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Socioeconomic and Environmental Impacts of Forest Concessions in Brazil

Permanent Link: http://ufdc.ufl.edu/UFE0022324/00001

Material Information

Title: Socioeconomic and Environmental Impacts of Forest Concessions in Brazil A Computable General Equilibrium Analysis
Physical Description: 1 online resource (216 p.)
Language: english
Creator: Banerjee, Onil
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: amazon, brazil, computable, concessions, deforestation, dynamic, equilibrium, forest, forestry, general, illegal, logging, public
Forest Resources and Conservation -- Dissertations, Academic -- UF
Genre: Forest Resources and Conservation thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Understanding the forces that drove policy in the past can inform our expectations of the effectiveness of policy implementation today. Historical analysis suggests that forest policies of countries with significant forested frontiers transition through stages reflecting the orientation of governments toward economic development on the frontiers, namely: settlement, protective custody and management. With respect to Amazonian forests, Brazil?s path is no exception from this trend. This dissertation begins by following the trajectory of forest policy in Brazil to identify its path through the stages of policy development. Brazil is on the cusp of a transition toward the management phase of policy development. As such, the question of whether this phase will represent a break from the historical tendency of largely ineffectual forest policy is addressed. For society to accept and support a forest policy, it should generate positive socio-economic and environmental benefits. Brazil's Public Forest Management Law (2006) and specifically the socioeconomic and environmental impacts of implementing forest concessions, are taken as a proximate indicators of whether the transition to management will in fact increase the relevance of forest policy. To evaluate these impacts, two quantitative experiments are conducted. In the first, a static computable general equilibrium model is developed to evaluate the short-run policy effect on welfare, the forestry sector and levels of legal deforestation. Given the economic importance of illegal logging and illegal deforestation in Brazil, the second experiment explicitly models these sectors. A recursive dynamic computable general equilibrium modeling framework is employed to consider the medium-term implications of the policy, to shed light on the resulting economic transition path, and to assess the short-term costs and longer-term gains resulting from policy implementation. Results of this analysis can provide important insights on forest sector and deforestation dynamics to policy makers, industry and civil society such that complimentary policies and programs may be developed to maximize benefits and minimize any negative impacts resulting from the implementation of forest concessions.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Onil Banerjee.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Alavalapati, Janaki R.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2009-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022324:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022324/00001

Material Information

Title: Socioeconomic and Environmental Impacts of Forest Concessions in Brazil A Computable General Equilibrium Analysis
Physical Description: 1 online resource (216 p.)
Language: english
Creator: Banerjee, Onil
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: amazon, brazil, computable, concessions, deforestation, dynamic, equilibrium, forest, forestry, general, illegal, logging, public
Forest Resources and Conservation -- Dissertations, Academic -- UF
Genre: Forest Resources and Conservation thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Understanding the forces that drove policy in the past can inform our expectations of the effectiveness of policy implementation today. Historical analysis suggests that forest policies of countries with significant forested frontiers transition through stages reflecting the orientation of governments toward economic development on the frontiers, namely: settlement, protective custody and management. With respect to Amazonian forests, Brazil?s path is no exception from this trend. This dissertation begins by following the trajectory of forest policy in Brazil to identify its path through the stages of policy development. Brazil is on the cusp of a transition toward the management phase of policy development. As such, the question of whether this phase will represent a break from the historical tendency of largely ineffectual forest policy is addressed. For society to accept and support a forest policy, it should generate positive socio-economic and environmental benefits. Brazil's Public Forest Management Law (2006) and specifically the socioeconomic and environmental impacts of implementing forest concessions, are taken as a proximate indicators of whether the transition to management will in fact increase the relevance of forest policy. To evaluate these impacts, two quantitative experiments are conducted. In the first, a static computable general equilibrium model is developed to evaluate the short-run policy effect on welfare, the forestry sector and levels of legal deforestation. Given the economic importance of illegal logging and illegal deforestation in Brazil, the second experiment explicitly models these sectors. A recursive dynamic computable general equilibrium modeling framework is employed to consider the medium-term implications of the policy, to shed light on the resulting economic transition path, and to assess the short-term costs and longer-term gains resulting from policy implementation. Results of this analysis can provide important insights on forest sector and deforestation dynamics to policy makers, industry and civil society such that complimentary policies and programs may be developed to maximize benefits and minimize any negative impacts resulting from the implementation of forest concessions.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Onil Banerjee.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Alavalapati, Janaki R.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2009-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022324:00001


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1 SOCIOECONOMIC AND ENVIRONMENTAL IM PACTS OF FOREST CONCESSIONS IN BRAZIL: A COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS By ONIL BANERJEE A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008

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2 2008 Onil Banerjee

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3 To my family.

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4 ACKNOWLEDGMENTS I am very grateful to my brother, Albert who has always had the utmost confidence in me; and to my mother, father and grandmother for their unconditional support. Special thanks go to my advisor Janaki Alavalapati for his guidance, inspiration and trust. Thanks also go to my committee members, Dan Zarin, Sherry Larkin, Richard Kilmer and Doug Carter for their encouragement and tutelage. I am grateful to Marco Lentini and Alexander Macpherson for sharing their expertise in fo rest policy and economics. Than ks go to Sherman Robinson for providing the dynamic version of the IFPRI CGE model and to Hans Lofgren, Andrea Cattaneo, Igncio Tavares de Arajo Jnior and Joaquim Bento de Souza Ferreira Filho for CGE modeling advice. Special thanks go to my br other from the barrio Greg Brown.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ............................................................................................................... 4LIST OF TABLES ...........................................................................................................................8LIST OF FIGURES .......................................................................................................................10LIST OF ABBREVIATIONS ........................................................................................................ 12ABSTRACT ...................................................................................................................... .............15 CHAP TER 1 INTRODUCTION .................................................................................................................. 17Overview ...................................................................................................................... ...........17Research Questions ............................................................................................................ .....182 TOWARD A POLICY OF SUSTAINABLE FOREST MANAGEMENT IN BRAZILAN HISTORICAL ANALYSIS .............................................................................................20Introduction .................................................................................................................. ...........20Settlement and Exploitation (1889 to 1964) ........................................................................... 20Protectionist Approach to Na tural Forests (1965 to 2000) ..................................................... 21Integrating the Brazilian Amazon into the National Economy ....................................... 23The Environmental Movement and Democratization ..................................................... 25Protected Areas ................................................................................................................27Constitutions, International Agreements and the 1990s .................................................. 29Political Economy Impacts on the Forestry Sector .........................................................31Sustainable Forest Manage ment (2000 to present) ................................................................. 32Contemporary Forest Policy and the Public Forest Management Law ........................... 35The Public Forest Management Law ............................................................................... 35Increasing Deforestation and Illegal logging ..................................................................38Forestry Sector Crisis ......................................................................................................40Escalating Violence ......................................................................................................... 42International Concern for the Amazon ............................................................................ 43The Workers Party ........................................................................................................... 44Discussion and Conclusions ...................................................................................................443 STATIC COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS OF FOREST CONCESSI ONS IN BRAZIL ................................................................................................ 51Introduction .................................................................................................................. ...........51The Brazilian Forestry Sector ................................................................................................. 52The Public Forest Management Law ...................................................................................... 55

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6 Overview of Computable General Equilibrium Models ......................................................... 57Computable General Equilibrium Applications in Forestry ................................................... 61Construction of a Social Accounting Matrix for Brazil ..........................................................63An Aggregated Social Acc ounting Matrix for Brazil ...................................................... 63Disaggregating Land Types and Regional Fo restry and Agricultural Activities ............ 64Disaggregating Labor and Households ...........................................................................67Taxes ......................................................................................................................... .......68Balancing the Social Accounting Matrix ........................................................................ 69Standard Computable General Equilibrium Model in GAMS ............................................... 69Production .................................................................................................................... ....70Factor Markets .................................................................................................................70Institutions .................................................................................................................. .....71Commodity Markets ........................................................................................................72Macroeconomic Balances ................................................................................................ 73Scenario Design ......................................................................................................................75Simulation Results ............................................................................................................ ......76Comparing Simulation Results under Ba lanced, Neoclassical and Johansen Closures........................................................................................................................76Simulation Results and Interpretation in a Balanced Macroeconomic Environment ...... 79Implications for Deforestation ......................................................................................... 84Conclusions .............................................................................................................................844 RECURSIVE DYNAMIC COMPUTABLE GENERAL EQUILIBRIUM MODEL W ITH ILLEGAL LOGGING AND DEFORESTATION ................................................... 116Introduction .................................................................................................................. .........116Illegal Logging ......................................................................................................................116Illegal Logging in Brazil .......................................................................................................119Treatment of Illegal Behavior in Com putable General Equilibrium Models ....................... 122Dynamics in Computable Ge neral Equilibri um Models ...................................................... 124Dynamic Extension to the Standard Computable General Equilibrium Model in GAMS ...125Customizing the 2003 Social Accounting Matrix for Brazil to Describe Illegal Forestry and Illegal Deforestation ................................................................................................... 127Illegal Deforestation ......................................................................................................127Illegal Forestry ...............................................................................................................131Summary of Key Assumptions ......................................................................................132Scenario Design ....................................................................................................................132Results ...................................................................................................................................136Discussion .................................................................................................................... .........1435 CONCLUSIONS .................................................................................................................. 167 APPENDIX A COMPLETE MODEL EQUATION LISTING .................................................................... 179

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7 B STATIC MODEL RESULTS: COMPARI NG THE B ALANCED, NEOCLASSICAL AND JOHANSEN CLOSURES ........................................................................................... 186LIST OF REFERENCES .............................................................................................................203BIOGRAPHICAL SKETCH .......................................................................................................216

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8 LIST OF TABLES Table page 3-1 Areas with forest management plans and areas with deforestation authorizations ........... 903-2 Area reforested in 2006 ......................................................................................................903-3 Mapping of activitie s to national accounts ........................................................................ 913-4 Mapping of products to national accounts ......................................................................... 943-5a Brazilian national accounts source s for the social accounting matrix ............................... 983-5b Key to table 3-5a ................................................................................................................993-6 Brazilian social accounting matrix accounts, reference year 2003 .................................. 1013-7 Aggregated social accounting matr ix for Brazil, reference year 2003 ............................ 1033-8 Percent change in macroeconom ic and institutional indicators ....................................... 1043-9 Percent change in institutional income ............................................................................ 1043-10 Equivalent variation ..................................................................................................... ....1053-11 Factor income ............................................................................................................ .......1053-12 Price of composite good...................................................................................................1063-13 Price of factor F for activity A .........................................................................................1073-14 Domestic activity ........................................................................................................ .....1133-15 Factor demand by sector .................................................................................................. 1143-16 Domestic sales and exports .............................................................................................. 1143-17 Composite goods supply ..................................................................................................1154-1 Macroeconomic and institutional indicators between 2003 and 2018 ............................. 1614-2 Level of domestic activity between 2003 to 2018 ........................................................... 1624-3 Quantity of composite supply between 2003 and 2018 ................................................... 1634-4 Quantity of domestic and export sales between 2003 and 2018 ...................................... 1634-5 Composite commodity pr ices between 2003 and 2018 ...................................................164

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9 4-6 Quantity of factor demand by industry between 2003 and 2018 ..................................... 1644-7 Institutional income between 2003 and 2018 .................................................................. 1654-8 Factor income between 2003 and 2018 ........................................................................... 1654-9 Factor wages and pric es between 2003 and 2018 ............................................................ 166

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10 LIST OF FIGURES Figure page 2-1 Roundwood production and trade, area planted for pulp and paper, and area deforested ...........................................................................................................................50 3-1 Relative output value of forest products ............................................................................ 87 3-2 Regional distribution of roundwood, char coal and fuelwood production value from natural forests .....................................................................................................................87 3-3 Regional distribution of wood, charcoal and fuelwood production value from forest plantations ................................................................................................................... .......88 3-4 Area deforested 1988 to 2007 ............................................................................................88 3-5 Structure of production ......................................................................................................89 4-1 Relationship between forestry, defore station, forest planta tions, agriculture, forestland and agricultural land ....................................................................................... 148 4-2 Real GDP growth .............................................................................................................149 4-3 Level of legal and illegal forestry activ ity in the north, north east, south, south east and center west .................................................................................................................149 4-4 Level of legal and illegal deforestati on activity in the nort h, north east and center west .......................................................................................................................... ........150 4-5 Level of forest plantation activity in th e north, north east, south east, south and center west .......................................................................................................................... ........150 4-6 Level of wood processing and pulp and cellu lose activity .............................................. 151 4-7 Level of agricultural activity in the north, north east, south east, south and center west .......................................................................................................................... ........151 4-8 Composite commodity supply of agricu ltural, forest, processed wood, and pulp and cellulose products .............................................................................................................152 4-9 Domestic and export demand for agricu ltural, forest, processed wood, and pulp and cellulose products .............................................................................................................152 4-10 Composite commodity prices of agricu ltural, forest, processed wood, and pulp and cellulose products .............................................................................................................153 4-11 Agricultural land stock in th e north, north east and center west ......................................153

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11 4-12 Forestland stock in the north ........................................................................................... .154 4-13 Forestland demand in the north east, south east and south .............................................. 154 4-14 Forestland demand in the north and center west .............................................................. 155 4-15 Agricultural land demand in the north east, south east and south ................................... 155 4-16 Agricultural land demand in the north and center west ...................................................156 4-17 Household and enterprise income .................................................................................... 156 4-18 Household expenditures ...................................................................................................157 4-19 Labor and capital income ................................................................................................ .157 4-20 Forestland income in the north, north east, south east, south and center west ................ 158 4-21 Agricultural land income in the north, nor th east, south east, south and center west ...... 158 4-22 Labor wages and the price of capital ...............................................................................159 4-23 Price of forestland in the north, north east, south east, south and center west ................ 159 4-24 Price of agricultura l land in the north, nor th east, south east, so uth and center west ...... 160

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12 LIST OF ABBREVIATIONS AAGR Average Annual Growth Rate ACPC Annual Compound Percentage Change APP Permanent Preservation Area Art. Article ATPF Transportation Authorization Permits BASA Bank of Amazonia BBC British Broadcasting Corporation BRACELPA Associao Brasilei ra de Celulose e Papel BRASAM Brazilian Social Accounting Matrix CEI Integrated Economic Accounts CES Constant Elasticity of Substitution CET Constant Elasticity of Transformation CGE Computable General Equilibrium CONAFLOR Coordinating Commission of the National Forests Program CONAMA The National Environmental Council CV Compensating Variation DETER Real Time Deforest ation Detection System EV Equivalent Variation FAO Food and Agriculture Organization FAOSTAT Food and Agriculture Organization Statistics FCO Central West Financing Fund FCU Foreign Currency Units FNDF National Fund for Forest Development FNE North Eastern Financing Fund

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13 FNO Northern Financing Fund G-7 Group of Seven GAMS General Algebraic Modeling System GDP Gross Domestic Product Ha Hectare IBAMA Brazilian Institute for the Environment and Natural Renewable Resources IBDF Brazilian Institute for Forestry Development IBGE Instituto Brasileiro de Geografia e Estatstica ICMS Tax on the Circulation of Merchandise and Services IFPRI International Food Policy Research Institute INCRA National Institute for Colonization and Agrarian Reform INPE National Institute for Space Research I-O Input-Output IPEA Research Institute for Applied Economics IPI Tax on Industrialized Products ISA Instituto Socioambiental LCU Local Currency Units LES Linear Expenditure System MDA Ministry of Agrarian Development MMA Ministrio do Meio Ambiente NGO Non-Governmental Organization No. Number OECD Organisation for Economic Co-operation and Development PAOF Annual Forest Granting Plan PFML Public Forest Management Law

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14 PIN National Integration Program PNF National Forest Program POLAMAZONIA Program of Agricultur al, Livestock and Mineral Poles POLONOROESTE The Northwestern In tegrated Development Program PRODES Program for the Calculati on of Deforestation in Amazonia PRONAF-Florestal National Program fo r Strengthening Family Agriculture PT Workers Party R$ Brazilian reais RADAM Radar of Amazonia SAM Social Accounting Matrix SBS Sociedade Brasileira de Silvicultura SEMA Secretariat for the Environment SFB Servio Florestal Brasileiro SISNAMA The National Environmental System SISPROF Brazils monitoring and control sy stem for resources and forest products SNUC National System of Nature Conservation Units SUDAM Superintendency for the Development of Amazonia SUDEPE Secretary of Fisheries Department SUDHEVEA Superintendence for Rubber T1 National Accounts Table 1 T2 National Accounts Table 2 US$ United States Dollar

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15 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy SOCIOECONOMIC AND ENVIRONMENTAL IM PACTS OF FOREST CONCESSIONS IN BRAZIL: A COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS By Onil Banerjee August 2008 Chair: Janaki Alavalapati Major: Forest Resources and Conservation Understanding the forces that drove policy in th e past can inform our expectations of the effectiveness of policy implementation today. Historical analysis suggests that forest policies of countries with significant forested frontiers trans ition through stages reflec ting the orientation of governments toward economic development on th e frontiers, namely: se ttlement, protective custody and management. With respect to Amazonian forests, Brazils path is no exception from this trend. This dissertation begins by following the trajectory of forest policy in Brazil to identify its path through the stages of policy development. Brazil is on the cusp of a transition toward the management phase of policy development. As such, the question of whether this phase will represent a break from the historical tendency of largely ineffectual forest policy is addressed. Fo r society to accept and support a forest policy, it should generate positive socio-economic and envi ronmental benefits. Brazils Public Forest Management Law (2006) and specifically the socioeconomic and environmental impacts of implementing forest concessions, are taken as a proximate indicator s of whether the transition to management will in fact increase the relevance of forest policy. To evaluate these impacts, two quantitative experiments are conduc ted. In the first, a static computable general equilibrium

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16 model is developed to evaluate the short-run policy effect on welf are, the forestry sector and levels of legal deforestation. Given the economic importance of illegal logg ing and illegal deforestation in Brazil, the second experiment explicitly models these se ctors. A recursive dynamic computable general equilibrium modeling framework is employed to c onsider the medium-term implications of the policy, to shed light on the resulting economic tran sition path, and to assess the short-term costs and longer-term gains resulting from policy implem entation. Results of this analysis can provide important insights on forest sect or and deforestation dynamics to policy makers, industry and civil society such that complimentary policie s and programs may be developed to maximize benefits and minimize any negative impacts resu lting from the implementation of forest concessions.

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17 CHAPTER 1 INTRODUCTION Overview Sixty-th ree percent or 4 million km of the Amazon biome is located in Brazil. Brazils Legal Amazon is approximately 5 million km or 59% of Brazils total land area; 2.6 million km of the Legal Amazon are forested1. This region is home to 22.5 million people (12% of Brazils total population), 5.3 million of whom live in forested areas (Celen tano & Verssimo, 2007, p. 9). Brazil is the largest producer and consumer of tropical timber pr oducts and as such, the forest industry is an important component of the econo my and in particular, the economy of the Legal Amazon. It is estimated that the forestry sector is responsible for 3.5% of Brazils gross domestic product, generating 2 million formal jobs and acc ounting for 8.4% of Brazilian exports (Servio Florestal Brasileiro [SFB], 2007a, p. 10). Furtherm ore, strategic sectors of the economy such as the steel and construction sectors have cl ose linkages with th e forest sector. Approximately 500,000 families in the Amazon depend at least in part on forestry for their livelihoods (Lima, Merry, Nepstad, Amacher, Azevedo-Ramos, Resque & Lefebvre, 2006, p. 33). With 1.15 million km of forests with a high poten tial for forestry activities, natural forest management presents a tremendous opportunity fo r promoting forest-based development and for maintaining environmental quality and economic value in the region (Verssimo, Junior & Amaral, 2000, p. 6). Until 2006, Brazil has lacked a mechanism to pr omote forest management on public lands. In March 2006, Brazils Public Forest Mana gement Law (PFML) was passed. One of the principal features of the law is the authorization of forest conce ssions which enables the state to sell the rights to harvest forest goods and services to private firms for a predetermined period of time. The implementation of such a framework fo r the promotion of natural forest management

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18 and forest-based development is unprecedented in the history of Brazilian forest policy. This research is concerned with analyzing the trajectory of Brazilian forest policy and the potential socio-economic and environmental impacts of forest concessions in the Legal Amazon. The remainder of this chapter outlines the purpose of this investigation and the research questions to be addressed. The second chapter provi des an in-depth analysis of forest policy in Brazil with an emphasis on the transformations in Brazils political and socio-economic structures that facilitated the a pproval of the PFML. The third chap ter is quantitative in nature and develops a computable general equilibrium model to assess the short-run socio-economic and environmental impacts of forest concessi ons. The fourth chapte r builds on the third by introducing recursive dynamics a nd an illegal logging and illegal deforestation sector into the model to evaluate the medium-run socio-econo mic impacts of forest concessions and the interactions between forest-based sectors. The final chapter unites the w hole with a summary of the research findings and discusses complimenta ry policies to reduce a ny negative impacts of forest concessions and future research directions. Research Questions In f ollowing the trajectory of Brazilian forest policy, chapter two is designed to answer the following questions: 1. Historically, how has the state acte d to develop the forestry sector? 2. What are the key legislative instruments that govern the forestry sector? 3. Given the poor implementation record of forest policy, does stat e action taken this decade represent a break from the past? 4. What factors were involved in facilitating the approval of the PFML? Chapter three develops a static computable general equilibrium model to address the following questions in the short-run:

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19 1. How are the forest and related sector outpu t and prices affected by the implementation of forest concessions? 2. How are household economies affected by forest concessions? 3. How are the regional dynamics of defore station affected by forest concessions? The fourth chapter, incorporating recurs ive dynamics and illegal logging and illegal deforestation sectors, addresses the same questions as the previous chapter in the medium-run, as well as the following: 1. What are the interactions be tween legal and illegal logging and deforestation as forest concessions expand? 2. How is the trajectory of the economy affected by the implementation of forest concessions through time? 1 The Legal Amazon is composed of the states of Ac re, Amazonas, Amap, Pa r, Rondnia, Roraima, Tocantins, Mato Grosso, and part of Maranho (west of the longitude of 44 west) and Gois (above the latitude of 13 south).

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20 CHAPTER 2 TOWARD A POLICY OF SUSTAINABLE FOREST MANAGEMENT IN BRAZILAN HISTORICAL ANALYSIS Introduction Understanding the forces that drove policy in th e past can inform our expectations of the effectiveness of policy implementation today. Historical analysis suggests that forest policies of countries with significant forested frontiers trans ition through stages reflec ting the orientation of governments toward economic development on th e frontiers, namely: se ttlement, protective custody and management (Marty, 1986). To present, with respect to Amazonian forests, Brazils path is no exception from this trend1. This chapter follows Brazilian forest policy from its beginnings in the late 19th centu ry to the colonization plans and paper parks of the military regime of the 1960s to the commitment to su stainable forest management of the current democracy to identify Brazils path through the stages of forest policy development. The military regimes prioritization of industrialization and integrating the Amazon into the national economy contradicted sharply with the protectionist forest policies of th e era thus marginalizing forest policy. This analysis provides evidence of profound changes in Br azils governance structures through democratization and civil societys role in influenci ng public opinion and political processes as well as increasi ng awareness of the biophysical importance of forests and the emerging vision of the Amazon as a region whos e primary vocation is sustainable forest management. Future implications of this transfor mation increase expectations of the relevance of forest policy for the region as the nation embarks into an era of sustainable forest management. Settlement and Exploitation (1889 to 1964) Early legislation on forests regu lated the harvest of valuable species, such as Brazil wood ( Caesalpinia echinata ) and the harv est of forests adjacen t to water. Land clearing occurred primarily in the Atlantic Forest Region to meet European demand for forest products, to produce

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21 energy, and to establish farms and ranches. W ith declining timber stocks and the drastic transformation of this countryside, the need to regulate forest use was recognized in the 1920s when the government of Getulio Vargas passe d the first Forestry Code in 1934 (Decree No. 23.793 January 23, 1934). With this law, private property rights were subordinated to the collective interest of society, an imposition that continues to resonate strongly today. A Legal Reserve requirement, which still exists although th e requirements have changed, dictated that no more than 75% of the forested land in private ru ral properties could be cl eared (art. 23). A fact rarely mentioned in current debates regardi ng forest concessions, a basic framework for concessions was written into the 1934 Code, a lthough they were not implemented during this period2. The law was ambitious for the time, but resu lted in few substantive changes in forest practices; government priorities were industrializ ation and integrating the Brazilian Amazon into the national economy through coloniza tion and agricultural expansion. Protectionist Approach to Natural Forests (1965 to 2000) The transition to a paradigm of forest protection often occurs when unrestricted exploitation of the forests renders them unable to sustain forest industr y capacity. Legislative command and control mechanisms are believed to be required to renew and protect natural forest resources. In the case of Brazil in the 1960s, how ever, while the Atlantic Forest was largely cleared or intensely fragmented, the forests of the Brazilian Amazon remained relatively intact (Fearnside, 1980; Torras, 2000 both cited in Siqueira & Nogueira, 2004, p. 5). Brazils protectionist period was characte rized by the promulgation of rest rictive legislation, the creation of large protected areas and the pr ovision of incentives for planta tion forests. Initiatives for the development of the natural forest management s ector were generally absent. Although a variety of legal instruments and institutions were put in place, they were largely ineffective in

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22 controlling deforestation as th e nations resource extraction for economic growth model took precedence over the rational use of forest resources3. With the poor implementation record of the pr evious Forestry Code, discussions about a new forestry code began in Congress in 1948 (Ahrens, 2003, p. 6; Ondro, Couto & Betters, 1995, p. 113). Seventeen years later and marking the transition to the paradigm of forest protection, the 1965 New Forestry Code (Law No. 4.771 September 15 1965) was passed by the military government of Humberto de Alencar Castelo Bran co. This code increased the restrictions on private property rights and removed landowne r entitlement to compensation for these restrictions. It introduced Perm anent Preservation Areas (APP) for the protection of sensitive areas and increased the Legal Reserve requireme nts in some regions to 50%. The law also created a range of conservation area categories: national, state and muni cipal parks, biological reserves for the protectio n of flora, fauna and aesthetics as well as national, state and municipal forests for meeting economic, sc ientific or social objectives. The military governments strategy of import substitution industrialization increasingly demanded raw materials to feed the nations industry. Charcoal made from timber was particularly important for the metal and mi neral industries (Kengen, 2001, p. 230; Mery, Kengen & Lujn, 2001, p. 245). To ensure supply of these products, subsidized cred it and tax exemptions for forest plantations were declared in the New Code and in a law passed in 1966 (Law No. 5.106, September 2, 1966). These incentives were the states principal instrument for forest sector development and resulted in the pl anting of 6 million hectares between 1965 and 1987 when the subsidies were eventually terminated (S ociedade Brasileira de Silvicultura [SBS], 1998 as cited in: Mery et al., 2001, p. 245).

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23 In 1967, the Brazilian Institute for Fore stry Development (IBDF; Decree No. 289, February 28, 1967) was created. The IBDF was Br azils first federal agency charged with the mandate of managing natural resource conservation (Drummond & Barros-Platiau, 2006, p. 91). Although the IBDF was created to engage in formulating forest policy, research, extension and creating conservation areas, given the importan ce of plantation incentives for the nations industrialization, the agencys main role in practice was the administration of incentives and the commercialization of wood products (Chadw ick, 2000, p. 153; Drummond & Barros-Platiau, 2006, p. 91; Kengen, 2001, p 26; Viana, 2004, p. 18). Integrating the Brazilian Amazon into the National Economy The m ilitary governments Operation Amazonia sought to develop, occupy and integrate the Brazilian Amazon with the national econom y. Geopolitical concerns including securing Brazils borders with other Amazonian countries and insuring ownership of mineral and other natural resources motivat ed the governments efforts to dem onstrate control of the region. To help achieve this goal, the government pursued major road building and agricultural colonization projects and provided fiscal incentives for industry and agri culture. A regional development agency and bank, the Superintendency for the Development of Amazonia (SUDAM) and the Bank of Amazonia (BASA), respectively were cr eated to manage and finance the strategy (Mahar, 1989, p. 11). The National Integration Program (PIN) was launched in 1970 and financed the Transamazon and the Cuiab-Santarm highways, which are now important commercial corridors as well as corr idors of severe forest loss and land conflicts. Agricultural settlement in the region was encouraged by allocat ing land in a 20 km strip on either side of these highways to smallholder colonists. Settlers were lured from Br azils drought stricken north east as well as the south by offers of housing subsidies, crop financing and loans for the purchase of farm plots. The

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24 Northwestern Integrated Development Pr ogram (POLONOROESTE) began in 1981 and involved paving the BR-364 highway from Cuiab to Porto Velho and the promotion of sustainable agriculture. The model of colonization and development of the Brazilian Amazon contradicted sharply with protectionist provisions in the New Fo restry Code. For example, a law passed in 1971 placed all land in the Brazilian Amazon within 100 km of a federal highway or 150 km of an international border under the juri sdiction of the National Institute for Colonization and Agrarian Reform (INCRA). According to INCRA policies, a settler would be granted transferable land titles in this area if they cleared it. Moreover, th e settlers were offered title to an area three times the size of the area cleared, up to a maximum of 270 hectares. This policy dramatically accelerated land clearing and speculati on in the region (Mahar, 1989, p. 37). Following the Oil Crises in the 1970s and the resulting increased demand for foreign exchange, the state placed less emphasis on road building and settlement and instead concentrated on the promotion of large-scale export oriented proj ects in livestock, forestry and mining around 15 development centers in the Brazilian Amazon (Mahar, 1989, p. 40). This program was known as the Program of Agricultu ral, Livestock and Mineral Poles in the Brazilian Amazon (POLAMAZONIA) and lasted from 1974 to 1987. The project aimed to develop infrastructure and through fiscal incentive s and subsidized credit, sought to improve the investment climate in the region while increas ing foreign exchange earnings. The Greater Carajs Program established in 1980 was another such program designed to exploit the reserves of iron ore in the Serra dos Carajs region in the state of Par (Mahar, 1989, p. 41). The military governments strategy for Amazonian development was arguably effective in generating economic growth although not equitable from a distributional perspective. The politic

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25 for the forest sector was aligned with the re gimes emphasis on industrialization and as such concentrated on the promotion of forest plantati ons. With the lions sh are of public resources devoted to industrialization, resour ces for promoting the sustainabl e use of forests were scarce. Institutions charged with fore st protection were weak and underfunded and the protectionist stage of Brazilian forest policy lived out primarily on paper. The Environmental Movement and Democratization Political opportunity for the for mation of an environmental movement began in late 1974 when then President Ernesto Geisels governme nt announced the opening (abertura) of the political system to the gradual implementation of democracy. The moderate government of President Joo Figueiredo declared amnesty for ex iles, terminated censorship in the print media, permitted the formation of new political parties, a nd called for direct elec tions of state governors (Alonso, Costa & Maciel, 2005, p. 5; Chadwick, 2000, p. 125). This opening enabled the growing environmental movement to partner with established sectors of civil society and align itself with an increasingly organized international environmental movement. Growth in the number of environmental nongovernmental organiza tions (NGOs) appears to be correlated with important events in Br azilian democratization. NGO growth increased with the abertura in 1974, the amnesty law passed in 1978/1979, and direct elections of state governors in 1982. A record of 77 new environm ental NGOs were established following the Constitution of 1988 (Chadwick, 2000, p. 163). Democratization also brought with it ne w government institutions which were more responsive to civil societys demands and environmental concerns. With direct governor elections in 1982, the number of government envi ronmental agencies increased as well as NGO counts. In 1995, there were three times more environmental agencies than at the beginning of the abertura (Chadwick, 2000, p. 159).

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26 In 1981, the National Environmental Policy (P oltica Nacional do Meio Ambiente, Law No. 6938 August 31, 1981) was instituted and co ntinues to be Brazils most important environmental policy. Passed during the abertura, th e enactment of this law provides evidence of civil societys increased presence and effectiv eness in influencing policy (Drummond & BarrosPlatiau, 2006, p. 92). Its principal motivation was to consolidate ex isting legislation pertaining to the work of the Secretariat for the Environment (SEMA; Paulo Nogueira-Neto interviewed in Urban, 1998, p. 316). The implementing agencies for this policy are organized as The National Environmental System (SISNAMA) which was cr eated in 1981. SISNAMA is composed of the institutions responsible for environmental protec tion at the federal, stat e and municipal levels. SEMA was SISNAMAs principal agency, while other institutions such as the IBDF remained sectoral in nature (Kengen, 2001, p. 28). Regulations for the SISNAMA were instituted in 1990 and its implementing agency was The Nationa l Environmental Council (CONAMA; Figueiredo, 2007, p. 65). CONAMA is linked to the Presidency above the Ministry of the Environment (MMA) and is responsible for deliberating on regulations for environmental protection (Figueiredo, 2007, p. 65); it is composed of fede ral, state and municipal agencies, business leaders, and scientists and ha s a strong representation from environmental NGOs (Rylands & Brandon, 2005, p. 29). In 1989, due to SEMA and IBDF s often overlapping mandates, they and the Secretary of Fisheries Department (S UDEPE) and the Superintendence for Rubber (SUDHEVEA) were replaced by the Brazilian In stitute for the Envi ronment and Natural Renewable Resources (IBAMA; Rylands & Brandon, 2005, p. 30). The emerging legal-bureaucratic structure provided political space and more responsive institutions for environmental claims as well as career opportunities with in those institutions (Alonso et al., 2005, p. 6). Furthermore, the enviro nmental movements shift from a biocentric

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27 focus which aimed to protect nature from human influence to a socio-environmental focus in the 1970s broadened the support for this movement4. At the end of the 1970s, social groups were mobilizing to inform the democratization process. The environmental movements increa sing social orientation enabled it to graft environmental concerns on to other socioeconomic and political agendas, effectively creating linkages between the environmental and democra tization movements (Alonso et al., 2005, p. 12). For example, the National Front for Ecologic Ac tion created in 1987 was dedicated to informing public opinion on environmen tal issues and successfully pressu red for the inclusion of a chapter on the environment in the 1988 Constitution (Alonso et al., 2005, p. 17). The formation of NGO networks was decidedl y important in consolidating the Brazilian environmental movement. These networks were st rategic for uniting and c oordinating the actions of individual NGOs working on similar matters; th ey enabled the exchange of experience and information and the mobilization of individual citizens. These networks also proved to be effective at promoting their policy agendas (Chadwick, 2000, p. 171). They were the basis for large campaigns and served as a vehicle for obtaining government and international grants. Environmental associations on the other hand were heavily engaged in teaching and served as specialized consultants to gove rnment, providing scientific information to support policy development (Alonso et al. 2005). Protected Areas At the United Nations Conference on th e Environm ent in Stockholm in 1972, Brazil committed to creating its first environmental mi nistry, the Secretariat for the Environment (SEMA; Decree 73.030 October 30 1973). SEMA was es tablished within th e Ministry of the Interior to develop policies for environmen tal protection and management (Drummond & Barros-Platiau, 2006, p. 91). Its main achievements include the establishment of 38 ecological

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28 stations and 11 environmental protec tion areas between 1977 and 1986 (Aquino, 1979; Drummond, 1988; Nogueira Neto, 1980 and 2001 a ll cited in Drumm ond & Barros-Platiau, 2006, p. 92). With SEMAs addition of 3.2 million hectares of ecologica l stations, protected areas reached 13 million hectares (Urban, 1998, p. 107). The IBDF created a protected areas system parallel to that of SEMAs a nd between 1979 and 1986, it established 8.5 million hectares of National Parks and National Biological Preserves. These protected areas make up some of the largest and most important of Brazils conservation areas today (Drummond & Barros-Platiau, 2006, p. 91). The Ministry of Mines and Energys Radar of Amazonia (RADAM) project was implemented between 1975 and 1983 to map the geology, geomorphology, hydrology, soils, and vegetation of the Brazilian Amazon. The projec t recommended the creation of 35.2 million hectares of protected areas and another 71.5 million hectares of sustainable use areas since these areas were considered unsuitable for mining or settlement (Rylands & Brandon, 2005, p. 30). Of the 25 priority conservation areas identified, only 5 national parks and 4 reserves were created (Figueiredo, 2007, p. 70; Mittermeier, Fonseca, Rylands & Brandon, 2005, p. 602). The Our Nature Program (Decree No. 96.944, 1988) was a direct response to the Constitution of 1988 as well as international pressures for environmental responsibility, geopolitical concerns about the internationalization of the Amaz on, and to strengthen Brazils position in international relati ons (Ioris, 2005, p. 182; Lopez, 2000, p. 57). Its mandate was to reduce predatory activities, stru cture the environmental protec tion system, protect indigenous and extractivist communities, regenerate degrad ed ecosystems, participate in environmental education, and regulate the use of the Legal Amazons natural resources by means of EcologicalEconomic Zoning5.

