each region

88

- Conifer Sawnwood Market

The general system of demand and supply equations of conifer sawnwood for

each region is deﬁned as follows:

Dcsw. = a0 + a. Pm. + aJCA. + as W. + a. me. + as I . + a6T + a7 Dem... + u,
SCSWt=b0+b1PCSWt+b2PCSLt+b3Wt+b4PNCSLt+b5T+b6SCSWt-l+vt

Dcsm = Scsw, + Icsw: ' E csm (material balance)

where:

DCSW, is the derived demand for conifer sawnwood, representing the forest input for the

sawnwood processing industry in time t;

SCSW, is the supply of conifer sawnwood, representing the sawmills’ output in time t;

Pcswt is the conifer sawnwood price, representing the input price for demand and the
output price for supply in time t;

ICAt is the index of construction activity, representing the price of the demand output in
time t;

Wt is the index of wages, representing a price of other inputs in the demand supply
equations in time t;

PCSI4 is the sawlog price, representing the price of the supply input in time t;

PNCSW, is the non-conifer sawnwood price, and represents the price of substitute inputs
in time t;

It is an index of economic activity expressed by per capita GDP in time t;

89

T is a trend variable;

DCSWH is the one period lagged demand for sawnwood in time t;

PNCSLt is the non-conifer sawlog price, and represents the price of substitute inputs in
time t;

SCSWH is the one period lagged supply of sawnwood in time t;

ICSWt is the imports of conifer sawnwood in time t;

ECSWt is the exports of conifer sawnwood in time t;

u, and v, are respectively the error terms of the demand and supply equations.

The time variable is included to remove any trend associated with increased input
inefﬁciencies or efﬁciencies (e. g. technological change), which may have occurred over
time. Consumption was estimated by the material balance relationship for each region,

with inventories assumed clear in the same year.

- Conifer Sawlog Market

The general system of demand and supply equations of conifer sawlogs for each

region is deﬁned as follows:

DCSLt = 00 + alPCSLt + 02 Pcsm + as W: + a4P~csu + as]: + aeT + a7Dcsu—r + u:
SCSL: : b0 + b1 PCSLt + b2 PCSW: + b3 W: + b4 PNCSL: + b5 T + b6 SCSLt—l + v:

DCSL: = SCSLt + Icsu - E csu (material balance)

where:

90

DCSL, is the derived demand for conifer sawlogs, representing the forest input for the

sawmills in time t;

SCSLt is the supply of conifer sawlogs, representing the output of wood producers in
time t;

PCSL, is the conifer sawlog price, representing the input price for demand and the output
price for supply in time t;

PCSWt is the conifer sawnwood price, representing the price of the sawmill output in
time t;

Wt is the index of wage, representing a price of other inputs for demand and supply in
time t;

DCSLH is the one period lagged demand for sawlogs in time t;

PNCSL, is the price of non-conifer sawlogs, and represents the price of substitute inputs
for demand or the price of substitute outputs for supply in time t;

It is an index of economic activity expressed by per capita GDP in time t, (an indirect
explanatory variable since demand for sawlogs - the dependent variable - is a
derived demand);

SCSLH is one period lagged supply of sawlogs in time t;

T is a trend variable;

ICSL‘ is the imports of conifer sawlogs in time t;

ECSL t is the exports of conifer sawlogs in time t.

u, and v. are respectively the error terms of the demand and supply equations.

The systems of equations for sawnwood and sawlogs were estimated separately.

In order to link both systems, which where estimated independently, the price of conifer

91

sawnwood was used as the output price in the sawlog demand equation, and the price of

conifer sawlogs was used as an input price in the sawnwood supply function (Figure 3.1).
The simultaneous systems were modeled by testing linear and logarithmic

functional forms for each commodity and region. The ZSLS and 3SLS procedures were

used to determine the parameter estimates.

3.5 Data Sources

Secondary annual data for the estimation of the system of equations were obtained
from various sources. Aggregate data at country level were used for Brazil, Chile, and
Argentina, and for Southern Brazil depending on data availability (Appendices 2-8).

Production, exports and imports of conifer sawlogs and sawnwood, as well as
total value of exports and imports for each individual country were taken from FAO
Forest Products Yearbooks, 1980-1998 (F A0, 2000). The quantity consumed was
estimated as the sum of production and imports minus exports. For Brazil, FAO data on
conifer sawlog production have been reported as almost constant values since the early
1980s and a preliminary attempt to use those quantities failed to generate meaningﬁrl and
reliable coefﬁcient estimates. As an alternative, data for domestic production and
consumption of conifer sawlogs were taken respectively from IBGE and ABPM.
Production of conifer sawlogs is the total ‘other roundwoods’ production from
plantations (except pulpwood and ﬁrewood/fuelwood) between 1980-96. Such
production is assumed to represent primarily pine sawlogs, given that sawlogs from other

major plantation species were negligible until very recently. Consumption of conifer

92

sawlogs was estimated for 1983-95 from data on pine lumber production reported for the
years 1980, 1983, 1986, 1987, 1990, 1991, 1994 and 1995 (Revista da Madeira, 1996) by
using the ratios of conifer sawlogs to conifer sawnwood from FAO for each year.
Quantities for years not reported were estimated through interpolation by using average
growth per year between two contiguous reported periods.

All monetary values were expressed in domestic currencies and deﬂated to 1995.
Current prices and other monetary values were deﬂated using the Consumer Price Index
(CPI) for each country from IADB (2000). For Brazil, the speciﬁc deﬂator was the
“General Price Index” (IGP - 1994=100) for the period 1980-97 (F GV, 1998).

Prices of conifer sawlogs and sawnwood were computed from various sources
depending on the country. For Brazil, unit values of production of IBGE ‘other
roundwoods’ production from plantations for Southern Brazil between 1980-95 were
used as proxies for market prices of pine/conifer sawlogs. Annual prices of pine
roundwood (stumpage and market prices) for the state of Parana between 1989-95 ﬁ'om
SEAB-EMBRAPA/CNPF were used for comparison. Prices of conifer sawnwood in
Brazil between 1981-95 came from APBM, published in Revista da Madeira (1996). For
Chile, domestic prices of radiata pine sawlogs and sawnwood, as well as for some
hardwood species, between 1980-98 came from INFOR (2000) and ﬁom F AO—Chile
(personal communication, 1997). For Argentina, domestic prices of conifer sawlogs were
obtained from SAGPyA (personal communication, 1999) from 1983-95, and unit values
of imports came from F A0 (2000).

Other sawlog prices for Brazil and Argentina, although not used in the regression

analysis, were used for veriﬁcation and use in the spatial equilibrium analysis (Chapter

93

4). They included monthly pine sawlog prices for the state of 850 Paulo/Brazil from
1993-96 (FF, 1998); Revista da Madeira (various years); and Angelo (1998). For
Argentina, additional time-series price data came ﬁom SAGPyA (1996).

Exchange and interest rates, consumer price indexes, population and total and per
capita GDP were obtained from the IADB intemet database (IADB, 2000) and from the
World Tables (Word Bank, 1995). The World Tables were also the source of GDPs and
GDP deﬂators. Average hourly earnings (or a wage index), and construction cost indexes
came from varied sources depending on the country (Appendices 1-8).

The systems of equations were estimated using EViews®, version 3 (EViews,
1998a and 1998b). In EViews®, F-statistic is reported only if the equation is speciﬁed in
list form and since systems cannot be speciﬁed in list form, the F-statistic cannot be
directly reported (EViews, personal communication). The equivalent of the F -statistic
was obtained by carrying out a joint test that all slope coefﬁcients are equal to zero as a

Wald coefﬁcient test.

94

3.6 Results and Discussion

The period of the analysis varies by country and commodity. For Brazil and
Argentina the period is from 1982—95 and for Chile, from 1980-98, for which time-series
of domestic prices of sawlogs and sawnwood were available. Combinations of
explanatory variables in each system of equations were tested in both the linear and
logaritlunic functional forms using both 28L and 3SLS estimation procedures. Results of
the estimates represent the equations with the most meaningful coefﬁcient and overall
goodness-of-ﬁt. The major criteria for choosing models were the expected signs and
magnitude of the coefﬁcients of the price variables, t-statistics, adjusted R2, and the
Lagrangean Multiplier (LM) test statistics. In some cases, more parsimonious models
without some of the proposed explanatory variables (Section 3.4), generated better

results.

3.6.1 Brazil

The initial estimation of the structural equations using conifer sawnwood and
sawlog data from F AO failed to provide meaningful results. This may be relatedto the
fact that FAO data on production, exports and imports of conifer sawlo gs were reported
with almost the same values between 1985-89, and 1991-95. In addition, the data

aggregated both Araucaria and pine products, which are not homogeneous. Alternative

95

estimates were obtained with domestic data ﬁ'om IBGE (1999) and ABPM (1994).
Results of the structural equations estimation (log and linear forms, through ZSLS
and 3SLS) for conifer sawnwood and sawlogs are shown in Tables 3.2 and 3.3,
respectively. For serial correlation, the Breusch-Godfrey test statistic, also called
Lagrange Multiplier (LM) test statistic is reported. It is an appropriate test for systems of
equations and when lagged dependent variable is present in an equation. The Durbin-h
statistic (DW-h) is another alternative to test serial correlation in autoregressive models,
however it is a large-sample test. The LM test is statistically more powerful not only in
the large samples but also in the ﬁnite, or small, samples, being preferable to the DW-h
test (Gujarati, 1995; Korosi, Matyas and Szekely, 1992). The LM test statistic was
computed for each equation to the 5th order autoregressive for residuals using 2SLS.
Results obtained using the 3SLS procedure tended to generate more signiﬁcant
coefﬁcients, although their magnitude did not depart signiﬁcantly from the 2SLS results.
Discussion in this section focuses on the 3SLS results. Overall, the signs and magnitudes
of the own-price coefﬁcients are consistent with production and consumption theory, and
within expected bounds. The adjusted R25 are close to 50% for the supply and between

61%-68% for the demand. The F statistic was highly signiﬁcant for all the equations.

96

Table 3. 2. Estimated structural equations for conifer sawnwood in Southern Brazil

 

 

(1982-95)
EXPLANATORY
VARIABLES REGRESSION COEFFICIENTS
ZSLS 3SLS

 

 

ngarithmic LLinear

 

Logarithmic [ Linear

 

- Demand for Sawnwood (m3)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Intercept 3.88 2346984 4.50 2663738
(1.51) (1.59) (2.52) "' (2.59) *
Conifer sawnwood ~0.04 -513 -0.04 -480
price (1995 RS/m’) (-l.81) (2.20) * (-2.53) * (-2.96) **
Dummy for 1986 0.04 700661 0.03 653502
(1.34) (1.92) (1.78) (2.59) "'
Interest rate 0.005 6006 0.003 4849
(0.67) (1.05) (0.65) (1.23)
GDP (1995 US$) 0.23 2.8E-O6 0.19 0.000
43.60) "”" (3.56) ** (4.39) ** (4.43) "
Real minimum 0.08 4568 0.07 3996
wage (index 1990) (3.30) " (3.05) " £1.75) " (3.85) "
Lagged demand 0.35 0.40 0.38 0.41
for sawnwood (m’) (2.09) * (2.42) * (3.25) ** (3.53) **
Adjusted R2 0.68 0.62 0.66 0.61
LM statistic *** 0.17 0.19 0.17 0.19
- Supply of Sawnwood (m3)
Intercept 10.03 5699225 9.55 5452035
(3.67) ** (3.79) ** (4.14) *"' (4.31) "
Conifer sawnwood 0.005 748.40 0.002 565.56
price (1995 RS/m") (0.27) (0.55) (1.15) (0.49)
Conifer sawlog -0.05 -l3601.44 -0.04 -11224.90
price (1995 RS/mi) (-l .86) (-1.95) (-1.70) (-1 .93)
Lagged supply of 0.38 0.37 0.41 0.39
conifer sawnwood (2.22) * (2.14) * (2.84) "' (2.69) "
(m3)
Adjusted R2 0.48 0.49 0.47 0.48
LM statistic *** 0.28 0.27 0.27 0.28
F-statistic 2.1E+07 77696 3.2E+07 117658
Notes:

Numbers in parentheses are t-statistics
(*) signiﬁcant at the 5 % probability level
(**) signiﬁcant at the l % probability level

(***) probability of obs*R

of 5th order autoregressive using 2SLS estimator

97

 

- Conifer Sawnwood

For conifer sawnwood, the demand equation was a function of its own price, a
dummy variable (=1 for 1986 to account for price variability for that year), interest rate,
the total GDP, wage index, and one-year lagged demand. In the supply equation,
explanatory variables were the own-price, the input price (conifer sawlogs), and one-year
lagged supply (Table 3.2). Time trend was not included to reduce collinearity problems.
The resulting, adjusted st were relatively low (0.47-0.49) for the supply equations and
higher (0.61-0.68) for the demand equations, depending on the estimation procedure. The
LM test statistics do not show evidence of serial correlation for either equation or
speciﬁcation. The own-price coefﬁcients (conifer sawnwood price) were both with the
expected sign and signiﬁcant at the 1% level for demand (linear form-3SLS) and non-
signiﬁcant for supply.

For sawnwood demand, the logarithmic function showed a slightly higher
adjusted R2 than for the linear form. In both forms the coefﬁcients were mostly
signiﬁcant and with the proper signs: demand for sawnwood was negatively related to its
own-price, and positively related to GDP and one-year lagged demand. Although
unexpected positive signs were found for wage index, a possible explanation for this sign
could be the economic of scale of lumber-consuming companies. The own-price elasticity
was signiﬁcant and negative, with an extremely low value of -0.04 and —0.05,
respectively for the log and linear forms in the 3SLS (Tables 3.2 and 3.9 - about
discussion on elasticities).

For sawnwood supply, in both functional forms and estimation procedures, supply

was positively related to its own price although non-signiﬁcant (Table 3.2). A signiﬁcant

98

coefﬁcient with the expected positive sign was noted for the lagged supply. An expected
negative sign was found for sawlog price (although not signiﬁcant), indicating an inverse
relation with sawnwood supply. The own-price elasticity was positive but not signiﬁcant,
with an extremely low value of 0.002 and 0.006, respectively for the log and linear forms
(Tables 3.2 and 3.10 — about discussion on elasticities). Given the data limitations for
sawnwood demand, an attempt was made to estimate a single equation for sawnwood
supply using ABPM data from 1982-96. Results through the OLS procedure failed to

provide meaningful coefﬁcients.

- Conifer Sawlogs

For conifer sawlogs, the failure to obtain meaningﬁrl results using F AO data led
to the use of a combination of IBGE (production and proxy for prices) and ABPM
(consumption estimates) data and the estimation of models, with good ﬁt and some
highly signiﬁcant coefﬁcients (Table 3.3). The demand equation was regressed as a
_ function of its own price, the price of the output (conifer sawnwood), the interest rate,
wage index, and a proxy for the price of hardwood sawlogs. The supply equation was
regressed as function of its own price, time trend, and one-year lagged supply. Data were
assumed to represent production and consumption of conifer sawlogs (primarily pines) in
Southern Brazil, where the sawmilling of softwood species is traditionally concentrated.
Overall, demand and supply equations of conifer sawlogs had adjusted st above 0.80 for
the logarithmic speciﬁcation and above 0.65 for the linear form. In general, coefﬁcients
were signiﬁcant at 1% or 5% signiﬁcance level. The LM test statistics do not show

evidence of serial correlation for both speciﬁcations.

99

Table 3. 3. Estimated structural equations for conifer sawlogs in Southern Brazil (1983-

96)

 

EXPLANATORY
VARIABLES

REGRESSION COEFFICIENTS

 

ZSLS

3SLS

 

Lo arithmic [Linear

 

Logarithmic I Linear

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

- Demand for Sawlo s (m )
Intercept 8.59 6060691 9.35 5626561
(2.61) * (0.91) (3.84) "'* (1.14)
Conifer sawlog -2.18 -557287 -1.94 ~519079
price (1995 RS/mj £2.32) * (2.03) (-2.93) ** (-2.61) *
Conifer sawnwood -0.20 8391 -0.40 -4369
price (1995 RS/m’) (-0.54) (0.30) 1-1 .65) (-0.22)
Interest rate 0.24 39113 0.29 54392
(1.63) (0.33) (2.84) "”" (0.62)
Real minimum 3.17 163124 3.17 171348
wage (index 1990) (4.43) ** (2.37) * (6.08) ** 43.36) "
Non-conifer 0.13 45775 -0.06 37950
sawlo price (0.18) (0.45) (-0.12) (0.51)
(RS/m
Adjusted n2 0.81 0.65 0.82 0.65
LM statistic *** 0.73 0.23 0.73 0.23
- Supply of Sawlogs (m3)
Intercept 7.12 -2600873 5.74 -2631568
Q .92) (-0.94) (1.95) (-1 . 12)
Conifer sawlog 0.24 61024.60 0.26 59906.40
price Q995 Rs/m3) (1.13) (1.19) (1.43) (1.38)
Time trend 0.06 370089 0.05 327161
(3.38) ** (2.91) ** (3.80) *"‘ (3.12) "”"
Lagged supply of 0.46 0.63 0.55 0.70
conifer sawlogs (2.10) "‘ (2.97) *"' (3.17) ** (3.99) "
(m’)
Adjusted R2 0.83 0.84 0.83 0.84
LM statistic *** 0.72 0.24 0.72 0.24
F—statistic 200136 915 252558 1153
Notes:

Numbers in parentheses are t-statistics
(*) signiﬁcant at the 5 % probability level
(**) signiﬁcant at the 1 % probability level

(***) probability of obs*R

of 5th order autoregressive using ZSLS estimator

100

 

For sawlog demand, a high adjusted R2 (0.82) was obtained with the log form.
The price of sawlogs was highly signiﬁcant and with the expected negative sign. The
interest rate and the real minimum wage rate were unexpectedly positively correlated to
the demand for sawlogs, and signiﬁcant depending on the functional form and estimation
procedure (Table 3.3). One explanation for the positive signs of these variables could be
related to the economy of scale of sawmills.

For sawlog supply,-both functional forms showed higher adjusted st, around
0.83. Serial correlation was corrected by adding lagged supply as an explanatory variable.
In both functional forms the signs of the coefﬁcients were as expected for all variables,
although not necessarily signiﬁcant. The supply of sawlogs was positively related to its
own price and with the trend variable, which conﬁrms the increased production of pine
sawlogs from plantations since the early 19803 in Southern Brazil (Table 3.3).

Estimates of short-run price elasticities of supply and demand of sawnwood and
sawlog in Brazil are summarized in Table 3.9. Discussion and comparison with ﬁndings

from other studies are also presented.

3.6.2 Chile

Estimations of the logarithmic and linear functional forms for both conifer
sawnwood and sawlogs were performed for Chile (Tables 3.4 and 3.5). Overall the signs
and magnitude of the coefﬁcients are consistent with production and consumption theory
and within the expected bounds, except for a few variables in both equations. The
adjusted R25 are high (over 0.80), the F statistics are signiﬁcant, with an overall good ﬁt

for the equations. Coefﬁcient estimates obtained through 2SLS and 3SLS procedures

101

differed to a certain degree, and 3SLS tended to generate more signiﬁcant coefﬁcients.

- Conifer Sawnwood

The conifer sawnwood demand equation was regressed as function of its own
price, an index of construction cost, the interest rate, and the exchange rate. Both the log
and linear functional forms generated equations with high adjusted R28 (0.89 and 0.88
respectively) either with 2SLS or 3SLS procedures and signiﬁcant F statistic for all the
equations (Table 3.4). However, the linear form equations seemed to show a better ﬁt,
with more coefﬁcients being signiﬁcant. This is particularly true for the estimates of the
own-price coefﬁcient which were highly signiﬁcant (0t=0.01) in the linear form and not
signiﬁcant in the log form. Coefﬁcients with unexpected positive signs and which were
highly signiﬁcant were the construction cost index and the exchange rate. As construction
cost increases, demand for conifer lumber would be expected to decrease since it
represents a cost for consumers. An explanation for the positive sign is the possibility that
the construction cost index includes materials other than conifer lumber, and therefore
lumber could be a substitute for other construction materials. For the exchange rate, an
increase in the rate would favor more exports to the detriment of the domestic market,
although an indirect effect of a higher exchange rate could favor an increase in the
derived demand. Although there is no clear explanation for such signs, both variables
were kept in the model as they improved the overall ﬁt of the system. Demand for conifer
sawnwood was negatively related to its own—price and to interest rate, as expected.
Adding GDP as an explanatory variable to the demand equation failed to generate a

system with a good ﬁt and meaningful results (not shown).

102

Table 3. 4. Estimated structural equations for conifer sawnwood in Chile (1980-98)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

EXPLANATORY REGRESSION COEFFICIENTS
VARIABLES
ZSLS 3SLS
Logarithmic Linear Lgarithmic Linear
- Demand for Sawnwood (mi)t
Intercept -3.92 -2135616 -0.64 -l883579
(0.68) (~2.37)* (-0.15) (-2.49) "”'
Conifer sawnwood -0.29 -57.15 -0.17 -55.28
price (1995 PS/m’) (-0.84) (-3.35) ** (-0.59) (6.79) **
Construction 2.40 2199 1 .85 2060
index (2.46) (3.69) "”" (2.49) "' $09) "
Interest rate -0.26 ~20185 -0.28 -20720
(-1.45) (1.84) (~2.12)* (~2.39) "‘
Exchange rate 0.69 6979 0.64 6848
11995 PS/m’) (5.01) (6.90) *"' (6.21) " (8.22) "
Adjusted R2 0.89 0.88 0.89 0.88
LM statistic *** 0.48 0.35 0.48 0.35
- Supply of Sawnwood (ml
8.11 584156 10.49 579352
Intercept (2.66)* (1.40) (4.94) ** (1.72)
Conifer sawnwood 0.11 13.57 0.84 28.19
price (1995 PS/ms) (0.13) 40.32) (1.39) (0.89)
Lagged sawnwood 0.42 0.59 0.14 0.44
exports (m’) 41.44) (2.46) * (0.91 ) (2.60) *
Time trend 0.04 109132 0.07 139878
(1.18) (1.85) (2.79) ** Q.18) **
Real minimum 019 -1539 -0.38 -1021
wage (index 1990) (057) (-0.33) (-1.70) (-0.29)
Conifer sawlog -0.03 -70.44 -0.60 -108.59
price (1995 Ps/m3) (-003) (-059) (-100) (-1.21)
Adjusted R2 0.85 0.90 0.84 0.90
LM statistic *** 0.02 0.02 0.02 0.02
F-statistic 283070 2347 267710 2436
Notes:

Numbers in parentheses are t-statistics

(*) signiﬁcant at the 5 % probability level
(**) signiﬁcant at the 1 % probability level

(**"') probability of obs*R

103

of 5th order autoregressive using ZSLS estimator

The conifer sawnwood supply equation was regressed as function of its own
price, the one-year lagged exports, the one-year lagged supply and the one-year lagged
price (Table 3.4). Both functional forms showed adjusted st above 0.82, with most of
the coefﬁcients signiﬁcant. Signs of most of the coefﬁcients were as expected. Domestic
supply of conifer sawnwood was positively related with its own-price and with the one-
year lagged supply and negatively related with the exports. The positive sign of the
lagged supply coefﬁcient indicates that supply tends to increase from one year to another,
as observed. Increasing exports in the previous year may lead to a decrease in the supply
of the domestic market. The sign of the coefﬁcient of the one-year lagged own price,
however, was not as expected.

