Economic Instruments to Protect the Amazon

Economic Instruments to Protect the Amazon The Manaus Industrial Pole experience

Economic Instruments to Protect the Amazon The Manaus Industrial Pole experience Alexandre Rivas James R. Kahn José Alberto da C. Machado José Aroudo Mota (Editors)

Curitiba, 2009

Copyright © 2009 - Institute PIATAM Managing Editor: Railson Moura Editorial Coordination: Simone Santos Cover: Roseli Pampuch Layout: Roseli Pampuch Review: The Authors Translation: James R. Kahn Photos: Nokia / Istockphotos Brazil

International Catalogue of Publication Data (CIP - Dados Internacionais de Catalogação na Publicação) Brazilian Chamber of Books (Câmara Brasileira do Livro, SP, Brasil) Economic instruments for the protection of the Amazon: the experience of the Industrial Pole of Manaus, Alexandre Almir Ferreira Rivas, James Randall Kahn, Aroudo José Mota, José Alberto da Costa Machado, Curitiba, Brazil: Editora CRV for Instituto PIATAM, 2009. Bibliography. ISBN 978-85-62480-33-1 1. Amazon - Commerce 2. Amazon - Economic conditions 3. Amazon - Social conditions 4. Amazon Industry 5. Amazon - Economic policy 6. Amazon - Sustainable development 7. Deforestation - Brazil - Amazon 8. The Industrial Pole of Manaus 9. Natural resources - Conservation - Amazon 10. Forest reserves - Amazon I. Rivas, Alexandre Almir Ferreira. II. Kahn, James R., III. Mota, José Aroudo. IV. Machado, José Alberto da Costa. 09-07821

CDD-333.709811

Catalogue Indexes: 1. Industrial Pole of Manaus in the Amazon region: Amazon: Sustainable development: Environmental Economics

333.709811

All rights reserved. Tel.: (41) 3039-6418 www.editoracrv.com.br E-mail: [email protected]

Tel.: (92) 3584-6882 www.institutopiatam.org.br E-mail: [email protected]

INDEX

Acknowledgements�� 7 Suframa’s presentation�� 9 Nokia’s presentation�� 11 About the authors�� 13 Preface�� 19 Presentation�� 23 Part I The Manaus Industrial Pole in the Context of the Amazon�� 29 Chapter 1 The Manaus Industrial Pole and its Dynamics�� 31 Aristides da R. Oliveira Jr., José Alberto da C. Machado Chapter 2 Deforestation of the Amazon in Perspective�� 51 Marcelo B. Diniz, José A. Mota, Alexandre Rivas Chapter 3 Migratory movements in the State of Amazonas�� 63 Peri Teixeira Part II Econometric Models�� 71 Chapter 4 A Mathematical Behavioral Model of the Manaus Industrial Pole�� 73 James R. Kahn

Chapter 5 A Correspondence Analysis of Deforestation in the State of Amazonas�� 87 Carlos Edwar de C. Freitas, Fabíola A. do Nascimento Chapter 6 Causalities, Convergence Clubs and Quantile Analysis�� 115 Marcelo Bentes Diniz, José Nilo de Olivera Jr. Chapter 7 The PIM’s Effect: a Counterfactual Analysis�� 143 José Aroudo Mota, José O. Cândido Jr. Chapter 8 The Demand for deforestation and the PIM’s Effect�� 155 Alexandre Rivas, Renata Mourão, Beatriz Rodrigues Part III�� 167

The future of PIM ..................

Chapter 9 Possible consequences of a potential end of the PIM�� 169 Alexandre Rivas Chapter 10 Compensatory mechanisms for the positive effects of the Manaus Industrial Pole�� 175 Alexandre Rivas, José Alberto da C. Machado, José A. Mota Chapter 11 Increasing the market value of products produced in the Manaus Industrial Pole�� 183 Aristides da R. Oliveira Jr., José A. Mota, José Alberto da C. Machado Chapter 12 Manaus Industrial Pole: beyond the purely economic benefits�� 191 Alexandre Rivas, José A. Mota, José Alberto da C. Machado References�� 195 Annexes�� 201

Acknowledgements

The authors are grateful to Nokia and to SUFRAMA for supporting the implementation of this study. They also thank the hard work of format and preliminary review done by Débora Ramos Santiago (Master’s degree student at UFAM/FES), and the grammatical review by Hostília Maria Lisboa Campos in the Portuguese version of this book. They thank the referees Jill Caviglia-Harris (Salisbury University, EUA), Norbert Fenzl (Community Research¬ & Development Information Services), Brussels, Belgium e Hercílio Castellano (Universidad Central de Venezuela) for his valuable contributions and criticism in the improvement of this study.

Suframa and its relevance for the Amazon

It

has taken a long time for society in the amazonian

to notice the beneficial effect of Industrial Pole of Manaus (IPM) on the forest of the state of Amazonas. Observing the impacts on a day-to-day basis, little attention was given to the necessity of demonstrating those effects empirically. As environmental issues began to consolidate in national and international agendas, people recognized the importance of PIM and the need to justify its continued competitiveness. region

The first place that this the verification process received attention was in the discourse of local institutions and local political authorities. After that, the first measurements, conducted in isolation, began to appear in institutional and academic studies. Following this, the issue has permeated debate in both technical-scientific and political spheres. This debate was based largely in opinion, without consistent support from scientific methods and sufficient data.

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rivas, KAHN, machado & mota

The need for a thorough analysis, based in scientific methods was recognized. This analysis would provide the basis for understanding that PIM has a beneficial on the Amazon rainforest and, in particular, the State of Amazonas. A sound scientific study would provide ample defense against skeptics who choose their position based on non-scientific factors. The Superintendenc of the Manaus Free Zone (SUFRAMA) sought support from the regional and national academy to formulate the problem properly and then presented the idea of such a study to companies within PIM obtain a partnership to achieve the study. Nokia of Brazil responded promptly and was the only one to do so in a timely fashion. Since then, their participation has been exemplary, not only for financial support, but also for institutional monitoring, including the participation of its global executives in the quality control of this study. Thus, while presenting this work, I would like to thank this partnership and commitment from Nokia and at the same time, register the immense satisfaction of seeing that a complex study with large magnitude can be implemented with regional researchers, and with cooperation of researchers linked to other institutions such as the Institute of Applied Economic Research (IPEA). Flávia Skrobot Barbosa Grosso Superintendent of SUFRAMA

Nokia’s presence in Manaus carries both responsibility and pride

The study presented in this report brings hope to an issue that has been discussed since the emergence of discussions about the creation of an economic center in the state of Amazonas: In addition to the obvious improvements to the economic development of the whole North region of the country, what would be the impact of industrial activity on the largest rainforest in the world? We, at Nokia, have always been aware that, when we chose Manaus as the site of our first factory in South America ten years ago, we would become part of that context, taking advantage of the benefits, as well as assuming the responsibilities. Being in Manaus surpasses the economic functions and places the company as an important cog in environmental preservation.

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The extensive research available to us now shows that we were right to evaluate our presence in the Industrial Pole of Manaus (IPM). The results show how the strategy of industrialization implemented here has contributed to the deceleration of deforestation of the Amazon. It is also important to contemplate what might occur if the pole did not exist, considering the consequences for the country and the region. While working for the development of IPM, we create direct and indirect employment and income for thousands of families, who start to have alternatives for growth and development without participating in the direct exploitation of forest. Adding to this the best practices of respect for the environment in our production processes, we have a very positive equation for all. Our commitment to the region has been renewed. Investments recently announced reinforce our expectation that the plant in Manaus will combine its role as a service market that also has the ability to supply other countries. The beginning of production of 3G handsets, in the first half of this year, was the first step in this process. It is impossible not to highlight the economic scenario from Manaus as motivational factor of our initiatives. Due to the efforts of the Government of the State of Amazonas and SUFRAMA, Manaus has grown in recent years at rates higher than the rate of the Brazilian economy. The PIM can be considered one of the most successful models around the world of economic and social development and environmental preservation. Because we believe in this model, we are working hard with the development agencies and government entities to improve it, so that the pole can receive better industries that accept the challenge of producing in the heart of the Amazon, as a competitive business decision and at the same time socially and environmentally responsible. Almir Narcizo Chairman of Nokia of Brazil

About the authors

Alexandre Rivas Postdoctor in Environmental Economics from Washington and Lee University (2005), Ph.D. in Environmental Economics and Public Finance - The University of Tennessee System ¬ (1998), Master of Arts in Public Finance – The University of Tennessee System (1997) and Batchelor’s degree in Fishing Engineering, Federal University of Ceará (1988). Professor of the Department of Economics and Analysis, Federal University of Amazonas, Visiting Full Professor at Washington and Lee University (USA), Chairman of PIATAM Institute and member of the Advisory, agreement between the United Nations University and the Bank of Brazil. He works mainly in the area of Environmental Economics and Natural Resources in the following areas: Economic valuation of environment, analysis of environmental impacts of major projects in the Amazon and policies regarding the use of economic instruments for environmental protection. He is coordinator of PIATAM Project (www.piatam.ufam.edu.br) and director of a consortium in Brazil-United States Program, under the auspices of the CAPES (Brazil) and FIPSE (USA)

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José Aroudo Mota Ph.D. in Sustainable Development at the University of Brasilia (2000), Masters in Financial Management from the University of Brasilia (1994) and batchelor’s degree in Economics from the Catholic University of Brasilia (1981). He is currently Coordinator of the Environment and Sustainable Development Institute of Applied Economic Research - IPEA and Coordinator of the IPEA Forum on Climate Change. He is visiting professor of environmental economics at the Federal University of Amazonas, a research professor associated with the Center for Sustainable Development at the University of Brasilia professor of the Center for Sustainable Development at the University of Brasilia. He worked as associate director and interim at the Board of Directors of Urban and Regional Studies of Institute of Applied Economic Research - IPEA. Has experience in Economics, with emphasis on Sustainable Development, acting on the following subjects: environmental economics, sustainable development, valuation of natural resources, environmental management and environmental impact.

José Alberto da Costa Machado Ph.D. in Socio-environmental Development (1999), Master’s degree in Systems Engineering and Computing (1990) and Batchelor’s degree in Business Administration (1978). Associate Professor, Department of Economics and Analysis, Faculty of Social Studies, Federal University of Amazonas, of the Post-Graduation Programs in Production Engineering and Regional Development, all from the Federal University of Amazonas. He is a researcher in theories, methods and metrics about sustainable development and dynamics of regional development in the Amazon. Currently has operations focused on the Free Zone of Manaus, Industrial Pole of Manaus and the Amazon development, issues on which he has developed hundreds of business and economic studies.

James Randall Kahn Ph.D. in Economics, University of Maryland (1981), M.A. in Economics, University of Maryland (1978) and B.A. in Economics, Washington and Lee University (USA) (1975). Holding the official chair John F. Hendon Professorship in Economics and is Director of the Environmental Studies Program at Washington and Lee University. He has also been a Collaborating Professor at the Federal University of Amazonas, since 1992. Among his previous activities, he was a professor at State University of New York, Binghamton (1980-1991) and professor at the University of Tennessee (1991-2000). In the same period in which he worked at the University of Tennessee, he was also a collaborating scientist at National

ECONOMIC INSTRUMENTS TO PROTECT THE AMAZON: The Manaus Industrial Pole experience

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Laboratory of Oak Ridge. The area of research of Professor Kahn is Environment Economics. The research areas include sustainable development in the Amazon, development of economic incentives for environmental preservation, valuation of the Environment, multi-criterial analysis, global climate change, fishery economics and modeling of the interaction between economic and ecological systems.

Marcelo Bentes Diniz Ph.D. in Economics, Federal University of Ceará (2005), Master’s degree in Economics, Federal University of Ceará (1997) and Bachelor’s degree in Economics, Federal University of Pará (1993). He is currently Associate Professor at the Federal University of Pará. Has experience in the areas of Agricultural Economics and Natural Resources. Works mainly on the following issues: Inequality and Poverty.

Carlos Edwar de Carvalho Freitas Doctor of Science in Environmental Engineering, University of São Paulo (1999). He is currently a Professor at the Federal University of Amazonas, Professor accredited by the National Institute of Amazonian Research (INPA), Research Productivity Fellowship from CNPq (Brazilian national science foundation), Member of the Advisory Board of the Sustainable Amazon Foundation and Fellow of the Linnean Society of London. He works in the area of Fishery Resources and Fishery Engineering, with emphasis on stock assessment of Inland Fisheries. He referees for numerous national and international scientific journals, agencies and funding programs for research.

Pery Teixeira Ph.D. in Demography, Federal University of Minas Gerais (1997) and bachelor’s degree in Mathematics, Institute of Mathematics and Statistics, University of São Paulo (1972). He is currently a professor at the Federal University of Amazonas. Has experience in the area of Demography, with emphasis on Components of Demographic Dynamics. Works mainly on the following subjects: Northeast, Differential, Mortality in Childhood, homogeneous Microregions.

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José Oswaldo Cândido Júnior Ph.D. in Economics, Fundação Getúlio Vargas (EPGE/RJ), M.A. (1998), and batchelor’s in Economics, Federal University of Ceará (1992). In the moment, he is a Planning and Research Technician of Institute of Applied Economic Research - DF and Economic Advisor of the Brazilian Federal Senate. Has experience in the Economy area, with emphasis on Growth, Fluctuations and Economic Planning. Research interests in the following areas Exchange Policy, Credibility, Rules of Economic Policy, Brazil.

José Nilo de Oliveira Junior Ph.D. in Economics, Federal University of Ceará. He is an economist with emphasis in Quantitative Methods. He research interests are in the areas of Economic Growth, Agriculture Sector and the Environment.

Aristides da Rocha Oliveira Junior Master in Business Administration, Fundação Getúlio Vargas (2002), Bachelor’s degree in Economics, Faculty of Social Studies - Federal University of Amazonas (1993). He is currently Assistant Professor in the Department of Administration of the FES/UFAM. Has experience in management and economics, working mainly in the following areas: Management of Regional Development, Sustainable Development in the Amazon, and Economics and Management of the Oil and Gas in the Amazon.

Renata Reis Mourão Master in Regional Development, the Federal University of Amazonas (2008) and Bachelor’s degree in Economics, Federal University of Amazonas (2003), visiting scholar - Washington and Lee University (2003). She is a researcher with the PIATAM Project. She works in the area of economics, with emphasis in the Environment, issues on environmental valuation and analysis of environmental impacts of major projects in the Amazon.


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Beatriz Furtado Rodrigues Master in Environmental Sciences, Federal University of Amazonas (2009), and Bachelor’s degree in Economics, Federal University of Amazonas (2003). Research in the area of Natural Resource Economics with emphasis on environmental economics, regional economics and regional accounts. Currently, she is a researcher in the area of Economics and Management of PIATAM Project and research on environmental valuation at the University of Florida.

Fabíola Aquino do Nascimento Master in Freshwater Biology and Inland Fisheries, National Institute of Amazonian Research (INPA) and a Bachelor’s degree in Fishing Engineering, Federal University of Amazonas (2002). Has experience in the management of fishery resources, with emphasis on user conflicts in floodplain lakes.

Preface Manaus Free Zone (ZFM): its importance to the Amazon and Brazil Denis Benchimol Minev1 Secretary of State for Planning and Economic Development of the State of Amazonas

This book provides a great service to brazil and the Amazon, as it demonstrates, scientifically, the beneficial effect of ZFM in the protection of the Amazon rainforest. Those who view the Free Trade Zone model with suspicion, this book will add the environmental argument in a definitive and convincing way to the other benefits we bring to the country, and also for the world. To those, Manaus and the Amazon should always be open to dialogue about the best ways to adapt and develop our economy, provided that they recognize our environmental contribution. What should be a dialogue, historically has been configured in a monologue, an empty speech of forest preservation curbing the existence of economic activities without conception of alternatives. It is thought that the economic alternatives in this distant land could be limited to picking Brazil nuts and dancing boi-bumba for foreigners - what the eyes can’t see, the heart can’t feel. Those who believe that the Amazon could be preserved in this fashion are wrong. Wealth is a precondition to preservation. To those who live in the Amazon and glimpse a prosperous future, for both the economy and the environment, this book fills a gap in the supporting arguments. We have always given arguments that the Free Zone is an economical and environmental policy, at its core, an economic-environmental policy, which we 1 Bachelor’s in Economics and Master’s Degree in Latin-American Studies, Stanford University, Master’s Degree in Business Administration, Wharton School of the University of Pennsylvania and former senior consultant for Goldman Sachs Group.

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tried to prove with anecdotes and examples. Now there is a literary work, a product of scientific research of highest quality, written and reviewed by persons of unblemished reputation and unquestionable impartiality, which substantiates our arguments, filling them with concrete data and conclusions. Forty years of gestation. Professor Samuel Benchimol, my grandfather, used to say in the 80’s that the sustainable development of the Amazon would have to respect four paradigms: be economically viable, socially just, environmentally appropriate and politically balanced. The Manaus Free Zone model, as this literary work well supports, responds to four. Our economic viability is proven by private capital, which was decided to locate in Manaus - in an open, globalized economy, capital votes with its feet. More than five hundred industries gave their vote of confidence to our model, and remain here, not by the goodness of their hearts or the beauty of the sunset in the Rio Negro, but because here they were able to execute their business plans and achieved the desired profitability for their investors. Let us not forget that much of the success of our model is due to the reception that was given to investors around the world, without discrimination or xenophobia. Economic strength in recent years has also enabled the establishment of training policies and improvement of the labor force which transformed Manaus in one of the poles of national technology development, based both on state investments, as well as the legal orders of the Computer Law which funnels resources for investment in research and development in the Amazon. In this way the new settlement of the Amazon began, not by farmers as back in 1970, but by doctors, as it should be. This literary work clearly defines the social difference between the Amazon and the other states in the North of Brazil. Let’s compare briefly with our neighbor Pará, a state of beauty and wealth at least comparable to ours. The Amazon has a per capita income which is nearly double compared to Pará - our total deforested area is 2% of the territory, against more than 20% in our neighbor. It is asked why. It is wrong anyone who thinks it’s because the Amazonians have better heart or greater love of the forest than the people from Pará is wrong. It is a matter of model of development and volume of income- industries take up little space, while agriculture, livestock, mining and timber, the main economic activities in our neighbor, occur over wide areas with high environmental impact. Furthermore, this differentiated regional level of income has allowed Amazonas build a policy and institutions that strengthen environmental conservation. Few governments in the world that, when visited by large cattle ranchers and farmers, can answer with certainty that such investments are not welcome in areas of native forest. The State of Amazonas can, thanks to social justice offered by the Free Trade Zone. Let us remember that the per capita income in Amazonas does not appear among the highest in the country, near only to the average, so there is here designed an excess of justice.


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The political balance, requirement of sustainable development, must begin with the external factor. Currently Brazil shouts, pounding its chest with pride, that we were the only one among the major countries to preserve our forests. This position of global environmental leadership, as demonstrated ably and beyond discussion this literary work is mainly due to the Manaus Free Zone. Continuing on foreign policy, the occupation of the Western Amazonia is an important national leadership in the Amazon and allowed the establishment of infrastructures that benefit all segments, from the scientific to national security. Within Brazil, the Free Zone also enjoys political balance, bringing great contributions via SUFRAMA, to the Western Amazon through the redistribution of resources that stimulate both scientific research and the training of human resources and establishment of infrastructure. Even states that do not participate in SUFRAMA benefit through this intense logistical movement. Nationally, the Manaus Free Zone is a large buyer of inputs for their industries and for local consumption (the State of Amazonas is a net importer of food). Finally, as already said by Professor Samuel Benchimol, instead of a tax haven, the Free Zone constitutes the tax paradise, as on the federal side, much more is collected in the Free Zone than it is spent on it. It is clear that more needs to move forward and leave behind the schizophrenic content of regional development. While a strong policy in fiscal incentives has been developed for forty years, appropriate and necessary logistical connections to the pole, such as river transport (best ports, dredging of waterways), roads (establishment and maintenance of roads), rail or air transport, was denied. While people are getting richer with more jobs, the electrical energy is of low quality and scarce, telecommunications are poor and expensive. In 1967 it would take 15 days to transport goods from Manaus to Sao Paulo, the leading national consumer center. Guess what it is today. The very design of the pole needs greater regional deepening in search of fulfilling a lifelong dream of integration of the abundant Amazonian wealth and advanced industrial technologies. Segments of timber, cosmetics, processing of products such as nuts and rubber are some of the alternatives for creating a foundation for this integration. Despite the many fronts of progress and even the tremendous challenges it is important that we pause for a moment to recognize the achievements of the past forty years. Given the tremendous success of policies related to the Free Zone, which is to achieve the paradigms needed for sustainable development, it’s time to strap it to the list of national policies for sustainable success. Nowadays, in this list, two established policies are already included: the implementation of power plants as a cheap and clean alternative throughout Brazil and experimentation with alcohol fuel. Both lifted Brazil to lead a world increasingly concerned about carbon emissions, climate change and global warming. The reader of this book will conclude that the Manaus Free Trade Zone is the third policy of great national environmental success, belonging to this select list.

Presentation The Manaus Industrial Pole and Protection of the Amazon Alexandre Rivas José Alberto da C. Machado José A. Mota

Discussions concerning the causes of deforestation in the Amazon were uncontroversial for a long time. The consensus causes included

expansion of the agricultural frontier, which, encouraged by tax incentives led to road construction, migration and land speculation. These causes are all interrelated (Reis and Margullis, 1991; Young, 1998). At the same time, a positive correlation was observed between the advancement of this economic frontier in the Legal Amazon and national economic growth. This situation has not been seen in recent years, because the deforestation rate has been increasing, despite a slowing in regional growth (Ferreira, 2005).

At least in Eastern Amazon1 or in the well-known consolidated border region there are indications that this new dynamic is linked to the export market and is being propelled along by highly profitable activities such as cattle ranching, logging and agribusiness. Thus, the responsibility for recent deforestation has been attributed to large and medium-scale livestock operations and their related consequences, including the cutting of trees, the clearing of land and road construction. 2

Although there are several controversial viewpoints on recent causes of deforestation, it is clear that the characteristics of the prevailing production structure 1 Eastern Amazon includes the States of Pará, Rondônia, Mato Grosso, Tocantins, and Amapá. 2 In principle, every frontier is. At the same time, the consolidated frontier does not in fact, strictly speaking, constitute a boundary. The practice of agents is expansive and, in the specific case of the Amazon, takes advantage of the conversion of forest into agricultural land. They are situated on the edge of the frontier and the term “consolidated” simply differentiates them from agents with more speculative strategies.

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have a very significant causal link in explaining this process as time goes on. In this region, two contrasting cases are quite representative, the state of Amazonas and the state of Pará. In the first case, processes related to the Manaus Industrial Pole have generated accelerating industrialization and the vertical integration of production, creating an economic environment that does not have its growth process linked to the more intensive use of the existing natural resources base, particularly the forest resources. In the second case, a production structure has been put in place based on the exploitation of forest and mineral resources, creating a perverse logic in which the land use and its appreciation process are linked to an overexploitation of natural resources. It has implemented a system based on the direct link between wood exploitation and the advance of the agricultural frontier. As a result, pressure on the natural resource base in the Amazon and resulting deforestation were completely different between these two states. Table 1 corroborates this relationship, with a comparison of the average contributions from each state to the total deforestation in the Amazon. Table 1: deforestation rate in the states of Legal Amazon from 1985-2003 States

Average (%)

Acre

3,11

Amapá

0,41

Amazonas

4,92

Maranhão

5,76

Mato Grosso

36,17

Pará

31,60

Rondônia

13,98

Roraima

1,56

Tocantins

2,49

Therefore, the state of Amazonas, through the economic dynamics generated by the Manaus Industrial Pole, produces products that have an additional value that are not apparent at first sight, but related to the avoided cost of deforestation associated with the economic activity in the state. Moreover, this effect on social welfare is not limited to the local population in the state or more broadly within the Amazon region. The effect generates external effects that improve social welfare throughout the world, even though these people may not be aware of the benefits that are being generated. Despite this, the current discussion of the justification for the incentives that the companies receive to settle in the PIM ignores this environmental effect and focuses on other reasons. Regionally, it evokes the historical justification for the creation of the Manaus Free Zone, which was the need to compensate for high


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logistical costs, associated with the distance of the city from the input sources and the great centers of consumption. In the rest of Brazil, the prevailing idea is that the economic incentives for the PIM only function to benefit companies or groups of companies to improve their economic performance. Because of this imperfect view, several measures, especially tax measures, have been taken to benefit some of the productive sectors based in the center-south area of the country to the detriment of the competitiveness of firms located in the PIM, weakening the important economic dynamics in the region. One of these measures is the differential treatment concerning the ICMS (revenue tax), such as is given to large states like São Paulo. This means that products manufactured in Manaus become relatively more expensive in these larger markets, thus causing damage to the competitiveness of enterprises and generating questions in concern to the desirability of maintaining operations in the Manaus Industrial Pole. In a scenario of global change and an increase in the social and environmental responsibilities of companies, this differential treatment imposes a high private cost to the PIM and an even higher social cost to the Amazon and the rest of Brazil. With the exception of Rivas (1998), there are no empirical studies that seek to measure the relationship between deforestation levels in the state of Amazonas and the levels in the PIM. From the aforementioned, it is possible to synthesize the subject in three ways: a. The first focuses on positive external effects that the PIM produces for Brazil and for the rest of the world, the reduction of deforestation pressure in the Brazilian Amazon; b. The second relates to imperfect information due to the fact that the federal government and state governments from other regions of the country do not internalize all the costs that must be considered in their decisions in relation to the tax treatment of goods that are similar to those produced in the PIM, but produced in different regions of Brazil; c. The third is that a mechanism does not currently exist to incorporate the benefits of the PIM’s positive environmental impacts into the market price of the products produced in the PIM. Based on this discussion, the objectives of this work can be defined at two levels. The first is to empirically demonstrate with appropriate econometric models that the PIM’s industrialization strategy has contributed to the slowing of deforestation in the Amazon forest, particularly in the State of Amazonas, estimate the magnitude of this effect and construct scenarios of the environmental consequences of a possible end to the PIM. The second level, which is an extension of the first, seeks to develop policies for compensating the PIM region for the positive environmental benefits that it generates. One method could be through awarding carbon credits for reduction in deforestation and associated reduction in

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CO23 emissions. Another method would be to create a mechanism that would incorporate these environmental benefits into the products produced in an environmental friendly way in the Manaus Industrial Pole. This study seeks to show that the PIM has managed to raise economic wealth, produce social improvements and generate positive environmental externalities. Such externalities generate national and global benefits by reducing CO2 emissions, but they could also mean future monetary gains through use of carbon credits by companies located there. The economic importance of the PIM in regards to the Amazon is so significant that in later years it has become a source of more than 60% of all federal taxes collected in the Northern region (not including Tocantins) of Brazil. Other indicators of its economic significance during the period from 2002 to 2007 are shown below: • Revenues increased by 182.22%, reaching USD 25.6 billion. • The number of direct manufacturing jobs increased by 70.63%, reaching 102,561. The number of indirect jobs rose to about 400 thousand. • Exports rose 4.04%, reaching about USD 1,107 billion. • Purchases of domestic inputs recorded an increase of 4% (even with the depression in the exchange rate with the dollar), totaling 51.16% of all inputs utilized in the Pole. 29.49% is purchased locally. • The total investment for annual approved projects rose 209.49%, reaching close to USD 3.5 billion. • Investments consolidated in the Pole rose 220.87%, reaching around USD 6.7 billion. • The total number of companies producing in the PIM increased by 43% to a total of 508. • Total revenues, including federal, state and municipal taxes, rates and contributions, recorded an increase of 111.52%, exceeding USD 12.4 billion. • The growth of industrial production was twice the Brazilian average. • The PIM has remained responsible for 57.66% of all tax revenue that Brazil collects in the northern region, except Tocantins. • In the states in western Amazon, in the years 2003 and 2004, the average growth in GDP reached almost 50% and GDP per capita almost 30%. In the period of 3 Within the strategy of the Clean Development Mechanism (CDM) and CREs - Certified Reductions of Emissions, the beneficiary projects would be those that help in the absorption of carbon dioxide from the atmosphere (in the case of reforestation) or those that prevent the release of greenhouse gases (in the case of energetic efficiency), or those that bring any advantage which would not occur without the project.


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2003-2006 the average growth was around 36.99% (AM grew 33.53, AC grew 49.29%, RO grew 34.45% and RR grew 33.72%) in GDP and 32.10 %% (AM rose 46.04, AC grew 33.40%, RO grew 27.25% and RR grew 21.73%) in GDP per capita. In addition to these economic benefits, there are also environmental benefits, the magnitude of which is estimated in this study. This task is associated with certain degrees of uncertainty. For example, when a tree of commercial value is harvest, approximately 20 others trees are damaged, resulting in the opening of clearings in the forest, which then become vulnerable to fire (Nepstad, 2001). In addition, CO2 emissions are not the only environmental damages generated by deforestation. Deforestation also causes a loss of environmental services such as: • A loss of productivity in agriculture, due to the disappearance of pollinating bees, • The expansion of lands that eventually become uninhabitable, • Negative impacts on water resources, with possible effects on the weather and rainfall regime, both in the region and in other places such as the state of São Paulo, • Destruction of biodiversity with effects on the reduction of value to its bio inventories and the irreversible loss of certain species of plants or animals. An expressive dynamic economy should not be replaced or weakened from one hour to the next. On the contrary, it should be preserved and strengthened, through other extra-tax mechanisms, including the accumulation of value to the products according to the possible positive environmental effects they have. In this way an appropriate measure could be a logo certification based policy that captures this intangible value and transfers the value to the products that bear its seal. Currently, the products produced in the PIM are required to use a seal, but it is based on an exclusively logical thought of visualization of the production site, with no accumulation of any additional benefits. Thus, in addition to showing the real effects of the PIM in preventing deforestation in the Amazon, particularly in the State of Amazonas, this study can provide a basis for the creation policies of a new seal of quality in order to transfer to the product that bears the seal the environmental benefits that the PIM may have in preserving the Amazon. The book is organized as follows: the first part contextualizes the Manaus Industrial Pole and its dynamics. The second develops econometric studies that test the hypothesis of the study. The last part discusses different aspects relating to an eventual end of the pole and possible compensatory mechanisms, showing an alternatives for the PIM products.

Part I The Manaus Industrial Pole in the Context of the Amazon

The Manaus Industrial Pole (PIM) is a government program created in the 60s aimed at stimulating economic development in the Amazon region. This section of the book presents a brief description of the Manaus Industrial Pole and its recent economic acceleration to highlight possible causal links between regional economic development and environmental preservation in the state of Amazonas.

Chapter 1 The Manaus Industrial Pole and its Dynamics1 Aristides da R. Oliveira Jr. José Alberto da C. Machado

The “Modelo Zona Franca de Manaus” (Manaus Free Zone Model) is an area of tax differentiation based on different legal and institutional arrangements that provide tax breaks and other incentives within the jurisdiction. The area with differentiated tax arrangements includes (1) Manaus, the capital of the state of Amazonas. The Manaus Industrial Pole (PIM) is the primary generator of economic activity for this city as will be discussed further in this book. Imports of consumer goods was once very relevant to the local economy, but this is no longer significant due to the existence of other ports of entry for foreign consumer goods. (2) An area consisting of Manaus and the neighboring cities of Rio Preto da Eva and Presidente Figueiredo, where SUFRAMA’s Agricultural District (SAD) operates. The Manaus Free Zone Model provides tax incentives for the development of activities in the primary production sector. Although some good results have been obtained with the implementation of rural projects in the vicinity of Manaus, (production of fruit, vegetables, and dairy for the local market), it remains the least developed of all the sectors of the model. (3) The Free Trade Areas (FTAs), which are present in border areas of the sub-region of the western Amazon and in the counties of Macapá and Santana (state of Amapá, in the Eastern Amazon). Other parts of this region (composed of the states of Acre, Amazonas, Rondônia, and Roraima) are not included in the program.

1 This section of text has OLIVEIRA Jr. (2006) and GARCIA (2004) as key references.

32


Each of these three areas receives funds authorized by federal legislation but are subject to different levels of tax incentives. Figure 1, below, illustrates the geographic location of the areas with differentiated tax schemes associated with the MFZ model. Figure 1 – The MFZ model: levels of tax-geographic coverage. Western Amazon.