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29 Although 11% of continental Brazil was allocate d to protected areas, the majority of the countrys 60 national parks were considered paper parks (Figueiredo, 2007, p. ii). Paper parks are areas declared by the government to be prot ected in law but not in practice; they are characterized by a lack of mana gement capacity, financing, infras tructure, and integration of local communities in their management, as well as contradictory legislation. In some instances, the creation of protected areas wa s a cost effective way of dem onstrating a commitment to the environment before domestic and international in terests without fulfilling the commitment in any substantive way. As an indication of this weak implementation, funding fo r the protected areas system in Brazil was low; between 1993 a nd 2000, federal spending for protected areas accounted for 0.3% to 0.5% of the MMAs bud get, most of which was allocated to administrative and financial expenditures (Young & Roncisvalle, 2002 as cited in Young, 2005, p. 757). Nonetheless, Brazils protected area s, although underfunded, have had a quantifiable effect on deterring deforestation and encroachment (Nepstad, Schwartzman, Bamberger, Santilli, Ray, Schlesinger, Lefebvre, Alencar, Prinz, Fiske & Rolla, 2006, p. 72). Constitutions, International Agreements and the 1990s Early trea tment of forest resources in Brazils Constitutions focused on the jurisdiction between state and federal governments. The 1891 Constitution granted states autonomy over forest resources while property rights were unlimited. The 1934 Constitution (art. 5, XIX, j) transferred responsibility for forestry law back to the Federal Government, although states could develop supplemental or complimentary le gislation. In the 1967 Constitution and the 1969 Amendment, the responsibility of forest mana gement was granted exclusively to the Federal Government (Viana, 2004, p. 9). The current Constitution of 1988 which followed democratization provides explicit treatment of forests and include s a chapter on environmental qual ity and protection. This chapter

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30 places environmental limits on the developmenta list model formerly pursued by the military government (Drummond & Barros-Platiau, 2006, p. 95) and charges federal and state governments with developing and implementing legislation pertaining to the environment. While municipal authority to legislate on forests is not explicitly stat ed, municipalities may legislate on issues of local interest thereby supplementing fe deral legislation (art. 30, I and II; Viana, 2004, p. 10). Chapter VI of the Constitution, On the E nvironment proclaims that an ecologically balanced environment is a civil right of present and future gene rations and confers the protection of this right to the state and the public. A nu mber of biomes, including the Brazilian Amazon, were declared national patrimony and as such may only be used in such a way that the environment and natural resources are preser ved and ecological functions are maintained. The 1990s were particularly active years for fo rest policy in Brazil, both through domestic and international engagement. Th e MMA was created in 1992 and is to date Brazils top-level institution in its hierarchy of environmental institutions (Figueiredo, 2007, p. 65). There was also a dramatic increase in international interest in the Amazon region as its importance for biodiversity and carbon sequestration became more ev ident, as did threats to its existence. The United Nations Environment and Development Conference held in Rio de Janeiro in 1992 resulted in Agenda 21 which dealt explicitly with forest resources. In 1998, the Environmental Crimes Law (Law No. 9.605, Lei de Crimes Ambien tais) was passed to systematize the sanctions outlined in numerous legislative instruments a nd to address the recommendations of Agenda 21 (Viana, 2004, p. 22). In Chapter V of this law, On Crimes against the Environment, the penalties for violations of the New Forestry C ode are described. An innovation introduced in the law is that companies would become subject to pr osecution; prior to this law, only citizens were liable for environmental crimes (D rummond & Barros-Platiau, 2006, p. 100).

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31 Political Economy Impacts on the Forestry Sector Figure 2-1 reveals potential corr elations between forest sector indicators and political and econom ic events between 1961 and 2007. First, roundwood production shows a steady increase over the period. Following the Oil Crisis in 1973, production grows at an unprecedented rate for the remainder of the decade. Growth in forest plantations follows an exponential trend, little affected by the elimination of plantation subsidies in 1987. Exports appear to follow deforestation levels closely which may be related to the fact that most timber harvested in the Amazon until the mid 1970s was exported due to the lack of infrastructure connecting the region to the south which would later become the larg est source of timber demand (Lima et al., 2006, p. 29). Peaks and troughs in exports appear to be co rrelated with the institution of the Plano Real, changes in Legal Reserve requirements, and the development of Regulations for the New Forestry Code6. Estimates on deforestation also follow these general trends. Until 2005, deforestation levels have generally increased steadily. Laurance, Albernaz and da Costa (2002, p. 11) show that although deforestation rates (absolute an d per capita) declined slightly in the first few year s of the 1990s compared with the period between 1978 to 1989, they returned to historically high levels between 1995 and 2005. Variation in the rate of deforestation between years appears to be closely correlate d with economic variables. For example, the relatively lower levels of deforestation betw een 1991 and 1994 are likely associated with the freezing of bank accounts which occurred in 1990, thus constraining investment and economic activity (Laurence et al., 2002, p. 12). The drasti c increase in 1995 is hypothesized to be a response to the increase in investment funds av ailable resulting from stabilization measures contained in the Plano Real (Fearnside, 1999 as cited in Laurence et al., 2002). To help contain the deforestation that followed the Plano R eal and to improve Brazils credibility in environmental policy with the international community, a provisional measure (Provisional

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32 Measure 1.511, August 22, 1996) was passed, increasing the Legal Re serve requirements to 80% in the Amazon biome (Hirakuri, 2003, p. 16; Toni, 2006, p. 28). The increasing trend in deforestation beginning in 2000 was a response to greater economic growth (Bugge, 2001 as cited in Laurence et al., 2002, p. 12). Reductions in deforestat ion following 2004 may be related to the governments action plan to combat deforestation and the strengthening of the Brazilian real relative to the US dollar. Sustainable Forest Management (2000 to present) As the influence of the environm ental move ment grew and civil society became more active in the political affairs of the country, forest policy began to transition to the management phase of forest policy development with the turn of the millennium. Four critical developments can be identified which signal this transition, namely: the institution of the National Forest Program (PNF) and the National System of Natu re Conservation Units (SNUC), the provision of fiscal incentives for natural forest management, and the Public Forest Management Law (PFML). These developments are discussed in turn. In 1997, the Federal Government and the Food and Agriculture Organization (FAO) of the United Nations developed the Positive Agenda fo r the Forestry Sector to manage forests for socioeconomic development while maintaining e nvironmental quality and ecosystem integrity. The Positive Agenda is one of Brazils first policy references to forest-based sustainable development, differing significantly from the biocentric, protectio nist paradigm of previous years. The PNF and the Secretariat for Biodiversity and Forests were instituted as a result of this agenda. The PNF is central to the political transiti on to balancing use and conservation, setting concrete and ambitious targets for the sustainabl e management of forest resources. It aims to increase Brazils share of international timb er markets from 4% to 10% by 2010, increase the

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33 area of sustainably managed forests on private land by 20 million hectares, create 50 million hectares of sustainable produc tion forests on public land, and in crease exports from natural forests from 5% to 30% by 2010 (Macqueen, Gr ieg-Gran, Lima, MacGre gor, Merry, Prochnick, Scotland, Smeraldi & Young, 2003, p. vi; Viana, 2004, p. 24). Implementation of the PNF rests with the Coordinating Commission of th e National Forests Program (CONAFLOR). CONAFLOR is composed of various government ag encies and civil society; its mandate is to develop policy in the areas of land tenure reform, credit and financing, environmental legislation, research, and training. Such a program for pr omoting sustainable forest management is unprecedented and marks the governments explic it recognition of the Brazilian Amazon as a region best suited for forest-based developm ent (MMA, 2001, p. 12). Affirming the significance of this program, the forestry sector was included as one of three priority program areas in the Governments Multi-Year Plan for 2000 to 2003, the federal strategy fo r capital expenditures during a Presidents tenure. The SNUC (Law 9.985, 2000) was created in 20 00 and details criteri a and guidelines for the creation and management of conservation areas. The SNUCs mandate is to protect biodiversity while promoting su stainable development (Viana, 2004, p. 23). The law provides for two main categories of conservati on areas, namely sustainable us e areas and strictly protected areas. While strictly protected areas have re source conservation as their main objective, sustainable use areas, which in clude national forests, seek to balance conservation with the sustainable harvest of natural resources. Demons trating this new approach to natural forest resources, legislators use the term management instead of protection and consider communities an integral component of the landscape (D rummond & Barros-Platiau, 2006, p. 98; Silva, 2005, p. 609). Between 2002 and 2004, over three million hect ares of protected ar eas were created and

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34 currently approximately 11% of Brazils area has been desi gnated by federal or state governments as protected ar eas (Figueiredo, 2007, p. 61). Since 1965, numerous provisional m easures have been issued to modify the New Forestry Code, most of which deal with aspects of the Legal Reserve and Permanent Preservation Areas. In force today, a Provisional Measure issued in 2001 (Medida Provisria No. 2.166-67, August 24, 2001) established Legal Reserv e requirements of 80% and 35% for the high tropical forest and cerrado biomes, respectively and 20% for other regions (Viana, 2004, p. 17)7 Restructuring of IBAMA in 2001 (Decree 3833) resulted in the creation of the Forestry Directorate to coordinate, supervise, regulate a nd orient federal action with rega rds to reforestation and access to and management of forest resources, and to provide recommendations on the creation and management of National Forests and Reserves. Financial incentives for promo ting natural forest management are new and coincide with the implementation of the PNF (Verissmo, 2006, p. 6). Incentives are financed through Constitutional Funds for Regional Financing es tablished by the 1988 Constitution; these funds include the Northern Financing Fund (FNO), th e Central West Financing Fund (FCO), and the North Eastern Financing Fund (FNE). Banks, accord ing to government directives, offer lines of credit with below market interest rates appropriate for the long maturation periods of forestry investments (Verissimo, 2006, p. 20). The PNFs creation of the forestry arm of the National Program for Strengthening Family Agriculture (P RONAF-Florestal) also provides resources to family farmers engaged in forest management and agroforestry. All of these programs have disbursed a small fraction of the resources available, however, which is largely due to the current scarcity of forestland for legal forestry operati ons. In the case of PRONA F-Florestal, lending is expected to increase in the future as farmers ar e informed of the program and better technical

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35 support for the development and implementation of projects becomes available (Verissimo, 2006, p. 7). Contemporary Forest Policy and the Public Forest Management Law The creation of the PNF, SNUC, PFML and ince ntives for natural forest management mark the transition from a protectionist to a sustaina ble management approach to forest resources. With impetus from the PNF and the opportunity created by prevailing social and political economy considerations, a law promoting the management of public forests became a source of intense debate. Although rudimentar y provisions for such a law were first made in the Forestry Code of 1934, the PFML details a comprehensive program for instituting forest management by private agents on public land. The implications of this law for promoting forest-based development in Brazil are unprecedented and thus the conditions that facilitated its approval merit an in depth analysis. In this section, the development of the law and its provisions are briefly described. The factors which interacted in such a way as to create a political window receptive to this policy are considered in detail. The Public Forest Management Law Until 2006, Brazil lacked a fram ework to regula te forest management on public land (SFB, 2007a, p. 10). Since the 1934 Forestry Code, the firs t serious proposal to pr omote the sustainable management of public forests for timber and ot her forest goods and services was submitted by the government of Fernando Henrique Cardoso in 2002. This proposal was motivated by the need to control the illegal use of public forests, maintain it s capacity to produce goods and services, and foster socio-economic developm ent (SFB, 2007a, p. 10). With President Luiz Incio Lula da Silvas government entering o ffice in 2003, however, the proposal was withdrawn and the consultation process was re-opened (Guevara, 2003, p. 3). A working group involving all levels of government, researchers, and leaders in business, social mobilization, environmentalism

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36 and politics met on various occasions over a period of 14 months to further develop the proposal. After numerous consultations and revisions, Brazils first Public Forest Management Law (Law 11.284) was approved by Congress and sanctioned by President Lula in March of 2006. The law regulates the management of public forests for sustainabl e use and conservation and creates the SFB and the National Fund for Fore st Development. Key principles of the law are the promotion of forest-based development, research, conservation, an d the creation of the necessary conditions to stimulat e long-term investment in forest management and conservation (art. 2). The law mandates the esta blishment of national, state a nd municipal forests and forest concessions. In the case of forests occupied or used by local communities, extractive reserves and sustainable development reserves will be created. Forest concessions, the laws principal mechanism for promoting forest sector development, are defined as the governments entrustment, through a competitive bidding process, to a legal private entity the right to practice sustainable forest management for the production of goods and services. Sustainable fore st management is defined in the law as management for the production of economic, soci al and environmental benefits, while respecting ecosystem structure and function which consider s the management of various tree species, multiple non-wood products, and other forest goods and services (art. 3, VI). Forest concessions auctioned in a given year are to be described in the Annual Forest Granting Plan (PAOF; art. 10). To facilitate th e participation of smaller enterprises in the concessions process, the PAOF will contain a variety of concession sizes to accommodate regional characteristics such as the structure of production, local infrastruc ture and markets (art. 33). To prevent concentration of concessions in the possession of only a few firms, firms and consortiums can only hold up to 2 concessions, wh ile the percentage of concession area in the

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37 PAOF that one firm may possess will be restrict ed (art. 34). Concessi on contracts are of a maximum duration of 40 years while only 20 years in the case of concessions for the provision of forest services such as carbon sequestration (art. 35). The price of a particular conce ssion is intended to be a function of the harvestable forest goods and services, the consideratio n of environmental, social and technical criteria, and some of the administrative costs incurred by the SFB in the concessions process. The minimum price is set to encourage competition and the competitivene ss of the forest sector, be competitive with forest management on private land, and promot e socio-economic development (art. 36). The SFBs main functions are to formulate the PAOF, create and maintain the National Forestry Information System, manage the National Public Forest Regist ry, and develop, manage and monitor concession contracts including the bi dding process (art. 54). Third-party monitoring of a concession must be conducted at least every three years, the cost of which is borne by the concessionaire (art. 42). The law establishes the Management Commission for Public Forests, composed of members of the business communit y, civil society, scientists, and the public service. Its mandate is to propose and evaluate regulations for public forest management and serve as the consultative arm of the SFB (art. 51). In March of 2007, a decree (Decree No. 6.063) was issued to regulate the PFML. In particular, it regulates the National P ublic Forest Registry, the allocation of forests to local commun ities, the PAOF, environmental licensing, the competitive bidding process, concession contra cts, monitoring, and public audiences. A number of political, economic and social va riables interacted to create a political window receptive to the PFML. Some of these vari ables evolved over time such as the growing influence of the environmental movement and pr ofessional capacity in sustainable resources management. The first six years of 2000 were al so marked by events wh ich created political

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38 opportunities for the development and eventual institution of the law. Record levels of deforestation in 2002 raised concerns about th is seemingly untenable problem. Between 2003 and 2006, numerous covert enforcement operati ons uncovered the pervasiveness of illegal logging and exposed the entrenched interests of fi rms as well as public officials. The murder of an activist from the United States in 2005, fuelled by land disputes, drew domestic and international attention to the in creasing violence in rural regions of the Brazilian Amazon. Crisis in the forestry sector in 2004 was brought about by government attention to questions of land tenure irregularities and the illegal use of public lands. Finally, the electi on of President Lulas Workers Party (PT) in 2002 and the appointment of key progressive-minde d leaders contributed to a shifting tide of political will to address these issues. These variables are discussed in detail below. Increasing Deforestation and Illegal logging Data released by the National Institute for Space Research (INPE) revealed that from August 2001 to 2002, there was a 40% increase in deforestation compared to the previous period (Fearnside & Barbosa, 2004, p. 7). Occurring during a period of economic contraction, this was the second highest level of deforestation in history, second only to the deforestati on that occurred in 1995. In light of acute domestic and internat ional pressure, the government was forced into action. In 2003, a Presidential D ecree was issued (July 3, 2003) creating the Permanent InterMinisterial Working Group for the Reduction of Deforestation Indices in the Legal Amazon whose mandate was to develop measures and coor dinate actions to reduc e deforestation in the Legal Amazon (Presidncia da Republica, 2004, p. 7) The main lines of action presented in their comprehensive Action Plan for the Prevention and Control of Deforestation in the Legal Amazon were land tenure reforms, improved environmental monitoring and enforcement, and support for sustainable forest-based development activities. Shortly after the plan was instituted, 19.5 million

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39 hectares of Federal Conservation Units were es tablished and activities with potentially negative environmental impacts were prohibited along the BR-163 and BR-319 highways in the states of Par and Amazonas, respectively. Reductions in deforestation between 2004 and 2006 indicate that this plan may be contributing to improving monitoring and enforc ement (Instituto Socioambiental [ISA], 2006b). Since 2004, for example, 19 field enforcement stati ons staffed with federal and military agents were located strategically within the so-called arc of deforestation8. Stations monitor satellite data on land cover change and target gangs invol ved in illegal logging an d the illegal occupation of public land. Deforestation stat istics released by INPE reveal that deforestation has been significantly reduced in areas proximate to these field stations (ISA, 2006b). The timeliness of deforestation statistics has also improved dr astically in recent years. Previously, reporting of deforest ation indices was delayed by a number of years, often for political reasons (Fearnside & Barbosa, 2004, p. 9). For example, the increas e in deforestation in 1992 was not reported until 1995, while the histori cal peak of deforestation in 1995 was not reported until one month following the Decem ber 1997 Kyoto Conference on Global Warming. In 2002, the government announced that future estimat es would be released as soon as they were available. Land use and land cover change mon itoring technology has also improved; the Real Time Deforestation Detection System (DETER) has allowed state agencies to monitor the Brazilian Amazon by satellite with a monthly coverage period. The states enhanced ability to obtain land cover change information in a timely manner and the increased transparency of the system is believed to be contributing to reducing levels of deforestation. Since the Action Plan for the Prevention a nd Control of Deforestation in the Legal Amazon, the state and police have taken unprecedente d action to tackle the problem of illegal

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40 logging on public land. Between October 2003 and 2006, 221 operations were conducted to detect and punish illegal logging; 814 thousand cubic meters of wood were seized, 800 million reais in fines were issued, a nd 186 people were incarcerated, 63 of which were public servants, as a result of these operations9. Of these operations, Operatio n Black September (state of Rondnia, 2003), Operation Farwest (state of Pa r, 2004) and Operations Curupira I and II (states of Mato Grosso and Rondnia, 2005) were the largest. The perpet rators of the crimes were identified as a highly orga nized network of loggers, business people, and public officials. Operations Belm I and II also recovered subs tantial information regarding the trade in fraudulent Forest Product Transportation Authori zation Permits (ATPFs) wh ich set the stage for the implementation of Operation Green Gold (C onsulate General of Brazil in San Francisco, 2005). As a result of this operation, in October 2005 the Federal Police temporarily suspended the transport of all logs from the Brazilian Amazon (Lima et al., 2006, p. 29). Forestry Sector Crisis The crisis in the forestry sector began in December of 2004 when the Ministry of Agrarian Development (MDA) and INCRA issued a Gove rnmental Decree (Porta ria Conjunta No. 10) requiring rural property owners to register their properties within 60 to 120 days depending on property size. In response to this order, IBAMAs Director of Forests issued a Memorandum (Memorandum No. 619, December 10, 2004) recommending that all management plans in the Legal Amazon be suspended until INCRA released a formal statement on the results of the registration process (ISA, 2005c). As a result on December 31, 2004, IBAMA in Par suspended 26 management plans in the region of Santarm. Since August of 2003, by the direction of IB AMA, forest management plans were no longer approved without proper documentation of legal land title. Prior to 2003, management plans were approved based on precarious doc umentation from INCRA and state property

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41 registries. Furthermore, as long as a firm provided proof that it had initiated the land legalization process, it was able to submit a forest manageme nt plan for approval. Often by the time INCRA reached a decision regarding the le gality of the claim, the property was harvested and the logger had moved on (Lima et al., 2006, p. 30). In 2000, there were 3000 management plans in the Brazilian Amazon. Following property registrati on and the inspection of existing forest management plans, close to 2000 management plans were canceled or suspended. With the forest industrys access to private forestland brought to a near-standstill, ac cess to public forestland became critically important and consequently fueled debate on the proposal for the PFML. In mid-2004, worker unions and forest sector associations petitioned the government to begin approving new forest management plans. De spite the fact that th e government had decided not to authorize new forest management plans on public land until the land tenure situation was resolved, it conceded to evaluati ng 49 areas of public forest for their potential management for timber. INCRA geo-referenced 33 of these areas and discussions took pl ace on whether or not they would be made available for harvest. Wh ile management plans were being developed, the Government Decree requiring the registration of all rural properties was issued (Portaria Conjunta No. 10, December, 2004). On December 28, 2004, the MDA in Par announced that the 33 areas would not be available until January of 2005. ISA (2005c) reports that the industry was under considerable strain a nd lacked sufficient volume of le gally harvested timber to meet the demand of processing facilitie s. The situation reached crisis proportions with the suspension of 26 forest management plans in th e region of Santarm on December 31, 2004. Loggers responded to the cris is by initiating a blockade on January 25, 2005 on the BR163 highway (Cuiab-Santarm) at Novo Progress o, paralyzing southwestern Par for 11 days. On February 3, 2005 government officials, members of parliament and state representatives from

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42 Par, along with leaders of timber and rural producers organizations, came to an agreement to end the blockade. The federal government con ceded to re-evaluate the suspended forest management plans, authorize new plans in settlement areas and send the proposal for a PFML to Congress (ISA, 2005c). Escalating Violence In the last 20 years, over 500 people have been killed due to land conflicts in the state of Par alone, from farmers and col onists, to leaders of agrarian re form and other social movements (ISA, 2006a). In the municipalitie s of Altamira and So Flix do Xingu in Par, local residents reported that armed bandits evicted sixty familie s from their land (90% of the population living along the margins of the Xingu and Iriri Rivers). In some cases, the bandits, accompanied by the state military police, looted homes and set them on fire. The state argues that the recent increase in violence was a reaction to the land tenure regularization process (ISA, 2005b). In February of 2005, the government responded to the killings of several rural workers and leaders of social movements, in particular, the murder of Do rothy Stang, a US missionary in Anapu, and the murder of Daniel Soares da Co sta, a Rural Workers Union leader (British Broadcasting Corporation [BBC], 2005b); the st ate sent 110 soldiers to Anapu and another 2,000 troops were deployed in Par to maintain or der (BBC, 2005a). The government also launched a program to interdict land clearing on 8.2 milli on hectares in the area of the BR-163 highway until a land management plan wa s developed for the area. One year following Dorothy Stangs murder, the Federal government, on February 13, 2006, created 7 new conservation areas and increased the size of the National Park of Amazonia. These areas sum to 6.4 million hectares of protected area along the BR-163 (ISA, 2006b) increasing the total conservation area in the Amazon to 45.8 million hectares. Fifteen million hectares of this area were created by the government of President Lula and make up the

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43 countrys first Sustainable Forestry District within which 5 million hectares were allocated for forest management. The creation of this distri ct is the first state action founded in the new regulatory framework established by the PFML which was approved by Congress the week prior (ISA, 2006b). International Concern for the Amazon Intern ational interest in the Amazon has in creased gradually in recent decades due to greater transparency, a globalized media, the timeliness of deforestati on figures and, a growing recognition of the importance of the Amazon fo rest for conserving biodiversity and carbon sequestration. In 1988, for example, deforestation wa s 8 million hectares. Th is figure, the intense media coverage of the fires burning in the Amazon, and news of the assassination of rubber tapper Chico Mendes in 1988 drew attention to the region, increasing concerns about global climate change and the rural workers struggle to earn a livelihood from managing the forest (Kolk 1996, p. 78). The World Commission on Envi ronment and Developments 1987 report, Our Common Future acknowledged the Amazons importance as a genetic storehouse thus piquing international interest in the regions potential reserves of medicines and chemicals. The size of the Brazilian Amazon also made it an easy target of inte rnational atten tion (Kolk, 1996, p. 137). The Group of Sevens (G-7) concern for the environment was formalized in a 1989 summit where tropical forests were re cognized for their important function in sequestering carbon. Out of the G-7s actions, the Pilot Programme for th e Brazilian Amazon was pa rticularly significant, contributing to capacity development in polic y, research, and management. The growth and mobilization of NGOs also assisted in bri nging attention to the Amazon. NGOs in the US campaigned heavily against the multilateral development banks, beginning in 1983, for their involvement in setting development agendas in developing countries. The World Bank funded

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44 POLONOROESTE and Carajs project s were principal targets fo r their poor environmental records (Kolk, 1996, p. 290). The Workers Party Finally, the election of the PT, led by Luis Inc io Lula da Silva, has contributed to creating a political environm ent which was more receptive to proposals for forest-based development. Lula is considered Brazils first left-leaning Pr esident in the last 4 de cades (Morton, 2005, p. 14). Becoming active in trade unions in 1978, he was a founding member of th e PT in 1980, and on his fourth attempt at the presidency, he wa s elected in October 27, 2002. The PT has committed itself to fighting poverty and encouraging the part icipation of grassroots organizations (BBC, 2002). Four years following the assass ination of Chico Mendes, Jorge Viana, member of the PT and a close associate of Mendes, was elected governor of the Amazonian state of Acre. His goal was to be a government for the forest and its people (Rohter, 2002). Viana in fact is a forest engineer and considers that Acres primary vocation is sustainable fore st management. Marina Silva joined the PT in 1985 and was elected to the Senate in 1994. Also a close friend of Mendes, Silva was appointed to the post of Minister of the Environment by Lulas government in January 2003 (Environment News Service, 2002). Discussion and Conclusions The period o f settlement and exploitation from 1889 to 1964 was characterized by land clearing, primarily in the Atlantic Forest Region, to meet European demand for forest products, to produce energy, and to establish farms and ra nches. With declining timber stocks and the visible degradation of the countryside, legisl ators became concerned by the destruction and passed the first Forestry Code in 1934.

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45 In 1965, Brazil entered the protectionist peri od of forest policy development. The 1965 New Forestry Code placed unprecedented restricti ons on private property rights. Numerous laws and institutions were created for regulating fo rests and environmental quality. Vast protected areas were established, though generally low levels of management capacity and financing left them vulnerable to destructive forces. The military governments push for industrialization and integration of the Amazon into the national economy was an overwhelming counterforce to protectionist policies, however, rendering them largely insi gnificant. Although legislators demonstrated preoccupation with the envi ronment and legislated on its behalf, a developmentalist agenda was the priority to which most resour ces were allocated. With the abertura in 1974, political sp ace was created for the beginnings of an environmental movement. As this movement strate gically aligned itself w ith already established domestic movements and the international e nvironmental movement, environmental issues gained increasing attention. A break with the military governments developmental model was written into the 1988 Constitution where grow th was to be limited by environmental sustainability constraints. It is argued that with Brazils democratiza tion, the growing influen ce of the environmental movement, and civil societys effective engagement in political affairs, forest policy began to transition to a sustainable management model w ith the turn of the millennium. Various policies and programs mark this transition such as the S NUC, PNF, fiscal incentives for natural forest management, and in particular, the PFML. Taking the PFML as the proximate indicator of this transition in approach to natural forest resources the variables that led to the political opening for the approval of this law ar e of considerable interest.

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46 Until 2003, deforestation increased steadily since reliable estimates have been available, reaching historic highs in 1995 and in 2002. With greater transparency and a globalized media, both domestic and international interests expressed concern for the fate of the region. In response to these statistics and the illegal use of public lands and illega l logging, the government established an inter-ministerial group to address the issue. These factors, combined with escalating violen ce in the Amazon and a forest sector crisis, prompted the government to send the PFML propos al to Congress as a constitutional emergency. This proposal, likely aided by the election of the left-leaning PT, passe d relatively swiftly through Congress and was signed into Law on March 2, 2006. The laws provision for the establishment of forest concessi ons represents the transition to a sustainable forest management model on public lands and can prove to be a pow erful mechanism for directing the development of the natural forest management sector. The next few years will reveal whether the ne w policies and instituti ons established since 2000 represent a break with the lo w levels of implementation of the protectionist policies and programs of the past. The experience with forest concessions, their ability to control the illegal use of public lands, achieve sustainable yields, and manage the forest for multiple uses will provide indication of whether fo rest policy has in practice move d towards sustainable forest management. With recent efforts to delimit public lands a nd conservation units, the state in partnership with civil society is making m easurable progress in regulati ng public land use and occupation. Government neglect of the ille gal exploitation of public lands is no longer an option from a political standpoint with the globalized civil society and media en suring that the illegal clearing of the countryside does not go unnoticed. Collu sion between bureaucrats, politicians and

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47 business has been exposed and the risks involved in the illegal exploitatio n of forest resources are becoming too great. Distinct from the protectionist phase of forest management, the state has committed the institutional support of government agencies and funding to gain c ontrol of forest resources in the Brazilian Amazon. The potential forests hold for creating socio-economic stability and generating revenue, and the role forest con cessions and conservation areas may play in broadening support for political part ies has rendered sustainable forest management an attractive alternative. As such, the state has recognized the Brazilian Amaz ons primary vocation as forestbased development. While prot ectionist policies were outweighed by the colonization and resource extractive agenda of the military regime forest policy is becoming more aligned with extra-sectoral and economic development policie s. The elimination of subsidies for cattle ranching, the allocation of forests for community use, and the management of public forests for the production of goods and services are indica tive of this increas ing policy coherence. A successful transition to sustainable manage ment will require the state to address the question of forest management on private land. Land rent between private land and concessions with similar characteristics should be equal if fore st management is to continue on private land at a socially optimal level (Merry & Amacher 2005, p. 29). Integrating smallholder timber producers into the legal timber market will involve inter-institutional co llaboration and greater flexibility. The main obstacle to their integration relates to the procedural burden of preparing forest management plans and obtaining deforestation permits. Although required by law, most smallholders do not obtain deforestation permits while forest management plans require the contracting of a professional for their prepar ation, which is often beyond the means of small producers. Creating a system that is more res ponsive to the particul ar characteristics of

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48 smallholders is required if sust ainable forest management is to be a paradigm that applies to forests in general regardless of tenure type. Mech anisms to facilitate sm allholder production can go a long way to reduce illegality and contribute to the socioec onomic development of the some 500,000 families settled in the Brazili an Amazon (Lima et al., 2006, p. 34). Brazils transition to a politic of sustainable forest manageme nt is occurring at a time when the very concept of sustainable management is evolving rapidly. The environmental movements focus on climate change and the environmental serv ices that forests generate has increased the significance of forests in achievi ng broader environmental sustaina bility goals. New instruments for the promotion of sustainable forest mana gement, such as payments for environmental services, carbon credits for car bon sequestration, tradable defore station permits, and potential incentives for avoided deforestation are some of the options under consideration. This rapidly changing environment presents challenges and potential synergies for the implementation of Brazilian forest policy. Though the complexity of instruments has increased, the broadening definition of sustainable forest management is adding value to standing forests not only for the socioeconomic benefits they generate, but fo r the environmental serv ices they provide. Brazil has undergone profound political and eco nomic changes over th e last two decades with democratization, a free and gl obalized media, an engaged civil society, and a more stable economy. The political opportunity for the PFMLs approval set in the context of consistent economic growth and public engagement in the democratic process leads us to expect a significant shift both in policy a nd in practice in how natural fo rest resources are managed and for whom.

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49 1 Given Brazils size and regional diversity, the particular period in time at which a transition to a subsequent phase of forest policy development occu rs and the impact of forest policy may differ by region. This analysis focuses on the overall trends in forest policy development with a bias towards the Brazilian Amazon. 2 Forest concessions enable the state to sell the right s to harvest forest goods and services to private firms for a predetermined period of time. 3 This model has economic expansion as its primary ob jective with little consideration for the sustainable uses of resources. In Brazil, this model was characte rized by pervasive state intervention in the economy, state-corporatist mechanisms and clientelism. To manage the ambitious developmentalist program, the state apparatus grew significantly, including state-run enterprises. 4 The biocentric approach viewed environmentalis m as the business of science and scientists whereas the socio-environmental approach viewed environmental issues from the perspective of the social sciences (Alonso et al., 2005, p. 10). 5 Ecological-Economic Zoning is a form of land use pl anning that identifies areas where particular land uses should be encouraged and areas with special ne eds that may require conservation considering the physical, biotic and socioeconomic environments (FAO, 1996). 6 The Plano Real was an economic stabilization plan developed by the government of President Fernando Henrique Cardoso. 7 The cerrado is a tropical grassland savannah and is considered the worlds most biologically rich grassland biome. 8 The arc of deforestation is formed by the BR-163 hi ghway in western Par, passing through the extreme northeast of Mato Grosso until the southern reaches of Amazonas in close proxim ity to the Transamazon highway. 9 The average exchange rate for the year 2003 was 3.1 reais to the US dollar.