For sawnwood demand, the LM test statistics do not show serial correlation, for
both functional forms and estimation procedures (Table 3.4). The LM statistic, however,
shows slight evidence of serial correlation for the supply function at 3% and 4% level,
respectively for the linear and logarithmic functions. Attempts to correct the problem by
adding other variables (as suggested by Gujarati, 1995) did not improve the results.
Estimates of price elasticities derived from equations with LM statistic below 5% were

not used in the spatial equilibrium model of Chapter 4.

- Conifer Sawlogs

For conifer sawlogs, the demand equation was regressed as function of its own
price, the price of the output (conifer sawnwood), interest rate, and a time trend. The
supply equation was regressed as function of its own price, one-year lagged supply and a

wage index. Estimates of both the log and the linear systems showed an overall good ﬁt

104

with adjusted st above 0.90 (Table 3.5). The linear function, however, is preferred over
the logarithmic one since it gave a greater number of more signiﬁcant coefﬁcients,
including the own-price coefﬁcients. For sawlogs both test statistics did not indicate
serial correlation for the supply equation, although the LM statistic suggests evidence of
serial correlation for the linear equation.

For sawlog demand, higher adjusted R25 (over 0.91) were obtained for both
functional forms. Coefﬁcients were overall highly signiﬁcant in the linear form and signs
were as expected, except for interest rate. The own-price coefﬁcient was highly
signiﬁcant and inelastic and with the expected negative sign. The coefﬁcient of the output
price was expectedly positive and signiﬁcant. The unexpected positive sign for interest
rate (signiﬁcant only in the 3SLS) could indicate that the composite rate used is not
representative of the rate that the sector faces. Other explanations may be related to the
mechanisms used for capital borrowing and use in the conifer lumber sector (Table 3.5).

For sawlog supply, both ﬁrnctional forms showed high adjusted st of 0.93 with
highly signiﬁcant coefﬁcient (0t=0.01). Serial correlation was corrected by using one-
year lagged supply as an explanatory variable. In both functional forms the signs of the
coefﬁcients were as expected from supply theory for all variables, and signiﬁcant except
for the wage. Sawlog supply was positively related to its own-price and to lagged supply,
although negatively related to the wage. In the logarithmic form, the price coefﬁcient
(elasticity) of supply was positive and inelastic with value between 0.7-0.8 (Table 3.5).

A summary with the estimates of the short-run price elasticities of supply and
demand of sawnwood and sawlogs in Chile is shown in Table 3.10. A discussion and

comparison with ﬁndings from other studies are also presented.

105

Table 3. 5. Estimated structural equations for conifer sawlogs in Chile (1980-98)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

exp! FLIAN, Big?“ REGRESSION COEFFICIENTS
2SLS 3SLS
Logarithmic ] Linear Logarithmic I Linear
- Demand for Sawlo gm!)
Intercept 8.35 -3395066 6.66 -3718588
(4.44) " (-2.01) "' (4.58) * (~2.90) "
Conifer sawlog -0.60 -651.49 -0.74 -711.99
rice (1995 PS/m3) (-l.13) (-2.15) * (1.64) (-2.87) **
Conifer sawnwood 1.07 252.94 1.34 280.33
price (1995 PS/m’) (2.37) "' (2.74) *" (3.65) *"' (3.96) "
Interest rate 0.16 48272 0.23 50223
(1.01) (1.47) (2.08) * (2.17) "'
Time trend 0.11 765018 0.11 777265
(4.25) " (5.25) " (5.14) *"' (6.33) "
Adjusted R7 0.93 0.91 0.91 0.91
LM statistic *** 0.02 0.31 0.02 0.31
- Supply of Sawmis (m3)
Intercept 1.67 297454 1.87 756784
(1.48) (0.27) (1.89) (0.86)
Conifer sawlog 0.27 152.77 0.34 138.54
price (1995 PS/m’) (1.59) (2.01) * (2.42) * (2.10) *
Real minimum -0.18 -5770 -0.09 -10412
wage (index 1990) (-1.03) (-0.58) (-0.69) (-l.34)
Lagged supply of 0.79 0.80 0.71 0.83
conifer sawlogs (4.44) " (5.502) " (4.99) " (6.76) "
(m3)
Adjusted R2 0.93 0.93 0.93 0.93
LM statistic *** 0.51 0.09 0.51 0.09
F-statistic 562354 2698 481692 2285
Notes:

Numbers in parentheses are t-statistics

(*) signiﬁcant at the 5 % probability level

(**) signiﬁcant at the 1 %

robability level

(**"') probability of obs*Rg of 5th order autoregressive using 2SLS estimator

106

 

3.6.3 Argentina and other Mercosur countries (Uruguay, Paraguay and Bolivia)

A major constraint in estimating the sawnwood equations for the Argentina,
Uruguay, Paraguay, and Bolivia was the unavailability of long-period price series for
conifer sawnwood. The price of conifer sawnwood imports for Argentina and a weighted
price of pine lumber for Brazil and Chile were considered as proxies for the sawnwood
prices. The supply and demand equations were estimated initially only for Argentina.
Argentinean social-economic variables, including wages (real minimum wage index), the
GDP, and an index of construction costs (Revista Construir, 1999) were used as

explanatory variables.

- Conifer Sawnwood - Argentina

For conifer sawnwood, the demand equation was regressed as ﬁrnction of a proxy
for its price, the index of construction cost and the GDP. The supply equation included a
proxy for its price, the input price (sawlogs) and the cross-price variable.

For sawnwood demand and supply the logarithmic speciﬁcation gave higher
adjusted st than did the linear form. Results obtained using 3SLS generated a greater
number of signiﬁcant coefﬁcients, although their magnitude was comparable to those
obtained through 2SLS (Table 3.6). Estimation of sawnwood demand and supply
equations for Argentina failed to generate equations with a good overall ﬁt for adjusted
R23 and signs of some coefﬁcients. Adjusted st were low (0.50-0.56) for the demand
and even lower (0.16-0.36) for the supply equation, the explanatory variables failing to
account for the variability to a large extent. However, signs, magnitude and signiﬁcance

of the own-price coefﬁcients for the demand and supply equations were as expected.

107

For sawnwood, GDP, as expected, was positively related to demand while the
index of construction cost showed a signiﬁcant and unexpected positive Sign. The
elasticity with respect to GDP was also highly elastic and signiﬁcant, with a value of 4.4.
It should be noted, however, that this is a derived demand and GDP is an indirect
explanatory variable through the chain of consumption from primary products (sawlogs)
to the ﬁnal or manufactured goods (furniture and housing construction). The LM
statistics do not show evidence of serial correlation for either ﬁrnctional form. Future
estimates using speciﬁc price and quantity data, as they become available may eventually
conﬁrm such range of coefﬁcients. The fact that the st and some coefﬁcient estimates
were not as expected, requires caution in interpreting and using the coefﬁcients as true
measures of the price elasticities (Table 3.6).

For sawnwood supply, the logarithmic function showed a better ﬁt than did the
linear form for adjusted R2, more coefﬁcients were signiﬁcant, and coefﬁcients were
more highly signiﬁcant. Both functional forms showed the expected signs for the own-
price coefﬁcients, but not for other coefﬁcients. The own price coefﬁcient of supply was
elastic for the logarithmic form, with an estimated value of 1.7. Unexpectedly, the input
price coefﬁcient (sawlogs) was positive, highly elastic, and signiﬁcant and the non-
conifer sawnwood price (eucalyptus and poplar species) was negative, highly inelastic,
and signiﬁcant. Insufﬁcient data may be a likely cause of such results (Table 3.6).
Although some caution should be practiced in using the coefﬁcient estimates from the
supply and the demand equations, they are a starting-point for ﬁnding the price

elasticities of conifer sawnwood in Argentina.

108

Table 3. 6. Estimated structural equations for conifer sawnwood in Argentina (1982-95)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Eﬂgfgsom REGRESSION COEFFICIENTS
2SLS 3SLS
Logarithmic I Linear Logarithmic 1 Linear
- Demand for Sawnwood (ml
—139.35 -l347222 -138.06 -1240328

Intercept (.393) " £2.93) * (-4.68) ** (-3.41) **
Conifer sawnwood -1.28 -l955.14 -1.34 -2200.98
price - proxy (-1.88) (-1.60) (-2.39) * (-2.24) *
(1995 AS/m’)
Construction 1.53 2330.82 1.40 1925.23
index (2.61) "‘ L237) "' (2.96) *"‘ (2.81) "‘
GDP (1995 A8) 5.68 4.76E-O6 5.67 4.81E-06

(4.10) *" (3.45) " (4.92) " (4.19) "
Adjusted 1?.2 0.56 0.51 0.55 0.50
LM statistic *** 0.18 0.06 0.18 0.06
- Supply of Sawnwood (m3)
Intercept 12.29 -77206 13.85 126931

(1.54) (-0.17) (2.14) "' (0.38)
Conifer sawnwood 1.77 2521.72 1.69 2255.37
price - proxy (2.36) * (2.11) * (2.74) * (2.34) *
(1995 AS/m’)
Conifer sawlog 2.75 19244.10 2.74 19056.10
price (1995 AS/m’) (2.76) * (1.88) (3.31) *"' (2.24) "'
Non-conifer -3.22 -2324.13 -3.42 -2793.48
sawnwood rice (-2.54) "' (-1.82) (-3.29) " (-2.89) *"'
(1995 AS/m )
Adjusted R2 0.34 0.16 0.36 0.18
LM statistic *** 0.33 0.10 0.33 0.10
F-statistic 35709 226 40201 285
Notes:

Numbers in parentheses are t-statistics

(*) signiﬁcant at the 5 % probability level
(**) signiﬁcant at the 1 % probability level

(***) probability of obs*R

109

of 5th order autoregressive using ZSLS estimator

 

- Conifer Sawlogs

For conifer sawlogs, the demand equation was regressed as function of its own
price, the cross price, the'output price (conifer sawnwood), and wages. The supply
equation includes the own-price, a trend variable, the wage, and one-year lagged supply.
The logarithmic form showed a better ﬁt than did the linear form, with adjusted R23 of
0.61 and 0.63, respectively, for the demand and supply equations (3SLS) (Table 3.7).

For sawlog demand, signs of the own-price and cross-price coefﬁcients are
consistent with demand theory, although they were highly elastic and signiﬁcant (Table
3.7). The output price (conifer sawnwood) was highly signiﬁcant with an unexpected
negative Sign. The data used in the estimation may have played a role in generating such
unexpected signs. The wage index may not account for the changes within the sector and
the output price was a proxy for the actual price. Both variables however, remained in the
equation to improve the overall ﬁt. The cross-price elasticity for non-conifer sawlogs is
positive, as hypothesized, and high in magnitude (elasticity of over 4.0 in the log form).
The LM statistic shows evidence of serial correlation for the linear demand function (at
3% level). Estimates of price elasticities ﬁ'om equations with LM statistic lower than 5%
were not used in the spatial equilibrium model of Chapter 4.

Results for sawlog supply show a better ﬁt for the logarithmic function, with
adjusted st of 0.65 and 0.63 using respectively 2SLS and 3SLS (Table 3.7). All
coefﬁcients showed the expected signs, although only the one-year lagged supply was
signiﬁcant, depending on the speciﬁcation. The own-price elasticity of supply for the log
function was positive although non-signiﬁcant (0.7 to 1.0). The LM test statistics do not

show evidence of serial correlation for the supply equation.

llO

Table 3. 7. Estimated structural equations for conifer sawlogs in Argentina (1982-95)

 

EXPLANATORY
VARIABLES

REGRESSION COEFFICIENTS

 

ZSLS

BSLS

 

Ltgarithmic l Linear

Lgarithmic [ Linear

 

- Demand for Sawlo

s (my)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Intercept 9.7 -324066 10.82 -285074
(1.43) (-0.37) (2.03) (-0.42)
Conifer sawlog -4.87 -76755.40 -4.39 -73745.70
paice (1995 AS/m’) (-2.77) * (-2.20) * (-3.23) ** (-2.77) *
Real minimum 2.95 12725 2.20 11334
me (index 1990) (1.29 (1.06) (1.24) (1.27)
Non-conifer 4.33 99530.50 4.06 97715.30
sawlo price (1995 (3.84) " (3.02) " (4.62) ** (3.83) *"
A$lm )
Conifer sawnwood -1.66 -4536.59 -1.28 -3 840.65
price @995 AS/m’) (-2.94) ** (-1.63) (-3.12) ** (-2.48) *
Adjusted R2 0.61 0.42 0.61 0.42
LM statistic *** 0.09 0.03 0.09 0.03
- Supplyof Sawlo s 1113)
Intercept 14.03 167960 12.710 141303
(1.95) 40.15) (2.27) * (0.20)
Conifer sawlog 0.69 1 1190.90 0.99 1 1635.00
price (1995 AS/ms) (0.58) (0.46) (1.08) (0.70)
Real minimum -2.83 -5800.23 -2.56 -5726.18
wage (index 1990) (-l .76) (-0.73) (-2.04) (-0.96)
Lagged supply of 0.76 0.84 0.66 0.83
conifer sawlogs (3.60) "”" (2.13) "' (4.22) "”" (3.37) "
(m’)
Time trend 0.02 37127 0.05 38292
(0.33) (0.88) (1.10) (1.70)
Adjusted RT 0.65 0.50 0.63 0.50
LM statistic *** 0.53 0.72 0.72 0.53
F-statistic 21986 127 25528 1 16
Notes:

Numbers in parentheses are t-statistics

(*) signiﬁcant at the 5 % probability level

(***) signiﬁcant at the l %

robability level

(***) probability of ObS*R§ of 5th order autoregressive using ZSLS estimator

lll

 

- Combined Estimation of Conifer Sawlog System - AUPB countries

As the results of the system estimation for conifer sawlogs in Argentina were
partially satisfactory, an attempt was made to improve its overall goodness-of-ﬁt by
aggregating data ﬁom Argentina, Uruguay, Paraguay, and Bolivia (AUPB countries). A
more parsimonious model was estimated by excluding wage rate (a non-signiﬁcant and
erratic variable) from the demand equation. The demand equation was regressed as
function of its own price, the output price and the cross price. The supply equation
included its own-price, a trend variable, wage and one-year lagged supply.

Overall, the regression analysis for the AUPB countries showed a better ﬁt for the
system in comparison with Argentina only, with an increase in the adjusted st for both
demand and supply (Table 3.8). The logarithmic form showed a better ﬁt than did the
linear form. Adjusted st were 0.80 and 0.67, respectively, for the demand and supply
equations (ZSLS). The LM test statistic shows slight evidence of serial correlation for the
linear form of the demand equation at 3% signiﬁcance level.

For sawlog demand, signs of the own-price and cross-price coefﬁcients are
consistent with demand theory. The magnitude of the coefﬁcients was relatively high
(over 2.6), and signiﬁcant in the log equation. The sawlog price was signiﬁcant and with
the expected sign, although smaller than those estimated for Argentina alone (over 4.0).
The output price (conifer sawnwood) was highly signiﬁcant in the log form and with an
unexpected negative sign. The cross-price elasticity for non-conifer sawlogs was positive,
as hypothesized, and high in magnitude (elasticity of over 2.5 in the log form) (Table

3.8).

112

Table 3. 8. Estimated structural equations for conifer sawlogs in the AUPB countries

(1982-95)

 

EXPLANATORY
VARIABLES

REGRESSION COEFFICIENTS

 

ZSLS

3SLS

 

 

Logarithmic I Linear

 

Logarithmic I Linear

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

- Demand for Sawlo s (m’)
Intercept 17.95 927297 17.40 877998
(12.11) " (2.15) * (14.50) *"' (2.52) "'
Conifer sawlog -2.66 -58783.70 -2.67 -59245.50
price (1995 AS/m’) (-6.32) ** (-4.12) ** (-7.64) ** (-4.99) **
Non-conifer 2.60 83170.80 2.56 83146.12
sawlog price (1995 (5.83) ** (3.80) *"‘ (6.91) ** (4.57) "
AS/m )
Conifer sawnwood -0.80 -2741.93 -0.64 -2090.88
price (1 995 2AS/mg) (-3.35) ** (-1.39) (-3.53) "“" (-1.69)
Adjusted R 0.80 0.60 0.79 0.59
LM statistic *** 0.06 0.03 0.06 0.03
- Supply of Sawlogs (m3)
Intercept 1.68 -7 35869 1.18 -863733
(0.40) (—0.72) (0.55) (-1.23)
Conifer sawlog 0.93 13194.31 1.23 32420.55
price (1995 AS/mj) (0.74) (0.33) 41.33) (1.25)
Real minimum 032 345 -0.40 -l 741
wage - AUPB (-0.67) (0.12) (-l.17) (-1.06)
@dex 1990)
Lagged supply of 0.72 0.591 0.66 0.81
conifer saglogs - (2.75) * (1.24) (3.48) "”" (2.67) *
AUPB (In
Time trend 0.08 76847 0.09 59064
2 (1.63) (1.65) (2.63)* (2.15) *
Adjusted R 0.67 0.61 0.65 0.52
LM statistic *** 0.09 0.09 0.09 0.09
F-statistic 59413 233 62635 212
Notes:

Numbers in parentheses are t-statistics
(*) signiﬁcant at the 5 % probability level
(**) signiﬁcant at the 1 % probability level

(***) probability of obs*R

of 5th order autoregressive using 2SLS estimator

The sawlog supply results did not show a signiﬁcant improvement from

combining data for the AUPB countries. The adjusted st remained around 0.65

113

 

regardless the estimation procedure. All coefﬁcients showed the expected signs, although
only the one-year lagged supply and the trend variable became signiﬁcant, depending on
the speciﬁcation. The own-price elasticity of the supply for the logarithmic function (0.9
to 1.2) was positive although non-signiﬁcant. The magnitude of the wage index reduced
considerably using an aggregate index from the countries under investigation (Table 3.8).

A summary of the short-run price elasticities of supply and demand of sawnwood
and sawlogs in Argentina is Shown in Table 3.11. A discussion and comparison with

ﬁndings from other studies are also presented.

3.6.4. Pooled Data Estimation

An alternative to overcome the problem of the low number of observations for the
three countries in this analysis, was to create panel data and run a cross-sectional and
time-series analysis. Although such an analysis is feasible from an econometric
standpoint, it is not economically sound for the region under investigation given the
heterogeneous nature of the countries, which are subject to speciﬁc market forces and
different national policies. It would not be justiﬁable to pool certain variables such as the
national GDP of each country or their wage rates and use them as explanatory variables
in the simultaneous equation estimation of demand and supply in a given country. A
cross-sectional data analysis, however, that was tried for both the sawlog and sawnwood
demand and supply failed to generate meaningﬁrl results, although it improved its
degrees of freedom (not shown). Although some results in this study were not ideal from
the econometric standpoint, they are the best estimates for each country, considering the

data constraints.

114

3.6.5. Own-Price Elasticities of Supply and Demand for All Regions

This section concentrates on the estimation of the price elasticities of supply and
demand for the products under investigation as that is the main focus of the spatial
equilibrium analysis of Chapter 4. Estimates of short-run price elasticities of supply and
demand for sawnwood and sawlogs in Brazil, Chile, and Argentina (and in the AUPB
countries) are summarized respectively in Tables 3.9, 3.10, and 3.11. Price elasticities of
demand and supply for the linear equations were computed at the sample means. Overall,
the estimates of the own-price elasticities were all with the expected signs, and mostly
within the range found in the literature for similar products in other countries.

For Brazil, sawnwood demand and supply were highly inelastic (close to zero)
and sawlog supply was inelastic (0.24 to 0.26), indicating that relatively large changes in
price cause relatively smaller changes in quantity demanded or supplied. Sawlog demand
was highly elastic (-1.9 to —2.5), indicating an opposite trend. Low elasticity estimates for
Brazil may have been inﬂuenced by the data, which may not accurately reﬂect the
average market transactions. For comparison reasons, arc-elasticities of demand and
supply of sawlog and sawnwood were estimated using various sources of data. Results,
however, were highly variable, with some unexpected signs and magnitudes (not shown).

For Chile, price elasticities were variable. Supply of sawnwood (0.37 to 0.77) and
sawlogs (0.27 to 0.34) were inelastic for either functional form. However, demand for
sawnwood and demand for sawlogs were inelastic and not signiﬁcant in the log form (-
0.34 to -0.47 for sawnwood, and -0.60 to -0.74 for sawlogs) and elastic in the linear form
(about -1 .1 for sawnwood, and -1.5 for sawlogs). The linear form, using either estimation

procedure, gave a greater number of signiﬁcant elasticity estimates than did the log form.

