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NON-TIMBER FOREST PRODUCTS (NTFP) AND THE GLOBAL COSMETIC INDUSTRY: EXPLORING THE CONTRIBUTIONS OF NTFP TO RURAL LIVELIHOODS IN THE BRAZILIAN AMAZON

AGHANE DE CARVALHO ANTUNES

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2019

To my family

ACKNOWLEDGMENTS

This thesis is the fruit of a long journey to professional and personal development that was accompanied by many phenomenal people who helped me in the development and completion of this work.

First, I want to express my sincerest gratitude to my advisor, Cynthia Simmons, who from the very beginning, believed in me giving me a life-changing opportunity. If I am here today, it is because she could catch my potential for growing into an avid researcher. She expressed confidence in me when doubted; evolved into a friend willing to countless hours of conversation not only addressing theoretical human geographic frames but also about the many challenges I have had during this enlightening journey.

With her encouragement and practical attitudes that go beyond just the rhetorical narrative of inclusion, diversity, and equality, she unconsciously drove me to a painful process of self-acceptance and reconnection with my parents, relatives, and ancestry, which enabled me to face up my feelings of insecurities and unworthiness within the excluding and elitist academic field.

I also want to thank the distinguished members of my thesis committee, Robert

Walker and Liang Mao, who generously shared their time, knowledge, and experiences with me.

I am especially indebted to Joao Paulo Candia Veiga, Professor of Political

Science, of the Department of Political Science at the University of São Paulo, for providing the data set used in the research, besides, being a reliable source of advice, information, and inspiration. Without his generosity, I never would have the ability to perform this study. Thanks also to Fausto Makishi, Professor of Management at the

Federal University of Minas Gerais and Murilo Alves Zacareli, Ph.D. at Institute of

International Relations at USP and researcher at the University of Wisconsin-Madison for sharing their work concerning the data used in this study.

Anwar Sounny-Slitine and Yin-Hsuen Chen are also deserving of my deepest gratitude for their invaluable help on this project, concerning the application of GIS tools.

Both were also fundamental to the development of this work.

I also wish to thank the Department of Geography at the University of Florida, for supporting me financially with an Assistantship and later a Pre-Doctorate Fellowship, particularly, the department chair Jane Southworth who additionally granted me with respect, and encouragement.

I want to acknowledge the support I also received from Alfredo Kingo Oyama

Homma, researcher of the Brazilian Agricultural Research Agency (EMBRAPA), who so generously shared his experience, outstanding knowledge, and time in long construtive conversations about the Amazon, small farmers, and his influential and controversial positions on the limitations of extractivism. Homma's extraordinary life history is an inspiration to me.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 9

LIST OF FIGURES ...... 10

ABSTRACT ...... 11

CHAPTER

1 THESIS OVERVIEW...... 13

2 BACKGROUND AND MOTIVATION ...... 20

The Changing Amazon ...... 20 Brazilian Amazon Development History ...... 23 Early Extractive Economy ...... 23 Order and Progress: Development under Military Regime ...... 27 National Integration Plan ...... 29 Agro-Industrial Expansion (1975 to 1984) ...... 32 Development and Environment: New Democratic Reform ...... 37 Multi-Annual Plans (Plano Pluriannual - PPA) ...... 40 Conclusions: Development Outcomes and the Urgent Call for Sustainable Alternatives ...... 43

3 NON TIMBER FOREST PRODUCTS ...... 46

The Rise of Forest Extractivism ...... 46 The Onset of NTFP as a Sustainable Development Approach ...... 48 Early Economic Studies of NTFP ...... 51 Income Derived from NTFP ...... 53 The Decline of Forest Extractvism ...... 57

4 COMPANY-COMMUNITY PARTNERSHIPS ...... 58

Pioneer Partnerships with Global Cosmetic Companies ...... 59 The Body Shop Partnership ...... 59 Other Partnerships Experiences ...... 61 NTFP and Sustainable Global Value Chains of Biodiversity Products ...... 68 Cooperatives ...... 74 Socio-Biodiversity Sourcing Partnerships in the Amazon: Current Scenarios .. 77

5 RESEARCH METHODOLOGY ...... 86

Theoretical Framework ...... 86 Political Ecology (PE) ...... 86 Economic Geography ...... 89 Spatial Econometric Theory ...... 90 Spatial Dependence ...... 92 Spatial Heterogeneity ...... 93 Spatial Econometric Models ...... 94 Weight Matrix ...... 95 The Mixed Autoregressive-Regressive Model (SAR) ...... 95 The Spatial Autoregressive Error Model (SEM) ...... 96 The Spatial Durbin Model (SDM) ...... 96 Bayesian Spatial Autoregressive Models ...... 97 The Heteroscedastic Bayesian Linear Models ...... 98 Quantification of Location Points ...... 98 Exploratory Spatial Data Analysis (ESDA) ...... 99 Data Preparation and Exploratory Data Analysis ...... 99

6 RESEARCH DESIGN AND METHODS ...... 111

Study Site ...... 112 Participants ...... 114 Sampling Methods ...... 115 Income from NTFP ...... 118 Econometric Multiple Regression Analysis Approach ...... 120 Subset Sample ...... 121 Hypotheses and Research Questions ...... 122 Data Generating Process and Variables Definition ...... 122 Distance Variables ...... 124 Coordinates Points ...... 124 Main Exploratory and Dummy Variables ...... 126 Empirical Statistical Modeling Approach ...... 126 Descriptive Statistics ...... 128 Bayesian Statistics ...... 129 Data Analysis ...... 131 Models Assessment ...... 139 Statistical Regression Models Results ...... 144

7 DISCUSSION ...... 146

Statistical Regression ...... 146 Multifunctional Production ...... 147 Tomé-Açu ...... 152 Igarapé-Miri ...... 159 Breves (Furo do Gil) ...... 162 Anajás ...... 164 Private Company Beraca ...... 165 Other Experiences ...... 168

Effect of Partnerships on Communities’ Welfare ...... 171 Income Average ...... 172 Intensive Agroforestry Systems ...... 173 Price Negotiation ...... 173 Transport Challenges ...... 175 Location ...... 175 Amounted Traded ...... 176 Unfeasible Extractivism of NTFP? ...... 176 Sustainability and Threats ...... 178 Competition with Other Economic Activities ...... 178 Public Policy ...... 182 Selecting Zones for Extraction or Collection ...... 184 Survey of Areas where the Species of Interest Occurs ...... 185 Benefits Sharing ...... 185 Remote Areas ...... 185 Diversification ...... 187 Socioeconomic Context ...... 189 Cooperatives ...... 190

8 CONCLUSION ...... 192

Research Contribution ...... 198 Limitations ...... 198 Future Research ...... 198