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50 Figure 2-1. Roundwood producti on and trade, area planted for pulp and paper, and area defore stedSources: Roundwood production, imports and exports (FAOSTAT, 2007); area planted (Associ ao Brasileira de Celulose e Papel [BRACELPA], 2005); area deforested (INPE, 2007) 0 500000 1000000 1500000 2000000 2500000 30000001961 1965: New Forestry Code 1973: Oil Crisis 1987: End of plantation subsidies 1989: Transition to democracy 1994: Plano Real 1996: Legal Reserve increase to 80% 1999: Regulation of forestry code 2003: Plan for combating deforestation 2006: Public Forest Management Law 2007Year C u b i c m e t e r s 0 500 1000 1500 2000 2500 3000 3500 H e c t a r e s Roundwood Production (100's m) Roundwood Exports (m) Area Planted (100's Ha/Yr) Area Deforested (1000's of Ha)

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51 CHAPTER 3 STATIC COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS OF FOREST CONCESSI ONS IN BRAZIL Introduction The m anagement of natural forests in Brazil is concentrated in the states of the Legal Amazon with almost all legal timber extraction o ccurring on private land. While the government has promoted the development of forest plantations through economic incentives, state involvement with natural forests has focused on regulation. Over 1 million km in the Legal Amazon have been identified as suitable for the production of forest goods and services and other resource-based activities (Verissimo et al., 2000, p. 6). Only recently, however, has the state taken action to harness the potential this vast public resource holds for promoting sustainable development. In March 2006, the P ublic Forest Management Law (PFML) was passed by the government of President Luiz Incio Lu la da Silva. A key feature of this law is a framework for creating forest concessions on public lands. With the goal of establishing up to 13 million hectares of forest concessions by the end of the decade, an initiative for the development of the forest sector is unprecedented in Brazili an history, marking the st ates recognition of the Amazons vocation as one of forest-based development. Forestry is an important economic driver in the Brazilian Amazon. Investment in forest management is low, however, since managing fo r high value timber speci es requires long-term investments in sometimes unstable political e nvironments (Rice, Gu llison & Reid, 1997 in Pinedo-Vasquez, Zarin, Coffey, Padoch & Rabe lo, 2001, p. 220). Compoundi ng this disincentive is the often unsecure tenure situation in th e Amazon, illegal logging, a nd deforestation. As a result, industrial forestry in the Amazon has foll owed a boom and bust cycle where, rather than investing in management, firms mi ne high value species until depl etion and then migrate further into the forest in search of new timber sources. Forest conces sions present an opportunity to

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52 counteract some of the negative incentives for forest management. By providing industry and communities with secure tenure, investment in management may increase. Increasing transparency in the regulatory environment further reduces risks and costs. Concessions, as in the case of protected areas, may also act as a barrie r to deforestation and encroachment (Nepstad, Schwartzman, Bamberger, Santilli, Ray, Schlesinge r, Lefebvre, Alencar, Prinz, Fiske, & Rolla, 2006, p. 72). Taking forest concessions as a proximate i ndicator of Brazils transition towards the management phase of forest policy developmen t, the economic, welfare, and environmental response to concessions provide an indication of the degree to whic h, if fully implemented, forest policy will be accepted and supported by society. In this chapter, a static computable general equilibrium (CGE) model is developed to ev aluate the short-run socio-economic and environmental implications of implementing forest concessions in the Brazilian Amazon. Following this introduction, the second section of this chapter provides a brief overview of the Brazilian forest sector while the third s ection describes key components of the PFML. The fourth section is an overview of CGE models and their applications in forestry. Next, the procedures followed in constructing the dataset are described and the CGE model is developed. Following model development, the scenario desi gn and modeling results are presented. The final section offers a discussion and conclusions. The Brazilian Forestry Sector Brazil is the largest pr oducer and consum er of tropical timber products and as such, the forest industry is an important component of th e economy and in particular, the economy of the Legal Amazon. The forestry sector is responsible for 3.5% of Brazils gross domestic product (GDP; Servio Florestal Brasileiro [SFB], 2007a, p. 10). The natural forest management sector in the Legal Amazon accounts for 15% of GDP (Verssimo, 2006, p. 23). The Brazilian forestry

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53 sector generates 2 million formal jobs and accounts for 8.4% of exports (Servio Florestal Brasileiro [SFB], 2007a, p. 10); forest product ex ports are third in importance to the Legal Amazon (Celentano & Verssimo, 2007, p. 22). In 2006, the value of forest sector output was over 10.9 billion reais1. Forest plantations were responsible for 66% of this output while natural forest management and non-timber forest products extraction accounted for 34% (Instituto Brasileiro de Geografia e Es tatstica [IBGE], 2007a). Roundw ood, charcoal and firewood made up the majority of this production (71%) with pulp a nd paper, and non-timber forest products accounting for 23% and 6%, respectively (Figure 31). Forest plantations produced over 69% of the value of roundwood, charcoal, fuelwood, and pul p and paper. While most of the timber volume harvested from natural forests is destined to wood products, over half of forest plantation production is processed into pulp and paper. The Brazilian forest industry is regionally distinct; the majority of natural forest management occurs in the states of the Legal Amazon while most forest plantation management occurs in the south and south east2. Total roundwood, charcoal and fuelwood production from natural forests totaled 3.18 billion reais in 2006, 43% of which was produced in the north, 27% in the north east, 17% in the cen ter west, 8% in the south and 5% in the south east (Figure 3-2). The value of roundwood, charcoal and fuelwood fro m forest plantations was 4.5 billion reais with 57% of production concentrated in the south, 33% in the south east, 4% in the north and 3% in both the north east and cen ter west (Figure 3-3). In 2006, approximately 14 .6 million cubic meters of timber (excluding charcoal and fuelwood), valued at over 1.4 billion reais, were harvested from natura l forests in the Legal Amazon. Forest plantations in the Legal Amazon produced 4 million cubic meters of timber

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54 (excluding charcoal, fuelwood and wood for pulp and paper), amounting to 187 million reais in value. Between 1998 and 2004, roundwood production in the Legal Amazon dropped by 15%. The number of logging centers, however, increased from 72 to 82 as the industry advanced to new logging frontiers. These new logging centers form an arc from the BR-163 highway in western Par to the northeast of Mato Grosso until the southern reaches of the state of Amazonas. The number of logging and proces sing companies increased from 2,600 to 3,100, mainly due to an increase in micro-mills in Par. Forest sector employment grew slightly (less than 3%) over this period, reaching 124,000 and 255,000 direct and indirect jobs, respectively, representing 3% of the economically ac tive population (Lentini et al., 2005a). Forest product exports incr eased between 1998 and 2004 from 14% to 36%. The value of forest product exports increased by 250% in part due to a fa vorable exchange rate and an increase in North American, European, and Asian demand for Brazilian forest products. The United States is Brazils larg est export market, consuming 31 % of Brazilian forestry exports, while China consumes 12% and France 11% (Lentin i et al., 2005a, p. 99). The internal market consumes 64% of production with the south and south eastern states consuming 42% of total production in 2004 (Lentini et al., 2005a, p. 67; 93). In 2003, there were 2.1 million hectares of forest with approved management plans, over 91% of which were located in the north (Table 3-1). Also in 2003, there were 210,032 hectares of forest authorized for defore station, over 59% of which were al so in the north. In 2004, 71% of timber was extracted by firms on forestland owne d by third-parties while 29% was extracted by firms owning or renting forestland (Lentini et al., 2005a, p. 72). In 2006, 627,000 hectares of

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55 forest plantations were establis hed, a 13% increase in relation to the previous year. Most new planting occurred in the south east (44 %) and the south (28%; Table 3-2). Illegal logging and illegal defo restation are pervasive problems in the Brazilian forest sector. As of 2004, 14% of the Amazon was deforested (Lentini et al., 2005a, p. 29). From 2004 to 2007, deforestation dropped significantly fr om 27,379 km to 11,224 km (Figure 3-4). Some estimates suggest that 80-90% of timber in Brazil is produced illegally; more conservative figures estimate that illegal logging accounted for approximately 43% of production in 2004 (Lentini, 2006). As of 2007, the Legal Amazon has 194 million h ectares of public forests, 56% of which are indigenous territories, 28% are conservation areas, 15% are other public lands, and 1% are sustainable development projects (SFB, 2007b). Th irty-three perc ent of the Legal Amazon is considered terra devoluta, which is land without legal title or land with a title in dispute (Lentini et al., 2005a, p. 32). The Public Forest Management Law Brazils 1934 Forestry C ode contained legislat ion for the concession of forest management on public land, though concessions were never imple mented. Motivated by the need to control the illegal use of public lands and to promote socio-economic development, a proposal for a public forest management law was submitted by the government of Fernando Henrique Cardoso in 2002 (SFB, 2007a, p. 10). With President Luiz Incio Lula da Silvas government entering office in 2003, the proposal was withdrawn a nd the consultation process was re-opened (Guevara, 2003, p. 3). A multi-stakeholder worki ng group was formed to debate and further develop the proposal. After this lengthy consul tation period, numerous public audiences, and congressional discussion, the PFML (Law 11,284) was approved by Congress and sanctioned by President Lula on March 2nd of 2006.

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56 The PFML regulates the management of public forests for sustainable use and conservation and creates the Brazilian Forest Service (SFB) and the National Fund for Forest Development (FNDF). Key principles of the law are the promotion of forest-based development, research, conservation, and the creation of the necessary conditions to stimulate long-term investment in forest management and conserva tion (art. 2). The law mandates the establishment of national, state and municipal forests, and forest concessions, and creates a framework for designating forests for community management. Forest concessions, the laws principal m echanism for developing the natural forest management sector, are defined as the governments entrustment to a lega l entity the right to practice sustainable forest management for the production of goods and services. This right is conferred through a competitive bidding process. The winning bidder must comply with all criteria in the published request for bids and demonstrate the capacity to meet all contractual requirements at its own risk for a pre-determined period of time (art. 3, VII). Sustainable forest management here is defined as management for the production of economic, social and environmental benefits, while respecting ecosyst em structure and function which considers the management of various tree species, multiple non-wood products and other forest goods and services (art. 3, VI). Forest concessions auctioned in a given year are to be described in the Annual Forest Granting Plan (PAOF; art. 10). To encourage the participation of smaller firms, the PAOF will contain a variety of concession sizes to acco mmodate regional characteristics such as the structure of production, local infr astructure, and markets (art. 33) To prevent concessions from being concentrated in the possession of only a few firms, firms and consortiums may only hold two concessions at a time, while the percentage of total concession area that one firm may

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57 possess will be restricted (art. 34). Concession contracts for the harvest of forest goods are valid for up to 40 years and up to 20 years in the case of forest services such as carbon sequestration (art. 35). The price of a particular conce ssion is intended to be a function of the harvestable forest goods and services, the consideratio n of environmental, social and technical criteria, and some of the administrative costs incurred by the SFB. Th e minimum price is set to encourage firm participation, promote forest sector competitiveness, be competitive with forest management on private land, and promote socioeconomic development (art. 36). The SFBs main functions are to formulate the PAOF, create and maintain the National Forestry Information System, manage the Nation al Public Forest Registry, and develop and manage concession contracts and the bidding process (art. 54). Third-party monitoring of a concession must be conducted at least every 3 years, the cost of which is borne by the concessionaire (art. 42). The law establishes the Management Commission for Public Forests, composed of members of the business communit y, civil society, scientists, and the public service. Its mandate is to propose and evaluate regulations for public forest management and serve as the consultative arm of the SFB (art. 51). In March of 2007, a decree (Decree No. 6.063) was issued to establish regulations for the PFM L, in particular, the National Public Forest Registry, the allocation of forests to local co mmunities, the PAOF, environmental licensing, the competitive bidding process, concession contra cts, monitoring, and public audiences. Overview of Computable General Equilibrium Models CGE models have their roots in the input-output fra mework developed by the economist Wassily Leontief in the 1930s (Dixon, Parmen ter, Powell & Wilcoxen, 1992, p. 19). Input-output (I-O) models are used for economic planning and are effective in elucidating the inter-sectoral linkages which result from the production and co nsumption of intermediate inputs (Bandara,

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58 1991, p. 6). This type of model is frequently appl ied to identify important economic sectors and estimate the effect of changes in demand on outpu t and employment. In addition, I-O models can shed light on the impact of private sector decisions and public sect or policies on the economy (Rose, 1995, p. 297). I-O models continue to be the most common approach to estimating the impact of public policies on the forestry sector (Alavalapati, Adamowicz & White, 1998a, p. 711). Despite their popularity, I-O models have a number of limitations which have prompted the development of CGE models. I-O models assu me fixed prices, unlimited factor supply, and that factors of production and in termediate inputs are used in fixed shares; final demand is treated exogenously (Alavalapati et al., 1998a, p. 711). As a result of these assumptions, final demand determines output levels, input substituti on is not possible, and producer and consumer behavior is not responsive to ch anges in relative prices (Banad ara, 1991, p. 7). Bridging the gap between I-O and CGE models is the work of Le if Johansen and his Multi-Sectoral Growth model. Further developing this framework was Irma Adelman and Sherman Robinson with their model of South Korea (Bandara, 1991, p. 10). A CGE model is a mathematical representa tion of the economy, from a household to a country, to the entire world economy. Creati ng a basic CGE model involves developing a theoretical structure of the economy which is formalized by equations representing demand for commodities, intermediate and f actor inputs, equations relating prices to costs, and market clearing equations for factors and commoditie s (Dixon et al., 1992, p. 87). Supply and demand equations describe the behavior of utility maximizing consumers and profit maximizing producers. The system of equations is solved simultaneously for the economic equilibrium (Bandara, 1991, p. 9).

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59 This class of models represents a sign ificant improvement over I-O models by incorporating an endogenous demand and pric e system, substitutability in production and demand, optimization of agent behavior, factor scarcity, and a more detailed treatment of institutions and the macroeconomic environment. Customization of the model in terms of the structure of production and cons umption, the macroeconomic environment, and institutional interactions enables the analyst to more realistically model th e economy of concern (Alavalapati et al., 1998a, p. 712). With producers compe ting for scarce resources and consumer expenditures, CGE models are effective in captu ring the distributional as pects of policy changes (Buetre, Rodriguez & Pant, 2003, p. 2). The principal data source for a CGE model is a social accounting matrix (SAM). A SAM is a square matrix representing an economy; it empiri cally describes the structure of production and transactions between sectors, institutions, and factors of production. A SAM has two main functions: the organization of data and to provide the statisti cal basis for the development of an economic model (King in Pyatt & Round, 1985, p. 17) SAMs are typically constructed based on national accounts data and government surveys such as household expenditure surveys and census data. CGE models have been critici zed on a number of grounds. Firs t, CGE models often require a large amount of consistent data in the form of a SAM (D evarajan & Go, 1998, p. 678). This data, where available, is collected with some de gree of error and the use of a variety of data sources may introduce inconsistencie s into the dataset. Constructing a SAM is also a very labor intensive process. Second, CGE models are typically calibrated using data from a benchmark year. At this benchmark year, the economy is assumed to be in equilibrium, that is, factors of production are

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60 fully utilized and optimally allocated given the po licy parameters and societ al preferences of the day. The quality of the model and the reliability of results are in large part dependent on the quality of the data in the benchmark year; ra ndom or extraordinary ec onomic events in the benchmark year may put model results into question (McKitrick, 1998, p. 544). Furthermore, since the calculated parameters are based on a si ngle year, they are typically not accompanied by measures of confidence. Third, additional data is required in terms of elasticities. Elasticitie s represent preferences and production technologies and can have a signifi cant effect on model resu lts (Alavalapati et al., 1998a, p. 712; McKitrick, 1998, p. 544). Elasticity estimates in the literature where they exist, may be at times contradictory, however (S hoven & Whalley, 1984, p. 1020). These estimates are often not available for some countries and as such, estimates from other countries are often applied or a best-guess is made (Bandara, 1991, p. 18). One approach that has been used to overcome uncertainty in el asticities is to produce central tenden cy tables of these parameters for each sector (Shoven & Whalley, 1992, p. 119). Sensitiv ity analysis may also be performed to determine how robust the model is to variations in elasticities (Stenberg & Siriwardana, 2005, p. 409). Fourth, the choice of macroeconomic balanc es can have a significant impact on model behavior (Dewatripont & Michel 1987, p. 66).There are three m acroeconomic balances in a CGE model: the current government balance, the current account of the balance of payments, and the savings and investment balance. Decision s regarding these balances are known as closure rules and are required to maintain a balanced economic environment. To avoid erroneous conclusions due to model sensitivity to closur e rules, it is good m odeling practice to conduct experiments under a variety of closure settings.

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61 Fifth, CGE models typically assume perf ect competition between producers. Perfect competition implies that one firm alone cannot in fluence market prices and producers earn zero pure profits. This assumption is often fair for pr imary sectors of the economy; it is an empirical reality however, that monopolies, monopsonies and oligopolies exist. Th is assumption ignores whatever pricing power a firm may have. More complex CGE mode ls have addressed this issue by incorporating imperfect competition in some sectors. Computable General Equilibrium Application s in Forestry CGE models are frequently used to study in ternational trade, taxes, economic policy packages such as structural adjustment programs, and climate change issues (Stenberg & Siriwardana, 2005, p. 412). More rece ntly, they have been applied to the study of forest sector policies. Dee (1991) developed a model to eval uate the impact of increasing the minimum harvest age of trees and variati ons in stumpage and discount rate s in Indonesia. Wiebelt (1994) studied how macroeconomic policies affect forest resource use in Brazil. Alavalapati, Percy and Luckert (1997) developed a regional model to analy ze distributional effects of an increase in the stumpage price in Canada. Thompson, Van Kooten and Vertinsky (1997) studied forest management options when non-timber values are considered in the model. Alavalapati, White, Jagger and Wellstead (1998b, p. 349) ev aluated the impact of land us e restrictions on a resource dependent economy in Canada. Dufournaud, Jerret t, Quinn and Maclaren (2000, p. 15) evaluated the economic impact of an export ban and an incr ease in royalties and export taxes. Gan (2004) evaluated the potential impacts of trade liberalization on China s forestry sector. Gan (2005) evaluated the impact of forest certification on welfare, output, prices and trade patterns. Stenberg and Siriwardana (2007) examined the ec onomic effects of selective logging, stumpage taxes, set-aside areas, and secure forest land tenure on the Philippine economy using a standard CGE model and a forestry sub-model.

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62 A few modelers have addressed the interac tions between land use and deforestation. Persson and Munasinghe (1995) developed a mode l of the Costa Rican economy to assess the impact of economy-wide policies on deforestation and compare ag ent behavior in the face of insecure property rights. The authors addresse d the question of defore station by introducing logging and deforestation activ ities under secure and ins ecure tenure regimes and by incorporating a market for defore sted land. Loggers clear land to harvest trees for sale to the market. The amount of land cleared is a functi on of the demand for forestland and the world market price of logs (Persson & Munasinghe, 1995, p. 267). Where property rights are secure, loggers incorporate the social value of forests in their utility function. Logging technology exhibits decreasing returns to scale to model the di minishing availability of forests due to illegal logging. A deforestation sector cl ears land to sell to the agricu ltural sector. In the case of insecure property rights, the cost of clearing land is a function of labor inputs; where property rights are secure, the deforestation sector incorporates the social value of forests in its utility function (Persson & Munasinghe, 1995, p. 266). Cattaneo (2001 and 2002) built on the work of Persson and Munasinghe (1995) and examined the relationship between economic grow th, poverty and natural resource degradation in Brazil. Cattaneo considered th e effects of currency devaluation, reduced transportation costs, changes in land tenure regimes, adoption of re gionally specified agri cultural technology, and fiscal incentives on land use cha nge. Cattaneos research emphasized the role of land types as factors of production, specifica lly, forestland, arable land, gr assland, and degraded land. To model deforestation, Cattaneo (2002, p. 36) included a deforestation sector which is responsible for land clearing, the am ount of which is a function of the returns to arable land and profit maximization subject to tech nological constraints. The price of arable land is a function of

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63 the returns to agri cultural land, taking into ac count the degradation and transformation of arable land into grassland and its subsequent transformation into degraded land which must be left to fallow. In order to simulate the presence of land tenure insecurity in the Amazon, returns to deforestation do not include potential returns from forested land. Construction of a Social Accounting Matrix for Brazil An Aggregated Social A ccounting Matrix for Braz il The SAM developed for Brazil follows the framework presented in Lofgren, Harris, Robinson, Thomas and El-Said (2002). In this framework, activities are distinguished from commodities, with activity and commodity account receipts valued at producer and consumer prices, respectively. The advantage of this structure is that on e particular activity can produce multiple commodities while one particular co mmodity may be produced by more than one activity. Marketing margins are also explicitly considered, which are th e costs involved in shipping a product from the producer to the consum er whether the good is an import, export, or domestically produced and consumed good (Lofgren et al., 2002, p. 7). The main data sources used in the constructi on of the Brazilian SAM are Brazils national accounts for 2003 (IBGE, 2004a). The year 2003 was chosen as the reference year since this is the most recent year for which definitive nati onal accounts were available, along with national household survey and expenditure data (IBGE, 2004b and IBGE, 2007b). The regional disaggregation of agriculture and forestry wa s supported by regional accounts for 2003 (IBGE, 2005) and IBGE data on production and extraction of forest products and silviculture (IBGE, 2004c). Additional sources include the 2000 de mographic census (IBGE, 2003), preliminary results from the 2006 agriculture and cattle ranching survey (IBGE, 2007c), The Research Institute for Applied Economics (IPEA) 2003 SAM for Brazil (Tourinho, Costa da Silva & Alves, 2006) and Cattaneos (2002) 1995 SAM for Brazil.

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64 The 2003 national accounts feature supply and use tables with 55 sectors and 110 goods and services. Since for the purposes of the present analysis, such sectoral and goods and services detail was not required, an aggregate SAM wa s created by aggregating sectors and commodities to 15 and 14, respectively (Tables 3-3 and 3-4). This aggregation was performed to the supplyuse tables for goods and service supply, activit y production, and imports of goods and services (Table 1 of the national accounts). This aggrega tion was then performed to the supply-use tables for intermediate consumption, final demand, and components of value added (Table 2 of the national accounts). Data on institut ional transfers, taxes, savings and investment were obtained from the national accounts Integrated Econom ic Accounts (CEI) tabl e and the IPEA SAM. These data were input into the SAM as described in Tables 3-5a and 3-5b. Disaggregating Land Types and Regional Fo restry and Agricultural Activities In the national accounts, expenditures on land ar e aggregated with capital. From Cattaneo (2002), approximately 20% of tota l agricultural and forestry expe nditure is on land. To calculate expenditure on land in the aggr egate SAM, the product of tota l agricultural and forestry expenditures and 20% was taken. This amount was then deducted from agricultural expenditure on capital and attributed to expenditure on land. In the national accounts, natural forest manage ment and forest plantation management are aggregated with agriculture. The following procedure was employed to disaggregate an aggregate forestry sector from agriculture. Firs t, indirect taxes on the forestry activity were calculated by applying the same proportion of agri cultures expenditure on indirect taxes to the forestry sectors total expenditure. Indirect taxes paid by the forestry sector were subtracted from the agricultural sectors indirect tax payments Next, forestrys expe nditure on intermediate consumption and factor inputs was calculate d. Total forestry expenditure less forestry expenditure on transportation in Cattaneo (2002) was calculated3. The intermediate and factor

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65 consumption as a proportion of total forestry expenditure in Cattaneo was then calculated. These proportions were applied to total forestry expenditure in the aggregate SAM to obtain expenditures on intermediate cons umption and factors; these expe nditures were then subtracted from the agricultural se ctors expenditures. To disaggregate the natural forest management, forest plantation management and deforestation sectors from the aggregate fore stry sector, the proporti onal output of forest products from natural forest management, forest plantations and defore station was calculated based on IBGEs production and extraction of fore st products and silviculture survey (IBGE, 2004c) and deforestation authorization permits fr om Brazils monitoring and control system for resources and forest products database (MMA, 20 08a). The proportions of total aggregate forest sector output for natural forest management, fo rest plantation manage ment, and deforestation were used to determine intermediate consum ption, factor inputs, and indirect taxes. Agriculture was regionally disa ggregated according to Brazils major administrative units. To determine regional agricultu ral expenditure on labor, the product of the number of people employed in agriculture by region (IBGE, 2007c) a nd the average wage in agriculture by region (IBGE 2003) was calculated. The proportion of agricu ltures total expenditure on labor by region was calculated and applied to agricultures total expenditure on labor. To calculate agricultures regional expenditure on capit al, the proportion of tractor s by region (IBGE, 2007c) was calculated and applied to agricultures total expenditure on capital. To calculate agricultures regional expenditure on land, the product of agricultural land area by region (IBGE, 2007c) and the average land price by region (Reydon & Pl ata, 2000) was calculated. This sectors proportional expenditure on land by region was calculat ed and applied to the agricultural sectors total expenditure on land.

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66 The agricultural sectors expenditure on intermediate consumption by region was calculated as the proportion of intermediate consumption by region based on regional accounts data (IBGE, 2005); these proporti ons were applied to agricu ltures total intermediate consumption. In the case of indirect taxes, the proportion of agricultu ral output value by region was calculated from the regional accounts (IBGE 2005). These proportions were applied to agricultures tota l indirect tax payment to obtain indirect taxes by region. Agricultural receipts by region were calculated as receip ts from each product as a proportion of agricultures total receipts. These proportions were ap plied to the agricultu ral activitys to tal receipts by region to determine regional agricultural receipts from each product. Next, the natural forest management, forest plantations, and defore station sectors were regionally disaggregated. The product of the area of forest under sustainable forest management plans by region (MMA, 2008a) and the average la nd price by region (Rey don & Plata, 2000) was calculated. The proportion of this product by regi on was calculated and applied to the natural forest management sectors expenditure on fo restland to obtain expenditure on forestland by region. The product of the area of forest plantations by region (Associao Brasileira de Celulose e Papel [BRACELPA], 2003) and the average land price by regi on (Reydon & Plata, 2000) was calculated. The proportion of this product by regi on was calculated and applied to the forest plantation sectors expenditure on agricultural land to obtain the forest plantation sectors expenditure on agricultu ral land by region. The product of the area deforested by region (MMA, 2008a) and the average land price by region (Reydon & Plata, 2000) was calculated. The proportion of this product by regi on was calculated and applied to the deforestation sectors expenditure on forestland to obtain the defore station sectors expend iture on forestland by region.

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67 Each forest sectors expenditure on indirect taxes was calculated proportional to the value of output from each of these sectors. The forest sectors intermediate consumption was also calculated in this manner. Re gional natural forest management, forest plantation, and deforestation sectors expenditure on labor was calculated following the same procedure as for the agricultural sector; expenditures on capital were calculated accordi ng to proportional capital expenditures based on Cattaneos (2002) SAM. The product produced by the deforestation en terprise requires sp ecial attention. The deforestation product was calculated as a function of the product of the price differential between forested and agricultural land (Reydon & Plata, 2000), and the area deforested (MMA, 2008a). This amount summed with the deforestation se ctors forest product output represents the deforestation sectors total receipts. Disaggregating Labor and Households Following T ourinho et al. (2003), labor was disa ggregated into 6 types based on skill level and formal or informal participation in the labor market. Labor that formally participates in the market makes indirect tax payments (i.e. social security contributions), while informal labor does not. Low-skilled workers possess 0-8 years of sc hooling, mid-skilled workers possess from 9 to 11 years, and high-skilled workers have more than 11 years of schooling. Households were disaggregated into 3 inco me categories: low-income, mid-income and high-income. Income disaggregation is a functio n of the number of minimum wages (240 reais per month is equal to 1 minimum wage) a household earns per month. The low-income household earns from 1 to 3 minimum wages per month, the mid-income household earns from 4 to 10 minimum wages per month, and the high-i ncome household earns 11 or more minimum wages per month (Tourinho et al., 2006, p. 36).

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68 The proportion of labor income paid to a particular household income class and to indirect taxes was based on pr oportions calculated from the IP EA SAM (Tourinho et al., 2006). A sectors payment to a particular labor class was based on the proportion of that labor class employed in that activity in the IP EA SAM (Tourinho et al., 2006, p. 34). Distribution of land rent to households and the enterprise was based on the IPEA SAM (Tourinho et al., 2006, p. 38) and inferred from national household survey data (IBGE, 2004b). Tourinho et al. (2006) determined that the large number of informal low-skilled workers in the agricultural sector earning up to 1 minimum wage per month and the large number of informal low-skilled workers in the agricultural sector earning over 20 minimum wages is accounted for by the fact that some of these workers claim land re nt as a portion of their income. On this basis, it was assumed that families earning up to 6 mi nimum wages do not receive land rent while those earning 8 or more minimum wages do. The amount of land rent include d in returns to labor is the difference between declared labor income and the average labor income of families earning up to 6 minimum wages. This provides the imputed value of land rent for each household income class participating in agriculture and forestry, and the proportional receipt of land income for each household class (Tourinho et al., 2006, p. 38). Household consumption by income class wa s based on national household expenditure survey data (IBGE, 2007b); a household classs share of the total consum ption of each product was calculated based on the IPEA SAM. With regards to savings, it was assumed that households earning 6 or less minimu m wages per month have neglig ible savings (Tourinho et al., 2006, p. 40). Taxes In the Brazilian SAM, indirect taxes on activit ies are the sum of effective social security contributions and other production taxes net of subsidies. Commod ity taxes are the sum of a tax

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69 on the circulation of merchandise and services (ICMS), a tax on industrialized products (IPI), and other taxes net of subsidies. Tariffs are import taxes on commodities. Direct taxes on households and enterprises are taxes on current income and property. Balancing the Social Accounting Matrix In constructing a SAM from a variety of data sources, as in the case of the Brazilian SAM, some imbalances between symmetrical row and column sums are unavoidable. To eliminate these imbalances, the cross-entropy balancing ap proach described in Robinson, Cattaneo and ElSaid (2001) was employed. The procedure was executed in th e General Algebraic Modeling System (GAMS), a software system designed for solving mathematical programming and optimization problems (GAMS, 2008). The program code is available in Robinson and El-Said (2000). Table 3-6 is a complete listing of all SA M accounts and Table 3-7 presents an aggregated version of the SAM developed for Brazil, reference year 20034. Standard Computable General Eq uilibrium Model in GAMS The model developed herein is based on the International Food Policy Research Institutes (IFPRI) Standard CGE Model. This model is im plemented in GAMS and is solved as a mixed complimentary problem using the PATH solver. Th is model was developed by IFPRI to facilitate the use of CGE models in developing countries (Lofgren et al., 2002, p. vi). Although this model is very well documented in Lofg ren et al (2002), the basic mode l structure is presented here following Lofgren et al. Appendix A provides a listing of the core model equations. While the SAM is a numerical representation of the equilibrium payments and receipts between agents in the economy, th e CGE model is developed to de scribe the behavior of these agents and their economic environment (Thur low, 2004, p. 3). The model is a system of equations describing the utility maximizing behavi or of consumers, profit maximizing behavior of producers, and the equilibrium conditions and constraints imposed by the economic

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70 environment. Agent behavior is represented by linear and non-linear first order optimality conditions while the economic environment is descri bed as a series of equilibrium constraints for factors, commodities, savings and investment, the government, and rest of the world accounts (Lofgren et al., 2002, p. 8). The model may be broken into a series of blocks, namely: production, factor markets, institutions, commodity markets, and macroeconomic balances. These are discussed in turn. Production The m odel allows for an activity to pr oduce more than one commodity, while any particular commodity may be produced by more than one activity. Producers maximize profits subject to nested technological constraints described in Fi gure 3-5. At the bottom of the technology nest, domestic and imported commod ities are aggregated into a composite intermediate input according to fixed shares. Va lue-added is created by a constant elasticity of substitution (CES) aggregation of primary fact or inputs. Constant elasticity of substitution functions enable non-unitary though c onstant price elasticities (i.e. identical elasticities between all pairs of commodities), non-zero but constant substitution elasticities, and a unitary income elasticity (Annabi, Cockburn & Decaluw, 2006, p. 9). Primary factors are used until the marginal revenue product for each factor is equa l to its wage (Lofgren et al., 2002, p. 8). The wage paid to a particular factor can vary for each sector depending on the factor market closure. Intermediate and value added inputs are aggreg ated according to fixed shares. Since any one sector can produce more than one commodity, at the activity level, the commodities that a particular sector produce s are determined by fixe d yield coefficients. Factor Markets Factor m arket closures describe the mechanis m by which the supply of a factor equilibrates with demand. The model allows for three main factor market closures, the choice of which

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71 depends on the application and the temporal scal e under consideration. The first closure fixes the quantity of a factor at the benc hmark level allowing the economy-wid e wage to adjust; the factor is fully employed and mobile between sectors. The second closure is a Keynesian closure where the economy-wide factor wage is fixed and the factor may go unemployed. The third closure is a segmented market closure where each industry hires the base-year quantities of a factor. In this closure, factor demand and the economy-wide wage are fixed and the indu stry-specific wage and supply are flexible (Lofgren et al., 2002, p. 9). This closure is of ten used for short-term analysis5. Institutions The 8 institutions in the m odel are three household incom e classes, a deforestation institution, a general enterprise, an interest account, a government, and the rest of the world. Households purchase marketed commodities accord ing to a linear expenditure system (LES) where households use their income to first c onsume a minimum level of subsistence goods and services. The income remaining after subsistenc e consumption (i.e. supernumerary income) is used to purchase commodities according to a linear relationship between income and consumption. The difference between the CES f unction and the LES function is that income elasticity in the LES function is non-unitary (Annabi et al., 2006, p. 13). All households pay direct taxes (income and property taxes); mi d and high-income households also save. All households receive income from labor and capit al, while mid and high-income households also receive income from returns to agricultural and forestland. In addition, households receive transfers from social security benefits, interest as property income, the en terprise (i.e. indirect income from factors), the government, and the rest of the world. Direct taxes and transfers to domestic institutions are computed as fixed shares of household income while savings are specified as flexible (Lofgren et al., 2002, p. 10). The deforesta tion institution receives all its

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72 income from the returns to agricultural land and spends its income entirely on the deforestation product. It does not pay ta xes, nor does it save. The enterprise transfers factor income to households, pays direct taxes, pays interest as property income, and saves. The enterprise r eceives income from capital and agricultural and forestland. The difference between the behavior of households and the enterprise is that the enterprise does not consume. As in the case of households, direct tax payments and transfers are fixed shares of enterprise income, while savi ngs are flexible. The in terest account receives income from the government, the enterprise, and th e rest of the world, a nd transfers its entire income to households. The government receives income from the indir ect, direct and commodity tax accounts as well as the tariff account. The government consum es, in particular, public goods and services produced by the public administration sector (e.g. public health, education and public security) and to a much lesser degree, private services The government makes transfer payments to households which are indexed by the consumer pri ce index, pays interest on property, and saves. Government savings may be negative and is trea ted as a flexible residua l (Lofgren et al., 2002, p. 10). The rest of the world purchases exports, ma kes transfers to households, and receives (when the column entry is negative) income from interest The rest of the world s savings is the current account deficit (when the column entry is negativ e), which is the difference between a countrys expenditure and its receipts (Lof gren et al., 2002, p. 11). The rest of the world receives income from imports. Commodity Markets Outputs of a particular commodity from different sectors are treated as imperfect substitutes due to potential differences in the timing and quality of output, and the distance to markets. As a result, commodity prices are sect or-specific. The demand for a sectors output is