115

Table 3. 9. Price elasticities of supply of and demand for conifer sawnwood and sawlogs

 

 

 

 

 

 

in Brazil
Price Elasticities 2SLS 3SLS
(Conifers) Logarithmic Linear Logarithmic Linear
Sawnwood Demand - 0.04 (*) - 0.06 C”) - 0.04 (***) - 0.04 Q‘ﬂ
Sawnwood Supply 0.005 0.008 0.002 C“) 0.006
Sawlog Demand - 2.18 (***) - 2.471 C“) - 1.94 (***) - 2.30 C”)
Sawlog Supply 0.24 0.24 0.26 0.24

 

 

 

 

 

 

 

(*) signiﬁcant at the 10 % probability level
(**) signiﬁcant at the 5 % probability level
(***) signiﬁcant at the 1 % probability level

Table 3. 10. Price elasticities of supply of and demand for conifer sawnwood and sawlogs

 

 

 

 

 

 

 

 

 

in Chile
Price Elasticities 2SLS 3SLS
(Conifers) Logarithmic Linear Lagarithmic Linear
Sawnwood Demand - 0.29 - 1.20 (***) - 0.17 - 1.16 (***)
Sawnwood Supply 0.11 0.07 0.85 0.14
Sawlgg Demand - 0.60 - 1.38 C”) - 0.74 - 1.50 (***)
Sawlog Supply 0.27 0.30 C“) 0.34 (***) 0.27 (*3

 

 

 

 

(*) signiﬁcant at the 10 % probability level
(***) signiﬁcant at the 5 % probability level
(***) signiﬁcant at the 1 % probability level

Table 3. 11. Price elasticities of supply of and demand for conifer sawnwood and sawlogs
in Argentina and in the AUPB countries

 

 

 

 

 

 

 

 

 

 

 

Price Elasticities ZSLS 3SLS
(Conifers) Lagarithmic Linear Lgarithmic Linear
Sawnwood Demand - 1.28 C“) - 1.09 - 1.34 (***) - 1.22 (***)
Sawnwood Supply 1.77 C”) 2.08 (***) 1.69 (***) 1.86 C")
Sawlog Demand - Argentina - 4.87 (**) - 4.38 (***) - 4.39 (***) - 4.21 (***)

- AUPB ~2.66 (***) -2.48 (***) -2.67 (***) -2.50 (***)
Sawlog Supply - Argentina 0.69 0.64 0.99 0.66

- AUPB 0.93 0.56 1.23 1.37

 

 

 

 

(“') signiﬁcant at the 10 % probability level
srgrrr Icant att e opro a rrty eve

(**)' 'f h5°/ bb'l' 1 1

(***) signiﬁcant at the 1 % probability level

116

For Argentina, signs of most of the elasticities were in the expected range and
signiﬁcant, although the magnitude varied signiﬁcantly for demand and supply of both
products. Sawnwood demand and supply price elasticities were both elastic and were
higher for supply (1.7 to 2.1) than for the demand (-l.1 to -1.3). Sawlog demand was
highly elastic for Argentina (-4.2 to -4.8) and for the AUPB countries (-2.5 to —2.7)
indicating that relatively small changes in demand price cause relatively larger changes in
quantity demanded. For sawlog supply the elasticity was in a range of inelastic (0.6) to
unit elastic for Argentina, and between 0.6 to 1.3 to the AUPB countries depending on
the estimation procedure. It is noteworthy that the sawnwood price used in this analysis

was a proxy for conifer sawnwood price.

- Comparison of Price Elasticities - Conifer Sawnwood

The estimates of price elasticities of conifer sawnwood supply were positive for
all countries and products, and signiﬁcant depending on the speciﬁcation and estimation
method (Tables 3.9, 3.10 and 3.11). Comparatively, the elasticities varied from as low
and inelastic as 0.002 to 0.008 for Brazil (signiﬁcant in the logarithmic speciﬁcation, 3
SLS) and ﬁom 0.07 to 0.85 for Chile (non-signiﬁcant in both forms and procedures) to as
high and elastic as 1.70 to 2.10 for Argentina (signiﬁcant in all forms and estimation
methods). Although these values differ signiﬁcantly from each other, they are in the
range reported in other studies, except for Brazil (Appendix 9). Kant et al. (1996) found

low price elasticities of solidwood and wood- manufacturing production in Canada from

117

0.15 to 0.32, with an average of 0.21. Chen, Ames and Hammett (1988) in a study of
softwood lumber demand and supply in the US found a signiﬁcant price elasticity of 0.31
for supply. McKillop and Liu (1989), modeling disaggregated demand and supply of
softwood lumber (Douglas-ﬁr and Hemlock-ﬁr) in the Western US estimated a wider
range of price elasticities varying from 0.39 to 0.68 for Hemlock-ﬁr clears, 1.35 to 1.37
for Hemlock-ﬁr commons, and 2.40 to 2.13, for all Hemlock-ﬁr, using conventional and
quadratic programming estimation procedures respectively. Adams and Haynes (1980),
in their study of the North America softwood lumber, plywood, and stumpage markets
(TAMM) found a range of elasticities for the lumber supply, between 0.21 (Paciﬁc
Northwest-West) and 0.79 (South Central region). The price elasticity of lumber supply
for Canada stayed in the middle, with a positive and inelastic value of 0.47 (Appendix 9).
The estimates of the price elasticities of conifer sawnwood demand were negative
and signiﬁcant for all countries (Tables 3.9, 3.10 and 3.11). Comparatively, the price
elasticities varied from as low and inelastic as -0.04 to —0.06 for Brazil (signiﬁcant in all
forms and estimation methods) to as high, signiﬁcant and elastic as -1.16 to —1.20 for
Chile (linear form, respectively 3SLS and 2SLS) and from -1.09 to -1.34 for Argentina
(signiﬁcant in the linear form with ZSLS and 3SLS, and in the logarithmic form with
ZSLS). Estimates of price elasticities of demand for conifer sawnwood from other studies
are highly variable depending on the location, product, and species group. Zhang,
Buongiomo and Zhu (1997), in a study of consumption, production and trade in the Asia-
Paciﬁc region estimated low price elasticities of demand for conifers in the range of -
0.07 to -0.13, respectively for low and high GDP countries, with an average of —0.08 to

the world. Kant et al. (1996) estimated price elasticities of demand for Canadian (solid)

118

wood in the range of -0.3 to -1.1, with average of -0.47, higher than the ones estimated
for Brazil but close to the ones found for Chile and Argentina (upper level). McKillop
and Liu (1989), estimated price elasticities of demand from -0.88 to -0.95 for Douglas-
ﬁr clears, -2.83 to -2.91 for commons, and -2.13 to -2.27 for all products for the Western
US, using conventional and quadratic programming estimators respectively. Chen, Ames
and Hammett (1988) found a low and non signiﬁcant -0.03 price elasticity of demand for
softwood lumber in the US, and Merriﬁeld and Haynes (1983) estimated price elasticity
of lumber and plywood products demand as -0.36 for the Paciﬁc Northwest region of the
US. In a study of demand for forest products in OEDC countries, Buongiomo and Chang
(1986) found a signiﬁcant price elasticity of -0.24 for coniferous sawnwood. In a study
about the demand for softwood lumber in the residential construction market in the US,
Rockel and Buongiomo (1982) estimated signiﬁcant price elasticity of -0.91. From a
review of literature on the subject, these authors cited elasticities varying from 0 to —0.87.
Adams and Haynes (1980), in TAMM, found price elasticities of demand for softwood
lumber in six regions of the US in a close range between -0.3 to -0.4 (Appendix 9).

In Brazil, price elasticities for conifer sawnwood were close to zero, indicating
almost perfectly inelastic demand and supply and a situation where large changes in price
would cause relatively small changes in quantity (Table 3.9). This market is expected to
be somewhat inelastic, given the limitations on installed capacity and the resource base
expansion. The estimates, however, may be a result of the limited F AO data on
production of the commodity. Alternative estimates using more speciﬁc data, as they

become available could eventually conﬁrm such low price elasticities.

119

- Comparison of Price Elasticities - Conifer Sawlogs

The estimates of the price elasticities of conifer sawlog supply were positive for
all countries and products but signiﬁcant only for Chile (Tables 3.9, 3.10 and 3.11). The
price elasticities of supply were inelastic for all countries, except for the AUPB region.
Comparatively, elasticities were low and stable for Brazil (0.24 and 0.26) and Chile (0.27
to 0.34) and higher for Argentina (0.69 to almost 1.00) and for the AUPB countries (0.56
to 1.37) depending on the functional form and estimation procedure. Price elasticities of
sawlog supply were also stable for the linear speciﬁcation in Argentina (0.64 to 0.66),
depending on the estimation procedure. Although these values differ from each other,
they are in the range found in other studies. Price elasticities of supply of coniferous
sawlogs tend to be in the range between 0 and 1.0. Newman (1987), in a study of
stumpage roundwood for lumber or plywood in Southern US found signiﬁcant elasticity
of supply of 0.55. Haeri (1987), in a spatial equilibrium model of the wood products
industry in the US, estimated regional elasticities of private stumpage supply varying
from 0.11 and 0.14, respectively for Rocky Mountains and the Southwest, to 0.46 and
0.49, respectively for the Northwest and South. Some price elasticities of sawlog supply
in this study were in the range of Haeri’s elasticities. Merriﬁeld and Haynes (1983),
found price elasticity of stumpage supply of 0.80 in the Paciﬁc Northwest. Adams and
Haynes (1980), estimated price elasticities of stumpage supply for forest industries and
other private owners. For forest industries, they were as low as 0.06 (Rocky Mountains)
up to 0.99 (North Central region). However, they remained between 0.26 to 0.47 for most

of other regions. For other private owners, the elasticities also ranged from 0.06 (Paciﬁc

120

Northwest-West and Paciﬁc Southwest) to 0.99 (Northeast), although for most of the
other regions they remained between 0.12 and 0.39. In their study, price elasticities that
were unreasonably high were constrained to levels found in a neighboring region (as it
was the case for the South Central — forest industry, for which the elasticity from
Southeast was assumed to hold) (Appendix 10).

The estimates of the price elasticities of conifer sawlog demand were all negative
and signiﬁcant for all countries, depending on the functional form and estimation
procedure (Tables 3.9, 3.10 and 3.11). Comparatively, the estimates of elasticities were
usually elastic in all regions, except for the logarithmic speciﬁcation in Chile (although
not signiﬁcant). The sawlog demand elasticities were as high and elastic as - 2.0 and over
for Brazil (signiﬁcant in both functional forms and estimation procedures), over -4.0 for
Argentina (signiﬁcant in both forms and estimation procedures), and around —2.5 for the
AUPB countries. For Chile, price elasticities were signiﬁcant and from -1 .38 to -1.50, for
the linear form using the ZSLS and 3SLS respectively. These elasticities are higher than
the elasticity of 0.55 found by Newman (1987), although his study referred to lumber and
plywood in the Southern US (Appendix 10). Klemperer (1996) also suggested that short-
run price elasticities of stumpage demand tend to be inelastic with estimates from other
studies ranging from —0. 14 to —0.57 (Cubbage and Haynes, 1988; Hyde and Newman,
1991). The results from this study, however, indicate a different trend in the Mercosur
countries during the period of analysis. This may be related to the characteristics of the
conifer lumber market in those countries, which faced a transition period in terms of
species substitution, product acceptance (domestically and for exports), and forest and

industrial investments.

121

3.6.6. Model Testing - Residuals

Considering the relatively low number of individual observations in each
system’s equations, an investigation of their residuals was carried out. The analysis
revealed that most of the estimates during the period of investigation are within less than
one standard error (SE) of the regression using the linear equations (either estimation
procedure), with a few exceptions depending on the region and product. Overall, this
indicates that the model is able to estimate the quantity demanded and supplied within an
acceptable range of the actual data.

Results of the residuals with respect to the standard error of the regression for
conifer sawnwood and for conifer sawlogs are shown respectively in Figures 3.12 and
3.13 for the linear demand and supply, considering the ZSLS estimation procedure.
Residuals expressed in terms of the standard error of the regression using either the 2SLS
or the 3SLS procedures gave similar results (comparison not shown). An investigation of
Figures 3.12 and 3.13 indicates that a long-term time effect is not inﬂuencing the data, or
has been accounted for by a time trend explanatory variable. This implies that the
assumptions that the errors are independent, have zero mean, a constant variance, 0'2, and
follow a normal distribution do not appear to be violated (Draper and Smith, 1966).

The validation of the models for years outside of the range used to build the
model proved difﬁcult for the time being. Data from the same series and sources for all
the explanatory variables used to estimate each model, for years either before or after the
period of the analysis are not yet available. Although data for some variables are
available, others are not. The models, therefore will be tested for validity as data from the

appropriate series become available.

122

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3.7 Conclusions

This study represented the ﬁrst attempt to obtain coefﬁcient estimates of price
elasticities of supply of and demand for conifer sawlogs and sawnwood for some
Mercosur countries through regression of systems of simultaneous equations. This
analysis investigated the main sawlog and sawnwood demand and supply shifting
variables in Brazil, Chile and Argentina. It indicated some signiﬁcant results, including
the range of the price elasticities and the coefﬁcients and elasticities of other major
variables affecting demand and supply. Some coefﬁcient estimates may be restrictive for
forecasting and for policy analysis, given data limitation for products and countries,
although they provide the range of elasticities for the products under investigation.
Important inferences based on the aggregate estimation of the systems are summarized as
follow:

- For the derived demand for conifer sawnwood, the major explanatory variables are its
own price, the wage index, the cost of construction, the price of substitute inputs, and
the lagged demand. Not all of these were signiﬁcant for each country or group of
countries,

- The total GDP is a macroeconomic variable that drives the consumption of sawnwood
and became a signiﬁcant indirect variable for speciﬁc countries,

- As for the input product — sawlogs - some of the major factors affecting its derived
demand by sawmills included its own-price, the index for wage, the lagged
consumption, the output price (sawnwood), and, indirectly, the GDP,

- For the supply of conifer sawnwood, its own price, the input price (sawlogs), the

wage index, the price of a substitute product for the industry (e.g. non-conifer

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sawnwood), the lagged supply, and sometimes the lagged export price of the product,
and a time trend were the major variables explaining its variability,

- For the supply of conifer sawlogs, besides its own-price, other variables explaining
supply were the export price, time, and the lagged supply.

The above inferences, signs and magnitude of different macro-variables can be
useful in designing short-term strategies for production and consumption of the forest
products under investigation. Such strategies make investment decisions more objective,
both in the forest product industries and in the forest management of conifer species, such
as pines in the Mercosur countries.

As more observations become available and are collected in each country, further
econometric studies may be carried out and the results compared with the ones ﬁom this
study. It may give an idea of the changes in the parameters and coefﬁcients of the main
variables over time. Selected elasticities, combined with others from the literature, are

used in the spatial equilibrium model of Chapter 4.

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CHAPTER 4

A SPATIAL EQUILIBRIUM ANALYSIS OF TIMBER MARKETS IN
SOUTHERN BRAZIL AND IN THE OTHER MERCOSUR COUNTRIES

Introduction and Rationale for the Study

The Southern Brazilian states of Parana, Santa Catarina, and Rio Grande do Sul,
although maintaining speciﬁc individual characteristics, form a distinctive geographic
region with some common climatic and topographic characteristics that have deﬁned the
proﬁle of the regional forest sector. One of its main distinctive characteristics, as
discussed in Chapter 2, is the large scale forest plantation with pines and the consequent
regional industrial proﬁle, led by the emergence of the conifer lumber industry (and their
related markets) as well as the pulp and paper and other minor forest products industry.

Over the past decades, conifer lumber has been produced and traded into both the
domestic (regional and national) and international markets. The southern region trades
domestically with other Brazilian states, notably with the highly populated southeastern
region. It also shares border with other Mercosur countries (Argentina, Paraguay, and
Uruguay), although it is geographically separated from the associate members (Chile and
Bolivia). It has had continuous trade with North America, EU, Asia, and other markets
through some of the major Brazilian ports, some located in the region.

The combination of the regional production of distinctive forest products, with the
limited supply of industrial roundwood, the consequent shortage estimates of sawlogs in
the coming years, and the existence of spatially separate markets (within and outside the

region) raise the question about the sustainable production, consumption, and trade of

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conifer products in the region, and in a large extent in Brazil as a whole.

Only few quantitative studies about the supply of and demand for forest products
in Brazil and in the other Mercosur countries. None of them, however, contemplated the
conifer lumber market where quantities and prices are endogenously determined in the
solution process in accordance with the economic theory.

The problem under investigation in this study ﬁts with in the concept of a spatial
equilibrium problem, where two or more regions with known supply and demand
ﬁrnctions produce and consume homogeneous product(s) with the regions separated but
not isolated, by known transfer (e. g. transportation, tariffs, taxes) costs. This chapter
focuses on the spatial equilibrium analysis of the demand and supply of solidwood
products (notably conifer sawlogs and sawnwood) in Brazil and their commercial
partners within and outside Mercosur. The main research question in this study is about
the ﬁnal competitive equilibrium of prices in all markets under investigation (sawlogs
and lumber), the quantities supplied and demanded at each region, and the pattern of

exports and imports between each pair of regions.

4.2 Objectives

The primary objective of any spatial equilibrium problem is to determine the
optimum level of production, consumption, and price in each region and the interregional
equilibrium trade ﬂows of commodities between regions. Thus, the basic feature of a
spatial equilibrium problem involves the estimation of a set of prices that equate supply

and demand among regions.

128

The sequential objectives of this chapter are:

(a) Developing a spatial equilibrium model of supply and demand for conifer sawlogs
and lumber for Mercosur;

(b) Solving the model in order to estimate the competitive equilibrium of prices and
quantities for all markets and regions in the base-period (static phase) and in multi-
periods (dynamic phase);

(0) Performing sensitivity analyses by changing the base scenario, primarily in terms of
changes in the major demand and supply shifting variables.

(d) Verifying major ﬁndings of the study by comparison with observed data for the base-

scenario.

4.3 Literature Review

4.3.1 Forest Sector Models - Overview and Application to the Forest Sector

Long-run projections of forest products market demand and availability of the
forest resources to achieve such demand are basic tools for any development planning, in
both public and private sectors. Succinct but comprehensive reviews of forestry sector
models are provided by Adams and Haynes (1980), Harou (1992), and Buongiomo
(1996). Previous forest sector studies can be categorized as ‘gap’, non-spatial market,
quasi-spatial market, and spatial market models, based on whether or not they provide
estimates of equilibrium prices and quantities, and the extent to which they recognize the
spatial attributes of forest products markets (Adams and Haynes, 1980). Andersson et al.

(1986) suggested that forest sector models can be disaggregated in a number of ways,

129

including disaggregation of inputs and outputs, treatment of time, treatment of regions,
treatment of nonconvexities and nonlinearities of relations (such as economies of scale),
and behavioral criteria such as optimization of single or multiple criteria.

In a synthesis of forest sector modeling, Buongiomo (1996) provided a detailed
overview of the evolution of different methods used in quantitative analysis and forecast
of forest sector markets. He showed that since the 1950s, forest sector modeling has gone
from strict applications of econometrics, mathematical programming, or system dynamics
to a combination of these methods in large models, with the idea of beneﬁting from the
best features of each approach. In his historical overview, the author pointed out that
during the 19805 forest economists pursued more complete models of sectors, particularly
in the US, with the desire to use the models for policy analysis, the Timber Assessment
Market Model (TAMM) (Adams and Haynes, 1980) for solidwood, PAPYRUS (Gilless
and Buongiomo, 1985) for pulp and paper, as well as others, with the culmination of the
Global Trade Model (GTM), developed by an international team of forest economists

(Kallio et al., 1987) at the International Institute for Applied Systems Analysis (IIASA).

4.3.2 Simple Sector Models

Basic forest sector models have consisted of ‘gap models’, which attempt to
determine the difference between the potential demand for and supply of a given forest
product over time at a speciﬁed price level or price trend (Adams and Haynes, 1980; and
Harou, 1992). Although ﬂexible in accommodating different levels of data available, gap
models do not show where and by how much production of raw materials should change

to fulﬁll ﬁrture requirements, nor how much should be produced domestically or

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imported (Buongiomo et al., 1994). Haynes (1993) pointed out that gap models do not
account for the fact that in a market economy a gap between demand and supply will
result in a price increase, which increases supply and decreases demand, automatically
ﬁlling the gap (also in Zhang and Buongiomo, 1994).

Despite the shortcomings with this approach, it has been a popular analytical
framework in analyzing forest sectors in developing countries (Haynes, 1993). As
forestry statistics and technical expertise improve, ‘market equilibrium models’ can

replace gap analysis.

4.3.3 Market Equilibrium Models

Market equilibrium models are characterized by a supply and demand adjustment
process, which endogenously determines both prices and quantities. It can be done for
one region (non-spatial market models), from one region to another (quasi-spatial market
models) or for various regions or markets that compete among themselves (spatial
equilibritun models) (Adams and Haynes, 1980; Harou, 1992, Bergh et al., 1996). In
another study, Adams and Haynes (1987), revising the major approaches for modeling
spatial markets, cited Thompson’s9 (1981) taxonomy of models, identifying ﬁve broad
classes: (1) two-region, non-spatial models; (2) multiregion, non-spatial price equilibrium
models; (3) spatial equilibrium models; (4) trade ﬂow and market share models; and (5)
transportation models.

Spatial equilibrium analysis has been formulated by many researchers and has a

9 Thompson, R.L. 1981. A Survey of Recent US Developments in International Agricultural Trade Models,
Bibliographies and Literature of Agriculture # 21 (ERS, USDA, Washington, DC).

131

variety of economic applications. Spatial equilibrium models differ from all other
models, in that demand and supply quantities, prices, and bilateral trade ﬂows are

determined endogenously in the model solution process.

4.3.3.1 Spatial Equilibrium Models

In 1951, Enkel0 formulated the problem of competitive equilibrium among
Spatially separated markets, suggesting a solution in the case of linear market functions.
Proceeding from the Enke formulation, Samuelson (1952), presented the theoretical
foundations for the computable spatial equilibrium modeling, showing how this purely
descriptive problem in non-normative economics can be cast mathematically into a
maximum problem, and related the Enke speciﬁcation to a standard problem in linear
programming (the so-called Koopmans-Hitchcook minimum-transport-cost problem).
Samuelson suggested that, after the problem in descriptive price behavior is converted
into a maximum problem, it can be solved by trial and error or by a systematic procedure
of varying shipments in the direction of increasing social payoff. He established the
desired formal equivalence between the equilibrium of interregional trade and a
maximum problem, restricted to a single commodity (in numerous markets). His
argument was that given equilibrium quantities, the equilibrium pattern of trade will
minimize total transportation costs (Meister et al., 1978). His formulation was developed
in the context of a spatial equilibrium model in which market supply and demand are
ﬁxed and given exogenously (Meister et al., 1978; and Willet, 1983).

Samuelson (1952) formulated his study in terms of maximum net social pay-off

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(NSP), deﬁned as the sum of the social pay-offs (difference between the areas under the
excess supply curve) in each region minus the transport cost. Samuelson illustrated his
concept by using two exports and two import regions, and respective local prices and
transport costs between and within regions (Appendix 11). Using the primal approach, he
showed that market equilibrium can be achieved through either the minimization of
transportation costs or the maximization of the net social payoff function.