SUFRAMA’s head office Free Trade Areas Regional Coordination Offices Western Amazon Source: SUFRAMA/COGEC

The following timeline provides a comprehensive overview of the history of the general MFZ model and its most important component, the PIM: • Phase 1 (1957 to 1967): The MFZ functioned primarily as a commercial warehouse or a “free port” form for importation. • Phase 2 (1967 to 1975): Growth of the domestic market for commercial goods and absence of import taxes on inputs attracted international companies to assemble consumer goods, primarily in the area of consumer electronics. • Phase 3: (1975 to 1990): This period was associated with import substitution and national policies that restricted competition. • Phase 4: (1990 to the present): This phase experienced the modernization of industry in the context of a global market system.


33

Phase 1: A commercial warehouse In the first stage, the Manaus Free Zone (MFZ) was established by Law 3173/1957, to allow Manaus to operate as a free port or a warehouse trade area for imported goods. This first aspect of the MFZ was not fully operational for the initial ten years of its existence.

Phase 2: Growth of the domestic market In phase 2, the Manaus Free Zone Model there was a reformation of laws governing the port´s warehouse center. The publication of Decree-Law 288 (February 28, 1967) created the Superintendence of the Manaus Free Zone (SUFRAMA) and determined that the MFZ should represent, “an industrial, commercial and livestock center in the heart of the Amazon “(Article 1 of DL 288). It would be based on the establishment of tax incentives in the agricultural, extractive, industrial, and commercial sectors. Through the implementation of this program, the federal government sought to offset local economic disadvantages inherent in the region such as high transportation costs. A set of tax incentives forms the basis of this program that has the objective of creating an economic center in the Amazon that increases employment and stimulates both the demand and supply sides of the economy. It focuses on the three basic sectors: agriculture, industry, and services. The intention was to create an economic center that would generate further economic development in the entire surrounding region. This policy stems from the economic and geopolitical thinking of the era. Its economic roots are the philosophy of developmentalism, which was then in vogue in Latin America. Its geopolitical basis is the policy of import substitution aimed at the formation of a significant internal market capable of reducing dependence on modern manufactured goods imported from industrial countries. The model was also aimed at promoting, through strong encouragement of the state, an accelerated transition from an economy based on the export of primary goods to an economy based on services and technology. On the other hand, the geopolitical focus needs to be understood in its context, a time during which Brazil was under full control of the military government (post-1964). The military government was concerned with economically and demographically occupying the Amazon in order to strengthen itself and in order to justify surveillance actions in its vast border. This was aimed at reducing perceived risks of territorial invasion by foreign forces, which were specifically related to leftist political movements in bordering countries. This economic-geopolitical strategy of the military government for the northern region of the country took place during the historical “Amazon Operation” during 1966 and 1967. During these two years the Federal Executive Body began

34


to put forth a number of institutions and special incentive mechanisms for modern economic activities in the Amazon for its integration with the more active areas of economic development, located in the central-southern area of the country. These institutions include: • The Superintendence for the Development of Amazon (SUDAM) was created to replace the Superintendence of Financial Appreciation of the Amazon (SPVEA). The area of responsibility of SUDAM consisted of all of what is constitutionally defined as the Legal Amazon (all states in the northern region, Mato Grosso, part of Maranhão, and more recently, the State of Tocantins). SUDAM had programs aimed at reducing firms’ income taxes, as well as investment funds available through the Amazon Investment Fund Resources (FINAM) for approved business projects. SUDAM was also in charge of regional development planning. • The Banco da Amazônia SA (BASA) was created through the restructuring of the former Banco de Crédito da Amazônia (Bank of Credit of the Amazon) which in turn had its origins in the Banco de Crédito da Borracha (Bank of Credit of the Rubber). BASA is a financial institution responsible for stimulating public and private projects that are economic in nature. It does so by managing the Constitutional Fund for Northern Financing Fund (FNO). • The Superintendence of the Manaus Free Zone (SUFRAMA) was specifically created to manage the Manaus Free Zone in an entirely different way from the original polices. SUFRAMA has a variety of functions: a. The first is to grant or sell land for agricultural and industrial use in the SUFRAMA Agricultural District (DAS) (located between the cities of Manaus and Rio Preto da Eva (AM)), and in the Manaus Industrial District; b. The second function is to provide federal and state tax incentives (reductions, suspensions and exemptions) that are levied on production and sales. These taxes include the industrialized products tax, import tax (federal), and the revenue tax, in the State of Amazonas; c. The third function is tax control of domestic and imported goods that enter the western Amazonian region. Those goods intended for domestic consumption or industrialization could benefit from the incentives listed above (Decree-Law 356/1967 and 1435/1975). During this second phase, the Brazilian market for durable goods was still of a modest size, but fairly open to imports. It is notable that in Manaus any industry that would have been installed under the MFZ aegis would not find strong economic barriers such as taxes or restrictions on the import of inputs. Even so, the Industrial District did not experience much industrial growth in this period, and did not increase its share of national capital and small companies. Neither the commercial nor the primary sector experienced significant development during this time.


35

Phase 3: The import substitution mechanism From 1975 to 1990, import substitution occurred in an environment of limited competition, two “oil shocks,” and a consequent explosion of Brazilian external debt. These factors ended up compelling the federal government to adjust industrial and overseas trade policies, closing the internal market through import restrictions, including resale and industrialization. Manaus was the only city in Brazil in which the distribution of goods of foreign origin was permitted, and was facilitated by means of tax incentives to the importer. This was associated with limited individual and family import quotas, made available for tourists and administered by SUFRAMA. This caused a wave of Brazilian tourists to Manaus who went to this city to purchase imported goods (especially clothing articles, electronic consumer goods, cameras, watches, etc.). Such events stimulated the creation of wealth among some segments of Manaus society, which only stopped after national trade liberalization in 1990. It was not until the second half of the 1970s that Manaus started hosting significant number of industries. Since the domestic market was closed to imports and domestic industry was sheltered from foreign competition by this protectionist policy, the result was the spread of factory production systems that were characterized by intensive use of labor, but low wage labor. This stage marked the beginning of the national information technology policy, whose recurring theme was the creation and consolidation of a national industry of computer goods within the Brazilian economy. At this stage, the rationale for supporting the PIM continued to be one of import substitution, but based on a new productive activity that addressed the legal requirement for high rates of nationalization in the production of inputs. This would discourage purchases of inputs from abroad, thereby stimulating the creation of jobs inside the country. Most of the stimulated industries operated within Manaus and included international brands in the consumer electronics sector (TVs, clock radios, stereos, etc.). In addition, there was a significant production of both two-wheeled vehicles (motorcycles and bicycles) and miscellaneous products such as watches and toys. A whole network of material and component suppliers (electronic and electrical components, plastics, metals, rubber, paper, cardboard, etc.), was created in order to support the factories that produced the finished goods. The supply factories were set up in the Industrial District, using mostly domestic and regional investment capital. Regarding the internalization of the MFZ model in phase 3, the enabling regulatory legislation (DL 1435/1975) limited the range of some of the associated tax benefits to the Western Amazon territory (Acre, Amazonas, Rondônia and Roraima) and the creation of Free-trade Areas (during the late 1980s early 1990s) in some of the counties located along the international borders of the Legal Amazon. The general aim of this policy instrument was to reduce the inter-regional disparities of cost of living experienced by the local populations. Further, it wanted

36


to stimulate agricultural, agribusiness, and mining activities in inland locations, all of which could benefit from regional primary inputs and whose outputs were intended for consumption within the Amazonian region. SUFRAMA was also in charge of promoting economic infrastructure to support production (transport, storage, electricity, machinery, equipment, etc.). In these states, as noted below, these programs were an important contribution to the development of regional economic infrastructure. This phase ended in 1988, but was followed by the first extension of the MFZ’s authorization until the year 2013. In addition, the model was incorporated into the new Brazilian Constitution of 1988 (Article number 40 of Acts of Transitional Constitutional Provisions (Brazilian ADCT)).

Phase 4: Industrial modernization and the high technology Phase 4 began in 1990 and continues until the present day. It is characterized by high-tech modernization within the context of a global economy, and is marked by two significant internal events. The first was the opening of Brazilian trade through policy change in 1990 and the subsequent industrial policies focused on productivity and quality control. The second event was the monetary policy change that resulted in lower inflation and stabilization during the 1993 and 1994. This was known as the “Real Plan”. In 1990, Brazil abruptly opened to imports and domestic industry was exposed to competition from similar products of a markedly superior technology. In the short term, this policy had a deleterious effect on significant portions of the domestic industry, resulting in the closure of manufacturing activities of entire industrial sectors, such as the toy and watch industries. Today, the watch industry is still found in Manaus and it owed its existence to MFZ’s incentives. The manufacturing segments of intermediate goods and components were particularly affected by trade liberalization due to the domestic industries’ inability to compete against similar imported items. Importers were also victims of the trade liberalization process. Unlike the industrial sectors such as intermediate goods and components, which recovered back part of their competitiveness in the PIM in 2000, the Manaus importing sector has yet to recover. This is because it does not have a big enough size or scale of purchasing power to compete against national retailers and chains. It is also due to the increases in costs imposed by regional warehousing, customs clearance, and transportation logistics. Suffering the consequences of the abrupt foreign competition in the import and components sector, Manaus began to encourage the growth of the manufacturing sector. The federal legislature amended the MFZ’s function so that it would be in accordance with the new economic environment. Law no 8387/1991 brought several important innovations:


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• The first innovation was a change in evaluation methods away from the old Nationalization index in favor of a different measure, the Basic Productive Process (BPP). The Nationalization Index was appropriate when the goal was to increase the value added of the domestic sector. However, with a greater freedom to purchase material, supplies, and components from any source, including imported sources, this criterion was no longer relevant. This change was put into place by Decree no 783/1993 and SUFRAMA has the legal responsibility to monitor compliance with the BPP’s contained in the approved industrial projects. It does so together with the Administrative council (CAS), in a fashion similar to the monitoring of compliance with nationalization indices. • The second criterion was related to the establishment of quality control for products manufactured with MFZ incentives. This was inspired by the National Program for Quality and Productivity (PNQP) and established by the federal government in order to set international parameters of quality and competitiveness in the domestic business sector, including the requirement for implementation of ISO 9000 systems in companies. • There was also a requirement for firms to allocate a portion of their gross revenue to research and development in the IT sector. Part of this portion of revenue could have been retained for technological projects within the companies themselves and part must have been allocated to scientific and technological projects in the Regional Science, Technology & Innovation System,(S, T & I). These same provisions were extended to IT industries in the rest of Brazil (laws 8.248, 8.587, and 1.991.) In this case, the Ministry of Science & Technology was in charge of monitoring compliance of both measures. These changes in the regulatory framework to support the MFZ radically transformed the targeted industries. Many shifted from intensively using labor to investing in the use of capital and technology. Brazil went under a period of fast paced modernization that has had no recent parallels. In the early years of the MFZ´s industrial conversion process, the amount of direct labor was significantly reduced, but this was compensated by a substantial improvement in the quality and conditions of those who remained employed. It also caused a significant jump in industrial productivity. The standing logic of the industrial policy in Manaus went from stressing the importance of import substitution to a focusing on international competitiveness in price, quality, and service. At this time, the PIM’s basic profile was transformed to a center for high-tech industry. The advent of the Real Plan (1993-1994) saw the stabilization of the national currency, which permitted the expansion of credit for consumers. The main industrial impact was a rapid and strong expansion of the durable consumables market, fueled by both rising imports (which were no longer restricted) and accelerated growth from national industries that produced goods such as home appliances, consumer electronics (TV’s, video, sound systems, etc.) and vehicles (cars and motorcycles).

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In the case of the Manaus Industrial Pole, pessimistic forecasts of the early 90s inevitably ended during the period from 1993 to 1996 with an industrial renaissance. This renaissance was driven not only by a leap in demand, but also by a top-down imposition of technological modernization on industry, with massive imports of machinery, equipment, economic inputs, and high technology components. Independently of whether the overall output of these industries in Manaus also experienced a significant leap forward, imports followed this trend and partly contributed to the Brazilian trade deficit that became chronic in the following years. Table 1 seeks to illustrate the general dynamic framework for the industrial reconversion that represents the beginning of the fourth phase of the MFZ. Table 1 – The industrial reconversion of the MFZ, in numbers (1990/1996). Years Indicators

1990

1991

1992

1993

1994

1995

1996

1. Gross Revenues 8.379,22 5.984,26 4.542,76 6.635,72 8.818,20 11.766,56 13.266,06 (in USD millions) 2. Direct Labor (companies)

76.798

58.875

40.361

3. Imports (in USD millions)

715,12

728,98

672,75

4. Exports (in USD millions)

61,78

62,46

115,13

5. Trade Balance (in USD millions)

-653,34

-666,53

37.734

41.477

1.275,98 1.841,55 97,27

114,57

Evolution 1990/1996 (%)

58,32

48.761

48.494

-36,86

2.823,26

3.186,86

345,64

101,76

105,31

70,47

-557,62 -1.178,71 -1.726,97 -2.721,50 -3.081,55

-371,66

Source: Suframa/SAP/CGPRO/COISE (2008) Obs.: The term direct labor refers to the monthly average of people that work in the industries.

In response to this new scenario, SUFRAMA policy was based on a threepronged strategy: • The first aspect of the strategy was to focus on the downstream development of the production chains of manufactured goods with incentives in Manaus. This entailed more systematic efforts to attract new producers, especially foreign corporations on a competitive basis. As this occurred, there was a significant pool of producers of economic inputs and components in Manaus. This pool of producers integrated the production chains of goods considered as the anchors of the PIM, such as televisions, cell phones, motorcycles, DVD players and stereos. Incentives such as infrastructure development, tax incentives, and the investment in science, technology and innovation (S, T & I) also spurred this transformation, which was led by SUFRAMA. It had R&D funding from both


39

its own budget and the money provided by the laws related to research in the information and technology sector. This budget became a standard component of its institutional agenda, and was put into place in partnership with local private and public research institutions, in order to forge a strong S, T & I system that could compete internationally, as well as develop emerging sectors such as microelectronics and biotechnology. • The second prong of the strategy was the internationalization of local industry. One mechanism was to stimulate exports and not rely on the Brazilian market as the sole source of demand for the products produced in Manaus. Programs were developed to promote trade and international cooperation, including, beginning in 2002, the implementation of a regular schedule for the Amazon International Fair (FIAM). This exposition promotes bilateral and multilateral commercial trade negotiations and exhibitions and trade missions led by either the state governments from the area or the Foreign Ministry (Itamaraty). • The final component of the strategy was the development of social infrastructure in Western Amazon to support production projects of either business or cooperative nature. This focused on the use of Amazonian natural resources and was supported by SUFRAMA with its own resources. These resources were generated from the collection of the Administrative Services Fee (TSA) from industries in the PIM’s sphere of influence.

The current PIM’s economic performance its regional benefits The trajectory of the MFZ from 1967 to the present as a regional development model clearly indicates the relevance of assessing the regional benefits that it generates and is based on two interrelated dynamics: (a) Performance indicators of the benefited industries (historical series 2000-2007); and (b) the effects of the sequence of employment-income-demand. This sequence is generated by the PIM and is expressed in Manaus and the State of Amazonas on the basis of its own collection of the administrative services tax (TSA). This tax is collected based on the value of imports associated with the industries of the PIM. These imports could be for use in production activities projects and or in economic and scientifictechnological infrastructure investments. The tax is implemented within Western Amazon and the Free Trade Area of Macapá and Santana - ALCMS). The PIM is now a regional production model made up of high-tech industries. Its strong performance in recent years is not only the result of business dynamics and the market itself, but also the result of policies made during the last ten years, which focused on marketing and development of production chains. Table 2 presents data that demonstrate the behavior of the PIM in the period from 2000 to 2007.

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Investments The incentives for the Manaus Free Zone model have attracted approximately 500 companies to the PIM. A large portion of these companies are subsidiaries of famous multinational corporations such as Nokia, Coca-Cola, Honda, Gillette, Harley Davidson, Sony, Philips, and Panasonic. All of these corporations are hightech companies and taken together they represent an accumulated foreign investment exceeding USD 6.7 billion (Suframa, 2008).

Commercial Performance of the benefited industries Total revenue of the benefited companies has reached more than USD 25.0 billion, a growth of 147.08% compared to 2000. In 2007, no less than 69.62% of this total turnover was concentrated in only three subsectors: • Home Appliances (29.35%), which includes products from consumer electronics (such as color TVs, DVD players, and audio systems), appliances (such as air-conditioners and microwave ovens), and components (such as printed circuit boards, electrical cables, and transformers); • Two-wheeled vehicles (23.22%), which includes the production of motorcycles and bicycles. In some cases, such as Honda and its suppliers, there is both a high degree of vertical integration and agglomeration effects; • Computer Goods (17.05%), which involves the manufacturing of mobile phones (the flagship product of this segment), as well as other products like computer screens, microcomputers, and peripherals. The remaining 30% of the revenues are scattered across products such as chemicals, thermoplastics, metal goods, mechanical goods, material suppliers, components, and parts and pieces for both the production of finished goods or subsectors of lower valued products such as disposable shavers, pens, cigarette lighters, and watches.

164,811

R$ 1.000,00

R$ 1.000,00

R$ 1.000,00

7.2. Suframa (TSA).

7.3. State of Amazonas (Gross Revenue Tax collected) 7.4. County of Manaus (PROPERTY + SALES + Taxes)

NA – Not Available

783,693

R$ 1.000,00

7.1. Federal

76,111

1,201,815

4,074,834

R$ 1.000,00

50,003

3,025,473

7. Total Tax Collection

USD1,000.00

5.2. Imports

741,625

People

USD1,000.00

5.1. Exports

-2,283,848

22,73

6. Direct Employment

USD1,000.00

5. Trade Balance

%

10,392,606

USD1,000.00

4. Regionalization degree of economic inputs purchase

3. Gross Revenue

3,619

US$ / person

2. GDP per capita (AM)

10,311,940

2000

US$ 1,000.00

Units

1. GDP (AM)

Indicators

154,154

718,704

51,955

1,157,789

4,895,023

54,762

2,701,677

829,042

-1,872,635

24,52

9,130,863

3,014

8,822,203

2001

Table 2 – Manaus Industrial Pole, in numbers (2000-2007)

147,815

710,664

45,561

1,114,675

5,896,974

57,823

2,583,732

1,025,799

-1,557,933

27,75

9,104,766

2,482

7,459,733

2002

62,086

761,957

50,694

1,217,950

6,441,889

64,969

3,223,339

1, 224, 940

-1,998,399

29,25

10,531,230

2,631

8,113,937

2003

99,102

949,662

65,725

1,773,021

8,448,617

79.380

3,758,994

1,084,893

-2,674,100

32,36

13,961,238

3,300

10,360,523

2004

118,496

1,316,078

86,930

2,343,924

9,413,065

89.224

4,763,075

2,021,195

-2,741,879

32,57

18,964,109

4,237

13,698,716

2005

156,337

1,476,660

98,664

2,780,192

10, 987,232

98.194

5,923,236

1,483,954

-4,439,282

31,90

22,858,368

NA

NA

2006

175,200

1,639,105

100,619

3,218,965

12,502,010

98.244

6,285,629

1,041,043

-5,244,586

29,47

25,677,762

NA

NA

2007

6.30

109.15

32.20

167.84

206.81

96,48

107,76

40,37

-129,64

29,65

147.08

17.07

32.84

Var. %

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Figure 2 – PIM’s turnover by subsectors of activity in 2007. 0,74

Others

2,20

Lighters, pens and disposable shavers

0,10

Toys

0,37

Optical

0,27

Non-metallic minerals

10,27

Chemicals

0,67

0,09

Timber

2,54

Mechanical

5,81

0,75

5,53

23,22 1,04 17,05 29,35

Paper and Cardboard

Metallurgical Beverage Thermoplastic

Two-wheeled

Watchmaker

Computer goods

5,00

10,00

15,00

20,00

25,00

Comsumer electronics

30,00

Source: SUFRAMA/SAP/CGPRO-COISE (2007). The authors own material.

By looking at the market destinations of the business´ gross revenue, one can perceive that almost the entire amount of production (95.95%) is destined for the internal market. Of this, 79.01% is outside the area of the PIM and 16, 94% is repurchased by the PIM. The remaining 4.05% goes to foreign markets, but such data require qualification. The trajectory of the PIM, as outlined in the previous section, clearly shows that the PIM model was not designed for exporting, but rather to supply the Brazilian market. This has become the driving force behind the investment decisions implemented in Manaus. As a regional development policy, the PIM had some aspects of an import substitution model as it increased production of domestic markets, which saved Brazil significant amounts of foreign currency which would have otherwise been used to purchase foreign imports demanded by the emerging middle class. In any case, it was expected, based on the acute trade deficit experienced by Brazil in the post-liberalization trade period and the currency crises that the country went through until early 2000, that the entire effort of industrial policies would focus on promoting and facilitating exports. In this way the PIM became one of the targets of these export promotion efforts.


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Thus, the constant promotion of trade by SUFRAMA and the Government of Amazonas as well as the marketing strategies of some companies promoted a significant change in the level of the PIM’s exports. While it is true that its exports grew rapidly in the period from 2001 to 2005, increasing from USD 830 million to more than USD 2.0 billion, the growth was concentrated almost exclusively on a single product, mobile phones. These were exported by a single company (Nokia) to meet the demand of NAFTA (USA, Mexico and Canada), and complement the production of Nokia´s other plant located in Reynosa (Mexico). This plant was at that time in process of expansion. In 2005, the cell phones produced by Nokia in Manaus represented over 50% of all PIM exports, about 1.0 billion US dollars. In 2006, the new Nokia facilities in Reynosa came into operation as a result of the expansion of the Nokia plant located there. In 2006-2007, exports of Nokia Manaus mobile phones declined substantially. This obviously caused a significant drop in PIM exports. Despite the transfer of production lines to Mexico, this company continued to be the largest exporter in the PIM over the last 3 years. It can therefore be assumed that part of the production of some models produced in Manaus and exported to NAFTA, have been shifted to Mexico. Two factors shaped this business decision. The first was related to chronic deficiencies in the regional logistics system, especially shortcomings in procedures for customs clearance processes made difficult by strikes from the employees of the Income Department and the Ministry of Agriculture. In addition to this there was an observable mismatch between the timing of the generation of the higher cargo volumes for export and the space availability on cargo planes. In addition, the gradual devaluation of the dollar against the Real, beginning in 2004, reduced the overseas demand for these exports. This sharp drop in cell phone exports took place in the face of growing export performance of other goods produced in the PIM, such as motorcycles, but this did not happen at the same pace. In 2000, the two-wheeled vehicle industry had exports valued at 66.8 million US dollars. These grew to 74.4 million in 2001, 91.0 million in 2002, 138.8 million in 2003, 199.6 million in 2004, 218.9 million in 2005, 256.1 million in 2006 and 267.9 million in 2007. The composition of the PIM’s exports in 2007 is shown in Table 3 next page.

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Table 3 – PIM’s exports, in 2007. Ranking

Products

US$ FOB

%

1º

Mobile Phones

300.424.443,00

28,86

2º

Motorcycles

267.031.350,00

25,65

3º

Concentrates for Soft drinks

187.630.625,00

18,02

4º

Shaving Products (razors, blades and cartridges)

73.298.480,00

7,04

5º

Color TV (picture tube, plasma, and LCD)

50.282.491,00

4,83

6º

Set-top boxes

32.640.946,00

3,14

7º

Wood Products

14.898.068,00

1,43

8º

Car stereos and audio devices

13.789.055,00

1,32

9º

Computer Screens (picture tube and LCDs)

4.709.704,00

0,45

10º

DVD players (recorders/players)

3.496.626,00

0,34

11º

Television picture tubes

3.189.608,00

0,31

12º

Others

89.651.762,00

8,61

1.041.043.158,00

100,00

Total Fonte: SUFRAMA/SAP/CGPRO-COISE (2008) The authors own material

On the other hand, imports by industries in the PIM maintained a high level, accounting in 2007 for 48.85% of the purchases of the all the PIM’s economic inputs. Table 4 shows the 10 top imported items of the PIM for 2007. Note that, unlike the PIM’s exports, imports are much more varied in terms of value distribution. They are also almost entirely concentrated in purchases of electronic economic inputs (except for styrene, used in polystyrene production as a raw material for the plastic processing subsector).

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Table 4 – PIM’s Imports, in 2007. Ranking

Products

US$ FOB

%

1º

Liquid Crystal Devices (LCD)

843.127.685,86

13,41

2º

Other parts for Radio (fusion receiver devices, television, etc.)

779.464.823,68

12,40

3º

Cathode tubes for color TV receivers, etc.

300.085.848,70

4,77

4º

Other integrated circuits and electronic micro-sets

299,503. 684,23

4,76

5º

Other parts and accessories for motorcycles, including motorized bicycles

251.803.714,42

4,01

6º

Set-up circuits, special surface setting. (SMD – “surface mounted device”)

198.859.993,51

3,16

7º

Other electronic parts and accessories

184.269.124,94

2,93

8º

Printed Circuits

157.609.863,91

2,51

9º

Head-disk joint of hard disk unit, set-up

137.650.492,28

2,19

10º

Styrenes

133.138.795,05

2,12

11º

Platinum in double or powdered forms

115, 983. 780,28

1,85

12º

Other for remote control/ TV cameras/ video devices

110.159.028,89

1,75

13º

CHIPSET-type circuits

108.181.668,96

1,72

14º

Other imports

2.665.790.895,29

42,41

6.285.629.400,00

100,00

Total Source: SUFRAMA (2008) The authors own material.

The degree of domestication of industrial inputs demanded by the companies (the purchase value of domestic inputs divided by the total purchased, originating from the PIM or other states of the federation) increased from 44.98% in 2000 to 51.15% in 2007. At the same time, the proportion of inputs purchased from within the PIM rose from 22.77% in 2000 to 29.47% in 2007. This shows that the local industrial chains are becoming progressively more vertical. These are the effects in recent years, after the development of policies that target supply chains by SUFRAMA and the government of Amazonas. In 2003, for example, the state government substantially changed the ICMS (sales tax) incentives to make components and production of intermediate goods more attractive in the PIM.

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Direct and indirect jobs In 2007, PIM was responsible for 98,244 direct jobs. This is based on the monthly average of all companies operating in the PIM. There was an increase of 96.48% over the year 2000, when PIM directly employed over 50,000 people. Indirect employment, spread out over the production system made up by companies which support the Pole, particularly in services, is estimated at around 450,000 jobs. This can be compared to the 90.000 employees that the PIM employed in 1990, a period when the production function of the companies in Manaus was labor intensive. This means that job creation preceding the restructuring period of the 90’s was surpassed at a time when it was very extensive (less staff employed, but with a greater number of companies). Another difference to be highlighted is the quality of jobs offered, which currently require much higher skills and offer benefit packages and higher wages than were available in the past. For instance, the average salary for PIM employees in 2000 was US$ 4509.57 per year and increased to US$ 8214,055 in 2007, reflecting an increase of 82.15% over this interval.

Macroeconomic Benefits: GDP, GDP per capita and ITV. The data analysis of regional accounts of IBGE for the period 2000-2005 (IBGE, 2008), in the state of Amazonas allows for comparison across Brazilian states and it leads to three conclusions regarding state and county GDP, GDP per capita, and the industrial transformation value (ITV): • The first result is that the state of Amazonas’ GDP grew at significant rates in the period 2000-2005 (76.76%), but in terms of proportion of national GDP, it was still quite low in 2005 as it represented only 1, 6% of Brazilian GDP. Amazonas lagged behind the State of Pará in the northern region, which produced 1.8 % of national GDP. • Second, there are two types of concentrations of a state´s GDP, a sectoral concentration and a geographical concentration. The sectoral concentration is due to the manufacturing industry of the Amazon which produced 35.7% of state GDP in 2005, a pattern contrary to that observed in other northern states ( where the secondary sector appears to be somewhat representative). Using the Gini index calculated by IBGE to assess the sector distribution of GDP across the states, it can be noted that industrial GDP shows an index of 0.97, while the GDP for the service sector index reaches 0.85. The geographic Gini index is high because in 2005, the county of Manaus had a GDP of USD 27,214 billion and a GDP per capita of USD16,547.00. This represents about 81.6% of state GDP (R$ 33,359 billion in 2005) and a per capita income over 60% higher than that observed in the state as a whole (average of USD 10,320.00). Because of this, Manaus´s GDP was considered the largest GDP in the entire northern region in 2005, a direct result of economic activity generated from the PIM.


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It encompassed a growing service sector in the capital to seize opportunities for reinvestment of the income that was being generated. It also gave rise to a significant amount of public investment in urban infrastructure, which resulted from a surplus in county and state public revenues. Manaus can be compared with the county of Coari, which ranked second in wealth generation for the state for the year 2005. Coari had a local GDP of US$ 980.17 million and a GDP per capita of US$ 11,626.00. In other words, it’s only 2.94% of Amazonas’s GDP but with a per capita income just 12.7% higher than the average state per capita income. The explanatory variable in this case is the operation of the Petrobras oil and gas exploration compound in the Urucu Basin. Other representative counties in the state of Amazonas (Parintins, Itacoatiara, Tefé, Manacapuru, etc) were much smaller, with the largest having a GDP that did not exceed USD 455 million in 2005. • Another indicator highlighting the county of Manaus as the direct and primary beneficiary of the PIM’s economic activity is its Gross Value Added (GVA) which comes from the processing industry. It stands at USD 11.3 billion, which according to 2005 data from IBGE, places Manaus as the fourth highest Brazilian county for industry in terms of wealth production, following São Paulo (R$ 52.6 billion), Rio de Janeiro (R$ 13.7 billion) and Campos de Goytacazes -RJ (R$ 12.9 billion). However, when we observe the list of the 100 counties with the highest per capita income, Manaus is far from the first position, which is occupied by the county of Pirajuba (MG), with GDP per capita of USD 31,372.00 in 2005. This reflects the relationship between important economic activities in a city versus a locality with a small population. This data suggest that despite the mass of wealth created in Manaus by the processing industry, the ownership coefficient of the generated income is very low. In other words, there is a strong “leakage” of income created in Manaus flowing to other Brazilian and overseas locations. An example is corporate profit transfers from the manufacturing subsidiaries of the PIM to their head offices, or the external investment of local residents’ own financial resources. The challenge the PIM faces is its ability to capture and maintain the income that it generates. This requires very specific regional development strategies.

Public Finance: Collection and Waiver of Taxes In 2007, tax collection (taxes, contributions, and rates), for federal, state, and the county of Manaus reached about US$ 12.5 billion, reflecting a growth of 206% from 2000 to 2007. Among other phenomena, we can highlight the State of Amazonas (and, more specifically Manaus) as the responsible for about 65% of all federal tax revenues in the 2nd Fiscal Region (all states in the north, except

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Tocantins). A tax analysis also draws attention to the relationship between tax collections and the PIM tax waiver, which shows that R$ 1.15 is collected for every R$ 1,00 foregone in terms of waivers. This reveals the fact that, despite foregone taxation opportunities, the model presents a surplus contribution to the national public accounts.

S, T & I and Environmental Strategic Investments The economic activity of the PIM generates massive amounts of federal tax revenue that is received by state agencies at three federal levels. During the last decade, this has allowed planners to design and implement strategies focused on incubating regional income. Among these strategies, we want to point out actions taken to strengthen regional science, technology, and innovation systems (S, T & I). These actions were taken as a priority in the agenda of the direct players linked to the management of the PIM (Government of Amazonas, SUFRAMA, participating companies and research institutions). Efforts to promote a broader scope and make possible scientific and technological development are well-developed. First, the Science, Technology and Innovation Center of the Manaus Industrial Pole (PIM-CT) was inaugurated in 2002 and is aimed at developing technological skills in key areas such as microelectronics and nanotechnology-based industries. Second, the Amazon Biotechnology Center (ABC) focuses on biotechnology to support the PIM’s bio-industries sources. Both of these institutions are formally included in the Industrial, Technology and Foreign Trade Policy (ITFTP) of the Brazilian government and in the Research & Development Institutions complex. The Research and Development Institutions complex includes approximately 20 organizations, which operate several research and development lines of high tech products. Processes are all properly accredited under the Research and Development Activities Committee of the Amazon (RDACA). RDACA became involved as representative of regional and federal agencies (MCT, science and technology state ministries, business entities) whose function is to regulate the application of R & D funds provided by the tax incentive law for the production of computer goods, which is generated from the collection of a statutory 5% tax on the computer goods industry in the aggregation of technology projects in the Amazon. The number of S, T & I’s industries accredited by the RDACA to receive R & D resources from the tax on computer goods now spans 74 organizations or specific academic units (academic departments of universities, R & D centers of companies, etc.). These are distributed across SUFRAMA territory. The R & D resources returned to the computer production firms in the PIM, with supervision and control of the RDACA, have directed several programs and research projects from the R & D centers of some companies, sometimes with institutions which are external partners with the company. These external partners include the Federal University of Amazonas (UFAM), the State University of Amazonas (UEA), and


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the entire network of foundations and private institutions of regional R & D that has rapidly developed in Manaus in recent years. It should be noted that the S, T & I actions mentioned above refer to a rational use of Amazonian natural resources through the generation of linkages within the high-tech productive sector. This features a challenging (some say unlikely) alliance between the biotechnology and microelectronic-based technologies for the purpose of exploring potential regional biodiversity. This bolsters the claim made by some of the PIM’s players that the PIM represents an important model for regional development.