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73 determined by minimizing the cost of supplying the aggregate commodity subject to the CES function (Lofgren et al., 2002, p. 11) Aggregate domestic output is allocated to domestic and foreign markets with producers maximizing reve nues subject to a constant elasticity of transformation (CET) function. Demand for exports is infinitely elastic at fixed world prices. Domestic consumer demand is for a composite commodity composed of imports and domestic output. In determining domestic demand, the Armington assumption is utilized where consumers minimize costs subject to imperfect substitutabi lity between domestica lly produced and imported goods. International supplies of goods are infini tely elastic at fixed prices. The Armington assumption allows for some flexibility between do mestic and world prices thereby assuring that the domestic market clears. Macroeconomic Balances There are th ree macroeconomic balances in the model: the government current account balance, the current account of th e balance of payments, and the savings and investment balance. Decisions regarding these balances are known as closure rules wh ich are required to maintain a balanced economic environment. Amartya Sen (1963 in Dewatripont & Michel, 1986, p. 65) determined that the assumption of equality of savings and investment is not guaranteed in an economy where labor is fully employed, factors of production are paid up until their marginal productivity, household consumption is solely a function of real income, and there is a fixed amount of investment. In order for economic equilib rium to be achieved, one of the four of these conditions must be relaxed. In essence, the system is over-determined with one more equation than variable. With regards to the government account, tax ra tes may be fixed with government savings calculated as a flexible residual. Alternatively, government savings may be fixed and direct tax rates flexible. The current account of the balance of payments may be maintained by a flexible

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74 real exchange rate and fixed foreign savings which implies a fixe d trade balance. Alternatively, the real exchange rate may be fixed allowing fo r a flexible current account deficit and trade balance. There are three main types of closures for th e savings and investment balance: a balanced closure, the Johansen closure and a neoclassical closure. The bala nced closure is a variation of the investment-driven closure where investment and government consumption shares are fixed while the quantities are flexible. Changes in absorption ar e distributed between household and government consumption, and investment. Nomi nal absorption shares of investment and government consumption are fixed at their base year levels. With other investment-driven closures, government consumption is fixed in real terms. The balanced closure is preferable for examining the probable economic impacts of po licy shocks since it is a more accurate representation of how real world economies have tended to behave (Lofgren et al., 2002, p. 16). The Johansen closure is investment-driven wher e the real quantity of investment is fixed and savings rates for non-government institutions adjust to equal the investment cost. It combines fixed foreign savings, fixed real investment, and fixe d real government consumption and is often used to examine the welfare implica tions of policies (Lofgren et al., 2002, p. 16). It is assumed that the government implements policies to generate sufficient non-government domestic institutional savings to cover the cost of the investment bundle (Lofgren et al., 2002, p. 15). The neoclassical closure is savings-driven where investment is the sum of private, government and foreign savings. In this case, in vestment is flexible while all non-government institutions have fixed marginal propensities to save. Changes in absorption are largely absorbed by investment (Lofgren et al ., 2002, p. 15). The savings and inve stment market is cleared by

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75 assuming that an interest adjustment mechanis m exists outsid e of the model (Bandara, 1991, p. 17). The choice of closure rules can have a signifi cant impact on model behavior (Dewatripont & Michel, 1987, p. 66). Robinson (1988, in Kraev, 2003, p. 16) determ ined that the sectoral distribution of income is sensitive to the closur e rule adopted. The Johansen closure for example, avoids the misleading result of household welfar e improving as a function of increased foreign savings and decreased investment (Lofgren et al. 2002, p. 16). Given the potential sensitivity of model behavior to the closure rules chosen, it is g ood practice to model policy shocks in a number of macroeconomic closure settings. Scenario Design Brazils 2007-2008 Annual Forest Granting Pl an (SFB, 2007c), the governm ents first annual declaration of priority areas for forest management on public land, identifies 3.96 million hectares of public forests in the Amazon as priority areas for forest concessions. Of this area, it is estimated that 1 million hectares will be a llocated to forest concessions in 2008. Annual production from these first concessions is es timated at 610,000 m of roundwood and 670,000 m of logging residuals, generating gross revenues of $120 million reais per year and creating 8,600 jobs6. With over 2.1 million hectares of forest with active Sustainable Forest Management Plans in 2003, establishing concessions on 1 million he ctares of public forestland implies a 47% increase in available forestland. This scenario is modeled by increasi ng the factor supply of forestland in Brazils northern region by an equivalent amount. Given the potential influence macroeconomic closures may have on model results the factor supply shock is examined under a balanced closure, neoclassical cl osure, and Johansen closure. With regards to factor closures, since the curren t analysis is short-run, it is appropriate to segment the labor and capital markets where eac h sector employs the base-year quantity of

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76 capital and labor (Lofgren et al., 2002, p. 9). In this case, economy-wide wages are fixed and activity-specific wages and supply are flexible. Forestland and agricultu ral land are fixed and mobile between sectors. A flexible real exchange rate is chosen for the rest of the world closure which reflects current Brazilian government policy. The government closure fixes direct tax rates while government savings are flexible. The domestic price index is chosen as the numeraire. Simulation Results Comparing Simulation Results under Balanced, Neoclassical and Johansen Closures The gross dom estic product at market prices under the balanced, neoclassical and Johansen closures were very similar (0.01%, 0.00%, and 0.01%, respectively; Appendix B, Table B-1). Under the balanced and Johansen closure, the consumer price index increased, while for the neoclassical closure, the index decrease d (0.02%, 2.00% and -1.19% respectively). The Brazilian currency depreciated under the balanced and neoclassi cal closures (-0.01% and -0.91%, respectively) and appreciated under the Johansen closure (2.00%). The government savings to GDP ratio remained unchanged in the balanced closure and increased under the neoclassical and Johansen closures (0.00%, 4.10% and 2.60%, respectively; Table B-1). Fixed investment wa s not affected under the balan ced and Johansen closures and increased under the neoclassical closure (0.39%). The investme nt to GDP ratio remained unchanged under the balanced closure and increased in the neoclassical and Johansen closures (0.00%, 4.70% and 0.50%, respectively). Househol d marginal propensity to save increased for mid-income and high-income households and the enterprise under the balanced and neoclassical closures (balanced closure: 0.57%, 0.04% and 0.0 1%, respectively; neoclassical closure: 0.01%, 0.15% and 0.41%, respectively), and decreased fo r mid-income and high-income households and the enterprise under the Johansen closure (218.82%, -14.11% and -5.35%, respectively).

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77 Low-income, mid-income and high-income household consumption expenditures were unchanged under the balanced closure. In the ne oclassical and Johansen closures, low and midincome household consumption increased (n eoclassical: 2.10% and 0.20%, respectively; Johansen: 0.70% and 0.10%, respectively; Table B-1) while high-income household consumption decreased in the neoclassical and Johansen closures (-0.90% and -0.20%, respectively). All household classes and the enterprise earned mo re under the balanced closure (0.05%, 0.05%, 0.04% and 0.01% for low, mid and high-income households and the enterprise, respectively; Table B-2). Under the neoclassical and Johansen closures, low income households and the enterprise earned mo re while mid-income and high-income households earned less (neoclassical: 0.96%, 4.88%, 0.99% and -2.13%; Johansen: 0.49%, 0.92%, -0.07% and -0.80%, respectively). Equivalent variation remained unc hanged under the balanced closure; it increased for low-income and mid-income households and decreased for high income households under the neoclassical and Johansen closures (neocl assical: 2.10%, 0.20% and -0.90%, respectively; Johansen: 0.70%, 0.10% and -0.20% respectively; Table B-3)7. Labor income increased for all labor classes under the balanced closure, but decreased for all but low-skilled informal labor in the neoclass ical and Johansen closures (Table B-4). Capital income was greatest under the neoclassical closur e, compared with the balanced and Johansen closures (5.30%, 0.13% and 0.98%, respectively). Factor income for forestland and agricultural land were generally quite similar between closures. Under the Johansen closure, all composite good prices ex cept for forest products and public services (-6.54% and -15.04% respectively; Table B-5) increased by between 1.88% and 3.22%. Composite good prices were similar between th e balanced and neoclass ical closures with the exception of construction goods and public services.

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78 With regards to the factor price for a particul ar activity, the most notable differences were found between the balanced and neoclassical cl osures for all labor skill classes in the construction sector (Table B-6) and between the balanced closure when compared to the neoclassical and Johansen closures for all labo r skill classes in the public services sector. The change in the price of capital was also much higher in the neoclassical closure for the construction sector and lower in the neoclassical and Johansen cl osures for the public services sector. Levels of domestic activity, the quantity of factor demand by industry and the quantity of domestic sales were quite similar under al l three closures (Tables B-7, B-8, and B-9, respectively). Changes in exports were also similar under all three closures with the exception of construction exports. The change in quantity of composite goods and serv ices supply was very similar between closures (Table B-10). In summary, with few exceptions, the price of composite goods, f actor demand, factor prices, factor income with the exception of la bor, levels of domestic activity, domestic sales, exports, and the quantity of composite goods supply were very similar between closures. Gross domestic product at factor prices was identica l between closures. The greatest variations in results were in savings and inve stment behavior and were driven by the manner in which savings and investment were br ought into balance. As previously discussed, under a balanced clos ure, changes in abso rption are distributed between household and government consumption and investment, while nominal absorption shares of investment and government consumption are fixed at base year levels. As a result, changes in absorption have a noticeable effect on institutional marginal propensity to save and private consumption.

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79 The neoclassical closure is savings-driven where investment is the sum of private, government, and foreign savings, while investment is flexible. With changes in absorption compensated for by adjustments in investment, th ere is an increase in the investment to GDP ratio and in fixed investment (4.10% and 0.39%, respectively) which is met by increases in mid and high-income household and enterprise marginal propensity to save, and an increase in the government savings to GDP ratio. As a result of increased private savings, private consumption declines overall. In the case of the investment-driven Johansen closure, the real quantity of investment is fixed while household and enterpri se savings rates adjust to purchase the investment bundle. Government consumption in this closure is fixed in real terms. Under this closure, overall, households consume more and save less. The increase in the investment to GDP ratio is largely met by an increase in the government savings to GDP ratio. Simulation Results and Interpretation in a Balanced Macroeconomic Environment Sim ulations undertaken in a balanced macroeconomic closure environment are preferable for evaluating the real-world impacts of policies and to aid the design of complimentary policies. This section presents detailed re sults of the forest concessions policy experiment in a balanced macroeconomic environment. Gross domestic product at market and factor prices increased by 0.01% (Table 3-8). There was a small increase in the consumer price index (0.02%) indicating po tential inflationary pressure. The Brazilian currency depreciated by -0.01%. Absorption increased by 0.01%. Imports declined by -0.01% while exports were unaffected. Real government consumption and the government to GDP savings ratio remain unchanged. Private consumption increased by 0.02% (T able 3-8). Low, mid, and high-income household income increased (0.05%, 0.05% and 0.04% respectively: Table 3-9). Enterprise

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80 income increased by 0.01%. The marginal pr opensity to save of mid and high-income households and the enterprise increased (0.57% 0.04% and 0.01% respec tively; Table 3-8). Equivalent variation remained unchange d for all households (Table 3-10). Labor became significantly more expensive for th e natural forest management sector in the north (143.65%; Table 3-13); the pri ce of labor for the natural forest management sector in other regions dropped by between -9.40% and -14.82%. The price of labor increased for the processed wood and pulp and cellulose sectors (2.68% and 2.50%, respectively); it also increased for the deforestation sector in the nor th (18.84%) and decreased in th e north east and center west (6.96% and -5.89%, respectively). The price of labor decreased by a small amount for the agricultural sector in the north, north east and center west, and increased in the south east and south (-0.13%, -0.11%, -0.13%, 0.54% and 0.32%, resp ectively). With regards to the forest plantations sector, the price of labor decreased substantially in all regions (-13.57%, -14.40%, 20.43%, -26.08% and -15.99% in th e north, north east, south eas t, south and center west, respectively). The price of labor increased margin ally for all other sectors with the exception of the mining and petroleum and commerce sect ors. The price of capital followed the aforementioned trends. The price of forestland for the forestry and deforestation sectors decreased by the same amount for both sectors (-64.47%, -14.25%, -9.40% -9.54% and -11.14 in the north, north east, south east, south and cente r west, respectively). Th e price of agricultural land for the agriculture and fore st plantations sectors decreased for both sectors by the same amount in the north, north east, south east, south and center west (-0.17%, -0.38%, -6.79%, 1.81% and -0.17%, respectively). Both labor and capital factor income in creased between 0.07% and 0.13% (Table 3-11). Forestland factor income declined in the north, north east, south east, south and center west (-

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81 48.13%, -14.25%, -9.40, -9.54% and -11.14%, respectiv ely). Agricultural land income in the north, north east, south east, sout h and center west declined, part icularly in the south east (0.17%, -0.38%, -6.79%, -1.81% and -0.17%, respectively). There was a substantial decline in forest product prices and a very small decline in the price of processed wood and pulp and cellulose (-8.44%, -0.01% and -0.01% respectively; Table 3-12). The composite good prices of agriculture, mining and petroleum, and commerce decreased by a small margin wh ile the prices of industrial go ods and services, processed food, utilities, construction, transpor tation, private services and public services increased by a small amount. In terms of the natural forest management sectors level of domestic activity, there was a large expansion in the no rth, a small contraction in the north east and center west, and no change in the south and south east (24.68%, -0.02%, -0.01%, 0.00% and 0.00%, respectively; Table 314). As a result of the implementation of fo rest concessions in the north, there was a simultaneous contraction of the fore st plantation sector, particularly in the south and south east (3.71% and -2.40%, respectively) and to a lesser degr ee in the north, north eas t and center west (0.56%, -0.62% and -1.29%, respectively). Deforestation expanded in the north, north east and center west (1.20%, 0.14% and 0.09%, respectively). In the agricultural sector, there was an expansion in all regions except the north on the order of between 0.01% and 0.19%. All other sectors exhibited no change in th eir level of activity. There were small changes in the quantity of composite goods supply, the most significant of which was for forest products (0.37%; Table 317). The supply of all other goods and services were little affected. Domestic sales of agricultural products, forest products, indu strial products and processed food all increased (0.05%, 1.95%, 0.01% and 0.02%, respectively; Table 3-16) while there were

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82 no changes in the domestic sales of all other good s and services. Agricultural product and forest product exports increased by 0.31% and 14.55%, respec tively. There were small contractions in industrial, processed food, construction, transporta tion and private services exports on the order of between -0.04% and -0.09%, and no change in all other sectors. Forest sector demand for forestland increased in the north (46.97%; Table 3-15), decreased in the north east (-0.13%) and cen ter west (-0.01%) and presented no change in the south east and south. Deforestation demand for forestland increased in the nort h, north east and center west (6.22%, 0.41% and 0.29%, respectively). Forest plantations demanded less agricultural land in the north, north east, south east, south and cen ter west (-2.84%, -2.99%, -3.11%, -5.52% and 3.39%, respectively). Agriculture demanded more agricultural land in the north, north east, south east, south and center west (0.01%, 0. 06%, 1.83%, 0.52% and 0.01%, respectively). In summary, the forest concessi ons policy resulted in an incr ease in gross domestic product as well as household income and private consump tion. As a result of the expansion of forestlands for natural forest management in the north a nd taking into considera tion the full employment assumption for land in the model, the overall dema nd for forestland in the north increased by the simulated amount of 47%. As a consequence, the price of forestland, par ticularly in the north, dropped significantly with the reduc ed scarcity. The large increase in forest sector activity in the north resulted in higher labor and capital costs for this sector, taking into consideration the segmented labor and capital markets. With a fixe d economy-wide wage for labor and capital, a full factor employment closure, and increased scarcity due to th e forest concessions policy, both labor and capital income increased. A significant implication of the forest conce ssions policy is the simu ltaneous expansion of natural forest management in the north and the c ontraction of forest plan tations in all regions.

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83 Smaller contractions in the natural forest manage ment sector also occurre d in the north east and center west. As a result of this contraction, labor, capital, fores tland and agricultural land became substantially less expensive for the forest planta tions sector in all regi ons as well as for the natural forest management sectors in all regions with the exception of the north. Increased natural forest management activity in the north resulted in a decrease in forest product prices and an increase in sales, particularly in forest product exports which increased by almost 15%. The reduction in forest product prices was not completely transf erred to its principal intermediate consumers, the processed wood and pulp and cellulos e sectors. This is in part attributable to the higher capital and labor costs incurred by these sectors as a result of the forest concessions policy. As a consequence of the contraction of the fo rest plantation sectors in all regions, these sectors demanded less agricultural land. Given th e full-employment assumption for land, this supply was taken up by the agricultural sector which simultaneously increased activity and output in all regions, in particul ar in the south east and south where forest plantation production was concentrated. The reduction in forest plan tations demand for agricultural land also translated into a reduction in the price and inco me of agricultural land in all regions, which had implications for levels of deforestation. The re duction in agricultural pr oduct prices benefited consumers to some degree, however, this reducti on in price was not transmitted to the processed food sector which produced a more expensive com posite good. This result is partially explained by the increased labor and capital costs that this sector confronted. Incr eased labor and capital income, however, enabled households to cope with a general price increa se as reflected by the consumer price index.

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84 Implications for Deforestation In the base year of 2003, there were 125,307 hect ares legally deforest ed in the north and 68,263 and 16,462 hectares legally defore sted in the north east and cen ter west respectively for a total of 210,032 hectares. Sim ulation results indicate d that the deforestatio n sectors demand for forestland increased by 6.22%, 0.41% and 0.29% in the north, nor th east and center west respectively. Overall, the forest concessions policy resulted in a 3.8% increase in legal deforestation. This result may be explained by th e interaction of the returns to agricultural and forestland, the price of forest pr oducts and the deforestation sect ors output of forest products. The price of agricultural and forestland declined in all regions due to the forest concessions policy. While the deforestation sectors output of forest products increa sed in the north, north east and center west, the price of forest products declined. While the reduction in agricultural land income and the value of forest product outp ut reduced the amount of income perceived by the deforestation institution, the decrease in the price of fore stland rendered deforestation cheaper. As a result, the reduction in income was more than offset by the reduction in the price of forestland and the deforesta tion sector increased output of its composite good (forest products and cleared land). Conclusions The forest sector is an important component of the Brazilian econom y, accounting for 3.5% of gross domestic product and 8.4% of exports, and generating 2 million formal jobs. Over 500,000 families settled in the Brazilian Amazon depend on forestry as a component of their livelihood system. Though the majority of the le gal timber harvest is currently conducted on private land, over 1 million km of public forestland has been identified as suitable for the production of forest goods and services. Brazils Public Forest Management Law which passed in 2006 includes a framework for establishing forest concessions on these public forestlands.

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85 Such a framework for promoting natural forest management in Brazil is unprecedented and presents a tremendous opportunity for fore st-based socio-econo mic development. In light of the proposed scale of forest c oncessions in the Amazon, the importance of the forestry sector to the economy and to the regions inhabitants, analysis of the forest concessions policy in a quantitative framewor k can provide important clues as to the potential impacts of the policy and indications of comp limentary policies that may se rve to counteract unintended negative consequences of policy implementation. As such, a computable general equilibrium model was developed to analyze the socio-ec onomic and environmental impacts of forest concessions. Simulating the implementation of forest con cessions in a general equilibrium framework, three general conclusions are made: 1. Household income and private consumption increase with the implementation of forest concessions. Though there is a general in crease in the price of consumer goods, households are able to cope with this increase due to their increased income. 2. The expansion of natural forest manageme nt in the north results in a significant contraction of forest plantati on production in all regions a nd to a lesser degree, natural forest management in the north east and center west. The increased output from the north squeezes out plantation production by bringing less expensive timber to the market. As the forest plantation sector de mands less agricultural land for production, the price of agricultural la nd decreases. The excess supply of agricultural land is taken up by the agricultural sector, which pays less for th e land and consequently is able to produce more of a less expensiv e agricultural product. 3. The implementation of forest concessions resu lts in an increase in legal deforestation by 3.8% in Brazil, with the greatest percentage increase in the north, followed by the north east and center west. This may be explaine d by the interaction of agricultural land prices, forest product prices and forestland prices. The deforestation institution perceived less income from agricultural la nd due to the drop in its price. Although the deforestation sectors output of forest products increased, the decline in forest product prices also resulted in a reduction in fore st product income. These results, all other things being equal, would imply a reduction in the level of deforest ation. However, with the substantial drop in the price of fore stland in all regions, the reduction in the deforestation institutions income was more than offset by the reduced expenditure on forestland. Thus, the net effect was that the deforestation sector expanded its deforestation and fo rest product output.

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86 What this analysis does not consider explicitly are the illegal forestry and illegal deforestation sectors and how they may be expect ed to behave with the implementation of the forest concessions. In order to more realistically and fully evaluate the potential socio-economic and environmental impacts of forest concession s, the chapter that follows disaggregates and models an illegal forestry and illegal deforesta tion sector. To enable th e updating of agricultural land stocks resulting from deforestation and to consider the medium-ter m implications of the policy, a recursive dynamic computable general equilibrium modeling framework is developed and employed. 1 The average exchange rate in 2006 was 2.2 reais to the US dollar. 2 Brazils administrative regions are north, north east south east, south, and center west. The northern region is composed of the states of Rondnia, Acre Amazonas, Roraima, Par, Amap, and Tocantins. The north eastern region is Maranho, Piau, Cear, Ri o Grande do Norte, Paraba, Pernambuco, Alagoas, Sergipe, and Bahia. The south east is Minas Gerais, Esprito Santo, Rio de Janeiro, and So Paulo. The south is Paran, Santa Catarina, a nd Rio Grande do Sul. The center west is Mato Grosso do Sul, Mato Grosso, Gois, and the Distrito Federal. 3 Expenditure on transportation is de ducted since Cattaneo employs a different treatment of transportation margins. 4 For the full disaggregated version of the social account matrix developed for Brazil, please contact the author. 5 The closures employed in the modeling experiment s to follow are detailed in the Scenario Design section. 6 The average exchange rate for the first 210 days of the year 2008 was 1.7 reais to the US dollar. 7 Equivalent variation is measured at the level of pr ices and income present prior to the implementation of a policy. It is the minimum payment the consumer woul d need to forgo the policy change. In other words, it is the amount the consumer would need to receive to be as well-off if the policy had been implemented. A positive equivalent variation indicat es an improvement in welfare.

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87 Figure 3-1. Relative output value of fo rest products. Source: IBGE, 2007a Figure 3-2. Regional distributi on of roundwood, charcoal and fuelwood production value from natural forests. Source: IBGE, 2007a 6% 23% 71% Non timber forest products Pulp and paper Roundwood, charcoal and fuel wood 43% 27% 5% 8% 17% North North east South east South Center west

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88 Figure 3-3. Regional distribution of wood, charcoal and fuelwood production value from forest plantations. Source: IBGE, 2007a 0 5000 10000 15000 20000 25000 30000 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Area deforested (Km2) Figure 3-4. Area deforested 1988 to 2007. Source: INPE, 2007 4% 3% 33% 57% 3% North North east South east South Center west

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89 Figure 3-5. Structure of production Commodity Outputs Activity Level Production Aggregate Value Added Aggregate Intermediate Input Imported Commodities Domestic Commodities Primary Factors Labor, Capital, Land Aggregated by Fixed Shares Aggregated by CES Function Aggregated by Fixed Shares Transformed by Fixed Yield

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90 Table 3-1. Areas with forest management plans and areas with deforestation authorizations Forest management plan Deforestation authorization Region (Ha) (%) (Ha) (%) North 1959976 91.9 125307 59.7 Northeast 5117 0.2 68263 32.5 Center west 166863 7.8 16462 7.8 Total 2131956 100.0 210032 100.0 Source: MMA, 2008a. Table 3-2. Area reforested in 2006 Region Area planted (Ha) Pe rcent area planted (%) North 34500 6 North east 94500 15 South east 275000 44 South 175000 28 Center west 48000 8 Total 627000 100 Source: MMA, 2008b.

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91 Table 3-3. Brazilian social acc ounting matrix mapping of activ ities to national accounts BRASAM sector BRASAM sector code National accounts sector National accounts sector code Agriculture, livestock, ranching and fishing A-AGR Agriculture, silviculture, forest harvesting 101 Ranching and fishing 102 Natural forest management A-FOR Agriculture, silviculture, forest harvesting 101 Forest plantations A-PLNT Agriculture, silviculture, forest harvesting 101 Deforestation A-DEF Based on other data Mining, other extractive industries, petroleum and natural gas AMINPET Iron mining 202 Other extractive industries 203 Petroleum and natural gas 201 Manufacturing A-IND Cement 319 Other products of non-metallic minerals 320 Manufacture of steel and derivatives 321 Metallurgy of non-iron metals 322 Metal production except machinery and equipment 323 Machines and equipment including maintenance and repair 324 Electro domestic appliances 325 Electric machines, equipment and materials 327 Office machines and informatics equipment 326 Electronic equipment and communications equipment 328 Medical equipment and instruments 329 Cars, pick-up trucks and jeeps 330 Trucks and buses 331 Parts and accessories for motor vehicles 332

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92 Table 3-3. Continued BRASAM sector BRASAM sector code National accounts sector National accounts sector code Other transportation equipment 333 Plastic and rubber articles 318 Alcohol 310 Chemical products 311 Agricultural pesticides/herbicides 314 Paint, varnish, enamels and lacquers 316 Diverse chemical products 317 Resin and elastomers 312 Petroleum and coke refining 309 Pharmaceutical products 313 Perfume, hygiene and cleaning products 315 Textiles 303 Clothing and accessories 304 Leather goods and shoes 305 Furniture and other diverse industries 334 Newspapers, magazines and disks 308 Wood processing A-SAW Wood products, except furniture 306 Pulp and paper products A-PULP Ce llulose and paper products 307 Food processing A-FPR Food and drinks 301 Smoking products 302 Public utilities A-UTI Electricity, gas, water, sewage and urban cleaning 401 Construction A-CON Construction 501 Commerce A-COM Commerce 601 Transportation, storage and postal services A-TRN Transportation, storage and postal services 701

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93 Table 3-3. Continued BRASAM sector BRASAM sector code National accounts sector National accounts sector code Private services A-SER Information services 801 Maintenance and repair services 1101 Food and shelter services 1102 Services rendered to enterprises 1103 Private education 1104 Private health 1105 Other services 1106 Finance and insurance brokers 901 Real estate and rental services 1001 Public services A-ADM Public education 1201 Public health 1202 Public administration and social security 1203 Notes: BRASAM is the Brazilian social accounting matrix, reference year 2003.

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94 Table 3-4. Brazilian social acc ounting matrix mapping of produc ts to national accounts BRASAM product BRASAM product code National accounts product National accounts product code Agricultural products P-AGR Rice 010101 Corn 010102 Wheat and other cereals 010103 Sugar cane 010104 Soybean 010105 Other agricultural products and services 010106 Yucca 010107 Tobacco 010108 Cotton 010109 Citric fruits 010110 Coffee 010111 Cattle and other animals 010201 Milk 010202 Swine 010203 Birds 010204 Eggs 010205 Fish and aquaculture 010206 Forest products P-FOR Forest products 010112 Deforestation product P-DEF Based on other data Mining, petroleum and natural gas P-MINPET Petroleum and natural gas020101 Iron mining 020201 Coal 020301 Non-iron mining 020302 Non-metallic minerals 020303 Industrial goods P-IND Cotton processing 030301 Textiles 030302 Other textiles 030303 Garments and accessories 030401 Leatherwork except shoes 030501 Shoes 030502 Paper and packaging 030702 Newspapers, magazines and disks 030801 Liquefied gas from petroleum 030901 Automobile gasoline 030902 Gas-alcohol blend 030903 Combustible oil 030904

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95 Table 3-4. Continued BRASAM product BRASAM product code National accounts product National accounts product code Diesel oil 030905 Other refined petroleum products and coke 030906 Alcohol 031001 Inorganic chemicals 031101 Organic chemicals 031102 Resins and elastomers 031201 Pharmaceutical products 031301 Agricultural chemicals 031401 Beauty and hygiene products 031501 Paint, varnish, enamel and lacquer 031601 Diverse chemicals 031701 Rubber goods 031801 Plastic goods 031802 Cement 031901 Other non-metallic mineral products 032001 Pig iron 032101 Steel tubes and sheets 032102 Metallurgical non-iron products 032201 Casted steel 032202 Metal products except equipment and machines 032301 Machines and equipment including maintenance and repair 032401 Electro domestic products 032501 Office machinery 032601 Machines, equipment and electronic materials 032701 Electronic and communication equipment 032801 Medical and optical instruments 032901 Automobiles, pick-up trucks and wagons 033001 Trucks and buses 033101 Auto parts 033201 Other transportation equipment 033301 Furniture and other diverse industrial products 033401 Scrap iron 033402 Wood products P-SAW Wood products except furniture 030601

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96 Table 3-4. Continued BRASAM product BRASAM product code National accounts product National accounts product code Pulp and paper P-PULP Cellulose and pulp 030701 Processed food P-FPR Animal slau ghter and preparation 030101 Fresh or frozen swine 030102 Fresh or frozen birds 030103 Industrialized fish 030104 Preserved fruits and vegetables 030105 Soybean derivatives 030106 Other vegetable and animal oils except corn 030107 Refined soybean oil 030108 Refrigerated, sterilized and pasteurized milk 030109 Milk products and ice cream 030110 Processed rice and derivatives 030111 Wheat flour and derivatives 030112 Yucca flour and others 030113 Corn oil, starch and rations 030114 Refined sugar and related products 030115 Toasted and ground coffee 030116 Instant coffee 030117 Other food products 030118 Beverages 030119 Tobacco products 030201 Utilities P-UTI Electricity, gas, water, sewage and urban sanitation 040101 Construction P-CON Construction 050101 Commerce P-COM Commerce 060101 Transportatio n P-TRN Freight transport 070101 Passenger transport 070102 Mail 070103 Private services P-SER Information services 080101 Financial intermediary services and insurance 090101 Real estate services 100101 Imputed rent 100102 Maintenance and repair services 110101 Housing and alimentary services 110201

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97 Table 3-4. Continued BRASAM product BRASAM product code National accounts product National accounts product code Services provided by businesses 110301 Commercial education 110401 Commercial health 110501 Family services 110601 Associated services 110602 Domestic services 110603 Public services P-ADM Public education 120101 Public health 120201 Public and social services 120301 Notes: BRASAM is the Brazilian social accounting matrix, reference year 2003.

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98 Table 3-5a. Brazilian national accounts s ources for the social accounting matrix Act Com Lab Cap Hh Itax Dtax Tar Ctax Int Ent Gov Row S-i Dstk Tot Act 1 2 Com 3 4 4 4 4 4 5 Lab 6 7 Cap 8 9 Hh 10 11 12 13 13 13 14 Itax 15 16 17 Dtax 18 18 19 Tar 20 21 Ctax 22 23 Int 24 24 25 Ent 26 27 Gov 28 29 30 31 32 Row 33 34 12 35 S-i 36 36 36 37 38 Dstk 39 40 Tot 41 42 43 44 45 46 47 48 49 50 51 52 53 54 40 Notes: Numbers in this table refer to the Brazilian national accounts sources descri bed in table 3-5b. Key to abbreviations: Act: activities; Com: commodities; Lab: labo r; Cap: capital; Hh: households; Itax: indire ct taxes; Dtax: direct taxes; Tar: tar iffs; Ctax: commodity taxes; Int: interest; Ent: enterp rises; Gov: government; Row: rest of the world; S-i: savings and investment; Dstk: c hange in stocks; Tot: total.

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99 Table 3-5b. Key to table 3-5a Reference number Brazilian national accounts source 1 T1: Activity production (includes margins) 2 Activity income 3 T2: Intermediate activity consumption 4 T2: Final demand 5 Demand 6 T2: Value added (salaries) 7 Labor income 8 T2: Value added (gross operating surplus and mixed income) 9 Capital income 10 T2: Value added (salaries net of social security payments) 11 T2: Value added (gross operating surplus and mixed income) 12 CEI: Property income 13 Calculated as a residual 14 Household income 15 T2: Value added (effective social security contributions and ot her production taxes net of subsidies) 16 Private contributions to offi cial social security system 17 Indirect tax receipts 18 CEI: Current income and property taxes 19 Direct tax receipts 20 T1: Supply of goods and services (import tax) 21 Tariff receipts 22 T1: Supply of goods and services 23 Commodity tax receipts 24 CEI: Property income 25 Interest receipts 26 T2: Value added (gross operating surplus and mixed income) 27 Enterprise income 28 Transfer of total indir ect tax to government account 29 Transfer of total direct taxes to government account 30 Transfer of total tariffs to government account 31 Transfer of total commodity taxes to government account 32 Government income 33 T1: Goods and services imports 34 CEI: Labor payment from the rest of the world 35 Foreign exchange outflow 36 CEI: Gross savings 37 CEI: Current account balance 38 Savings 39 T2: Final demand 40 Change in stocks 41 Activity expenditure 42 Supply expenditure

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100 Table 3-5b. Continued Reference number Brazilian national accounts source 43 Labor expenditure 44 Capital expenditure 45 Household expenditure 46 Indirect tax transfers 47 Direct tax transfers 48 Tariff transfers 49 Commodity tax transfers 50 Interest transfers 51 Enterprise expenditure 52 Government expenditure 53 Foreign exchange inflow 54 Investment Notes: T refers to table. CEI refers to Inte grated Economic Accounts. Households include nongovernment institutions. Enterprise s include financial enterprises.