Takayama and Judge (1964, 1971) extended Samuelson’s concept to a multi-
commodity equilibrium among spatially separated markets using a quadratic
programming formulation, assuming appropriate linear dependencies between regional
supply, demand and price. A computational algorithm was speciﬁed to obtain directly
and efﬁciently the competitive optimum solution for regional prices and quantities and
regional ﬂows. They represented manufacturing activities in a spatial equilibrium model
by activity analysis, i.e., in terms of the inputs per unit of output, the unit cost of
manufacturing net of these inputs, and regional manufacturing capacities (Gilles and
Buongiomo, 1985). Although Takayama and Judge’s formulation has been extensively
used in the determination of spatial price equilibrium, particularly in the agriculture
economics literature, some authors (cited by Brooks and Kincaid, 1987) have questioned
their imposition of symmetry conditions, particularly to demand equations, constituting a
restriction to the problem formulation. A simpliﬁed exposition of the quadratic single and
multi-commodity models of spatial equilibrium is found in Martin (1981).

Following the initial studies on spatial equilibrium, several theoretical extensions
and empirical applications have been implemented to different sets of commodities and

‘0 Enke, S. 1951 Equilibrium among spatially separated markets: Solution by electric analogue.
Econometrica 19: 50-57.

133

regions. In the solution of spatial equilibrium models, some direct optimization or
iterative scheme are used to ﬁnd a set of demands, supplies, prices, and trade ﬂows that
satisﬁes the conditions for market equilibrium for the particular market structure

(competitive or not) (Adam and Haynes, 1987).

4.3.3.2 Spatial Equilibrium Models in Forestry

Many studies incorporating spatial equilibrium models applied to forestry and
trade of forest products have been developed in the past decades. A review of literature of
spatial equilibrium in forest sector and trade models (Buongiomo et al., 1981; Shim,
1985; Adams, 1985; Greber and Wisdon, 1985; Kallio et al., 1986; Andersson et al.,
1986; Buongiomo, 1986; Fowler and Nautiyal, 1986; Cardellichio et al., 1987; Kallio et
al., 1987; Sarkar, 1991; Zhang, Buongiomo, and Ince, 1993; Buongiomo, 1996;
Buongiomo et al., 1996; Valverde et a1, 1999 and others) has revealed different aspects of
model construction regarding the objective of the study, the optimization tool, the
products under study, and the period of time involved.

In terms of the objective function, studies have either maximized the net social
welfare (the sum of both producer and consumer surpluses), as well as market requital,
proﬁts, and increase of GNP, or minimized total costs (including transportation and
processing costs). Several optimization tools have been used, including econometrics,
linear or quadratic programming, mixed integer programming, goal programming,
chance-constrained, and reactive programming, depending on the nature and objectives of
the problem. Limitations or speciﬁc problems with one or another programming tool

have also been a justiﬁcation for the use of alternative tools.

134

In terms of the products involved, studies have been classiﬁed into single or
multi-products, the latter involving one or more stages of production, which requires
more detailed data and a different theoretical framework. Regarding the time period
involved, models can be subdivided in static or dynamic. In such models prices and
quantities have been either endogenously or exogenously determined, although
endogenous price-quantity models are more theoretically consistent and require more
detailed data and information in comparison with price-exogenous models.

Models of spatial equilibrium applied to forest have been developed since the
early 1980’s. Adams and Haynes (1980) developed a spatial econometric model of the
North America softwood lumber, plywood, pulp, and stumpage markets, which provided
long-range projections of price, consumption, and production trends. In their study, six
geographic demand regions and nine supply regions were considered. The model, called
the Timber Assessment Market Model (TAMM), is sub-divided into ‘ﬁnal product’s and
‘stumpage’, and is based on a combination of systems of equations for each industry,
with prices of the products as the driving variables. The model was used for policy
simulation evaluating the impact of levels of harvesting, tariffs, and lumber costs.

Gilles and Buongiomo (1985) developed a spatial equilibrium model of the North
American pulp and paper industry (PAPYRUS), which was designed to provide long-
range projections of production, consumption, imports, exports, equilibrium prices, and
ﬁber inputs. Their model is described to consist of price endogenous linear programming
that describes the industry in each year, and a set of recursive relationships that update
this linear programming from year to year to reﬂect endogenous and exogenous changes.

The optimal solution is a competitive equilibrium for each year. The model consists of 14

135

commodities, 11 supply and 9 demand regions, with the rest of the world consisting of 3
net demand regions. As an extension of that model, Zhang, Buongiomo, and Ince (1993)
developed a price endogenous linear programming system (PELPS HI), combining both
regional demand and supply equations (with their respective price elasticities),
transportation costs, as well as speciﬁc information of the forest industry, existing tariffs,
and exchange rates. This system models multiregional equilibrium, for one or multi-
products, determining quantities and prices that satisfy the market conditions under static
or dynamic phases. The PELPS model has been applied to different market conditions
and its ﬂexibility seems to accommodate a wide range of different market proﬁles and
interactions.

In a study of the market of southern pine lumber in the US, Shim (1985) evaluated
the current and future demand-supply and estimated the optimum allocation and pricing
pattern. He integrated a system of regional demand and supply ﬁmctions using a
quadratic spatial equilibrium formulation as developed by Takayama and Judge (1964a)
and Liew and Shimll (1978). In estimating the demand and supply ﬁrnctions, a panel data
was considered and regional dummy variables were used in to derive the functions for
each area. The author validated his model indicating, in the optimal spatial solution, what
prices, production, and consumption, and ﬂows of trade should have been in 1981 and
should be in 1984 subject to speciﬁc constraints. His study represents a theoretical
improvement over traditional methods of planning since it incorporated demand
equations into a spatial model, allowing price and quantity demanded in each region to
ﬂuctuate in response to changes in either available timber supplies or the level of total

demand. A comparison of that actual behavior with the optimal behavior for 1981

136

indicated the regions where the optimum shipment was achieved.

Spatial equilibrium models for other countries have also been studied in recent
years. Buongiomo et al. (1994) developed a model to ﬁnd the optimal economic location
and timing of new forestry projects in Nigeria, representing wood production, processing
and transportation, and meeting the needs of each state at least costs. Given severe data
limitation on roundwood prices, the demand for forest products was framed in terms of
ﬁrture uses consistent with estimates of ﬁrture income and population growth. The supply
of forest products was based on the resources available by state and set in terms of
possible production under sustainable yield. A ‘gap analysis’ indicated a deﬁcit of all
future wood raw-materials at national level, and a linear programming model (as stated
by Samuelson, 1952) was developed to balance the demand and supply of each product in
each state, minimizing the total cost of production, transportation, and capacity
expansion. The results indicated the economic location and magnitude of changes in
production in forestry and forest industries throughout Nigeria.

In a previous study, Buongiomo et al. (1981) developed a model to evaluate long-
term development strategies under economic efﬁciency for the Indonesia’s forestry
sector. Given speciﬁc targets for domestic demand and exports of timber products, the
model (a static mixed integer programming with goal programming variables) estimates
the economic and geographic patterns of timber production, industrial processing and
transportation that minimized total costs. The constraints represented resource
limitations, industrial capacity utilization, balance of raw materials and ﬁnal products,
processing, residues formation, product transshipment, port-handling capacity

constraints, location of export facilities, and domestic and foreign demand achievements.

" Liew, C.K. and Shim, 1.x. 1978. A Spatial equilibrium model: another view. J Urban Econ. 52 526-534.

137

Results showed domestic and foreign supply, resources utilization, industrial expansion,
ports activity and location of export outlets, and inter-islands shipping patterns. The
model is viewed as a description of methodology rather than a recommendation for
policy, and it could be improved by further product disaggregation and inter-temporal
linkages. It is ﬂexible on analyzing the effects of various aspects of a given strategy.
Adams (1985) developed a spatial equilibrium model of African-European trade
in tropical logs and sawnwood. His model was designed to simulate sawnwood
production and consumption in tropical Africa and Europe, log production in Africa, and
the prices and trade ﬂows resulting from market exchange. Assuming competitive
markets, a system of supply and demand equations representing different products
(sawnwood and logs) and regions (East, Central, and West Africa; and Europe) was
initially estimated. The spatial equilibrium solution was given by the reactive
programming algorithm of Tramel and Seale12 (1959), and the Gauss-Seidel method for
solution of simultaneous equation systems. His study involved two interrelated market
levels (logs and sawnwood), with the relation between both markets being viewed as two
groups of simultaneous equations linked by vectors of prices. The combined approach
demonstrated the ﬂexibility of reactive programming in dealing with multilevel spatial
market problems. The Gauss—Seidel reactive programming, as pointed out by the author,
was the ﬁrst such application of the technique, adding to the pool of solution tools (e. g.,
linear and non-linear programming) with some possible advantages relative to other
techniques. Results for bilateral trade ﬂows appeared to be consistent with the limited
available ﬂow data for 1980. The resulting solution minimized the transportation cost,

‘2 Tramel, TE. and Seale, A.D. Jr. 1959. Reactive Programming of Supply and Demand Relationships -
Applications for fresh vegetables. J. Farm Econ, 41: 1012-1022.

138

given the optimal set of regional production and demand levels.

Sarkar (1991) developed a long-run timber model for Bangladesh, using a system
of semi-logarithmic supply-demand equations, with equilibrium solutions obtained for
stumpage price and quantity of timber. The model also provided the optimal land, labor
and capital allocation for timber production. The theoretical basis was the multi-period
production theory using the premise that the optimal level of output maximizes the
present net social beneﬁts, or the end-value of net beneﬁts in any given target year.

In a review of available spatial price equilibrium studies, Webb et al. (1994)
indicated that those models are either single commodity multi-region models or multi-
commodities and multi-region models for which all regions are forced to have identical
demand and supply functions (e. g. assuming no cross price effects). He developed a
multi-crop model for China encompassing commodities with multiple cross price effects
in the supply and demand functions.

A few studies have been focused on spatial equilibrium markets in Mercosur
countries. Ochoa (1996) studied the effects of Mercosur on the plywood industry in the
states of Parana/Brazil and Misiones/Argentina, focusing on the effect of the program of
tariff reduction over the net social payoff as proposed by Samuelson (1952), of both
regions. Ochoa used the static phase of PELPS III as the mathematical programming tool,
and made use of price elasticities of demand and supply previously estimated by
Sperandio13 (1992). He found a signiﬁcant effect of the creation of Mercosur on the
regional net payoff, quantities demanded and supplied, and prices in both countries.

Valverde et al. (1999) analyzed the impacts of international agreements on the

'3 Sperandio. J. Demand and Supply of Plywood in the State of Parana: An Econometric Analysis. Federal
University of Parana, Curitiba, Brazil. Ph.D dissertation 147 p. 1989.

139

Brazilian forest economy as a whole. The market liberalization agreements were the
Uruguay Round, Mercosur, NAFTA, the inclusion of Chile in Mercosur, F TAA and the
Mercosur-EU agreement. The authors used the Global Trade Analyses Project (GTAP), a
general equilibrium model (citing Hertel and Tsigas”, 1997). In their study forest
products were aggregated as one single commodity. Overall results indicated that the
agreements affect more the international trade rather than the domestic production.
Speciﬁcally for the inclusion of Chile in Mercosur, results indicated that the impact is
non signiﬁcant for the production, however it can affect the international trade, increasing
imports and reducing exports, contributing to the trade deﬁcit. Their study, however, did
not address the conifer lumber market speciﬁcally.

Analyzing international trade of agriculture products among Mercosur countries,
Waquil and Cox (1995) formulated a spatial equilibrium model using quadratic
programming allowing the existence of stages of production with intermediate products.
The model took into account the economic allocation of sets of primary and ﬁnal
commodities among spatially separated markets, given that all primary and ﬁnal
commodities could be traded among regions. The model considered a quantity
formulation, in which the decision variables are quantities in terms of production,
consumption and trade ﬂows. Starting from restricted cost functions, the authors
maximized the net social payoff, as cast by Samuelson (1952), by using the Takayama
and Judge (1964a) formulation. The model was implemented with four stages of
production, analyzing the optimal allocation and pricing of animal products, grain and
oilseeds in the Mercosur countries.

“ Hertel, rm. and ME. Tsigas. 1997. Structure of GTAP. In: Hertel, T.W. (ed) Global Trade Analysis:
Modeling and Applications. New York: Cambridge University Press. 403 p.

140

Many studies of long-range projection procedures have ignored the spatial
characteristics of forest products markets. This reduces the value of the projections to
decision makers and may preclude the possibility of identifying opportunities or needs for
speciﬁc policy actions (Adams and Haynes, 1980). The studies presented above provide,
to different extent, possible approaches and methods to be combined in the development
of this proposed study.

The theoretical basis for the approach of ﬁnding a competitive solution to the
spatial equilibrium problem may be found in the multiproduction theory (with
intermediate products) using, for instance, the premise that the optimal level of output in
a base year is that which maximizes the net social payoff (N SP), or the sum of the excess
demand and excess supply subtracting the transportation cost, as proposed by Samuelson

(1952) and later extended by Takayama and Judge (1964, 1971).

4.3.4 The Conceptual Model

The conceptual spatial equilibrium model (SEM) can be approached either with
prices as the dependent variables (in the quantity domain) or quantities as the dependent
variables (in the price domain). The model presented here considers the maximization of
Samuelson’s net social payoff as the objective function, with a quadratic programming
representation in a quantity domain (primal), in which the decision variables are
quantities (production, consumption, trade ﬂows). The Lagrangean multipliers are
interpreted as shadow prices. When the model is presented in a price domain (dual), in

which the decision variables are prices, the Lagrangean multipliers are interpreted as

141

shadow quantities.
Assuming the existence of known linear supply and demand functions for each
product (primary or intermediate) and for each region, the representation (here price

expressed as the dependent variable) can be shown as follow:

P3, = am. + but, D” + + u. demand function for intermediate products (1)

P: = d I“. + eh, . S“ + + Vt supply function for primary products (2)
where:
i - 1, ..., I regions,
k - l, ..., K primary products,
11 = 1, ..., N intermediate products,

Dnj - quantity demanded at different prices for intermediate product ‘n’ in region ‘i’,

Ski - quantity supplied at different prices for primary product ‘k’ in region ‘i’,

PDnj - demand price at different output levels for intermediate product ‘n’ in region ‘1’,
Pski - supply price at different output levels for primary product ‘k’ in region ‘i’,

am - intercept of the demand response function for intermediate product ‘n’ in region ‘i’,
bni - the slope of the demand response function of Dni to PDjn in region ‘i’,

dki - intercept of the supply response function for primary product ‘k’ in region ‘i’,

ekj - the slope of the supply response function of Ski to Pskj in region ‘i’,

Demand and supply shifter variables may also be included in the equations above.

The conceptual ﬁ'amework is developed with production occurring in a two-stage

process, using a multiproduct formulation, adapted from Waquil and Cox (1995) and

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Zhang, Buongiomo, and Ince (1993). Considering the allocation of a set of primary
products (k), and a set of secondary (intermediate) or ﬁnal products (11), among spatially
separated regions (1), the primary products can be produced and processed into secondary
(or ﬁnal) products in each region, the secondary/ﬁnal products can be consumed in each
region, and all primary and secondary products can be traded among regions. The
production of secondary products involves two kinds of inputs: the primary products and
other inputs. With more than one stage of production, it is necessary to know and subtract
from the objective function the cost of transformation (e. g.: manufacturing) in each stage.
The cost of transformation can be considered ﬁxed per unit of production or variable and
represented as function of explanatory variables such as prices and quantities of the
inputs. Following the Zhang, Buongiomo, and Ince formulation (1993), a ﬁxed cost of
transformation is considered in this study.

Samuelson’s maximization of the net social payoff (N SP) objective function for
one commodity and two-regions can be extended to multi-commodities and multiregions.
This can be done by aggregating the NSP functions across commodities and regions and
subtracting the costs of transportation of products from one region to another, and (in
case of multi-stages of production) the cost of transformation (Waquil and Cox, 1995).

The maximization of the aggregate NSP objective function is subject to a
constraint set which consists of two conditions that characterize a competitive spatial
market equilibrium solution. The ﬁrst condition states that demand in any region is less
than or equal to trade ﬂows to that region (either as a primary or intermediate product),
while the second condition states that trade ﬂows from a region must be less than or equal

to production in that region. The ﬁrst constraint set eliminates the possibility of excess

143

demand in the optimal solution while the second set allows for the possibility of excess
supply. Non-negativity constraints are another set of constraints.

The model described in this section is adapted and adjusted to the purpose of the
study considering its objectives, the characterization of the regions, and the proposed
maximization function. Based on the previous premises and assumptions, the
mathematical formulation for the objective function can be written as:

Maximize:

St ,1‘

Dim PF”
NSP = Z {Z IP,§(D;’,)dD —Z IPkf(S,f,)dS -2 ICT..}
i n o k a n o

_ g 2.- Z]: TPkinPky. —; Z Z] TFnrjXFm-j (3)

where:

‘k’ holds for the primary product (conifer sawlog)

‘n’ holds for intermediate product (conifer sawnwood)

‘i’ and ‘j’ hold for regions (e.g. Brazil, Chile, and Argentina)

Pski = price-dependent supply function for the primary product ‘k’ in region ‘i’,

PDni = price-dependent demand function for the intermediate product ‘n’ in region ‘1’,

Ski = quantity supplied of primary product ‘k’ in region ‘i’, for k= 1,...,k and i= 1,...,j
Dni = quantity demanded of intermediate product ‘n’ in region ‘i’, for n= 1,...,n and i=
1,...,j

PFni = quantity produced of intermediate product ‘n’ in region ‘i’, for n= 1,...,n and i=

1,...,j

CTni = cost of transformation for the intermediate product ‘n’, in region ‘1’,

144

XPkij = exports of primary product ‘k’ from region ‘i’ to region ‘j’, for k= 1,...,k,
i= 1,...,j, andj= 1,...,j
XFnjj = exports of intermediate product ‘n’ from region ‘i’ to region ‘j’, for
k= 1,...,k, i= 1,...,j, andj= 1,...,j
Tijj = unit cost of transportation of the primary product ‘k’ from region ‘i’ to region ‘j’,
TFnij = unit cost of transportation of the secondary/ﬁnal product ‘k’ from region ‘1’ to
region ‘j’,
Dki and Sni are represented as functions of their own-prices and cross-prices, possibly
including lagged quantities. CTni is a ﬁxed cost, although it can be a variable ﬁrnction.

Substituting linear demand and supply functions (from equations (1) and (2)
simpliﬁed here as function only of their respective prices) in (3):

PKJ

Pu-
NSP = Z {2 [(am- +bniDr‘11i)dD:i - Z I(dki + eki [fins/ii
i n o k 0

”ZCTni}-;ZZTR@XB<0 —ZZZTFnUXFkij (4)
n I J n i j

which becomes a quadratic expression in D (demand) and S (supply) - area under all
demand curves minus the area under all supply curves, subtracting the transportation

costs of all trade ﬂows:
5 l 2 l 32
NPS = Zi{[zkaikDri1i -2kdimkSki +§ZkbimkD:,i _§Zkeimk ki ]

-2CT,,}-;ZZTRW.X%—ZZZTFW.XFW (5)
n I j '1 1 J

Subject to the following constraints (equations 6):

145

1. Ski 2 Xj Xijj — where production of a primary commodity (k) in region (1) should be
greater or equal to the exports of primary commodity (k) from region (i) to all other
regions (j);
2. Zj Xiji .2 2n aknj anj —— where total exports of primary commodity (k) from region
(i) to all other regions (i) has to be greater than or equal to the production of intermediate
commodity (n) in region (i);
3. PFni 2 Zj XFnij — where production of intermediate products (n) in region (i) should
be greater than the exports of the intermediate commodity (n) from region (i) to all other
regions (j);
4. Zj XFnij 2 Dnj — where exports of the intermediate commodity (n) from all regions (j)
to region (1) should be greater or equal to the consumption of the commodity (n) in region
(i);
5. Ski 2 0; XPkij 2 0; PFni 2 0; XFnjj _>. 0; Dni 2 0 - all non-negativity constraints (6)

In this study, the transformation of primary into secondary/ ﬁnal products is given

by ﬁxed coefﬁcients, where ak,n,i is the amount of primary product ‘k’ used as input to

produce one unit of the secondary or ﬁnal commodity ‘n’ in region ‘i’. The Kuhn-Tucker
conditions for optimality are derived after substituting constraints (equation 6) in the
maximization problem (equation 3) (see Appendix 12 for details).

This is the general formulation for spatial price equilibrium for multiple products.
Takayama and Judge (1971) proved that this type of programming formulation is
. solvable and would yield a solution that satisﬁes the competition equilibrium condition.
The equilibrium prices and quantities for individual products in a speciﬁc region are then

used to calculate the net social beneﬁt (N SP).

146

Zhang, Buongiomo, and Ince (1993) proposed the solution of this problem with
linear programming efﬁcient computation, by using stepwise approximations to the area
under these non-linear curves using PELPS III. Other authors as well have suggested
different approximations in the attempt to linearize quadratic formulations like the NSP
objective ﬁrnction (Willet, 1983; and Apland, 1986). The ﬂexibility of PELPS III
(versions F PL-TPELPSI and the GFPM) and easy adaptation to different conditions,
make it a suitable tool for the solution of the problem.

The optimal solution to this problem is characterized by three equilibrium
conditions. First prices differ between any two regions by an amount that is less than or
equal to the transfer costs. For the second condition it is assumed that the quantity of a
good which is produced and consumed in the same region is viewed as a trade ﬂow to
that region itself, with the demand in a given region being equal to the sum of the trade
ﬂows to that region. The third condition implies that equilibrium prices and quantities
must lie on the supply and demand functions (Willet, 1983).

In the solution of spatial equilibrium models, direct optimization or iterative
scheme are used to ﬁnd a set of demand, supply, prices, and trade ﬂows that satisﬁes the
conditions for market equilibrium for the particular market structure (Adam and Haynes,
1987). The optimal solution may be obtained through the use of different quadratic
programming (QP) solvers. In Waquil and Cox’s model of agriculture commodities
(1995) the optimal solution for the QP problem was obtained by using LINDO or GAMS
(General Algebraic Modeling System) software. Other studies have considered different
algorithm solvers. Zhang, Buongiomo and Ince (1993) developed the Price Endogenous

Linear Programming System (PELPS III), a specially designed interface which interacts

147

with LINDO in the solution process. Buongiomo et al. (1994), in solving a GAP analysis
for timber production in Nigeria used the Excel solver in the solution process. Adam and
Haynes (1987) used reactive programming and the Gauss-Seidel method to ﬁnd the
spatial market equilibrium of timber trade for some Aﬁican countries. In this study, the
quadratic programming formulation of net social surplus (from Samuelson, 1952) is
found by using the so-called F PL-TPELPSI, a version of PELPS III adapted by the

FPL/U SF S (Lebow, 1999, personal communication).