The MFZ Model as a cause of development Western Amazon Another strategic aspect of the internalization of the income generated by the PIM is linked to the internal development actions of the Western Amazon region. These can come from direct public investment, from government transfers, including partnerships with transfers to the county governments, state governments, civil organizations, and companies financed by the PIM. Today, the major sources of funding generated by the PIM’s enterprises for these purposes are the Administrative Services Rate (ASR), the Revenue Tax, ,the Tourism and Development Internal Fund Tax (TDIFT), and the State University of Amazonas fund fee. Administrative Services Rate (ASR) is a federal revenue source and is paid by the benefited industries and commercial enterprises for regulatory activities exerted by SUFRAMA on the entrance of imported goods and supplies. With the resources of the ASR, SUFRAMA has already invested more than USD 820 million into Western Amazonian states, counties and Macapá-Santana (AP) between the years 1997 and 2007. This represents one of the most important and perhaps the largest of all investments sources for infrastructure in the region. The investment distribution, by federal unit and by type of application, is shown respectively in Tables A1 and A2 in Appendix A. At the Amazonas state government level there are several sources of funding. These include the unwaived portion of the Revenue Tax, the Tourism and Development Internal Fund Tax (TDIFT), and the State University of Amazonas funding fee. These state revenues grew 178% in the period from 2000-2007, reaching US$ 4.0 billion in 2007. The overview of this data, as well as the data shown in Tables A1 and A2 (attached), clearly show the size and capacity of the MFZ Model as a dynamic source of income generation and ownership processes in the Western Amazon region and the Free-Trade Area of Macapá - Santana. Therefore, it does not seem logical that any public initiative (policy, program or project), whose focus is regional sustainable development can be planned or implemented without setting some sort of connection with the MFZ Model and its government or private action components.

Chapter 2 Deforestation of the Amazon in Perspective Marcelo B. Diniz José A. Mota Alexandre Rivas

The Reasons for Deforestation Many previous studies conducted in various countries have sought to identify the causes of deforestation. Although the results vary across studies, Geist and Lambin (2001) report that there is some consensus concerning the phenomena that drive deforestation. These findings show that a general pattern cannot be found. For example, Walker (1987) finds that deforestation results from a complex socioeconomic process and, in many situations, it is impossible to isolate a single cause. Angelson (1995) indicates that there is no clear definition of deforestation and no consensus estimates concerning its size or the phenomena that underlie the primary causes. Rodel and Roper (1996) suggest that deforestation in tropical forests occurs under various different circumstances, which complicates the construction of a common deforestation pattern. This leads to the conclusion that deforestation has many causes and specific causes are dependent on the location. Hedges (1997) indicates further that the factors that have influenced deforestation are different across regions, so generalizations cannot be made concerning the relative importance of each factor. The International Human Dimensions Program on Global Environmental Change (IHDP, Geist and Lambin 2001) sought to understand patterns and changes in the environmental transformation rates, focusing specifically on the driving forces that act globally, regionally and at the decision-making levels that are responsible for these changes. The authors present a broad review of existing international literature on the causal factors that are labeled as responsible for deforestation in most countries. An analysis of the primary and underlying causes, as well as their interconnections in 152 national case studies, demonstrated

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that there is regional variation in the causes and their interactions. Differences in economic and institutional factors and national policies account significantly for this variation. In classifying these different causes according to commonalities, the authors divided the causal factors into three broad groups. First, primary aggregate causes such as agriculture expansion, wood extraction and infrastructure expansion are the prominent features at the local level. Second, demographic, economic, institutional, policy, cultural and socio-political factors form the environment in which the primary causes take place. Third, there is substantial variation in exogenous variables such as biophysical forces and random events related to them. There is also a range of random social phenomena, such as war, economic and health crises, intense migratory movements that motivate and intensify the pressure on the use of existing natural resources, thus promoting higher levels deforestation. Among primary causes of deforestation is the expansion in farming of annual crops, perennial crops and livestock farming, as well as the extraction of wood. Existing and new infrastructure allows the presence of these agricultural and wood extraction pressures to occur. For example, internal migration, colonization and population resettlement programs, in addition to the factors that motivate these changes (transport logistics, trade, and the urbanization process (with its various public services)), could function as primary causes of deforestation. Underlying causes include economic, social, cultural and institutional environmental factors that limit, inhibit or encourage the expansion of agricultural activities. This level involves the entire legal system, including issues related to properties rights, which defines the behavior of private economic agents and the public sector. This is influenced by public policies targeted directly or indirectly at that sector. Also included in this category are restrictions on technological processes related to the productive use of natural resources, especially land and timber forest resources. Further, cultural factors that define household behavior, including the demographic implications of family size such as population growth, population density and the spatial distribution of population have important impacts. One additional factor is cultural values that affect the way land and natural resources are used. Economic factors, especially those linked to the level of urbanization and industrialization, also play a role in demand for and pressure on natural resources. Another important topic is the concept of environmental poverty, the direct relationship between human poverty and environmental degradation. What has been referred to as a vicious circle between poverty and environmental degradation has also been discussed by several authors, such as Reardon and VOST (1995), Cavendish (1999) and Chomitz (2007). However, similar to the first topic, the literature on this subject is still rather inconclusive.


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The Evidence for the Amazon The dynamics involved in the deforestation of the Amazon have a direct relationship with internal migration and settlement processes as well as the various forms of land use in the region. However there are very distinct features among the group of states that make up the Amazon, especially when comparing the western and eastern portions of the area. According to the Amazon Sustainable Plan (ASP), in 2004, about 62% of the Legal Amazon was covered by original tropical forest, 20% of which corresponded to the savannas and transition ecosystems and about 18% of which would have been lost or altered by human action. Different patterns can be indentified in three large regions within the Amazon based on natural characteristics and demographic processes. The first of these three regions encompasses an arc of dense population (corresponding to the states of Mato Grosso, Rondônia, Tocantins and parts of southeastern and northeastern Pará, southeastern Acre and south Amapá). The second region corresponds to the Central Amazon, which includes the western and northern regions of the State of Pará, the northern State of Amapá and the Vale do Rio Madeira in the state of Amazonas. The third region is the western Amazon, which includes the state of Roraima, and all the rest of the state of Amazonas and parts of central and western state of Acre. This regional grouping is directly and indirectly related to the various pathways of access into the region and how these means of access serve as conductors of migration processes, population growth and rural and urban settlement patterns. The population arc, for example, is directly related to the presence of a road network, with a marked increase in population density in a belt that is between 300 to 500 km² wide. (Becker, 2006). The history of deforestation, according to MIN/MMA’s numbers (2004), has at least two distinct periods, one that prevailed until about 1980 and the other that began in the 1980s. The periods are associated with temporal and spatial differences in the patterns of deforestation. In the first period the Amazonian region lost approximately 300 thousand km² of original forest (6% of the forested area). This deforestation was related to a clearing process caused by the opening of roads in areas that did not have roads and to official resettlement projects. In the second period, the clearing process was more related to individual decision-making and was triggered by alternative economic drivers, such as market forces seeking to maximize the profits associated with the exploitation of natural resources, especially cattle ranching and logging enterprises. During the 1980s, deforestation reached about 130 thousand km², whereas in the 1990s it was at 150 thousand km², and in the first years of 21st century it was about 120 thousand km². (MIN/MMA, 2004).

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In regards to the eastern Amazon (which consists of the arc of dense population and the central Amazon), the states of the western Amazon have experienced a less devastating process, with the exception of the state of Acre, which has seen more pressure on its natural resources. Table 1 shows GDP, population and deforestation contribution growth rates, in average terms between the 1980s and 2004. Table 1 – Average annual growth rates of GDP, population and deforestation– Legal Amazon: 1985 - 2003 States of Legal Amazon Acre Amazonas Amapá Maranhão Mato Grosso Pará Rondônia Roraima Tocantins

Average annual growth rate of the resident population 3,66 3,86 5,90 1,94 3,51 3,25 4,14 6,24 1,03

Average annual growth rate GDP 4,55 2,71 5,82 3,60 6,95 2,91 3,03 3,74 8,44

Average annual percentage contribution to deforestation 3,11 4,92 0,41 5,76 36,17 31,60 13,98 1,56 2,49

Source: Made from IPEADATA. Note: growth rates calculated by adjustment of exponential functions (linear in logarithms), estimated by least squares, where for the population the adjusted R² was higher than 0,90 in all cases, and for GDP was greater than 0,45.

According to Fearnside (2007) the deforestation of the Amazon takes place in a context of a plethora of causal factors with a great difference between their locations. In fact, the factors affecting deforestation in the Amazon have been under fiery debate. For some time the debate was calm with the thesis that deforestation was caused largely by the expansion of the cattle ranching frontier being driven by easy credit offered through tax incentives; and also by the positive correlation between the “creation” of pathways into the region such as roads, migration and land speculation (Reis and Margullis, 1991, Young 1998). At the same time, a positive correlation was noted between the advancement of this economic frontier in the Legal Amazon and national economic growth. However, in recent years, according to Ferreira (2005), this relationship began to change as a result of increasing deforestation despite certain stagnation in the growth of the region. This suggests that the new dynamics are linked to the export market and driven by the high profitability of the major economic activities such as livestock, logging and more recently, agribusiness.

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Cattle ranching in the Eastern Amazon1, or what is referred to as the eastern frontier2, is highly profitable from a private point of view with return rates higher than those of livestock farming in the traditional livestock regions of the country. The rate of return on an investment in livestock ranching was consistently above 10%. These values are possibly being achieved by established cattle ranchers that have capital resources in the consolidated frontier of the Eastern Amazon. In other words, recent deforestation in the Amazon is basically driven by medium and large scale cattle ranching activities and all the consequences that accompany these activities, such as logging and road construction, which generate higher levels of deforestation. Figures 1 and 2, below illustrate the growth of cattle ranching in the Amazon when compared with developed centers in other states and centers of production. Figure 1 – Total cattle herds – Legal Amazon and Rest of Brazil.

140,000,000 120,000,000 Head of Cattle

100,000,000 80,000,000 60,000,000 40,000,000 20,000,000 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Legal Amazon Rest of Brazil

Source: IBGE, 2007 – Survey of Monthly Slaughter of Cattle

1 Eastern Amazon includes the states of Pará, Rondônia, Mato Grosso, Tocantins, and Amapá. 2 Every economic frontier is, in principle, speculative and no longer is a border when the process no longer has this feature. At the same time, the consolidated frontier does not constitute a boundary in the strict sense. But as the practice of causing agents is expansive and, in the specific case of the Amazon, it takes advantage of the conversion of forests into agricultural lands. In this case, they are situated on the edge of the border and the term “consolidated” simply differentiates them from agents with more speculative strategies (Margulis, 2004).

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The growth of cattle ranching is attributed to a number of factors including the return on investment, which is relatively higher than in other economic activities that can take place in the region. Indeed, the research of Piketty et. al. (2004) concerning causal factors in Eastern Amazon shows that the economic environment in this region is very conducive to livestock expansion in the region. The number of cattle as well as their growth rate is higher than what is typical for Brazil. Figure 2 – Annual growth rates of the cattle herd.

11.00 10.00 9.00 8.00

Annual var. %

7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 -1.00 -2.00 -3.00 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Legal Amazon Rest of Brazil

Source: IBGE, 2007 – Municipal Livestock Research.

The theory that the high profits seen in cattle ranching has been stimulating the expansion of deforestation in the Amazon is also supported by Margulis (2003). In fact, for this author, livestock (most of all medium and large scale operations) is the main deforestation agent. This expansion has been a continuous process with considerable momentum. Based on the high return rate observed for the livestock sector (even excluding the proceeds from timber sales on cleared land), this growth was above 10% in different parts of the arc of deforestation, which was much higher than other areas of the country.


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Thus, even without government subsidies (which were eliminated by SUDAM in 2002), the profitability of cattle ranching was the propelling factor that fed the momentum of deforestation. Factors that have contributed to such high profitability include favorable geological conditions (despite the high temperatures), high rainfall, and humidity which all work to ensure good pasture and high productivity per hectare. In addition the availability of cheap land that requires little labor and the low cost of unskilled labor result in a low cost to the producer. There are other subsidiary factors that enhance the effect of the livestock growth. For example, land speculation and the opening of roads (occurring jointly with logging activities) are processes that cause deforestation because of the resulting cattle ranching. The very existence of these activities relies on the opening of transportation routes through the Amazon. Moreover, the effect of land speculation on the agricultural frontier is directly proportional to the lack of state government oversight. Although the opening of roads was implemented more for exogenous geopolitical goals, the roads themselves were responsible for these high rates of deforestation. As a matter of fact, the profitability of livestock production in turn leads to pressure for the opening of more roads, created by the ranchers themselves in order to lower transportation costs. At the same time, the effect of the exogenous roads (those emerging for geopolitical causes) has a considerable effect on deforestation through the same set of forces as the creation of endogenous roads in order to maintain the profitability of the livestock sector. For these reasons, subsidies and credits from the government (which showed a decrease in the 1990s) could not be considered as factors in explaining the deforestation process. Referring specifically to the correlation between the infrastructure provision (such as roads) and population growth, Weinhold (2001) and Reis (1996) conclude that there is more empirical evidence to support that urban population growth leads to infrastructure development and not vice versa. Consequentely, they conclude that improvements made to infrastructure in the Amazon were unlikely to be the major cause of deforestation in the region. These improvements seem instead to favor urban population growth. On the other hand, urban improvements such as provision of electricity and potable water break the need to exploit forests as a source of energy and other services, so that urban infrastructure improvement can help to mitigate the impact of urban areas on the environment. In addition to the direct and indirect effects of the cattle and timber activities on deforestation, highlighted above, other factors are cited as important by Fearnside (2007). These factors include deforestation for the purpose of maintaining ownership of an area and protecting investments against settlers or expropriation from the government (as vacant or unutilized land); forms of deforestation used in respect to money laundering, especially when funds are derived from illegal sour-

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ces such as drug trafficking, corruption, sales of stolen goods or tax avoidance; and loss of the vegetative coverage as in the case of inundations caused by hydroelectric dams. In Brazil these hydroelectric dams would have already added about 10 million hectares to the loss from deforested areas (2% of the total land area of Legal Amazon and 3% of the original forest). In order to test the effects of these different causes, many authors have used econometric models of differing scopes as in the cases of Garcia and Moro (2006) and Andersen et al. (2002). The first authors used a methodology of spatial deforestation modeling, utilizing three models aimed at assessing deforestation from the years 1997 to 2001. They used a sampling of 339 districts and showed that the growth of deforestation during this period was associated with a spatial correlation. The dependent variable in the first model was the areal percentage of the original cleared forest, per district, in 1997. In the second one, the areal percentage of the original cleared forest, per district, in 2001; and in the third one, the difference between the area percentage of the original cleared forest in 2001 and 1997, per district. The variables tested by the authors included: 1) the average distance of paved highways in the district; 2) the density of the urban network; 3) number of cattle per km²; 4) crop value per km²; 5) percentage of the land area under crops; 6) population density; 7) rural population density; 8) rural population density adjusted by the land concentration index; 9) Net migration rate (1995/2000); 10) migration overbalance (1995/2000); 11) migration overbalance per km²; 12 ) migration volume; 13) migration volume per km²; 14) natural population percentage per district, 15) population employed in agriculture, 16) employed population; 17) total population; 18) employment percentage in the agricultural sector; 19) population employed in the agricultural sector per km²; 20) income percentage from the agricultural sector; 21) income from the agriculture sector; 22) per capita income in the agricultural sector per km²; 23) protected area; 24) protected area percentage; 25) demographic concentration index; 26) governance index; 27) economic development index; 28) agricultural infrastructure index; 29) agricultural and forest activities index; and 30) socioeconomic development index. The results of the model, after adjustment for outliers, led to some interesting conclusions. The variables which proved to be statistically significant included average road distance, number of cattle per km², rural population density, per capita income in the agricultural sector per km², net migration rate 1995/2000, governance index, and percentage of the municipal planted in crops in 2000. To some extent these variables corroborate those tested by Andersen et al. (2002), in which the authors used local data from the period 1970 to 1996, and data from the Agriculture Censuses of 1975, 1980, 1991 and 1996. The authors arrive at a “reduced form model, beginning with a very broad set of potential explanatory variables, and then let the data reveal which variables should be included in the model. This was done using a strategy called “general-to-simple”, which has

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the advantage of controlling the omitted-variable bias. Other studies on this subject are listed in Table 2. Table 2 – Econometric Models for Deforestation. Studies IPCC (2000)

Explanatory Variables Lagged Population until 25 years old

Comments

Geiste and Lambim (2002)

Agricultural expansion Logging Expansion of infrastructure

Based on 152 case studies

Angelsen and Kaimowitz (1999)

Accessible land (by highways) Prices of agricultural products Wood prices Wage levels Employment in rural areas Employment in urban areas Population Growth Poverty National income External debt Economic reforms

Summary of 140 models

Vicent and Yusuf (1991) Vicent and Ali (1997)

Population density Population growth Income growth rate

Vicent and Ali (1997)

Spatial density Quality of the soil, forests, etc. Work Access to markets (infrastructure) Agricultural Activities Highways expansion

Reis and Margullis (1991)

Population density Size of the cattle herd Areas of agricultural crops Timber extraction Roads

Young (1998)

Credits Changes in agricultural prices Land prices Rural wages

Ferraz (2000)

Production Price Input Prices (rural wage and land) Highway Length Agricultural credit

Source: Rodrigues (2004)

Used a logistics curve model

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According to CEPAL (2007), about 98% of the tropical rain forest in the state of Amazonas is still preserved, and the deforestation rates have been decreasing. Currently, more than 22% of the total area of the state is in officially preserved areas, either federal or state. In addition, there are 45.7 million hectares of indigenous lands. The deforestation process in Amazonas is very irregular. In the south of the state, particularly in certain districts of Apuí, Humaitá and Lábrea, there have been higher and increasing deforestation rates in comparison to the other regions of the state. An important feature found in the state of Amazonas was the industrialization strategy adopted in the state by the PIM. This strategy has resulted in the state having “high-levels of nature and biodiversity preservation” (CEPAL, Op cit.). The results show that the industrial model has generated positive externalities, in addition to benefitting the economy and other states in the range of SUFRAMA’s tax incentives. These positive externalities include environmental externalities related to the avoided cost of deforestation and other harmful actions to the Amazonian environment. The fact that the positive effects of the PIM extend past the sphere of the specific local economies in which the programs were implemented is an important proof of the national and even international benefits of the incentives. Some of these externalities are related to avoiding the release of carbon dioxide (CO2) and methane (CH4) into the atmosphere due to deforestation, which would otherwise increase global warming. Thus, it is very clear that the characteristics of the prevailing production structure and the regional economic drivers are significantly linked as causes in explaining the temporal process of deforestation time. Two contrasting cases are illustrative, the state of Amazonas and the state of Pará. In the first case, the industrialization model and the vertical integration of the production change in the Manaus Industrial Pole that was implemented in the State of Amazonas have created a set of economic drivers that is not linked linked to the more intensive extraction and use of the existing natural resources base, particularly forest resources. In the second case, as the productive structure was assembled and organized from the exploitation of forest and mineral resources, it created a perverse economic environment in which the land use and the appreciation process are linked to an overexploitation of the natural resources. A system has been implemented based on the direct link between timber exploitation and the advance of the agricultural frontier. Rivas (1998) was the first to estimate the PIM’s effects on the pattern of deforestation in the State of Amazonas. His results suggest that the growth of the capital stock has been negatively impacted by the rate of deforestation. The author concluded that the PIM is a factor of deforestation inhibition, since capital is the basic input for the industries, which have received incentives for the accumulation of capital. Referring exclusively to the deforestation problem in the State of Ama-


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zonas, in the period from 1980 to 1985, he uses the following variables to explain deforestation. Deforestation is measured with a proxy variable as the sum of the areas (Km²), with perennial crops, pasture (livestock area) and fallow area. The stock of deforestation in 1980 is measured using the same proxy. Other variables include actual GDP range between 1980 and 1975, road density (proxy of government consumption) in km / km² calculated by dividing the extension of federal and state paved roads and non-paved area of the districts, electric power consumption by the industrial sector (proxy of the physical capital stock) in Kwh, and the number of students enrolled in high school (human capital proxy). The empirical results of the study show that deforestation (the dependent variable) was influenced by the many variables. These include the stock of the deforested area (negative), GDP variation, (positive), road density (positive), the proxy variable for physical capital (negative), and the proxy variable for human capital (positive). In addition to the variables identified above, the population issue is a relevant factor in understanding deforestation. Therefore, the following section deals with migration in the state of Amazonas.

Chapter 3 Migratory Movements in the State of Amazonas Peri Teixeira

During the second half of the 20th century, migration into the state of Amazonas, from both other parts of the Legal Amazon and other

locations in Brazil, experienced a markedly different pattern than migration into the other states of the Legal Amazon of Brazil. The most important aspects include a lack of immigration from other locations into rural areas of the state of Amazonas, and the significant population and economic growth of the state capital, Manaus. As in the case of other Brazilian states, including northern states, Amazonas has seen a quickening of the urbanization process throughout the entire extent of the state. Demographic change in Amazonas is similar to what happened in Acre with regard to a lack of significant migration to rural areas. In that state, the lack of projects for rural settlement had limited migration to that state, and was characterized by rural exodus and consequent growth of major cities. A similar process has occurred in Amazonas through migration from rural areas of the state. However, a significant portion of rural migrants coming from small communities were emigrating to both more prominent local towns and to the rapidly growing state capital. Thus, the state did not show the sub-regional migration patterns that occurred in other states of the region, of which Rondônia and Pará are the most important examples. There, the various programs that focused on the expansion of the agricultural frontier and the settlement of rural areas during the 60’s provided a significant increase in rural population through the 80’s. During this time, Amazonas has gone through a significant urbanization process, followed by emigration from rural areas, whose growth

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has been almost nonexistent1. However, unlike the state of Amazonas, which shows no decrease in its rural population, Pará and Rondônia, had initially huge increases to their effective rural areas, followed by declines in rural populations since 1990 (Teixeira, Brazil, 2008). Despite being one of the states crossed by the Transamazonian highway, the state of Amazonas was not demographically affected by the construction of the road, in contrast to the state of Pará. In Pará, the migration flows from outside the state have contributed to an unprecedented growth in rural population, in addition to having also fueled urban growth (Martin and Turchi, 1988). From a social standpoint, the conflicts for land possession, which began to occur in the area of the highway’s influence and have lasted for more than three decades, are far from being solved. The state of Amazonas has not seen the same conflicts because the state’s portion of the Transamazonian highway was never completely finished.2 As noted above, the more prominent feature has been a strong and continuous internal migration from rural to urban areas. In the rural counties, migration from other states with growing population has been reduced in most cases. This migration, from foreign origin, has the capital of Amazonas as a preferred destination and, to a lesser extent, the other urban areas within state boundaries. In general, the main inter-state migration flows into the different counties of the state of Amazons depend on the proximity of these counties to the areas of origin of the migration. Manaus is a notable exception to this relationship. Between 1995 and 2000, the most significant groups of immigrants who reached the border counties of Alto Solimões came from neighboring countries, such as Colombia and Peru. This applies, for example, to Tabatinga, where 419 of 1,357 immigrants in that period had come from Colombia. For the same period, almost all interstate immigrants from Envira and Guarajá, neighboring counties to Acre, came from that state. The number of these immigrants was larger the number who came from the rural area of Amazonas (21% and 32%, respectively, of the total local immigrants). Similarly, the main portion of the interstate migrants living in counties situated east of the state originated in Pará. The same phenomenon occurred with the counties located in the south, where the main population movements came from Rondônia.

1 Annual growth was below 0.5% a year, between 1970 and 1991 and between 2000 and 2007. The largest growth noted in the 90’s (1.6% pa) may be due to the reduction in migration towards Manaus, which Free Zone had a decrease in 44% of the employed workforce between 1990 and 2000 (SUFRAMA, 2005). 2 In the State of Amazonas, in the drivable part of the road, INCRA (National Institute for Colonization and Agrarian Reform) has implemented, through a precarious way, an initial settlement, which today is the city of Apuí on the border with the State of Mato Grosso. This occurred in the late 70’s. This area has seen conflicts between settlers and squatters or even squatters against old farmers and founders of the county (Brito, 2004).


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The same is not true for the counties located in the center of the state, far from foreign borders. As already seen, most counties receive very few interstate migrants. However, several counties close to Manaus are the destination of a large number of immigrants from other states and regions. They are usually in the micro-regions of Manaus and Rio Preto da Eva. These two micro-regions have been showing, especially in the last two decades, high rates of population growth. Two micro-regions are particularly important with regard to cross-border migration. One is the aforementioned Tabatinga with migration from Colombia and the other is Sao Gabriel da Cachoeira. These are both border counties located respectively in the micro-regions of Alto Solimões and Alto Rio Negro. The contribution of migration on local population growth in a given period is measured by the net migration (the difference between the amount of immigrants and emigrants) and the net migration rate3 nover the period. Table 1 shows the information needed to calculate this contribution for the 20 counties in Amazonas that had the highest proportions of immigrants in its total population at the time of the last census (2000).

3 The Net Migration Rate between 1995 and 2000 is given by the ratio between net migration of people older than five years old in this period and the population of this age group in 2000.

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Table 1: State of Amazonas 5-year-old or older residents Number of immigrants and emigrants, net migration and Net Migration Rate (between 1995 and 2000) of the counties with the largest proportions of immigrants on the total population in 2000. Counties

Immigrants

Emigrants

Net Migration

Net Migration Rate

Rio Preto da Eva

6.242

852

5.390

35,6

Pres. Figueiredo

5.992

1.824

4.168

27,9

Apuí

4.198

1.215

2.983

25,6

Iranduba

3.934

981

2.953

10,7

Novo Airão

1.222

563

659

7,9

Tabatinga

3.914

2.030

1.884

5,9

Silves

821

507

314

4,8

Manaus

113.345

63.592

49.753

4,0

Guajará

847

492

355

3,3

Boa Vista do Ramos

903

628

275

3,1

Caapiranga

566

398

168

2,3

Urucará

1.136

819

317

2,0

Itapiranga

691

577

114

1,8

Autazes

1.679

1.468

211

1,0

Humaitá

2.013

2.373

-360

-1,3

Anamã

451

539

-88

-1,6

Itacoatiara

5.352

6.605

-1.253

-2,0

Tefé

3.950

5.421

-1.471

-2,7

Juruá

511

679

-168

-3,1

Boca do Acre

1.620

3.389

-1.769

-7,7

Source: Microdata from 2000 demographic census. Prepared by the authors.

The counties with the three highest net migration rates (Rio Preto da Eva, Presidente Figueiredo, Apuí) are among the most populous of the state, but have gone through remarkable growth towards the end of the last century, approximately 10% per year, which is due to the meaningful net migration that they had for that period. In fact, in just five years (1995/2000), these net migration numbers accounted for significant portions of the population of the two counties. In the case


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of Rio Preto da Eva, net migration accounted for 35% of its population in 2000. This growth rate has played a major role in recent years, as the census showed in 2007. These cities have different economic bases, such as mining and tourism (Presidente Figueiredo), agriculture (Rio Preto da Eva), agriculture and livestock (Apuí), and have passed through a significant development stage, with predicted continued growth in the coming years. Among the 14 counties that had a positive net migration between 1995 and 2000, 12 had experienced this throughout the 1990s. One intriguing exception appears in the data of Novo Airão, which showed one of the highest migration rates in the period 1995/2000, and had the second largest drop in population of the state (45.3%) in the 1990s (Santa Izabel do Rio Negro lost 46% of its population during this period). Considering the identification of this strong discrepancy and assuming the good quality of data on migration in the 2000 census, we can not discard the hypothesis of demographic over-counting in Novo Airão through the census of 1991. The suspicion is strengthened by the fact that the county’s population has started growing again between 2000 and 2007. On the contrary, the counties of Fonte Boa and Barcelos each doubled its population between the censuses of 1991 and 2000, despite being characterized in this period with zero (Boa Fonte) or a very (Barcelos) net migration. In the absence of a change in land cover to justify this demographic dynamism, the explanation should be in the net migration rates, which were not positive. In both cases, the population over-counting may have occurred in the 2000’s census. The same phenomenon of atypical population growth seems to have occurred with Coari, Parintins, and other counties with annual growth rates above 5% and negative net migration in the same decade. All those would show net migration growth rates of close to zero between 2000 and 2007, which reinforces the over-counting hypothesis in 2000.

Manaus’s migration flows from the interior of the State of Amazonas There is no record in the State of Amazonas’s rural area of significant migration between counties, as shown by the population censuses of 1991 and 2000. The most important intra-state movements are the ones that occur toward the main sub-regional poles of the state, Parintins, Tefé, Itacoatiara, Coari, and others, beyond those that are close to Manaus. There are also relatively intense migratory flows in several areas of the state. These include the three counties located along the border with Peru and Colombia (Atalaia do Norte, Benjamin Constant and Tabatinga), the several counties in the south of the state (see Teixeira e Brazil, 2008) and a few counties with an intense demographic and economic development (Presidente Figueiredo, Rio Preto da Eva and Apuí). In addition to the migrants from within the state, these cities are destinations of migration originating in other regions of Brazil.

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Even considering that their current population contains significant numbers of migrants, the most important counties of the state of Amazonas are characterized by a clearly negative net migration with Manaus. This indicates that, apart from residents born in the counties themselves, some of their immigrants will remigrate to the state capital in the future. Manaus is a city that has received a large number of migrants from all over the country since the 1960’s. Migration to this capital was very intense in the 1970’s and 1980’s but has been less intense recently. As indicated by the 2000 census, the current immigrants come from rural areas, primarily from other states. Between 1995 and 2000, out of about 113 thousand migrants that the capital received, 72 thousand were from other states, with 44 thousand of these immigrants from the north of Brazil and 28 thousand from the rest of the country. Table I illustrates that the capital of the state, despite having grown by about 400 thousand inhabitants between 1991 and 2000, did not have a high net migration rate in the last five years of the twentieth century. This implies a relatively reduced migration balance compared to those observed in previous decades and in several other counties in the state of Amazonas. As a matter of fact, the migration to Manaus began to fall after the extraordinary growth in the 1970’s, declining with the progression of time. The annual population growth rate of Manaus has been falling from 7% a year in (1970’s) that decade down to 2.3% between (2000 and 2007). The cooling of the migration process across the nation, the reduction of the relative migrant stock in the rural part of the state, the discontinuity in the county’s economic development and the general fertility decline are all contributory factors in the slowing of the population growth in the capital of the state. The great mass of migrants from the rural areas who leave their towns, prefer to go towards the city of Manaus. Between 1986 and 1991 the amount of migrants who went to Manaus and the amount of those moving to rural areas of the state (29 thousand and 28 thousand, respectively) were almost equal. Ten years later (1996/2000), migration directed to the counties of the State’s rural areas began to overtake the migration towards Manaus. Out of 100 thousand intra-state migrants in Amazonas, the capital received 41 thousand, and the rest went to the other towns in the state. It’s true that the migration towards Manaus has increased relatively, but its increase has been shown to be significantly lower than that found in the rural area. The number of migrants in the state of Amazonas increased from 57 to 100 thousand between the last five years of the 1980s and the last five years in the 1990’s show, with no doubt, dynamic demographic changes in the state. However, the decline in the importance of Manaus in the migration process could indicate that the capital has lost power in terms of attracting population. Most likely, the cause of this phenomenon is found in the labor market crisis associated with trade liberalization and the opening of the Brazilian economy in the early 90’s, which had dramatic effects on employment in Manaus.