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101 Table 3-6. Brazilian social accounting matrix accounts, reference year 2003 Account Description A-NAGR Agricultural sector north A-NEAGR Agricultural sector north east A-SEAGR Agricultural sector south east A-SAGR Agricultural sector south A-CWAGR Agricultural sector center west A-NFOR Natural forest ma nagement sector north A-NEFOR Natural forest mana gement sector north east A-SEFOR Natural forest management sector south east A-SFOR Natural forest management sector south A-CWFOR Natural forest management sector center west A-NPLNT Forest plantation sector north A-NEPLNT Forest planta tion sector north east A-SEPLNT Forest planta tion sector south east A-SPLNT Forest plantati on sector south A-CWPLNT Forest plantation sector center west A-NDEF Deforestation sector north A-NEDEF Deforestation sector north east A-CWDEF Deforestation sector center west A-MINPET Mining, petroleum and natural gas A-IND Manufacturing sector A-SAW Wood processing sector A-PULP Wood pulp sector A-FPR Processed food sector A-UTI Utilities A-CON Construction A-COM Communications A-TRN Transportation A-SER Private services A-ADM Public administration P-AGR Agricultural products P-FOR Forestry products P-DEF Deforestation product P-MINPET Mining, petroleum and natural gas products P-IND Manufactured products P-SAW Wood products P-PULP Pulp products P-FPR Processed food products P-UTI Utilities P-CON Construction P-COM Communications

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102 Table 3-6. Continued Account Description P-TRN Transportation P-SER Private services P-ADM Public services F-LF Low-skilled formal labor F-LIF Low-skilled informal labor F-MF Mid-skilled formal labor F-MIF Mid-skilled informal labor F-HF High-skilled formal labor F-HIF High-skilled informal labor F-CAP Capital F-NAGLD Agricultural land north F-NEAGLD Agricultural land north east F-SEAGLD Agricultural land south east F-SAGLD Agricultural land south F-CWAGLD Agricultural land center west F-NFRLD Forest land north F-NEFRLD Forest land north east F-SEFRLD Forest land south east F-SFRLD Forest land south F-CWFRLD Forest land center west H-HL Low-income household H-HM Mid-income household H-HH High-income household H-DEFLND Deforestation institution INDTAX Indirect tax DIRTAX Direct tax TAR Tariffs COMTAX Commodity taxes INT Interest ENT Enterprises GOV Government ROW Rest of the world S-I Savings and investment DSTK Change in stocks

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103 Table 3-7. Aggregated social accounting ma trix for Brazil, reference year 2003 Aagr Afor Aroe Pagr PforPdefProe LabCapAldFldHh Tax Int Ent GovRowS-i DstkTot Aagr 0 0 0 163 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 176 Afor 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 Aroe 0 0 0 21 0 0 27770 0 0 0 0 0 0 0 0 0 0 0 2798 Pagr 18 0 108 0 0 0 0 0 0 0 0 35 0 0 0 0 20 10 7 197 Pfor 1 0 5 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 9 Pdef 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Proe 54 2 1353 0 0 0 0 0 0 0 0 1010 0 0 0 327 234 2351 3216 Lab 27 1 500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 528 Cap 36 1 708 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 744 Ald 33 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 35 Fld 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Hh 0 0 0 0 0 0 0 497 127 2 0 0 0 135242193 9 0 0 1205 Tax 7 0 123 8 0 0 221 31 0 0 0 58 0 0 90 0 0 0 0 539 Int 0 0 0 0 0 0 0 0 0 0 0 0 0 0 91 94 -50 0 0 135 Ent 0 0 0 0 0 0 0 0 617 34 1 0 0 0 0 0 0 0 0 652 Gov 0 0 0 0 0 0 0 0 0 0 0 0 539 0 0 0 0 0 0 539 Row 0 0 0 6 0 0 204 0 0 0 0 0 0 0 0 0 0 0 0 211 S-i 0 0 0 0 0 0 0 0 0 0 0 102 0 0 229-75 -2 0 0 253 Dstk 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 7 Tot 176 7 2798 197 9 0 3216528 744 35 1 1205 539 135652539 211 2537 Notes: Values are in 1,000,000,000 Brazilian reais. Key to abbreviations: Aagr: agricultural activity; Afor: forestry activity; Aroe: other activities; Pagr: agricultural products; Pfor: forestry produ cts; Pdef: deforestation product; Proe: other products; Lab: labor; Cap: capital; Al d: agricultural land; Fld: forestland; Hh: households; Tax: tax; Int: interest; Ent: enterprises; Gov: government; Row: rest of the world; S-i: savings and invest ment; Dstk: change in stocks; Tot: total.

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104 Table 3-8. Percent change in macroec onomic and institutional indicators Indicator Percent change Absorption 0.01 Private consumption 0.02 Fixed investment 0.00 Change in stocks 0.00 Government consumption 0.00 Exports 0.00 Imports -0.01 GDP at market prices 0.01 GDP at factor cost 0.01 Net indirect taxes 0.01 Real household consumption Low-income household 0.00 Mid-income household 0.00 High-income household 0.00 Deforestation enterprise 0.80 Consumer price index 0.02 Exchange rate -0.01 Investment share of absorption 0.00 Foreign savings 0.00 Marginal propensity to save Mid-income households 0.57 High-income households 0.04 Enterprises 0.01 Investment to GDP ratio 0.00 Private savings to GDP ratio 0.00 Foreign savings to GDP ratio 0.00 Trade deficit to GDP ratio 0.00 Government savings to GDP ratio 0.00 Table 3-9. Percent change in institutional income Institution Percent change Low income household 0.05 Mid-income household 0.05 High-income household 0.04 Deforestation institution -0.24 Enterprises 0.01 Interest 0.02

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105 Table 3-10. Equivalent variation Institution Percent change Low-income households 0.00 Mid-income households 0.00 High-income households 0.00 Deforestation enterprise 0.80 Total 0.00 Table 3-11. Percent change in factor income Factor Percent change Low-skill formal labor 0.08 Low-skill informal labor 0.07 Mid-skill formal labor 0.08 Mid-skill informal labor 0.07 High-skill formal labor 0.07 High-skill informal labor 0.08 Capital 0.13 Agricultural land north -0.17 Agricultural land north east -0.38 Agricultural land south east -6.79 Agricultural land south -1.81 Agricultural land center west -0.17 Forest land north -48.13 Forest land north east -14.25 Forest land south east -9.40 Forest land south -9.54 Forest land center west -11.14

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106 Table 3-12. Percent change in price of composite good Good or service Percent change Agriculture -0.21 Forestry -8.44 Deforestation -1.07 Mining and petroleum -0.01 Industrial 0.01 Processed wood -0.01 Pulp and cellulose -0.01 Processed food 0.02 Utilities 0.05 Construction 0.17 Commerce -0.01 Transportation 0.09 Private services 0.05 Public services 0.04

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107 Table 3-13. Percent change in pr ice of factor F for activity A Factor Sector Percent change Low-skilled formal labor Agriculture north -0.13 Low-skilled formal labor Agriculture north east -0.11 Low-skilled formal labor Agriculture south east 0.54 Low-skilled formal labor Agriculture south 0.32 Low-skilled formal labor Agriculture center west -0.13 Low-skilled formal labor Forestry north 143.65 Low-skilled formal labor Forestry north east -14.82 Low-skilled formal labor Forestry south east -9.40 Low-skilled formal labor Forestry south -9.54 Low-skilled formal labor Forestry center west -11.18 Low-skilled formal labor Fo rest plantations north -13.57 Low-skilled formal labor Fore st plantations north east -14.40 Low-skilled formal labor Fore st plantations south east -20.43 Low-skilled formal labor Fore st plantations south -26.08 Low-skilled formal labor Forest plantations center west -15.99 Low-skilled formal labor Deforestation north 18.84 Low-skilled formal labor De forestation north east -6.96 Low-skilled formal labor De forestation center west -5.89 Low-skilled formal labor Mining and petroleum -0.09 Low-skilled formal labor Industry 0.01 Low-skilled formal labor Processed wood 2.68 Low-skilled formal labor Pulp and cellulose 2.50 Low-skilled formal labor Processed food 0.65 Low-skilled formal labor Utilities 0.06 Low-skilled formal labor Construction 0.33 Low-skilled formal labor Commerce -0.03 Low-skilled formal labor Transportation 0.10 Low-skilled formal labor Private services 0.05 Low-skilled formal labor Public services 0.03 Low-skilled informal labor Agriculture north -0.13 Low-skilled informal labor Agriculture north east -0.11 Low-skilled informal labor Agriculture south east 0.54 Low-skilled informal labor Agriculture south 0.32 Low-skilled informal labor Agriculture center west -0.13 Low-skilled informal labor Forestry north 143.65 Low-skilled informal labor Forestry north east -14.82 Low-skilled informal labor Forestry south east -9.40 Low-skilled informal labor Forestry south -9.54 Low-skilled informal labor Forestry center west -11.18

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108 Table 3-13. Continued Factor Sector Percent change Low-skilled informal labor Fo rest plantations north -13.57 Low-skilled informal labor Fore st plantations north east -14.40 Low-skilled informal labor Fore st plantations south east -20.43 Low-skilled informal labor Fo rest plantations south -26.08 Low-skilled informal labor Fore st plantations center west -15.99 Low-skilled informal labor Deforestation north 18.84 Low-skilled informal labor Deforestation north east -6.96 Low-skilled informal labor De forestation center west -5.89 Low-skilled informal labor Mining and petroleum -0.09 Low-skilled informal labor Industry 0.01 Low-skilled informal labor Processed wood 2.68 Low-skilled informal labor Pulp and cellulose 2.50 Low-skilled informal labor Processed food 0.65 Low-skilled informal labor Utilities 0.06 Low-skilled informal labor Construction 0.33 Low-skilled informal labor Commerce -0.03 Low-skilled informal labor Transportation 0.10 Low-skilled informal la bor Private services 0.05 Low-skilled informal la bor Public services 0.03 Mid-skilled formal labor Agriculture north -0.13 Mid-skilled formal labor Agriculture north east -0.11 Mid-skilled formal labor Agriculture south east 0.54 Mid-skilled formal labor Agriculture south 0.32 Mid-skilled formal labor Ag riculture center west -0.13 Mid-skilled formal labor Forestry north 143.65 Mid-skilled formal labor Forestry north east -14.82 Mid-skilled formal labor Forestry south east -9.40 Mid-skilled formal labor Forestry south -9.54 Mid-skilled formal labor Forestry center west -11.18 Mid-skilled formal labor Fo rest plantations north -13.57 Mid-skilled formal labor Fore st plantations north east -14.40 Mid-skilled formal labor Fore st plantations south east -20.43 Mid-skilled formal labor Fore st plantations south -26.08 Mid-skilled formal labor Forest plantations center west -15.99 Mid-skilled formal labor Deforestation north 18.84 Mid-skilled formal labor De forestation north east -6.96 Mid-skilled formal labor De forestation center west -5.89 Mid-skilled formal labor Mining and petroleum -0.09 Mid-skilled formal labor Industry 0.01

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109 Table 3-13. Continued Factor Sector Percent change Mid-skilled formal labor Processed wood 2.68 Mid-skilled formal labor Pulp and cellulose 2.50 Mid-skilled formal labor Processed food 0.65 Mid-skilled formal labor Utilities 0.06 Mid-skilled formal labor Construction 0.33 Mid-skilled formal labor Commerce -0.03 Mid-skilled formal labor Transportation 0.10 Mid-skilled formal labor Private services 0.05 Mid-skilled formal labor Public services 0.03 Mid-skilled informal labor Agriculture north -0.13 Mid-skilled informal labor Agriculture north east -0.11 Mid-skilled informal labor Agriculture south east 0.54 Mid-skilled informal labor Agriculture south 0.32 Mid-skilled informal labor Agriculture center west -0.13 Mid-skilled informal labor Forestry north 143.65 Mid-skilled informal labor Forestry north east -14.82 Mid-skilled informal labor Forestry south east -9.40 Mid-skilled informal labor Forestry south -9.54 Mid-skilled informal labor Forestry center west -11.18 Mid-skilled informal labor Fo rest plantations north -13.57 Mid-skilled informal labor Fore st plantations north east -14.40 Mid-skilled informal labor Fore st plantations south east -20.43 Mid-skilled informal labor Fo rest plantations south -26.08 Mid-skilled informal labor Fore st plantations center west -15.99 Mid-skilled informal labor Deforestation north 18.84 Mid-skilled informal labor Deforestation north east -6.96 Mid-skilled informal labor De forestation center west -5.89 Mid-skilled informal labor Mining and petroleum -0.09 Mid-skilled informal labor Industry 0.01 Mid-skilled informal labor Processed wood 2.68 Mid-skilled informal labor Pulp and cellulose 2.50 Mid-skilled informal labor Processed food 0.65 Mid-skilled informal labor Utilities 0.06 Mid-skilled informal labor Construction 0.33 Mid-skilled informal labor Commerce -0.03 Mid-skilled informal labor Transportation 0.10 Mid-skilled informal la bor Private services 0.05 Mid-skilled informal la bor Public services 0.03 High-skilled formal labo r Agriculture north -0.13

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110 Table 3-13. Continued Factor Sector Percent change High-skilled formal labor Agriculture north east -0.11 High-skilled formal labor Agriculture south east 0.54 High-skilled formal labor Agriculture south 0.32 High-skilled formal labor Agriculture center west -0.13 High-skilled formal labor Forestry north 143.65 High-skilled formal labor Forestry north east -14.82 High-skilled formal labor Forestry south east -9.40 High-skilled formal labor Forestry south -9.54 High-skilled formal labor Forestry center west -11.18 High-skilled formal labor Fo rest plantations north -13.57 High-skilled formal labor Fore st plantations north east -14.40 High-skilled formal labor Fore st plantations south east -20.43 High-skilled formal labor Fore st plantations south -26.08 High-skilled formal labor Forest plantations center west -15.99 High-skilled formal labo r Deforestation north 18.84 High-skilled formal labor Deforestation north east -6.96 High-skilled formal labor Deforestation center west -5.89 High-skilled formal labor Mining and petroleum -0.09 High-skilled formal labor Industry 0.01 High-skilled formal labor Processed wood 2.68 High-skilled formal labor Pulp and cellulose 2.50 High-skilled formal labor Processed food 0.65 High-skilled formal labor Utilities 0.06 High-skilled formal labor Construction 0.33 High-skilled formal labor Commerce -0.03 High-skilled formal labor Transportation 0.10 High-skilled formal labo r Private services 0.05 High-skilled formal labor Public services 0.03 High-skilled informal labor Agriculture north -0.13 High-skilled informal labor Agriculture north east -0.11 High-skilled informal labor Agriculture south east 0.54 High-skilled informal labor Agriculture south 0.32 High-skilled informal labor Agriculture center west -0.13 High-skilled informal labor Forestry north 143.65 High-skilled informal labor Forestry north east -14.82 High-skilled informal labor Forestry south east -9.40 High-skilled informal labor Forestry south -9.54 High-skilled informal labor Forestry center west -11.18 High-skilled informal labor Fo rest plantations north -13.57

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111 Table 3-13. Continued Factor Sector Percent change High-skilled informal labor Fore st plantations north east -14.40 High-skilled informal labor Fore st plantations south east -20.43 High-skilled informal labor Fo rest plantations south -26.08 High-skilled informal labor Fore st plantations center west -15.99 High-skilled informal labor Deforestation north 18.84 High-skilled informal labor Deforestation north east -6.96 High-skilled informal labor Deforestation center west -5.89 High-skilled informal labor Mining and petroleum -0.09 High-skilled informal labor Industry 0.01 High-skilled informal labor Processed wood 2.68 High-skilled informal labor Pulp and cellulose 2.50 High-skilled informal labor Processed food 0.65 High-skilled informal labor Utilities 0.06 High-skilled informal labor Construction 0.33 High-skilled informal labor Commerce -0.03 High-skilled informal labor Transportation 0.10 High-skilled informal labor Private services 0.05 High-skilled informal labor Public services 0.03 Capital Agriculture north -0.13 Capital Agriculture north east -0.11 Capital Agriculture south east 0.54 Capital Agriculture south 0.32 Capital Agriculture center west -0.13 Capital Forestry north 143.65 Capital Forestry north east -14.82 Capital Forestry south east -9.40 Capital Forestry south -9.54 Capital Forestry center west -11.18 Capital Forest plantations north -13.57 Capital Forest plantations north east -14.40 Capital Forest plantations south east -20.43 Capital Forest plantations south -26.08 Capital Forest plantations center west -15.99 Capital Deforestation north 18.84 Capital Deforestation north east -6.96 Capital Deforestation center west -5.89 Capital Mining and petroleum -0.09 Capital Industry 0.01 Capital Processed wood 2.68

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112 Table 3-13 Continued Factor Sector Percent change Capital Pulp and cellulose 2.50 Capital Processed food 0.65 Capital Utilities 0.06 Capital Construction 0.33 Capital Commerce -0.03 Capital Transportation 0.10 Capital Private services 0.05 Capital Public services 0.03 Agricultural land north Agriculture north -0.17 Agricultural land north Forest plantations north -0.17 Agricultural land north east Agriculture north east -0.38 Agricultural land north east Fo rest plantations north east -0.38 Agricultural land south east Agriculture south east -6.79 Agricultural land south east Forest plantations south east -6.79 Agricultural land south Agriculture south -1.81 Agricultural land south Forest plantations south -1.81 Agricultural land center west Agriculture center west -0.17 Agricultural land center west Fore st plantations center west -0.17 Forestland north Forestry north -64.47 Forestland north Deforestation north -64.47 Forestland north east Forestry north east -14.25 Forestland north east Deforestation north east -14.25 Forestland southeast Fo restry south east -9.40 Forestland south Forestry south -9.54 Forestland center west Forestry center west -11.14 Forestland center west Deforestation center west -11.14

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113 Table 3-14. Percent change in level of domestic activity Sector Percent change Agriculture north 0.00 Agriculture north east 0.01 Agriculture south east 0.19 Agriculture south 0.13 Agriculture center west 0.01 Forestry north 24.68 Forestry north east -0.02 Forestry south east 0.00 Forestry south 0.00 Forestry center west -0.01 Forest plantations north -0.56 Forest plantations north east -0.62 Forest plantations south east -2.40 Forest plantations south -3.71 Forest plantations center west -1.29 Deforestation north 1.20 Deforestation north east 0.14 Deforestation center west 0.09 Mining and petroleum 0.00 Industry 0.00 Wood processing 0.00 Pulp and paper 0.00 Food processing 0.00 Utilities 0.00 Construction 0.00 Commerce 0.00 Transportation 0.00 Private services 0.00 Public services 0.00

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114 Table 3-15. Percent change in quant ity of factor demand by sector Sector Factor Percent change Agriculture north Agricultural land north 0.01 Forest plantations north -2.84 Agriculture north east Agricu ltural land north east 0.06 Forest plantations north east -2.99 Agriculture south east Agricu ltural land south east 1.83 Forest plantations south east -3.11 Agriculture south Agricu ltural land south 0.52 Forest plantations south -5.52 Agriculture center west Agricu ltural land center west 0.01 Forest plantations center west -3.39 Forestry north Forest land north 46.97 Deforestation north 6.22 Forestry north east Forest land north east -0.13 Deforestation north east 0.41 Forestry south east Forest land south east 0.00 Forestry south Forest land south 0.00 Forestry center west Fore st land center west -0.01 Deforestation center west 0.29 Table 3-16. Percent change in quant ity of domestic sales and exports Good or service Percent change domestic sales Percent change exports Agriculture 0.05 0.31 Forestry 1.95 14.55 Deforestation 0.84 0.00 Mining and petroleum 0.00 0.00 Industrial 0.01 -0.04 Processed wood 0.00 0.00 Pulp and cellulose 0.00 0.00 Processed food 0.02 -0.07 Utilities 0.00 0.00 Construction 0.00 -0.09 Commerce 0.00 0.00 Transportation 0.00 -0.08 Private services 0.00 -0.04 Public services 0.00 0.00

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115 Table 3-17. Percent change in qua ntity of composite goods supply Sector Percent change Agriculture 0.03 Forestry 0.37 Deforestation 0.84 Mining and petroleum 0.00 Industrial 0.01 Processed wood 0.00 Pulp and cellulose 0.00 Processed food 0.02 Utilities 0.00 Construction 0.00 Commerce 0.00 Transportation 0.00 Private services 0.01 Public services 0.00

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116 CHAPTER 4 RECURSIVE DYNAMIC COMPUTABLE GE NERAL EQUILIBRIUM MODEL W ITH ILLEGAL LOGGING AND DEFORESTATION Introduction The static C GE model evaluated the policy impact of forest concessions in the absence of illegality. Given the economic importance of illegalit y in the Brazilian forestry sector (56% of forest sector output in 2003), in order to more r ealistically and fully eval uate the potential socioeconomic and environmental impacts of forest concessions, the analysis conducted herein disaggregates and models illegal forestry and illegal deforestation sectors. To enable the updating of agricultural land stocks resulting from deforestation and to consider the mediumterm implications of the forest concessions policy, a recursive dynami c computable general equilibrium modeling framework is developed and employed. Following this introduction, illegal logging is defined and its key causes and effects are summarized1. Next, illegal logging in Brazil is discussed and estimates of its magnitude and level of prosecution are provided. Treatment of illegality in CGE models is then reviewed. Following this review, dynamics in CGE models are introduced. Next, the recursive dynamic extension to the IFPRI Standard CGE Model in GAMs is presented. The experimental design of the modeling exercise is developed and is follo wed by simulation results. The chapter concludes with a discussion of the key findings. Illegal Logging Illegal logging can be ch aracterized as consis ting of illegal logging ac tivities, the illegal movement of forest products, and the evasion of taxes and forestry related payments. Following Callister (1999, p. 7), illegal logging activities incl ude logging without authorization on public or private land, logging in breach of contractual obli gations, logging that does not respect forestry law, and the illegal attainment of timber harvest rights. The illeg al transport of forest products

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117 includes the import and export of tree species protected or banned under national and international law, the unauthorized transport of specified amounts of timber across withincountry and between-country borders and the illegal transportation of logs from the forest to the market. The evasion of taxes and other forest-related payments includes the evasion of fees and duties through incorrectly reporting timber grades and dimensions, the ev asion of license fees, royalties, taxes and other government fees, and false declarations of input and output costs (Callister, 1999, p. 8). In this chap ter, illegal logging is defined si milarly to Callister (1999) with the exception that the illegal im port and export of timber across international borders is not considered. Corruption is a special form of illegality; it is closely linked with illegal logging since each facilitates the presence of the other (A macher, 2006, p. 86). Corruption involves engaging public officials and involves public property or power; it is c onducted for private gain and is intentional and clandestine (Cont reras-Hermosilla, 2002, p. 9). Corr uption enables illegal logging to take place or allows it to continue without consequence. Numerous reasons for the presence of illegality in the forest sector are cited in the literature. The Food and Agricult ure Organization (FAO, 2005) repor ts 5 root causes of forest sector illegality, namely: flawed policies a nd legal frameworks, low enforcement capacity, insufficient data on the characteri stics of the forest resource and illegal activities, corruption, and high demand for timber. The legal framework for forestry can create incentives for illegal operations when laws are unrealistic and their implementation and en forcement are not supported by sufficient human and financial resources (FAO, 2005, p. 7). Laws that are too complex or burdensome can create a significant barrier for producers legal compliance, particularly in the case of producers of a

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118 smaller stature. Often these frameworks we re developed with lit tle public and industry participation (FAO, 2005, p. 7). Where government s have low implementation and enforcement capacity, particularly in highly regulated envir onments, incentives are created for producers to engage in illegal activities give n the low probabilities of dete ction and subsequent punishment (FAO, 2005, p. 9). A lack of information on the forest resource in ques tion and its potential remoteness render monitoring, detection, and enforcement difficult (FAO, 2005, p. 9). Corruption and a lack of transp arency can induce a mutually be neficial relationship between illegal operators and government officials (FAO, 2005, p. 12). Corruption is bred through faulty legal frameworks and programs, weak government and non-government institutions, few checks and balances resulting in low levels of account ability and transparency, and low public sector wages (FAO, 2005, p. 11). High levels of demand for timber due to excess processing capacity create incentives for the i llegal acquisition of wood (Contreras-Hermosilla, Doornbosch & Lodge, 2007, p. 21). Furthermore, road building into previously inaccessible areas can significantly lower the costs of illegal logging in the absence of mechanisms of control and incentives for sustainable forest manageme nt (Contreras-Hermosilla et al., 2007, p. 23). It is thus reasonable to assume that with the price of illegal and legal wood practically equal, economic incentives for operating illegally are created. Rational profit maximizing firms that base decisions on purely economic criteria w ill choose to engage in illegal logging when the benefits are greater than the co sts, penalties, and associated risks of operating in illegality (Tacconi, Boscolo & Brack, 2003, p. 8). Illegal logging results in ne gative socio-economic and envi ronmental outcomes. Firms that evade forestry related payments such as royalties are able to produce an identical and cheaper product compared to those that operate legally. The world pric e reduction in forest

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119 products due to illegal logging is estimated between 7% and 16% (Seneca Creek Associates & Wood Resources, 2004). Consequently, firms operati ng legally suffer from reduced profits, are discouraged from investing in fo rest management and ultimately may be forced out of business (Contreras et al., 2007, p. 19). Depressed prices al so create perverse incentives for inefficient harvesting operations (Gutierrez-Velez & Ma cDicken, 2008, p. 249; Seneca Creek Associates & Wood Resources, 2004, p. 55). Illegal logging can lead to future fo rest productivity losses with firms operating in illegality shirking the hi gher management standards required by law (Gutierrez-Velez & MacDicken, 2008, p. 249). Society as a whole tends to suffer from the effects of illegal logging. The government loses a potentially important sour ce of revenue and its ability to provide social services is reduced (Callister, 1999, p. 19). Lost revenue fr om illegal logging in developing countries is estimated at over US$15 billion per year (Contreras-Hermosilla et al., 2007, p. 18). For Brazil, illegal logging costs the state and society upwards of US$1.1 b illion per year (Gutierrez-Velez & MacDicken, 2008, p. 254). Furthermore, a forests capacity to produce critical environmental services is diminished due to inappropriate logging practices, includ ing increased residual damage and the logging of sensitiv e areas and protected species. Finally, illegal operations can have a nega tive employment effect; legal operations require minimum salaries and working condition s whereas illegal operations do not (GutierrezVelez & MacDicken, 2008, p. 249). Short-term em ployment may be created by illegal logging, however, the boom and bust nature of these operations does not lend to community stability. Illegal Logging in Brazil Illegal logging in Brazil is a function of flawed policies and legal fram eworks, low enforcement capacity, insufficient data on the charact eristics of the forest resource and illegal activities, corruption, and high demand for timber as discussed in the previous section. Rhodes,

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120 Allen and Callahan (2006, p. 3) argu e that illegal loggings pervasiv eness in Brazil is largely due to the absence of the rule of law where petty co rruption prevails. The aut hors assert that in many cases, firms once operating legally were forced in to illegality due to increasingly uncertain property rights, a changing policy environment cr eating new restrictions on forest operations, and a lack of forestland on which firms ma y operate legally (Rhodes et al., 2006, p. 3). Estimates on the extent of illegal logging in Brazil tend to vary considerably. Smeraldi (2004, p. 39) reports that the classification and an alysis of forest harvesting, commerce, and illegal logging in Brazil bega n in the early 1990s. A repor t prepared in 1994 by the environmental NGO Amigos da Terra reported that all mahogany harvested in the Brazilian Amazon was harvested illegally. A second pape r prepared by the NGO reported that illegal harvesting activities were advancing on to publ ic forests and conser vation areas in 1994. A confidential government report leak ed to the media in 1997 estimated that 80% of all the timber in the Brazilian market prior to 1997 was harves ted illegally (Smeraldi, 2004, p. 40); this figure has subsequently been cited in numerous publicati ons (Lele, Viana, Verssimo, Vosti, Perkins & Husain, 2000, p. 20; Marquesini & Edwards, 2001, p. 1; Tacconi et al., 2003, p. 8). Seneca Creek Associates and Wood Resources International (2004, p. 11) report estimates of between 20-90% of wood harvested in Brazil is done so illegally. More recently, considering roundwood consumption and the volume authorized thr ough deforestation authorizations and forest management plans for 2004, over 43% of the timber volume harvested was done so illegally (calculation based on Lentini et al., 2005a). This figure incl udes wood sourced from both selectively logged and deforested areas. With the approval of Brazils Environmental Crimes Law in 1998 and a supporting Decree in 1999 (Federal Law 9.606/98 and Federa l Decree 3.179/99, respec tively), efforts to

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121 punish illegal logging have increased dramati cally. The number of fines applied since 1999 has increased substantially, though th eir collection has remained low. The fine for illegal deforestation is approximately US$700 per he ctare (Barreto, Souza, Noguern, Anderson & Salomo, 2006, p. 20) while the fine for illegally transporting wood products ranges from US$28.50 to US$143.00 per cubic meter (Brit o, Barreto and Rothman, 2005, p. 13). An estimated US$16.2 billion in fines were issued in 2001 which is 8 times the fines issued in 1997; the percentage of these fines collected was 6% (Macqueen, Grieg-Gran, Lima, MacGregor, Merry, Prochnick, Scotland, Smeraldi & Young, 2003, p. 54). In 1999, The Brazilian Institute of the Environment and Natural Resources (IBAMA) collected 14% of the value of fines owed between 1998 and 1999. Be tween 2001 and 2004, the total value of fines issued by IBAMA increased 180% from US$103 million to US$290 million. Only 2% of the fines issued for environmental crimes during this period were collected, however. During the same period, the illegal timber harvest reportedl y fell from 47% to 43% (Brito & Barreto, 2006, p. 2). Brito et al. (2005, p. 13) conducted an analysis of a sample of 55 judicial actions for forestry violations committed in Par and file d with the Federal Court of Belm between 2000 and 2003. Of this sample, 98% of the cases involv ed illegally taking, acqu iring, selling, storing, or transporting wood, firewood, charcoal, and other forest products (art. 46 of the Environmental Crimes Law). Illegal transport and storage accounted for 48% and 24% of the crimes committed, respectively (Brito et al., 2005, p. 9) while only 8% of the crimes involved illegal logging (6%) and deforestation (2%; Brito et al., 2005, p. 10). With regards to fine collection, 9% of the cases or 2% of the value of the fines assessed were paid. In 2003, the average civil liability in 3 cases

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122 was US$0.70 per cubic meter while the average criminal liability was US$45.00 per cubic meter for an overall average of US$45.70 per cubic meter (Brito et al., 2005, p. 13). Treatment of Illegal Behavior in Co mputable General Eq uilibrium Models Illegal activities in CGE models have received little attention in the literature. The closest parallel to modeling illegality ha s involved distinguishing between formal and informal activities and labor. Informal sector activi ties and labor are characterized by firms and individuals that do not comply with regulations and tax codes. Informality emerges where the government imposes excessive regulations and taxes and lacks the capacity to enfo rce them (Loayza, 1997, p. 2). Alternatively, informality may occur when stat e officials or interest groups profit from informality and as such they create a regulatory environment that renders operating in the informal sector beneficial or unavoidable (Loa yza, 1997, p. 3). Costs involved in engaging in the informal sector include fines and penalties, lack of access to public services, unenforceable property rights on capital and outpu t, and the inability to enter into legally binding contracts including access to capital markets and insurance (Loayza, 1997, p. 3). Kelley (1994) analyzed the macroeconomic impl ications of informal sectors and informal labor in Peru considering an increase in government and investment demand, and the impact of an increase in formal sector wages and informal sector productivity. In this model, a shortage of formal employment results in a parallel informal sector, employing informal labor and producing a similar, though imperfectly substitutable good or service. Kelley (1994) characterized informal sector production as organized around an individual, family, or small group where the producer receives the net product from produc tion rather than a wage, and pays little or no taxes. Informal sector production technology is la bor intensive to reflect capital sc arcity. Output from parallel formal and informal sectors is aggregated into a composite good; relative prices and imperfect substitutability between the tw o similar goods determine output levels. Full labor employment is

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123 assumed which is made possible through adjustme nts in the level of informal employment (Kelley, 1994, p. 1395). Gibson (2004) developed a dynamic model with an informal sector to evaluate the longrun effects of macroeconomic and trade reform policies on growth, distribution, human capital formation, and poverty (Gibson, 2004, p. 61). In th is model, non-traded goods sectors have parallel informal sectors which serve as an em ployer of last resort, absorbing surplus labor during recessions and supplying labor in expans ionary periods (Gibson, 2004, p. 62). The price of informal sector output is determined by the fo rmal sector. The informal sector always operates at full capacity and as in Kelley (1994), info rmal sector income is earned by the operator (Gibson, 2005, p. 65). Fortin, Marceau and Savard (1997) evaluated the effect of taxation and wage controls in an economy with informal sectors. The informal sector is distinguished fro m the formal sector by technological and organizational factors which are reflected through the scale of the firm, a wage differential between formal and informal workers, and regulatory evasion. In their model, firms operating in the informal sector do not comply w ith minimum wage and tax laws. Informal firms face the risk of being detected and fined unle ss they engage in a costly activity to avoid detection. The authors assume that firms would rath er pay a sure cost to avoid the risk of being detected; this sure cost function is described by an inverted L curv e where the cost of detection is zero until firm size reaches a critical limit and the co st of informality rises to infinity (Fortin et al., 1997, p. 298). The Australian Bureau of Agricultural and Resource Economics model of Papua New Guinea incorporated a kidnapping and informal sector in their modeling framework with

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124 unskilled labor as the primary input to these sect ors. Fines applied to th e criminal sector are treated as production taxes in the mo del (Levantis & Fairhead, 2004, p. 15). Dynamics in Computable General Equilibrium Models Policy m akers are often interested in how current decisions will affect future socioeconomic outcomes. Static models provide an indication of the order of magnitude and direction of effect of a policy shock and are typically either short-run or long-run depending on the factor and macroeconomic closures chosen by the modeler. In contrast, dynamic models are used to simulate the impact of a policy on th e economy for a definite time period. The main advantages of this class of models is their abi lity to shed light on the economic transition path resulting from a policy shock and the short-term costs and longer-term gains resulting from policy implementation (Cattaneo, 1999, p. 17). Furt hermore, dynamic models typically involve a deeper treatment of investment behavior a nd enable the modeler to update key growth parameters such as population, labor force, f actor productivity, world prices, and government consumption as well as rates of capital deprecia tion. Dynamic models are thus a useful tool for informing policy decisions. Four main approaches to dynamic modeli ng may be distinguished. Following Dixon and Parmenter (1996, p. 24), the distinction between approaches is based on the treatment of investment and capital accumulation. In the firs t approach, investment is modeled exogenously with agent expectations modeled as static. Agents exhibiting static expectations assume no changes in decision parameters over time. This type of model is recu rsive dynamic where the inter-temporal trajectory of the economy is determ ined by calculating a series of static equilibria which are related to each other by capital formati on processes and exogenous changes in growth parameters (Pereira & Shoven, 1988, p. 402).