APPENDIX

A EXPLORATORY DATA ANALYSIS ...... 201

B COMPLETE DATASET DESCRIPTION ...... 268

LIST OF REFERENCES ...... 269

BIOGRAPHICAL SKETCH ...... 291

LIST OF TABLES

Table page

6-1 Total Income by Municipality (R$) ...... 113

6-2 Descriptive Statistics Income Source Activities (N=286) ...... 118

6-3 Dependent and Exploratory Variables ...... 127

6-4 Dependent and Exploratory Variables (n=286) ...... 128

6-5 OLS, SAR and SEM model comparisons (n=286) ...... 133

6-6 SDM model comparisons (n=286) ...... 134

6-7 SAR_g and SEM_g model comparisons (n=286) ...... 135

6-8 Bayesian SDM_g model comparisons (n=286) ...... 136

6-9 Bayesian SAR_g model comparisons (n=286) ...... 137

6-10 Bayesian SEM_g model comparisons (n=286) ...... 138

6-11 Bayesian Models Assessment (SAR_g, SEM_g and SDM_g models) ...... 139

6-12 Bayesian Models Assessment (SAR_g and SEM_g) ...... 139

6-13 OLS model comparisons (n=286) ...... 140

B-1 Variables and their description for the full data set ...... 268

LIST OF FIGURES

Figure page

4-1 NTFP into cosmetic chains...... 70

4-2 NTFP actor’s interactions ...... 71

4-3 IBGE data on annual production of acai (elaborated by the author)...... 80

4-4 IBGE data on annual production of oilseeds (elaborated by the author)...... 81

5-1 Regression total income versus explanatory variables ...... 100

5-2 Regression total income versus explanatory variables by membership status . 102

5-3 Explanatory variables by location...... 104

5-4 Histograms variables ...... 106

5-5 Relationships between Total Income and explanatory variables ...... 108

6-1 Study Site ...... 113

6-2 Percentual Production ...... 116

6-3 Income source activities ...... 119

6-4 Correlational Matrix...... 123

6-5 Model Builder Workflow to create distance variables from the major road, river and capital networks...... 125

6-6 Adapted from Lesage and Pace Simplified Approach for Spatial Models. Courtesy of author...... 130

6-7 Diagnostic plots for linear regression analysis in R...... 142

7-1 Location of the municipality of Tomé-Açu-PA, highlighting the infrastructure ... 152

7-2 Location of the municipality of Igarapé-Miri, highlighting the infrastructure ...... 159

7-3 Location of the municipality of Breves, highlighting the infrastructure ...... 162

7-4 Location of the municipality of Anajás, highlighting the infrastructure ...... 164

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

NON-TIMBER FOREST PRODUCTS (NTFP) AND THE GLOBAL COSMETIC INDUSTRY: EXPLORING THE CONTRIBUTIONS OF NTFP TO RURAL LIVELIHOODS IN THE BRAZILIAN AMAZON

Aghane De Carvalho Antunes

August 2019

Chair: Cynthia Simmons Major: Geography

Commercialization of non-timber forest products (NTFP) has been advocated as a compatible strategy to mitigate climate change and poverty by maintaining forest biodiversity and ecosystem services, reducing deforestation in Amazonia, while also generating income for small farmers. This study explores the engagement of small farmers in NTFP production and partnerships with the multinational cosmetic industry.

The objectives were to critically assess: (1) how income generated from market-oriented

NTFP extraction impacts small farmers’ livelihoods; and (2) whether membership in cooperatives linked to company-community partnerships is a factor in improved livelihood.

Household-level data from 286 surveys conducted in remote communities in four municipalities in the Northeast of Pará, provide empirical insight into NTFP extraction and processing activities in the Brazilian Amazon, as well as qualitative interview data with key informants. A spatial econometric approach is employed using the household- level data to assess if engagement in NTFP extraction and membership in cooperatives result in statistically significant increases in the household income. To estimate these

relationships, a series of conventional and spatial regression models are employed, including OLS, SAR, SEM, SDM, and alternative Bayesian models. Results show that the NTFP does not have done any bearing effect on the total income. The outcome also indicates that membership in cooperatives tied to partnerships is positive and significant, and results in increases in total income at the household level. The findings suggest that participation in partnerships is the most robust factor influencing total income with consistent results across the various estimation methods implemented in the study.

CHAPTER 1 THESIS OVERVIEW

The Brazilian Amazon is a biodiverse region with vast carbon sequestration potential, and despite declines in deforestation in the millennial decade, rates are once again edging up as global demand for mineral and foodstuffs continue to encroach on the basin (PRODES 2018). While the Amazon is resource-rich, its resident rural populations continue to suffer from perennial poverty given their limited access to opportunities for income increase (Brondízio 2008; Guedes and Brondízio 2012;

Simmons 2004). Market-oriented extraction of Non-Timber Forest Products (NTFP) has been advocated as a win-win approach for alleviating rural poverty and ensuring conservation since NTFP generate income that translates into livelihood improvements, while NTFP extraction maintains biodiversity with negligible impacts on ecosystem service functioning (Anderson and Clay 2002; Mayers and Vermeulen 2012; Neumann and Hirsch 2000). Nevertheless, few studies have empirically assessed the beneficial claims of NTFP commercialization in the context of company-community partnerships with global cosmetic companies, and whether said corollary improvements to rural livelihoods and mitigation of environmental harms result (Belcher and Schreckenberg

2007; Mayers 2000; Morsello 2006b; Shanley et al. 2006). Limited research has raised concerns that what NTFP contribute to income is relatively small, and the opportunity costs are high for farmers with access to more lucrative economic activities (i.e., cattle- ranching, agriculture), which has increasingly become evident as new roads make once remote locations accessible, reducing transportation costs and making market engagement viable (Hecht 2005; Killeen 2007; Souza 2014). Indeed, studies have shown that small farmers across the basin have shifted from annuals production and

NTFP extraction to cattle ranching, corresponding to the expansion of deforestation

(Pereira et al. 2016; Salisbury and Schmink 2007; Simmons 2016; Vadjunec et al. 2011;

Walker et al. 2008).

Despite the lack of consensus on outcomes, NTFP harvesting supported by market-based mechanisms (e.g.,entrepreneurship training on cooperative management, production certification) is still promoted as a sustainable development strategy by conservation foundations and NGOs (e.g., ISA, WWF, Imaflora, Idesam), international development agencies (e.g, World Bank, USAID, GIZ, the German aid agency), as well as federal, state, and local governments (e.g., government programs) (FGV, 2014; Laird

& Wynberg, 2008; Morsello et al., 2012; Scherr et al. 2003). Partnerships between companies and Amazonian communities for trading NTFP are rising. Some of these agreements involve international and national cosmetics companies (e.g., Natura;

L’Oréal; Aveda; The Body Shop) as this sector has progressively replacing synthetic chemicals with natural products or organic active ingredients to manufacture functional and plant-based products, in addition to expanding product diversification and corporate social responsibility investments (Millennium Ecosystem Assessment, 2005; Morsello,

2009). The number of partnerships for trading NTFP to supply cosmetic industry chain are also growing in the Brazilian Amazon (Becker, 2001; Brondízio, 2011; Makishi,

2015; Nobre & Nobre, 2019). In addition to major cosmetics industries, there are also

“green” biochemistry processing companies responsible for specialized stages of the production process (e.g., Beraca; Cógnis; Symrise) (Morsello, 2009; Veiga et al. 2016).