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In the second half of the twentieth century demographic evolution of the state of Amazonas has been characterized by significant urban growth, accompanied by the loss of rural population, which translates into low growth rates for this last demographic stratum. Both the accelerated urbanization and the gradual emptying of rural areas are characteristic of the Brazilian population as a whole, although they have slowed down in the states of the Amazon region. However, in the state of Amazonas, contrary to what happened in other northern states, less migration was observed into rural areas. This led the respective populations to grow substantially (1970’s and 1980’s), with subsequently strong falls. At the same time and for the same reason, Amazonas did not have, except for Manaus, areas where the population could become denser by the proliferation of small population cores that would become cities of small or medium size, as happened in Pará or Rondônia. Amazonian demographic processes spawned a unique and great metropolis, as well as increased urbanization in the interior of the state. This has not resulted from immigration from outside the state, but with migration from rural to interior urban areas. Although the growth of Manaus has contributed to a reduction in rural population of the state, the rural-urban migration to the capital cities of the various counties of the rural area was stronger than that from the rural areas to Manaus or from small cities to Manaus, as illustrated in Table 2. As it can be noted in this table, the increase in the urban areas of the interior of the state was inferior to that of Manaus in the 70’s, when the capital population doubled. However, this trend has reversed since the 1980s and is still being observed in the initial years of the 21st Century. Considering that migration was slowed between the rural communities, we are forced to conclude that urban growth in the interior of the state was due, and continues to be due, largely to rural to urban migration within these interior counties of the state of Amazonas. Table 2 – The State of Amazonas, county of Manaus and interior area of the state. Evolution of the population growth rates between 1980 and 2007.

Area

1970/80

1980/91

1991/2000

2000/2007

Urban

Rural

Urban

Rural

Urban

Rural

Urban

Rural

Amazonas

7.7

0.4

5.2

0.4

3.8

1.8

2.4

0.5

Manaus

7.9

-2.7

4.6

-12.6

3.7

7.0

2.3

1.1

Interior

7.2

0.5

6.6

0.7

4.1

1.7

2.7

0.5

Source: IBGE. Microdata of demographic census

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Unlike almost every state in the north, the rural areas of the Amazon, although losing population through emigration, have not at any time seen a drop in the absolute values of their populations. This can be seen in the table, by the positive growth rates over time. Those who have migrated from the Amazonian rural areas have the urban centers of other counties as their preferred destination, swelling the local population, and the city of Manaus remains a less important destination. However, the demographic importance of the capital in the state, which was going through a substantial growth period through the end of the 80s, continued in the following years. Thus, from 48.1% of total statewide population in 1991, the population of Manaus grew to represent 49.9% in 2000 and 51.1% in 2007. It would not be an exaggeration to raise the hypothesis that the reduction of migration to the capital, from the 90’s, can be explained by a greater absorptive capacity of the population from the rural towns. Some policies and initiatives taken by the federal, state and municipal governments to attempt to improve living conditions within the rural area may be contributing to the increase of this capacity.

Part II Econometric Models

This part of the book presents a series of models developed to test the hypothesis that there is a positive effect of the PIM on the state of Amazonas’s forest. Thus, it initially presents a behavioral mathematical model developed with basis on the economy of the Pole. Then a correspondence analysis is developed, in which some of the factors observed in the behavioral mathematical model, among others, are assessed as potential candidates for inclusion in the econometric analysis. After this segment, a model that tests the causality of variables that lead to deforestation is developed, as well as an analysis of deforestation clubs of districts. These two analyses are developed for the north region and the state of Amazonas. Finally, two models, one based on cross-section data and the other on panel data estimate the potential existence of the “PIM effect”.

Chapter 4 A Mathematical Behavioral Model of the Manaus Industrial Pole James R. Kahn

The economic development of the Manaus Industrial Pole can be considered as one of the most interesting economic phenomena of the post-World War II era. Over the course of several decades turned from a collapsed economy after the second rubber boom, into in a global manufacturing center. Clearly the economic incentives associated with the PIM have led to exceptional economic growth, to the creation of quality jobs and to an improvement in the quality of life for both Manaus and surrounding areas. However this impact itself is not necessarily sufficient justification for the continued existence of incentives. If these incentives had not existed, much of the economic growth could have still occurred in other regions of Brazil. Although a strong argument could be made that the reduction of economic disparities between the Northern and Southern regions of Brazil is of the national interest, it is efficacious to examine the impact of these incentives in the context of their total social benefit to the nation as a whole. Thus, where available data permitted, it was sought in this section to measure whether the benefits of the economic incentives are more encompassing than the increase in GDP that the incentives have created. An examination of the quality of life and economic vitality of Manaus and the surrounding region and of how this has changed the life of the PIM suggests preliminary evidence that the Industrial Pole has been very successful in transforming this region for the better. However, many people in Brazilian society have questioned the wisdom of PIM, arguing that it promotes an inefficient transfer of wealth from the south to the north of Brazil. There are several ways to think about these concerns. First, the transfer of wealth associated with the PIM occurs in the context of many other transferences of wealth, including a large flow of benefits such as raw materials that are extracted and then sent to the southern and southeastern Brazil, where there is value added. One cannot isolate one policy in this regard and then say that it’s an inappropriate transfer of wealth. Second, the transfer of resources or wealth in search of greater equity is a legitimate function

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of the government. Third, such a transfer may be economically beneficial in that the incentives are creating net benefits for Brazilian society as a whole and that it is not simply a transfer of benefits from one region to the other. One of the objectives of this study is the demonstration of this third response. Economic policy is often regarded as a search for Potential Pareto Improvements, the search for changes that can raise the well-being of one component of society by a great amount than the decline in well-being in other parts of society. For example, a well structured and appropriate tax on carbon is considered by many economists as an overall Potential Pareto Improvement. Although the oil industry and those industries that rely heavily on fossil fuels are penalized by a carbon tax, society as a whole will be better off as the impacts of global climate change will not be as severe due to the reduction in the use of fossil fuels. In other words, this study examines the question of whether the continued existence of the PIM constitutes a Potential Pareto Improvement for the Brazilian nation as a whole. An important analogy for the PIM could be seen in the United States during the Great Depression when the Tennessee Valley Authority (TVA) was founded to create economic growth in an area of the country which had little economic activity. The existing economic activity was one dimensional (coal extraction) and people in the region did not enjoy the same quality of life as the rest of the country. It is interesting to note that Tennessee is the sister state of Amazonas and Manaus is Knoxville’s sister city. McCormick (1992) wrote about social and economic similarities between the pre-PIM Amazon and pre-TVA Tennessee. The Tennessee Valley Authority was slightly different from the PIM in that it relied more on direct investment (mainly in the construction of hydroelectric power plants to boost economic development) than in economic incentives as a basis. Nevertheless, the economic transformation created by the TVA was significant and allowed the region to become a major part of American society, similar to the way in which the PIM has transformed Manaus and its surrounding area. While PIM has had a transformational impact on the region, those who oppose the transfers associated with the PIM may argue that these industrial development benefits would have occurred in other regions of Brazil if they had not occurred in Manaus. Nokia, Honda, Philips and other companies could be located in Sao Paulo, Fortaleza and Rio Grande do Sul if they had not been located first in Manaus. This leads to two important questions. The first, which is not part of this study, is whether they really would be located in another part of Brazil if they had not been located in Manaus. In fact, if they had not been located in Manaus, they could have been located in Chile, Venezuela or Argentina or in countries in other regions such as Mexico, the United States or China. However, the determinants of industrial location are not the main focus of this work. The second issue, which is the main focus of this work, is whether there are other social benefits (that accrue at the local, regional, national and global levels) from the presence of the PIM in Manaus. Our working hypothesis, based on the original study of Rivas (1998), is


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that the PIM has led to a reduction in deforestation in Manaus and that the economic incentives associated with the PIM have reduced incentives for deforestation in those areas impacted by the PIM. There are two broad categories of these potential social benefits. The first can be called a “direct impact of foreign investment”. These are the benefits generated if the incentives lead to direct foreign investment that would have been located outside of Brazil, if such incentives did not exist. This effect of direct foreign investment could be generated both by direct and indirect causes. The direct cause would occur if the incentive itself could provide the stimulus for the companies to locate in the PIM. The indirect cause would occur if the incentives could alter the business environment (labor quality, public infrastructure, general quality of living) in such a way that the area of Manaus became a preferred location for industrial activity. The second may be called “positive external effects”, and would occur if the economic activity associated with the PIM created positive externalities or public goods, such as reduced deforestation. While we believe that the direct impact of foreign investment is present, the existence of such an effect would be difficult to confirm through empirical testing. This confirmation would require primary data generated by questionnaires to firms located in the PIM, firms located in Brazil but not in the Pole, as well as firms located outside of Brazil. Then, a multinomial logit analysis could be conducted to identify the determinants of the industrial location. This type of data set does not currently exist and the creation of this set is beyond the scope of this project. Consequently, this research will focus on testing the existence of the second effect, which is the existence of positive externalities associated with the presence of this economic activity. Specifically, it will test the hypothesis that the economic incentives have had a significant impact in reducing deforestation pressure in the vicinity of PIM.

Location Analysis Most economic models do not have a spatial dimension. For example, traditional growth models, production models, and supply and demand models do not have spatially defined variables, although the models are defined for a specific market or geographic region. However, this lack of spatial consideration in many economic models, indeed, implies a lack of literature in which to base the conceptual models of social benefits of the PIM that are taken into consideration here. A small but very strong body of literature has been developed to address the spatial forces and their impacts on economic behavior. It began with the classic work of Von Thünen (1966), continued by the work of Muth (1969) and Mills (1972) on the monocentric urbanization model, and followed by the regional science literature (eg Harris, 1985), where critical nature of location has been amply demonstrated.

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Quite simply, spatial and location characteristics are important determinants of the comparative advantage of a region. The comparative advantage of a region is an important determinant of the economic activities of a region, and the economic activities in a region are important determinants of the quality of living found in a region. The quality of living can then in turn influence the comparative advantage for feedback effect, as a better quality of living can make a region a better alternative for investment. For example, the higher the quality of living in a region, the easier it is to attract management and the technical labor force of a business. This chain of influences is represented in a flow diagram shown in Figure 1. Figure 1 – Characteristic of location and comparative advantage

Locational Characteristics

Comparative advantage

Mix of economic activities

Quality of living

The economic models developed by Von Thünen and others focused on transport cost as a determinant of economic activity. These models were constructed in the context of a single urban center, but the importance of transportation cost remains central even in the context of continental- sized region such as the Amazonia. In fact, the vast distances and the lack of transportation infrastructure in the Amazonia magnify the importance of the transportation costs. In remote regions such as the Amazon that are distant from markets, comparative advantage leans toward extractive goods for two primary reasons. First, extractive outputs do not require extensive use of manufactured inputs. These inputs are often associated with very high transportation costs. Second, once extractive products (such as iron ore, grain, and wood) are loaded onto ships or barges, they have relatively low marginal transportation costs since the fuel cost per unit of product is relatively low. In contrast to this, manufactured goods tend to have high marginal transport costs because their special handling needs prevent them from being treated as bulk goods, generating relatively high fuel cost per unit of output per kilometer. Given that that the comparative advantage of the Amazonian region is found in extractive activities, one would expect that entrepreneurs would invest in these types of activities. Although the initial wave of deforestation was caused by the immigration of small scale farmers, more recent deforestation has been caused by


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extractive activities on an industrial level, such as soybean cultivation, cattle and industrial logging. Although one can explain the difference between deforestation rates in Amazonas and in the peripheral states in the 70’s and early 80’s as being generated by the migration to the periphery states, it’s not possible to explain the differences in the 90’s to the present in this fashion. As it will be seen in the following sections, there is an apparent relationship in the State of Amazonas between the PIM and its low deforestation rates. However, before testing the hypothesis suggested, it is necessary to explore the behavioral mechanism by which the PIM might impact deforestation. The discussion above leads to the question about how the PIM has affected comparative advantage, which will be investigated in the following section.

Capital markets and comparative advantage The impact of PIM on the choice of economic activities has to do with the interplay between capital markets and comparative advantage. The economic incentives change the competitive advantage so that it influences the choice among investment options. The options chosen lead to less deforestation than the options that would have been chosen in the absence of PIM. First, it is important to note that the major multinational companies that were located in Manaus have complete access to capital, either from retained earnings or share issues or from loans from global credit markets. They make their investment decisions based on conventional criteria, such as the risk and expected return rate (after taxes). Although the high transportation costs associated with location in the Manaus region would lower potential rate of return, the adjustment of the post-tax rate of return because of reduced tax liability compensates for the high transportation costs and tips the comparative advantage of the Manaus region away from extractive resources1, towards manufactured goods. Moreover, agglomeration effects have developed in the Manaus region. Agglomeration effects refer to a set of economies of scale that exist when a large number of similar industries are located in the same area. The labor specialization, specialized public infrastructure, and the development of local industries that supply inputs, reduce production costs and continue to define the comparative advantage towards manufacturing. It is through the development of local industries that externalities on the Amazon forest begin to develop, because they represent a diversification of investment capital away from extractive industries and towards the manufacturing and service industries that support the multinational manufacturing companies. 1 Of course, if there is a large deposit of ore available to be extracted at relatively low cost (such as the case in Pitinga), the comparative advantage of the region surrounding the mineral reserve is in mining.

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Although one can assert with great certainty that the multinational industrial firms have open access to capital markets, the same cannot be said in respect to local entrepreneurs. The isolation of Amazonas from global credit markets and very high interest rates associated with the Brazilian credit market imply that there is a limited amount of investment funds in the Amazon and investment opportunities compete against each other for these funds. The agglomeration economies and the goods and services demanded and created by multinational enterprises (and large Brazilian ones) in the PIM create non-extractive investment opportunities in Manaus that do not exist elsewhere in the Amazon. The diversion of investment funds in activities that support manufacturing in Manaus reduces the number of deforesting activities in the region. Figure 2 illustrates the change of events that occur when the comparative advantage favors extractive activities, as a consequence of high transportation costs. Investments are made in extractive activities, such as logging and agriculture that lead to deforestation. Deforestation and extractive activities further steer investment activities, as the access provided by deforestation, connecting roads, grain depots and other agglomeration and external effects associated with extractive activities and further tip comparative advantages toward the extractive activity, creating creates a well-observed notion that deforestation leads to even more deforestation. Figure 2 – Investment decisions and feedback effects in areas outside of the PIM

Locational characteristics favoring the extractive activities

Investment Decisions Changes in locational characteristics

Extractive actities

Deforestation

In contrast, the PIM area, manufacturing activities provide for more manufacturing activities, as well as supply of inputs from non-extractive services in support of manufacturing activities. This is illustrated in Figure 3, where the manufacturing activities spawned by the PIM’s economic incentives have directly spur investment decisions into industrial supply and service activities. These agglomeration effects also provide an indirect route by which the incentives further modify comparative advantage and the investment decisions in favor of non-extractive activities. Furthermore, there is a further feedback, as industrial service activities intensify the agglomeration effects. At the same time, the reduced deforestation pressure is associated with less opening of the forest, thereby reducing potential access and the ability to engage in activities of further deforestation.


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Figure 3 – The investment decisions and feedback effects in PIM

Low rates of deforestation

Economic incentives

Manufacturing activities

Investment decisions

Agglomeration externalities

Industrial supplies and service activities

Although the above diagram shows a complicated flow of cause and effect relationships, the intuition underlying the diagram is very simple. An entrepreneur located in PIM is more likely to invest in non-extractive activities because the PIM environment makes investing in non-extractive activities more attractive than investing in extractive activities.

Formal Economic Model The seminal treatment of the impact of the economic incentives of PIM on deforestation was conducted by Rivas (1998). Rivas developed a model that looked at the problem of maximizing societal consumption, where deforestation and land were an input to the production process. A principal assumption of the model, (which was to be estimated with data primarily from the 1970s and 1980s) was the social planner did not include the benefits of intact rainforests in the optimization process, except to the extent that forests contributed to the production of GDP. Rivas formulated an optimal control model, maximizing consumption, but subtracted the economic incentives from consumption, since these incentives represented a reduction in consumption elsewhere in Brazil. The maximization took place subject to several constraints, including one that incentives remain above a minimum level to induce the development of the Free Trade Zone which took place during the 1970s and 1980s. Rivas used both Cobb-Douglas (Log-linear) and quadratic production functions. He then solved the optimization problem for the optimal level of deforestation. He found that increasing incentives could either reduce or increase deforestation, depending on the interaction with other

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inputs. Since the solution of the theoretical model indicates that deforestation could be positively or negatively impacted by increasing the level of economic incentives, the determination of the actual impact would await empirical analysis. Rivas’ empirical analysis showed the economic incentives reduced the level of deforestation. Since the importance of the interaction between inputs and incentives seems to be a key finding of the Rivas dissertation, we begin our modeling effort in this paper to more explicitly model the relationship among inputs, in particular, differentiating between capital used for extractive industries and capital used for manufacturing industry. In order to even more explicitly capture this interaction, the model focuses on the optimization behavior of the individual firm or entrepreneur, examining how the level of incentives affects individual decision-making.

The mathematical model The mathematical model of a firm’s behavior in the Amazon will focus on the behavior of an individual firm and its decision concerning the magnitude of investment in extractive activities, manufacturing activities, or a mixture of both. Following the discussion above, one can view the financial capital for investments as limited, thus the investment amount in extractive capital (IE) and the amount invested in the manufacturing capital (IM) are limited to the pool of investment funds that are available (IO), or IO = IE + IM. In order to simplify the solution to the optimization model, simple loglinear production functions have been employed. The product from the manufacturing (QM) is written solely as a function of the amount of capital devoted to manufacturing (KM), or

(1)

Similarly, extractive output is considered to be a function of capital (K), and deforested land (D) or

(2)

In this model the assumption is that the land is only productive for one period of production and this eliminates the need to treat the land as a stock variable and to add an additional state equation to the model. Although this is true


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for forest products, it is not strictly true for agriculture. However, this is not a bad modeling assumption for agriculture, as rainforest that is converted to agricultural land loses soil fertility quickly. This assumption does not compromise the modeling process. The production cost for manufacturing is simply the cost of capital employed in manufacturing, or rKM. Similarly, the cost function for the extractive product is the sum of the capital cost and the cost of converting the forest to agriculture, or rKE + y0Dy . 1

An individual firm’s profit maximization problem can be written as

(3)

Where TcM is equal to the unit travel cost of manufacturing output, TcE is equal to the unit travel cost of extractive output, τe is equal to the general corporate tax rate (including extractive output) and τm is equal to the reduction of the corporate tax rate on manufacturing output which is eligible for the economic incentives of PIM. The above maximization problem is subject to the following constraints. Note that τe > τm and the higher the level of economic incentives, the lower the τm. The maximization problem above is subject to the following restrictions:

(4)

(5)

(6)

Equation (4) represents the constraint on the amount of financial capital available for investment and Equations (5) and (6) represent state equations for the variables that measure the capital, with δm and δe referring to the indices of manufacturing and extractive capital depreciation. The first constraint shown in equation (4) can be replaced in equation (5) and the maximization problem can be rewritten as the Hamiltonian function of equation (7).

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(7)

The first-order conditions for the maximizing of the Hamiltonian equation (7) are shown below in the following equation (8) and are completely written to the letter in equations (8a) to (8d).

(8)

(8a)

(8b)

(8c)

(8d)

From equation (8a) λM = λE, which implies in

and that the

right-hand side of equation (8c) can be set equal to the right-hand side of equation (8d). If equation (8b) is substituted into the resulting equation and the terms rearranged to solve for the optimal level of deforestation, it produces equation (9).

(9)

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The equation above, constructed from a dynamic model, mathematically shows how deforestation occurs in concern to the Manaus Industrial Pole. The equation differs in regards to σ, a factor of tax reduction, to determine how this important factor can affect the deforestation. The new expression is shown in Equation 10.

(10)

It is very difficult to evaluate the sign of the derivative in equation (10), because that term is raised to the (1-γ1)/γ1 power and therefore could be either positive or negative. This means that the whole expression (10a) can be positive, negative or even imaginary.

(10a)

However, it is unlikely that it is either negative or imaginary, as the only unambiguously negative component in the equation (10a) is (-rσ), which is the product of two numbers that are each significantly less than one. could be negative if the depreciation rate of manufacturing capital was greater than the depreciation rate of extractive capital. Every other component in the equation (10a) is unambiguously positive. Therefore, it is likely that this equation (10a) is positive (not negative or imaginary) and the sign of the derivative will take on the sign of expression to the right of the power, (1-γ1)/γ1, in equation (10). Before analyzing

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this expression, it is possible to remove some of the ambiguity associated with the (10b) sign of equation (10a), by assuming the γ1 is equal to one. This implies the cost of clearing forest is proportional to the amount of forest cleared. If this is the case, then the equation (10a) would be equal to one, and equation (10) could be rewritten as equation (10b).

(10b)

Without any ambiguity, all terms in this equation are positive, except (α1-1) and (δE-δM) that can be negative or positive. ∂KE/∂σ and –r are definitely negative. This implies that the impact of economic incentives on deforestation can be either negative or positive. However, the more the incentives discourage investment in extractive capital the more likely its signal will be negative. The nature of optimal control models is that they are very complex, and solutions are even more complex. The solution of the first-order conditions for the level of deforestation and the derivative of this expression show that a pathway exists by which subsidies lead to less deforestation. Certain values of the parameters could imply that deforestation increases with increased subsidies, which leads to the necessity for an empirical investigation of the direction and magnitude of the impact. An intuitive discussion of the behavior of firms, based on the literature in location theory, shows that a strong argument can be made that the subsidies modify comparative advantage towards manufacturing and away from extractive industries. Similarly, two different optimal control theory models show that there exist pathways by which a deforestation is lessened by economic incentives for manufacturing. Rivas’s original model was from the perspective of the social planner, and found this to be the case. The model presented above based on individual firm maximization derives the same result. Our intuitive discussion and both formal mathematical models indicate that there is substantial reason to believe that economic incentives have an impact on deforestation, that could be negative or positive, but is likely to be negative (reducing the deforestation rate). One cannot use the results of this modeling to prove that economic incentives reduce or incre-


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ase the level of deforestation. However, the economic models provide sufficient evidence to formulate a hypothesis that could be tested using regression analysis of data on the relevant variables in the model. In essence, one would estimate a simplified form of equation (10) or (10b), with the rate of deforestation a function of prices, transportation costs and economic incentives, among other explanatory variables, and look to the estimated coefficients for the answer to this important policy questions. The mathematical behavioral model of dynamic optimization that has been developed in this chapter highlights as a set of variables that can be used in the econometric tests. Thus, the next steps will analyze these variables under the light of the specialized literature on tropical deforestation, particularly that which focuses on the Amazon. In order to do this there will be developed matching, quintile and deforestation clubs setting analyses are developed to carry out the econometric tests that will estimate the PIM’s effect.

Chapter 5 A Correspondence Analysis of Deforestation in the State of Amazonas Carlos Edwar de C. Freitas Fabíola A. do Nascimento

More than two billion years ago, Africa and South America were still a single continent. When South America began to separate from Africa, the region that lay west of the Amazon Basin drained to the Pacific Ocean, while the eastern part flowed to the new Atlantic Ocean. After the Andes Mountains were formed and as Africa and South America were being separated, the area that became the Amazon basin began to take shape. Today the Amazon basin is a complex region drained by over a thousand rivers forming the largest watershed on the planet. The drainage totals approximately 7,000,000km² of the drainage area, with a discharge on the order of 20% of the world’s freshwater (Sioli, 1968). Currently, the Amazon contains the largest rainforest in the world, and contains over one third of all remaining rainforest in the world. This watershed drains nine South American countries, but 69% of its area is in the Brazilian territory (approximately 4.8 million km²) (Ab’Sáber, 1977). The forest system generates a huge amount of carbon, and has a great biological wealth with millions of species, many of them still unknown to science. The region has a rich cultural diversity, with traditional peoples and indigenous peoples with a long and deep tradition of contact with the forest. The Amazon holds about 1/3 of the total genetic stock of our biosphere. Although the data is not definitive, it is estimated that the region contains about 60,000 species of plants, 2.5 million species of arthropods, and up to 5,000 species of fish and 300 species of mammals, as presented in Table 1.

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According to Poore & Sayer (1987), in regions such as the Amazon and Southeast Asia, less than a third of the species have been described by scientists. However, approximately 15,000 species of plants have been used for some time in common non-timber purposes including pharmaceutical, food, and germplasm banks for genetic improvement of diverse cultures by the people in those regions. According to Gentry it seems clear that from a global perspective the tropical forests deserve more attention than the ecosystems of temperate zones (1986). This is not only because of the greater wealth of flora and fauna but also because of the higher concentration of local endemism. The Brazilian land mass contains seven biomes (Figure 1). The Amazon biome extends from the Atlantic Ocean to the slopes of the Eastern Andes Cordillera, up to approximately 600 meters in altitude (Ab’Saber, 1977). In Brazil, this biome includes the states of Acre, Amapá, Amazonas, Goiás, Maranhão, Mato Grosso, Pará, Rondônia and Roraima, hosting a population of more than 20 million inhabitants (IBGE, 2007), as shown in Figure 2. Figure 1 – Brazilian biomes

BIOMA AMAZÔNIA BIOMA CAATINGA

BIOMA CERRADO BIOMA PANTANAL

BIOMA

MATA

ATLÂNTICA

BIOMA PAMPA

Source: IBGE (www.ibge.gov.br)

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Figure 2 – Population of the Brazilian Amazon, by state. 8,000,000 7,000,000

Inhabitants number

6,000,000 5,000,000 4,000,000 3,000,000 2,000,000 1,000,000 0 Pará Maranhão Amazonas

Mato Grosso

Rondônia

Acre

Amapá

Roraima

Brazilian states

Table 1 – Estimated number of species on Earth and in Brazil. Group

Number of Species Planet

Brazil

Plants

270,000

55,000

Birds

9,000

1,650

Mammals

4,600

540

Reptiles

6,300

480

Anphibians

4,200

600

Fish

20,000

7,000

Source: Wilson, 1988.

The current preservation status of the Amazon and the prospect for its future has been well-studied, but the debate remains contentious. There is a consensus that the loss of forest coverage will have an adverse effect on the entire ecosystem, degrading environmental functions and causing a loss of biodiversity on a catastrophic scale. Therefore, one way to assess the current status of the Amazon ecosystem is to analyze the process of deforestation that has been occurring.

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Deforestation in the Amazon The historical settlement process of the Amazon has resulted in a significant increase in deforestation rates. This is mainly in the southern and eastern portions of the Amazon Basin. According to Ferreira et al. (2005), the most urgent issues in terms of preservation and use of natural resources in the Amazon are related to the large-scale loss of the critical functions of the Amazonian ecosystem undergoing deforestation. The authors link increased deforestation with the expansion of economic activities such as land speculation along roads, city growth, a dramatic increase in cattle ranching, logging, small-scale farming and newly mechanized agriculture that is mainly linked to soybeans and cotton cultivation. It is also important to note that the trend of increasing deforestation rates continued until 2003 and 2004, and then deforestation rates were lower in the years 2005 and 2006, which can be seen in Table 2. As seen in Figure 3, deforestation in the Amazon is not homogeneous. The states of Mato Grosso and Pará have the highest deforestation rates, a pattern that has remained constant since the last decade. The other accentuated areas of deforestation are concentrated and are located around state capitals, medium-size cities or are associated with large projects such as mines. Table 2 – Deforestation data per state in Legal Amazon (km²/year) for the period 2000 to 2006 (Source: PRODES / INPE - available at www.obt.inpe.br / PRODES.

State

Year 2000

2001

2002

2003

2004

2005

2006

Acre

574

419

762

1,061

729

539

323

Amazonas

612

634

881

1,587

1,211

752

780

Amapá

-

7

0

46

46

33

30

Maranhão

1,065

958

1,014

755

755

922

651

Mato Grosso

6,369

7,703

7,892

11,814

11,814

7,145

4,333

Pará

6,671

5,237

7,324

8,521

8,521

5,731

5,505

Rondônia

2,465

2,673

3,067

3,834

3,834

3,233

2,062

Roraima

253

345

84

311

311

133

231

Tocantins

244

189

212

158

158

271

124

91


Figure 3 – Critical areas of deforestation in the Amazon for 2003. Source: PRODES/ INPE – available at www.obt.inpe.br/prodes.

Contribution of 20% of deforestation Contribution of 80% of deforestation 2003 Critical scenes

Source: PRODESDIGITAL - INPE

INSTITUTO DO HOMEM E DO MEIO AMBIENTE DA AMAZÔNIA

The concentration of deforestation areas in the states of Mato Grosso, Pará and Roraima can be verified by the predominance of counties among those with larger deforested areas in the last five years, as seen in Table 3. The state of Amazonas contains only one such area, the county of Lábrea, located in the south of the state, which is among the 15 counties with the highest deforestation rates. Simplistic attempts to explain the distribution of deforestation pressure in the Amazon in general tend to list separate factors such as geographic features, the presence of roads (official and clandestine), the existence of projects for agricultural settlement programs, agricultural funding, market pressure, agribusiness (mainly soybeans and cattle ranching), and so on. However, deforestation is very complex; a large number of variables and interactions of variables all act in a multivariate framework. Moreover, difficult-to-quantify variables such as political phenomenon have an important impact on the rate of deforestation for a given period.

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Table 3 – Fifteen counties in the Legal Amazon with the most deforested areas (sq. km) in the last fifteen years. County

FU

São Félix do Xingu

Year 02

03

04

05

06

PA

795,9225

898,18

571,2875

982,74

435,195

Juara

MT

409,77

309,775

315,755

401,2525

199,12

Santana do Araguaia

PA

370,155

342,2575

285,2225

486,0925

136,27

Cumaru do Norte

PA

360,5125

347,92

400,28

576,92

174,8025

Porto Velho

RO

234,51

353,825

503,1725

402,785

198,1175

Novo Repartimento

PA

169,79

501,465

436,675

205,335

425,2475

Pacajá

PA

55,0575

203,28

261,8375

279,64

213,69

Nova Ubiratã

MT

302,1225

368,385

381,6275

254,535

72,855

Altamira

PA

346,05

393,71

364,2375

293,9775

192,68

Aripuanã

MT

344,9575

402,68

380,12

329,8875

51,8625

Novo Progresso

PA

492,805

297,5725

422,905

139,34

177,1375

Colniza

MT

316,2625

477,7525

567,57

514,935

211,5075

Nova Maringá

MT

193,06

229,895

445,435

384,7275

42,1075

Lábrea

AM

204,81

446,8525

335,0975

178,9425

237,075

Nova Bandeirantes

MT

188,375

223,8025

371,9425

290,2925

122,9575

In an attempt to observe general ordination patterns of the states that make up Legal Amazon, deforestation rates are analyzed on the basis of geographical variables and public policies. The statistical approach uses a multivariate method of correspondence analysis, considering the states as objects and variables based on indicators of the economic activities listed below. LAV - Perrenial Crop GDP - Gross Domestic Product REB – Cattle Herd DESM – Average Deforestation (2000 to 2006) Given the low deforestation levels observed in the state of Amapá, this state was not included in the analysis. A similar decision was made in regards to the states of Maranhão and Tocantins based on their distance in relation from the Manaus Industrial Pole.

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Because of the high value of the eigenvalue1 of the first dimension, which accounted for about 95% of the model variability, only this dimension will be interpreted. The state of Amazonas has very different characteristics from the other states being analyzed. As it has a high Gross Domestic Product (GDP) and low values for the “average deforestation,” “crop “and “cattle herd” variables, it appears that other economic activities, probably with a low relationship to deforestation, are responsible for the economy of the state (Figure 4). On the opposite side of dimension 1, the states of Mato Grosso and Rondonia have high rates of deforestation that are directly related to agricultural activities, Figure 4. The states of Acre and Roraima have low deforestation rates and also low GDP values. This represents low economic activity. On the other hand, the state of Pará has a high GDP, but in contrast also has high observed values of “deforestation”, “cattle herd” and “crop” variables. This places it in an intermediate position in the chart, under the influence of the two variable categories. In the state of Amazonas, the deforestation process also occurs in an uneven manner. In southern Amazonas and in particular in the counties of Apuí, Humaitá and Lábrea, there are higher and increasing deforestation rates in comparison to other regions of the state.