4.3.5 Description of PELPS III and its latest versions

The price endogenous linear programming system, PELPS III (Zhang,
Buongiomo, and Ince, 1993) is a general microcomputer system for modeling economic
sectors and combines regional information on supply and demand curves, manufacturing
technologies, and transportation costs into spatial sector models. PELPS HI extends
concepts that have been applied in previous interregional models of the forestry sector,
including TAMM - Timber Assessment Market Model (Adams and Haynes, 1980) and
the Global Trade Model (Kallio et al., 1987; Zhang, Buongiomo, and Ince, 1993).

Although PELPS was developed to model the pulp and paper sector in North
America, it accommodates other economic sectors, becoming an useﬁrl and powerful tool
in forest products modeling. PELPS HI has a static and a dynamic phase. In the static
phase, a multiregion, multicommodity equilibrium is computed with the solution given
by quantities and prices that clear all markets at a point in time. In its dynamic phase, the
evolution of the spatial equilibrium is predicted over time, explaining how a sector

adjusts gradually to changes in exogenous variables.

148

PELPS’ static phase solves a generalized version of Samuelson’s (1952) classical
spatial equilibrium problem represented by production, transport, transformation, and
consumption of one or more commodities in two or more regions. For each region, price-
quantity relationship through domestic demand and supply curves are used to determine
the equilibrium prices in all the markets, the supply and demand in each place, as well as
the trade ﬂow (Zhang, Buongiomo, and Ince; 1993). Commodities are described as a
primary raw material, recovered waste or a consumed commodity (virgin or recycled).
Demand and supply regions are described by equations that give quantity as function of
prices. The system has also manufacturing regions, where the output production, and the
input consumption are modeled as processes described by activity analysis. Each process
has a limited capacity, with a commodity made with input mixes, deﬁned by
manufacturing coefﬁcients giving the amount of each input needed per unit of output,
with a corresponding manufacturing cost. The solution of the static phase is obtained by
price endogenous linear programming (Hazell and Norton15 , 1986; cited by Zhang,
Buongiomo, and Ince, 1993). The equilibrium quantities produced, transformed,
transported, and consumed are given by the maximization of the sum of the producer and
consmner surplus throughout the sector, minus the transportation and transformation
costs. As result, the static phase gives the price that clears all markets at a given point in
time, subject to the positions of each pair of supply and demand curves, the capacities of
production by region and process, the transformation and transportation costs, the taxes
and exchange rates, and the recycling constraints, if applicable.

In its dynamic phase, PELPS breaks down a multiperiod spatial equilibrium

'5 Hazell, P.B.R. and Norton, RD. 1986. Mathematical Programming for Economic Analysis in
Agriculture. New York: MacMillan, 400 p.

149

problem into a sequence of problems, as in the recursive programming approach
described by Day16 (1973) and cited by Zhang, Buongiomo, and Ince (1993). It is
characterized as a succession of static phases, one for each period of the forecast,
simulating partial long-run optimization behavior. The static calculation in each period
gives the short-term equilibrium, subject to the demand, supply, costs, and capacity in
that period. The parameters of the programming problem that condition the equilibrium
change from period to period as function of exogenous changes and of changes in
capacity are determined endogenously by the model (Zhang, Buongiomo, and Ince;
1993). The capacity of production in the next period is a function of the shadow price of
capacity in the previous period, past production, and the cost of increase in capacity.

PELPS HI maximizes the net social payoff, which is deﬁned by the area under all
demand curves minus the area under all supply curves, also subtracting the
transformation and transportation costs. Although these areas are by nature non-linear
functions, PELPS III linearizes them by using stepwise approximations to the area under
these curves so that the spatial equilibrium can be computed efﬁciently by linear
programming. More details on the theoretical approach used by can be found in Zhang,
Buongiomo, and Ince (1993). In recent years, some versions had incorporated additional
features. The USFS-Forest Products Laboratory’s version, FPL-TPELPSl includes a
resource worksheet (Lebow, 1999; personal communication) to deal with the resource
base, and the Global Forest Products Model - GFPM (Zhang, Buongiomo, and Zhu,
1997) includes trade inertia speciﬁcation (Buongiomo, 2000; and Turner, 2000, personal
communication).

'6 Day, RH. 1973. Recursive programming models. In: Judge. G.G. and T. Takayama, eds. Studies in
Economic Planning over Space and Time. American Elsevier. 329-344.

150

4.4 Empirical Method

In section 4.3.4, a conceptual framework for the multiregional, multi-industry
model was outlined and detailed. Based on the theoretical aspects and information about
the forest sector in Brazil and in the other Mercosur countries an empirical industry-
speciﬁc spatial equilibrium model is described. The model is based on the mathematical
programming formulation developed by Samuelson (1952) and extended by Takayama
and Judge (1964a), using the USFS/F PL version’s of PELPS III, the FPL-PELPSI
(Lebow, 1999, personal communication).

The model is both static and dynamic and involves partial equilibrium. The model
also assumes perfect competition and homogeneous products. Prices and quantities are
determined along supply and demand functions, which remain unchanged in the basic
model, since no structural changes in either supply or demand are considered from a
starting point to a new equilibrium. Production takes place in speciﬁc regions, which are
separated from each other and from consumers, with trade incurring a transportation cost.
There are no barriers to trade and local producers can compete freely. Whether locations
trade, however, depends on the underlying parameters of the model.

Since pines are vastly dominant as the primary species in conifer sawmills,
Araucaria and hardwoods were not part of the analysis. Including them would require
additional data, some nonexistant. With respect to products, the model focuses on
primary (sawlogs) and intermediate (lumber or sawnwood) products and by-products
(sawmill residues). Residues from sawmills can be consumed as ﬁrewood, woodchip for

papermaking, or simply burned and were considered in a broad category in the analysis.

151

4.4.1 Boundaries of the Study

The problem of establishing model boundaries is a frequent issue in econometrics
and systems modeling. Although there is no deﬁnitive rule, some of the characteristics
that shape the model and the trade-offs that must be weighted in its construction can be
identiﬁed. Adams (1987) pointed out that basic characteristics to be taken into account
are: (a) the question being asked (or the user’s information needs), (b) the characteristics
of the regional markets for the products of interest, and (c) the cost and resources needed
to build and use the model under alternative degrees of model complexity. Regarding the
information needs, one basic criterion is the scope of direct (target region) and indirect
(regions outside the target region) trade ﬂows, the projection period, and the simulation
needs. As for market characteristics, the array of products to be incorporated into the
model is deﬁned by the purpose of the analysis and the information needs of the users.

The boundaries in this analysis differ slightly from the boundaries in the multiple
regression analysis (Chapter 3), primarily for Brazil, which was split in two regions to
account for the proﬁle of the conifer sawmilling sector in the Southern region. In this
model, six regions were considered: Southern Brazil (S-BZ), Rest of Brazil (RBZ), Chile
(CHI), Argentina (ARG), Rest of Mercosur (ROM - formed by Uruguay, Paraguay and
Bolivia), Asia (ASIA-S formed by Japan, China, Hong Kong, and Republic of Korea),
and the Rest of the World (ROW). The ASIA-S and ROW regions are modeled in terms
of their import demand ﬁom Mercosur, simplifying the model while allowing Mercosur
to trade with outside members, a more realistic representation of the real world. The
sawlog and lumber demand and supply regions are listed in Table 4.1, which identiﬁes a

representative geographic demand and supply center for each region.

152

Table 4. 1. Conifer sawlogs and lumber supply and demand regions

 

 

 

 

 

 

 

 

 

 

 

 

# R ions Producer and Consumer Supply and/or
eg Centers Demand Regions

1 S-BZ Northern Parana/PR D
(Southern Brazil) Curitiba/PR S

2 RBZ ‘ sao Paulo/SP D
(Rest of Brazil) Western Sio Paulo/SP S

3 CHI Santiago D
(Chile) Concepcion S

4 ARC Buenos Aires D
(Argentina) Misiones S

5 ROM 2 North-Central Uruguay D
(Rest of Mercosur) Montevideo, Uruguay S

6 ASIA-S 3 Nagoya, Japan Imported D

7 ROW ‘ Savannah/GA, USA Imported D

 

Note: D for Demand, S for Supply

‘ RBZ - all the country, except Southern Brazil
2 ROM —— formed by Uruguay, Paraguay, and Bolivia
3 ASIA-S - Japan, China, Hong-Kong, and Republic of Korea

4 ROW — Rest of the World

4.4.2 Economic Actors and Major Elements

The transformation process from conifer sawlogs into lumber, common to most of

the countries under investigation is characterized by technological, geographical and

market-oriented specialization. The economic actors of the study are suppliers and

consumers of conifer sawlogs and lumber, assumed to operate under perfect competition

(Figure 4.1). Conifer sawlog producers in Southern Brazil, as well as in most of the other

Mercosur countries, are primarily pine plantation owners (either private companies, or

small to medirun non-industrial private owners). Sawmills processing pine sawlogs are

both conifer sawlog consumers and lumber producers. Lumber consumers are all

categories of industries or lumber-related sectors, including furniture companies, the

153

 

construction sector, and lumber exporters, the latter also represented by sawmills.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

W CONIFER (PINE) CONIFER (PINE)
M ! BELT SAWLOG MARKET ——> LUMBER MARKET
A A
DOMESTIC
MARKET m
o I E: V
CONIFER ; CONIFER E DOMESTIC
FOREST LUMBER
OWNERS m: ' SAWMILLS ‘73, MARKET
a: re
E TRANSFORMATION E
w SUPPLY OF DEMAND FOR SUPPLY OF DEMAND FOR
D AND SAWLOGS SAWLOGS LUMBER LUMBER

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 4. 1. Product linkages in the conifer lumber market in Southern Brazil and in other
Mercosur countries

Conifer sawlog and lumber producers in the ROW countries, notably in North
America and EU, differ from Mercosur producers in terms of the source of raw-material
(forest type and ownership) and lumber end-uses. In North America, conifer sawlog
supply comes mostly from natural forests (Northern/Westem US and Canada) and from
plantations (Southern US) from both public and private lands, and conifer lumber is
consumed primarily in housing construction and wood-manufacturing industries.

The major elements of the static phase of the study are the demand and supply of
sawlogs and lumber in each region, their own-prices, sawlog availability, the industrial
capacity and production, the sawmilling-processing costs, the import tariffs and the

export taxes, the transportation costs, and the exchange rates.

154

As an optimization model the maximizing objective function is subject to various
constraints. Sawlog supply constraints were related to the annual growing stock and
harvesting levels, which are related to the commercial area with pines, age distribution,
and yield in each region. Environmental aspects related to legal forest protection, the
legal requirement of reforestation for wood-consuming companies and restrictions on
harvesting native species, although relevant as policy issues were not direct part of the
model. Lumber supply constraints were related to the existing industrial capacity and
investment capital in each region. Demand constraints were related to the existing
industrial capacity in both the sawlogs and lumber markets, which are somehow related
to macroeconomic factors such as income, population growth, the overall performance of
the economy, the terms of trade, as well as production and transportation costs.

In multicommodity models, the inclusion of stages of production with
intermediate products seems to be more realistic, particularly for forest products that are
freely traded. In a competitive world, each region has the option of producing both
primary and intermediate products regionally, importing primary products and
transforming them regionally into intermediate products or directly importing
intermediate products. In each stage, the products are allocated as intermediate products
for the production of new products in the next stage. In each stage, commodities are
potentially transported between regions.

In this study, one transformation process (sawmilling) and two stages of
production (sawlogs being processed into lumber and residues) were considered. The
ﬁnal consmnption is not modeled, given that lumber may go through several processes

before being transformed into diverse ﬁnal products.

155

According to Waquil and Cox (1995) previous studies dealing with intermediate
products assumed constant costs of processing, which in fact may differ among regions.
Waquil and Cox’s formulation (1995) assumed transformation cost functions. Given the
lack of time-series on costs of transformation of sawlogs into lumber in the countries

under investigation, the cost of transformation is assumed to be ﬁxed.

4.5 Data Requirements and Sources

Speciﬁc data was required to run FPL-TPELPSI under the static and the dynamic
phases of the model. For the supply and demand of each commodity in each region, basic
data were the price of the commodities and their respective quantities demanded and
supplied (in the base-period and in one lagged-period), their own-price elasticities of
demand for conifer sawnwood and supply of conifer sawlogs, and the upper bound in the
quantity supplied of sawlogs and lower bound in the quantity demanded of sawnwood in
each region. For the manufacturing activity, data include the net manufacturing cost, the
input mix, and the amount of input commodity per unit of output commodity. For the
transportation costs and taxes, data included the freight cost of shipping one unit of
commodity from origin to destination, the import ad-valorem tax rate, and the export ad-
valorem tax rate. In addition, speciﬁc data on the manufacture activity and industrial
capacity were considered (Appendices 13 to 21).

For the dynamic phase, changes in exogenous variables were modeled
considering some of the major regional demand and supply shifters over the period of the
analysis. Some possible changes included, but are not restricted to: shift in demand or

supply curves, changes in net manufacturing costs, new manufacturing coefﬁcients, new

156

capacities, depreciation rates, capacity costs, changes in transportation costs, and changes
in import and export ad-valorem rates, and changes of exchange rates.

The base-period was set in the year 1995, for which data on trade between some
pairs of countries are available. Monetary values were all in a common currency, in the
case the US dollar. Prices and quantities for the base-period and for the period before the
base-period for each commodity and region come from different sources (Appendices).
Price elasticities of demand and supply come from the elasticity estimates for each
commodity for Brazil (Southern), Chile and Argentina from Chapter 3 (Table 4.2). For
Brazil, considering that the price elasticity of demand obtained from Chapter 3 was close
to zero (0.005) and is related to events in a period (1980-1995) that may not reﬂect the
period of the analysis (1995-2010), a different set of elasticities were used. For both
Southern Brazil and the RBZ region, price elasticities of demand of —0.40 were used,
which are average for other regions cited in the literature (see discussion in Chapter 4).
The price elasticities of import demand for ASIA-S and ROW ﬁ'om Mercosur products
were intermediate values reported from other studies. Prestemon and Buongiomo (1996),
investigating the Mexican demand for imports of US softwood lumber found price
elasticities of softwood imports between 032 (other softwoods) and —2.30 (Douglas-ﬁr),
with intermediate elasticities of —0.72 and —0.77, respectively for ponderosa pine and
Southern pine. Hseu and Buongiomo (1993) estimated price elasticities of US import
demand of Canadian softwood lumber between -0.94 and —1 .01. In another study, Chen
et a1. (1988) found price elasticity of —0.84 for US demand for imports of Canadian
softwood. In this study, the price elasticities of import demand of conifer sawnwood and

sawlogs for both ASIA-S and ROW regions were set as —0.50, within the range found in

157

the literature for import demand of softwood lumber in some countries (Prestemon &
Buongiomo, 1996; Hseu & Buongiomo, 1993; Chen, Ames & Hammett, 1988).
Secondary data was obtained ﬁ'om various sources. Production and unit value
price of roundwood products and some aggregate secondary products for Brazil were
obtained from IBGE. For Brazil, price of pine roundwood/sawlogs was ﬁ'om SEAB/PR
(SEAB, 1998 obtained through CNPF/EMBRAPA) unit value of ‘other roundwoods’
production was ﬁ'om IBGE, and time—series on price of pine lumber was from Revista da
Madeira. For Chile, radiata pine sawlog and lumber prices came from INFOR (2000), and
for Argentina were from SAGPyA and LaRobla (personal communication, 1999). Import
sawlog and sawnwood prices for the ASIA-S and the ROW regions came (or were

estimated) respectively from F A0 and from RISI (1997).

Table 4. 2. Price elasticities in different regions

 

 

 

 

 

 

 

 

 

 

 

 

 

Conifer Conifer Sawnwood
Region Source Sawlog L ____________________________________

Supply Demand Supply
S-BZ Chapter 3 0.256 -040 3 0.002 C
RBZ r Chapter 3 0.256 -040 3 0.002 C
Chile Chapter 3 0.268 (3) -1.081 3 0.739 C
Argentina Chapter 3 0.993 -1342 9 1.693 3
ROM 2 Chapter 3 0.993 -1342 3 1.693 B
ASIA-S Assumption - -0.50 -0.50
ROW Assumption - -0.50 -0.50

 

Notes:1 RBZ — Rest of Brazil, repeated from estimates for Southern Brazil;
2 ROM -— Rest of Mercosur, repeated from estimates for Argentina;

 

3 Assumption of higher elasticity as compared with estimated —0.05
from Chapter 3

A signiﬁcant at 1% level

3 signiﬁcant at 5% level

C signiﬁcant at 10% level

158

Production, exports and imports of conifer sawlogs and sawnwood were available
ﬁom FAO for all countries and various years. Consumption in each region was derived
from those data in a material balance formulation. Speciﬁc demand and supply data for
Brazil, in particular for Southern Brazil, came from ABIMCI (1999) - pine lumber -— and
ABPM, ANF PC (1994), STCP (2000, personal communication), and IBGE (1999, pine
sawlog supply as ‘other roundwoods’ production). For Chile, data on production of pine
sawlogs and lumber was obtained from INF OR. For Argentina, data on pine sawlog
production also came from SAGPyA-INDEC (1999).

Estimates of the limiting supply of pine roundwood (sawlog in particular) in
Brazil came from various studies including Ramos (1993), Tomaselli (1998), and Dos
Santos (1995). For other countries, estimates were from INFOR (Chile), INDEC-
MECON (Argentina), and information from papers by Flynn & Associates (several years,
including 1996, 1999a, and 1999b). Forest grth and roundwood production by
category of product for pines were estimated using Ramos (1993)’s procedure, adapted
speciﬁcally for Southern Brazil using data from IBAMA (ex-IBDF).

Exchange rates and import tariffs came from various sources including the WTO
and the IADB online database. Socio-economic data from all Mercosur countries (such as
population, producer price indexes - PPI, interest rates) were obtained from IADB.
Speciﬁc demographic and economic data for Brazil came from FGV (1998), IPEA
(1998), and IBGE (1980-1995 and 1996-2000).

Prices represent average prices of each product in each region and, as all
monetary values, are expressed in a common currency (e. g. US$), deﬂated by the

producer price index (PPI). Import tariffs and export taxes between each pair of regions

159

were considered when appropriate and data was obtained from the IADB (2000) and
Valverde et al. (1999). In addition, secondary data on prices, quantities, costs came from
varied sources including papers, online database, and expert opinion (Graca, 1996-2000,
Jmendes, 2000, STCP, 2000, Wiecheteck, M.R.S., 2000 — personal communication).

Import prices and quantities (from Mercosur) in ASIA-S and ROW came from
various sources. For conifer sawlogs, proxies for prices and quantities were estimated
with 1995 data of exports of ‘conifer saw, veneer logs’ obtained from the International
Trade Statistics Yearbook (UN, 1996). Proxies represent weighted quantities and
weighted unit value of exports, with the weights being the proportion of the total exports
(volume and value) of ‘wood, conifer rough, untreated’ (SITC classiﬁcation) from
Mercosur to ASIA-S and ROW obtained from UN (1995). The same procedure was used
to estimate proxies of prices of conifer sawnwood, except that the data of sawnwood
exports are from FAO (2000a). Exports of ‘conifer saw, veneer logs’ from Mercosur to
ASIA-S and ROW in 1995, as reported by the UN (1996) were lower than expected and
unrealistic.

Data on directions of trade of conifer sawnwood from Mercosur to ASIA-S and
ROW (F AO, 2000a and FAO, 200b) between 1995-1997 indicated an increase of 108%
and 11.9% respectively. This increase was incorporated in the model in Period 3 of the
dynamic forecast (corresponding to 1997). The total conifer sawnwood quantity exported
from Mercosur to those regions in 1997 reached 2.34 million m3. However, FAO (2000a)
reports only 1.82 million In3 as being exported, showing conﬂicting reports. Most of this

difference is related to Chilean exports.

160

- Transportation cost estimates

Based on each center’s distance to the other regions (Appendices 18 and 19),
transportation costs (in common currency per m3) between pairs of regions were

estimated through simple regression equations taking into account the method of
transportation (either trucking or shipping) and range of distances (short-, middle- and
long distances). Separate equations were estimated for short and middle-distance

(trucking transportation) and for long-distance (shipping transportation) (Table 4.3.).
Unitary cost of transportation ($/m3.mile) was regressed as function of the distance

between two respective centers.

Data for the regression were obtained from SIFRECA/Brazil (1998) and from
international shipping freight costs (Hardwood Review, several years). Distances of
transportation between trade points were obtained from the DNER (2000) and from
World-Port (2000). Estimation of inland distances, within Mercosur, was made (when

data was not readily available) by approximation using road maps for South America.

4.6 Results and Discussion

A model for analyzing conifer timber markets in Mercosur was developed in
order to analyze and forecast conifer sawlogs and sawnwood consumption, production
and trade within and outside the bloc. The model followed the theoretical framework
proposed by Samuelson (1952) and used F PL-TPELPSI, a FPL/USFS version of PELPS
III (Zhang, Buongiomo, and Ince, 1993), taking into account the assumptions outlined in

the previous section, which reﬂect the most likely scenarios given the data availability.

161

Table 4. 3. Transportation cost equations

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Distance Method Fitted Equation A}?! Obs.
Short - Sawlogs Trucking / tcss = 0.047859 -5.994E-05.d 0.50 20
(<350 km) roundwood * (HE-12) (0-00033)
Short — Lumber Trucking / tcsl = 0.037603 -4.7096E-05.d 0.50 20
(<350 km) roundwood * (1065-12) (000033)
Medium - Sawlogs Trucking / tcms = 0.06436 - 9.73E-06.d 0.84 07
(1000-3000 km) general load ** (3925-05) (0-00229)
Medium - Lumber Trucking / tcml = 0.05057 - 7.64E-06.d 0.84 07
(1000-3000 km) general load ** (3325-05) (000229)
Long - Sawlogs Ocean freight / tcls = 0.00628 -1.78E-07 0.68 119
(5,000-23,000 km) container *** (1795-66) (3905-31)
Long — Lumber Ocean freight / tcll =0.0049 - 1.4OE-O7 0.68 119
(5,000-23,000 km) container *** (17913-66) (39013-31)
comm sawmgs 93:???" tcs = 0.690*exp(3.69+0.3981nD-0.049an)
Conifer Lumber Wisdom _ *
(1987) H" tel — 0.541 exp(3.84+0.3191nD)

 

Note: Numbers between parenthesis are p-values.
All estimate were signiﬁcant at 0t<0.01

(*) Short-distance trucking transportation cost (toss and tcsl) in RS/m3.km within
State of S50 paulo/Brazil (source: SIFRECA, 2000 - online source at
hﬂpJ/sifrecaesalg.usp.br/madeira.htm), distance (d) in km.