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125 The second approach differs from the first in that investment is modeled endogenously and agent investment behavior is based on ad aptive expectations. Agents with adaptive expectations use information on previous rates of return to capital to inform their current investment decisions. This class of models is also solved recursively. Third, investment may be modeled endogenous ly where agents engage in forwardlooking investment behavior. These agents consider both the present and future to inform their current investment decisions (Spr inger, 1999, p. 5). In this case, an agents expectations about expected rates of return to capital for a given year are equal to the actual rates of return for that year. Differing from recursive dynamic models, this class of models is solved simultaneously for all time periods. In the final case, investment behavior is optimized and modeled as an inter-temporal decision where inter-temporal substitution possibilities exist (Dixon & Parmenter, 1996, p. 2536; Springer, 1999, p. 6). This class of models is also solved simultaneously for all time periods. Since perfect foresight is not generally representative of how investment behaves in the real world, the model in the next section fo llows the second approach, where investment behavior is based on adaptiv e expectations and the model is solved recursively. Dynamic Extension to the Standard Comput able Genera l Equilibrium Model in GAMS The Recursive Dynamic Computable General Equilibrium Model is a dynamic extension to the IFPRI Standard CGE Model in GAMS an d was developed by Robinson and Thurlow (no date). The within period (year) sp ecification of the model is identical to that of the Standard IFPRI model described in Chapter 3. The betwee n period specification contains adjustments to account for endogenous investment, and exoge nous population and labor force growth, depreciation, and changes in total factor produc tivity (Robinson & Thurlow, no date, p. 1). The sectoral allocation of capital is modeled as a function of the rate of capital depreciation and the

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126 differential in profits between sectors from the previous period (Thurlow, 2004, p. 11). Endogenous adjustments to account for capital accumulation and exogenous adjustments to population, labor force and total factor productivity are discussed in turn. Capital supply is based on the previous periods capital stoc k and allocation of investment spending. Investment is carried out in proportion to a se ctors share in economy-wide capital income and is adjusted by the ratio of a sectors rate of profits and the economy-wide average rate of profit. This spec ification implies that a sector with higher than average profits will receive a larger share of investment than its average share in aggregate capital income (Robinson & Thurlow, no date, p. 4; see Appendix A, equations 50 to 54). Population growth has a direct and positive impact on household consumption expenditure with the quantity of income-independent consumption increasing at the same rate as population growth (Robinson & Thurlow, no da te, p. 2). The level of minimum household consumption expenditure also increases proporti onally with population growth. Growth affects average rather than marginal consumption demand implying that new consumers share the same preferences as existing consum ers (Robinson & Thurlow, no date, p. 3). The parameter, the subsistence consumption of marketed commodity c for household h, and h ach the subsistence consumption of home commodity c from activit y a for household h, are both adjusted upwards by the rate of population growth (see Appendix A). With regards to labor force growth, with a fi xed labor supply, flexib le nominal wages and full employment, the between period levels of la bor supply are adjusted according to the rate of labor force growth (Robinson & Thurlow, no date, p. 4; see Appendix A). The parameter f QFS, the quantity of factor supply, is adjusted upwards by the ra te of labor force growth. m ch

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127 Changes in total factor productivity are imposed exogenously by introducing a technological parameter in the model equations fo r the calculation of the quantity of aggregate value-added (Robinson & Thurlow, no date, p. 6; see Appendix A). The parameter va a the efficiency parameter in the Constant Elasticity of Substitution value-adde d function, is adjusted upwards by total factor productivity growth. The dynamic extension to the Standard CGE Model in GAMS is programmed and solved as a mixed complementarity problem using th e General Algebraic Modeling System and the PATH solver. Customizing the 2003 Social Accounting Matrix for Brazil to Describe Illegal F orestry and Illegal Deforestation Illegal forestry and illegal deforestation ac tivities are not explicitly described in the national accounts data from which the SAM fo r Brazil was construc ted. Expenditures and receipts from these activities are aggregated with those of the legal forestry sector, however, since once timber is extracted from the forest, it is presented to consumers as a legal product and accounted for as such. Therefore, in customizing th e SAM to describe illega l forestry and illegal deforestation activities, expenditu res are disaggregated from the le gal forestry sector (with two exceptions noted below). The following subsections describe the procedures used in disaggregating illegal forestry and illegal defo restation in the Brazilian SAM and provide a summary of the key assumptions made. Illegal Deforestation The starting point for disaggregating the illega l d eforestation sector in the SAM is to estimate the total area illegally deforested in 2003. Illegal deforestation in th is analysis is defined as the removal of timber and the subsequent clearing of forests in th e absence of a valid deforestation authorization and the illegal transport of timber from the forest to the mill. As with

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128 the legal deforestation considered in Chapter 3, illegal deforestation occu rs in Brazils north, north east and center west regions. Total deforestation in Brazils Lega l Amazon in 2003 was 2,528,200 hectares (The National Institute for Space Researchs [INPE] Pr ogram for the Calculation of Deforestation in Amazonia [PRODES]). Deducting legally authorized deforestation registered with Brazils monitoring and control system for resources and forest products database (SISPROF; 210,032 Ha), illegal deforestation in 2003 was a pproximately 2,318,168 hectares (MMA, 2008a). Forest product output from illegal deforestati on is calculated next. It is reasonable to assume that only timber harvested in proximity to new logging centers can be fully utilized since the difficult access and the states reduced monito ring and enforcement capabilities decrease the probability that illegality will be detected and penalized (Lentini, 2008, personal communication)2. Therefore, it is assumed that illegal deforestation produces the same amount of timber per unit area as legal deforestation only in proximity to new logging centers. To calculate the area deforested in proximity to these centers levels of deforestati on by municipality were obtained from the PRODES online server wh ile the location of new logging centers was extracted from Lentini et al. (2005a, p. 63). Illega l deforestation in municipalities in which new logging centers are located amounted to 15.7% and 27.1% of total illegal deforestation in the north and center west, respectively. Therefore, illegal deforestation produces 15.7% and 27.1% of the timber that the legal deforestation sect or produces per unit area in the north and center west, respectively3. Given the lack of data on logging cen ters in the north east and its easier access, it is assumed that illegal deforestation in this region pr oduces 10% of the timber per unit area when compared with legal deforestation.

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129 The illegal deforestation sectors expenditu res on intermediate consumption, labor and capital for producing forest products is identical to that of th e legal deforestation sectors expenditure per unit area. These expenditures, alo ng with the value of th e illegal deforestation sectors forest product out put value are deducted from the legal forestry sector. The treatment of the illegal deforestation sectors expenditure on forestland and its payments to the indirect tax account differs fr om that of the legal deforestation sectors expenditures and payments. First, with regard s to forestland, it is assumed that illegal deforestation operations occurring on 50% of Special Areas (military zones, quilombola communities which are areas settled by the desc endents of slaves, Environmental Protection Areas, and rural settlements) and on private land in dispute and terra devoluta make reduced forestland payments. This is a reasonable assump tion since, private land aside, most illegality occurs on land with these tenure ty pes. Furthermore, it is assume d that these illegal operations pay 75% of the legal deforestation sectors fore stland rent per unit of output. The rationale for this reduced payment is that firms that acquire land illegally are assume d to pay less than those that do so legally due to weaker property rights guarantees and th e risks inherent in the illegal acquisition of land. To calculate th e percentage reduction in forestla nd payments, the total area in Special Areas, private land, private land in disp ute, and terra devoluta is calculated based on IBGE data (1996; cited in Lentin i et al., 2005a, p. 32). Based on this value, the proportion of area in 50% of Special Areas, private land in dispute, and terra devoluta is ca lculated and represents the proportion of production that is subject to reduced fores tland payments (0.76, 0.57 and 0.34 in the north, north east and center west, resp ectively). These proportions are multiplied by 0.75 and each regions legal forest sector payment to forestland for a given unit of forest product

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130 output to determine the illegal sectors fores tland payment per unit of forest product output by region4. With regards to activity taxes, the illegal de forestation sector does not pay activity taxes. Illegal deforestation operations are, however, subject to fines if their illegal activities are detected and they are successfu lly prosecuted. It is therefore ne cessary to estimate the value of fines issued in 2003 as well as the collection rate for the same year. The value of fines issued to the illegal sectors is extrapolated from IBAMA and Macqueen et al. (2003, p. 54) data. Based on estimates from Macqueen et al. (2003, p. 54), Br ito et al. (2005, p. 10), Brito and Barreto (2006, p. 3), and IBAMA data, a conservative estimate of 6% of the rate of collection for fines issued is made and applied to the estimate of the value of fi nes issued. The value of fines paid by region is distributed according to the weight of the value of each regions illegal output of forest products. Under the balanced macroeconomic closure used in the modeling experiments to follow, government savings are flexible and tax rates are fixed (i.e. the activity tax or fine is treated as a fixed share parameter and is calibrated from the SAM). Treating the fine as a fixed share implies that as an illegal sectors output increases, so does the amount of the fine that it pays. Next, the additional expenditures on labor fo r clearing forestland are accounted for. From Cattaneo (2002), clearing one hectare of forestla nd requires half a month of low-skilled labor. The cost of labor for forest clearing is thus the product of half of the average monthly wage for the forestry sector and the fore st area cleared. The imbalance in expenditure on labor which is generated as a result of this additional labo r payment is distributed among all sectors in accordance with the weight of each sectors la bor expenditure. The imbalance created in the indirect tax account resulting from the illegal sectors payment of fines is distributed among all sectors according to the weight of each sectors indirect tax expenditure.

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131 The difference between the illegal deforestation sector and the legal de forestation sectors expenditure to produce a unit of output is referre d to as the deforesta tion product in the SAM. This is considered to be the level of above nor mal profits earned for ope rating illegally and is allocated according to an assumption matrix which describes how these profits are distributed to each labor class. Finally, adjustments to the SAM are made to account for the consumption of the deforestation product. With the disaggregation of the illegal defore station sector, th e size of the deforestation product is larger than when only legal deforestation was considered. The sole consumer of the deforestation product is the defore station institution. This institution derives all of its income from the returns to agricultural land. To provide the additional income that the deforestation institution requi res to consume the deforesta tion product, the value of the deforestation product is deducted fr om the total agricultural land re nt distributed to households and enterprises. This amount is deducted accordi ng to the weight of each institutions share of agricultural land rent and is then reallocated as income to the deforestation institution. Illegal Forestry The starting point for disaggrega ting an illegal f orestry sector in the SAM is to calculate the Legal Amazons total illegal forest product output. First, the total volume of timber authorized through forest management plans and deforestation aut horizations in 2003 is calculated. Next, total roundwood c onsumption for 2003 is extrapolated from data for 1998 and 2004 (Lentini et al, 2005a, p. 69; Lentini et al ., 2005b, p. 1). The authorized volume is deducted from total consumption revealing that the illegal harvest volume from illegal deforestation and illegal forestry was 56% of total output in 2003. The difference between total illegal forest product output and the illegal defo restation sectors forest product output is the illegal forestry sectors output.

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132 The illegal forestry sectors expenditures on intermediate consumption, labor and capital are assumed to be the same as that of the lega l forestry sectors expe nditures per un it of output. These expenditures are deducted from the legal forestry sectors expenditures. The illegal forestry sectors payments to forestland and its payments of fines are calculated in the same way as for the illegal deforestation sector. Since the illegal sector generally pays less forestland rent and makes smaller payments to the indirect tax acc ount (interpreted as fines), the illegal sectors expenditure for a given unit of output is less than that of the legal sector. As in the case of the illegal deforestation sector, this difference is interpreted as above normal profits and is distributed to labor in the same manner as in the case of the illegal deforestation sector. Summary of Key Assumptions 1. Illegal def orestation produces less timber per unit area than the legal deforestation sector. 2. The illegal deforestation and illegal forest ry sectors pay less for forestland per unit of forest product output than their legal counterparts. 3. The illegal deforestation and illegal forestry sectors do not pay activity taxes rather they pay fines based on the amount of fines a nd the collection rate estimated for 2003. 4. The deforestation institution derives all of its income from the returns to agricultural land which enables it to finance the production of forest products and the clearing of forestland. 5. The difference between an illega l and legal sectors (for both forestry and deforestation) expenditure to produce a unit of forest product output is cons idered to be the level of above normal profits earned for operating illega lly; this profit is allocated to labor. Scenario Design Two scenarios are s imulated in this chapter. The baseline scenario projects the Brazilian economy from the base year of 2003 to 2018 in th e absence of forest concessions. The policy shock scenario uses the results of the base ye ar projection and simulate s the establishment of forest concessions in the Brazilian Amaz on from 2008 to 2018. The difference between the

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133 baseline and policy shock scenarios is the polic y impact of forest co ncessions on the Brazilian economy. This section develops the baseline and policy shock scenarios in detail. In contrast to the static CGE model of Chapter 3, the re cursive dynamic model enables the updating of factor stocks. In both the baseline and the policy s hock scenarios, labor supply is updated based on the estimated labor force growth rate. Capital stocks are updated endogenously based on the previous periods allocation of i nvestment and the rate of capital depreciation5. In both the baseline scenario and the policy shock scenar ios, the stock of agricultural land is also updated each year. Since the legal and illegal de forestation sectors clea r forestland, the quantity of forestland cleared in one year is used to update the factor s upply of agricultural land in the subsequent year and is thus ma de available to the agricultural and forest plantations sectors6. Figure 4-1 represents the relationship between na tural forest management, forest plantations, agriculture, forestland, and agri cultural land. Equation 4-1 demons trates the updating of the agricultural land stock based on th e previous periods level of de forestation using the stock of agricultural land in the north as an example. 4-1 Where: = Quantity of factor f demanded by activity a in time t. = Quantity of factor supply for activity a in time t In the policy shock scenario, the forest conces sions policy is simulated as an increase in the factor supply of forestland in the north. Of the 193.8 million hectares of public forests registered in Brazils National Registry of P ublic Forests, 43.7 million hectares (22.5%) are legally eligible to be designated as forest con cessions; 99.8% of these forests are located in the

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134 Legal Amazon. Of this area, 11.6 million hectares may be designated as Management Units for forest concessions for the 2007 to 2008 period, over 99% of which are located in Brazils northern administrative region (i.e in the Legal Amazon with the exception of the state of Mato Grosso). The state of Par alone accounts for over 82% of this area. The majority of these forests are National Forests with approved forest ma nagement plans or National Forests with management plans that are in the process of de velopment. Of the 11.6 milli on hectares of forest, 3.9 million hectares were designated as priority areas for forest concessions, all of which are located in the states of Par and Rondnia. One million hectares of this area in 2008 are expected to be designated as Management Units and put up for bidding as forest concessions (SFB, 2007, p. 30). Over the next 10 years, th e maximum area that the state plans to designate as forest concessions is 13 million hectares or 3% of Amaznia (MMA, 2005, p. 5). Taking a more conservative estimate of 10 million hectares over the next 10 years, the policy scenario introduces 1 million hectares of forestland stock per year in the north beginning in 2008, with the final 1 million hectares introduced in 20177. A mathematical formulation of this addition of forestland is as follows. The sum of all activities demand for a given fact or is equal to the total factor supply as given by equation 4-2. For simplicity, the element of time is dropped. 4-2 Where: = Quantity of factor f demanded by activity a. = Quantity of factor supply. = Initial quantity of factor supply.

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135 As an example, in the first year of fore st concessions implementation, forestland supply is increased by 46% in the nort h. Given equation 4-3, this incr ease enters the model as in equation 4-4. Since: 4-3 4-4 Illegal forestry operations produce less timber and generate more wood waste than their legal counterparts. Following Gutierrez-Velez and MacDicken (2008, p. 252), planned logging is used as a proxy for legal forestry and unplanned logging is used as a proxy for illegal forestry. Barreto, Amaral, Vidal and Uhl (1998, p. 13) found th at planned forestry operations harvest 30% more timber than unplanned operations due to le ss waste during felling and skidding. To account for inefficiencies in illegal operations as well as the increasing scarcity of forestland available for use by the illegal logging and illegal deforest ation sectors in the nor th, a yield distortion parameter is introduced into the illegal forestry and illegal deforestation sectors equations for forest and deforestation product output per un it of activity. Equation 4-5 is a mathematical representation of the imp act of the distortion parameter on a s ectors activity level. In this equation, as a sectors activity leve l increases, its output increases at a slower rate as determined by the yield distortion parameter. 4-5 Where: = Quantity of household home consumption of commodity c from activity a for household h. = Quantity of marketed output of commodity c from activity a. = Yield distortion parameter for activity a and

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136 commodity c. = Yield of output of c pe r unit of activity a. = Activity level. In order to calculate the dist ortion parameter, the area of fo rest concessions established each year is deducted from the total federal public forests eligible for forest concessions. The proportion of the value calculated above and the to tal federal public forests eligible for forest concessions is calculated and is interpreted as the distortion parameter. With 1 million hectares of public forests designated as fore st concessions per year, this di stortion parameter is smaller in value and larger in effect year upon year8. With regards to model closure rules, in both the baseline and policy shock scenarios, the modeling experiment is run in a balanced m acroeconomic environment where investment and government consumption shares are fixed while the quantities are flexible. Nominal absorption shares of investment and government consumpti on are fixed at their ba se year levels. With regards to factor closures in the baseline and policy shock scenarios, la bor, capital, agricultural land, and forestland are fully employed and mobile between sectors. A flex ible real exchange rate is chosen for the rest of the world closure while the government closur e fixes direct tax rates enabling flexible government savings. The domestic price index is chosen as the numeraire. Results Changes in m acroeconomic indicators in the ba seline scenario are presented in Table 4-1. The annual compound percent change (ACPC) in r eal GDP at market prices is 5.5%. The annual ACPC in household consumption, investment, government consumption, exports, and imports are 5.9%, 6.2%, 4.4%, 5.1%, and 5.8%, respectively. Slight deflationary pre ssure is reflected in the ACPC of the consumer pri ce index (-0.2%). There is a reduc tion in private savings as a percentage of GDP (-0.6%) which is compensate d for by an increase in government savings

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137 (0.6%). There is an increase in the ACPC in low-income, mid-in come and high-income households consumption (3.7%, 5.6% and 6.6%, re spectively). The welfare of all households improves; the ACPC in equivalent variation is 5%, 8.6% and 11.2% for low, mid and highincome households, respectively. The impact of the forest concessions policy on the economy is calculated as the difference between the value of indicators in the baseline scenario and th e policy shock scenario. The policy impact on real GDP at market prices is positive for all years with the exception of 2009 (Figure 4-2). The policy impa ct on other macroeconomic indicators is small with the exception of the deforestation institutions c onsumption and welfare (Table 4-1). This institutions consumption in th e baseline grows at 3.3% ; it decreases with the concessions policy scenario at a rate of -9.2% pe r annum for a difference of -12.5% Equivalent variation in the baseline grows at 17.3% and decreases by -3.0% in the policy scenario for a yearly average difference of -20.3%. A large and positive impact on activity levels is experienced by the legal and illegal forestry sectors in the north (Figure 4-3). The policy has a small negative impact on legal forestry in the north east, south east and south, and on illegal forestry in the north east. There is a small positive effect on the legal and illegal forestry sectors in the center west. Forest concessions have a larg e positive impact on the av erage annual growth rate (AAGR) of legal forestry activity in the north and center west while they have a negati ve effect on the AAGR of activity levels in th e north east, south east and sout h (13.53%, 4.09%, -2.22%, -0.08% and 0.04%, respectively; Table 4-2). The impact on ill egal forestrys AAGR in activity is positive and the highest in the north and center west a nd negative in the north east (10.59%, 2.93% and 2.78%, respectively).

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138 With regards to policy impacts on legal deforest ation activity levels, there is a positive effect, the largest of which o ccurs in the north, followed by the center west and north east (Figure 4-4). The policy has a very small negative impact on illegal deforestation in the north east and a large negative impact in the north, fo llowed by the center west The policy has a large and positive effect on the AAGR of legal deforestati ons activity level; the effect is the greatest in the center west, followed by the north east and north (10.22%, 9.92% and 8.74%, respectively; Table 4-2). Illegal deforestations AAGR in activit y is negatively impacted, most dramatically in the north, followed by the north east a nd center west (-26.08%, -7.47% and -5.19%, respectively). The policy impact on forest plantation activity is negative and the largest in the south, followed by the north east, north, center west and south east (Figure 4-5). The policy negatively affects the AAGR of the forest plantations sectors activity, with the larges t effect in the center west followed by the north, north east, sout h and south east (-3.28%, -3.11%, -2.65%, -2.56% and -1.87%, respectively; Table 4-2). There is a large positive effect on pulp a nd cellulose and wood processing activities (Figure 4-6). The processed wood and pulp and cellulose AAGR in activity levels is positively impacted (0.38% and 0.75%, respectively; Table 4-2). The forest concessions policy has a relative ly large and positive impact on agricultural activity in all regions but the center west (Figure 4-7). The imp act on the AAGR of agricultural activity levels is also positive in the north, north east, south east and south, and negative in the center west (0.45%, 0.36%, 0.09%, 0.18% and -0.05%, respectively; Table 4-2). The policy impact on composite commodity suppl y for agricultural and forest products, processed wood, and pulp and cellulose is positiv e and most pronounced fo r agricultural product

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139 supply (Figure 4-8). The effect on the AAGR in composite commodity supply is positive for agricultural and forestry pr oducts, processed wood, and pulp and cellulose (0.09%, 0.47%, 0.14% and 0.76%, respectively; Table 4-3); the s upply of all other goods and services is also positively impacted with the exception of commercial services. Figure 4-9 reveals that the policy has a cons iderable positive impact on domestic demand for agricultural and forestry products. Domestic demand for processed wood and pulp and cellulose, and export demand for ag ricultural and forest products also increase. The AAGR for domestic demand of agricultural and forestry products, proce ssed wood, and pulp and cellulose increases by 0.10%, 1.10%, 0.15% and 0.76%, respectively; the AAGR in exports increases by 0.21%, 2.53%, 0.86% and 0.74% for ag ricultural and forestry pr oducts, processed wood, and pulp and cellulose, respectively (Table 4-4). With regards to composite commodity prices the forest concessi on policy has a large negative impact on forest produc t prices, a smaller negative impact on processed wood and agricultural product pr ices, and a negligible effect on pulp and cellulose prices (Figure 4-10). The policy impact on the AAGR for agricultural and forest products prices, processed wood, and pulp and cellulose is -0.09%, -0.94%, -0.32% and 0.00%, respectively (Table 4-5). Figure 4-11 and 4-12 depict the policy impact on the stock of agricultural land and forestland in the north, north east and center west. The policy impact is positive for agricultural land stocks in the north and the north east as a result of deforestation a nd negative in the center west. Forestland stock in the north increases beginning in 2008 as forest concessions are established. The policy impact on the legal deforestation se ctors demand for forestland in the north east is positive and pronounced after 2008 and rema ins relatively constant thereafter (Figure 4-

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140 13); the effect on the illegal and legal forestry sectors demand is negative. There is a small negative effect on the illegal deforestation sectors demand for forestland in the north east; there is no policy impact on demand in the south and s outh east. In the north east, the AAGR of legal deforestations demand for forestland increases while legal and illegal forestry and illegal deforestation demand decrease (9.93%, -2.16%, -2.77% and -7.45%, respectively; Table 4-6); there is no effect on demand in the south and south east. The legal and illegal forestry sectors dema nd for forestland in the north is positively and strongly affected by the forest concessions policy, t hough the latter to a lesser degree (Figure 414). The legal deforestation sectors demand for forestland in the north is positively impacted by a small degree while the illegal deforestation se ctors demand is negatively affected. In the center west, the legal forestry, legal deforesta tion and illegal forestry sectors demand for forestland is positively affected while the illegal deforestation sector is negatively impacted. The policy impacts on the AAGR in demand for fores tland are positive for legal forestry, illegal forestry and legal deforestation in the north, while illegal deforestation in the north suffers a marked negative impact (13.69%, 10.67%, 8.92% a nd -25.78%, respectivel y; Table 4-6). The AAGR of legal forestry, illegal forestry and le gal deforestation demand for forestland in the center west is positively affected while illega l deforestations demand is negatively affected (4.14%, 2.95%, 10.27% and -5.10%, respectively). Figure 4-15 describes the policy impact on demand for agricultural land in the north east, south east and south. The agricultural sectors demand for land in the south is the most positively affected of these 3 regions, followed by the agricultural sector in the north east and south east. The forest plantations sector is the most negatively impacted of these 3 regions in the south followed by the south east and north east. The ag ricultural sectors demand for agricultural land

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141 in the north is positively impacted to a large degree and negatively affected in the center west (Figure 4-16). The forest planta tion sectors demand for land is negatively impacted the most in the center west and then in the north. The effect on the agricultural sect ors AAGR in demand for agricultural land is positive in the north, north east, south east and south; it is negative in the center west (0.48%, 0.50%, 0.18%, 0.22% and -0.04% respectively; Table 4-6). The forest plantation sectors AAGR in demand for land is negatively impacted in all regions (-3.06%, 2.54%, -1.87%, -2.55% and 3.27% in the north, north east, south east, south and center west, respectively). The policy impact on household income is depicted in figure 4-17. High-income household income is the most positively affect ed, followed by enterprise and mid-income and low-income household income. From 2009 to 2012 and 2009 to 2010, the policy impact on lowincome and mid-income households, respectivel y, is negative; the impact on high-income households is always positive, though the differe nce between the baseline and the policy shock also dips slightly in 2009. The AAGR in inco me increases for low-income, mid-income and high-income households and the enterprise (0.01%, 0.02%, 0.03% and 0.02%, respectively; Table 4-7). Policy impacts on household expenditure s follow the same trends as those described for household income (Figure 4-18). Figure 4-19 describes the polic y impact on labor and capital income. Low-skilled labor income in the policy scenario is less than in the baseline from 2009 to 2016, though the effect becomes positive after 2016. Mid-skilled and high-ski lled labor and capital experience a negative impact on their income in 2009, though the effect is positive thereafter. Th e policy impact on the AAGR for low-skilled, mid-skilled and high-skille d labor, and capital income is positive (0.01%, 0.03%, 0.03% and 0.03%, respectively; Table 4-8).

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142 Forestland income in the north increases dram atically with the implementation of forest concessions (Figure 4-20). Fore stland income in other regions changes little in comparison, though it tends to be generally negatively affected as a result of the polic y. The policy impact on the AAGR in forestland income increases markedly in the north and declines in the north east, south east, south and center west (5.39%, -0.66%, -1.32%, -1.14% and -2.39%, respectively; Table 4-8). In figure 4-21, the policy impact on agricu ltural land income is depicted. The policy effect in the north is negative in 2008, positive in 2009, negative from 2010 to 2014, and positive thereafter. In the north east, the effect is negative in 2008, positive in 2009 and negative in all subsequent years. In the south east, south and center west, it is negative for all years. The policy impact on the AAGR in agricultural land income in the north is posi tive, while it is negative in the north east, south east, south and center west (0.08%, -0.49%, -0.47%, -0.30% and -0.14%, respectively; Table 4-8). Figure 4-22 depicts the policy impact on labor wa ges and the price of capital. The effect on low-skilled labor wages is negative and quite pronounced from 2009 to 2016; the effect becomes positive after 2016. The impact on mid-skilled labor wages is negative only for 2009. Mid-skilled and high-skilled labor experience a po sitive impact on their wages as a result of the policy while the price of capital is positively affected to a much lesser degree. The impact on the AAGR in low-skilled, mid-skilled and high-skilled labor wages and the price of capital is positive (0.01%, 0.03%, 0.03% and 0.02%, respectively; Table 4-9). The impact on the price of forestland in the north east spikes positively in 2009 and declines steadily thereafter; the effect is negative in 2008, positive from 2009 to 2014 and negative thereafter (Figure 4-23). The impact is negative in all other regions with the greatest

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143 effect on forestland prices in the north follo wed by the center west. The policy impact on the AAGR in forestland prices is negative in al l regions (-6.20%, -0.66%, -1.32%, -1.14% and 2.39% in the north, north east, south east, sout h and center west, resp ectively; Table 4-9). The policy impact on agricultural land prices is negative for all regions (Figure 4-24). Prices are negatively affected the most in the s outh east and north east, an d less dramatically in the south and north. Although prices are negatively affected in the center west, this effect is less pronounced than in other regions. The impact on the AAGR in agricultural land prices is negative in all regions (-0.36%, -0.78%, -0.47%, -0.30% and -0.07% in the north, north east, south east, south and center west, respectively; Table 4-9). Discussion The baseline m odel results reveal a 5.5% ra te of real GDP growth over the time horizon (Table 4-1). This figure is a reasonable estimate when compared to other real GDP growth projections that vary between 4% and 5% (Jaeg er, 2006, p. 1; Ministrio de Minas e Energia, no date; Patusco, 2002; Purushothaman, no date; The World Bank, 2008, p. 22). In the baseline scenario, legal forestry in the north and center west contract while expanding in all other regions (Table 4-2). W ith forest concessions implemented in 2008, the opposite becomes truethe policy im pact is positive in the north and center west and negative in other regions. This is an indication of the curr ent scarcity of forestland for forest management. The increased availability, however, results in greater growth in the illegal forestry sector in the north and center west rather than the contraction experienced in the baseline. Illegal forestry grows at a slower rate than the legal forestry se ctor given the increasing scarcity of forestland for illegal activities with the estab lishment of forest concessions. With increased activity and output resulting fr om forest concessions, the policy impact on the forest plantation sector, alt hough it continues to grow, is negativ e in all regions (Table 4-2).

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144 This result, however, does not take into consider ation investments that have already been made in forest plantations which are yet to bear their full economic impact. Legal deforestation in the base line expands in the north and contracts in the north east and center west. The policy impact, however, leads to positive and faster growth in all regions. Illegal deforestation grows in the north and cent er west and contracts in the north east in the baseline; the policy impact, however results in a contraction in a ll regions. This contraction in illegal deforestation is a functi on of the increasing scarcity of forestland on which firms may operate illegally and the reduced returns to agricultural land which fund the deforestation institution (Table 4-2). In summary, the policy impact of forest concessions on illegality is to reduce the rate of growth of illegal forestry in th e north east and the rate of illegal deforestation in all regions. As a result of the forest concessions policy, th e rate of agricultural growth increases in all regions with the exception of a small decrease in the center west. While the greater rate of expansion in the north and north east are a result of faster ra tes of legal deforestation and therefore the production of agricultural land, the declin e in the center west appears to be the result of a relatively large decrea se in illegal deforestation, given th e importance of this sector in generating agricultural land. Various economic indicators such as real GDP, household income, agri cultural and forest product supply and forest planta tion activity exhibit a positive or negative spike in 2008 or 2009 as a result of the forest concessions policy. Where these spikes occur in 2008, they are the result of the increased availability of forestland in the north. Where they occur in 2009, it is reasonable to assume that this variation is the result of the combined economic impa ct of greater forestland

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145 availability in the north as well as increased s carcity in forestland for illegal activities. These spikes represent a brief period of economi c adjustment to policy implementation. With a growing population and labor force, it is expected that through time, the domestic demand for aggregate sectoral output will increase. In the baseline, this is the case with domestic demand growing between 3% and 6% (Table 4-4). Th is is also true in the forest concessions policy scenario where the largest impacts on dome stic demand are for forestry and agricultural products, processed wood, and pulp and cellulose; in the case of exports, the policy impact is also positive for these products. With regards to composite commodity prices, forestry and agricultural product as well as pr ocessed wood product prices increa se in the baseline (Table 45); the policy impact of forest c oncessions, however, results in a re duced rate of growth in these prices. Just as the level of activity of the legal and illegal forestry sectors in the north and center west are positively affected by the policy, growth in their demand for forestland also increases. In the case of illegal deforestation in the north and center west, the growth in demand for forestland is positive in the baseline; the policy, howe ver, results in a negative rate of growth for this activitys demand for forest land. In the north east, the polic y impact on growth in demand for forestland is positive although still decreasing. The policy impact on the rate of growth in the agriculture sectors demand for agricultural land increases in all regions but the ce nter west. This increased growth in demand is met by a reduced rate of growth in the forest plantations sectors demand for land and an increase in the rate of growth of legal defo restation and therefore an increasing stock of agricultural land.

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146 With regards to household income, there is a positive overall policy impact on income levels (Table 4-7). With high and mid-income households receiving a greater share of income from deforestation among other th ings, their incomes also grow at a faster rate. Low-income and mid-income households experience an adjustment period to the policy shock with their incomes being negatively affected in comparison to th e baseline for 4 and 2 y ears, respectively; highincome households are positively affected throughout the simulation period. Through time, in the baseline, labor and capital income increases; forest concessions have a positive impact on the income of all labor classes as well as capital (Table 4-8). The policy impact on growth of forestland income is large and positive in the north and negative in other regions. With the price of forestland negatively imp acted by the policy in a ll regions (Table 4-9), the increase in forestland income in the north is explained by the in crease in the stock and demand for forestland in the north. The greatest negative effect on forestland prices is in the north followed by the center west. With regards to agricultural land income, the av erage rate of growth is increasing in the baseline; in the policy shock scen ario, the policy impact is negativ e in all regions but the north (Table 4-8). With agriculture in all regions w ith the exception of the center west exhibiting greater growth in demand for agricultural land as a result of the policy, the negative impact on agricultural land income is due to the magnitude of the negative impact of the policy on the price of agricultural land in all regions (Table 4-9). Interestingl y, in the north, the impact on agricultural land income is nega tive in 2008 and from 2010 to 2014. This is an indication that the policy impact on agricultural land income is mi xed initially requiring a number of years for adjustments to take place.

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147 1 Although beyond the scope of the current discussion, the drivers of deforestation are reviewed in Kaimowitz & Angelsen (1998); a literatu re review of the drivers of deforestation may be found in Glantz, Brook &Parisi (1997). 2 New logging centers were established less than 10 year s ago (as of 2004) and are located primarily in western Par and the extreme north west of Mato Gr osso (Lentini et al., 2005a, p. 37); these centers form an arc from the BR-163 highway in western Par to the extreme north west of Mato Grosso until the southern reaches of the state of Amazonas close to the Trans-Amazon highway (Lentini et al., 2005a, p. 62). 3 In the case of the center west, given the high levels of deforestation and the large number of new logging centers, this value is further adjusted dow nwards since calculated as described above, illegal deforestation on its own would account for over 53% of the total (legal deforestation, legal logging and illegal logging) forest sector output. 4 As better data becomes available, a closer approxima tion of illegal sectors payments to forestland will be possible. 5 Estimates on population and labor force growth rat es were obtained from the Population Division of the Department of Economic and Social Affairs of th e United Nations Secretariat. Projections on the depreciation rate and total factor productivity gr owth rates were taken from the Organisation for Economic Co-operation and Devel opments [OECD] Economic Surveys (2006). The average capital to output ratio was obtained from Morandi and Reis (2004). 6 Deforestation driven by land speculation may not necessarily be put towards a productive use. Furthermore, deforested areas that are put towards production may be later abandoned due to low productivities. These processes are beyond the scope of the present analysis, however (see Cattaneo (2002) for the incorporation of land de gradation in a static CGE framework). 7 Forestland in the model is treated as homogenous, specifi cally, it is not distinguished by tenure type. As a result, forestland that enters the model as forest concessions is technically av ailable to both legal and illegal forestry and deforestation sectors. Deforestation is of course not permitted by law on forest concessions and the illegal deforestation of forest c oncessions is not likely to occur at any significant level given a concessionaires vested interest in prohi biting encroachment by third parties. It is thus assumed that within the aggregate forestland base, de forestation occurs on non-concessioned forestland. 8 The yield distortion parameter calculated in this wa y likely underestimates the inefficiency of illegal operations as well as the increasing scarcity of for estland for illegal operations as forest concessions expand. As data on the real economic effects of fo rest concessions become available, the ability to estimate this parameter will improve. Though beyond the scope of this research, as with all model parameters, systematic sensitivity analysis may be conducted to examine the sensitivity of results to changes in the assumptions embodied in exogenous pa rameter inputs. Furthermore, confidence intervals may also be generated for any endo genous variables in the model (see Alavalapati, Adamowicz & White, 1999 and Arndt & Pearson, 1998, for details).