Dimension 2; Eigenvalue: 0,00477 (4,711% of inertia)

Figure 4 – Correspondence Analysis using states as objects and economic indicators as descriptors. 00 00

AM

00

pibPA

MT

AC RO

-00

reb desm

-00 -00 -00

RR

-01 -01 -01 -01

lav

-01 -01 -01 -0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

Dimension 1; Eigenvalue: 0,09651 (95,28% of inertia)

1 Eigenvalues are a special set of scalars associated with a system of linear equation (Hoffman and Kunze 1971).

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Seeking to clarify this unequal distribution of deforestation, we performed a “discriminant analysis” by grouping the counties of the state of Amazonas. Table 4 – Division of the state of Amazonas in microregions. Group of counties

Alto Solimões (AS)

Boca do Acre (BA)

Coari (CO)

Itacoatiara (IT) Japurá (JP)

Juruá (JU)

Madeira (MA)

Manaus (MN)

Counties Amaturá Atalaia do Norte Benjamin Constant Fonte Boa Jutaí Santo Antonio do Içá São Paulo de Olivença Tabatinga Tonantins Boca do Acre Pauiní Anamã Anori Beruri Caapiranga Coari Codajás Itacoatiara Itapiranga Nova Olinda do Norte Silves Urucurituba Japurá Maraã Carauari Eirunepé Envira Guarajá Ipixuna Itamarati Juruá Apuí Borba Humaitá Manicoré Novo Aripuanã Autazes Careiro Careiro da Várzea Iranduba Manacapuru Manaquiri Manaus (it continues)


Group of counties

Parintins (PA)

Purus (PU) Rio Negro (NE) Rio Preto da Eva (RP) Tefé (TE)

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Counties Barreirinha Boa Vista dos Ramos Maués Nhamundá Parintins São Sebastião do Uatumã Urucará Canutama Lábrea Tapauá Barcelos Novo Airão Santa Isabel do Rio Negro São Gabriel da Cachoeira Rio Preto da Eva Presidente Figueiredo Alvarães Tefé Uarini

The following variables were used as dependent variables in the discriminant analysis: PC AREA

Perennial crop area (hectare in 2006)

BOVINE

Cattle herd (heads in 2006)

BUFFALO

Buffaloes herd ( heads in 2006)

GOATS

Goats herd (heads in 2006)

SWINE

Swine flock (heads in 2006)

SHEEP

Sheep flock (heads in 2006)

DEFORESTATION

Deforestation in 2006

FIREWOOD

Amount of wood used for firewood in 2006

The variables for perennial crop and buffalo ranching are considered as discriminants. The counties of Apuí (Madeira group) and Maués (Parintins group) stand out to the perennial crop, Figure 5. The county of Parintins (Parintins group) has a significant buffalo herd in the state of Amazonas. Autazes (Manaus group) occupies an intermediate position between the variables. This shows that either the agricultural activity or the buffalo ranching is important to this county.

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PC AREA

Figure 5 – Ordination of the counties, according to the groups presented in Table 4, using perennial crop and buffalo herd area as discriminants and including the county of Coari.

BUFFALOES HERD

When we consider the perennial crop and cattle herd area as discriminants (Figure 6), it is possible to observe the importance of livestock activity, mainly in the county of Lábrea (Purus group). Apuí and Boca do Acre (Boca do Acre group) also have both significant agriculture and livestock production. Whereas the perennial crop and goat herd areas are discriminants, note that the counties of Parintins, Autazes and Itacoatiara (Itacoatiara group) have significant goat ranching activity in relation to other counties in the State of Amazonas (Figure 7). It can also be observed that Autazes is in an intermediate position; the two related activities are important for this county.


97

PC AREA

Figure 6. Ordination of the counties, according to the groups presented in Table 4, using the perennial crop and cattle herd area as discriminants and including the county of Coari.

BOVINES

PC AREA

Figure 7. An ordination of the counties according to the groups presented in Table 4. This is using the perennial crop and sheep flock area as discriminants and includes the county of Coari.

GOATS

98


The counties of Careiro and Autazes (Manaus group), isolated in the right portion of the graph, stand out in relation to sheep herding (Figure 8). Other counties with significant participation in the sheep flock are Itacoatiara, Parintins and Barreirinha (Parintins group). When the discriminants are the variables of perennial crop and swine, the counties of Pauini (Boca do Acre group), Manicoré (Madeira group) and Parintins can be identified as important swine breeders. In addition to having a significant swine flock, the county of Manicoré has a reasonable area set apart for the perennial crops (Figure 9). The counties of Apuí and Maués remain the highest in use of land for agricultural production.

PC AREA

Figure 8. Ordination of the counties, according to the groups presented in Table 4, the perennial crop and sheep flock area as discriminants, including the county of Coari.

SHEEP


99

PC AREA

Figure 9. Ordination of the counties according to the groups presented in Table 4. This is using the perennial crop and swine flock area as discriminants and includes the county of Coari.

SWINE

The scenario with the perennial crop and firewood area descriptors show that Tefé (Tefé group) and Tapauá (Purus group) are the counties that extract the most firewood in the state of Amazonas (Figure 10). In addition to these, there are the counties of Uarini, Manicoré (Madeira Group) and Fonte Boa (Alto Solimões group). The last two counties are located in an intermediate position between the two variables, showing that both activities are significant in the areas.

100


PC AREA

Figure 10. Ordination of the counties, according to the groups presented in Table 4 and using perennial crop and firewood as discriminants and including the county of Coari.

FIREWOOD

When the deforestation and firewood variables are used as discriminants, we observe that extraction of firewood does not significantly contribute to deforestation in most counties of the state of Amazonas (Figure 11). Counties that have seen the most deforestation (Itacoatiara and Lábrea) are not among those that have the highest rates of firewood extraction (Tefé and Tapauá). The county of Manicoré is in an intermediate position between these variables. Figure 11 shows that the high rate of deforestation in the counties of Itacoatiara and Lábrea is not related to firewood extraction. However, when deforestation and cattle herd are used as discriminants, it is possible to relate the rate of high deforestation in the county of Lábrea to the great number of cattle in the region (Figure 12). In addition to Lábrea, it is also possible to identify the counties of Apuí, Parintins and Boca do Acre as having a significant direct relationship between deforestation and bovine herd variables.


101

DEFORESTATION

Figure 11. Ordination of the counties according to the groups presented in Table 4 using deforestation and firewood extraction as discriminants.

FIREWOOD

DEFORESTATION

Figure 12. Ordination of counties, according to the groups presented in Table 4, using deforestation and cattle herd as discriminants and also excluding the county of Coari.

BOVINE

102


When deforestation and buffalo herd variables are used as discriminants, we can identify buffalo ranching as one of the probable causes of high deforestation in the county of Itacoatiara (Figure 13). In addition to Itacoatiara, it is possible to identify the counties of Parintins, Autazes and Manaus as having significant direct relationships between deforestation and buffalo flock variables. When the discriminant variables are deforestation and domestic swine herd, the county of Pauini has the highest level of swine herds. However, such activity appears not to negatively affect the rate of deforestation in the region (Figure 15). This is different from Manicoré and Parintins, where this activity has a significantly negative effect on deforestation in these counties. The counties of Itacoatiara, Lábrea, Careiro, Manaus, Parintins, Itacoatiara and Barreirinha have a large number of domestic sheep flocks (Figure 16). The type of activity in these counties has had a negative impact on the forest because the domestic sheep flock variable is directly proportional to the rate of deforestation.

DEFORESTATION

Figure 13. Ordination of counties according to the groups presented in Table 4, using deforestation and water buffalo herd as discriminants.

BUFFALOES


103

DEFORESTATION

Figure 14. Ordination of counties according to the groups presented in Table 4, using deforestation and domestic goat flock as discriminants.

GOATS

DEFORESTATION

Figure 15. Ordination of counties according to the groups presented in Table 4 using deforestation and domestic swine as discriminants.

SWINE

104


DEFORESTATION

Figure 16. Ordination of counties according to the groups presented in Table 4 and using deforestation and domestic sheep flock as discriminants.

SHEEP

The county of Manaus has a comparatively high level of deforestation. However, this is not related to agricultural activity. The deforestation is most likely due to an expansion of urban area (Figure 17). The worst case can be seen in the counties of Itacoatiara, Lábrea, and Manaus. This deforestation is not related to agricultural activities. Unlike the above mentioned counties, the counties of Apuí, Maués, Boca do Acre and Autazes have high levels of deforestation, which is likely related to the high level of agricultural activity in these counties.


105

DEFORESTATION

Figure 17. Ordination of counties according to the groups presented in Table 4 and using the level of deforestation and perennial crop as discriminants.

PC AREA

When the variables firewood and cattle herd are the discriminants, it can be seen that the counties of Tefé and Tapauá are the main extractors of firewood (Figure 18). These counties use only a small part of their land area for cattle ranching, unlike Lábrea and Apuí, which have large amounts of cattle. When firewood extraction and water buffalo herd are the discriminants, it can be seen that several counties have a significant amount of water buffalo. These include areas such as Parintins, Autazes, Itacoatiara and Manaus (Figure 19). However, they are not major extractors of firewood.

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FIREWOOD

Figure 18. Ordination of counties according to the groups presented in Table 4 and using deforestation and bovine herd as discriminants.

BOVINE

FIREWOOD

Figure 19. Ordination of counties according to the groups presented in Table 4 and using the level of deforestation and water buffalo herd as discriminants.

BUFFALOES


107

When firewood extraction and domestic goat herd are the discriminants, Parintins is a major breeder of goats in the state of Amazonas (Figure 20). Regarding domestic swine, it can be seen that the county of Pauini stands out among the other counties in the State of Amazonas (Figure 21). It can be seen that the county of Manicoré has either a reasonable level of firewood extraction or swine production in the state of Amazonas. When the variables firewood extraction and domestic sheep flock are the discriminants, the counties of Manaus and Careiro stand out for their large number of domestic sheep, as seen in Figure 22.

FIREWOOD

Figure 20. Ordination of counties according to the groups presented in Table 4 using deforestation and domestic goat flock as discriminants.

GOATS

108


FIREWOOD

Figure 21. Ordination of counties according to the groups presented in Table 4, using deforestation and domestic swine herd as discriminants.

SWINE

FIREWOOD

Figure 22. Ordination of counties according to the groups presented in Table 4 using deforestation and domestic sheep flock as discriminants.

SHEEP


109

In an effort to conduct an exploratory analysis, GDP will be considered as a discriminant variable. This procedure is targeted at determining the GDP’s behavior as a function of the other discriminant variables previously used in this chapter. The county of Manaus stands out for its high per capita GDP, as shown in Figure 23, with a small area of perennial crop. On the other hand, the counties of Apuí and Maués have comparatively large crop areas, but low per capita GDP. The county of Manaus presents a comparatively high level of deforestation, probably due to the expansion of the urban area along with high GDP per capita (Figure 24). The worst scenario can be observed in the counties of Lábrea and Itacoatiara, both of which have low per capita GDP and high levels of deforestation.

GDP

Figure 23. Ordination of counties, according to the groups presented in Table 4, using GDP and perennial crop as discriminants.

PC AREA

110


GDP

Figure 24. Ordination of counties, according to the groups presented in Table 4, using GDP and deforestation as discriminant variables.

DEFORESTATION

The scenarios for GDP per capita and extraction of firewood (Figure 25), cattle herds (figure 26), water buffalo herds (figure 27), goats (Figure 28), swine (Figure 29) and sheep (Figure 30) remain unchanged on the low GDP when related to these different economic activities.

GDP

Figure 25 – Ordination of counties according to the groups presented in Table 4 and using GDP and firewood extraction as discriminant variables.

FIREWOOD


111

GDP

Figure 26 – Ordination of counties, according to the groups presented in Table 4, using the GDP and the bovine herd as discriminant variables.

BOVINE

GDP

Figure 27 – Ordination of counties, according to the groups presented in Table 4, and using GDP and water buffalo herd as discriminant variables.

BUFFALOES

112


GDP

Figure 28 – Ordination of counties according to the groups presented in Table 4 using GDP and domestic goat flock as discriminant variables.

GOATS

GDP

Figure 29 – Ordination of counties, according to the groups presented in Table 4 and using GDP and swine as discriminant variables.

SWINE


113

GDP

Figure 30 – Ordination of counties according to the groups presented in Table 4 and using GDP and domestic sheep as discriminant variables.

SHEEP

A short conclusion A synthesis of the analysis of the current status of preservation in the state of Amazonas, based on the level of general economic activity (particularly agricultural and cattle ranching), lead to the following conclusions: I. Firewood extraction does not significantly contribute to deforestation in most counties in the State of Amazonas. II. In some counties, there is a direct relationship between the level of agricultural activity and deforestation (the counties of Apuí, Maués, Boca do Acre and Autazes). However, the worst case is seen in counties such as Lábrea and Itacoatiara, where deforestation is related to agricultural activity. III. It is possible to blame high levels of deforestation found in the county of Lábrea on the large amount of cattle present in the region. IV. Water buffalo, domestic sheep and domestic goat farming are the most likely factors contributing to deforestation in the county of Itacoatiara. V. Deforestation shows a correlation in relation to GDP per capita. With the exception of Manaus, every county with a high level of deforestation had low GDP per capita. VI. The economic activities discussed in this paper show a low relationship to GDP per capita.

Chapter 6 Causalities, convergence clubs and quantile analysis Marcelo Bentes Diniz José Nilo de Olivera Jr.

This section will develop and discuss the applied empirical

used in the analysis1. First, methodological discussion about the purpose of the analysis will be discussed. Following this, the causality test will be developed. A causality test is a preliminary empirical result in the sense that it indicates which variables could influence the explained variable (dependent) in statistical models, in this case, deforestation2. After this preliminary analysis, as set of different analytical tools and estimation techniques for estimating will be employed. models

Units of observation The first aspect to be considered concerns the identification of the units of observation units to define the dimensionality in the descriptive analysis. This is especially important for the conformation of the econometric model. As some authors suggest, the adoption of the definition of the Comparable Minimum Area (CMA) allows a more consistent analysis of geographically varying phenomenon over time. The CMA is a spatial differentiation concept that in our analysis is consistent with the municipal level of data collection (ANDERSEN et al. 2006).

1 Although the goal of this chapter is to make the text as simple to understand as possible, some terms and technical specific jargon are inevitable, since their absence would compromise the interpretation of the results. 2 In a regression model of the type Y = β1 + β2X the variable on the left side of the equality (Y) is called the dependent variable and the variable(s) on the right are called independent or explanatory variable(s) (GUJARATI, 2006).

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Between the mid 1990´s and 2006 a small number of new counties were created. This could imply that a spatial comparison at the municipal level for this period could lead to statistical issues associated with changing sample sizes. However, this does not cause significant problems because the number is so mall. Furthermore, because of the lack of data on some of the variables, some counties created during this period were excluded from the sample. The data used for the model, including the preliminary causality test, covered the period from 1997 to 2006, which necessitated the use of data interpolation. In this case two procedures were used, a variable calculation from the moving average, based on observed data or calculation using a known growth rate. In the end, those counties with predicted behavior that could be viewed as “outliers”3. Holtz-Eakin et al. (1988) argue that a sufficiently long time series data set is required for the application of Granger’s causality test. In this case, the number of variables used in the temporal dimension would require at least five periods (years) of data on independent variables to improve robustness of the test. The choice of explanatory variables relies on the data available for the specific period, at least for the years 1997 and 2006. The groups that were utilizing the land, however, were considered the major forces causing deforestation, although other factors such as the presence of certain types of infrastructure (highways, for example) are viewed as intensifying the impact of the primary causal factors. In this case the primary causal factors are cattle ranching, and both annual and perennial crops. The selected variables were tested using four models, focused on the main objective of the study, testing the PIM’s effect on deforestation in the Amazon. The first step was to apply Granger’s causality test, using a model in the form of the dynamic panel as described in the following section. The second step, spatial differences in deforestation were observed under the convergence mode using deforestation as the dependent variable with the identification of statistical convergence clubs. After that, quantile regressions were performed. This technique allows you to check the degree of influence of the explanatory variables selected for different percentiles4 of the dependent variable, which in this case is deforestation. This has become relevant, since each “deforestation club” could be linked to the different forms of the explanatory variables. Moreover, even in a cross-sectional structure, a first check on the influence of deforestation on the PIM can be taken. Finally, the parameters of a panel-data model were estimated, taking into consideration the random effect identified by Hausman test.

3 An outlier is defined as observation whose behavior describes different patterns from what is expected a priori from it, in light of its past behavior or from the comparison of the behavior of another observation unit with similar characteristics. 4 A separatrix of p order (with 0
9.740*

Node 3.2 420 dist 11.118 ≤

> 11.118*

Terminal node *

Threshold value

Node 4.1 163 dist

Node 4.2 257 dist

Source: Authors’ compilation.

According to Figure 1, we can observe the existence of three decision nodes, as well as four terminal nodes. This implies the existence of four deforestation convergence clubs in The Legal Amazon. The club 2.1, corresponding to the club with the smallest deforested area, encompasses 40 counties with deforested area smaller than 45.369 Km2. The node 3.1 corresponds to the intermediate convergence club, with 93 counties, which have deforested area of between 45.369 and 169.83. The other intermediate club 4.1 (with 163 counties) is the club with deforested area of between 169.83 and 673.73. But the terminal node 4.2 corresponds to the convergence club with the largest deforestation area, including the 257 counties that have areas of deforestation larger than 673.7319 Km2. Table 5 next page shows that the club with the smallest deforested area (2.1) and the intermediate club (4.1) show differences between their counties, which may indicate that they may be in a process of migration toward the poles, or possibly forming new clubs. The intermediate club (3.1) show convergence between the counties. Finally, with club (4.2), the most deforested, there is no divergence and and convergence. Regarding the aggregate sample, this shows evidence of global convergence. 19 This value is derived from the application of the exponential in the corresponding threshold value in Figure 2.

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As for the convergence club with the smallest deforested area (2.1), only the GDP variable, the variable that was used as a proxy for the degree of economic development of the counties, was important in explaining the process. The other variables were insignificant in explaining the deforestation process in this club. This result indicates that the economic activity of this club is the only thing responsible for the deforestation process, and this in turn should be tied to the livelihood process of the resident populations that, in general, is not an intense cause of deforestation. In the intermediate club (3.1) the GDP, the total cattle herd and annual crops were important in explaining the deforestation process in this club, indicating that the level of economic activity in the counties is directly connected to the deforestation process. The results indicate that the livestock and the perennial crops, began to intensify in this club and the deforestation process follows the same trajectory. All variables that the literature has shown to be linked deforestation are important in explaining the deforestation process within the clubs with the largest deforested area (4.1 and 4.2). This is because the economic activities of these counties are directly linked to livestock and annual and perennial agriculture. It can be noted that the clubs with the largest deforested area (4.1) and (4.2) are the clubs already established in terms of deforestation, with both livestock and extensive agriculture acting intensely in these counties. With respect to the clubs with the smallest deforested area (2.1) and (3.1), economic activity based on livestock and agriculture is still in its initial development stage, growing as possible areas for the expansion of deforestation. Table 5 – Regression Analysis of Ordinary Least Squares (OLS) for The Legal Amazon’s Clubs The Legal Amazon

Clube 2.1

Clube 3.1

Clube 4.1

Clube 4.2

553

40

93

163

257

Constant

0,82* (0.28)

2.97* (1.51)

2.01* (1.01)

-0.81* (0.29)

-0.02 (0.02)

lnDesm2000

-0.09* (0.02)

0.25* (0.14)

-0.14* (0.13)

0.05* (0.02)

-0.03 (0.03)

lnPIB

0,001 (0.03)

0.56* (0.27)

0.13* (0.06)

0.05* (0.02)

0.02** (0.01)

lnRBOV

0.08* (0.02)

-0.55 (0.37)

0.04* (0.002)

0.02* (0.01)

0.09* (0.03)

lnCULTPERM

0.01* (0.006)

-0.03 (0.07)

0.01 (0.02)

0.009* (0.004)

0.03* (0.005)

lnCULTEMP

0.023* (0.001)

0.08 (0.07)

0.07** (0.04)

0.03* (0.01)

0.01* (0.008)

Counties

(it continues)

129


Counties ln (Occupied Area)

The Legal Amazon

Clube 2.1

Clube 3.1

Clube 4.1

Clube 4.2

553

40

93

163

257

0.09* (0.04)

0.54 (0.41)

0.01 (0.06)

0.09* (0.04)

0.13* (0.04)

R2 ajusted

0.21

0.27

0.38

0.22

0.20

Variance Residue

0.14

0.74

0.25

0.16

0.10

Note: * significant at 5%; ** significant at 10%; Values in parentheses are standard deviations. Source: Author´s compilation.

Figure 2 shows the spatial dispersion of convergence clubs in the Legal Amazon. The most deforested clubs are concentrated in the counties of the states of Pará, Mato Grosso and Rondônia.

Figure 2 – Spatial Dispersion of Legal Amazon’s counties according to Convergence Clubs Classification

A The Legal Amazon The Legal Amazon Does not belong to the sample Convergence Club 2.1 Convergence Club 3.1 Convergence Club 4.1 Convergence Club 4.2


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The State of Amazonas The results for the state of Amazonas follow the same pattern of results as for Legal Amazon. Table 6 below summarizes the results found for the sample division between the decision nodes and this one has the same characteristics in Table 6. The results reveal the existence of two decision nodes and three terminal nodes. The decision nodes correspond to the initial node (full sample) and one of them in the first interaction (first division of the sample) (1.1), as shown in Figure 3 below. Table 6 – Decision of the Sample Division Decision Node

Defor. 97 Test

Division Decision Threshold value Reliable Interval

Deforestation

19.00

5.79

[4.57; 6.29]

1.1

12.30

5.02

[5.02; 5.06]

Note: Significance level used was 95% and H0: there is no sample division.

As in the previous section, a tree diagram was drawn up with the same characteristics, pointing out that the left side of each decision node contains the observations in which the logarithm of the production value is smaller than or equal to the threshold value. According to Figure 3, we can observe the existence of two decision nodes, as well as three terminal nodes. This implies the existence of three deforestation convergence clubs in the state of Amazonas. The club 2.1, corresponding to the club with the smallest deforested area, covers 26 counties that have less than 151.86 km² of deforested area. The node 2.2 is the intermediate convergence club, with 17 counties which have deforested area of between 151.86 and 329.63 km². The terminal node 1.2 corresponds to the convergence club with the largest area of deforestation, including 19 counties that have more than 329.6320 Km2 of deforested area.

20 This value is derived from the application of the antilog in the threshold value corresponding to Figure 3.


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Figure 3 – Decision diagram of the threshold effect for the State of Amazonas

5.789* ≤

Node 1.1 43 dist 5.023 ≤

> 5.023

Deforestation

> 5.789*

Node 1.2 19 dist Subtitle:

Node 2.1 26 dist

Node 2.2 17 dist

Decision node Terminal node *

Threshold value


But the intra-clubs results, as they can be seen in Table 7 next page, show that the club with the least amount of deforested area presents convergence of its counties. The intermediate club presents divergence between the counties, which may indicate that they may be in a process of migration toward the poles, as well as building new clubs. Otherwise, the club with the largest deforested area presents neither convergence nor divergence, which may indicate that the counties are in a transition stage of forming new convergence clubs or are simply stagnant.

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Table 7 – Regression analysis of Ordinary Least Squares (OLS) for the clubs of the State of Amazonas Amazonas

Club 2.1

Club 2.2

Club 1.2

63

26

17

19

Constant

-6.11* (1.14)

-4.51* (1.26)

-15.5 (5.05)

-37.97* (0.04)

ln(DESM)1997

-0.58* (0.10)

-0.81* (0.12)

2.45* (1.00)

-0.38 (4.65)

ln(PIB)

0.79* (0.35)

-0.81 (0.92)

0.90** (0.59)

2.15* (0.55)

ln(RBOV)

0.13* (0.04)

0.13* (0.04)

0.12* (0.05)

0.46* (0.15)

ln (CULTPERM)

0.29* (0.10)

-0.23* (0.11)

-0.67 (0.14)

-0.77 (1.19)

ln(CULTEMP)

0.41* (0.17)

0.57* (0.13)

-0.64** (0.59)

-0.80 (1.71)

Counties

R2 ajusted

0.35

0.77

0.60

0.67

Variance Residue

2.07

0.88

1.21

1.46

Note: * Significant at 5%; ** Significant at 10%; Values in parentheses correspond to the pattern error.

For the convergence club with the smallest deforested area (2.1), the perennial crop and cattle herd variables were significant and with positive sign. This indicates that they are essential in explaining the deforestation process for this club. The perennial crop variable, despite having been significant is associated with a negative sign. This is not a surprise, because most often perennial crops are linked to subsistence crops, which in turn have contributed to slowing deforestation. But GDP, the variable that was used as a proxy for the economic development degree of the counties, was not important for explaining the deforestation. In the intermediate club (2.2), GDP was important in explaining the deforestation process, indicating that the economic activity level of the counties is directly related to the process. The perennial crop variable proved to be insignificant, indicating that it has little power in explaining the deforestation process in this club. This is when the explanation follows the logic outlined in the club with the smallest deforested area. The annual crop variable was significant, indicating that it is an important factor in explaining the process, but it has a negative sign, which does not mean that it is helping to keep the rainforest intact. On the contrary, what may be happening is that this agricultural process is taking place on land that has already been cleared. The same may be happening with the cattle herd variable, which was also significant, but with a negative sign.


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In regards to the club with the largest amount of deforested area (1.2), the Gross Domestic Product and cattle herd variables prove to be major causal factors. This result may be an indication that there is a direct relationship between the counties’s economic activity, livestock and the deforestation process. Long and short-term crop variables proved to be insignificant factors, which may indicate that these agricultural processes are taking place in areas already cleared for cattle. In aggregate terms the results indicate evidence for global convergence and show that all explanatory variables are significant in explaining the deforestation process within the state of Amazonas. The geographic dispersions of the various counties throughout the state of Amazonas can be observed in Figure 4 in terms of convergence clubs. The club with the smallest amount of deforestation concentrates the counties of the northern area of the state. The intermediate club concentrates, mainly, the counties of the central region of the state and the club with the largest deforested area concentrates, mainly, the counties which border the states of Pará, Mato Grosso, Rondônia and Acre. Figure 4 – Spatial Dispersion of deforestation in State of Amazonas

The State of Amazonas Convergence Club 2.1 Convergence Club 2.2 Convergence Club 1.2

The results found here corroborate Cepal (2007), who shows that most deforestation is concentrated in counties in the south of the state. With a total area of 393,875 km² (approximately 25% of the state), and a population of 258,674 inhabitants (9% of the state), approximately 30% (10,406 km²) of the total deforestation in the state was concentrated in this area until 2004. This process is explained mainly by the expansion of the agricultural frontier resulting from bordering states.

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The model with quantile regressions The idea behind quantile regression is to estimate what is the effect under any separatrix in the distribution of y given x, when x changes. Thus, quantile regression allows us to analyze the impact of explanatory variables at different points of the conditional distribution to the dependent variable. The motivation for creating this methodology arises in what Koenker and Basset (1978), writing in their seminal article, called a “robustness of the distributional hypothesis” problem in the data generating process. In fact, the presence of outliers makes the least squares estimators poor estimators in many situations in which data is derived from non-Gaussian distributions, especially the long-tailed ones. Thus, the distribution of errors with tails longer than the Gaussian distribution would require the estimators to modify the average sample, placing smaller weight on the extremes of the distribution. This fact, as emphasized by Koenker and Basset (op. cit.) would suggest that there could be a class of estimators superior to least squares linear for the linear non-Gaussian models, since they would add more information than the average conditional estimators. Moreover, the quantile regressions show some interesting features, as highlighted by Buchinsky (1998). First, these models can be used to characterize the entire conditional distribution of a response variable given a set of regressors. Second, the objective function of the quantile regression is a weighted sum of absolute deviations, providing a locally robust measure, so that the vector of estimated coefficients is not sensitive to the extreme observations on the dependent variable. Third, when the errors do not follow a normal distribution, the quantile regression estimators can be even more efficient than the least squares estimators. Finally, different solutions for different quantiles may be interpreted as differences in the response of the dependent variable to changes in the regressors at various points of the conditional distribution of the dependent variable.

The statistical model

(6) Where the variables are described in Table 8 and stochastic error term, ui, is assumed to be independently and identically distributed (iid).


135

From the results shown, we find that quantile analysis can provide an influential analysis of the explanatory variables selected for the counties at different deforestation levels. Thus from the reference values of the calculated convergence clubs, it is possible to estimate the percentile that each club belonged to. In this case, the first club matches the 20th percentile. The inclusion of the second club requires moving to the 33 percentile and finally, including the last club requires going to the last percentile. From this division, three quantile estimates were taken in order to evaluate possible changes in the significance and effect of the selected variables for the quantil intervals and thus to each convergence club. Table 8 – Variables used in the statistical analysis Variable

Description

Desmi98

deforestation (Km2) in 98

lncultperm i98

Natural log of the perennial crop in 98

lncultemp i98

Natural log of the annual crop in 98

lnrebbov i98

Natural log of the cattle herd in 98

lnestcred i98

Natural log of the credit stock in 98

lneduadu i98

Natural log of the adults’ education in 98

lnmatrícula i98

Natural log of the school registration in 98

densdemog i98

Demographiv density in 98

densboi i98

Cattle density in 98

tcresboi i98

Growth rate of the cattle herd in 98

desmat i97

Deforestation in 97

lncultperm i97

Natural log of the area in perennial crops in 97

lncultemp i97

Natural log of the area in annual crops in 97

lnrebbov i97

Natural log of the cattle herd in 97

lnpibpc i97

Natural log of the GDP per capita in 97

lneduadu i97

Natural log of the adult’s education in 97

lnestcred i97

Natural log of the credit stock in 97

As this model is estimated in a cross-sectional structure, the verification of the PIM’s variable relevance was made during 1997-1998 only.

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Analysis of the evidence from the quantile analysis Based on the results for the quantile regressions presented in Table 9, the analysis for each one of convergence clubs is developed. Table 9 – Results for Quantile Regressions 1998-1997. Variables

Quantis 20th

Quantis 33th

Quantis 99th

PIM

-25. 1780 (27. 6429)

-27. 4690 (15. 2672)

-602. 4912* (0. 0468)

lncultperm i98

17. 4396 (18. 0539)

40. 1851* (11. 5794)

18. 8493* (0. 0388)

lncultemp i98

-7. 7368 (14. 8865)

-15. 9145 (10. 5983)

64. 2884* (0. 0375)

lnrebbov i98

153. 7479* (50. 5434)

71. 4807* (34. 8581)

-291. 3017* (0. 1341)

lnestcred i98

17. 9710* (10. 3159)

15. 2291* (7. 6100)

-26. 0594* (0. 0191)

lnmatrícula i98

-36. 2595* (16. 3088)

-26. 0401* (9. 7904)

149. 2981* (0. 0301)

lncultperm i97

10. 1630 (17. 7165)

-11. 3883 (11. 3133)

-16. 2097* (0. 0386)

Lncultemp i97

-6. 4225 (15. 2491)

10. 4255 (11. 0331)

-32. 5234* (0. 0387)

lnrebbov i97

-63. 6255 (49. 5354)

6. 81171 (34. 7528)

376. 1926* (0. 1350)

lnpibpc i97

5. 5116 (7. 8654)

1. 9721 (5. 5366)

-33. 4508* (0. 0162)

lneduadu i97

45. 2909* (10. 6111)

42. 7904* (6. 9837)

-1. 9012* (0. 0217)

lnestcred i97

7. 5313 (8. 9028)

18. 7915* (6. 1642)

81. 7577* (0. 0169)

densdemog i98

-1. 6534* (0. 3205)

-0. 7780* (0. 2408)

0. 6989* (0. 0065)

densboi i98

0. 3102 (0. 3525)

0. 1762 (0. 2186)

-4. 3323* (0. 0006)

tcresboi i98

2. 1132* (0. 8910)

2. 071* (0. 4792)

13. 6974* (0. 0016)

Const.

-1131.37* (141. 6223)

-4. 4146 (17. 6822)

-1455. 256 (0. 2762)

p-seudo r2

0. 3367

0. 3658

0. 5883

n. obs.

232

232

232

Note: the brackets refer to the pattern errors. * Significant estimates in at least 10%, the rest is not significant. Note: The variables lnEDUADU and DESMAT(-1) are excluded due to high multicolinearity.


137

The quantile regression performed for the quantile 20, which would be associated with a convergence club with the smallest deforestation shows that contemporaneous impacts on deforestation arise21 from the size of the cattle herd, the growth of the cattle herd, credit stock and elementary-school registration. Registration in adult education programs is the only variable that had a significant lagged impact.22 Cattle herd size and the cattle herd growth rate variables appear with an expected sign, emphasizing that even for the quantiles with the lowest deforestation these variables are important in explaining deforestation. In turn, the current stock of credit variable (with a positive estimated coefficient) shows the credit availability is driving activities (or increases in those activities) that promote deforestation for that date in time. The school registration variable, with a negative sign, leads us to infer that individuals with a greater number of children enrolled in school (elementary school) have less “arms” to work in activities that promote deforestation. On the other hand, information or knowledge accessed by the adult population through this type of education has a stimulating effect, in the next year, on the engagement in occupations with activities that promote deforestation. Thus, an improvement in the workforce skills from access to education, can be the difference between being busy or not busy in the next year, which given the existing opportunities in the agricultural sector is usually in activities that promote deforestation. Furthermore, a possible impact of the environmental awareness from the access to education is tempered by the need for survival itself that stimulates the search for occupations in the few opportunities available. After incorporating the counties that are part of the intermediate convergence club, the variables contributing to contemporaneous deforestation include the size, density and growth of the cattle herd (all positive), land area in perennial crops and the stock of credit. Only elementary-school registration currently appears as a counter force to deforestation. On the contrary, adult education and stock of credit in the past promote deforestation in the present. PIM has a statistically significant negative impact on deforestation. The analysis shows that in the counties in the area of influence of SUFRAMA, the economic activities spawned by the economic drivers of PIM end up inhibiting those activities with the greatest potential for deforestation. The other percentiles are combined to account for the last convergence club, which has greatest deforestation. In this group all tested variables are significant. In this convergence club it is clear that not only livestock, but also perennial and annual crops increase deforestation. With respect to livestock in particular, the joint impact of a group of variables must be examined. While the cattle herd gro21 Variables are in natural logarithms. 22 That is, being influenced by the explanatory variable in the previous year.