There have been cases considered successful partnerships for biodiversity conservation, sustainable use of natural resources and fair sharing of benefits with local

communities. A well-known example is the partnership between the Brazilian cosmetics multinational company Natura and small producers of the Amazon. The company was one of the pioneering companies to undertake these partnerships. The first agreement was signed 19 years ago, in the 2000s, with the Iratapuru community, at Amapá state, north of the Amazon. Today, Natura works with 37 supplier communities of forest inputs from the Amazon region, comprising 4,636 families (Natura 2018). Beraca, a global processing company that provides biodiversity-based ingredients to major cosmetics corporations including L’Oréal, L'Occitane, and The Body Shop, is another widely known example. The company adopted a model of supply chain management, one of the few companies that do so more intensively. The goal is to raise the value of oilseeds and nuts on-site, and as part of the strategy the company provides financial and technical support for enabling organic certification, machinery and equipment, access to diversified market chains, as well as training for improving sustainable harvesting and welfare benefits. Currently, Beraca leads a supply chains with around 150 rural communities, comprised of approximately 2,500 small farmers families to date. The company, with the support of a network comprising universities, NGOs, governments and certification bodies, commercialize NTFP associated with the long-term regional development goal (Veiga et al. 2016). Accordingly, the Brazilian government has recently announced that it wants to increase the number of partnerships between small producers and companies or investors interested in business opportunities with

Amazonian “socio-biodiversity” products. The government points out that the Amazon region houses over 400 thousand forest-dependent families producing food, cosmetics, handicrafts, among other multiple-use NTFP that help to conserve the Amazon by

reducing deforestation. More than 341 cooperatives and associations of small producers were already mapped, as well as over 121 private companies, potential buyers of the biodiversity products (GIZ 2019).

Most notably in the case of the Brazilian Amazon is the appeal of the rainforests and its traditional peoples (i.e. indigenous peoples and poor peasant farmers) which have stimulated more partnerships associated with “sustainable” and socially responsible forest commodities, such as medicinal plants, fibers, nuts, seeds, fruits, resins and essential oils (Drummond & De Souza, 2016; Morsello, 2006b).

Notwithstanding the hype cosmetic industries and bio-processing partners have promoted among these company-community partnerships, descriptive as well as empirical studies addressing their implications on forest-extractivists livelihoods and forest environments remains still inconclusive, in part because of a lack of reliable data on household income (Mayers & Vermeulen, 2012; Morsello, 2006b; Turner, 1995;

Brazilian Forest Service, 2013). Further, despite the worldwide attention sustainable development alternative has garnered, the central question remains: To what extent does the extraction of NTFP tied to market-based partnerships with cosmetics multinational companies provide a viable approach to long-term sustainability for small peasant farmers and the Brazilian Amazon environment? The aim of this thesis is to help fill this knowledge gap in the related literature.

This research explores the engagement of small farmers in NTFP commercialization, and membership in company-community partnerships as linked to cosmetic industry demand for natural biodiversity-based products, with the purpose of understand the long-term implications of these arrangements for communities’

livelihoods. The objectives are to critically assess: (1) how income generated from market-oriented NTFP extraction impacts farmers’ livelihoods; and (2) whether membership in a cooperative linked to corporation-community agreements is a factor in improved livelihood.

This research employs a mixed methods approach. An extensive literature review of relevant academic scholarship, government documents, NGO assessments, and corporate reports provides a summary of the diverse NTFP projects that have been established, their expectations and outcomes to date, the various strategies employed, as well as the successes and challenges encountered with such projects. Key informant interviews with community leaders, government officials, scientists and corporate executives provide first-hand insight into the experiences and challenges of NTFP commercialization as a sustainable development alternative to improve welfare and conserve the environment. Finally, household-level data from 286 surveys conducted in

37 remote communities, located in four municipalities in the Northeast of Pará state, provide empirical insight into market-based NTFP extraction practiced by family-farmers in the Brazilian Amazon. A spatial econometric approach is employed using the household level data to assess if engagement in NTFP commercialization and membership in company-community partnerships results in statistically significant increases in overall household income. To estimate this relationship, a range of conventional and spatial regression models were used, including OLS, SAR, SEM,

SDM, and alternative Bayesian models. I argue that the membership in cooperatives associated with NTFP-oriented community-company partnerships does affect a small farmer’s household income positively resulting in higher income. Although positive and

statistically significant, this contribution is minimal, and not enough to improve social welfare. The processing of biodiversity-based products within the communities offers an opportunity to small farmers households to increase their income and well-being.

The research provides mixed results when it comes to the value of NTFP extraction as a sustainable development alternative. While the econometric results show there is not a statistically significant relationship between engagement in NTFP extraction and total household income, they do suggest that membership in cooperatives tied to corporate-community partnerships have statistically significant effects with increases in total income across the various estimation methods implemented. Overall, key informants concur, suggesting that commercialization of

NTFP alone is not adequate to alleviate rural poverty, but instead long- term partnerships between cosmetic corporations (i.e., Natura, Beraca) and producer’s cooperatives must be encouraged. In terms of environmental outcomes, the role of

NTFP in conservation is performed by a series of intertwined actions aimed at increasing the income of the families as to support and influence them to not shift to other more harmful to the environment to generate their income. These actions combine the payment for the sale of NTFP itself, and the promotion of the cooperative businesses, including certification of biodiversity-based production, besides benefit- sharing payment for end-products commercialized, and in some cases, payment for environmental services (PES) such as carbon sequestration based on avoided deforestation on their property related to carbon credits market.

Although this research does not directly measure the environmental impact from

NFTP production, it does consider some of the problematic issues that arise with

uncritical expectation that the commercialization of NTFP results in conservation.

Results from this study can inform the debate on the role of the commercialization of

NTFP, and membership in product-oriented partnerships to produce biodiversity-based ingredients to globalized niche markets. Such an understanding is essential to tailor appropriate policy recommendations to enhance small producer’s remuneration from forest-based activities, enabling conditions for effective sustainable development in the

Amazon. We conclude by discussing the significance of these findings for research, addressing the intersections involving market and forest-based livelihoods, environmental change and conservation, climate change, poverty and inequality mitigation, and public policies.

The thesis is organized as follows. Chapter 2 provides motivation for this research with a brief discussion of the importance of the Amazon biome, an overview of the region’s development history and the unintended outcomes that include both deforestation and systemic rural poverty of resident populations. Finally, a review of

NTFP extraction as a sustainable development strategy is presented, including the contemporary scholarship debating its merits. Chapter 3 presents the study region and description of the household characteristics, NTFP extraction activities, and their involvement in corporate-community cooperatives. Lastly, it presents the methods used to address the primary research questions. Chapter 4 present the results and findings, as well as policy implications.

CHAPTER 2 BACKGROUND AND MOTIVATION

The Changing Amazon

Tropical forests are the most biologically diverse ecosystems on earth, housing more than half of the world’s biodiversity (Myers et al. 2000; Soares-Filho et al. 2006).