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148 Figure 4-1. Relationship between forestry, deforestation, fore st plantations, agriculture, forestland and agricultural land Legal forestry Illegal forestry Legal deforestation Illegal deforestation Agriculture Forest plantations

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149 Figure 4-2. Policy impact on real GDP growth Figure 4-3. Policy impact on the leve l of legal and illegal forestry activity in the north, north east, south, south east and center west -0.2 0 0.2 0.4 0.6 0.8 1 1.2 20032006200920122015201810^10Reais Real GDP growth -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 20032006200920122015201810^10 Reais Legal forestry north Legal forestry north east Legal forestry south east Legal forestry south Legal forestry center west Illegal forestry north Illegal forestry north east Illegal forestry center west

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150 Figure 4-4. Policy impact on the level of legal a nd illegal deforestation activity in the north, north east and center west Figure 4-5. Policy impact on level of forest plantation activity in the north, north east, south east, south and center west -0.12 -0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 20032006200920122015201810^10 Reais Legal deforestation north Legal deforestation north east Legal deforestation center west Illegal deforestation north Illegal deforestation north east Illegal deforestation center west -0.08 -0.07 -0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 20032006200920122015201810^10 Reais Forest plantations north Forest plantations north east Forest plantations south east Forest plantations south Forest plantations center west

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151 Figure 4-6. Policy impact on the level of wood processing and pulp and cellulose activity Figure 4-7. Policy impact on the leve l of agricultural activity in th e north, north eas t, south east, south and center west 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 20032006200920122015201810^10 Reais Wood processing Pulp and cellulose -0.05 0 0.05 0.1 0.15 0.2 20032006200920122015201810^10 Reais Agriculture north Agriculture north east Agriculture south east Agriculture south Agriculture center west

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152 Figure 4-8. Policy impact on composite commodity supply of agricultural, forest, processed wood, and pulp and cellulose products Figure 4-9. Policy impact on domestic and export demand for agricultural, forest, processed wood, and pulp and cellulose products 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 20032006200920122015201810^10 Reais Agricultural products Forest products Processed wood Pulp and cellulose 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 20032006200920122015201810^10 Reais Domestic demand agricultural products Export demand agricultural products Domestic demand forestry products Export demand forestry products Domestic demand processed wood Export demand processed wood Domestic demand pulp and cellulose Export demand pulp and cellulose

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153 Figure 4-10. Policy impact on composite commodity prices of agricultural, forest, processed wood, and pulp and cellulose products Figure 4-11. Policy impact on agricultural land stoc k in the north, north east and center west -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 20032006200920122015201810^10 Reais Agricultural product price Forest product price Processed wood price Pulp and cellulose price -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05 20032006200920122015201810^10 Reais Agriland stock north Agriland stock north east Agriland stock center west

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154 Figure 4-12. Policy impact on forestland stock in the north Figure 4-13. Policy impact on forestland demand in the north east, south east and south 0 0.05 0.1 0.15 0.2 0.25 0.3 20032006200920122015201810^10 Reais Forestland stock north -0.001 -0.0005 0 0.0005 0.001 0.0015 20032006200920122015201810^10 Reais Forestland NE:legal forestry NE Forestland NE:illegal forestry NE Forestland NE:legal deforestation NE Forestland NE:Illegal deforestation NE Forestland SE:legal forestry SE Forestland S:legal forestry S

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155 Figure 4-14. Policy impact on forestland demand in the north and center west Figure 4-15. Policy impact on agricultural land demand in the north east, south east and south -0.02 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 20032006200920122015201810^10 Reais Forestland N:legal forestry N Forestland N:illegal forestry N Forestland N:legal deforestation N Forestland N:illegal deforestation N Forestland CW:legal forestry CW Forestland CW:illegal forestry CW Forestland CW:legal deforestation CW Forestland CW:illegal deforestation CW -0.03 -0.02 -0.01 0 0.01 0.02 0.03 20032006200920122015201810^10 Reais Agriland NE:agriculture NE Agriland NE:forest plantations NE Agriland SE:agriculture SE Agriland SE:forest plantations SE Agriland S:agriculture S Agriland S:forest plantations S

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156 Figure 4-16. Policy impact on agricultural land demand in the north and center west Figure 4-17. Policy impact on household and enterprise income -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05 0.06 20032006200920122015201810^10 Reais Agriland N:agriculture N Agriland N:forest plantations N Agriland CW:agriculture CW Agriland CW:forest plantations CW -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 20032006200920122015201810^10 Reais Change in low-income household income Change in midincome household income Change in highincome household income Change in enterprise income

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157 Figure 4-18. Policy impact on household expenditures Figure 4-19. Policy impact on labor and capital income -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 20032006200920122015201810^10 Reais Low-income household expenditure Mid-income household expenditure High-income household expenditure -0.2 0 0.2 0.4 0.6 0.8 1 20032006200920122015201810^10 Reais Low-skilled labor Mid-skilled labor High skilled labor Capital

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158 Figure 4-20. Policy impact on forestland income in the north, north east, south east, south and center west Figure 4-21. Policy impact on agricultural land inco me in the north, north east, south east, south and center west -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 20032006200920122015201810^10 Reais Forestland north Forestland north east Forestland south east Forestland south Forestland center west -0.16 -0.14 -0.12 -0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 20032006200920122015201810^10 Reais Agriland north Agriland north east Agriland south east Agriland south Agriland center west

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159 Figure 4-22. Policy impact on labo r wages and the price of capital Figure 4-23. Policy impact on price of forestland in the north, north east, south east, south and center west -0.008 -0.006 -0.004 -0.002 0 0.002 0.004 0.006 0.008 0.01 20032006200920122015201810^10 Reais Low-skilled labor wage Mid-skilled labor wage High-skilled labor wage Capital price -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 20032006200920122015201810^10 Reais Price of forestland north Price of forestland north east Price of forestland south east Price of forestland south Price of forestland center west

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160 Figure 4-24. Policy impact on the price of agricult ural land in the north, north east, south east, south and center west -0.4 -0.35 -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 20032006200920122015201810^10 Reais Price of agriland north Price of agriland north east Price of agriland south east Price of agriland south Price of agriland center west

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161 Table 4-1. Annual compound percent change in macroeconomic and institutional indicators between 2003 and 2018 Indicator Baseline Policy shock Difference Annual compound percent change Real household consumption (L CU at base prices) 5.9 5.9 0.0 Real investment (LCU at base prices) 6.2 6.2 0.0 Real government consumption (LCU at base prices) 4.4 4.4 0.0 Total real exports (LCU at base prices) 5.1 5.1 0.0 Total real imports (LCU at base prices) 5.8 5.8 0.0 Purchasing Power Parity real exchange rate (LCUs per FCU) -0.3 -0.3 0.0 Consumer Price Index (100 for base) -0.2 -0.2 0.0 Investment (% of nominal GDP) 0.0 0.0 0.0 Private savings (% of nominal GDP) -0.6 -0.6 0.0 Foreign savings (% of nominal GDP) 0.0 0.0 0.0 Trade deficit (% of nominal GDP) 0.1 0.1 0.0 Government savings (% of nominal GDP) 0.6 0.6 0.0 Direct tax revenue (% of nominal GDP) 0.0 0.0 0.0 Real household consumption Low-income household 3.7 3.7 0.0 Mid-income household 5.6 5.6 0.0 High-income household 6.6 6.6 0.0 Deforestation enterprise 3.3 -9.2 -12.5 Equivalent Variation Low-income household 5.0 5.0 0.0 Mid-income household 8.6 8.7 0.1 High-income household 11.2 11.2 0.0 Deforestation enterprise 17.3 -3.0 -20.3 Nominal GDP at market prices 5.5 5.5 0.0 Nominal GDP at factor cost 5.6 5.6 0.0 Real GDP at market prices 5.5 5.5 0.0 Real GDP at factor cost 5.5 5.6 0.0 Notes: LCU and FCU are local and foreign currency units respectively

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162 Table 4-2. Average annual growth rate in th e level of domestic act ivity between 2003 to 2018 Activity Baseline (%) Policy shock (%) Difference (%) Agriculture north 1.85 2.29 0.45 Agriculture north east 1.69 2.05 0.36 Agriculture south east 5.06 5.15 0.09 Agriculture south 2.00 2.17 0.18 Agriculture center west 1.65 1.60 -0.05 Legal forestry north -0.02 13.51 13.53 Legal forestry north east 5.91 3.69 -2.22 Legal forestry south east 1.18 1.10 -0.08 Legal forestry south 1.03 0.99 -0.04 Legal forestry center west -3.67 0.42 4.09 Illegal forestry north 1.65 12.24 10.59 Illegal forestry north east 5.37 2.58 -2.78 Illegal forestry center west -2.13 0.80 2.93 Forest plantations north 14.40 11.28 -3.11 Forest plantations north east 12.40 9.75 -2.65 Forest plantations south east -7.31 -9.19 -1.87 Forest plantations south 2.84 0.27 -2.56 Forest plantations center west 12.40 9.12 -3.28 Legal deforestation north 0.28 9.02 8.74 Legal deforestation north east -10.52 -0.60 9.92 Legal deforestation center west -0.55 9.67 10.22 Illegal deforestation north 5.07 -21.01 -26.08 Illegal deforestation north east -0.70 -8.17 -7.47 Illegal deforestation center west 3.19 -2.00 -5.19 Mining and petroleum 6.28 6.25 -0.02 Industry 5.72 5.73 0.01 Wood processing 4.05 4.43 0.38 Pulp and cellulose 3.49 4.24 0.75 Food processing 3.61 3.71 0.10 Utilities 5.92 5.94 0.02 Construction 6.04 6.07 0.03 Commerce 6.86 6.79 -0.08 Transportation 5.91 5.92 0.01 Private services 5.96 5.97 0.01 Public services 4.39 4.39 0.00

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163 Table 4-3. Average annual growth rate in the quantity of composite supply between 2003 and 2018 Activity Baseline (%) Policy shock (%) Difference (%) Agriculture 3.90 3.98 0.09 Forestry 3.98 4.45 0.47 Deforestation 3.34 -9.19 -12.52 Mining and petroleum 5.72 5.74 0.01 Industrial 5.77 5.80 0.03 Processed wood 5.21 5.34 0.14 Pulp and cellulose 3.82 4.58 0.76 Processed food 4.72 4.78 0.05 Utilities 5.85 5.87 0.02 Construction 6.04 6.07 0.03 Commerce 6.42 6.37 -0.05 Transportation 5.90 5.91 0.01 Private services 5.94 5.95 0.01 Public services 4.39 4.39 0.00 Table 4-4. Average annual growth rate in the quantity of domestic and export sales between 2003 and 2018 Baseline Policy shock Difference Baseline Policy shock Difference Good or service Domestic sales (%) Domestic sales (%) Domestic sales (%) Exports (%) Exports (%) Exports (%) Agriculture 3.56 3.66 0.10 0.91 1.12 0.21 Forestry 2.17 3.27 1.10 -4.28 -1.75 2.53 Deforestation 3.34 -9.19 -12.52 0.00 0.00 0.00 Mining and petroleum 5.95 5.95 0.00 7.03 6.96 -0.07 Industrial products 5.82 5.84 0.02 6.10 6.07 -0.03 Processed wood 5.16 5.31 0.15 2.36 3.22 0.86 Pulp and cellulose 3.78 4.54 0.76 3.39 4.13 0.74 Processed food 4.67 4.73 0.05 1.01 1.16 0.15 Utilities 5.91 5.92 0.02 0.00 0.00 0.00 Construction 6.04 6.07 0.03 6.25 6.27 0.02 Commerce 6.04 6.03 0.00 5.35 5.43 0.08 Transportation 5.90 5.91 0.01 6.10 6.08 -0.02 Private services 5.96 5.97 0.01 6.12 6.11 -0.01 Public services 4.39 4.39 0.00 0.00 0.00 0.00

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164 Table 4-5. Average annual growth rate in composite commodity prices between 2003 and 2018 Good or service Baseline (%) Po licy shock (%) Difference (%) Agriculture 1.68 1.59 -0.09 Forestry 4.23 3.29 -0.94 Deforestation 2.54 15.02 12.49 Mining and petroleum -0.78 -0.76 0.02 Industrial products -0.43 -0.43 0.01 Processed wood 0.89 0.57 -0.32 Pulp and cellulose -0.20 -0.20 0.00 Processed food 0.86 0.81 -0.04 Utilities -1.01 -0.99 0.02 Construction -0.75 -0.75 0.00 Commerce 0.52 0.41 -0.11 Transportation -0.59 -0.56 0.02 Private services -0.55 -0.54 0.02 Public services 1.29 1.31 0.02 Table 4-6. Average annual growth rate in the quantity of factor demand by industry between 2003 and 2018 Activity Factor Baseline (%) Policy shock (%) Difference (%) Agriculture north Agricultural land north 0.91 1.39 0.48 Forest plantations north 13.41 10.35 -3.06 Agriculture north east Agricultural land north east -0.05 0.45 0.50 Forest plantations north east 10.84 8.30 -2.54 Agriculture south east Agricultural land south east 2.25 2.43 0.18 Forest plantations south east -8.45 -10.31 -1.87 Agriculture south Agricultural land south -0.15 0.07 0.22 Forest plantations south 1.60 -0.95 -2.55 Agriculture center west Agricultural land center west 0.56 0.51 -0.04 Forest plantations center west 11.34 8.07 -3.27 Legal forestry north Forestland north -1.13 12.56 13.69 Illegal forestry north 0.75 11.42 10.67 Legal deforestation north Forest land north -0.69 8.24 8.92

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165 Table 4-6. Continued Activity Factor Baseline (%) Policy shock (%) Difference (%) Illegal deforestation north 4.01 -21.78 -25.78 Legal forestry north east Forestland north east 3.55 1.40 -2.16 Illegal forestry north east 4.19 1.42 -2.77 Legal deforestation north east -11.56 -1.64 9.93 Illegal deforestation north east -1.92 -9.37 -7.45 Legal forestry south east Forestland south east 0.00 0.00 0.00 Legal forestry south Forestland south 0.00 0.00 0.00 Legal forestry center west Forestland center west -4.75 -0.61 4.14 Illegal forestry center west -3.02 -0.07 2.95 Legal deforestation center west -1.51 8.76 10.27 Illegal deforestation center west 2.15 -2.96 -5.10 Table 4-7. Average annual growth rate in institutional income between 2003 and 2018 Institution Baseline (%) Policy shock (%) Difference (%) Low income household 3.11 3.12 0.01 Mid-income household 4.79 4.81 0.02 High-income household 5.54 5.57 0.03 Deforestation institution 5.28 5.25 -0.04 Enterprises 5.88 5.90 0.02 Interest 5.28 5.31 0.03 Table 4-8. Average annual growth rate in factor income between 2003 and 2018 Factor Baseline (%) Policy shock (%) Difference (%) Low-skill labor 4.86 4.86 0.01 Mid-skill labor 5.03 5.05 0.03 High-skill labor 5.23 5.26 0.03 Capital 5.79 5.82 0.03 Agricultural land north 4.97 5.05 0.08 Agricultural land north east 8.04 7.55 -0.49 Agricultural land south east 11.09 10.61 -0.47 Agricultural land south 9.09 8.79 -0.30 Agricultural land center west 5.48 5.35 -0.14 Forest land north 9.22 14.61 5.39 Forest land north east 14.38 13.71 -0.66 Forest land south east 8.29 6.96 -1.32 Forest land south 7.64 6.50 -1.14 Forest land center west 9.69 7.30 -2.39

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166 Table 4-9. Average annual growth rate in factor wages and prices between 2003 and 2018 Factor Baseline (%) Policy shock (%) Difference (%) Low-skill labor 3.69 3.70 0.01 Mid-skill labor 3.86 3.89 0.03 High-skill labor 4.07 4.10 0.03 Capital -1.46 -1.44 0.02 Agricultural land north 3.97 3.61 -0.36 Agricultural land north east 7.62 6.84 -0.78 Agricultural land south east 11.09 10.61 -0.47 Agricultural land south 9.09 8.79 -0.30 Agricultural land center west 4.87 4.80 -0.07 Forest land north 9.22 3.02 -6.20 Forest land north east 14.38 13.71 -0.66 Forest land south east 8.29 6.96 -1.32 Forest land south 7.64 6.50 -1.14 Forest land center west 9.69 7.30 -2.39

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167 CHAPTER 5 CONCLUSIONS Historical analysis suggests that forest policies of countri es with significant forested frontiers transition through stages of forest policy developm ent reflec ting the orientation of governments toward economic development on th e frontiers, namely: se ttlement, protective custody and management. To present, with rega rds to the forests of the Amazon, Brazil is no exception. The period of settlement as the name suggests was characterized by land clearing to provide raw materials and establish farms and ranc hes. Policy makers co uld not, however, ignore the apparent devastation that re sulted, particularly in the Atlantic Forest Region, and passed the first Forestry Code in 1934. A basic framework fo r forest concessions was included in the law, although they were never implemented. With gove rnment priorities focusing on colonization and agricultural expansion, the destruction continued largely unabated. As forests in the Atlantic regi on continued to be reduced in quantity and quality and with the poor implementation record of the 1934 Forest ry Code, policy makers began to develop a New Forestry Code which was subsequently ap proved in 1965. This New Code was significantly more protectionist and marks the transition in forest policy de velopment to the protective custody phase. An important featur e of this code is the limitati on placed on private property rights. During this period, pr otective legislation flourished, al ong with the creation of large protected areas; initiatives to pr omote the development of the natu ral forest management sector were lacking, however. With the forest plantation sect or feeding the nations metal and mineral industries, the provision and management of ince ntives for forest plantations was a substantial component of forest policy. Institutions and programs during this period were weak and underfunded, however, and although th e forest policy framework developed significantly more than in the previous period, the states prioritization of industrialization a nd integration of the

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168 Amazon into the national economy resulted in the marginalization of forest policy rendering it largely ineffectual. As such, the protectionist st age of Brazilian forest policy lived out primarily on paper Signs of change in the relevance of forestry and environmental policy began with the political opening in 1974 when the seeds of an environmental movement were sown. The movement, aligning itself with already established democratic and international environmental movements, was successful in creating politic al space for the assertion of environmental interests. Brazils democratizat ion, the growing influence of e nvironmental movements and civil societys effective engagement in political affairs pushed forest policy towards the management stage of policy development by the beginning of the millennium. This transition is marked by various forest and environment sector initiativ es such as the National System of Nature Conservation Units (SNUC), The National Fo rest Program (PNF), incentives for the development of the natural forest management sector, and the Public Forest Management Law (PFML). The PFML is of considerable interest; a significant component of the law involves the authorization of forest concessi ons on public lands, whereas prev ious forest policies relating to timber extraction were focused on the regulation of forestry on private land. Taking the PFML as a proximate indicator of the tran sition to the management stage of forest policy development, the variables that led to the political opening for th e approval of the law are important to understand. First, levels of deforestat ion reached historic heights in 1995 and 2002. With greater transparency and domestic and international concern for the Amazon region, the state was pushed to act in defense of the forest resources of the region and develop frameworks to combat deforestation. Escalating violence caused by land tenure disputes, changes in the regulatory framework for natural forest management, the ensu ing scarcity of forestland for legal forestry,

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169 and the resulting forest sect or crisis urged the government to send the PFML proposal to Congress as a constitutional emergency. Aided by the left-leaning Workers Party and greater representation for the interests of forestry-b ased communities, the la w passed swiftly through Congress and was approved in March of 2006. The level of success that is experienced in the implementation of the PFML will reveal whether the new policies and institutions establis hed since 2000 represent a break with the past with regards to the low levels of policy implem entation that characterize d the protective custody period. There are convincing reasons to believe that the management stage of forest policy will be significantly more effectively implemented. Efforts to delimit public lands and state-civil society partnerships are making progress in regulating public land use. With a globalized civil society and media, the economic and political costs of the illegal exploitati on of forest resources are becoming a significant obstacle to illegality in the forest sector. The state has committed unprecedented institutional support and funding toward s gaining control of forest resources in the Amazon. Various contradictory extra-sectoral policies have been eliminated. Furthermore, the economic opportunities presented by sustai nable forest management are increasing. Payments for environmental services, carbon cred its for carbon sequestrati on, and incentives for avoided deforestation may serve to increase the value of standing, managed forests as opposed to an agricultural alternative. The outlook for a tr ansition to management that is lived out in practice is decidedly optimistic. Taking the PFML and the establishment of forest concessions in partic ular as a proximate indicator of a substantive transi tion to the management phase of forest policy development, the economic, welfare, and environmental response to concessions provides an indication of the degree to which, if fully implemented, forest policy will be accepted and supported by society.

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170 Using a static computable general equilibrium model, the short-run socio-economic and environmental implications of implementing fo rest concessions in the Brazilian Amazon were evaluated. Simulating the establishment of 1 million hectares of forest concessions in Brazils northern administrative region, three conclusi ons with regards to socio-economic and environmental impacts are made. First, households experience growth in their income with the implementation of forest concessions. Although pr ices of goods and serv ices increase somewhat, households are more than able to co pe with their increased income. Second, forest plantations contract as a result forestry expansion in the north. With the decline in the price of forestla nd in the north, the forestry se ctor is able to produce a less expensive product thus squeezing out some of the production from the forest plantations sector. The contraction in the forest plantations sect or results in a reduction in its demand for agricultural land which in turn de presses the price of agricultura l land. This less expensive land is taken up the agricultural sector enabling it to produce a more a ffordable agricultural product. Finally, the forest concessions pol icy results in an increase in legal deforestation, with the largest increase occurring in the Amazon. The de forestation institution perceives less income from returns to agricultural la nd, and although the sectors output of forest products increases, the depression in forest product pr ices also results in a reduction in its forest product income. All other things being equal, levels of deforestation would also decline as a result of the policy. However, with the price of fores tland also depressed, the sectors reduced income was more than offset by the decline in the price of forestland, enabling the deforestation institution to acquire more forestland, clear more land, an d produce more forest products.

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171 Given the significant economic importance of th e illegal forestry and illegal deforestation sectors in Brazil and to more realistically evaluate the socio-economic and environmental impacts of forest concessions, illegal forestry an d illegal deforestation sectors are disaggregated and modeled. A recursive dynamic computable general equilibrium modeling framework is chosen to consider the medium-term implications of the implementation of forest concessions. Advantages of the dynamic modeling framework incl ude: the ability to update the factor supply of agricultural land based on annual levels of deforestation; the model sheds light on the economic transition path resulting from the policy shock; and, it provides information on the short-term costs and longer-term gains resu lting from policy implementation. The baseline scenario projects the Brazilian economy from 2003 to 2018 by accounting for growth in the labor force, population, and total fact or productivity, as well as acc ounting for capital accumulation and depreciation. The policy shock scenario uses the results of the baseline scenario and introduces the forest concessions shock into the economy. The difference between the value of indicators in the baseline and the policy shock scenario is the net policy impact of forest concessions. The overall policy impact of forest concessi ons on the Brazilian economy is relatively small with the exception of the negative welfar e impact on the illegal deforestation institution. In the absence of forest concessions, legal forestry activities contract in the north and center west. Increased demand for forest products is met by rela tively large growth in forest plantations with the exception of the south east. In troducing the concessions policy, le gal forestry in the north and center west expand, which may be an indication of the current scarcity of forestland for legal forestry operations. Illegal forestry activities incr ease in the baseline in the north and north east and decrease in the center west; in the policy scen ario, growth in activity increases dramatically

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172 in the north and to a slightly lesser degree in the center west, while growth is reduced in the north east. Legal deforestation in the baseli ne expands slightly in the no rth and contracts in the north east and center west. The policy imp act, however, leads to faster growth in all regions. It should be noted that although growth rates in legal de forestation activities are large, the legal deforestation sectors output of forest products and demand for forestland is initially quite small. Illegal deforestation grows in the north and cent er west and contracts in the north east in the baseline; the policy impact, however results in a contraction in a ll regions. This contraction in illegal deforestation is a functi on of the increasing scarcity of forestland on which firms may operate illegally and the reduced returns to agricultural land which fund the deforestation institution. In addition, since legal deforestation is able to more fully utilize timber prior to land clearing, it is able to out-compete the illegal deforestation sectors in terms of forest product output. The overall policy impact of forest conce ssions on illegality is to reduce the rate of growth of illegal forestry in the north east a nd reduce the level of illegal deforestation in all regions; the legal deforestation sectors gr ow faster as a re sult of the policy. As a result of increased forest product output from legal and illegal forestry and legal deforestation in the north, the fo rest plantations sectors experien ce a negative policy impact in all regions. Nonetheless, forest plan tation activities continue to grow with the exception of the south east. With increased supply of raw material s from forest product producing sectors, the processed wood, and pulp and cellulose sectors grow at a faster rate as a result of the policy. Both domestic and export demand increase for forestry, agricultural, processed wood, and pulp and cellulose products. Agricultural and forest product prices grow at a slower rate as a result of forest concessions.

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173 Agricultural activity grows in all regions in the baseline; the policy impact is positive in all regions with the exception of a small reduction in growth in the center west. While the greater rate of expansion in the north a nd north east are a function of faster rates of legal deforestation (i.e. the production of agricultural land) as well as the forest plantations sectors reduced demand for agricultural land, the d ecline in the center west appears to be the result of a relatively large decrease in illegal defo restation, given the importance of this sector in generating agricultural land. With regards to household income, the policy impact is positive. Since high and midincome households receive a grea ter share of income from legal and illegal deforestation among other things, their incomes grow at a faster rate. Low-income and mid-income households experience an adjustment period to the policy shoc k with their incomes negatively affected for a short period in comparison to the baseline. The overall impact on income growth over the time period, however, is positive. In the baseline, labor and capital income in crease; forest concessions have a positive impact on the income of all labor classes as well as capital due to increased growth in demand and higher rates of growth in wages. The po licy impact on growth of forestland income is positive and large in the north and negative in other regions. With the price of forestland negatively impacted by the policy in all regions, the increase in income in the north is explained by the increase in the stock and demand for fore stland. With regards to agricultural land income, the average growth rate in all re gions is increasing in the baseline. The policy impact is large and positive in the north and negative in all other re gions. With all regions experiencing growth in demand for agricultural land in the policy scenario with the exception of the center west, the

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174 negative impact on agricultural land income in all regions but the north is explained by the magnitude of the decrease in the price of agricultural land. The positive policy impact on household welfare and household income, and growth in the legal forestry and agricultural sectors revealed by the models in Chapters 3 and 4 are positive indicators of the potential societ al acceptance of a new forest po licy paradigm of management. If forest concessions are fully implemented as they are assumed to be in the modeling experiments, the management phase of policy development prom ises real and on balance, positive changes to the Amazons economy and forest sector. The benefits that forest concessions can generate as suggested by these results, particularly in the legal forestry sector, will very likely serve to increase the relevance of forest policy as a tool for promoting socio-economic development. The implementation of forest concessions pr esents favorable outcomes with regards to reducing illegality in the case of deforestation. The reduction in th e levels of illegal deforestation in the north, north east an d center west is certainly a palata ble proposition from both a societal and political point of view. Less desirable, how ever, is the disappointi ng response of illegal forestry and legal deforestation to the forest concessions poli cy. Although forest concessions result in a reduction in the rate of growth in illegal forestry in the north east, the contraction of illegal forestry in the center west in the baseline is reversed and the sector expands. The illegal forestry sector in the north also experiences a considerable increase in growth in the policy scenario. In the case of legal deforestation, the contraction experienced in the baseline in the north east and center west is reversed in the policy scenario; in the north, legal deforestation expands at a faster rate. These pol icy impacts on illegal forestry a nd legal deforestation are likely not what policy makers and advocates for forest concessions would have desired.

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175 The disappointing policy impact on illegal fore stry must be qualified, however. In the modeling exercises, forest concessions are implemented in the absence of any increases in the detection and prosecution of illegality. Improved monitoring, enforcement and the subsequent successful prosecution of violators would incr ease the costs of doing business illegally. The consequences of improvements in this area are intuitivehigher costs of illegality would force firms into legal compliance (or out of business) Although this would resu lt in an expansion of the legal forestry sector, the overall level of forestry activity when compared to the results of the policy experiment in Chapter 4 would likely be somewhat lower. The forest plantation sectors would probably not experience su ch a negative policy impact, a nd forest product prices might increase slightly. Complimentary policies to improve monitoring, enforcement and prosecution of illegality could go a long way in reduci ng illegality in th e forestry sector. Another result that signals the need for comp limentary policies as forest concessions are implemented is the negative effect on low and mid-income households in the early years of policy implementation. From 2009 to 2012 and 2009 to 2010, the policy impact on low-income and mid-income households, respectively, is negative. This result is likely driven by the negative policy impact on low-skilled labor wages between 2009 and 2016. Economic development programs and social programs could be eff ective in counteracting the negative impacts on income growth experienced in the in itial years of policy implementation. Four limitations to the modeling approach adopt ed in this research should be mentioned. First is with regards to the po licy impact of concessions on lega l deforestation. The policy impact on legal deforestation resulted in increased rates of growth in the north and center west. Though initial levels of deforest ation are low, this result is still troublesome and highlights a limitation in the simulation design. This increase in the rate of growth of deforestation is in part explained by

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176 the fact that the legal deforestation sector is not constrained by increa sing factor scarcity of private forestland, rather, it is c onstrained by the total supply of forestland. For deforestation to be legal, it must be legally authorized on priv ate land. Private property ow ners in the Amazon are permitted by law to clear up to 20% of their forestland. The amount of forest area that can legally be cleared on private land, however, is likely some what scarce; reliable estimates of the size of this area are required to increase the realism of the simulation with regards to forestland constraints on the legal deforestation sector. The second limitation is elucidated by the polic y impact on the illegal forestry sector. The illegal forestry sectors use of forestland is constrained by the total s upply of forestland. The potential increasing scarcity of forestland for illegal activities is modeled as reduced yields per unit of illegal forestry activity. With the impl ementation of forest concessions, the 1 million hectare per year increase in fo restland stock is made availabl e to all forest product producing sectors. With the implementation of forest concessions, however, both the state and the concessionaire have a vested interest in prohibiting the illegal logging or clearing of the concession by a third party. Illegal activities engaged in by the concession aire aside (i.e. overharvesting, under-reporting, harvesting in sensitive areas), it is expected that the level of monitoring and enforcement in the concession and in proximity to the concession would increase as a result of the policy. To some degree, this heightened vig ilance is modeled by the illegal forestry sectors reduced yields per unit of activit y. The real effect of c oncessions in reducing the availability of forestland for illegal operations is uncertain, however, though it is likely lower than that modeled in Chapter 4. The third model limitation is with regards to the world prices of goods and services. World prices and therefore export prices remain constant in the dynamic modeling experiment.

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177 Often, depending on the time horizon of analys is, world prices are updated according to projections made by industry experts. The highl y aggregated social accounting matrix developed for this analysis, however, is not conducive to exogenously updati ng world prices. For example, the industrial sector produces everything from leather goods to motor ve hicles, whose general rate of growth in prices may be quite differe nt. To avoid erroneous conclusions that may be driven by the inflexibility in the world price sy stem as modeled, a medium-term time horizon of 10 years was chosen. Since domestic prices are a llowed some flexibility in the model due to imperfect substitution between imported goods and domestically produced goods, domestic prices tend to grow while export prices remain constant. The implications of fixed world prices on the modeling results are a likely lower growth rate in exports th an would occur if world prices were updated according to industry projections. Finally, the fourth limitation to the modeling approach involves the aggregation of households. Disaggregation of households accordi ng to region would be advantageous to understand the regional income impacts of policy implementation. As the results from Chapters 3 and 4 indicate, there is a small increase in aggregate household income and welfare. With a regional disaggregation of households, the polic y impact on household income growth in the northern region would likely prove to be substantially greater than in other regions. Data limitations, however, restricted the current analysis of household income to the economy-wide aggregate level. Three areas for promising future research ma y be mentioned. First, the policy experiment is conducted in the absence of potential increases in the detection and prosecution of illegal deforestation and illegal forestry. The computable general equilibrium framework applied in this research is conducive to iteratively and exogenously shocking the value of the fine paid per unit

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178 of illegal output. Determining the level of fines th at would be required to reduce illegal forestry and illegal deforestation to the socially optimal level would be useful to policy makers in their decisions on budgetary allocations fo r monitoring and enforcement. Second, policy makers may be interested in the longer run (10+ years) socioeconomic and environmental impacts of forest concessi ons. The debate surrounding Reduced Emissions from Deforestation and Degradation can signific antly increase the demand for this type of information. A greater disaggregation of the so cial accounting matrix for Brazil in terms of activities, commodities and households, as well as estimates on changes in the world price of goods and services would enable more realistic, longer-term analysis of the impacts of forest concessions. Finally, the modeling exercises treat forestla nd as capable of producing the same quantity and quality of forest product output through time. Forestry law sets diam eter, species and volume constraints on forest operations. Whether these limits, assuming they are followed, result in sustainable yields is an open que stion, though recent research appear s to indicate that they are not. In the short and medium term as modeled in the current analysis, declining quantity and quality of timber stocks may not present any measurable impact. If the policy is modeled in the longer-term, however, in teresting results may be borne out of modeling changes in the characteristics of the timber supply.