138


wth rate contributes to increased deforestation, more productive pastures with the accompanying higher density of cattle reduces the pressure for new land and therefore deforestation. However, the negative sign of the cattle herd size in the current year (in contrast to the positive sign of the lagged measure of herd size), may be imply that the herd growth occurs first, occupying new pasture areas and then becoming more efficient over time. Again it is shown that the PIM, with a negative sign, works against deforestation caused by the most intensive exploration of natural resources. Perennial and annual crops work differently on deforestation depending on time. Thus, current valued variables increase deforestation, but lagged variables tend to limit current deforestation. This can be attributed to the fact that when they are planted they use areas once occupied by pasture or already degraded areas. However, later on they require new areas, and they become forces expanding the agricultural frontier, in their drive for increasing agricultural areas. Following this reasoning, the in primary cause of deforestation in the most devastated areas was blamed primarily put on livestock and later on other economic activities. This corresponds to the positive sign on the current variable and the negative sign on the lagged variable. The representative variables of human capital were also relevant in explaining deforestation, and, while current adult education registration operates to increase deforestation, the opposite occurs with lagged adult education, which mitigates deforestation. This suggests that the opportunity of education for adults promotes the creation of economic alternatives for the families that are not necessarily related to increased pressure on the use of natural resources. On the other hand, this may also mean that adults with greater knowledge occupy themselves in economic activities in other areas such as services, for example, and not those related to environmental degradation such as logging and cattle ranching.

Model with panel data The model proposed to be used in a framework of panel data is the following:

(7) Wherein, the variables follow the description as Table 10 and it is assumed that the error uit is independently and identically distributed (iid).


139

Empirical evidence A primary and relevant empirical result is found using the Hausman test. In fact, as the test statistic (12.84) is lower than the critical value (5%) of a chi-square statistic with (k = 16) freedom degrees (26.41), the null hypothesis that the equality in parameters is non-systematic was not rejected, which implies that the model indicated for the estimation is a random-effect type. Thus, the model to be estimated is presented in Table 10 below and the variables used are quickly defined: Table 10 – Variables used in the statistical estimation Variables

Description

desm it

Deforestation of county i at time t;

lncultpermit

Natural log of the area of perennial crops of county i at time t;

lncultempit

Natural log of the area of annual crops of county i at time t

lnrebbovit

Natural log of the cattle herd of county i at time t;

lnestcredit

Natural log of the stock of credit of county i at time t;

lneduaduit

Natural log of the registration in adult education in county i at time t;

lnmatrículait

Natural log of school registration in of county i at time t;

lndemogit

Demographic density in county i at time t;

densboiit

Cattle density in county i at time t;

tcresboiit

Cattle herd growth rate in county i at time t;

desmatit

Deforestation in county i at time t-1;

lncultpermit-1

Natural log of the area of perennial crops in county i at time t-1;

lncultempit-1

Natural log of the area of annual crops in county i at time t-1;

lnrebbovit-1

Natural log of the cattle herd in county i at time t-1;

lnpibpcit-1

Natural log of the GDP per capita in county i at time t-1;

lneduaduit-1

Natural log of registration in adult education in county i at time t-1;

lnestcredit-1

Natural log of the stock of credit in county i at time t-1.

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The results presented in Table 11 indicate a significant and positive influence of the both current and lagged values of the area of annual crops, but perennial crops don’t have a significant effect. This may indicate that this first crop, led by soybeans and rice, is the expansion path of the agricultural frontier, as argued Puty; Almeida; Rivero (2007) and Nepstad et al. (2006). The results of the livestock activity on deforestation have to be considered more carefully. On the one hand, the positive sign of the cattle herd growth rate is as expected, as shown in other results cited in this report. However, the negative sign of cattle herd size, vis a vis, the density of the herd per hectare have to be discussed in conjunction with this first result, and also with the positive sign of the lagged herd variable. In fact, the greater the number of animals per hectare, the greater the efficiency in land use and thus the lower the need for new land as the herd grows. On the other side, given a higher herd size, one would expect a positive influence on deforestation. However, this would be true only considering one type of extensive livestock such as livestock for beef. Thus, the negative result of the cattle herd size per county may be tied to the outcome of the other two variables, and may present an increase in the diary cows in the Amazon region, as opposed to cattle ranching. For example, this is happening, in the state of Pará23, where the more confined dairy operations lead to a better use of the existing area, with less impact on deforestation impact. Table 11- Results for panel Regressions - Random Effect. Variables

Estimates

PIM

-17.5421* (6.7059)

lnCULTPERM

6.9625** (12.1861)

lnCULTEMP

41.2181* (10.3955)

lnREBBOV

-20.27126* (11.6405)

lnESTCRED

4.0925* (2.1429)

lnEDUADU

-3.6013** (6.0342) (it continues)

23 According to Guillhotto et. al. (2007), the contribution to GDP of dairy operations in the state of Pará grew almost twice as fast as cattle ranching between 2002 and 2005.


Variables

Estimates

lnMATRICULA

13.2166* (6.5679)

DESMAT (-1)

0.5183* (0.0602)

lnCULTPERM (-1)

8.6312** (11.4593)

lnCULTTEM (-1)

-32.4623* (10.3329)

lnREBBOV (-1)

40.1508* (10.4458)

lnPIBPC (-1)

21.0976* (7.7787)

lnEDUADU (-1)

-10.40262* (4.6364)

lnESTCRED (-1)

-5.2904* (2.7479)

lnPIBPC (-2)

-27.9750* (7.1289)

DENSDEM

-0.0118** (0.0907)

DENSBOI

-0.3382* (0.0776)

Txcrescbov

0.00003* (0.0000095)

Const.

-81.3127* (35.1804)

R-sq

within = 0.2446 Between = 0. 9320 Overall = 0. 4220

Wald chi2(18)

= 1882

n. obs.

= 3502

* Significant at least 10%, ** not significant. Note: Brackets refer to pattern errors.

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However, when put together, the positive signal of the lagged cattle herd in association with negative sign of the current valued variable, may represent a temporal “gap” between deforestation and the size of the cattle herd. As a matter of fact, considering only extensive cattle, herd growth between t and t-1, may imply the need for new areas to be incorporated as a pasture area and because of this the advance of deforestation. The positive sign of the current valued stock of credit was as expected. In other words, the greater the availability of credit in present time, the greater the advancement of cattle ranching, creating an indirect positive impact on deforestation. Meanwhile, the negative sign of the lagged value may indicate that funding in previous time is used for activities that require a longer maturation time, such as equipment, and are not serving to increase sources of deforestation, corroborating the results found for quantile regressions. The proxies for human capital show the same results previously obtained with quantile regressions in which school registration contributes to increasing deforestation. This occurs at the same time that adult education mitigates deforestation, but this is only true for the lagged variable. The per capita GDP experienced a sign change from positive to negative between the first and second lag, with both coefficients significant. This reflects that deforestation may not be directly related to economic growth, but that economic activities were prevalent in leveraging this growth. Finally, the behavior of the PIM variable is these estimations are consistent with those found in the quantile regressions. In both cases, the negative sign of the estimated coefficient shows that PIM has deforestation.

Chapter 7 The PIM’s effect: a counterfactual analysis José Aroudo Mota José O. Cândido Jr.

This segment of the study shows an econometric analysis that uses cross-sectional data to assess the effects of the Manaus Industrial Pole (PIM) within the district of Manaus.

Model specifications A model for deforestation was specified and estimated in the state of Amazonas. The model is subdivided in three stages. During the first stage, the main determinants of deforestation Amazonas´s districts are mapped out, excluding Manaus, which is where the PIM is located. When mapping we use an adjustment of an econometric equation of deforestation determinants and obtain a deforestation average for the districts that do not feel the direct influence of the PIM. In the second stage, a counterfactual exercise is carried out. This exercise can be summarized in the answer to the following question: If Manaus followed the same average pattern of deforestation as seen in other districts in the State of Amazonas, what would the predicted level of deforestation for this state capital? The idea is to attribute to Manaus the same influence of deforestation determinants as seen in other districts, as if Manaus had similar characteristics to the other districts with respect to the deforestation problem. The “as if” aspect of the study is what makes this analysis counterfactual. What actually occurs is strongly influenced y the fact that Manaus houses the PIM, and it is hypothesized that this is an important feature for deforestation in relation to other districts. Therefore, in the third stage the predicted level of deforestation by the Manaus’ model is compared against the actual deforestation in Manaus, If the difference between these values is small, it would be because the PIM is not an

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important factor in preventing deforestation. Otherwise, the results would confirm the importance of the PIM, as an example of public policy that was and continues to be responsible for limiting deforestation in its area of influence. These steps are described below in more detail. Initially, an equation is derived for the districts that do not experience or have only limited influence from the PIM, such as:

(1)

An expansion of equation (1) yields equation (2).

(2)

Where Y is a variable representing deforestation, X is a vector of explanatory variables and ε is an error term. The initial model estimated an equation for the deforestation in 1997. The estimated model used available data, which included variables for the size of the cattle herd, the land area in perennial crops, the actual GDP of the districts (deflated to 2006 prices), demographic density and district population. In addition to these variables, other explanatory variables included rural credit stock of credit (per capita), total number of rural credit contracts (per capita), a cost of transportation index (road and water) from the municipal seat of government to the nearest state capital, a cost of transportation index (road and water) from the municipal seat to São Paulo.1 The initial idea was to focus on the key determinants that recent literature has linked to deforestation, such as the expansion of the agricultural frontier and livestock activity in the state of Amazonas, as the most relevant factors, especially recently. However, other economic activities or determinants could be tested in the model. For example, charcoal production (associated with pig-iron production) and logging have been identified by experts as important causes of deforestation in the Amazon. Margulis (2003) suggests that economic gains realized through livestock activity in the Amazon are relatively low, and that one of the major factors in explaining the increase of this activity in the region is the existence of government incentives such as subsidies or credits. Therefore, this argument justifies the choice of rural credit variables for cost and investment and the total number of contracts for rural credit. On the other side, Young, et al. (2007) highlight other factors such as profita1 These indices were obtained and constructed from IPEA-Date. The index includes road and water ways for the northern region. For further details see Castro (2004).


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bility of land use and the use of the cattle herd as value reserve. They also state that “policy and institutional issues are also crucial: failure to implement the command and control (inadequate environmental monitoring, land chaos, etc.)”. Demographic variables are traditionally included in deforestation models, especially population size and population density. A variable that can also be tested would be migration flow. Cost of transportation is also an important factor, since it is one of the elements that affects the profitability of economic activities in the region. A negative relationship between deforestation and cost of transportation is expected. In other words, the lower the cost of transportation, the higher the level of deforestation. Thus, the PIM’s effect is estimated as:

; where

represent the

explanatory variables for Manaus, the estimated coefficients of the deforestation determinants for all the other districts (except Manaus). The estimation method is ordinary least squares. Therefore, is the estimated deforestation for the district of Manaus, in the counterfactual case the PIM did not exist. So, if > YM ; the PIM acts to diminish deforestation rates in the state, in which YM represents the effective deforestation in the city of Manaus.

Results Several determinant models of deforestation were estimated and tested for the districts of Amazonas (excluding Manaus in these estimations). Four final models were selected and estimated taking into account the significant degree of deforestation determinants proposed by the literature in this area and the nature of the problem in the state of Amazonas. On this last point, it is important to note that the Department of Sustainable Development of the state government states that the greatest pressure exerted on deforestation comes from the expanding process of the agricultural frontier in the south of the state. This can be explained by three contributing factors. First, in the regions of Apuí, Manicoré and New Aripuanã the major rural settlement projects of INCRA (Acari, Juma and Matupi) led to the replacement of family-run cattle ranching. Second, the increase in migration from Acre and Rondonia and other districts such as Canutama, Lábrea and Boca do Acre led to the population seeking alternative incomes in work such as logging and cattle ranching. Third, land in the districts of Manicoré, Humaitá, Canutama and Lábrea was used for agriculture with modern technology and capital intensive agricultural practices. In Amazonas, irregularities in land registration and ownership and low human development (a human development index ranging between 0.4 and 0.7) permeate the whole deforestation process. This is true not only in the southern part of the state, but also in other areas that do not have sustainable economic alternatives. This is what di-

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fferentiates Manaus from other districts, as the presence of the Industrial pole fits the requirement to develop an economic activity compatible with the maintenance of forest coverage. The estimated models seek to reproduce the situation of the main deforestation determinants in Amazonas with the following structure. The first group of variables are representative of agricultural activities and include the size of the cattle herd and the area in perennial crops (LAV). The next set of variables are economic and demographic and include population (POP), credit variables (the stock of rural credit per capita capita (ECRUR), the number of rural credit contracts per capita (CCRUR), and road and river transportation costs (cost of transportation from a district´s headquarter to the closest capital (CTCAP) or cost of transportation from the district´s headquarter to São Paulo (CTSP)). Other determinants suggested by the literature were tested, but because they are not statistically significant they were excluded from the models. These included GDP or per capita GDP and demographic density. Other variables representing the physical capital were also tested, such as urban or rural residential capital (per capita) of the districts in Amazonas. However, the estimated coefficients were statistically insignificant. The signs coefficients of the variables that were significant, which are shown in Table 1, adhere to reality. The livestock and agriculture representative variables show positive signs, as do incentives granted for these activities by means of credit. Furthermore, the population pressure in the districts of Amazonas that do not have a consolidated economic base is an additional pressure factor increasing deforestation pressure. The cost of transportation variables reflect what is actually happening in the state of Amazonas. In other words, higher transport costs inhibit the profitability of agricultural and other resource extraction activities, and thus contribute to reduction in deforested areas. It’s worth emphasizing that the models were robust. In other words, the results (coefficient sign and statistical significance) did not change when different measures of transportation cost or rural credit were employed. For example, the results are similar when using the stock of rural credit per capita (measured in monetary terms) or rural credit contracts per capita (number of contracts).

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Table 1 – Deforestation determinant models in the state of Amazonas. Variables

Estimate Coefficients Model 1

Model 2

Model 3

Model 4

Constant

156, 155

109, 767

283, 701

221, 901

RBOV

(2. 771) 0, 006

(1. 871) 0, 006

(3. 041) 0, 006

(2. 332) 0, 006

LAV

(3. 707) 0, 023

(3. 678) 0, 026

(3. 599) 0, 024

(3. 590) 0, 027

POP

(1. 778) 0, 005

(2. 133) 0, 005

(1. 827) 0, 005

(2. 454) -

ECRUR

(2. 390) 1, 630

(2. 520) -

(2. 345) 1, 619

(2. 454) -

CCRUR

(2. 558) -

10, 900, 790

(2. 990) -

10.854,060

CTCAP

-0, 046

(3. 328) -0, 042

-

(3. 226) -

CTSP

(-3. 134) -

(-2. 960) -

-0, 037 (-2. 864)

-0, 033 (-2. 627)

Estatistical R²

0. 699

0. 723

0. 690

0. 713

F

20, 885

23, 512

20, 003

22, 386

Infor. criterion Akaike

13, 311

13, 227

13, 341

13, 262

Infor. criterion Schwarz

13, 538

13, 454

13, 586

13, 483

Note: RBOV – Size of the cattle herd, LAV – Perennial Crop Area; POP (Population); ECRUR (Stock of rural credit per capita); CCCRUR (number of rural credit contract per capita); CTCAP (Cost of transportation from district’s headquarter to the nearest capital); CTSP (Cost of transportation from district’s headquarter to São Paulo.

Although the coefficient signs of the deforestation determinants in the state of Amazonas have been statistically significant and of the right sign, the results from the table above do not allow a comparison in terms of magnitude. In other words, the magnitude of the coefficients does not show which variables are the

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most important in explaining deforestation. The problem is that the variables are measured in different units. To compare these magnitudes, the models were reestimated using standardized data. This can be understood as a transformation of the original data trying to preserve the same variability of the primary data. This is important for the econometric estimates. By definition, the standardized value of a variable is given by the value of each observation minus the sample average, divided this result by the standard deviation of the sample. Expressed mathematically: VPXi = (Xi – MX)/DPX (18) Where, • VPXi is the standardized value of the observation i of the variable X; In the particular case i is a district of Amazonas and X is a variable of interest, e.g. cattle herd size; • Xi is the observed value of the variable X for the unit i; • Mx is the average of the sample values of the variable X. For example, the district average of the cattle herd size in Amazonas; • DPx is the standard deviation of the sample values of the variable X. In performing this transformation in the variables values, they lose their original scale measurement and will be pure dimensionless numbers. The estimated coefficients are between -1 and 1 or in module terms, between 0 and 1. Therefore, the magnitude of the coefficients may be compared. Table 2 shows the results of standardized models. Livestock is the main deforestation factor in the state of Amazonas when compared to other factors. The value of this activity coefficient stood at 0.33 in all models. These results match with recent evidence reported by Young, et.al. (2007), which shows that, in the case of Amazonas, the greatest pressure on the forest coverage comes from the expansion of cattle ranching. According to IBGE, between 2001 and 2005, the growth in the number of cattle in this state was 39% above the Brazilian average of 22%. In the background, with very close coefficients but lower still are the variables of population size, rural credit access and transportation costs. It is noteworthy that the number of credit contracts per capita showed a coefficient of 0.32, almost equal to the coefficient on the number of cattle. Therefore, rural credit and livestock are additional factors with significant importance in explaining deforestation. As for transport cost, the assessment is done observing the absolute value of the coefficient and also serves as a warning, since meritorious initiatives to reduce the cost of transportation, such as improvements in the traffic conditions of BR-319, BR 364 and BR 317 should be accompanied by strict environmental governance. Otherwise, these highways could attract

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an increase in migration into the state of Amazonas and the expansion of intensive deforestation activities. Finally, agricultural activity is a lower-pressure factor on deforestation in Amazonas. For example, the average of the estimated coefficients in the four models for the perennial crop area is about 59% of the average value of the estimated coefficient the size of the cattle herd. Table 2 – Deforestation determinant models in the State of Amazon. Variables

Estimated Coefficients Model 1

Model 2

Model 3

Model 4

Constant

0, 021

0, 038

0, 021

0, 037

RBOVP

(0. 284) 0, 335

(0. 524) 0, 333

(0. 279) 0, 335

(0. 515) 0, 335

LAVP

(3. 726) 0, 178

(3. 891) 0, 206

(3. 669) 0, 184

(3. 849) 0, 212

POPP

(1. 886) 0, 287

(2. 302) 0, 272

(1. 931) 0, 287

(2. 340) 0, 271

ECRURP

(2. 819) 0, 267

(4. 023) -

(2. 787) 0, 625

(2.770) -

CCRURP

(3. 155) -

0, 319

(3. 074) -

0, 317

CTCAPP

-0, 270

(3. 328) -0, 239

-

(3.95) -

CTSPP

(-3. 192) -

(-2. 921) -

-0, 258 (-2.986)

-0, 223 (-2. 650)

Estatistical R²

0. 700

0. 727

0. 694

0. 720

F

24, 770

28, 272

24, 057

22, 386

Infor. criterion Akaike

1, 819

1, 725

1, 840

1, 750

Infor. criterion Schwarz

2, 030

1, 936

2, 051

1, 961

Note: RBOV – cattle herd, LAV – Perennial Crop Area; POP (Population); ECRUR (Rural Stock Credit per capita); CORUR (Rural Credit Contracts); CTCAP (Cost of transportation from district’s headquarter to the nearest capital); CTSP (Cost of transportation from district’s headquarter to the nearest capital to São Paulo.

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Estimates of the PIM’s effect as a factor in the reduction of deforestation Deforestation consolidated by 2007 shows the district of Manaus with 1.2 million km² (11%) of deforested area. This can be compared to other state capital districts such as Rio Branco (31%) and Porto Velho (21%). Macapá is actually lower than Manaus, with deforestation still standing at 6%. But the PIM’s effect is extensive and ongoing throughout the State of Amazonas, therefore the rate of deforestation in the state is also the lowest recorded by the year 2007, with the exception of the State of Amapá with total deforestation at 1.8%. The state of Amazonas presents a consolidated deforestation rate at 2.1% of its territory until the year 2007, whose results are very different than those seen in other states in Legal Amazon, except for Amapá. Thus, in order to evaluate the PIM’s effect on deforestation in Amazonas it is necessary to use the estimated coefficients of the models without the presence of Manaus. As mentioned earlier, the idea is to impose on Manaus the average deforestation behavior of other districts on deforestation. It is plausible to assume that the PIM’s influence is stronger and more significant in the state capital. If this influence reduces deforestation, then the forecasted value in the model (pairing Manaus’s data with estimated coefficients from the other districts) be higher than that observed in Manaus. If the actual and predicted deforestation values are very close, then the PIM’s effect on deforestation would be negligible. The predicted deforestation for Manaus is a counter-factual exercise. In other words, it’s the deforestation for Manaus under the hypothesis that there was no system of economic incentives that generated this center of activity. This exercise becomes possible when it is trying to predict estimated coefficients of deforestation determinants for the other districts in the State of Amazonas under Manaus’s data. The deforestation predicted by the model in Manaus is controlled by several factors, such as agriculture, population, cost of transportation and access to rural credit. The only difference in the predicted model is that related to effective deforestation and is the presence of an industrial agglomeration in the PIM’s modeling. This is present in Manaus and absent in other districts of the State of Amazonas. Therefore, as shown in Table 3, for 1997, the actual value of deforestation in Manaus was only about 14% to 15% of deforestation predicted by the model artificially, imposing on Manaus the same deforestation patterns as seen in other districts in Amazonas and by using the coefficients of the deforestation regression models. When it’s assessed in another perspective, the presence of the PIM in Manaus, by developing economic activities with no or low use of forest resources in their inputs and by boosting other economic sectors with the same productive standard, such as services, collaborates with a reduction of 85% to 86% of defo-


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restation in the region of Manaus. Therefore, the PIM’s existence contributed to the preservation of the Amazon, avoiding the destruction of about 5.2 thousand km² of rainforest in 1997. Table 3 - Estimates of the PIM’s effect in reducing deforestation in the State of Amazonas. Model 1

Manaus

Predicted Deforestation

Effective Deforest.

PIM’s Effect

C

156, 1550

1

156, 155

RBOV

0, 0059

6932

40, 808684

LAV

0, 0228

876,9

19, 9915662

POP

0, 0051

1157357

5914, 09427

ECRUR

1, 6300

1, 46494339

2, 387879704

CETCAP

-0, 0460

7.1875

-0330668125 881,19

5.251,92

Effective Deforest.

PIM’s Effect

6.121,65

881,19

5.240,46

Effective Deforest.

PIM’s Effect

881,19

5.193,58

Model 2

Manaus


C

109, 7666

1

109, 7666

RBOV

0, 0056

6932

38, 916248

LAV

0, 0261

876,9

22, 8774441

POP

0, 0051

1157357

5945, 342909

ECRUR

10900, 7900

0, 00046312

5, 048419321

CETCAP

-0, 0421

7.1875

-0, 302342188

Model 3

Manaus


C

283, 7009

1

283, 7009

RBOV

0, 0059

6932

40, 572996

LAV

0, 0237

876,9

20, 8132215

POP

0, 0051

1157357

5895, 576558

ECRUR

1, 6119

1, 46494339

2, 361380343

CETCAP

-0, 0368

4570, 0995

-168, 2619234 6, 074,76

(it continues)

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Model 4

Manaus


C

221, 961

1

221, 901

RBOV

0, 006

6932

38, 95784

LAV

0, 027

876,9

23, 7166374

POP

0, 005

1157357

5900, 205986

ECRUR

10854, 060

0, 00046312

5, 026777528

CETCAP

-0, 033

4570, 0995

-150, 3425633 6.039,47

Effective Deforest.

PIM’s Effect

881,19

5.158,28

Note: RBOV – cattle Herd; LAV – perennial Crop Area; POP (Population); ECRUR (sock of rural credit per capita); CORUR (Rural Credit Contracts); CTCAP (Cost of transportation from district’s headquarter to the nearest capital); CTSP (Cost of transportation from district’s headquarter to the nearest capital to São Paulo.

Estimated Benefits of the PIM in Relation to Avoidance of Deforestation in Manaus The average avoided deforestation in Manaus according to the estimated models is 5.2 thousand km². Therefore, this value will be used as an annual reference to estimate the annual effect that the PIM has on maintaining forest coverage in Manaus. The value of the forest can be computed as the sum of direct, indirect, option and existence use values. The direct use values occur when there is an observable behavioral link between the diverse biological resources of the forest and production or consumption activity. Direct uses would include the use of forest resources as sources of raw materials, medicinal products, recreation and other consumer goods in general. The value of the forest´s indirect use is equivalent to the services provided indirectly to society, such as carbon capture, nutrient flow, maintenance of natural water ways and climate balance. Option value involves the risk of the resources becoming extinct, which could be used directly or indirectly in the future. Bioprospecting and innovations in biotechnology and knowledge can be taken from the rainforest and this can generate benefits for local people. Existence value refers to the ecosystems survival and perpetuation and encompasses the subjective values that individuals place on natural resources; ethical, altruistic, contemplative and moral positioning (Mota, 2006)

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Because the Amazon rainforest is a global public good, the benefits of the Amazon forest go beyond the area of direct influence of the PIM and extend to the whole world. Therefore, the estimate of the environmental benefits of the PIM will focus on the possible effects on avoided deforestation. This will be implemented by partially adopting the methodology and parameters of the studies completed by Soares Filho, et al. (2004) and Alencar, et al. (2005). For the value of indirect use, the monetary value of carbon stock was used, estimated at 120 t per hectare of the forest at a price of USD 6 per t of carbon, which results in an estimated of USD 720 per ha ( Margulis, 2003 Alencar, et al.). In addition, it is added to the value of the hydrological cycle, estimated at USD 10 per ha/year (Andersen, 1997 Alencar, et al. 2005). In order to partially measure option value the potential returns to “bio-prospecting” measures are used (development of pharmaceutical and agricultural products with information and use of the native flora). These have an estimated value of USD 2.50 per hectare / year (Alencar, et al. 2005). Existence value is given by the willingness to pay for the biodiversity protection obtained in studies such as Horton et al. (2003). The estimated existence value is USD 31.20 ha/year for the protection of the rainforest. Furthermore, the benefit of avoided forest fires is added, because of the fires set to aid in economic activities. These fires cause a loss of 20% of biomass and increase the release of carbon into the atmosphere. It is estimated that the area affected by avoided fires corresponds to 10% of the area of the avoided deforestation (Diaz et al., 2002 apud Alencar, et al., 2005). Alencar, et al. (Op. cit.) present the following parameter estimates that will be used in this study to determine the annual benefit valuation obtained by avoided deforestation in Manaus taking into consideration the presence of the PIM. Table 4 presents the parameters used for the preparation of the monetary value estimates of the non-deforested forest. Table 4 – Parameters used for development of monetary values Parameters

Value

Units

Carbon stock

120.00

t/ha

Carbon price

6.00

USD/t

Value of indirect use – Carbon stock

720

USD/ha

Value of indirect use – water cycling

10.00

USD/ha/ano

Value of option – Bioprospecting

2.50

USD/ha

Existence value – Biodiversity protection

31.20

USD/ha/ano (it continues)

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Parameters

Value

Units

Carbon emission in cases of forest fires

20%

% biomass

Area of forest fires on the total deforestation

10%

% deforestation

Value of fire risk per hectare of deforestation

12.00

USD/ha

Sources: Margulis, 2003; Andersen, 1997, Simpson et al., 1996, Cochrane et al., 1999; Diaz et al., 2002.

Table 5 shows the monetary valuation of the PIM’s benefits by the avoided deforestation in the district of Manaus. Looked at from another angle, the annual benefit of the PIM is given by its contribution in the maintenance of the forest coverage in the district of Manaus, which has an area of 5.2 thousand km². The estimated monetary value of this annual benefit is USD 399 million, which is concentrated in carbon retention. That represents 94% of the total, USD 375.2 million. The monetary value of indirect use for the hydrologic cycle is estimated at USD 5.2 million. As the monetary value of future use or the option to preserve the forest stand so that future generations have access to the region’s natural assets, the bioprospecting value reaches USD 1.3 million. The estimated benefit related to the biodiversity protection, i.e. only by the existence of intact forest is equivalent to USD 16.2 million. As for the avoidance of fires in relation to non-deforested area, the monetary value is equivalent to USD 625,300. Table 5 – Annual estimated benefit of the PIM by the avoided deforestation in Manaus Benefit source Are of Avoided Deforestation in 1997 (Km2) Are of Avoided Deforestation in 1997 (hectares) Benefit of the avoided deforestation Value of the indirect use – Carbon stock (USd) - A

Measure 5.211 521.106 USD 375,196,320.00

Value of the indirect use – water cycling (USD/year) - B

5,211,060.00

Value of Option – Bioprospecting (USD/year) - C

1,302,765.00

Existence Value – Biodiversity protection (USD/year) - D Value of fires risk per hectare of deforestatio (USD) - E Total Monetary Benefit = A+B+C+D+E Source: Authors’ compilation.

16,258,507.20 625,327.20 398,593,979.40

Chapter 8 The demand for deforestation and PIM´s effect Alexandre Rivas Renata Mourão Beatriz Rodrigues

The model developed in this segment follows the tradition of Balestra and Nerlove (1966). The authors analyzed a dynamic cross-section model and longitudinal data that continues to be a current reference for studies of dynamic panels of econometric development. The starting point is to determine an appropriate classification for the environmental resource being analyzed the Amazon forest. According to Kahn (2005), an environmental resource can only be indirectly analyzed and in qualitative terms. The Amazon forest is a typical case. Although a single tree is considered a natural resource, a set of trees organized with other systems is an environmental resource. This is an important dynamic for the model, because although it is possible to plant trees to replace harvested trees, the replacement of a forest by a forest plantation does not work the same way. The point that must be emphasized here is that a forest may be consumed to the point of extinction. It may not be a durable good. The privately profitable predatory use of this forest would generate deforestation at a rate above the socially optimal rate. The higher the rate of deforestation, the stronger could be the incentives to accelerates deforestation, generating a positive feedback cycle. Considering the points above, it is clearly possible to infer that deforestation is a dynamic process, and that its demand function must incorporate the effect of the forest stock size and all the adjustments that may occur as time goes by. The dynamic model developed in this segment considers the demand for new deforestation caused by economic forces originating in PIM. The logic behind this construction is simple. If at first PIM uses the natural resources in the Amazon as inputs into its production process in a predatory way,

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then economic agents who profited from this process would continue it, continuing to extract forest resources as inputs (see Chapter 4). It is important to highlight two aspects of this line of thought. The first is that the forest can be used either in a predatory or non-predatory way1. Here the predatory aspect is directly considered. The second is that economic activity, which can lead to deforestation, may have a low endogenous level in relation to the use of inputs. This is not necessarily a negative aspect, but is an important consideration for PIM. Because of this rational behavior, it is often assumed that there is a demand for deforestation which is consistent with the traditional economic theory of demand. For this reason, this theory should be considered at two distinct moments in time: past and present. Initially, it is assumed that there is a new demand for deforestation, D*, which is a function of the price of the products derived from the forest with direct use value, as well as the value of the bare land, or the substrate of the forest. These prices are simply established in existing markets and are represented by p. Questions concerning the failure of markets to establish the correct price for goods from or associated with the forest will not be taken into consideration. This is not an unrealistic assumption, because the economic agents typically respond to stimuli and these stimuli are associated with the possibility of generating short term income. In short, this is the price that encourages deforestation. The other element of the deforestation rate, D*, is all the deforestation demands directly arising from PIM, Z*. Equation (1) shows this relationship.