Its destruction through deforestation and environmental degradation is a prime global concern since forest loss triggers destructive ecological consequences at local, regional and global levels (Hall 2011; Pfaff and Walker 2010), including impacts on biodiversity, climate hydrology and global carbon cycle and sustainable development (Simmons

2004). Forests play a critical role in the earth’s terrestrial carbon sinks and exert strong control on the evolution of atmospheric CO2 (Yude Pan 2011).

Brazil is one of the economically affluent and environmentally diverse countries on earth with an estimated 463 million hectares of natural and planted forests, ~ 55% of its territory (Serviço Florestal Brasileiro 2013), comprising the world’s largest contiguous intact tropical forest (Pereira et al. 2016; Arima et al. 2016; Myers 1988; Soares-Filho et al. 2004), roughly 30% of all remaining global tropical forests (IPCC 2013)1. In recent discussions about the future of the changing Amazon, a controversial issue has been whether or not the Amazon region should be kept apart from development. Many scholars believe the Rainforest deserves more extensive protection since little is known about its existing biodiversity. The total biota, for example, accounts for 1.8 million

1 The concept of "mega-diverse country" was coined by Russell Mittermeier from Conservation International (Conservation International 2005). According to the IUCN (International Union for the Protection of Nature and Natural Resources - 2000), the countries classified as mega biodiverse are: Brazil, Peru, Ecuador, Colombia, Venezuela, Mexico, United States, South Africa, Madagascar, Democratic Republic of the Congo, India, Indonesia, China, Papua New Guinea, Malaysia, Philippines and Australia. Brazil is the most important because of its magnitude and the variety of its ecosystems and species.

species, assumed to be an underestimated figure, given challenges inherent to documentation and conservation (Lewinsohn and Prado 2005; Peres 2005; Silva et al.

2005). The Amazon Rainforest today comprises an area of approximately 5.4 million km2, about 87% of its original extent. Brazil alone accounts for 62% of the entire

Amazon biome (Malhi et al. 2008; Myers 1988; Soares-Filho et al. 2006), consequently

Brazilian policies to promote development and conserve the environment are crucial for the future survival of the Amazon biome.

The stability of forest-climate balance in the Amazon is disturbed by anthropogenic drivers of change. Tropical deforestation have been identified as one of the most critical of those drivers (IPCC 2013). A number of studies have indicated that continued land clearing will result in drastic consequences, including the conversion of more than 50 percent of the Amazon into a degraded savannah (Lenton et al. 2008;

Lovejoy and Nobre 2018). This change will compromise the forest’s capacity to provide critical ecosystems services, such as climate regulation, carbon sequestration, and fire control (Nobre and Borma 2009). Deforestation can also accelerate global warming and its ‘side effects’ that include severe flooding, changes in rainfall recycling patterns, river regimes and soil productivity (Laurance et al. 2015; Salazar, Nobre, and Oyama 2007).

The most harmed by all those changes will be the poorest of the poor small farmers because they have few other alternatives available to meet subsistence needs and their livelihoods rely mostly on the existing biodiversity (Nepstad et al. 2008; Brondizio and

Moran 2008).

The urgent need to conserve Amazonia’s natural riches and support social development of its resident population are widely accepted by all. And, sustainable

development, the balancing of economic, social, and environmental goals, along with decentralized local level planning and participation, have become common discourse since the 1990s as employed by the State (i.e., Eletrobras; Eletronorte), large corporate interests (i.e., Vale S/A), environmental NGOs (i.e., The Nature Conservancy –TNC;

Rainforest Alliance), and social movements (i.e., National Council of Extractivist

Populations - CNS). Nevertheless, the meaning of sustainable development is the subject of great debate, and the actual outcomes of approaches pursued since Nossa

Natureza was codified in the 1988 Constitution stand in stark contrast with the advance of deforestation and deterioration of social welfare, especially among the region’s traditional populations (Adams et al. 2009; Simmons 2002). Indeed, the Brazilian

Amazon is presently experiencing intense violent land conflict involving a myriad of actors, including the landless peasantry, small farmers, large ranchers, miners, loggers, fishermen, and indigenous peoples (Simmons 2005). In addition, social welfare, as reflected by malnutrition and infant mortality rates, is more severe here than anywhere else in the country. Despite a temporary lull in deforestation, forest loss is once again on the rise (PRODES 2018).

Since the turn of the new millennium, centralized large-scale infrastructure projects have accelerated, beginning with Avança Brasil in 2000 and expanded to continental scale with the Initiative for Infrastructure Investment in South America

(IIRSA), regardless of environmental and social harms, which raises the specter whether “sustainable” development was ever more than rhetoric. Despite the potential magnitude of harms from these mega infrastructure projects, environmental and social advocates continue to press for local grassroots governance and NFTP extraction to

ameliorate harms and empower traditional and local populations. Did the modernist development agenda of the State ever give way to a sustainable development paradigm that privileges local governance and the environment? If not, what is the potential that local level NTFP extraction will provide a sustainable alternative to improve social welfare and conserve the environment? In an effort to partially address these questions, the sections that follow provide a brief overview of the development history in the

Brazilian Amazon, and a summary of sustainable development strategies pursued, specifically efforts to promote NTFP activities as a way to balance conservation with social welfare improvements for the region’s rural poor.

Brazilian Amazon Development History

Early Extractive Economy

Historically, the Amazon was of great importance to Brazil's economy because of the many resources that could be extracted from its tropical forests, including rubber trees (Hevea brasilienses) and Brazil nut (Bertholletia excelsa). These multiple uses trees dominated the Amazon region’s land cover up until the mid-1970s, when impacted by consecutive “boom and bust” cycle and cleared to make way for cattle. Rubber latex, for example, came to be the third most exported Brazilian product between 1887-1917

(Homma 1992). Today, however, the natural rubber2 is a meaningless product for most

Amazonian communities due to its low profitability (Jaramillo-Giraldo et al. 2017; Nugent

2017), except for those working in extractive reserves in Acre given specific conditions and policies (i.e., cattle ownership and rubber subsidy), and yet combined with other

2 Today, natural rubber still has numerous industrial applications. The global production of natural rubber is strongly concentrated in Southeast Asia. Ironically, Brazil is not self-sufficient in rubber and imports 70% of only two countries (i.e., Indonesia and Thailand).

activities such as farming, fishing, different wage labor, among other typical forms of multifunctional Amazonian livelihoods (Nugent 2017). Rubber extraction took off in the

1800s and attracted many impoverished Northeastern rural peasants, fleeing from the endless drought and the misery that plagued that region of the country, especially

Ceará state. With the onset of the car industry, following discovery of the vulcanization, the remote Amazon rainforest turned into the hub of a lucrative global rubber market for many decades. Manaus and Belém, until then small towns on the Amazon, blossomed into modern metropoles (Bueno 2012a; Pinedo-Vasquez et al. 2011). In the words of

Nugent (2018), this phase is "monumentalized" in the literature through examples of icons like the Manaus Opera House, often cited as the material expression of the rubber boom. According to these accounts, this phase intensified economic growth in some regions of the Amazon and local elites accumulated tremendous assets. However, the experience of rubber tappers was very distinct. Unlike local urban entrepreneurs and trade-lenders (aviadores) who enjoyed the financial benefits of the rubber industry and the rewards of the nascent global capitalism, enslavement, in a form of an exploitative labor relation based on debt-bondage and peonage (debt slavery), and other human dignity abuses abounded in the forest domain (Bueno 2012a; Gomes et al. 2012).