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179 APPENDIX A COMPLETE MODEL EQUATION LISTING Table A-1. Model sets and param eters Symbol Explanation Symbol Explanation Sets Activities Commodities not in CM ACES( A) Activities with a CES function at the top of the technology nest Transaction service commodities ALEO ( A) Activities with a Leontief function at the top of the technology nest Commodities with domestic production Commodities Factors Commodities with domestic sales of domestic output Institutions (domestic and rest of world) Commodities not in CD Domestic institutions Exported commodities Domestic non-government institutions Commodities not in CE Households () cCMC Imported commodities Parameters Weight of commodity c in the CPI Base-year quantity of private investment demand Weight of commodity c in the producer price index Share for domestic institution i in income of factor f Quantity of c as intermediate input per unit of activity a Share of net income of i to i (i INSDNG; i INSDNG) Quantity of commodity c as trade input per unit of c produced and sold domestically Tax rate for activity a Quantity of commodity c as trade input per exported unit of c tec Export tax rate Quantity of commodity c as trade input per imported unit of c tff Direct tax rate for factor f Quantity of aggregate intermediate input per activity unit Exogenous direct tax rate for domestic institution i aA () cCMNC () cCTC () cCXC cC f F () cCDC iINS () cCDNC () iINSDINS () cCEC () iINSDNGINSD () cCENC () hHINSDNG ccwts cqinvcdwtsifshifcaica' iishii'ccicdata'ccice'ccicmainta itins

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180 Table A-1. Continued Symbol Explanation Symbol Explanation Quantity of aggregate intermediate input per activity unit 0-1 parameter with 1 for institutions with potentially flexed direct tax rates Base savings rate for domestic institution i Import tariff rate 0-1 parameter with 1 for institutions with potentially flexed direct tax rates Rate of sales tax Export price (foreign currency) Transfer from factor f to institution i Import price (foreign currency) tvaa Rate of value-added tax for activity a Quantity of stock change Base-year quantity of government demand Greek letters Efficiency parameter in the CES activity function CET function share parameter Efficiency parameter in the CES value-added function CES value-added function share parameter for factor f in activity a Shift parameter for domestic commodity aggregation function Subsistence consumption of marketed commodity c for household h Armington function shift parameter Yield of output c per unit of activity a CET function shift parameter CES production function exponent a Capital sectoral mobility factor CES value-added function exponent h ach Marginal share of consumption spending on home commodity c from activity a for household h Domestic commodity aggregation function exponent Marginal share of consumption spending on marketed commodity c for household h Armington function exponent CES activity function share parameter CET function exponent Share parameter for domestic commodity aggregation function a f at Sector share of new capital (dynamic model) aivaitins01 impsctmimps01ctqc p we iftrnsfrc p wmcqdst cqga a t c va a va f a ac c m ch q c ac t c a a va a ac c m ch q c a a t c ac ac

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181 Table A-1. Continued Symbol Explanation Symbol Explanation Armington function share parameter f Capital depreciation rate (dynamic model) ca Yield distortion parameter Source: Lofgren et al. (2002); R obinson and Thurlow (no date). Table A-2. Model variables Variable Explanation Variable Explanation Exogenous variables Consumer price index Savings rate scaling factor (= 0 for base) Change in domestic institution tax share (= 0 for base; exogenous variable) Quantity supplied of factor Foreign savings (FCU) Direct tax scaling f actor (= 0 for base; exogenous variable) Government consumption adjustment factor Wage distortion factor for factor f in activity a Investment adjustment factor Endogenous variables a f t A WF Average capital rental rate in time period t (dynamic model) Quantity demanded of factor f from activity a Change in domestic institution savings rates (= 0 for base; exogenous variable) Government consumption demand for commodity Producer price index for domestically marketed output Quantity consumed of commodity c by household h Government expenditures Quantity of household home consumption of commodity c from activity a for household h Consumption spending for household Quantity of aggregate intermediate input Exchange rate (LCU per unit of FCU) Quantity of commodity c as intermediate input to activity a Government consumption share in nominal absorption Quantity of investment demand for commodity Government savings Quantity of imports of commodity c Investment share in nominal absorption Quantity of goods supplied to domestic market (composite supply) Marginal propensity to save for domestic non-government institution (exogenous variable) Quantity of commodity demanded as trade input Activity price (unit gross revenue) Quantity of (aggregate) value-added q c CPI M PSADJ DTINS f QFS F SAV TINSADJ GADJ f aWFDIST I ADJ f aQFDMPScQG D PIchQH E GachQHAhEHaQINTA EXRcaQINTGOVSHRcQINVGSAVcQM I NVSHRcQQi M PScQTaPAaQVA

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182 Table A-2. Continued Variable Explanation Variable Explanation Demand price for commodity produced and sold domestically Aggregated quantity of domestic output of commodity Supply price for commodity produced and sold domestically Quantity of output of commodity c from activity a Export price (domestic currency) f RWF Real average fact or price (dynamic model) Aggregate intermediate input price for activity a Total nominal absorption f tPK Unit price of capital in time period t (dynamic model) Direct tax rate for institution i (i INSDNG) Import price (domestic currency) Transfers from institution i to i (both in the set INSDNG) Composite commodity price Average price of factor Value-added price (factor income per unit of activity) Income of factor f Aggregate producer price for commodity Government revenue Producer price of commodity c for activity a Income of domestic non-government institution Quantity (level) of activity Income to domestic institution i from factor f Quantity sold domestically of domestic output a f atK Quantity of new capital by activity a for time period t (dynamic model) Quantity of exports Source: Lofgren et al. (2002); R obinson and Thurlow (no date). Table A-3. Model equations cc CTc c c c cicmPQ EXRtm pwmPM' ')1( (1) cc CTc c c c cicePQ EXRtepwePE' ')1( (2) cc CTc c c cicdPQ PDS PDD' (3) cc cc cc cQMPMQDPDDQQtqPQ )1( (4) cccc ccQEPEQDPDSQXPX (5) ac Cc ac aPXAC PA (6) ca Cc c aicaPQ PINTA (7) a a a a aaQINTA PINTA QVAPVAQAtaPA )1( (8) c Cc ccwtsPQ CPI (9) cPDDcQXcPDSacQXACcPEaPINTATABSiTINScPM' iiTRIIcPQ f WFaPVA f YFcPXYGacPXACiYIaQAifYIFcQDcQE

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183 Table A-3. Continue d c Cc cdwtsPDS DPI (10) (11) (12) aa aivaQAQVA (13) aa aintaQA QINTA (14) va va a a1 vavavaf aafafafa fFQVA QF (15) 1 1 '1va va aafa faaa vavafvavaf fafafafafafa fFWWFDISTPVAtvaQVA QFQF (16) a ca caQINTA ica QINT (17) aac Hh ach acQA QHA QXAC (18) 1 1ac c ac cac ac cc ac ac aAQX QXAC (19) 11 'ac ac ccac ac c a cca ca ca ca c aAPXAC = QXQXAC QXAC PX (20) 1 1ac c ac cac ac cc ac ac aAQX QXAC (21) 1 1t ct c cc t c c cQE 1 PE = QD PDS (22) cc c = QDQE QX (23) qq q cc c1 -qq q cc c ccc = + (1-) QQQM QD (24) q c1 q 1+ c cc q c c cQM PDD = 1 QD PM (25) ccc = QQQDQM (26) '' '' '' Cc ccc cccc cc cQDicdQEiceQMicm QT (27)

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184 Table A-3. Continued Institutions fafa Aa f fQF WFDIST WF YF (28) ififf row fYIF =shifYFtrnsfrEXR (29) '' iifiiigovirow fFiINSDNGYI = YIFTRIItrnsfrCPItrnsfrEXR (30) ''''i iiii i iTRII = shii(1-MPS)(1-tins)YI (31) 11h hihhh iINSDNG E H = shiiMPS(1-tins)YI (32) '' '' '' mm m m cchcchchh cch acach cC aAcCPQQHPQ EHPQ PXAC (33) '' '' '' hh m m acach acachachh cch acach cC aAcCPXACQHAPXAC EHPQ PXAC (34) c cQINV =IADJqinv (35) c cQG =GADJqg (36) i a iac acc iINSDNGaAcCMNR crcccgovfgovrow crcr rRcCMRcCfFYGtinsYIta tm EXR QA pwmQM PA tmr EXRtqPQQQYFtrnsfrEXR pwmrQMR (37) cc igov cC iINSDNGEGPQQGtrnsfrCPI (38) System constraints f af aAQFQFS (39) cc ac hcccc aA hHQQQINTQHQGQINVqdstQT (40) cc crcr rowf cCMNR rRcCMR fF cc crcr irow cCENR rRcCER iINSDpwmQMpwmrQMRtrnsfr p weQEpwerQERtrnsfrFSAV (41) YGEGGSAV (42) (43) (1 01) 01ii i i M PSmpsMPSADJmpsDMPSmps (44) 1i ii ccc c iINSDNG cC cC M PStinsYIGSAVEXRFSAVPQQINVPQqdst (45) cch acach hHcC aAcChH cccccc cC cC cCTABSPQQH PXACQHA PQQGPQQINVPQqdst (46)

PAGE 185

185 Table A-3. Continued cccc cCcC I NVSHRTABSPQQINVPQqdst (47) cc cCGOVSHRTABSPQQG (48) Capital accumulation (dynamic model) 'f a t a f tf t f a t a f a' t aQF AWFWFWFDIST QF (49) '11f a t ft f a t aa f a t a f a' t f t aQF WFWFDIST QF AWF (50) c tc t aa c f a tf a t f tPQQINV K PK (51) c t f tc t c c' t cQINV PKPQ QINV (52) 1a f a t f a t+1f a t f f a tK QFQF QF (53) 11f a t a f tf t f f tK QFSQFS QFS (54) Source: Lofgren et al. (2002); Robinson and Thurlow (no date).

PAGE 186

186 APPENDIX B STATIC MODEL RESULTS: COMPARING THE BALANCED, NE OCLASSICAL AND JOHANSE N CLOSURES Table B-1. Percent change in macro economic and institutional indicators Balanced closureNeo classical closure Johansen closure Absorption 0.01 0.00 0.01 Private consumption 0.02 -0.09 0.02 Fixed investment 0.00 0.39 0.00 Change in stocks 0.00 0.00 0.00 Government consumption 0.00 0.00 0.00 Exports 0.00 -0.11 -0.04 Imports -0.01 -0.13 -0.04 GDP at market prices 0.01 0.00 0.01 GDP at factor cost 0.01 0.01 0.01 Net indirect taxes 0.01 0.00 0.01 Real household consumption Low-income household 0.00 2.10 0.70 Mid-income household 0.00 0.20 0.10 High-income household 0.00 -0.90 -0.20 Deforestation enterprise 0.80 0.80 0.80 Consumer price index 0.02 -1.19 2.00 Exchange rate -0.01 -0.91 2.00 Investment share of absorption 0.00 30.77 3.32 Foreign savings 0.00 0.00 0.00 Marginal propensity to save Mid-income households 0.57 0.01 -218.82 High-income households 0.04 0.15 -14.11 Enterprises 0.01 0.41 -5.35 Investment to GDP ratio 0.00 4.70 0.50 Private savings to GDP ratio 0.00 0.60 -2.10 Foreign savings to GDP ratio 0.00 0.00 0.00 Trade deficit to GDP ratio 0.00 0.00 -0.10 Government savings to GDP ratio 0.00 4.10 2.60

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187 Table B-2. Percent change in institutional income Institution Balanced closure Neo cl assical closure Johansen closure Low income household 0.05 0.96 0.49 Mid-income household 0.05 -0.99 -0.07 High-income household 0.04 -2.13 -0.80 Deforestation institution -0.24 -0.82 1.87 Enterprises 0.01 4.88 0.92 Interest 0.02 2.81 3.78 Table B-3. Percent change in equivalent variation Balanced closure Neoclassical closure Johansen closure Equivalent variation Low-income households 0.00 2.10 0.70 Mid-income households 0.00 0.20 0.10 High-income households 0.00 -0.90 -0.20 Deforestation enterprise 0.80 0.80 0.80 Total 0.00 -0.10 0.00

PAGE 188

188 Table B-4. Percent change in factor income Factor Balanced closure Neo clas sical closure Johansen closure Low-skill formal labor 0.08 -2.24 -2.81 Low-skill informal labor 0.07 14.64 1.30 Mid-skill formal labor 0.08 -10.53 -6.50 Mid-skill informal labor 0.07 -1.51 -1.96 High-skill formal labor 0.07 -16.38 -10.58 High-skill informal labor 0.08 -9.29 -4.89 Capital 0.13 5.30 0.98 Agricultural land north -0.17 -0.77 1.94 Agricultural land north east -0.38 -0.94 1.73 Agricultural land south east -6.79 -7.26 -4.81 Agricultural land south -1.81 -2.37 0.27 Agricultural land center west -0.17 -0.75 1.94 Forest land north -48.13 -48.37 -47.03 Forest land north east -14.25 -14.55 -12.37 Forest land south east -9.40 -9.93 -7.51 Forest land south -9.54 -10.06 -7.65 Forest land center west -11.14 -11.59 -9.27 Table B-5. Percent change in price of composite good Good or service Balanced closure Neo classical closure Johansen closure Agriculture -0.21 -0.88 1.88 Forestry -8.44 -9.03 -6.54 Deforestation -1.07 -1.64 3.22 Mining and petroleum -0.01 -0.91 2.00 Industrial 0.01 -0.92 2.01 Processed wood -0.01 -0.91 2.00 Pulp and cellulose -0.01 -0.91 2.00 Processed food 0.02 -0.74 2.12 Utilities 0.05 -0.95 2.12 Construction 0.17 69.40 2.00 Commerce -0.01 -0.91 2.00 Transportation 0.09 -1.36 2.07 Private services 0.05 -1.53 1.95 Public services 0.04 -20.85 -15.04

PAGE 189

189 Table B-6. Percent change in price of factor F for activity A Factor Sector Balanced closure Neo classical closure Johansen closure Low-skilled formal labor Agriculture north -0.13 -0.74 1.97 Low-skilled formal labor Agriculture north east -0.11 -0.68 2.01 Low-skilled formal labor Agriculture south east 0.54 0.06 2.70 Low-skilled formal labor Agriculture south 0.32 -0.27 2.44 Low-skilled formal labor Agriculture center west -0.13 -0.71 1.98 Low-skilled formal labor Forestry north 143.65 142.52 148.80 Low-skilled formal labor Forestry north east -14.82 -15.10 -13.00 Low-skilled formal labor Forestry south east -9.40 -9.93 -7.51 Low-skilled formal labor Forestry south -9.54 -10.06 -7.65 Low-skilled formal labor Forestry center west -11.18 -11.63 -9.32 Low-skilled formal labor Forest plantations north -13.57 -13.98 -11.75 Low-skilled formal labor Forest plantations north east -14.40 -14.78 -12.59 Low-skilled formal labor Forest plantations south east -20.43 -20.87 -18.78 Low-skilled formal labor Forest plantations south -26.08 -26.29 -24.52 Low-skilled formal labor Forest plantations center west -15.99 -16.40 -14.23 Low-skilled formal labor Deforestation north 18.84 18.15 22.68 Low-skilled formal labor Deforestation north east -6.96 -7.50 -4.34 Low-skilled formal labor Deforestation center west -5.89 -6.42 -2.92 Low-skilled formal labor Mining and petroleum -0.09 -3.44 1.99 Low-skilled formal labor Industry 0.01 -1.22 2.02 Low-skilled formal labor Processed wood 2.68 1.85 4.73

PAGE 190

190 Table B-6. Continued Factor Sector Balanced closure Neoclassical closure Johansen closure Low-skilled formal labor Pulp and cellulose 2.50 1.45 4.54 Low-skilled formal labor Processed food 0.65 -0.15 2.84 Low-skilled formal labor Utilities 0.06 -0.85 2.19 Low-skilled formal labor Construction 0.33 136.64 1.99 Low-skilled formal labor Commerce -0.03 -0.80 2.03 Low-skilled formal labor Transportation 0.10 -1.46 2.11 Low-skilled formal labor Private services 0.05 -3.09 1.92 Low-skilled formal labor Public services 0.03 -36.56 -25.72 Low-skilled informal labor Agriculture north -0.13 -0.74 1.97 Low-skilled informal labor Agriculture north east -0.11 -0.68 2.01 Low-skilled informal labor Agriculture south east 0.54 0.06 2.70 Low-skilled informal labor Agriculture south 0.32 -0.27 2.44 Low-skilled informal labor Agriculture center west -0.13 -0.71 1.98 Low-skilled informal labor Forestry north 143.65 142.52 148.80 Low-skilled informal labor Forestry north east -14.82 -15.10 -13.00 Low-skilled informal labor Forestry south east -9.40 -9.93 -7.51 Low-skilled informal labor Forestry south -9.54 -10.06 -7.65 Low-skilled informal labor Forestry center west -11.18 -11.63 -9.32 Low-skilled informal labor Forest plantations north -13.57 -13.98 -11.75 Low-skilled informal labor Forest plantations north east -14.40 -14.78 -12.59 Low-skilled informal labor Forest plantations south east -20.43 -20.87 -18.78

PAGE 191

191 Table B-6. Continued Factor Sector Balanced closure Neoclassical closure Johansen closure Low-skilled informal labor Forest plantations south -26.08 -26.29 -24.52 Low-skilled informal labor Forest plantations center west -15.99 -16.40 -14.23 Low-skilled informal labor Deforestation north 18.84 18.15 22.68 Low-skilled informal labor Deforestation north east -6.96 -7.50 -4.34 Low-skilled informal labor Deforestation center west -5.89 -6.42 -2.92 Low-skilled informal labor Mining and petroleum -0.09 -3.44 1.99 Low-skilled informal labor Industry 0.01 -1.22 2.02 Low-skilled informal labor Processed wood 2.68 1.85 4.73 Low-skilled informal labor Pulp and cellulose 2.50 1.45 4.54 Low-skilled informal labor Processed food 0.65 -0.15 2.84 Low-skilled informal labor Utilities 0.06 -0.85 2.19 Low-skilled informal labor Construction 0.33 136.64 1.99 Low-skilled informal labor Commerce -0.03 -0.80 2.03 Low-skilled informal labor Transportation 0.10 -1.46 2.11 Low-skilled informal labor Private services 0.05 -3.09 1.92 Low-skilled informal labor Public services 0.03 -36.56 -25.72 Mid-skilled formal labor Agri culture north -0.13 -0.74 1.97 Mid-skilled formal labor Agriculture north east -0.11 -0.68 2.01 Mid-skilled formal labor Agriculture south east 0.54 0.06 2.70 Mid-skilled formal labor Agricu lture south 0.32 -0.27 2.44 Mid-skilled formal labor Agriculture center west -0.13 -0.71 1.98

PAGE 192

192 Table B-6. Continued Factor Sector Balanced closure Neoclassical closure Johansen closure Mid-skilled formal labor Forestry north 143.65 142.52 148.80 Mid-skilled formal labor Forestry north east -14.82 -15.10 -13.00 Mid-skilled formal labor Forestry south east -9.40 -9.93 -7.51 Mid-skilled formal labor Forestry south -9.54 -10.06 -7.65 Mid-skilled formal labor Forestry center west -11.18 -11.63 -9.32 Mid-skilled formal labor Forest plantations north -13.57 -13.98 -11.75 Mid-skilled formal labor Forest plantations north east -14.40 -14.78 -12.59 Mid-skilled formal labor Forest plantations south east -20.43 -20.87 -18.78 Mid-skilled formal labor Forest plantations south -26.08 -26.29 -24.52 Mid-skilled formal labor Forest plantations center west -15.99 -16.40 -14.23 Mid-skilled formal labor Deforestation north 18.84 18.15 22.68 Mid-skilled formal labor Deforestation north east -6.96 -7.50 -4.34 Mid-skilled formal labor Deforestation center west -5.89 -6.42 -2.92 Mid-skilled formal labor Mining and petroleum -0.09 -3.44 1.99 Mid-skilled formal labor Industry 0.01 -1.22 2.02 Mid-skilled formal labor Processed wood 2.68 1.85 4.73 Mid-skilled formal labor Pulp and cellulose 2.50 1.45 4.54 Mid-skilled formal labor Processed food 0.65 -0.15 2.84 Mid-skilled formal labor Utilities 0.06 -0.85 2.19 Mid-skilled formal labor Construction 0.33 136.64 1.99 Mid-skilled formal labor Commerce -0.03 -0.80 2.03

PAGE 193

193 Table B-6. Continued Factor Sector Balanced closure Neoclassical closure Johansen closure Mid-skilled formal labor Transportation 0.10 -1.46 2.11 Mid-skilled formal labor Private services 0.05 -3.09 1.92 Mid-skilled formal labor Public services 0.03 -36.56 -25.72 Mid-skilled informal labor Agriculture north -0.13 -0.74 1.97 Mid-skilled informal labor Agriculture north east -0.11 -0.68 2.01 Mid-skilled informal labor Agriculture south east 0.54 0.06 2.70 Mid-skilled informal labor Agriculture south 0.32 -0.27 2.44 Mid-skilled informal labor Agriculture center west -0.13 -0.71 1.98 Mid-skilled informal labor Forestry north 143.65 142.52 148.80 Mid-skilled informal labor Forestry north east -14.82 -15.10 -13.00 Mid-skilled informal labor Forestry south east -9.40 -9.93 -7.51 Mid-skilled informal labor Forestry south -9.54 -10.06 -7.65 Mid-skilled informal labor Forestry center west -11.18 -11.63 -9.32 Mid-skilled informal labor Forest plantations north -13.57 -13.98 -11.75 Mid-skilled informal labor Forest plantations north east -14.40 -14.78 -12.59 Mid-skilled informal labor Forest plantations south east -20.43 -20.87 -18.78 Mid-skilled informal labor Forest plantations south -26.08 -26.29 -24.52 Mid-skilled informal labor Forest plantations center west -15.99 -16.40 -14.23 Mid-skilled informal labor Deforestation north 18.84 18.15 22.68 Mid-skilled informal labor Deforestation north east -6.96 -7.50 -4.34 Mid-skilled informal labor Deforestation center west -5.89 -6.42 -2.92

PAGE 194

194 Table B-6. Continued Factor Sector Balanced closure Neoclassical closure Johansen closure Mid-skilled informal labor Mining and petroleum -0.09 -3.44 1.99 Mid-skilled informal labor Industry 0.01 -1.22 2.02 Mid-skilled informal labor Processed wood 2.68 1.85 4.73 Mid-skilled informal labor Pulp and cellulose 2.50 1.45 4.54 Mid-skilled informal labor Processed food 0.65 -0.15 2.84 Mid-skilled informal labor Utilities 0.06 -0.85 2.19 Mid-skilled informal labor Construction 0.33 136.64 1.99 Mid-skilled informal labor Commerce -0.03 -0.80 2.03 Mid-skilled informal labor Transportation 0.10 -1.46 2.11 Mid-skilled informal labor Pr ivate services 0.05 -3.09 1.92 Mid-skilled informal labor Pub lic services 0.03 -36.56 -25.72 High-skilled formal labor Agriculture north -0.13 -0.74 1.97 High-skilled formal labor Agriculture north east -0.11 -0.68 2.01 High-skilled formal labor Agriculture south east 0.54 0.06 2.70 High-skilled formal labor Agriculture south 0.32 -0.27 2.44 High-skilled formal labor Agriculture center west -0.13 -0.71 1.98 High-skilled formal labor Forestry north 143.65 142.52 148.80 High-skilled formal labor Forestry north east -14.82 -15.10 -13.00 High-skilled formal labor Forestry south east -9.40 -9.93 -7.51 High-skilled formal labor Forestry south -9.54 -10.06 -7.65 High-skilled formal labor Forestry center west -11.18 -11.63 -9.32

PAGE 195

195 Table B-6. Continued Factor Sector Balanced closure Neoclassical closure Johansen closure High-skilled formal labor Forest plantations north -13.57 -13.98 -11.75 High-skilled formal labor Forest plantations north east -14.40 -14.78 -12.59 High-skilled formal labor Forest plantations south east -20.43 -20.87 -18.78 High-skilled formal labor Forest plantations south -26.08 -26.29 -24.52 High-skilled formal labor Forest plantations center west -15.99 -16.40 -14.23 High-skilled formal labor Deforestation north 18.84 18.15 22.68 High-skilled formal labor Deforestation north east -6.96 -7.50 -4.34 High-skilled formal labor Deforestation center west -5.89 -6.42 -2.92 High-skilled formal labor Mining and petroleum -0.09 -3.44 1.99 High-skilled formal labor Industry 0.01 -1.22 2.02 High-skilled formal labor Processed wood 2.68 1.85 4.73 High-skilled formal labor Pulp and cellulose 2.50 1.45 4.54 High-skilled formal labor Processed food 0.65 -0.15 2.84 High-skilled formal labor Utilities 0.06 -0.85 2.19 High-skilled formal labor Construction 0.33 136.64 1.99 High-skilled formal labor Commerce -0.03 -0.80 2.03 High-skilled formal labor Transportation 0.10 -1.46 2.11 High-skilled formal labor Private services 0.05 -3.09 1.92 High-skilled formal labor Public services 0.03 -36.56 -25.72 High-skilled informal labor Agriculture north -0.13 -0.74 1.97 High-skilled informal labor Agriculture north east -0.11 -0.68 2.01

PAGE 196

196 Table B-6. Continued Factor Sector Balanced closure Neoclassical closure Johansen closure High-skilled informal labor Agriculture south east 0.54 0.06 2.70 High-skilled informal labor Agriculture south 0.32 -0.27 2.44 High-skilled informal labor Agriculture center west -0.13 -0.71 1.98 High-skilled informal labor Forestry north 143.65 142.52 148.80 High-skilled informal labor Forestry north east -14.82 -15.10 -13.00 High-skilled informal labor Forestry south east -9.40 -9.93 -7.51 High-skilled informal labor Forestry south -9.54 -10.06 -7.65 High-skilled informal labor Forestry center west -11.18 -11.63 -9.32 High-skilled informal labor Forest plantations north -13.57 -13.98 -11.75 High-skilled informal labor Forest plantations north east -14.40 -14.78 -12.59 High-skilled informal labor Forest plantations south east -20.43 -20.87 -18.78 High-skilled informal labor Forest plantations south -26.08 -26.29 -24.52 High-skilled informal labor Forest plantations center west -15.99 -16.40 -14.23 High-skilled informal labor Deforestation north 18.84 18.15 22.68 High-skilled informal labor Deforestation north east -6.96 -7.50 -4.34 High-skilled informal labor Deforestation center west -5.89 -6.42 -2.92 High-skilled informal labor Mining and petroleum -0.09 -3.44 1.99 High-skilled informal labor Industry 0.01 -1.22 2.02 High-skilled informal labor Processed wood 2.68 1.85 4.73 High-skilled informal labor Pulp and cellulose 2.50 1.45 4.54 High-skilled informal labor Processed food 0.65 -0.15 2.84

PAGE 197

197 Table B-6. Continued Factor Sector Balanced closure Neoclassical closure Johansen closure High-skilled informal labor Utilities 0.06 -0.85 2.19 High-skilled informal labor Construction 0.33 136.64 1.99 High-skilled informal labor Commerce -0.03 -0.80 2.03 High-skilled informal labor Transportation 0.10 -1.46 2.11 High-skilled informal labor Private services 0.05 -3.09 1.92 High-skilled informal labor Public services 0.03 -36.56 -25.72 Capital Agriculture north -0.13 -0.74 1.97 Capital Agriculture north east -0.11 -0.68 2.01 Capital Agriculture south east 0.54 0.06 2.70 Capital Agriculture south 0.32 -0.27 2.44 Capital Agriculture center west -0.13 -0.71 1.98 Capital Forestry north 143.65 142.52 148.80 Capital Forestry north east -14.82 -15.10 -13.00 Capital Forestry south east -9.40 -9.93 -7.51 Capital Forestry south -9.54 -10.06 -7.65 Capital Forestry center west -11.18 -11.63 -9.32 Capital Forest plantations north -13.57 -13.98 -11.75 Capital Forest plantations north east -14.40 -14.78 -12.59 Capital Forest plantations south east -20.43 -20.87 -18.78 Capital Forest plantations south -26.08 -26.29 -24.52 Capital Forest plantations center west -15.99 -16.40 -14.23 Capital Deforestation north 18.84 18.15 22.68 Capital Deforestation north east -6.96 -7.50 -4.34 Capital Deforestation center west -5.89 -6.42 -2.92 Capital Mining and petroleum -0.09 -3.44 1.99 Capital Industry 0.01 -1.22 2.02 Capital Processed wood 2.68 1.85 4.73

PAGE 198

198 Table B-6. Continued Factor Sector Balanced closure Neoclassical closure Johansen closure Capital Pulp and cellulose 2.50 1.45 4.54 Capital Processed food 0.65 -0.15 2.84 Capital Utilities 0.06 -0.85 2.19 Capital Construction 0.33 136.64 1.99 Capital Commerce -0.03 -0.80 2.03 Capital Transportation 0.10 -1.46 2.11 Capital Private services 0.05 -3.09 1.92 Capital Public services 0.03 -36.56 -25.72 Agricultural land north Agriculture north -0.17 -0.77 1.94 Agricultural land north Forest plantations north -0.17 -0.77 1.94 Agricultural land north east Agriculture north east -0.38 -0.94 1.73 Agricultural land north east Forest plantations north east -0.38 -0.94 1.73 Agricultural land south east Agriculture south east -6.79 -7.26 -4.81 Agricultural land south east Forest plantations south east -6.79 -7.26 -4.81 Agricultural land south Agricultur e south -1.81 -2.37 0.27 Agricultural land south Forest plantations south -1.81 -2.37 0.27 Agricultural land center west Agriculture center west -0.17 -0.75 1.94 Agricultural land center west Forest plantations center west -0.17 -0.75 1.94 Forestland north Forestry north -64.47 -64.63 -63.72 Forestland north Deforestatio n north -64.47 -64.63 -63.72 Forestland north east Forestry north east -14.25 -14.55 -12.37 Forestland north east Deforestation north east -14.25 -14.55 -12.37 Forestland southeast Forestry south east -9.40 -9.93 -7.51 Forestland south Forest ry south -9.54 -10.06 -7.65 Forestland center west Forestry center west -11.14 -11.59 -9.27 Forestland center west Deforestation center west -11.14 -11.59 -9.27

PAGE 199

199 Table B-7. Percent change in level of domestic activity Sector Balanced closure Neo classical closure Johansen closure Agriculture north 0.00 0.00 0.00 Agriculture north east 0.01 0.01 0.01 Agriculture south east 0.19 0.19 0.19 Agriculture south 0.13 0.12 0.13 Agriculture center west 0.01 0.01 0.01 Forestry north 24.68 24.68 24.68 Forestry north east -0.02 -0.02 -0.03 Forestry south east 0.00 0.00 0.00 Forestry south 0.00 0.00 0.00 Forestry center west -0.01 -0.01 -0.01 Forest plantations north -0.56 -0.56 -0.56 Forest plantations north east -0.62 -0.61 -0.62 Forest plantations south east -2.40 -2.41 -2.41 Forest plantations south -3.71 -3.67 -3.71 Forest plantations center west -1.29 -1.28 -1.29 Deforestation north 1.20 1.20 1.20 Deforestation north east 0.14 0.14 0.15 Deforestation center west 0.09 0.09 0.11 Mining and petroleum 0.00 0.00 0.00 Industry 0.00 0.00 0.00 Wood processing 0.00 0.00 0.00 Pulp and paper 0.00 0.00 0.00 Food processing 0.00 0.00 0.00 Utilities 0.00 0.00 0.00 Construction 0.00 0.00 0.00 Commerce 0.00 0.00 0.00 Transportation 0.00 0.00 0.00 Private services 0.00 0.00 0.00 Public services 0.00 0.00 0.00

PAGE 200

200 Table B-8. Percent change in quant ity of factor demand by industry Sector Factor Balanced closure Neo classical closure Johansen closure Agriculture north Agricultural land north 0.01 0.01 0.01 Forest plantations north -2.84 -2.82 -2.84 Agriculture north east Agricultural land north east 0.06 0.06 0.06 Forest plantations north east -2.99 -2.96 -2.99 Agriculture south east Agricultural land south east 1.83 1.84 1.84 Forest plantations south east -3.11 -3.12 -3.12 Agriculture south Agricultural land south 0.52 0.51 0.52 Forest plantations south -5.52 -5.47 -5.52 Agriculture center west Agricultural land center west 0.01 0.01 0.01 Forest plantations center west -3.39 -3.38 -3.40 Forestry north Forest land north 46.97 46.97 46.97 Deforestation north 6.22 6.22 6.28 Forestry north east Forest land north east -0.13 -0.13 -0.14 Deforestation north east 0.41 0.40 0.44 Forestry south east Forest land south east 0.00 0.00 0.00 Forestry south Forest land south 0.00 0.00 0.00 Forestry center west Forest land center west -0.01 -0.01 -0.01 Deforestation center west 0.29 0.28 0.34

PAGE 201

201 Table B-9. Percent change in quantity of sales Balanced closure Neoclassical closure Johansen closure Balanced closure Neoclassical closure Johansen closure Good or service Domestic sales Domestic sales Domestic sales Exports Exports Exports Agriculture 0.05 0.09 0.06 0.31 0.04 0.22 Forestry 1.95 1.96 1.95 14.55 14.17 14.45 Deforestation 0.84 0.84 0.85 0.00 0.00 0.00 Mining and petroleum 0.00 0.00 0.00 0.00 0.00 0.00 Industrial 0.01 0.00 0.00 -0.04 0.02 -0.02 Processed wood 0.00 0.00 0.00 0.00 0.00 0.00 Pulp and cellulose 0.00 0.00 0.00 0.00 0.00 0.00 Processed food 0.02 0.09 0.06 -0.07 -0.46 -0.30 Utilities 0.00 0.00 0.00 0.00 0.00 0.00 Construction 0.00 0.15 0.00 -0.09 -23.44 0.00 Commerce 0.00 0.00 0.00 0.00 0.00 0.00 Transportation 0.00 -0.01 0.00 -0.08 0.33 -0.05 Private services 0.00 -0.01 0.00 -0.04 0.41 0.03 Public services 0.00 0.00 0.00 0.00 0.00 0.00

PAGE 202

202 Table B-10. Percent change in quantity of composite goods supply Activity Balanced closure Neo cla ssical closure Johansen closure Agriculture 0.03 0.09 0.05 Forestry 0.37 0.43 0.38 Deforestation 0.84 0.84 0.85 Mining and petroleum 0.00 0.00 0.00 Industrial 0.01 -0.01 0.01 Processed wood 0.00 0.00 0.00 Pulp and cellulose 0.00 0.00 0.00 Processed food 0.02 0.10 0.07 Utilities 0.00 0.00 0.01 Construction 0.00 0.32 0.00 Commerce 0.00 0.00 0.00 Transportation 0.00 -0.01 0.00 Private services 0.01 -0.07 -0.01 Public services 0.00 0.00 0.00

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216 BIOGRAPHICAL SKETCH Onil Banerjee earn ed a Bachelor of Science in forest resources management from the University of British Columbia in Vancouver in 1999. Focusing on international forest management, he spent a semester at the Institu te of Technology of Cost a Ricas Department of Forest Engineering. Upon graduation, he wo rked in community development and natural resources planning in Mexico and in forest re sources management in the USA and Canada. In 2002, he and a colleague established a firm, RMGEO Consultants Inc., for which he has undertaken various projects internationally in community-based resources management, forest plantations and geographic information system s development. After gaining professional experience at the grassroots level, he was insp ired to learn more about the macro context in which development policy is imbedded; he returned to Canada to study for a masters degree in public administration from Carleton University in Ottawa, focusing on international development policy and practice. During his studies, he work ed at the Canadian International Development Agencys Americas Branch, condu cting research on the economics of international trade and participated in strategic pla nning exercises. After graduation in 2004 and conducting work with indigenous communities in Paraguay, he commen ced his Ph.D. in 2004 at the University of Floridas School of Forest Resources Management focusing on fo rest policy and economics in Brazil.