(1)

Note that in the equation p acts on the rate for new deforestation and not the absolute value of the total area of deforestation. The problem addressed in this segment of the study is defining the concepts of new demands for deforestation that originate in MIP’s activities. They must therefore be incorporated into a model that can be expressed in terms of observable variables. Consider that there is a demand for deforestation derived from PIM, Zt, in the present and, Zt-1, in the previous period, where changes in this demand are expressed as the equation:

1 In this segment the term sustainable or non-sustainable will not be used, due to the fact that the sustainability concept requires a deeper theory that goes beyond the scope of this work.


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(2)

The first term on the left represents the change in deforestation caused by PIM, but not the total demand for deforestation. This is due to the fact that there are many other causes of deforestation. Part of the current change of this deforestation occurs due to changes in behavior in both the present and past. Note also that not all of the existing demand in period t-1 will be felt in period t. This is because part of this demand in the last period could be slowed down by the implementation of public policies of monitoring and control. In the previous period, t-1, consider that the forest stock, F, is given by Ft-1. For this period, the deforestation rate is given by λt-1. It is important to emphasize that this rate measures the use of the forest stock in a predatory way. So,

(3)

It is possible to say there is a depreciation rate r of this deforestation, because of economic activities with low demand for deforestation. In other words, the total demand for deforestation can be mitigated by a resulting counter force able to minimize or neutralize it. Under these conditions, from the forest stock Ft-1 only a fraction given by (1-r)Ft-1 will be effectively available. This fraction will be used at a rate of deforestation caused by predatory activities of λt. Thus, the total fraction of the forest that is used is given by λt (1-r)Ft-1

(4)

The amount above expresses the portion of the deforestation caused by Zt other activities that, in period t, are related to the existing forest stock at the beginning of the period. In other words, it represents the demand for deforestation that was translated from the past period to the present period and the beginning of the present period, t. The forest stock in period t will be given by Ft. This stock is associated with the total deforestation caused by Zt, as shown in equation Zt = λtFt

(5)

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The new demand for Z*t can be defined as

(6)

Although the deforestation rates in the Brazilian Amazon have increased in recent decades, they have remained relatively constant and low in the state of Amazonas. According to ECLAC (op. cit.), the rate of deforestation in the state decreased by 12.1% in 2003, 8.2% in 2004, and 4.7% in 2005. Even if this was not the case, because of the regional overflow of the problem´s negative effects, it is important to note that Amazonas is the Amazon state with approximately 98% of its forest stock preserved, according to the same data source. Thus, it is assumed that λt = λ for every t in the state. Equation (6) then can be rewritten now only in terms of the variables of Z. However, as λt = λ, Zt = λtFt and can be rewritten as follows:

(7)

, then the above equation

(8)

The new total demand per deforestation associated with PIM will be the sum of the incremental demand (in parentheses) plus the last demand that was mitigated by the effect of r. Similarly, the new total demand per new deforestation, will be given by:

(9)

Where rd represents the forces that lower deforestation pressure throughout the region and r ≠ rd.

Assuming linearity, the equation (1) can be rewritten as follows: (10)


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Note that r and rd do not need to be known, a priori, because (10) shows that they can be estimated. The literature on tropical deforestation indicates that there are two types of causes of deforestation: the primary causes and the underlying ones. Many studies have been carried out in this fashion and these causes can be used to estimate Zt in the following model:

(11)

Where, P represents the primary factors and S the underlying ones. Substituting (11) in (10), we obtain:

(12) The Equation (12) so can be perfectly estimated. The implicit parameter r is super identified, since the estimates for it are possible from the ratio between and . A restricted maximization for the following conditions is necessary.

(13)

(14)

By using (13), equation (12) becomes:

From the model above, and based on (10), PIM’s effect, rd, in order to reduce the deforestation is given by:

(15)

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The empirical test Equation 14 was developed by using variables that are primary and underlying causes of deforestation using data from 2000 to 2006 in the state of Amazonas. Different models were tested and a number of useful observations ranged from 399 to 436, depending on the model. The regressions were estimated as a panel using the ordinary least squares method, adjusting for fixed effects. Out of the variables that were tested from the databases mentioned above, five were chosen. A primary one, QTBOV and four underlying ones: VALORMAD, PIBCAP, SUFRAMA and LAGDES. The variables are defined in Table 1 below. Table 1 – Primary and Underlying variables Variables

Definitions

DESAM

Deforested area in km2

QTBOV

Cattle herd (No. of animals in 2006)

VALORMAD

Value of production of extracted wood, in logs (Thous. Reais)

PIBCAP

Per capita district GDP (Brazilian 2006 Reais of 2006(thousand)) – Deflated by the National GDP Implicit Deflator

SUFRAMA

1 = if district has received resources from SUFRAMA, 0 = otherwise

LAGDES

Lagged Deforestation in the State of Amazonas (lagged one year)

Equation 14 was estimated by using various combinations of primary and underlying variables. Variables that attempted to incorporate the spatial dimension in the model were also used, but they have not produced satisfactory results and were excluded from the tests. From these various combinations, five of them were chosen to be presented in Table 1 below. In two of the chosen models, all counties of the State of Amazonas were considered, including Manaus. In models 3 and 4, the city of Manaus as well as Humaitá, Labrea, Manicoré and Apuí (4 counties in southern Amazononas) were all removed from the sample. This occurred in an attempt to assess the overall outcome of the model without the main economic center of the state, as well as without the counties that are close or make up a part of the set of counties with high rates of deforestation. A fifth combination, Manaus and southern Amazonas was tested as well.

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Table 2 – Estimated results using panel data in relation to deforestation in the State of Amazonas for the period 2000/06. Model 1

Model 2

Model 3

Model 4

Model 5

With Manaus

With Manaus

Without Manaus, Without southern AM

Without Manaus, Without southern AM

Without Manaus, With southern AM

QTBOV

0.0003* (5.95)

0.0003* (5.91)

0.0003* (13.83)

0.0003* (13.76)

0.0003* (5.88)

VALORMAD

0.0117* (4.01)

0.118* (4.04)

-

0.0009 (0.59)

0.011* (4.03)

PIBCAP

-

-0.008** (-2.17)

-0.004** (1.95)

-0.004** (0.59)

-0.01** (-2.37)

SUFRAMA

-3.324 (-0.79)

-2.388 (-0.57)

-0.353 (-0.16)

-0.362 (-0.17)

-2.50 (-0.58)

LAGDES

0.228* (7.86)

0.234* (8.07)

0.297*** (1.72)

0.297*** (0.017)

0.235* (8.05)

Constant

6.084 (2.49)

3.685 (1.37)

3.92 (1.30)

3.62 (1.39)

3.09 (2.78)

R2

0.26

0.27

0.36

0.36

0.27

F

38.36

31.90

56.35

45.07

31.66

Figures in brackets are the value of the test t. Significant at 1% ** Significant at 5% *** Significant at 10% *

The R2 of the models ranged from 0.26 to 0.36. This indicates that they are able to explain from twenty-six to thirty-six percent of its variations. This is an acceptable result for this type of analysis. The F- statistic is the aggregate test of the null hypothesis (H0), testing the hypothesis that all estimated coefficients are equal to zero (Greene, 2008). The test results indicate high significance level, above 1%, which leads to the failure of accepting H0 In the five models presented, the coefficients for the cattle herd remained virtually the same and the signs remained constant. The test t shows that the variable was significant at the level of 1% in all models. It also confirms that in the State of Amazonas extensive livestock is a major cause of deforestation. However, one detail should be highlighted. The estimated coefficient is very small, which indicates a very low effect for this type of activity in the state. In the models already presented, the timber value, VALORMAD, was insignificant only when the counties of Manaus or the southern Amazonas’ counties were not considered. The variable signs went as expected. In other words, an in-

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crease in the timber value leads to an increase in deforestation. Although there is rationale for this affirmative, further analysis should be developed because there are still important gaps in the available data for this variable. GDP per capita, PIBCAP, presented the expected and significant sign at the 5% level. As a matter of fact, in model 3 the variable was marginally significant at this level. However, the coefficients values are very small. In the various configurations tested the sign of the variable remained negative. It is possible that one of the reasons for this behavior is that, with the increase of individual income, families are able to buy imported goods, leading to a pressure reduction on the stock of local natural products2. However, because of the coefficient magnitude, the effect of the individual income increase is small. It does not mean that the effect on deforestation will always be that way. The SUFRAMA variable is a dummy variable that seeks to capture possible effects of the financial resources applied by Suframa in the State of Amazonas on deforestation through partnerships for transfers, applications of infrastructure investments, and human resource training, among others. In several of the tested combinations, the variable sign indicated that the investments from Suframa, in the counties of the state, work to break deforestation. However, given the degree of variable significance this cannot be conclusive. In the tests, increasing by 10 and 20%, the number of investments has changed the variable behavior. Perhaps an analysis that takes into consideration the nominal value of the investments may be more enlightening. The variable of greatest importance for this model was the lagged deforestation variable, LAGDES. The logic behind this variable is current deforestation is influenced by primary causes of the past and by the history of deforestation. There are two ways to explain this behavior. The first one is that if the economic activities are strong users of natural resources, then there will always be a demand for these inputs to be computed in the following period. The second one occurs in a similar way, but it is more to do with a contamination effect. That is, if the economic agents that understand that the forest can generate short-term income, and that this forest is continuously decreasing in size, then it is possible that these utility maximizing agents accelerate the use of this forest to obtain the most possible income in the next period. Still, only in the estimated models without counties near the Arc of Deforestation in southern Amazonas, 3 and 4, the coefficients were significant at a level of 10%, while with the others it was 1%. A possible consistent explanation for this fact (and one not only for the results shown in other models, but also for those shown in the table above) is that if only those counties far from the border are taken into consideration, with little interaction from the economy of Manaus (PIM’s economy) deforestation from the past period did not have much importance. It

2

For more details on this analysis, see Rivas (1998) and Kahn (Chapter 4 above).

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is likely that this occurs for two reasons: First existing pressures in the southern Amazonas are not reaching the most eastern portion of Amazonas. The second reason is that the other economic activities of the State are not directly or indirectly intense in the use of the forest. As the performance of Amazonas’s economy has a high correlation with the performance of PIM’s economy, it seems that this is the main underlying force acting beneficially in the state. Although the analysis can be extended to a better scenario if the results are extended, the point is to estimate, from the reduced model shown in Equation 14, PIM’s effect. This effect is implicitly estimated through α6, in the case of the models in Table 1, the coefficient of the LAGDES variable, according to Equation 15. Table 3 below, presents the calculation of PIM’s effect, rd, for the different significance levels. Table 3 – PIM’s effect, rd, to the significance levels from 1 and 10% Significance level of 1%

PIM’s effect, rd

Significance level of 10%

Model 1

Model 2

Model 5

Model 3

Model 4

0. 772

0. 766

0. 765

0. 703

0. 703

Regardless of the significance level, PIM’s effect varies between 70.3 and 77.2%. PIM’s effect is the opposite effect (measured in terms of rate) generated by the Manaus Industrial Pole in preventing deforestation in the State of Amazonas.

Conclusions This component has developed a dynamic panel analysis to estimate the effect that the Manaus Industrial Pole has in reducing deforestation pressure in the State of Amazonas. The analysis shows that without PIM, thedeforestation rate in the state could be up to 77.2% higher. Table 3 below calculates PIM’s effect and the amount of carbon emissions avoided from the estimated values. For this purpose an average density of carbon at 120 tons per hectare was used. From the minimum and maximum estimated values for PIM’s effect, the areas corresponding to the avoided deforestation in each year were calculated and presented in the third row of the table. The last line shows the value in tons per hectare of avoided carbon emissions.

Max

Min

2684

2,061

2,134

1,500

2,780

2,146

Max

2,966

2,085

Min

881 Max

3,864

2,983

2002

5,343

3,756

Min

Max

6,960

5,373

1587

2003

4,077

2,866

Min

Max

5,311

4,100

1211

2004

2,532

1,780

Min

752 Max

3,298

2,546

2005

2,626

1,846

Min

780 Max

3,421

2,641

2006

144,900 207,200 150,000 214,600 208,500 298,300 375,600 537,300 286,600 410,000 178,000 254,600 184,600 264,100

2,072

634

612

1,449

Min

2001

2000

Source: http://www.obt.inpe.br/prodes/prodes_1988_2007.htm ** , where, DEV is avoided deforestation and DEF is effective deforestation.

Avoided carbon emission, in t/ha

Possible deforestation without rd , in Km2

DEV (PIM’s effect- rd), in Km2 **

Effective Deforestation (DEF), in Km2 year-1 *

Table 4 – Calculation of PIM’s effect, minimum and maximum, and the avoided carbon emission in the State of Amazonas for the period 2000 - 2006.


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Whereas, that only the average price of one carbon ton ranges from USD 6.00 at “Chicago Climate Exchange” up to about USD 38.00 at “European Climate Exchange”3, in the period analyzed (2000 to 2006), PIM may have avoided one to 10 billion USD in carbon emissions, as shown in Table 5 below.

Table 5 – Total value estimate of the avoided carbon emissions due to PIM’s effects in the period from 2000 to 2006. Average value of carbon ton, in USD

Market

Maximum and Minimum values, in USD 1.000

6.00

USA

1,100,304.00 1,573,992.00

38.00

Europe

6,968,592.00 9,968,616.00

If these estimates consider other indirect use values (see Mota and Cândido Jr., Table 4, in this book) such as water cycling, bioprospecting, biodiversity protection and fire risk, they would be significantly altered.

Quote from 10/05/2008. http://www.chicagoclimatex.com/ http://www.europeanclimateexchange.com/

3

Part III The future of PIM

In the following texts the main conclusions of the study will be presented. The first conclusion discusses the possible consequences that may result from a potential end to the PIM. Furthermore, this chapter offers proposals that might increase the positive effects of the economic model put forth in the state of Amazonas and therefore contribute to greater conservation in the Amazon. These proposals include the use of marketing and economic policies.

Chapter 9 Possible consequences of a potential end of the PIM Alexandre Rivas

So far, the study has shown that the PIM has been vastly important with regards to the preservation of the Amazon forest in the state of Amazonas. The factual evidence presented during the first part of this book illustrates an inverse relationship between the PIM and deforestation in the state. As the econometric modeling shows, the observed evidence was confirmed. The model that examined causality and convergence clubs showed that the behavior of the PIM variable served to reduce deforestation. This result was corroborated by the estimation with quantile regressions, and which also showed that PIM has reduced deforestation. In the counterfactual analysis, the first quantification of this deforestation reduction effect in Amazonas was estimated. Models were estimated in both the presence and absence of Manaus. Thus, it showed that Manaus reduces deforestation. Thus the predicted value of the models with the city data would record a higher deforestation level. Otherwise, the actual and predicted deforestation values would be close and the deforestation effect on the PIM would be negligible. The analysis showed that the presence of the PIM in Manaus, by developing economic activities with little use of forest resources in their inputs and by boosting other sectors of the economy with the same standard products, such as services, has helped to reduce deforestation by 85% to 86% around Manaus. From the conclusions found in the two previous econometric models, the PIM’s effect was estimated by taking into account a specific behavioral model. This model came from a principle different from the previous ones. In other words, it took into consideration that there is a demand for deforestation. Thus, this demand was estimated with reference to principles of microeconomic theory.

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In this fashion, the first model examined the importance and significance of the variables, but did not estimate the PIM’s effect. The second model estimated the PIM’s effect from a counterfactual analysis, but used only the year 1997 in the modeling because of lack of date for previous periods. And finally, the third model estimated the PIM’s effect for the most recent period. For this reason, the analysis presented below will use only the study with the most recent data to infer what would be the effect on deforestation if the PIM was ended. For this, Model 2 presented in Table 1 in Rivas and Mourão (this book)1, will be rewritten below. The variables are the same as defined in Table 1 of the above-mentioned chapter of this book. The reason for choosing this framework, and not another is because this framework deals with regression. The city of Manaus is used and tested against all variables. In addition, the results are similar to the others and the estimation covers a longer period of time. Therefore the equation is as follows: DESMAT = 6,084 + 0,0003QTBOV + 0,118VALORMAD - 0,008PIBCAP – 2,388SUFRAMA + 0,234LAGDES In order to find the actual deforestation for the desired period, the actual values for each variable in the period are substituted into the equation. Although the final value is very important, it’s even more important to understand how the dimensions considered in the equation may contribute to deforestation in the state of Amazonas, in a possible slowdown or in the absence of the PIM. In this case, ceteris paribus, the analysis should be done in a systematic fashion, considering one variable at a time.

What if PIM no longer existed? The equation above shows that the variables used can be grouped into three categories: economic, physical and dummy. The economic category includes timber value (VALORMAD) and per capita Gross Domestic Product (PIBCAP) of the districts. The physical category includes the size of the cattle herd size (QTBOV), and lagged deforestation (LAGDES). Finally, the dummy category includes the variable SUFRAMA, which can show if the presence or absence of Suframa investments have any effect on deforestation for this district. Starting the analysis with the binary (dummy) variable, in a situation where the Manaus Industrial Pole would lose dynamics, Suframa would inevitably lose its power to invest. This would hamper its estimated ability to inhibit deforestation. Investments from Suframa fight against deforestation in at least two ways. The

1 See section The Demand for Deforestation and the PIM’s Effect above.


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first one is by generating income and contributing to the improvement of the local purchasing power. The increase in purchasing power can lead consumers to buy manufactured products from other regions of the country and in so doing, reduce the pressure on local natural resources. The second one is through the improvement of infrastructure, material and/or human resources. It is possible that their combined effect causes the results that were found. However, that does not mean that it will always be so. If the investments that are made by Suframa change, a completely different result can arise. Since the investments from Suframa are characterized by caution in relation to environmental degradation, it is more important to consider the effect of their absence, and not an increase of these investments. Considering the two contributing forms of the effects of the Authority’s investments made in the previous paragraph, the reduction of its investments could lead to a decrease in income level and, therefore, change in the individuals and families expectations. This change could lead these economic agents to take a more aggressive stance in their use of natural resources. If the reduction of Suframa’s investments adversely affect the stock of human capital and material infrastructure, this can lead to a decrease in the ability to generate income, causing the same result. If the PIM, were for some reason to be hit by an adverse policy, the Gross Domestic Product of the state would be drastically affected. This already occurred to some extent in 1996. Considering that in the short and medium term the population of the state does not change rapidly, a reduction in GDP would imply a decrease in total income and therefore a higher level of widespread poverty. As people react to incentives and think about the margin of profitability, the first action to be taken would be to use relatively cheaper natural resources, which would thereby encourage environmental degradation. This behavior is demonstrated in the tested models. Note that both the SUFRAMA and the PIBCAP variables act as to reduce deforestation, so that their signs of the estimated coefficients are negative. In other words, if the variables increase, deforestation decreases. The fact that the sign of the PIBCAP variable is negative reinforces the argument developed for SUFRAMA. Thus, understanding the composition and dynamics of the state GDP is fundamentally important, not only in guiding economic policy in the state of Amazonas, but also to protect the Amazon forest. Although VALORMAD is an economic variable of the model, the estimated result is not directly tied to the PIM’s behavior. This is because this variable measures the timber value in logs and logs are a commodity, the price of which is determined by markets that are not related to the PIM. However, it is important to note that a decline in economic performance, similar to those described in previous paragraphs, might make logging become a relatively more lucrative activity, especially if it is performed outside the law. The sign of this variable indicates that an increase in the timber value would lead to an increase in deforestation. Thus,

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it is not necessarily the absolute timber value that is relevant, but the relative one. This way, if income decreases, the relative timber value might increase and there might be a stimulus for the increase in deforestation. The physical category is analyzed last. The first variable is the size of the cattle herd. The equation shows that, for the whole Amazon region, the cattle herd size has a direct effect on deforestation. We can not say that cattle ranching would fully replace the PIM, in case of a loss of significance of the latter. However, given how cattle ranching has been developing in the border areas of the region, cattle ranching would be expected to increase in importance in the absence of the PIM. This could occur for two main reasons. The first reason is similar to the other cases, increasing the relative value of this activity in relation to the PIM. The second reason, the simple need to supply the main consumer markets of the state with cheaper food, due to the new lower income level. In this scenario extensive livestock could become economically attractive, but significantly harmful from an environmental standpoint. It should be noted that although the variable is statistically significant, the absolute value of the coefficient is very small. The last variable to be considered is lagged deforestation. As previously explained, this variable aims to consider how past deforestation affects future deforestation. The results show a direct relationship with the dependent variable DESMAT. When this variable was evaluated for incorporation in the model, the supporting logic assumed that if the economic agents in PIM used the forest in the current period as a significant part of their production processes, in the following period they would look back and say that using the forest helps to increase their short term profits. Thus, the economic agents in the PIM would use more forest and more deforestation would occur. In other words, the total amount of deforestation would increase, because short term profits would be attractive. Actually, the analysis of this relationship is more complex than described here. However, it is important to understand how a decrease in the importance of the PIM can affect deforestation through the variation of this deforestation stock. If PIM economic activity began to collapse, there could be another prominent effect, a harder fight for economic survival. The families and companies, now with lower incomes, would seek to adjust their capacity to generate income, since they are now affected by the absence of the PIM. As explained earlier, the rational behavior would be to exploit the natural resources that generate income in the short term, particularly those resources that had lower marginal costs of extraction. The economic agents could then look at the previous period and find that some income was generated at the expense of deforestation and repeat the same behavior in the following period. This would cause the total stock of deforestation to increase, and increase the inherent consequences. If no new activity with an appropriate scale and low impact on the forest were found to replace the PIM, this would further increase deforestation in the state of Amazonas.


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Population dynamics The State of Amazonas has a current population estimated at approximately 3.2 million inhabitants. According to Teixeira (this edition) the majority of the population is located in urban areas. Manaus is considered, to a certain extent, to be a city-state and lays claim to approximately 81.5% of the Gross National Product of the state, at the current market price of 2005 (IBGE, 2008). This obviously makes the PIM a major attraction for the population, because of employment opportunities. This fact has made the population of Manaus increase rapidly in the last two decades, currently reaching about 1.7 million. A reduction in the PIM growth rate, or even the possible extinction of PIM, could be catastrophic in many ways. From the economic perspective, the state’s production would be reduced substantially, which in turn would affect the family income level and the business profitability. This could make economic agents begin to search for alternatives to generate income and primarily seek those with lower operational costs. This could result in an increase in the level of the use of natural resources. There could be an increase in fishing, logging and mining, especially those less dependent on scale economies. This effect could be would be greater the higher the population of Manaus, particularly those directly dependent on the PIM for income. Another population effect likely to occur under a possible absence of the PIM, would be the migration of population to frontier regions. This would stimulate the increase of pressure on its natural resources. In this scenario, there would be a population displacement in several directions. One of them would be to southeast Amazonas, where the problem is widespread because of advances in livestock and soybean cultivation. Another area likely to receive immigration would be the State of Roraima. Here, there is the possibility of developing agriculture, livestock and mineral exploration. Another vector is toward the east, headed to western Amazon, where the level of environmental preservation is very high. Finally, a population shift towards western Amazon would be likely, into to the State of Pará, along the path of the Amazon River and from there, branching out into several directions. These assumptions could be magnified, depending on a number of other factors. The net result of this scenario could be more pronounced, as per capita income depends on the rate of reduction of income in relation to population size. Since the total income may fall even faster than the population leaves the area, per capital income would fall even faster.

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Aspects of Industrial Location As discussed in this segment the Mathematical Behavioral Model of the Manaus Industrial Pole, the question of current industry locations in the PIM could be completely revised. In a globalized world, geographic location is important, but not decisive. The industries located in the PIM could easily be installed in other regions of the country or in South America or really any other place in the world. From a strictly economic point of view, if the first situation occurred, the Amazon economy would be negatively affected. However, nationally, it would not have a lesser impact because of the fact that there has been only a relocation, and not an elimination of industries and jobs in the national territory. Yet, the implications described in other parts of the study could occur and have a very strong adverse environmental effect, producing very negative externalities. In the second case, if the industries switched to other countries, Brazil would take a double loss. The first loss would be due to the decline in national GDP, and the second by the environmental degradation that would occur in the Amazon.

Absence of the State and its consequences An end to the PIM would harm the states revenue generating power. State revenues would plummet, the level of the industrial activity would fall and also reduce the dynamics in the services sector. All of these factors combined with those mentioned above could lead the state to act in those areas of highest priority by emergency. The presence of the state would inevitably be reduced. This perhaps would not be a problem if government infrastructure was well established and consolidated in the Amazon. But this is not the case. Currently, there is a great effort at all levels of government to increase the state’s presence in the region. The task is Herculean and involves enormous resources to achieve this. Without the state, the Amazon would be even more vulnerable to the actions of poachers, illegal loggers (both Brazilian and non-Brazilian), extensive livestock would become totally uncontrolled, violence, biopiracy, and among many other evil actions. This could cause degradation to increase to higher levels than those existing today, and what could be worse, in areas where it currently does not occur. All of this could lead to serious problems relating to national sovereignty. Far from being a remote possibility, this last point has appeared consistently in the international media.

Chapter 10 Compensatory mechanisms the positive effects of the Manaus Industrial Pole Alexandre Rivas José Alberto da C. Machado José A. Mota

Considering the results shown so far, it is relevant to broaden the understanding of the PIM, in regards to its role as reducer of deforestation in the Amazon. It is no longer simply a question of economic efficiency or tax incentives that are received, which was fully demonstrated to be positive. It is to account for its contribution in increasing national and international well-being - especially for its contribution in protect the Amazon - and to find ways to give back to the region some share of these benefits. For the analysis of this issue, it would be good to start with the fundamentals of economic efficiency. These grounds are the ones developed by Economic Theory, particularly microeconomics. In an economy where markets work perfectly, all scarce resources are allocated in such a way that promotes maximum well-being, through price mechanism. The problem occurs when markets fail to determine prices and quantities that truly reflect the scarcity of resources. In other words, it means all resources and not only the inputs from the production process. When this occurs there are differences between benefits and social and private costs, what gives rise to different degrees of inefficiency. There are several reasons that lead markets to fail, but this study will consider only externalities1. Externalities may be understood as effects arising from an economy which are generated from a non-intentional cause and whose costs or benefits are received by the families or companies not directly involved in the production process. They can be positive and negative. Negative externalities are those which, once produced, adversely affect social and private well-being. One example is

1 See Kahn (2005) to a further study about the issue.

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the increase in atmospheric emissions of gases that contribute to the greenhouse effect, resulting from a production process. In turn, positive externalities are those that produce a beneficial effect on social and private well-being. An example of a positive externality is avoided deforestation in the Sate of Amazonas due to the Manaus Industrial Pole. Economic studies show that negative externalities can be corrected with the help of economic instruments. A classic example is the Pigou tax. This is a tax applied on the externality and not on the product that generates it. The textbooks about Environmental Economics tend to give much emphasis to the analysis of negative externalities. Here the theoretical framework will remain the same, but the analysis is focused on how the positive externality generated by the PIM can be properly computed and used as a basis for the creation and implementation of compensatory mechanisms that promote a higher level of social equity, through local or regional internalization of the positive externalities from the PIM. That is, compensating those who are bearing the burden of producing a good for the country and the planet. When considering the issue of compensating someone or something, you should have a clear understanding about what is being compensated and how this compensation should be. In the case of an environmental resource such as the Amazon forest, the answer about what is being compensated is answered in this study, which showed that the PIM has a fundamental role in reducing deforestation in this state. In chapters 7 and 8 above we estimated the PIM’s monetary benefits resulted from its contribution to prevent deforestation in Amazonas. The question about how this compensation should be done is more problematic, owing to the fact that assigning a value to environmental services and goods provided by the forests, which have no market value (or they are still incipient) and whose information about their role and importance are still incomplete and / or imperfect, and are associated with a high degree of subjectivity. This chapter seeks to identify some mechanisms that can be considered as instruments for such compensation, so that to enable the positive externalities that the PIM provides to Brazil and the world can be internalized in the region as a way to compensate it. In the following section an environmental labeling will be proposed in order to associate these positive externalities generated by the PIM to the products manufactured through it. It is assumed that they would occur by the marketing compensation that such products, produced in line with the preservation of the Amazon, would have along the marketplace. Therefore, it would be a competitive gain against the competing products not manufactured in the region. Obviously, such initiative will need a policy that considers factors other than simply the fact that the product is physically made in the PIM.


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Theoretical fundamentals for the compensatory mechanisms Some mechanisms can be used to compensate the State of Amazonas in its effort to keep about 95% of its forest still intact. The mechanisms presented in this section are from a primarily economic nature and consistent with national and international concern concerning the Amazon. An interesting study was developed by the Environmental Protection Agency of the United States, EPA (2004), where an international analysis on the use of economic incentives for environmental protection is taken. Some results obtained by other countries and referred to in the mentioned study are taken into account here. Besides these, some proposals of domestic solutions will also be suggested. Benchimol (1989 and 1990) was the first one to suggest the collecting of an international tax, as compensation to the Brazilians for the preservation of the Amazon. However, the proposal was missing a more scientific rigor in regards to eventual implementation. With the results presented earlier in this study, it is possible to propose something more consistent in the direction advocated by the researcher. However, in advance, it is needed to understand better what are the economic incentives. A broad definition is the one that considers economic incentives as any tool that is able to promote continuous induction, financial or not, in order to encourage responsible parties to reduce their negative externalities, such as environmental damage, or expand their positive externalities, such as reducing deforestation. These incentives provide monetary or non-monetary reward for the expansion of the positive externalities or impose more costs for the negative ones. The economic compensation forms suggested here follow the tradition of Pigou and are designed to get the internalization for the Amazon society, which is directly responsible for preserving the forest, part of the benefits of this preservation. This happens because there are large social costs involved in accomplishing this. For example, the traditional Amazonian populations, by renouncing the use of the forests as a resource, deprive themselves from profit and thus deprive themselves also from better access to services such as healthcare, education, transportation and other factors that lead to a more dignified life. These circumstances make the majority of the population survive by exploitation of natural resources on a very low-scale, they remain without access to developed markets and therefore do not have opportunities to improve their well-being. From the perspective of the industries located in the Manaus Industrial Pole, the situation is no different. The PIM is considered by some segments of the domestic industry as highly inefficient, because the fact it benefits from tax incentives in order for it to survive. However, this vision is poor and lacks foundation. The eventual private costs, not fully internalized by the PIM, are more than compensated by the social benefits that it produces itself, when it turns into economic

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activity with high employment and wages levels relatively higher than the national average; when it contributes in supporting the Brazilian regional development, by moving to the northern part of the industrial axis of the south-southeastern Brazil; when it contributes in a compelling way to the construction of the Brazilian geopolitical strategy in regards to Amazon; and when it contributes in a highly positive way for avoiding deforestation, which creates adverse climate effects on a national and international scale such as the loss of biodiversity and conservation of important water resources of the region. In this particular point, only to illustrate the large gap in the accounts of its positive externality, it has been recorded that if the deforestation levels were high in the State of Amazonas, perhaps the levels in the Brazilian Southeast were already being strongly impacted, because of the cycle change and the rainfall intensity in the region. This could be monetarily computed in terms of a loss of crops and urban environmental damage. In a situation where overall balance is sought, the gain in terms of wellbeing provided by the PIM, can be seen as a huge Pareto gain. That is, in an overall structural balance of trades of the economic system under the well-being theory, the allocation of the PIM’s GDP versus the low deforestation level, as a positive externality of the Pole, shows that the system has a much more global than local² significant efficiency2. This fact also refers to the subject of social equity field, since the Pareto gains mentioned indicate that the national effort to develop an industrial pole in Manaus has contributed in several ways to national economic growth, while it has allowed the country to exercise its sovereignty in relation to the Amazon.

Some possible compensatory mechanisms The identification of possible compensation mechanisms has as its sole purpose the demonstration of several available options to be used. Certainly, each related mechanism has its own circumstances that will need further data for eventual consideration.

a) The compensatory tax In this scenario, an international compensation tax for the protection of the Amazon (Amazon Protection Compensation Tax - APCT) could be implemented. It could be collected by one of the multilateral agencies of the UN System (UN) and passed onto Brazil, which, in turn, would pass it on to the state governments in Amazonas, and mainly to the state of Amazonas, in order to strengthen the regional society and the PIM itself.