In 1876, an English botanist named Henry Wickham stole 70,000 rubber tree seeds and smuggled to Kew Gardens, England, where they were domesticated and planted in large rubber plantations in Britain’s Asian colonies, resulting in the rubber bust by 1910. In 1900, Brazil produced approximately 27 thousand tons of rubber, a figure that in 1919 rose to 34 thousand tons. But in Southeast Asia plantations of the hevea crops, as a substitution for the Amazonian wild rubber extractive model, allowed

favorable efficiencies (e.g., standardization, better control of supply, establishment of a commodity chain) that enabled an increase in productivity from 3 thousand tons the first year of the 20th century, to more than 381 thousand tons in 1919, absorbed mostly by the American car industry. Later, the American entrepreneur Henry Ford, one of the ideologists of the Amazonian industrialization, even tried to reproduce the successful

Asia plantations experience in the Amazon, but his ambitious project failed (Drummond and De Souza 2016). The fungus Dothidella ulei took over the trees planted in the city of Fordlândia. There were other attempts of rubber management, but the plantation was again devastated by the leaf-blight (Bueno 2012b). Isolated and with little access to public services, many northeasterners rubber rappers returned home, and those who remained in the region switch to subsistence activities such as fishing, hunting, floodplain agriculture and forest extractivism (Simmons et al. 2015).

Forty years later, World War II then brought a second Amazon “rubber boom.”

This aspect of WWII has not been commonly reported in the literature, and according to

Neeleman and Neeleman (2017) it may have been one of the most important events of the war. Outside Brazil, few people have heard of the saga of the “Rubber Soldiers,”

(soldados da borracha), an army of 55,000 northeasterners, who were recruited and sent to the Amazon jungle by the government under an agreement between Brazil and the United States to supply natural rubber for the Allied war effort. In 1942 Japan invaded Malaysia and Indonesia, taking control of most of the world’s rubber supply, formed by those plantations developed through biopiracy. Rubber was crucial to the war effort, because everything in WWII depended upon rubber, especially tires, wiring to warships. The Allies struck a deal with the populist President Getúlio Vargas through

the Washington Agreement of 1942 to quick reactivate rubber supplies from the

Amazon. The Brazilian government thus established an agency (SEMTA: Special

Service of Mobilization of Workers for the Amazon) to mobilize, recruit, transport, and allot tens of thousands of northeasterners to work as rubber tappers along the Amazon rivers seduced by promises of fortunes and heroism. The reality was very different though. They were literally slaves by debt, violently forced by the patrons to work long hours extracting latex in harsh conditions for little or no pay (Scott, and WWF 2014). As

Pacheco (2011) puts it, many of them “died in the battlefields of the North.” After the end of the WWII the rubber economy collapsed again, and many rubber tappers were left abandoned in the rainforest. Eventually, some died or settled in urban cities such as

Manaus and Rio Branco (Bueno 2012; Neeleman and Neeleman 2017), including the parents of renowned rubber tapper Chico Mendes (Neeleman and Neeleman 2017). A few of them were fortunate in going back to their hometowns, and many remained in the rubber estates, extracting latex and other non-timber forest products, and also working with agriculture (Gomes et al. 2012; Pacheco 2011). As Hall (2002) observes, poverty became prevalent amongst them, with high levels of malnutrition, illiteracy, and diseases such as malaria and leishmaniasis.

The Brazilian government was determined to expand into the vast Amazon region, and clearly articulated its desire to integrate the Amazon with the political and economic core of the country in the Brazilian Constitution of 1946, which called for a

Comprehensive Plan to see it to fruition. In 1953, the federal government established the Superintendence for the Planning of Economic Valorization of Amazonia (SPVEA) to design 5-year development plans for bringing the region into productive use by

focusing primarily on agriculture, transport, and health. However, the Brazilian Congress was skeptical of the plans, and instead approved a limited number of individual projects

(Santana et al. 1997). The major accomplishment of SPVEA was the construction of the

Belém-Brasília highway that linked Amazonia to the new national capital of Brasília, which was intentionally and symbolically moved from Rio de Janeiro to a more central location.

Order and Progress: Development under Military Regime

Development efforts were greatly hastened in 1964 when the military regime took power. In 1966, the Operation Amazonia was initiated, seeking to accommodate both economic and geopolitical concerns. One of the first initiatives of the program was the replacement of SPVEA with the Superintendence for Development of Amazonia

(SUDAM), under the auspices of the Ministry of Interior, whose primary responsibility was to develop the region by attracting private investment. In addition, the military government reformulated the Credit Bank of Amazonia (BCA), creating the Banco da

Amazonia S.A. (BASA), which became the financial agent of SUDAM. They also promulgated the Law 5.174 establishing a program of fiscal incentives applicable in the

Amazon region. This legislation made it possible for registered companies to deduct

50% of their owned income tax and invest it in agricultural, cattle-ranching or basic industry projects considered to be essential to the development of the region, and exemption from income tax and other federal taxes (Brasil 1975). These development strategies reflected the neoclassical paradigm of the 1960s, emphasizing policies that promoted accelerated economic growth (Thiesenhusen and Melmed-Sanjak 1990).

Nevertheless, the military’s geopolitical concerns necessitated the settlement of the

Amazonian frontier to secure the nation’s borders (Foresta 1992). Consequently, throughout this period development plans attempted to appease the land demands of both small farmers and large corporate interests.

The approach taken for the northern region as outlined in the First National

Development Plan from 1967 to 1971 (I PND), attempted to: (1) concentrate resources in areas of potential and existing populations; (2) promote stable and self-sufficient populations in the frontier region through managed in-migration; and (3) stimulate economic growth (Browder 1988; Hall 1989; Mahar 1979). In order to actualize such a plan, the government provided a 100 % tax exemption for agriculture, livestock, and industrial investments, and the provision of basic services for all activities pursued in the

Amazon region. Investors also received income tax credits, import and export tax exemptions, subsidized credit through BASA for land acquisition, and access to a special credit fund established by SUDAM (Browder 1988; Hall 1989; Mahar 1989;

Santana et al. 1997). Based on analyses of regional peculiarities, the Plan for the

Development of Amazonia (PDAm) for the period 1972-1974, following the model determined by the federal government for stimulating progress in new areas, initiated the process of colonization of said empty tracts of land in Amazonia. This occupation strategy had an emphasis on the Northeast region of Brazil. The Plan also emphasized the construction of massive highways such as the Belém-Brasília, Brasília-Acre,

Cuiabá-Santarém, and the Transamazonic Highway, which links the Northeast with the

Amazon region. To settle the Northeasterns in the region, SUDAM created priority projects for areas of agriculture and cattle-breeding. In addition, studies focused on the market, production, and commercialization of mineral and vegetable products of the

Amazon were also initiated, as part of the Basic Studies for Regional, Sectorial, and

Spatial Planning of SUDAM (Brasil 1975).