(**) Medium-distance trucking transportation cost (tc2) in R$/t.km ﬁ'om Brazil to
Argentina (source: Associacao Brasileira do Transporte Intemacional — ABTI
cited by Avogrado, 2000 at
http_://www.artrade.com/esp/info/sintrans.htm#costo),distance (d) in km.

(***) Long-distance sea shipping transportation cost (tc3) in US$/m3.km (source:
Hardwood Review Export, J ul/98 at hm;://www.hardwoodreview.com/);
distances between ports were estimated mostly using ‘World Port Distances’
(l_1t_tp://www.distances.com[) and some using the ‘Bali online system’
(ht_tp://www.indo.com/cgi-bin/dist), the latter based on information from the US.
Geological Survey.

(****) Transportation cost equations for conifer sawlogs (tcs) and conifer lumber
(tcl) by Wisdon (1987); distance (D) in Nautical miles and quantity transported

(Q) in Ton.

162

 

Results of the empirical analysis of the Spatial equilibrium model are presented
for the base scenario - base period (static phase) and for multi-periods(dynamic phase)
projected up to 2010. In addition, results of the sensitivity analysis from changes in the

base scenario are presented.

4.6.1. Base Scenario

4.6.1.1. Base Period (Static Phase)

The base period was set as 1995, considering this was the latest year when most
of the data was available for all the countries and regions under investigation. The results
of the static phase serve to calibrate the model and to outline the pattern of production
and trade among the regions, from which the dynamic forecast is based on. The summary
statistics of the model is shown in Appendix 22, which also indicates the total value of

the objective function.

- Conifer Sawlogs

Estimates of the supply of conifer sawlogs in the Mercosur, were within 17.5% of
the actual data, with estimates for Brazil about 5.7% and 2.2% above the observed values,
respectively for Southern Brazil and the RBZ. Estimates for Argentina and ROM were

17.5% and 11% above the actual data respectively (Figure 4.2).

163

 

20000

15000

10000

000ln3

5000

 

SBZ RBZ CHI ARG ROM

 

EIActual Data Base Period Estimates (1995ﬂ

 

 

 

 

Figure 4. 2. Conifer sawlog supply by region for base period and published data

The largest individual producing region was Southern Brazil with 15.8 million In
of conifer sawlogs, followed by Chile (10.1 rrrillion m3), and the RBZ (estimated 7.0
rrrillion m3). Although the RBZ region produces pine sawlogs, the observed quantity is
not reported. The quantity used in the model was a proxy assuming that all the ‘other
roundwoods’ reported by IBGE for the RBZ (31.4% of the Brazil’s total in 1995)
represents the regional production of conifer sawlogs. Argentina and ROM were minor
producers, with 1.7 million In3 and 300 thousand m3, respectively. The results of the
model suggest its ability to predict the levels of conifer sawlog supply produced in each
region within an acceptable range (Figure 4.2).

Estimated domestic prices of conifer sawlogs in all the Mercosur countries
remained between 14.7% to 29.4% above the 1995 observed prices (or proxies), except
for Chile, with prices 1.8% below its observed value (Figure 4.3). This difference
indicates that some actual prices were undervalued in the Mercosur countries, except in
Chile, in that year. That could be the case in Brazil considering that conifer roundwood,

as opposed to that in Chile, had not been internationally traded in commercial volumes

until 1995. Overall, estimates of the domestic prices laid between US$32.6/m3
(Argentina) and US$42.3/m3 (RBZ). In Southern Brazil and Chile, prices were
US$37.7/m3 and US39.8/m3, respectively. For regions outside Mercosur (ASIA-S and the
ROW), prices represent import prices and were, respectively, 26.7% (US$91 .4/m3) below
and 21.5% (US$76.5/m3) above the proxies used in the model.

These differences could be related to the way proxies for prices were estimated
for the ASIA-S and the ROW countries using UN (1996) data. Proxies represented
weighted averages of the unit value of exports of conifer saw/veneer logs ﬁ'om Mercosur
countries and may not represent, to a certain extent, the actual conifer sawnwood import
prices in both ASIA-S and ROW. Another explanation could be the fact that conifer
sawlogs are, to a certain extent, a non-homogeneous product. For the purpose of the
analysis, and considering the data availability, sawlogs were assumed to be homogeneous
and traded inter-changeably in this model. The model, however, showed that prices in the
ASIA-S region tends to be higher than in the ROW, as expected, given the higher
transportation costs from Mercosur.

Estimates of the demand for conifer sawlogs can be derived from the estimates of
the sawnwood production (using conversion factors), also adding the total net volume
traded (exports and imports) following the material balance formulation (values not

shown).

165

 

 

 

 

 

 

150
O
E 100 I
a 2
= 50 I I I
o ’ : U
0

 

S-BZ RBZ CHI ARG ROM ASIA-S ROW

 

 

 

0 Actual Data I Base Period Estimates (1995)

 

 

 

 

Figure 4. 3. Conifer sawlog prices by region for base period and published data

- Conifer Sawnwood

Estimates of the demand for conifer sawnwood in the Mercosur were within
13.6% of the actual data range (Figure 4.4). For Southern Brazil and ROM, the estimates
were respectively 2.7% (5.05 million m3) and 3.1% (103 thousand m3) below the actual
data used in the model, and the same value for RBZ (2.9 million m3). For Chile and
Argentina respectively, estimates were 2.9% above and 13.6% below the volumes
observed in those countries in 1995. Overall, the model predicted the conifer sawnwood
demand in each region under investigation within a close range. For ASIA-S and the
ROW, estimates were within 5.7% and 2.9% above the proxies used in the model.

As for prices of conifer sawnwood, the model gave reliable estimates within 15%
of the actual data for all the Mercosur countries, higher to Southern Brazil and RBZ,
Argentina, and ROM (3.7%, 2.8%, 8.6% and 2.4% respectively) and lower for Chile

(-3.8%). Equilibrium prices varied from as low as US$122.7/m3 in Argentina up to

166

US$137.3/m3 in RBZ (as compared to US$125.7/rn3 in Southern Brazil). Prices of
US$132.9/m3 were estimated for Chile and US$124.0/m3 in ROM. Estimates of the
import conifer sawnwood prices from the bloc were US$192.2/m3 for the ASIA-S
countries and US$178.0/m3 for the ROW, respectively 14.1% and 9.2% lower than the

proxies used in the model (Figure 4.5).

 

6000
5000
4000
3000
2000
1000

0

000 m3

 

S-BZ RBZ CHI ARG ROM ASIA-S ROW

 

El Actual Data Base Period Estimates (1995)

 

 

 

 

Figure 4. 4. Conifer sawnwood demand by region for base period and published data

 

250.00
200.00

150.00

US$/m3

100.00

50.00

 

0.00

S-BZ RBZ CHI ARG ROM ASIA-S ROW

 

9 Actual Data I Base Period Estimates (1995)

 

 

 

 

Figure 4. 5. Conifer sawnwood prices by region for base period and published data

167

Estimates of the total supply of conifer sawnwood (both domestic and
exported/imported) can be derived from the estimates of the sawnwood demand by
adding the net volume traded (exports and imports) following the material balance

formulation (values not shown).

- Directions of Trade — Conifer Sawlogs and Sawnwood

Estimates of trade of conifer sawlogs in Mercosur for 1995 was restricted,
according to the model, to Chile, Argentina and ROM (although signiﬁcantly low), with
no exports or imports reported for the other members (Table 4.4). Although actual data
on trade of conifer sawlogs for individual countries for 1995 were not available, Pinus
roundwood was exported in small scale ﬁ'om Brazil in that year. The lack of trade among
Mercosur members is expected and is explained by the fact that its countries are mostly
net exporters of forest products, with their own source of roundwood, and importing

sawlogs may not be economically justiﬁable.

Table 4. 4. Estimates of conifer sawlog trade in Mercosur countries in 1995

To => REGIONS
From U S-BZ RBZ CHI ARG ROM ASIA-S ROW

BZ

TOTAL

1 145 32 1472
G 43 43
OM
AL 1 145 3
Note: values in thousand m

 

168

The estimates of conifer sawlogs exports from Chile were expected, since this
country has intensively pursued exports of forest products (including sawlogs) in the past
decades. The estimates of total volume exported by Chile in 1995 (1,15 million m3), is in
accordance with the actual data used in the model, although below the total reported by
INFOR (2000). Chile exported 1.66 million In3 and 1.39 million m3 of radiata pine
sawlogs in 1995 and 1996 respectively (INFOR, 2000). On the other hand, the 1994
Commodity Trade Statistics (UN, 1995) reports 1.12 million In3 and 87 thousand m3 of
rough conifer untreated wood exported ﬁom Chile to ASIA-S countries and ROW,
respectively. Traditionally Chile has aimed at exporting to the Paciﬁc Rim countries,
making the Asia-S countries its major group of conifer sawlog importers. The UN study
also reported as much as 394 thousand m3 of rough conifer untreated wood exported from
Brazil (all to ROW) and 25.5 thousand m3 from Argentina (almost all to ASIA-S
countries). Considering that the deﬁnition of rough conifer untreated wood may not be
the same as conifer sawlogs, and the data are 1994 volumes, the results from this study
cannot be readily compared with the Commodity Trade Statistics data.

For conifer sawnwood, the results indicate a total of 2.5 million m3 exported from
the bloc in 1995, mostly to the ROW (1.46 million m3) and to ASIA-S countries (892
thousand m3) (Table 4.5). As expected, Brazil leads the conifer sawnwood exports to the
ROW (traditionally to the US and to some EU countries), with estimated 1.0 million m3
(although part refers to domestic trade), and Chile (with 1.1 million m3 total) is the sole
exporter to the Asia-S countries (estimated 892 thousand m3). Minor exports come from
Argentina and the ROM. The result also shows the internal trade of 162 thousand m3 of

conifer sawnwood from Southern Brazil to the RBZ. This magnitude of trade is a result

169

of the assumption made that about 35% of the production from the Southern region go to
the RBZ. Since this percentage could not be veriﬁed due to data unavailability, trade
estimate between both regions may be either over— or underestimated, subject to future

validation as data become available.

Table 4. 5. Estimates of conifer sawnwood trade among Mercosur countries in 1995

To 2 REGIONS
From U S-BZ RBZ CHI ARG ROM ASIA-s Row
DZ 1 101 11

TOTAL

892 223 111
G 197 1
OM 2
AL 162 2513
Note: values in thousand m

 

Trade data from the 1994 Commodity Trade Statistics (UN, 1995) and from the
1995 International Trade Statistics Yearbook (UN, 1996) indicate conﬂicting trade
statistics for conifer sawnwood from/to Mercosur countries. The 1994 Commodity Trade
Statistics reports only 195 thousand m3 of ‘conifer wood, sawn’ exported from Brazil in
that year (90% to the ROW, and the remaining 10% to Argentina, ROM, and the ASIA-S
countries), and only 484 thousand m3 from Chile (66% to the ROW, 32% to ASIA-S
countries, and 1.5% to Argentina). On the other hand, the 1995 International Trade
Statistics Yearbook reports only 195 thousand m3 of ‘conifer shaped (sawn and planned)
lumber’ exported from Brazil and 720 m3 from Chile in 1995. INFOR (2000) reports a
total of 1.2 million In3 of radiata pine sawnwood exported in 1995, about 67% above the

total reported by the International Trade Statistics Yearbook and 7.7% above the ﬁndings

170

from this study (Table 4.5). In addition, FAO (2000) reported 1997 trade statistics
indicating that Brazil exported 649 thousand m3 of conifer sawnwood (94% to the ROW),
Chile exported 1.56 million m3 (53% to ASIA-S countries and 47% to the ROW), with
the combined Mercosur countries exporting 2.3 million m3. The statistics over the period
1994-97 from both studies are highly variable.

Overall, the results from this study are not closely matched by the trade statistics,
particularly for Brazil, which could be explained by lack of consistent deﬁnitions for the
products reported by different sources, data discrepancy, or a higher domestic
consumption of conifer sawnwood than initially assumed for the RBZ (resulting in a
larger internal trade from S-BZ to the RBZ). The model, however, conﬁrms the pattern of
sawnwood trade for Brazil and Chile, the dominant net exporters within the bloc.

Even though the Mercosur countries started their economic integration in 1991,
the ﬁndings of this study suggest that the creation of the bloc had (as far as 1995) none or
a small impact on trade among the members, in part as a result of being net exporters and
self-sufﬁcient, to an extent, in raw material for sawmills. However, other indirect beneﬁts
have been in the form of direct investments from one country in another country’s forest
sector (e. g. Chile investing in Argentina), and increases in overall trade and consumption
of goods and services, which may indirectly impact the conifer lumber market.

Increased trade of conifer sawlogs and sawnwood within the bloc in the ﬁrture
still remains a possibility. The perspective of roundwood shortage in Southern Brazil in
the coming years, and increasing demand for forest products, changes in plantation rate in
some countries, and the possibility of Chile and Bolivia becoming ﬁrll members in the

near future, with all countries beneﬁting from tariff reduction are considerations that can

171

impact trade of conifer lumber. Some of these variables were considered in the dynamic

forecast of the base scenario and in the sensitivity analysis.

4.6.1.2. Dynamic Forecast

The dynamic forecast predicts the possible changes occurring over time. A
simulation taking into account the most likely scenario (base scenario) as well as changes
in some of the major variables of the model was carried out. In this study the dynamic
phase was modeled with exogenous changes in the growth rate of the GDP (as a demand
shift variable), the grth rate of the upper bound on the conifer sawlogs in each region,
and the change in import ad-valorem tax rate for Chile, assuming this country could
become a full member of Mercosur by 2005 (Appendix 21).

Results of the dynamic simulation of the base scenario indicates that the demand
for conifer sawnwood is expected to increase steadily for most of the Mercosur countries,
except for Chile, between 1995-2010 (Figure 4.6). Such a decrease in domestic demand
in Chile may be the result of increasing exports of conifer lumber over the period, rather
than domestic processing. Other causes could be related to its relatively small population
and consequently low domestic demand for lumber, and government policies targeting
exports. For ASIA-S import demand for conifer sawnwood from Mercosur is expected to
increase, although for the ROW it could decrease. This decrease could be the result of

reduced production in Southern Brazil caused by shortage of sawlogs (Figure 4.6).

172

 

 

 

 

 

000 m3

 

 

 

 

 

 

ROM ASIA-S ROW

 

 

 

[131995 132000 .2005 532010I

 

 

Figure 4. 6. Estimates of the demand for conifer sawnwood in the dynamic forecast —
base scenario

Prices of conifer sawnwood are expected to increase across Mercosur, given the
increasing demand in most of the countries and relatively limited supply of the sawlogs in
some countries. The state of the forests in the region is not well known, nor is the
magnitude and timing of the future availability of the plantation forests. The price
increase therefore represents an overall trend, although the range of values may vary both
in magnitude and timing (Figure 4.7). Prices in the ASIA-S and in the ROW represent
equilibrium prices of imports from Mercosur. Such price differences reﬂect adjustments
in the transportation costs and in the existing taxes/tariffs. Estimates of the average prices
in the ASIA-S tend to be higher than in the ROW as result of higher transportation costs

to that region.

173

 

 

 

 

US$/m3

     

 

 

S-BZ RBZ CHI ARG ROM AS lA-S ROW

 

 

 

[.1995 132000 .2005 632010]

 

Figure 4. 7. Estimated conifer sawnwood prices in the dynamic forecast — base scenario

For sawlogs, the base scenario reveals a situation where an almost ﬁxed volume is
consumed by the manufacturing industry, except for Argentina and ROM, which may
experience a slight increase in supply (not shown). Prices of conifer sawlogs show an
increasing trend over the simulation period, with slightly higher prices in the RBZ and
Chile (Figure 4.8). With respect to the exports, the model forecasts Chile as the sole
exporter of conifer sawlogs to selected-Asian countries over the period. An average of
1110 thousand m3 are forecast to be exported from Chile to Asian-S countries and 365
thousand m3 to the ROW for the period 2000-2010 (numbers not shown). Although some
exports from countries such as Brazil, Uruguay, and Argentina, could be expected, the

model does not account for such potential exports.

174

 

 

 

 

 

 

US$/m3

 

 

 

AS IA-S ROW

 

.1995 [32000 l2005 .2010]

 

 

 

 

Figure 4. 8. Estimates of conifer sawlog prices in the dynamic forecast — base scenario

In terms of exports of conifer sawnwood, Chile and (Southern) Brazil may remain
the regional leaders (Table 4.6). The steady reduction in potential lumber exports from
Brazil during 1995-2010, however, can be attributed to the expected decrease in timber
availability over the period. An opposite trend is predicted for Chile and Argentina,
where the expansion of the forest resource base, and consequently the industrial capacity,
is expected in the next decade. Under this scenario an increase in exports of conifer
sawnwood of about 5% would be expected for Mercosur, from 2.51 million In3 exported
in 1995 to up to 2.63 million In3 in 2010. Chile could experience an impressive increase
of 52%, followed by Argentina with 14%, with Brazil suffering a decrease of about 47%
over the period. This may represent a signiﬁcant change in the pattern of the Brazilian
exports of conifer lumber for a country that has solidly expanded its participation in the
global market of pine lumber over the past two decades, due to a large extent to the use of
pines. This base scenario takes into account an average decrease in the resource base for

Brazil, and drives exports to the large domestic market as compared to the other

175

Mercosur countries. In fact, the increase in the Chilean exports is a result of a relatively
small domestic derived demand associated with expanded radiata pine plantations. A
similar trend is noticed for Argentina, although in a smaller scale, reﬂecting Argentinean
pine plantations and recent investments in industrial capacity. The ROM countries are
lead by Uruguay which has the potential for greater conifer sawnwood exports (increase
of over 200%) in comparison to its small export level in 1995, but still signiﬁcantly lower

than its neighbor countries.

Table 4. 6. Potential exports of conifer sawnwood ﬁom Mercosur countries (1995-2010)

 

 

 

 

 

 

 

 

 

 

 

Region YEAR
1995 2000 2005 2010
S-BZ 1178 1025 830 627
RBZ 0 0 0 0
CH1 1115 1315 1517 1699
ARG 197 203 214 225
ROM 24 54 69 76
TOTAL 2513 2597 2629 2627

 

 

 

 

Note: values in thousand m3

For the direction of trade of conifer sawnwood for 2000 and 2010, (Southern)
Brazil, Argentina, and Uruguay will likely continue exporting to the ROW countries,
while Chile, given its geographic location, will keep its Asia-Paciﬁc market, mostly the
ASIA-S countries (Japan, China and Republic of Korea) (Tables 4.7 and 4.8).

Southern Brazil’s major markets for conifer sawnwood will possibly remain the
rest of Brazil (RBZ) and the ROW. In the mid—19905, ROW exports have been lead by
the US as Brazil has been the second largest exporter, following Canada (Flynn, 1999).
For Southern Brazil, however, the decrease in exports between 2000 and 2010 may be

even bigger as a result of an increase in regional pine sawnwood trade to the rest of the

176

country, a major consumer region with expanded demand (Tables 4.7 and 4.8).

Table 4. 7. Potential directions of trade of conifer sawnwood for 2000 - base scenario

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

To :5 REGIONS

From U S-BZ RBz CHI ARG ROM ASIA-S ROW TOTAL
S-BZ 250 0 0 0 0 775 1025
RBZ 0 0 0 0 0 0 0i
CHI 0 0 0 0 937 378 1315
ARG 0 0 0 0 0 203 203
ROM 0 0 0 0 0 54 54
TOTAL 0 250 0 0 0 937 1410 2597

 

 

 

Note: values in thousand m

Dal

Table 4. 8. Potential directions of trade of conifer sawnwood for 2010 — base scenario

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

To => REGIONS
From U S-BZ RBZ CHI ARG ROM ASIA-S ROW TOTAL
S-BZ 486 0 0 0 0 141 627
RBZ 0 0 0 0 0 0 0-
CH1 0 0 0 0 1005 694 1699
ARG 0 0 0 0 0 225 225
ROM 0 0 0 0 0 76 76
TOTAL 0 486 0 0 0 1005 1136 2627

 

 

 

Note: values in thousand m

For the base scenario during the period under investigation, the model predicts the
possibility of Chile increasing its exports toward the ROW (694 thousand m3 by 2010),
compensating for the decrease in the exports from Brazil, if the ROW maintains its level
of imports from the region. In fact, such an assumption may be the reason for the
predicted decrease in domestic demand for pine sawnwood in Chile. Also Argentina and
the ROM countries may likely face an increase of pine sawnwood exports to the ROW,

although such volumes would not compromise their domestic demand for the commodity.

177

4.6.2. Sensitivity Analysis

In any modeling project, the primary interest of potential users converges to the
usefulness of the model for analyzing policy issues. In order to test the ability of the
Mercosur model to serve as an acceptable representation of the conifer lumber sector and
to show its capability for analyzing policy changes, alternative scenarios were studied.

The base scenario presented in the previous session may represent the most likely
scenario for the period 1995-2010. However, given the large number of variables
involved and the model assumptions, the magnitude of some variables could change,
consequently changing the results both in the static and in the dynamic forecasts.
Sensitivity analysis of the model considers potential changes in the magnitude of some
key variables associated with the demand and supply of the commodities. The results
from the sensitivity analysis presented below are results from changes in individual
variables for each time. This section examines changes in (1) the grth of supply of
conifer sawlogs in Brazil, (2) the growth of GDP in Brazil, and (3) the price elasticities of

demand for conifer sawnwood in Brazil and Chile.

4.6.2.1. Change in the Growth of the Availability of Conifer Sawlogs in Brazil

An important objective of this study is to investigate the effect on the production,
consumption, trade and prices ﬁom the expected shortage of pine sawlogs in Southern
Brazil in the next decade. To investigate sensitivity of the model to changes in this
variable, the following range of growth rates and timing of changes in the minimum

availability of conifer sawlogs were considered in Brazil (Table 4.9).