2 The terms “global” and “local” are used here in the balance sense.


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Two aspects are relevant in this proposal. The first one is from “who” and “how” this rate would be collected. The suggestion from this study is that it is calculated based on the level of CO2 emissions of UN Member States. That is, countries with high emissions of carbon would pay more than countries with less emission. The APCT’s value can be determined according to the results found in this study. For example, for a given year the rate could be calculated as follows: APCTannual = [(100% of the value of direct use of 1 ha of forest + 30% of the value of indirect use of 1 ha of forest) x deforestation area avoided by the PIM in the year]. It is noteworthy that several aspects of the calculation need to be thoroughly discussed. One of them is the percentage of indirect use value considered. Another one would be whether or not to take into account the years prior to the start of tax collecting. The second point is about how to use the income from the rate. As the Environmental Economics Theory predicts, a tax on the externality must be used to reduce or remove it in case of it being negative, or to keep and extend it in case of it being positive. In other words, the APCT should necessarily be used in the compensation of those activities that produce the externality, in this case, to the benefit of economic agents, companies and families involved directly and indirectly in the construction and maintenance of the PIM.

b) Carbon tradable actions There is currently, and it is in continuous development, an international market for trading carbon credits. This negotiation is performed in specialized stock markets and consists in the sale and purchase of carbon stocks. There are companies in the market that pollute more and others that pollute less. There are countries with greater environmental restrictions and others with less. Thus, these companies can invest in some type of technology or simply buy stocks in the carbon market to allow them to operate within the legal standards. For example, a polluting company can buy stocks of low-pollution companies. Thus, the polluted company wins credits to pollute, but the total emissions of carbon in the planet’s atmosphere decreases3. In this proposal, the companies of the PIM could participate in this market with acquired stocks, from the volume of avoided carbon in accordance to its role in the Pole. The idea is simple. The amount of area that was not deforested because of the PIM was estimated in this study. According to the figures used above, 1 hectare of the Amazon forest has stored on average 120 tons of carbon. If in the international 3 PIn order to deepen in the subject see Kahn (2005).

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carbon market a tradable stock is worth 1 ton of carbon, then the PIM would have 120 tradable carbon stocks per hectare of avoided deforestation. How much would each company get? A public policy in this regard would have to determine the most appropriate method. But in order to demonstrate the possibilities of such a mechanism, the amount each company would receive could be defined based on its added value to the GDP of the state or the PIM. What to do with this credit? There are many companies in the international market that need to buy carbon credits to be able to continue working, as well as companies that due to their environmental responsibility policies buy carbon credits to help in reducing the element in the global atmosphere. The PIM’s companies, with proper support from SUFRAMA, the Federation of Industries of the State of Amazonas and other similar institutions could negotiate directly or indirectly, by BOVESPA, their carbon credits in international stock markets. The credits obtained could makeup the company’s revenue or could be used in activities of social and environmental responsibility. This could serve as a strong economic stimulus to make these companies strengthen and expand their roles in reducing deforestation directly through several available mechanisms, as well as indirectly by strengthening the PIM.

c) Government Compensation The PIM has made since its beginning, a large environmental contribution to Brazil and to the world. In this sense, it has acted against deforestation in the State of Amazonas, acted against the exploitation of environmental services such as nutrient flow, the use of water resources and the excessive exploitation of local biodiversity, especially when compared with other states to the north in the Amazon biome. From this line of reasoning, Brazilian society has judged the contribution of the PIM without the due compensation, as the region’s natural resources were not only dilapidated because of the PIM’s effect has exercised, over all these years, substantial gains in well-being for the population of the region. Even so, the financial compensation from the federal government has been translated into fragile and mutants tax benefits, which do not reflect the actual monetary and environmental value of the national wealth preservation and the wealth biodiversity provided by the PIM’s installation in the state of Amazonas. This can not be considered as an appropriate contribution, since, as has already been seen, it is not restricted to the simple preservation of the common asset of the Amazonian environment, but it concerns a new way of developing large arrays of information


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and technical knowledge4, which have all helped in containing deforestation in the State of Amazonas. According to this perspective, technical advancement through the prioritization of research centers in science and technology would be a good practice for the federal government to repay the PIM’s contribution in order to mitigate deforestation in Amazonas. It would not work to simply install centers quality research centers in the region. It is necessary to make them work effectively.

d) Competitive aggregation by certification of origin The idea here is to let the market itself compensate for the environmental benefits that the PIM provides. Then the products could carry an identifying mark that they contribute to the preservation of the Amazon. It is assumed that they would be treated by the market in a different way from similar products. Either accepting to pay more for them or giving them preference before the competing products. In this case these products would have a competitive advantage that would not be found anywhere else. Such a proposal needs a disciplined public policy with complete transparency in order to inform the market about all the different ways that the PIM benefits the environment. Furthermore, membership of the companies with such arrangements must be necessarily voluntary, so that this mechanism could be sought out as a competitive advantage and not as a legal obligation. The next segment of this study addresses this important issue.

4 Castells, 1985, 2000 (apud Becker, 2007), “The technological revolution in microelectronics and communication does not amount to a new technique, but to a new form of production based on information and knowledge, which involves social political, civil, military organization, and also power relations”.

Chapter 11 Increasing the Market Value of Products Produced in the Manaus Industrial Pole Aristides da R. Oliveira Jr. José A. Mota José Alberto da C. Machado

This segment presents a policy proposal for increasing the market value of products produced in the Manaus Industrial Pole. The proposal is based in a certification and management system of a seal applicable to these products, and would be granted by SUFRAMA as part of its macro-policy for an institutional invigoration of the Manaus Free Zone Model (MFZ) in general, and of the PIM in particular. The PIM and MFZ have existed for 42 years. Thus, this intervention proposal amounts to a social-environment certification policy for products produced in the Manaus Industrial Pole (PIM), and at the same time will allow them to: a. certify the geographic origin and the direct and indirect social-environmental benefits associated with its products, based upon scientifically valid means of measuring these benefits; b. add distinct commercial value to these products, making them more competitive to both domestic and foreign consumers, who have a growing sensitivity to social and environmental issues in general and the Amazon in particular; c. motivate companies to obtain certification, through the adoption of positive social and environmental practices, according to pre-established standards that are nationally and internationally validated. This would be included in marketing strategies, thereby linking their respective trademarks to the mark “Amazon” by means of a certification through a seal or logo, and; d. provide SUFRAMA and the other institutions linked to the PIM (the state governments of Western Amazon, business’, research institutions, etc.), a powerful tool for reversing the constraints and limitations imposed on its strength and continuity. This would mark a return to one of the original reasons for establishing

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the Manaus Free Zone Model, as an incentive for economic activity. It will be based on location and its ability to balance inter-regional disparities. In addition, it would serve to legitimize the national and international environmental benefits of the PIM, and serve as a model of regional development for the Amazon, worthy of continued support and incentives (tax and others).

Problem Context This section deals with the proposition of a unique and more robust certification policy, from a scientific point of view, for PIM products. The basic proposition is for the certification to go beyond the current practice of a mere indication of geographic origin of these products. The certification program would indicate the direct and indirect social, economic and environmental benefits associated with production in the Amazon. The evaluation of this potential policy occurs in an institutional and environmental context that is somewhat paradoxical. The large scale success of the PIM has been well documented, promoting investment and economic infrastructure throughout its 42 year history as the economic driver creating important economic benefits for the region, as well as the environmental benefits documented throughout this book. However, a general review of the history of the MFZ model clearly shows that its original fundamentals (in particular its tax incentive instruments) were subverted, by virtue of new legal interpretations of several regulatory bodies and by the imposition of limitations and constraints in its ability to attract new investments, to its export potential and to its systemic competitiveness against similar industry segments. This was promoted by identifiable alliances of political and economic interests of various origins. The structuring of the current certification policy adopted by SUFRAMA for logo certification of products made in the PIM does not show much potential in reversing the hurdles in the scenario mentioned above. The current compulsory seals do not add a distinctive marketing value to these products nor provide a link for the companies’ trademarks to the “Amazon”. Thus, the PIM logo does not influence purchasing decisions of markets that might have higher demand for environmentally beneficial products made in the Amazon. On the other hand, the Manaus Industrial Pole has become the main development engine of the western Amazon in general and the State of Amazonas in particular. PIM is anchored in industries with a high coefficient of added value and which show important indicators of economic success. In Table 2 by Machado and Oliveira Jr. (this edition)1, several indicators of the PIM’s economic performance in the year 2007 are synthesized. The analysis 1 The Manaus Industrial Pole and its dynamics


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can be further extended by looking at other positive effects generated by the MFZ model over the past 42 years in three great categories, economic, scientific-technological and environmental. On the economic side, the main positive effects are linked to increases in production, demand, employment and income that resulted from the attraction of high technology industries for Manaus (consumer electronics, computing, engineering, transport equipment, plastic processing, watches, etc.). This was augmented by the subsequent development of input-supplying industries, intermediate materials and components of various types as well as the creation of a service economy (specialized trade, hotels, restaurants, transportation, consulting and technical supporting services, etc.), which is consolidated in the environment of this dynamic industrial pole. These services support the needs of factory workers, executives, and technicians from industrial and service companies, as well as the companies themselves. The collection of federal, state and district taxes in the Amazon, the use of the state and federal revenue for infrastructure and economic projects in outlying districts, has internalized many of the effects of PIM. In this way SUFRAMA has played an important role, through the collection of substantial revenue through its Administrative Services Fee (ASF) on the values imported by companies participating in PIM. The excellent performance of the PIM’s industries in the last decade allowed Suframa to be the source of federal resources for projects throughout Western Amazon (such as small airports, roads, acquisition of machinery and equipment for civil works in various districts, support for rural cooperatives, fishing ports, and agro-industrial plants for processing of regional products, etc.). The budget for these projects exceeded USD$ 500 million in the period from 1997 to 2007. However, this strong support from SUFRAMA has suffered severe restrictions in recent years, according to the limitation of its resources by the federal government, as part of an ongoing adjustment of fiscal policy. The scientific-technological effect refers to the development of a regional science, technology and innovation system as a fundamental supporting element for the strengthening of industry. This has in turn encouraged development of the product and process engineering area through funded R&D funding projects by SUFRAMA or by universities or other local research institutions. In addition, this effort has stimulated the progress of regional scientific and technological skills in several other areas such as regional development, health (e.g., combating tropical diseases), business management and biotechnology. The environmental effect made it possible for the PIM to attract rural labor, allowing, the concentration of jobs in Manaus. This made opportunities for activities using aquatic and forest resources less attractive, as these opportunities could not offer the salaries, social and labor benefits that were available to workers employed PIM’s industrial center. This phenomenon of not exploiting natural resources in Amazonas can be characterized as an apparent environmental savings

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originating from the PIM’s economy. That is corroborated by the high degree of preservation of the original forest coverage, which satellite imagery shows to be 95% (SIVAM). The PIM also seems to have positive environmental effects according to the types of industries that it hosts: the non-smokestack industries, which combine process technology with reduced generation of waste pollutants. Table 1 below reproduces recent classification results from the different industry segments, by type of environmental pollutant generated. It shows that the manufacturing sectors covered by the PIM (consumer electronics, computing, transport equipment, plastic processing, etc.) do not appear to have significant environmental impacts. Table 1 - Industrial classification by potential emission of pollutants Type of Pollutant Water Pollutants

Organic loading (BOD)

Non-ferrous metallurgy, paper and printing, chemicals, non-petrochemical, sugar

Suspended solids

Steel. Non-ferrous metallurgy, steel, oil refining and petrochemicals Refining of petroleum and petrochemicals, steel. Non-ferrous metallurgy, steel, oil refining and petrochemicals; various chemicals. Steel, oil refining and petrochemicals; various chemicals. Steel; vegetable oils and fats for food, non-metallic mineral.

Sulfur dioxide (SO2) Nitrogen Dioxide (NO2) Air Pollutants

Type of Industry

Carbon Monoxide (CO) Volatile Organic Compounds Inhalable particulate matter

Source: Lustosa & Young (2002)

A development model for the Amazon Despite all the positive externalities generated by the PIM, it is still seen as an enclave and privileged economy by large segments of Brazilian and international society. It is seen as an enclave because people believe that it does not have any connection to Amazonian natural resources and it is seen as privileged because of its basis in tax incentives to the participating companies. This view is based on a mixture of the ignorance of the PIM’s general characteristics and a biased view by competing regional economic interests. This widely held but unfair view limits the advocacy for PIM in various negotiations and forums, as domestic and foreign competitors use these arguments to try to reduce the incentives given to PIM.


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Since the competing forces have more power, over time there have been continuous and progressive limitations to the ability of PIM to meet competition. These limitations have many dimensions. First, measures have been adopted to provide tax reductions or exemptions to industries similar to those in PIM, but in other areas of Brazil. This constrains the ability of the PIM to attract new investments, particularly in areas on the forefront of technology (such as Digital TV and mobile phones). A second problem has resulted from regional and national tax authorizes adopting questionable interpretations of the legislation which provides the tax basis for the Manaus Free Zone Model (Article 40 of the ADCT in the Federal Constitution). This has resulted in progressively tighter limits to tax incentives offered by the PIM. Initially, the incentives were based on the location of the industrial project in Manaus, the heart of the Western Amazon, and on its ability to generate economic effects throughout the region. By the end of the 70s, the tax provisions began to benefit groups of products, through the acceptance of “lists of exceptions” and other restrictions. A third problem was that new interpretations established limitations to the type of tax eligible for incentives (explicit case of PIS and COFINS, taxed at 3.65% for final sales of the PIM, out of its jurisdiction). Finally as disputes developed about rules for computers and the provisions of the Digital TV law, the possibility was discussed of whether any industrial sectors in Manaus should receive incentives. The problem can be seen from another angle. It is apparent that the relevant stakeholders in the PIM (labor, industry associations, state government, SUFRAMA, legislators, etc.), have not been able to steer the opinion of opinion makers (press, academe) and government officials in the face of the arguments of exogenous political and economic interests. Institutional arguments of sufficient technical and scientific force must be made in support of PIM, to justify in a more stable economic environment. This stability would be generated by removing the constraints that have gradually reduced its competitiveness and restoring its original properties that are explicitly defined by the Decree Law 288/67. Under this law, tax incentives for industrial activities are based on their regional location, but related to the effect of value added, local income and the generation of positive externalities (e.g. environmental and scientific-technological externalities). It is notable that many groups in the region, such as a large portion of regional academics, view the PIM largely as if it were a mere machine for generating jobs and tax revenue, and not necessarily as carrier of relevant social virtues that justify its protection, its strengthening and its stability. A quick overview of the thesis, dissertations and monographs produced in local academic institutions, especially in the economics and social sciences areas, shows the essentially critical character of these works in regards to the value perception of the model in general.

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Beyond the forest: the environmental labeling of the PIM’s products The current regulation of the PIM’s institutional disclosure process is anchored in Resolution No. 202, set forth on May 17, 2006, by the Board of Directors of SUFRAMA (CAS). This resolution deals with systematic presentation, analysis, approval and monitoring of industrial projects. In its Title IV (“The General Dispositions”), more precisely in its Chapter I (“Disclosure of the Manaus Industrial Pole”), the law imposes the requirements that companies must meet in order to use the inscription ‘Made in the Manaus Industrial Pole’, along with the stylized symbol of a heron flying, a logo which identifies the geographical origin of their products. Before this law, the inscription used was ‘produced in the Manaus Free Zone’, with the same symbol. The driving concept behind such an obligation for the companies is to perform an institutional disclosure, with the idea that consumers would be informed that the goods were manufactured in the geographical center of the Amazon, through a special public policy of incentives to promote regional development. However, the empirical observation by SUFRAMA, the regulatory body that oversees compliance with the law, and by the companies themselves, seems to reveal three perceptions about this experience of compulsory institutional disclosure through the current logo certification program: a. It is merely an informative certification of the regional origin of the products. It adds no additional meaningful content concerning possible social and environmental benefits generated by the act of purchasing these products; b. It is in essence compulsory and bureaucratic. It does not involve the companies or their distribution channels nor does it integrate into their marketing strategies. These strategies are directed at strengthening their own brands through association with the preservation of the Amazon and the generation of socioeconomic and environmental benefits to its people. In other words, due to the way it was conceived and is practiced, the current certification does not give the PIM companies any particular incentive to use it as a means of increasing market value. In addition, it does not provide a unique benefit for consumers that are increasingly concerned about environmental issues; and c. The current certification policy cannot provide the necessary evidence that products produced in the PIM have additional benefits. Thus it is hard to argue in defense of stabilizing policy higher levels of government and public opinion makers (including the academe and the press). Therefore, the concern here is to define the shape of a policy that would allow SUFRAMA to offer to companies affected by the PIM the use of a social certification capable of adding actual commercial value to products and services produced in the PIM. This would create a competitive advantage for these products in domestic


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and foreign markets. This initiative involves the adoption of a management system for this certification, with international and national validation, to legitimize and strengthen PIM. A social-environmental certification based on a formal institutional policy and its management system would give PIM products more competitiveness and would represent a non-tax incentive for the attraction of new businesses. In this context the adoption of an environmental label for the use on products and services provided by the PIM would be a tool that seeks to protect the natural environment of the region, and which would guide consumers, producers and other market decisions. This would encourage technological innovation and stimulate high-technology research and product development with the use of regional assets. It would strengthen local research institutions, providing society a new way of giving value to the natural resources of the region. The labeling idea would identify the PIM’s products as environmentally friendly, as demonstrated in this study. It would begin a new cycle of PIM in which the seal would have the clear purpose of aggregation of competitiveness by certification of origin, without disregarding the existing experience. However, this would need to be backed up by generally accepted methodological procedures, which would give the proposal a scientific anchor capable of giving legitimacy and credibility to the logo or environmental seal for the PIM’s products. A proposal with this purpose requires studies to ensure that it is properly conceived and implement. In addition to the research, other actions would be required such as a broad discussion with companies and business entities, interaction with public and private agencies and legal entities involved with the certification, and, above all, a clear policy decision from the institution towards the adoption of the proposal and allocation of institutional energy to its effectiveness. Without such measures, this proposal for a new certification system will always remain in the proposed stage. The environmental value of the PIM, which is now scientifically proven, will continue being treated as cheap talk and the opportunity for developing a new a competitive factor for the PIM’s products would be lost.

Chapter 12 Manaus Industrial Pole: beyond the purely economic benefit Alexandre Rivas José A. Mota José Alberto da C. Machado

The results presented so far respond to two important points regarding the goals of this research. The first one is to empirically demonstrate with appropriate econometric models that the strategy of industrialization that has been implemented in the Manaus Industrial Pole has contributed to the slowing of deforestation in the Amazon rainforest, particularly in the State of Amazonas. The second one is to estimate the magnitude of this effect, in order to make a reasonable approximation of the contribution of the PIM in the reduction of deforestation, and if possible, the contribution of the different industrial sectors located in the PIM. The analysis of these issues was structured to present a little of the Pole’s history, its performance and the main entities participating in it. Accordingly to this direction, it was found that in 2007 the electronic products sector accounted for about thirty percent of the revenues of PIM, while the two-wheeled vehicles and computer goods sectors was approximately 23% and 17%, respectively. It is important to note that a large increase in the PIM’s sales, from 2001 to 2005, was due to Nokia. In 2005, cell phones alone from this company accounted for over 50% of PIM global exports. After discussing the history of the PIM this study presented a history of deforestation in the Amazon and identified its primary and underlying causes. Following the logic established, causes of deforestation in the State Amazonas were analyzed.

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In order to better understand this deforestation profile, general patterns of ordering of the states of the Legal Amazon and the State of Amazonas were observed. This was done through the analysis of deforestation rates in terms of geographical variables and public policies, using a multivariate approach by means of the correspondence analysis. This analysis has tested the importance of variables identified in the literature and has substantiated the next steps of the study. From the results of the correspondence analysis and other relevant evidence, a mathematical model was developed that provided a theoretical and conceptual basis for all the specific models tested. From this point on, three groups of empirical models were implemented. The first one used causality tests and estimated panel models in order to identify the relevance and spatial profile of deforestation in the Amazon and the State of Amazonas. These estimates have shown that the PIM is important in inhibiting the activities with a greater devastating potential in the State of Amazonas. The second modeling effort took data from 1997 and estimated that, for this year, the PIM was responsible for a reduction of about 85% in the level of deforestation in the region of Manaus. This value was calculated from the difference between the values estimated in the econometric model and the ones measured by means of satellite images. The model estimated that the annual benefit of the estimated avoided deforestation in Manaus in the period from 1997 to 2007 that was approximately USD 400 million. The third model developed a behavioral model to directly calculate the effect of the PIM. This model was estimated by using panel data for the period from 2000 to 2006, and only for the State of Amazonas. An advantage of this third model is that some of the variables used had their relevance tested by the other models. This model estimated that the PIM had the ability to reduce deforestation in Amazonas within a range from 70 to 77% compared to what could have happened without the Pole. This study also estimated that the value of avoided carbon emissions over the period studied, based on the value of one ton of carbon in both the European and American markets. During this period, the estimates ranged from just over one to ten billion dollars, just considering the indirect use value of the avoided carbon emission. This is equal to an annual benefit ranging from USD 160 million to about USD 1.4 billion. The study has shown that the PIM really generates a countering force, capable of alleviating the pressures that lead to deforestation in Amazonas. This countering force varies from 70 to 84% of what would have been the level of deforestation in the state in the absence of the PIM. In the years following the study, particularly in the years 2000 to 2006, the participation of the mobile phone industry was about fifty percent of the revenues of the PIM. This industry had an important role in the magnitude of the PIM’s effect.


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In addition to assessing the positive effects brought about by the PIM, the study developed an exercise examining what might occur if the pole did not exist. To this end, some of the econometric tested models were used, in addition to considering aspects of population dynamics as well as locations of the PIM businesses. For this scenario of the absence of PIM, possible national and regional consequences were studied, in regards to the role and presence of this Brazilian state in the region. Compensation mechanisms were presented to partially or totally internalize the positive externalities produced by the PIM. All mechanisms that were considered were economic in nature. In this compensatory context, efficiency and equity aspects associated with the PIM were discussed in order to explain the important role that the Pole has in preserving the Amazon. The study has proposed a marketing policy to increase the value of PIM products based on a certification and management system of a certificate logo which would be placed on these products by SUFRAMA, as part of its policies for strengthening the Manaus Industrial Pole. With the results obtained in this study, it is clear that the PIM produced a significant positive externality for Brazil and the rest of the world, the preservation of the Amazon rainforest. Although PIM was created with the primary purpose of bringing economic development to an isolated area, rich in environmental and natural resources, its benefits went beyond economic benefits. As shown, the State of Amazonas has about ninety-seven percent of its area still preserved. This evidence shows the importance that economic policies have on environmental control. This kind of policy is still rarely used for environmental protection in Brazil. With this very positive outcome, efforts should be implemented to increase the understanding of these types of instruments. Understanding the PIM better could lead its benefits to reach an increasing number of people, and at the same time would contribute a lasting solution to the maintenance of “the standing forest.” This fact would also give better conditions to other economic activities such as ecotourism and bioprospecting that could be developed in a faster, more beneficial and more well-defined way in order to foster new industry sectors with a higher degree of endogeneity. Finally, the study presents scientific evidence that the economic incentives given to the PIM produce benefits that are larger than the costs associated with the tax incentives. Such evidence will help entrepreneurs, politicians and decision makers in general to extend and continue the positive cycle of benefits that the PIM has been generating since its creation.

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Annexes

23 13,543,131.83 19 11,295,354.35 25 9,500,000.00 55 58,129,469.27 212 153,751,11.45

QT Value QT Value QT Value QT Value QT Value

3 8,400,000.00 2 415,118.00 0 0.00 1 2,300,000.00 29 37,177,336.00

2 1,180,309.004 4 7,108,619.00 7 7,108,619.00 3 2,968,355.00 7 7,800,000.00 1,000,000.00 0,00

4 487,854.00 17 8,481,757.00 23 11,083,338.00 2 6,000,000.00 34 22,872,560.00 9 9,211,125.00 1 3,146,582.00

Source and elaboration: SUFRAMA/SAP/CGDER

Total Acum

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

Amapá

Acre

QT Value QT Value QT Value QT Value QT Value QT Value QT Value

Total of resources by application

4 35,964,000.00 26 10,547,413.00 9 16,122,870.84 32 28,192,448.53 236 224,780,740,00

20 44,266,775.1328 28 10,857,199.32 32 25,259,346.24 20 10,560,580.00 40 28,238,000.00 18 11,361,461.00 7 3,410,000.00

Amazonas

59 18,450,000.00 54 11,053,282.85 25 4,900,000.00 46 25,144,950.00 359 133,396,847.16

20 18,491,701.0033 33 8,830,901.00 43 13,797,092.00 21 7,008,955.00 53 24,190,470.00 5 1,529,446.00 0,00

Rondônia

Discrimination

4 11,160,000.00 10 10,547,413.00 0 0.00 11 22,425,000.00 147 123,911,897.16

29 10,891,065.0023 23 9,561,416.00 18 12,150,414.00 8 16,263,202.76 26 19,737,165.00 16 10,176,222.00 2 1,000,000.00

Roraima

23 19,797,495.13 21 17,923,384.45 6 3,421,016.18 42 56,008,361.00 185 153,327,508.88

7 2,411,164.00 5 2,968,109.00 3 950,000.00 11 9,306,676.00 28 14,817,056.00 24 13,576,599.17 15 12,1477,707.95

Entities

116 107,314,626.96 132 61,781,965.65 65 33,943,887.02 187 192,200,108.80 1,168 826,345,442.40

82 77,728,868.13 110 47,808,001.32 126 69,245,125.24 65 52,107,768.76 188 117,665,897.40 72 46,854,903.17 25 19,704,289.95

Total

Table A1 – Distribution of applications from Suframa’s resources on projects/actions of internalization of regional development, by federal unit (1997-2007)

Annex A

3 742,190.18

9 3,360,984.33

12 3,651,335.24

12 3,651,335.24

7 1,441,945.22 6 3,217,595.69

0 0.00

13 1,939,091.72

17 2,135,280.00

12 4,191,426.41

21 5,562,379.31

104 27,944,231.73

QT Value

QT Value

QT Value

QT Value

QT Value QT Value

QT Value

QT Value

QT Value

QT Value

QT Value

QT Value

1997

1998

1999

2000

2003

2004

2005

2006

2007

Total Acum,

Source and Elaboration: SUFRAMA/SAP/CGDER

2002

2001

Production

Total of resources by population

895 681,355,97.31

117 150,675,131.15

46 18,005,502.88

98 43,057,282.14

82 88,162,592.16

11 7,806,582.00

52 47,331,538.54 4 37,659,993.14

110 64,663,790.00

110 64,663,790.00

96 42,971,591.05

74 74,576,857.21

Support to infrastructure

36 18,456,443.64

3 3,525,000.00

1 281,434.18

2 1,818,427.00

2 910,000.00

6 1,381,994.00

1 200,000.00 6 2,454,034.40

3 630,000.00

3 630,000.00

3 788,856.62

4 2,209,820.79

Promotion of investment/ turism

69 69,576,863.20

19 16,056,951.83

5 11,113,523.55

6 11,465,305.51

12 14,008,178.66

7 10,485,313.95

2 2,794,22.00 7 1,190,331.94

1 300,000.00

1 300,000.00

2 686,569.32

1 200,000.00

R&D

Discrimination - Projects of:

64 29,011,925.46

27 16,380,646.51

1 352,000.00

9 3,305,665.00

7 2,294,764.42

1 30,400.00

3 340,065.00 4 2,332,948.00

0 0.0

0 0.0

0 0.00

0 0.00

R,H, Training

1168 826,345,442.40

187 192,200,108.80

65 35,943,887.02

132 61,781,965.65

116 107,314,626.96

25 19,704,289.95

65 52,107,768.76 72 46,854,903.17

126 69,245,125.24

126 47,808,001.34

110 47,808,001.32

82 77,728,868.13

Total

Table A2 – Distribution of applications from Suframa’s resources on projects/actions of internalization of regional development, by category of application (1997-2007)

204


Annex B Unit Root Test for Panel Data There are several tests that explore the panel conformation for an integration test of macroeconomic variables. The tests that are found can be classified into two groups. The first group includes those tests that assume the existence of a common unit root process such that the parameters for persistence for each unit (or group) have the same autoregressive structure (AR (1)), besides allowing for the existence of an individual effect. The tests proposed by Levin, Lin and Chu (2002) and Breitung’s (2000) integrate this group. These tests can be considered as an augmented test of Dickey-Fuller (ADF) with grouped data. The null hypothesis is that each series of the panel is integrated of order one, against the hypothesis that all series are stationary. The other group includes tests that allow for the existence of an individual process of unit root so that the persistence parameters can vary freely for each unit (group). Because of this the tests are constructed from individual statistics. For example, the test statistic proposed by Im, Pesaran and Shin (2003) is the result of an average of the t-statistics of Dickey-Fuller on each panel unit. The null hypothesis assumes that all series are non-stationary, while in the alternative hypothesis at least one series is stationary. The test acquires the structure of the ADF when enabling that the lags for the dependent variable can be inserted, which allows for the error autocorrelation for each series. In the present work there were used tests proposed by Levin, Lin and Chu (2002, LLC) and the test from Im, Pesaran and Shin (2003, IPS). The tests were performed for the series level, using the selection criterion for the number of lags of Hannan-Quinn. Table 1 next page presents the results.

205


Table B1 - Unit Root Tests in Panel Levin, Lin and Chu Variable

Im, Pesaran and Shin

Probability With Individual Intercept

Probability

With Individual Intercept and Trend

With Individual Intercept

With Individual Intercept and Trend

Deforestation

0.000

0.003

0.000

1.000

Occupied Area

0.000

0.000

0.001

0.000

Long-term Crop

0.000

0.000

0.000

0.000

Short-term Crop

0.000

0.000

0.000

0.011

Bovine Herd

1.000

1.000

0.000

0.952

Bovine Herd

0.000

1.000

0.000

0.174

Adults Education

0.000

0.000

1.000

0.275

Stock Credit

0.000

0.000

0.000

0.000

Registration

0.000

0.000

1.000

0.002

Per capita GDP

0.000

0.000

0.000

0.000

Population Demograp. Den.

0.000

0.000

1.000

1.000

0.000

0.000

1.000

1.000

Source: Prepared by the authors. Notes: The lags in the tests were determined by the Hannan-Quinn’s criterion. The probabilities for the tests assume asymptotic normality. Test Levin, Lin and Chu - Null Hypothesis: Unit root (assumes a common unit root process). Test Im, Pesaran and Shin - Null Hypothesis: Unit root (assumes individual unit root process).

As can be seen, all variables considered are stationary in first order, I(1), in at least one test at a level of 5% of significance. The deforestation variable is stationary in both tests, except in the test of Im, Pesaran and Shin when considering the individual intercept and trend; Bovine Herd is stationary whereas the test of Im, Pesaran and Shin and only the individual intercept; Adult Education is stationary only on the test of Levin, Lin and Chu, considering both the individual intercept as intercept plus individual trend; Registration is stationary in both tests, except the test of Im, Pesaran and Shin when considering only the individual intercept; but the Occupied Area, Long-term Crop, Short-term crop, Stock Credit and GDP per capita are stationary at both tests, considering both individual intercept and intercept plus individual trend.

206


Annex C As pointed out by Wooldridge (op.cit.), the presence of an omitted variable, due, for example, to and therefore of endogeneity, it becomes the motivation itself of the use of panel data. With the presence of an omitted variable the error term would consist of two components (so called composite errors):

(20)

would be common to all equations of the panel, and therefoin that re would be called an individual effect, individual heterogeneity, or fixed effect (Hayashi, 2000). On the assumption that the idiosyncratic error was not correlated with any explanatory variables that comprise the three vectors of variables, it would still be possible the correlation of with some explanatory variable, which would make the produced estimates by pooled OLS as biased and inconsistent. Assuming the equation (18) is valid, the most appropriate empirical model at first glance would be the fixed effect one, also called as estimator within, rather than the application of the OLS method occurs in the transformed model based on deviations from a group of averages (HAYASHI , 2000), which uses the time variation in the dependent variable and within each cross-sectional observation unit (Wooldridge, 2002). Assuming that the geographical variables don’t like to represent the omitted variable, then the most appropriate model to be adopted would be the fixed effect one. This means that is treated as a parameter to be estimated at each crosssection observation. In order to be estimated with the desirable properties for the estimators, the model would need some certain additional assumptions. Thus, in order to verify these hypotheses through an easier way, we can write the generalized model in the form:

(21)

where yit is the dependent variable as defined and Xit is the matrix of explanatory variables in the order i x k containing the observable variables that change between t, but not between I; between i, but not between i and t.


207

In the case of the random effect estimator, in the same way that the fixed effect arises from a transformation now subtracting each explanatory variable from its fraction on the average time, which depends on the error variance and on the variance of the omitted variable. This transformation allows the use of explanatory variables that are constant over time and results in a kind of model of Generalized Least Squares - GLS, which eliminates the serial correlation in the errors, and therefore the random effect estimator is a type of GLS feasible estimator (Wooldridge, 2002) This structure is necessary because the method of the random effect exploits the serial correlation in the error composed in a structure of generalized least squares (GLS). In this context it is necessary that the strict exogeneity extends between the explanatory variables and the composite error.

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