Despite initial concern expressed in the plan for permanent settlement and small- scale agriculture in the region, development during the 1960s resulted in the monopolization of land and government subsidies for commercial and speculative purposes (Branford and Glock 1985; Hall 1989; Hecht 1985; Ianni 1979; Mahar 1979).

The vast majority of development aid went to livestock activities with the number of approved projects increasing from 4 in 1966 to 162 projects by 1969. Emphasis on cattle activities reflected global interest in the third world livestock sector, with substantial investment coming from the World Bank and Inter-American Development

Bank (Hall 1989). Despite the lack of financial support for resettlement efforts, the construction of roads that opened Amazonia facilitated the spontaneous in-migration of an estimated 174,000 (Martine 1980) to 320,000 settlers (Katzman 1977) from 1960 to

1970.

National Integration Plan

The focus of Amazonian development shifted in 1970 from commercial interests to populist issues. This change was due, in part, to humanitarian concerns created by the Northeastern drought of 1970 that left many landless peasants in dire circumstances. A contributing factor involved the political appeal of a populist agenda, meant to diffuse attention away from the brutal repression of the military regime and, by advocating small farmer resettlement in the Amazon, would allow the government to avoid the politically sensitive issue of land reform in the Northeast and South (Hall

1989). This new emphasis was realized in the National Integration Plan (PIN) designed to integrate the Amazon with the national economy of Brazil.

The primary strategy of PIN was the construction of an extensive highway network connecting the Amazon with the economic mainland of Brazil at an estimated cost of US$100 million dollars, 10 % of which was provided by USAID funding (Hall

1989). The plan proposed two highways bisecting the region: the East-West Trans

Amazon Highway and the Santarém-Cuiabá Highway. The overall objective of this highway network was to create a complete line of roads from the Atlantic Ocean at

Recife to the Peruvian border at Cruzeiro do Sul.

The secondary strategy of PIN was the creation of an elaborate three-tier system of central places to promote rural urbanism. This hierarchy of central places involved the development of: (1) agrovilas at 10 kilometer intervals consisting of 48 to 66 households; (2) agropoles of 600 households at 100 kilometer distances with banks, post offices, schools, and cooperatives (designed to serve 8 to 22 agrovilas); and lastly,

(3) ruropoles at 140 kilometer intervals with populations of 20,000 and a full range of urban services (Browder and Godfrey 1997; Browder 1988). In an effort to promote orderly settlement of the region, this model of urban hierarchy was to be applied to the frontier landscape along the Transamazon Highway.

The final strategy of PIN was aimed at promoting small farmer settlement schemes to provide land for the growing number of landless peasants displaced by agro-industries and land consolidation in the Northeast and South, seasonal labor for shortages experienced in extractive industries, increases in staple food crop and export food production, and finally, security for national sovereignty in the frontier region. The entire endeavor hinged on the government expropriation of State land, as mandated by decree 1164. Of the 100-kilometer wide strip of land on each side of the Transamazon

highway, a 10-kilometer-wide buffer along the road was to be divided into 100-hectare plots for purchase by colonists, and all remaining land was to be distributed for commercial interests. In the Transamazon region, three Integrated Colonization Projects

(PIC) were planned with headquarters in Marabá, Altamira, and Itaituba, all cities in

Pará. In addition, the Fundo para Investimento Privado no Desenvolvimento (FIDAM) was established to finance agricultural activities, while BASA was assigned administrative responsibility.

The military government employed much propaganda to promote the colonization schemes with the nationalistic mottos Occupar para Não Entregar (occupy so as not to surrender), A Amazônia é Nossa (the Amazonia is ours), and Terra Sem Homens para

Homens Sem Terras (land with no people and people with no land). The initiative was massively advertised on buses, T-shirts, and billboards to stimulate a higher number of colonists to move to the Amazonian frontier, also reducing the migration to the south

(Ianni 1979; Schmink and Wood 1992; Simmons et al. 2016). Impoverished small farmers and rural landless migrated to the untouched Amazon rainforest with assurances of arable lands, agricultural credit and technical assistance. However, the reality encountered was different from the official propaganda.

Overall, PIN was deemed a failure by government officials and academics alike.

The PICs, for example, fell far short of the desired objectives and the strategy to create rural urbanism was never realized (Browder 1988).The failure of PIN has been attributed to many structural, institutional, and environmental factors. One argument is that INCRA was poorly equipped to handle the rapid influx of migrants and, therefore, could not process land claims; as a result, credit could not be obtained and crop

plantings were delayed (Bunker, 1985; Hall, 1987; 1989). In addition, promised infrastructure was not forthcoming and agricultural extension was inadequate.

Ultimately, the regional planners rigidly applied the spatial organization of the rural urbanism scheme without consideration of plot location in terms of access to transportation and soil fertility (Browder and Godfrey 1997; Mahar 1979; Moran

1983).Those colonists with plots along the highway and with fertile soil succeeded, while the majority sold their land to farmers from the South and became wage laborers or migrated to squatter settlements in one of Amazonia’s many expanding towns. An additional factor, not readily discussed in the literature, is the lack of political follow- through, which is made evident by the abandonment of PIN less than four years after it was first initiated.

Agro-Industrial Expansion (1975 to 1984)

In 1975, development policy changed sharply to an emphasis on agro-industrial ventures, reflecting the political power of both foreign and domestic interests. The

Second National Development Plan (PND II) from 1975 to 1979 stressed the importance of the national economy and capacity of Amazonia for generating foreign exchange. In this conext, the government defined economic functions of the Amazon region based on comparative advantages over other areas and products, such as its privileged position in Brazilian and international markets. Two essential functions were explicitly determined for the Amazon in the national context: (1) To contribute towards

Brazil’s balance of payments via exports; (2) To complement the national economy through the supply of basic raw materials and the expansion of the internal market. The main goals established for the Amazon region was to promote acceleration of regional

growth, raising population’s standard of living through an increase in the number of productive jobs and the consequent increase on production figures (Brasil 1975).

PND II called for the extension of transportation and communication networks and the promotion of export-oriented activities such as beef, timber, and minerals

(Browder 1988; Hall 1989; Mahar 1979; Santana et al. 1997). Development efforts focused on the Programa de Polos Agropecuários e Agrominerais da Amazonia

(POLOAMAZONIA), a strategy to attract capital to 15 growth poles by providing a wide range of government subsidies and tax incentives, including investors tax credit subsidies, income tax deductions and exemptions, and import duty exemptions

(Santana et al. 1997).