178

Table 4. 9. Percentage change in the minimum availability of conifer sawlogs in Brazil

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

SCENARIOS PERIOD
REGION (stock growth) 1996-2000 2001-2005 2006-2010
_I_I_igh 5.0% 2.0% 0.0%
S-BZ Medium * 2.0% 0.0% -2.0%
Low 0.0% -2.0% -5.0%
Lower 0.0% -5.0% -10.0%
_H_igh 5.0% 2.0% 0.0%
RBZ Medium * 2.0% 0.0% -2.0%
Low 0.0% -2.0% -5.0%
Lower 0.0% -5.0% -10.0%
CHI Base scenario 2.0% 3.7% 3.7%
ARG Base scenario 1.0% 1.0% 1.0%
ROM Base scenario 1.0% 1.0% 1.0%

 

 

 

(*) Medium scenario represents the base scenario (section 4.6.1.)

Most of the changes in the outcomes from changes in the growth in supply were
noticed by 2010, considering the cumulative effect of the decrease in timber availability.
Negligible changes were observed in the prices and quantities (consumed, produced and
traded) between the high and medium-value scenarios. With a more drastic reduction in
sawlog availability (low-growth scenario), however, prices may increase for both sawlogs
and sawnwood (not shown).

As result of the price-quantity interaction, the direction of trade changed
signiﬁcantly when comparing the high and low-value scenarios (Tables 4.10 to 4.12 as
compared with Table 4.8 — base scenario). With a drastic decrease of timber availability
(low scenario), Southern Brazil could eliminate its exports, supplying the domestic
market exclusively. Higher prices, and lower distances (transportation costs) to the RBZ,
could justify trade within Brazil only (Table 4.10). Extending the simulation to 2015,

Brazil could face imports by that year (results not shown).

179

Table 4. 10. Potential directions of trade of conifer sawnwood for 2010 —— Low-grth
scenario for the supply of conifer sawlogs

To:> REGIONS

From U S-BZ RBZ CHI ARG ROM IA-S Row

Bz 1308 0 1
0

TOTAL

0 877 822
0 0 225

OM 0 7
AL 1308 1 123
Note: values in thousand m

 

In a more drastic scenario, considering 0%, 5% and 10% decrease in the resource
base grth between 1995-2000, 2001-05, 2006-10 could potentially make Brazil (RBZ)
to face imports of conifer sawnwood, more likely from Chile, considering their forest
sector proﬁles and both countries’ membership in Mercosur (Table 4.11). The country, a
traditional net exporter and leader producer of pine lumber within the bloc, could become
importer potentially creating an impact in the domestic industry and the forest operations.
This scenario, however, may not occur if changes in the forest products markets take
place. These changes could include regulation of the production and consumption of
sawlogs and sawnwood, species and product substitutability, forest regulation and
management towards sustainable yield, technological changes, and more investment in
forest plantations. Each factor, to varying extents, could play a role in this scenario.
Investments in plantations, although a possible medium- to long-term solution, need to be
made soon to generate long-term results.

In the event of a considerably smaller decrease in pine timber availability (high-
scenario), the prospect for Brazil exporting pine sawnwood instead of importing still

exists. Exports, however, would be kept at a minimum level, considering that Southern

180

 

 

..
I

Brazil would supply mostly its own market (Table 4.12). In this scenario, the other
Mercosur countries would export slightly less than in the low-value scenario, although in
quite signiﬁcant volumes, particularly from Chile to both the ROW and the ASIA-S
countries.

Table 4. 11. Potential directions of trade of conifer sawnwood for 2010 — Lower-growth
scenario for the supply of conifer sawlogs

To:> REGIONS
FromU S-BZ RBZ CHI ARG ROM
61

 

AL
Note: values in thousand m

Table 4. 12. Potential directions of trade of conifer sawnwood for 2010 — High-grth
scenario for the supply of conifer sawlogs

To 2 REGIONS
S-BZ RBZ CHI ARG ROM

 

AL
Note: values in thousand

4.6.2.2. Change in the Growth of GDP in Brazil

Another possible change is the GDP growth rate in certain regions, which is
expected to affect the demand for forest products, changing consequently the equilibrium

prices and quantities. To investigate the model sensitivity to this change in Southern

181

Brazil, different scenarios were considered (Table 4.13).

Table 4. 13. Change in the Growth of GDP in Brazil

of in Southern and RBZ — 2000-2010

— 2000-2010
of in and — 2000- 10
(*) Medium scenario represents the base scenario (session 4.6.1.)

 

Results indicate that the model is not very sensitive to a small change in this

 

variable (between 3% to 5%). The scenarios held similar outcomes for sawnwood
equilibrium prices (Figure 4.12) and quantities demanded for conifer sawnwood.
Difference in prices was only within 1.7% range between the high and low growth
scenarios, with lower prices estimated for the low GDP growth rate scenario. Similar
results were observed for conifer sawlog prices (within 2% range between both

scenarios).

 

 

 

 

US$/m3

 

 

II

ARG

 

 

 

 

 

IEHIGH (5%) EIBase Scenario ILOW (3%)

 

Figure 4. 9. Estimates of conifer sawnwood prices in the sensitivity analysis for change in
GDP growth rate between 2000-2010 (prices in 2010)

182

For exports of conifer sawnwood, however, the effect of this simulation is more
noticeable, mostly by 2010. A higher economic growth, represented by a sustained 5%
GDP growth rate would likely reduce the sawnwood exports faster than with a 3%
growth rate. Indirectly a higher rate could increase the domestic consumption.
Considering that a signiﬁcant percentage of the total trade for S-BZ represents domestic

trade to RBZ, the estimated exports are even lower (Table 4.14).

 

Table 4. 14. Estimates of conifer sawnwood exports in the sensitivity analysis for change

in GDP grth rate between 2000-2010

 

 

 

 

 

 

 

 

Exports HIGH (5%) MEDIUM (4%) | LOW (3%)
2000 2005 2010 2000 2005 2010 | 2000 2005 2010

S-BZ * 1015 792 553 1025 830 627 1035 887 736

(296) (398) (553) (250) (363) (486) (245) (330) (423)
RBZ 0 0 0 0 0 01 0 0 0
CH1 1315 1517 1699 1315 1517 1699 1315 1517 1699
ARG 203 214 225 203 214 225 203 214 225
ROM 54 69 76 54 69 76 54 69 76
TOTAL 2588 2592 2553 2597 2629 2627 2607 2687 2736

I
I.
i
I
I .

 

 

 

 

 

 

 

 

 

 

 

 

Note: values in thousand mi3
* numbers in parenthesis are the estimates of the domestic trade to RBZ

4.6.2.3. Change in the Price Elasticity of Demand for Conifer Sawnwood

- Change in the Price Elasticity of Demand for Conifer Sawnwood in Brazil

 

Estimates of the price elasticities of demand for conifer sawnwood in Brazil, and
in few other Mercosur countries, revealed heterogeneous results through the regression
analysis from Chapter 3. Estimates in some cases were within a range of highly variable
elasticities (from around 0 to over —l). Such variability may be inﬂuenced by the data

available and used in this study. In addition, extremely low elasticities for conifer

183

sawnwood in Southern Brazil reﬂect the characteristics of the timber market during the
period of the analysis (19805 through mid-19905), which may differ from the period
covered by the spatial equilibrium analysis (1995-2010). Sensitivity analysis focusing on
the change of the price elasticity of demand for conifer sawnwood in Brazil (Southern
Brazil and RBZ) considered a range of elasticities varying between —0.05 (low), -0.40
(medium — base scenario) to —1.00 (high). This range was set based on the results found
for the elasticities in Brazil in Chapter 3.

This sensitivity analysis indicated that both elasticities between —0.05 and —0.40
held similar results in terms of prices of conifer sawlogs and sawnwood in all regions and
relatively similar quantities demanded of conifer sawnwood. A higher elasticity of -l .00
gave overall lower equilibrium conifer sawnwood prices (Figure 4.10) and sawlog prices

across the regions.

 

 

 

 

   

      
       

  

 

a a»

 

S-BRZ RBZ CHI ARG ROM ASIA-S ROW

 

In. HIGH (-1.00) :1 Base Scenario (-0.40) LOW (0.05) I

 

 

 

 

Figure 4. 10. Estimates of conifer sawnwood prices in the sensitivity analysis for price
elasticity of demand (2010)

In terms of trade, the low and the medium elasticities held similar results

suggesting that a range of relatively low price elasticities (between —0.05 to —0.40) would

184

’ I

 

not affect the overall export estimates as well (Table 4.15 for elasticity of —0.05). A
higher elasticity gave overall higher conifer sawnwood exports from Mercosur,

particularly for Brazil, considering that Chilean exports changed slightly (Table 4.15).

Table 4. 15. Potential directions of trade of conifer sawnwood for 2010 with price
elasticity of domestic demand in Brazil of -0.05

 

 

 

 

 

 

 

 

To => REGIONS

From U S-BZ RBZ CHI ARG ROM ASIA-S Row TOTAL
S-BZ 486 0 0 0 0 127 612
R32 0 0 0 0 0 0 0
CHI 0 0 0 0 1001 698 1699
ARG 0 0 0 0 0 226 226
ROM 0 0 0 0 0 76 76
TOTAL 0 486 0 0 0 1001 1127 2613

 

 

 

 

 

 

 

 

 

 

 

Note: values in thousand m

Table 4. 16. Potential directions of trade of conifer sawnwood for 2010 with price
elasticity of domestic demand in Brazil of -1 .00

To = REGIONS

From U S-BZ RBZ CHI ARG ROM IA-S Row TOTAL

32 3 0 0 1025 1414
0 0

1022 575 1
0 0
OM 0 19
AL
Note: values in thousand m

 

- Change in the Price Elasticity of Demand for Conifer Sawnwood in Chile

Chile is a major regional producer and exporter of conifer sawlogs and sawnwood
and possible change in its elasticity of demand for sawnwood would likely change the
regional equilibrium of prices and quantities. A sensitivity analysis focusing on the

change of the price elasticity of demand for conifer sawnwood in Chile was carried out.

185

Two elasticities were considered: -1.5 (high) and — 0.40 (low), as compared with the base
scenario elasticity (1.081) (Table 4.2.).

The sensitivity analysis for a range of price elasticities of conifer sawnwood
demand in Chile indicates opposite results for domestic demand depending on the
magnitude of the elasticity (Figure 4.11). For higher elasticities (between —1.20 and
—1.50) the decreasing trend in domestic consumption is forecasted. This decrease in
domestic consumption is compensated by an increase in exports (Table 4.17). For an
elasticity as low as —0.40, an increase in domestic demand is expected over time, causing
a direct decrease in exports. This result suggests a high sensitivity of the Chilean market
to a change in the price elasticity of the demand for conifer sawnwood. Prices change as

well with higher prices observed with the lower elasticity (about 3.6% higher than the

In.‘
I
u I
.1
1“ 1.
I‘I
ll.‘
1.1
.II
H'l- .
“Hi |

LOW (-0.40) MEDIUM-1.20) I-BH (-1.5)

base scenario - not shown).

 

 

 

     

 

 

   

 

 

 

E1995 :1 2000 I 2005 mtﬂ

 

 

Figure 4. ll. Demand for conifer sawnwood in Chile in the sensitivity analysis for price
elasticity of demand

As for exports, an opposite trend is observed, as expected, in comparison with the

domestic demand trend (Table 4.17). The Southern Brazil could increase its exports to

186

the ROW as result of the decrease in exports from Chile under a scenario with a lower

elasticity of demand in the Chilean market, all other variables constant.

Table 4. 17. Estimates of conifer sawnwood exports from 1995-2010 in the sensitivity
analysis for change in the price elasticity of conifer sawnwood in Chile

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Ex Om HIGH (4.50) I MEDIUM (4.20) 1 LOW (.040)
p 1995 2000 2005 201011995 2000 2005 201011995 2000 2005 2010
S-BRZ 1244 1092 897 732 1178 1025 830 627 1178 1215 1223 1223
(162) (250) (363) (486) (162) (250) (363) (486) (162) (250) (363) (486)
RBZ 00001000010000
CHILE 1049 1372 1820 183911115 1315 1517 169911115 985 874 747
ARG 197 143 61 651 197 203 214 2251 197 227 239 251
ROM 24 46 65 761 24 54 69 76I 24 59 69 76
TOTAL 2513 2653 2843 271212513 2597 2629 262712513 2486 2404 2297

 

Note: values in thousand m3
* numbers in parenthesis are the estimates of the domestic trade to RBZ

Overall these sensitivity analyses indicate the ability of the model to represent the

conifer lumber market in the Mercosur countries under different scenarios. The results

also indicate the complexity of the markets under investigation and reveals how sensitive

the model is to changes in speciﬁc variable (one each time) in terms of the magnitude and

timing of the change. However, a group of variables is more likely to change each time

and their interactions may shift the overall equilibrium prices and quantities consumed,

produced and traded. Indirectly the results also suggest the possible allocation of forest

plantations in the Mercosur countries.

4.6.2.4. Summary of the Sensitivity Analyses

Sensitivity analyses were performed for scenarios that represent change in the

timber availability in Brazil, growth in GDP in Brazil, and price elasticities of demand for

187

 

conifer sawnwood in Brazil and Chile (Tables 4.9 and 4.13, and p. 181-182).

Overall the results of these sensitivity analyses indicate the ability of the model to
represent the conifer lumber market in the Mercosur countries under different scenarios.
The results also indicate the complexity of the markets under investigation and reveal
how sensitive the model is to changes in speciﬁc variables (one at a time) in terms of the
magnitude and timing. However, a group of variables is more likely to change each time
and their interactions may shift the equilibrium prices and quantities consumed, produced
and traded. Indirectly the results also suggest the possible allocation of forest plantations.

A summary of the sensitivity of the model to the changes in the key variables,
with respect to the percentage change in exports of conifer sawnwood for the regions
under investigation is shown in Table 4.18 and 4.19 (sensitivity analyses 1, 2, 3 and 4).
Although the changes can be signiﬁcant in relative terms in some other countries, their
total volume may be small or negligible in terms of the total exports from the bloc. It is
important to consider that the Southern Brazil and Chile are the major regional exports.

As for a change in the growth of the availability of conifer sawlogs in Brazil, the
results are expressed in terms of the percentage change in trade of conifer sawnwood in
2010 as compared with the base scenario (Table 4.18 — sensitivity analysis 1). As
expected, a faster decrease in timber availability reduces the exports from Southern
Brazil, and increases the imports in the Rest of Brazil, representing either imports from
other countries or intra-trade from Southern Brazil. The ﬁrst two scenarios suggest a
minor impact on conifer lumber exports of the other Mercosur countries, with a major
impact predicted for Chile and Argentina as a more drastic reduction of the timber

availability occurs.

188

The model seems to be less affected by a change in GDP grth rate in Brazil
than by changes in the availability of supply in Brazil and in the price elasticities of
demand for conifer sawnwood in both Brazil and Chile (Table 4.18 — sensitivity analysis
2, 3, and 4). For a change in the price elasticity of demand in Brazil, the model indicates
an increase in exports from Southern Brazil with a price elasticity of —l .0, and a
signiﬁcant decrease in exports over 45% with lower elasticities (-0.40 and —0.05)
between 2000-10 (Table 4.19 — sensitivity analysis 3). The same trend is predicted for
Chile, with a signiﬁcant increase in exports with higher elasticities (respectively 34% and

52% for elasticities of -1 .5 and —1.2) and a signiﬁcant decrease of over 30% in conifer

sawnwood exports for a price elasticity of —0.40 (sensitivity analysis 4).

Table 4. 18. Summary of the results of the sensitivity analysis for a change in timber
availability in Brazil

 

SENSITIVITY ANALYSES
.-1 .2 .Ciialrge. 3911.19. 9:933!!! attire. Axeilehiiity. 9.! Earlier. .Sarrlagija Emil. £8.21}! .2395) .....
- Change in Trade of Conifer Sawnwood for 2010 as compared with the Base Scenario

 

 

 

 

 

 

 

in percent
Scenario S-BZ RBZ CHI ARG ROM ASIA-S ROW Total
5% 0 -1 0 0 0 0.5 0 0
2% imports imports
0%
-12 6 6
0% . . . 26
_2% 109 Imports 0 0 0 Imports Imports overall
_ o
5 /o exports trade
205 -18 - 7
0
0 {0 imports 0 imports imports 28
-5 /o overall
40% ' 2 31 99 trade
exports exports exports

 

 

 

 

 

 

 

 

 

189

Table 4. 19. Summary of the results for the sensitivity analysis of changes in the GDP
grth in Brazil and price elasticities of demand for conifer sawnwood in Brazil
and Chile

 

SENSITIVITY ANALYSES
.2--..(21391939191118 9:9th .9? .91).? 319.1359?! ...........................................................
— % Change in Conifer Sawnwood Exports (2000-2011)

 

 

 

 

 

 

 

Scenario S-BZ in“ * CHI ARG ROM ASIA-S ROW Total
mports

5% -46 86 -1

4% (BS) -39 94 29 11 41 na na 1

396 -29 73

 

 

 

 

 

 

 

 

 

.31-: 91.12929 3911.19. Prise. Elastisity. .Qt.l.).e.rr1595!.f.9r.§9.liifsr ﬁannwmd ill. Brazil. .................
- % Change in Conifer Sawnwood Exports (2000-2010)

 

 

 

 

 

 

Scenario S-BZ RBZ * CHI ARG ROM ASIA-S ROW Total
Imports

e=-l.0 11 143 34 -100 65 17

e = -0.4 (BS) -47 200 52 14 217 na na 5

e = -0.05 -48 200 52 15 223 4

 

 

 

 

 

 

 

 

 

..4: 91.13.1139 11311.19. £11199. Elastiaitx at P91112115! far. 99.1.1if9li ﬁemuwad. in. 91311.9. ..................
— % Change in Conifer Sawnwood Exports (2000-2010)

 

 

 

 

 

 

Scenario S-BZ RBZ * CHI ARG ROM ASIA-S ROW Total
Imports

e = -1.5 -33 94 34 -55 65 2

e = 42 (BS) -47 200 52 14 217 na na 4

e = -o.4 4 200 -33 27 217 -9

 

 

 

 

 

 

 

 

 

 

 

* imports of conifer sawnwood from Southern Brazil (S-BZ)
BS - Base Scenario
na — not applicable

190

4.7 Conclusions

In this chapter, a spatial equilibrium model to analyze the consumption,
production and trade of conifer timber markets in Mercosur was developed. Overall, the
model behaves well under different scenarios and predicts within an acceptable range the
results for changes in some of the most important variables under investigation. The
model captures mostly changes in the sawnwood market, with the results for the market
of sawlogs (production, consumption, and trade) subject to less variability. That may be a
consequence of well-deﬁned demand and supply markets, with the conifer sawmilling
industry already established in countries such as Brazil and Chile.

The results of the static phase (in the base scenario) serve to calibrate the model
and to outline the pattern of production, consumption, and trade among the regions, or
which the dynamic forecast is based. The results from the sensitivity analyses indicate the
many possibilities for changing key variables related to the demand and supply of the
commodities under investigation. Different scenarios revealed the possibility of
expanding or reducing exports, primarily in Southern Brazil, given the expected shortage
of conifer timber in this decade.

A spatial equilibrium analysis is data intensive, and given the data limitation in
this study some results should be viewed as trends of possible outcomes. It is important to
note that the timing and the magnitude of the results may change as some assumptions
change. As more speciﬁc data are collected and become available, the model could be
improved and the results and trends tested over time.

This study concentrated on the economic aspects only. However, political, social

and environmental aspects of related to the domestic supply, demand, and trade of forest

191

products are also important and may be addressed properly as they become relevant
issues to the model.

The model can be used, to a certain extent, as a ﬂexible tool for policy simulation
of the conifer lumber markets in the Mercosur countries. The usefulness of the model is
attested by a range of possible applications, most noteworthy the simulation of alternative
scenarios not contemplated in this study, such as a change in one variable or a group of
variables each time. Minor changes in the model could accommodate other. likely
scenarios such as the advent of the Free Trade Agreement of the Americas (F TAA), the
inclusion of other members to the bloc, and bilateral bloc trade negotiations (such as the

possible Mercosur-EU agreement).

192

CHAPTER 5

CONCLUSIONS

This study was the ﬁrst attempt to integrate the econometric results with
secondary data in developing a partial equilibrium analysis to investigate forest products
markets in Southern Brazil, and to a large extent, in the Mercosur countries. As a result, a
workable and useful model to analyze the consumption, production and trade of conifer
timber markets in the region was developed and tested.

Chapter 2 investigated qualitatively and quantitatively the forest resources and the
lumber industry in Mercosur, with focus on the Southern Brazilian market for conifer
sawlogs and sawnwood. It provided a critical view of the regional conifer lumber market,
its opportunities and shortcomings in the region under unvestigation, helping to deﬁne
the setting for the economic analysis that followed.

The econometric analysis for Brazil, Chile and Argentina in Chapter 3 provided a
better understanding about the relationship between demand, supply and prices of conifer
sawlogs and sawnwood in those countries. The evaluation also revealed the magnitude
and range of price elasticities of the demand for and supply of the commodities under
investigation, also identifying other major explanatory macroeconomic variables
affecting them. Price and GDP elasticities were used in building the trade model in
Chapter 4.

A spatial equilibrium model was developed in Chapter 4, which simulates the
optimal trade pattern, consumption, production, and capacity that satisfy the price and the

supply/demand quantity relationship. Sensitivity analyses indicated the possible changes

193

in the outcome as a result of changing one key variable each time. The ﬁrture pattern of
production, consumption and trade of conifer lumber in Southern Brazil is likely to be
highly inﬂuenced by the magnitude of changes in the availability of pine sawlogs from
plantations. A sensitivity analysis with respect to changes in this variable indicated that
the pine lumber market in Southern Brazil could face a signiﬁcant reduction in its
production and consequent exports, even becoming a net importer, if a drastic decrease in
availability of pine sawlogs occurs. Such a scenario, however, will be positively or
negatively inﬂuenced by the interaction with other major macroeconomic variables and
the expansion of the resource base, both in Brazil and in some of the other Mercosur
countries.

This study conﬁrmed the need for industries and government agencies in those
countries to address important policy issues such as the future availability of pine
sawlogs from plantations, the level of forest and industrial investments, and the need for
industrial modernization of the sawmilling sector in terms of competitiveness, and cost
and technology efﬁciency. Particularly for Brazil, the future shortage of conifer sawlogs
requires that effective actions be taken by the interested sectors (private and public) to
expand the pine plantation base, guaranteeing the sustainable production of pine sawlogs.
The effectiveness of these actions may be related to important ongoing forest debates at
national and regional levels. Those include the direction to be taken by the recently
created National Program for Forests, (decree 3.420, 2000), which aims at creating
incentives for the sustainable use of natural and planted forests across the nation. Another
important debate regards the existing Forest Legislation, which if effective, will likely

inﬂuence the land use patterns and the consequent allocation of reforestation projects

194

across the country by both private and public investments.