Approximately 200 priority projects were incorporated in the five-year plan period

(1975-1979), including colonization, farming, catlle raising, electric energy supply and urban development (Brasil 1975). An important activity targeted in this phase was cattle ranching, particularly by large corporate interests. Cattle ranching was the obvious investment choice due to the existence of plentiful, inexpensive land and high world beef prices, nearly three times the price in 1960. The focus on livestock as the key strategy for economic development was greatly influenced by national and international interests (Hall 1989). In Brazil, the Association of Amazon Businessmen (AEA), a São

Paulo-based group, exerted pressure on SUDAM to subsidize livestock interests

(Bunker 1985). At the same time, mechanized agriculture and livestock in Latin America were being promoted as a means for alleviating world hunger and, as a result, received tremendous financial support from various international institutions, such as World Bank and FAO (Hall 1989). The Latin American Agribusiness Development Corporation,

comprising 13 major US industrial and financial corporations, was established in 1970, with the explicit intent of financing ranching activities. Finally, the United States encouraged overseas investment in such activities by instituting tax laws that allowed tax payments to be deferred until profit repatriation occurred (Hall 1989).

By the end of the decade, the importance of cattle ranching as a national development strategy declined and Amazonian policy shifted with the Second Amazon

Development Plan from 1980 to 1985 (PDA II) with a greater emphasis on mineral extraction. The exploitation of mineral wealth in the Serra dos Carajás (Carajás

Mountains), west of the town of Marabá in southern Pará, was the target area for development; it was estimated that this region had the world’s largest reserve of iron ore, Brazil’s second largest source of manganese, and an abundant supply of other important minerals (Hall 1989).Viability studies recommended the transportation of the iron ore by railway to the port of Itaqui in São Luís, capital of the Maranhão state (Brasil

1975).

In 1980 the Programa Grande Carajás (PGC) was formally established promoting large-scale, capital-intensive, export-oriented mineral and agricultural development, and the expansion of related industrial activities. The primary objective of

PGC was the servicing of Brazil’s growing foreign debt (Hall 1989; Santana et al. 1997).

The PGC was divided into two principal components, one focusing on mineral exploration, and the other, Programa Grande Carajás Agrícola, concentrating on agricultural activities.

The realization of this elaborate project necessitated an alliance between the

State, private agents, and foreign actors, with substantial investment coming from

foreign sources (Hall 1989). The incentives for investment in PGC included: (1) total income tax exemption for investors in infrastructure or productive projects; (2) exemption of taxes on manufactured goods; (3) exemption from import duties; (4) bank credit; (5) subsidized electricity; (6) infrastructural improvements financed by the government; (7) guaranteed supply of iron-ore below world market prices; (8) lax environmental laws; and (9) cheap labor (Hall 1987, 1989). The mineral wealth of the

Carajás mine was expected to satisfy around 7.5 % of world iron demand. In return for good loan terms, Brazil signed contracts with many countries agreeing to provide iron- ore at favorable pricing below market value.

The stated objectives of the Programa Grande Carajás Agrícola were to increase agricultural production and employment opportunities, halt environmental degradation, and reverse the trends toward land concentration by creating a class of small family farms within the PGC region. To increase agricultural production and employment, the plan designated large tracts of land for pasture, soybean production, and sugarcane

(Hall 1987, 1989) In an attempt to halt environmental degradation, the plan called for

3.6 million hectares along the Carajás railway for Eucalyptus plantations to provide charcoal for the pig-iron smelters, thereby not impacting primary forest. To accommodate the creation of a small farmer class in the region, the plan focused on agricultural development poles with specified areas designated for small producers with up to 15 hectares, medium producers with 60 to 80 hectares, and large producers with farms of 80 to 500 hectares (Hall 1987, 1989). Moreover, in Altamira and in the

Maranhão region, projects offering assistance to farmers attracted to the area were designed (Brasil 1975).

Overall, despite the plan’s expressed concern with creating a small farmer class and providing employment for the increasing migrant population, the project was biased toward large-scale, capital-intensive endeavors. The plan expressly allocated a limited

17 percent of the project area for small farmers and 36 percent for medium producers, whereas large farmers received 47 percent of the allocated land (Hall 1987, 1989).

Nevertheless, approximately 51 percent of the PGCA region as of 1987 was dominated by 0.7 percent of the landowners, those with greater than 1000 hectares. In addition, social projects such as cooperatives, colonization, and agricultural extension were allocated a limited 15 percent of the budget, while credit for large producers, transportation, and agro-industry received 70 percent of the budget (Hall 1987, 1989).

In 1982, the POLONOROESTE was created as a special plan intended to develop the states of Mato Grosso and Rondônia. This program aimed to promote population growth in those states through the paving of BR-364 (Cuiabá-Porto Velho highway) and through the expansion of infrastructure, agriculture sectors, rural incomes, and social welfare (Mahar 1989; Serra and Fernández 2004). The World Bank financed this plan with US$ 1.5 billion; and at that moment, the Cuiabá-Porto Velho road (BR-

364) was paved, connecting the capitals of Rondônia and Mato Grosso (Millikan 1992).

Besides POLONOROESTE program, more colonization and land regulation projects, health protection, and indigenous and environmental defense programs were initiated (Hagemann 1994; Mindlin 1991). After the BR-364 was paved, the official rate of arrival of migrants in Rondônia exceeded governmental estimates. In

1980, there were around 490,000 people living in that state, and by 1986, the official figures were approximately 1.2 million people (Millikan 1992). According to IBGE

(2008), in 2000 there were 1.4 million people residing in Rondônia, meaning a surge of

185% in the population between 1980 and 2000. Lentini et al., (2005) notes that between the 1970s and early 2000s, the Amazon population experienced an increase of over 150%. In the same period, Brazil recorded a population increase of

42%.

The final project under the military regime was the Projeto Calha Norte (PCN) of

1985, designed to promote the occupation of the northern river area of the Amazon and

Solimões rivers, and the 6,500-kilometer border with Colombia, Venezuela, Guyana, and Surinam (Hall 1989). The project region encompasses an area of 1.3 million km2, or about 14 percent of the Brazilian Amazon. The stated objectives of the project were: (1) to create a permanent military presence; (2) to improve bilateral relations with neighboring countries; (3) to set up development poles with infrastructure such as road and hydroelectricity; and (4) to define a new policy with the indigenous groups beneficial to all parties. In effect, the PCN was a military project with approximately 78 percent of its budget allocated for that purpose (Hall 1989). According to some, the true rationale behind the project is the protection of the border region from infiltration of rebel groups from Guyana and Suriname, drug traffickers, and gold smugglers (Hall 1989; Santana et al. 1997).

Development and Environment: New Democratic Reform

Economic development activities in Amazonia changed little with the return to democracy in 1985. An assessment of planning documents reveals that the new government adopted a traditional approach that directed resources at productive activities with the expectation that benefits would “trickle down,” thereby redistributing income, alleviating poverty, and providing for basic needs (Santana et al. 1997). A

secondary concern emphasized in the planning rhetoric was the need to address land reform, and consequently the Plano Nacional de Reforma Agrária was enacted under law number 91.766 (10/10/85). The First Amazon Development Plan of 1986 focused efforts and development incentives on resource extraction activities such as mining and timber, and land redistribution. Nevertheless, growing international and national concern about an environmental crisis in the Amazon influenced the development; consequently, land redistribution efforts were impeded, tax incentives for land extensive activities such as ranching were abolished, and road construction and maintenance was halted (IDESP

1990). Despite such moves, population in the region continued to increase, doubling between 1970 and 1990 to approximately nine million people (Schneider 1995).