Continuous foreign investments in plantations and in the forest-based industries in
Argentina and in Uruguay towards pines may expand their resource base and their
industrial capacity, creating incentives for increased domestic consumption and exports
of solidwood products. Likewise, continuous expansion of the radiata pine base in Chile,
associated with intensiﬁed forest management practices (e. g. pruning and pre-commercial
and selective harvesting), will expand the future production and exports of both high-
grade pine sawlogs and sawnwood. These issues to varying extents may inﬂuence the
market equilibrium for pine timber within the trade bloc.

In terms of policy-related issues, the dynamic model in this study can be used to
analyze future policy scenarios. The model can be used as a starting point to address the
impact on the lumber markets from changes in national policies and aggregated private
investments in pine plantations, changes in the installed capacity and in the industrial
technology, and also in terms of species substitution (e. g. Eucalyptus for pines).

Policy related issues with a global scope that may inﬂuence the markets under
investigation and, to a certain extent, could be accommodated in the model are the Free
Trade Area of the Americas (FTAA), bilateral trade bloc agreements (e. g. Mercosur-EU),
the Kyoto Protocol on global climate change, and forest certiﬁcation. Other
developments and macropolicies at stake in the Mercosur countries that could inﬂuence
the pine lumber markets include the extent of hill integration of its members into the
bloc, the inclusion of other regional countries as members, the ongoing discussion on
trade issues among the current members, the dollarization and currency policies in some

countries, and possible economic crises. In addition, the evolution of Mercosur and the

195

full integration of its members (in terms of free trade and in other socio-economic areas)
can create direct and indirect incentives for the beneﬁt of the forest sector in each
country.

Overall, the study contributes to a better understanding of the conifer timber
markets in the Southern Brazil and in the other Mercosur countries. Although it
accomplished its objectives, it is important to consider the limitations of the available
data from different regions used in the development of the models. Future data as it
becomes available may be used to reﬁne and to conﬁrm some of the ﬁndings of this

study.

- Recommendations

As a recommendation, the disaggregation of the commodities (sawlogs and
sawnwood) into different grades, species and price ranges would be useful as data
become available. It would also be of interest to explore the substitutability between
groups of species including hardwoods from natural forests and from plantations (such as
the Eucalyptus, which only in recent years entered the lumber market). Eucalyptus is not
a substitute species for pines in the manufacturing industries and its market niche is still
being developed. However, considering the likely shortage of pine sawlogs in Brazil and
that key forested countries in Mercosur have extensive Eucalyptus plantations, this
species can play a future role in regional timber markets.

With the respect to improving and expanding the model, it is important to
consider that some sawmills operate under an integrated process, adding value to their

products, before exporting or commercializing them. For instance, some mills also make

196

mouldings, frames and other lumber-related products, and therefore part of the lumber
production may have not been accounted for in the process. The inclusion of these
products and markets into the model would give a better representation of the sector as a
whole. Further expansion of the model could also include trade inertia considering
possible trade linkages between pairs of regions. Expanding the model to incorporate
other countries or regions requires changing the geographic boundaries of the study. This
would be interesting when simulating the possible expansion of Mercosur, either in terms
of new members or new bloc integration (FTAA).

In addition, national agencies and institutes in Mercosur should develop standard
procedures for data collection, either in terms of product standardization, as well as
location (producer and consumer markets), and periodicity. As additional data and
information is collected, further analysis may provide a better indication whether or not

the future scenarios and the respective results will hold.

197

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210

APPENDICES

211

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219

Appendix 9 - Price Elasticities of Demand and Supply of Lumber — Literature

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

. . ____Qg_n-Price Elastlci
Authors Region Species/Product S Wy*r~—‘L—‘Deman d
Zhang, High GDP — -0.13 (0.04)*
Buongiomo Low GDP Conifer Lumber - -0.07 (0.04)*
and Zhu (1997) World - -0.08 (0.03)*
Kant et a]. Canada Solidwood and 0.15 to 0.32 -0.3 to -1.08
(1996) manufacturing (av. = -0.47
McKillop and Douglas-ﬁr — clears — -0.88 to -0.95
Liu (1989) — commons - -2.83 to —2.91
' — all - -2.13 to -2.27
Western US Hemlock-ﬁr — clears 0.39 to 0.68 -
— commons 1.35 to 1.37 -
— all 2.40 to 2.13 -
Chen et a1. USA Softwood lumber 0.309 -0.029
(1988) (Douglas ﬁr) (t=5.77)** it=-0.47)
Buongiomo OEDC Coniferous - -0.24 (0.08)*
and Chang countries sawnwood
Q 986)
Mem'ﬁeld and PNW US (All species) lumber - -0.359
Haynes (1983) and plywood (t=-1 .44)
products
Rockel and USA Soﬁwood lumber in - -0.91
Buongiomo the residential (0.02)M
(1982) construction
Citation — different authors - 0.00 to — 0.87
Adams and NW — Northwest US - -0.30 (nr)
Haynes (1980) SW — Southwest US - 0.341an
- TAMM - RM — Roclgy Mountains 0.35 (nr) -O.40 (nr)
NC — North Central US - -0.40 (nr)
NE — Northeast US - -0.39 (nr)
SO — South US - —0.34 (nr)
PNWW — Paciﬁc NW - Softwood 0.21 (nr) -
West US lumber
PNWE — Paciﬁc NW — 0.60 (nr) -
East US
PSW — Paciﬁc Southeast 0.23 gnrl -
SC — South Central US 0.79 (nr) -
SE — Southeast US 0.31 (nr) -
Canada 0.47 (nr) -

 

 

 

 

 

 

standard errors are in parenthesis.
nr - not reported
"‘ 5% signiﬁcance, "”" 1% signiﬁcance

220

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221

Appendix 11 - Samuelson’s conceptual framework

In 1951, Enkel7 formulated the problem of competitive equilibrium among
spatially separated markets, suggesting a solution in the case of linear market functions.
Proceeding from the Enke formulation, Samuelson (1952), presented the theoretical
foundations for computable spatial equilibrium modeling, showing how this purely
descriptive problem in non-normative economics can be cast mathematically into a
maximum problem, and related the Enke speciﬁcation to a standard problem in linear
programming, the so-called Koopmans-Hitchcook minimum-transport-cost problem.
Samuelson established the desired formal equivalence between the equilibrium of
interregional trade and a maximum problem, restricted to a single commodity (in
numerous markets) and the objective function was the difference between the areas under
the excess supply and demand curves for each market minus transportation cost. His
argument was that given equilibrium quantities, the equilibrium pattern of trade
minimizes total transportation costs (Meister et al., 1978).

Samuelson’s formulation was developed in the context of a spatial equilibrium
model in which market supply (S) and demand (D) are ﬁxed and given exogenously
(Meister et al., 1978; and Willet, 1983). On developing his study, he related his problem
of ﬁnding the minimum total transportation costs to the Koopmans’s linear programming
problem, which anticipated the ‘dual problem’ theory, which states that“... every
minimum problem in linear programming can be, so to speak, turned on its side and can
be converted into a related maximum problem, and the answer for this maximum problem

also gives the correct answer for the quantity that was to be minimized”. Samuelson

222

illustrated his concept by using two exports and two import regions, local prices and
transport costs between and within regions (Figure 4.1). Using the primal approach, he
showed that market equilibrium can be achieved through either the minimization of
transportation costs or the maximization of the net social payoff function (Figure

Appendix 11.1.).

Region A Region B
p SB

SA ESA
\a M kd/ e f
P1
X / DB
9 DA EDB

qA o qu qB

 

 

 

 

 

 

 

(a) (b) (C)

Source : Adapted from Hawk”, 1992 (cited by Ochoa, 1996)

Figure Appendix 11. 1. Trade model between two regions

The trade model between two pairs of regions, can be represented as shown in
Figure 4.1, stimulated by the price difference in each region as result of the respective
domestic supply and demand functions (graphs “a” and “c”). Graph “b” indicates the
equilibrium price and quantity given BSA and ESD, representing respectively the excess
supply and excess demand ﬁmctions derived from the relationship between the supply
and demand functions in region A and B.

Figure Appendix 11.2 represents Samuelson’s description of the so-called net

'7 Enke, S. 1951. Equilibrium Among Spatially Separated Markets: Solution by Electric Analogue.
Econometrica 19: 50-57.
'8 Houck, 1.. Elements of Agricultural Trade Policies. USA : Waveland Press, Inc. 1992.

223

social payoff, derived by subtracting transportation costs from the social payoff (net

surplus given by the area under the demand curve minus the area under the supply curve).

 

 

 

REGION B P REGION A
N U
Social Payoff
Transportation Cost Tr 3089003th COSl
U
Net Social Payoff

EBA: - EAB EAB

0 M \

BSL
N BSL

Source: Samuelson, 1952.

Figure Appendix 11. 2. Graphical representation of the Samuelson’s Net Social Payoff
(N SP)

Takayama and Judge (1964a, 1964b, 1971, 1996) extended Samuelson’s concept
to a multicommodity equilibrium among spatially separated markets using a quadratic
programming formulation, assuming appropriate linear dependencies between regional
supply, demand and price. Given this quadratic programming formulation a
computational algorithm was speciﬁed to obtain directly and efﬁciently the competitive
optimum solution for regional prices, quantities and regional ﬂows. The authors
represented manufacturing activities in a spatial equilibrium model by activity analysis,
i.e., in terms of the inputs per unit of output, the unit cost of manufacturing net of these

\inputs, and regional manufacturing capacities (Gilles and Buongiomo, 1985).

224

Appendix 12 - Kuhn-Tucker conditions for optimality

After substituting constraint (6) in the maximization problem (3), the
corresponding Lagrangean function becomes:

St..-

L = Z {Z in? (DMD - Z IP£<S>dS - Z iCTni}

—;ZZTPHU'XPI(U —ZZZTF"U°XF’<U

' 1 n l j

—Z;1h|:Ski —ZXPkij]:| _Z;#b.[z XPkij —Zakm.PFm.:|
I n I J n

— Z Z ”ni'|:Sni _ Z XFm’j] -2 Z rui°|:Z XFnji]— BM] (7)

where, km, mg, 111,1 are the corresponding Lagrangean multipliers.

The Kuhn-Tucker conditions for optimality associated with the problem are both
necessary and sufﬁcient conditions for the optimal global maximum solution, provided
that the suitable concavity and differentiability conditions of the objective function are
satisﬁed in the presence of linear constraints (Hillier and Lieberman, 1974; and Sposito,
1975; Takayama, 1985, 1994, both cited by Waquil and Cox, 1995). The associated

Kuhn-Tucker conditions are:

(a) 5L/5Dni = Pdni - mi s (=) o ifDni = (2) 0; (BL/BDIIIDIII = 0
(b) smash = Pski - Md 5 (=) o if Sn; = (2) o; (ESL/681d). ski = 0
(c) 5L/8PFni = - 2k aknj + mu 3 (=) o if PFni = (2) o; (ESL/6 PFni). PFni = 0

(d) 5L/5XPkij = - TPkij - Md + Ilki s (=) 0 if XPkij = (2) 0; (5L/5XPkij)- XPkij = 0

225

(e) 8L/6XFnij = - TFnij - nni + rm 3 (=) 0 if XFnij = (2) 0; (8L/6XFnij). XFnij = O
(f) 6L/5Md = Snki - Ej XPkij s (=) 0 if 1.191 = (2) O; (BL/811d). Md = 0

(g) 5L/5Hki = 2 XPkij - 2n akni -PFni s (=) 0 if llki = (2) 0; (8L/8Iuki). Pki = 0

(h) 5L/5nni = PFni - Zj XFnij S (=) 0 if nni = (2) 0; (5L/5nni)- nni = 0
(i) ESL/51m = 2 XFnij - Dni s (=) 0 if Tni = (2) 0; (ESL/51m). rni = 0 (8)
which imply the following interpretation: (9)
Dni > 0 i Pdni = Tni

Ski > 0 : Pski = Md

PFni > 0 : 2k akni. llki = nni

Xijj > 0 : TPkij = Ilki - in

XFnij > 0 : TFan = Tn,i - Tlni

Md > 0 : Ski = Zj XPkij

Pki > 0 I Zj Xiji = 231c 0tkni- PFni

nni > O : PFni = Zj XFnij

Tni > 0 : Zj XFnji = Dni

The Lagrangean multipliers are interpreted as shadow prices in competitive

equilibrium.

226

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228

Appendix 15 — Input data for PELPS’s manufacture activity (sawmilling)

 

MANUFACTURE

 

A : Record type (three types of records are used, M, P and B)

-> Record type M (manufacturing cost) :

B : Region number (01 to 99, in ascending order)

D : Commodity (primary) number (01 to 99, in ascending order within each region)

E : Commodity (secondary) number (01 to 99, in ascending order within each primary comm;
leave blank if not applicable)

F : Process number(Ol to 99, in ascending order within each commodity)

G : Input mix number(l to 9, in ascending order with each process)

H : Net manufacturing cost in common currency

-> Record type P (manufacturing coefﬁcients) :

B : Region number (01' to 99, in ascending order)

D : Input commodity number (01 to 99, in ascending order within each output commodity)

E : Output commodity number (01 to 99, in ascending order within each region)

F : Process number(Ol to 99, in ascending order within each commodity)

G : Input mix number(l to 9, in ascending order with each process)

H : Amount of input commodity per unit of output commodity

-> Record type B (by-product coefﬁcients) :

B : Region number (01 to 99, in ascending order)

D : Primary commodity number (01 to 99, in ascending order within each output commodity)
E : Secondary commodity number (01 to 99, in ascending order within each region)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

F : Process number(Ol to 99, in ascending order within each commodity)

G : Input mix number( 1 to 9, in ascending order with each process)

H : Amount of secondéry commodity per unit of prima_ry commodity
A B C D E F G H
M 01 03 02 2 29.80
M 01 03 04 02 2 0.00
M 02 03 02 2 29.80
M 02 03 04 02 2 0.00

_M 03 03 02 2 31.00
M 03 03 04 02 2 0.00

___1\_/1 04 03 02 2 40.00
M 04 03 04 02 2 0.00
M 05 03 02 2 31.00
M 05 03 04 02 2 0.00
P 01 02 03 02 2 2.54
P 02 02 03 02 2 2.54
P 03 02 03 02 2 2.56
P 04 02 03 02 2 2.31
P 05 02 03 02 2 2.31
B 01 03 04 02 2 1.54
B 02 03 04 02 2 1.54
B 03 03 04 02 2 1.56
B 04 03 04 02 2 1.31
B 05 03 04 02 2 1.3l

 

 

 

 

 

 

 

 

Note: Adapted from PELPS IH (Zhang, Buongiomo, and Ince; 1993)

229

 

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Appendix 17 - Input data for PELPS’s transportation costs

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TRANSPORTATION COST AND TAX
A : Origin region number (01 to 99)
C : Destination region number (01 to 99, in ascending order within each origin)
E : Commodity number (01 to 99, in ascending order within each origin-destination)
F : Freight cost of shipping one unit of commodity from origin to destination
G : Import ad-valorem tax rate
H : Export ad-valorem tax rate

A B C D E G

01 02 02 13.00 0 0
01 02 03 1 1.62 0 0
01 03 02 37.54 0 0
01 03 03 28.70 0 0
01 04 02 61.91 0 0
01 04 03 22.30 0 O
01 05 02 72.42 0 0
01 05 03 22.15 0 O
01 06 02 59.18 0.108 0
01 O6 03 46.50 0.108 0
01 07 02 38.13 0.108 0
01 07 03 31.34 0.108 0
02 01 02 13.00 0 0
02 01 03 11.79 0 0
02 03 02 38.20 0 0
02 03 03 28.66 0 0
02 04 02 66.54 0 0
02 04 03 22.84 0 O
02 05 02 79.05 0 0
02 05 03 22.68 0 0
02 06 02 59.13 0.108 0
02 06 03 46.46 0.108 0
02 07 02 38.08 0.108 0
02 07 03 29.92 0.108 0
03 01 02 37.54 0.100 0
03 01 03 29.36 0.100 0
03 02 02 38.20 0.100 O
03 02 03 29.62 0.100 0
03 04 02 87.99 0.100 O
03 04 03 27.82 0.100 0
03 05 02 35.98 0.100 0
03 05 03 27.93 0.100 O
03 06 02 47.59 0.100 0
03 06 03 37.39 0.100 0
03 07 02 32.66 0.100 0
03 07 03 25.66 0.100 0

 

 

231

 

continued from Appendix 17

PORTATION COST AND TAX
A B C D E
04 01 02
04 01 03 50.
04 02 02 66
O4 02 03 55.13
04 03 02 87.
04 03 03 64.18
05 02 70.22
05 03 58.13
06 02 59.05
06 03 46.3
07 02 41.43
04 07 03 34.15
05 01 02 72.42
05 01 03 55.4
05 02 02 79.05
05 02 03 60
05 03 02 35.98
05 03 03 31.53
05 04 02 63.91
05 O4 03 9.23
05 06 02 57.
05 O6 03 45.4
05 07 02 39.
05 07 03 32.98
07 01 02 38.13
07 01 03 29.83
07 02 02 38.
07 02 03 29.53
07 03 02 32.
07 03 03 28.73
07 04 02 41.43
07 04 03 31.1
07 05 02 39.
07 05 03 31.
Note: Adapted from PELPS III (Zhang, Buongiomo, and Ince; 1993)

 

232

Appendix 18 — Input data for PELPS’s exchange rate

 

 

 

 

 

 

 

 

 

 

EXCHANGE RATE
A : region number (including demand, supply and production, 01 to 99)
B : Exchange rate (expressed as the ratio of regional currency to U. S. dollar)
A B
01 1.00
02 1.00
03 1.00
04 1.00
05 1.00
06 1.00
07 1.00

 

 

 

 

Note: Adapted from PELPS III (Zhang, Buongiomo, and Ince; 1993)

233

Appendix 19 - Distance between the main centers in each region (sawlogs)

Sawlog

Sawlog

 

1
Supply Main Center # Demand Main Center nce (UST$(/:m3)
Brazil 421 13.00
Southern Brazil taGrossa Chile 7858 37
Southern Brazil ' 673 61.91
Southern Brazil OM acuarembo 1 72.42
Southern Brazil IA-S ' 22867 59.18
Southern Brazil OW SA Atlantic 38.13
Southern Brazil 421 13.
831 38
967 66
OM acuarembo 1 761 79.05
ASIA-S ' 22833 59.13
OW SA Atlantic 823 38.08
Southern Brazil taGrossa 785 8 37
8319 38
2328 87.
OM acuarembo 6781
ASIA-S ‘ 1
OW SA Atlantic 447
Southern Brazil
96
OM acuarembo
IA-S '
OW SA Atlantic
OM acuarembo Southern Brazil taGrossa
OM acuarembo
OM acuarembo Chile
OM acuarembo '
OM acuarembo IA-S
OM acuarembo 7 ROW SA Atlantic
OW SA Atlantic Southern Brazil
OW SA Atlantic
OW SA Atlantic 447
OW Atlantic 1
OW Atlantic acuarembo 9541
Values also referred in Appendix 17
2 Savannah/GA
3 in Uruguay

234

Appendix 20 - Distance between the main centers in each region (sawnwood)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Sawnwood Sawnwood Dista TC'

# Supply Main Center # Demand Main Center nce (US$/m3)
Region Region (km)

01 Southern Brazil PontaGrossa 02 RBZ S50 Paulo 523 11.62
01 Southern Brazil PontaGrossa 03 Chile Santiago 7160 28.70
01 Southern Brazil PontaGrossa 04 Argentina Buenos Aires 1512 22.30
01 Southern Brazil PontaGrossa 05 ROM Montevideo 1379 22.15
01 Southern Brazil PontaGrossa 06 ASIA-S Asia (Nagoya) 22867 46.50
01 Southern Brazil PontaGrossa 07 ROW USA (Atlantic)2 9486 31.34
02 RBZ Baum 01 Southern Brazil Curitiba 535 11.79
02 RBZ Bauru 03 Chile Santiago 7126 28.66
02 RBZ Bauru 04 Argentina Buenos Aires 1983 22.84
02 RBZ Baum 05 ROM Montevideo 1844 22.68
02 RBZ Bauru 06 ASIA-S Asia (Nagoya) 22833 46.46
02 RBZ Bauru 07 ROW USA (Atlantic)2 8237 29.92
03 Chile Concepcion 01 Southern Brazil Curitiba 7744 29.36
03 Chile Concgcion 02 RBZ Sic Paulo 7974 29.62
03 Chile Concepcion 04 Argentina Buenos Aires 6379 27.82
03 Chile Concepcion 05 ROM Montevideo 6481 27.93
03 Chile Concg3cion 06 ASIA-S AsiaJNagoya) 14830 37.39
03 Chile Concepcion 07 ROW USA (Atlantic)7 4476 25.66
04 Argentina Posadas 01 Southern Brazil Curitiba 787 50.06
04 Argentina Posadas 02 RBZ S50 Paulo 1197 55.13
Q4;Ar_gentina Posadas 03 Chile Santiago 1928 64.18
04 Argentina Posadas 05 ROM Montevideo 1439 58.13
04 Argentina Posadas 06 ASIA-S Asia (Nagoya) 22772 46.39
04 Argentina Posadas 07 ROW USA (Atlantic)2 11971 34.15
05 ROM Tacuarembo3 01 Southern Brazil Curitiba 1226 55.49
05 ROM Tacuarembo3 02 RBZ sao Paulo 1631 60.50
05 ROM Tacuarembo’ 03 Chile Santiago 9656 31.53
05 ROM 'I‘acuarembo3 04 Argentina Buenos Aires 350 9.23
05 ROM Tacuarembo3 06 ASIA-S Asia (Nagoya) 21973 45.49
05 ROM Tacuarembo3 O7 ROW USA (Atlantic)2 10934 32.98
07 ROW USA (Atlantic): 01 Southern Brazil Curitiba 8156 29.83
07 ROW USA (Atlantic)2 02 RBZ 850 Paulo 7892 29.53
07 ROW USA (Atlantic)2 03 Chile Santiago 7189 28.73
07 ROW USA iAtlantic)2 04 Argentina Buenos Aires 9360 31.19
07 ROW USA (Atlantic)2 05 ROM Montevideo 9241 31.06
' Values also referred in Appendix 17
2Savannah/GA
3 in Uruguay

235

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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