The 1988 Constitution marked a decisive step toward the formulation of environmental policy. For the first time in Brazilian history, a constitution had an entire chapter discussing the environment, charging the government and society with responsibility for its preservation and conservation (IBAMA, 2011). Our Nature Program

(Programa Nossa Natureza - PNN) and the Brazilian Environmental Agency (Instituto

Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis - IBAMA) were established to provide national environmental goals and local actions toward environmental protection (Serra and Fernández 1990). Although PNN did not have specific strategies, it was a step in adopting important environmental development concepts.

In June of 1992, the United Nations Conference on Environment and

Development was held in Rio de Janeiro and was attended by 170 nations. The Rio-92 had three main objectives: (1) to identify strategies for regional and global actions

related to important and current environmental issues; (2) to examine the environmental condition of the world and the changes occurring after the Stockholm conference; and

(3) to examine strategies to promote sustainable development and poverty eradication in developing countries. As result of Rio-92 (also known as ECO-92, Earth Summit or

UNCED) and driven by international repercussions of discussions raised at the World

Conference on the Environment, the Brazilian Ministry of Environment (Ministério do

Meio Ambiente; MMA) was created an institution with the power and structure to guide environmental policy in Brazil (Pokorny Benno et al. 2012; UNCED 1992).

SUDAM, recognizing the negative environmental and social consequences resulting from accelerated economic growth in the 1970s and 80s, called for new efforts to promote sustainable development. The four main directives outlined in the Second

Amazonian Development Plan of 1992-95, later reformulated in the PDA 1994-97, include: (1) natural resource exploitation without environmental degradation; (2) spatial integration and development in conjunction with ecological and economic zones; (3) quality of life improvement and reduction of social inequalities; and (4), strengthening of democratic institutions and popular participation in the development process (Santana et al. 1997). Although not every objective was pursued, several programs were formulated to promote economic development, distribute technical and financial support to small producers, and incorporate environmental concerns.

In terms of the environmental priorities outlined in PDA II, some important initiatives have been pursued. One example was the formulation of a plan for ecotourism (1992/1995), to balance economic, social, and environmental concerns. Another leading strategy was the Pilot Program to Conserve the Brazilian Rainforest (PPG7), a

joint multilateral initiative including the governments of Germany, Netherlands, Italy,

France, Japan, Canada, the United Kingdom, and the United States, in addition to the

European Commission and the Brazilian Government that provided financial support to

Amazonian States for creating innovative strategies for sustainable use of the Amazon.

The main goal was facilitate the implementation of pioneering projects to reduce deforestation rates in Brazil. Broadly, the PPG7 aimed to protect biodiversity, to reduce the emission of carbon, to promote quality of life of the local population, and to build international cooperation in global environmental questions including the creation of use zoning plans, with demarcation of indigenous lands, in addition to designations for economic activities and conservation, comprising the expansion of protected areas and extractive reserves(World Bank 2005).

Multi-Annual Plans (Plano Pluriannual - PPA)

In 1998, the Multiannual Plan (Plano Pluri-annual; PPA) approach was launched in decree 2.829 of 10/29/1998, which involves a set of actions, investments, and goals to be followed by federal, state, and municipal governments during four years, always starting in the second year of each presidential term and ending in the first year of the next term (Cardoso, 1998; Executive Decree #2,829). Despite the international interest in sustainable environment, the primary focus of the PPAs have been on large scale infrastructure projects that constitute the vast majority of financial investments. Since the first term of Fernando Henrique Cardoso in 1995, several plans were in devised that focused on large scale infrastructure investments to pave roads and construct dams, locks, railways, waterways, pipelines, and roads in the Brazilian Amazon, all of them part of PPAs. Brasil em Ação (PPA 1996-1999) and Avança Brasil (PPA 2000-2003)

(Becker 2001; Théry 2005). Both programs aimed to connect the Amazon to the

productive space that was rising in the rest of Brazil, and to integrate Brazil through the

Initiative for Infrastructure Investment in South America (IIRSA), a continental wide coordinated planning approach under the auspices of the United Nations of South

America (UNASUR) (Simmons et al. 2018).

The Brasil em Ação (Brazil in Action) program concentrated efforts mainly in the paving of BR-364, connecting Brasília to Rio Branco, capital of Acre; BR-163, connecting Cuiabá, capital of Mato Grosso, to Santarém, in Pará State; and BR-174, connecting Manaus, capital of Amazonas, to Boa Vista, capital of Roraima. In addition to those efforts, Brasil em Ação also aimed to connect the region through river transportation in the Araguaia, Tocantins, and Madeira Rivers (Brasil, 2001). Avança

Brasil continued efforts started in the previous program, and its plan mainly concentrated on the transportation corridors connecting south and southeast Brazilian regions to the Amazon. The Brasil de Todos plan (2004-2007) was elaborated under the rhetoric of inaugurating a long-term development strategy aimed at promoting structural changes in Brazil. This PPA was the key piece of the social and economic planning of the Luiz Inácio Lula da Silva’s administration. The following plans, entitled

Desenvolvimento com Inclusão Social e Educação de Qualidade (2008-2011) and Mais

Brasil (2012-2015) likewise held the same guidelines of the earlier PPAs3 with relation to infrastructure. As Walker et al. (2009) notes, the PPAs are predominantly directed at infrastructure projects for roads, ports, waterways, airports, and electricity. In general,

3 Currently, the PPA is regulated by Article 165 of the Brazil’s Constitution, and together with the budget guidelines (LDO) and annual budgets (LOA), the plan is part of the Union’s Integrated Planning and Budget System (Constituição da República Federativa do Brasil 2015).

plans across administrations have showed continuity, at least concerning Amazonia, complementing and expanding earlier PPA’s projects.

The Programa de Aceleração do Crescimento (PAC), launched in 2007 in Lula’s administration illustrates this point clearly. Part of the Brasil de Todos PPA (2004-2007), this program pursued the same developmentalist agenda of massive infrastructure investment and economic policies aimed at accelerating Brazilian economic growth

(Walker, Defries, et al. 2009). Although the program had emphasized as its main goal the reduction of social and regional inequalities, approximately 55% of the PAC’s budget was applied to finance energy infrastructure, mainly large hydropower plants

(Zhouri 2010) and other infrastructure network for the development of extractive resources (Baletti 2012). The PAC, as well as the PAC versions of subsequent administrations, fit into the plans for the Initiative for the Integration of South American

Regional Infrastructure (IIRSA) released a few years later in a meeting sponsored by

Cardoso and the American Development Bank (Zhouri 2010). The Program is based on the National Plan for Logistics and Transport (PNLT), a long-term planning instrument drawn up in 2006 by the Ministry of Transport, aiming at tracing actions for the transport sector until 2030 (Verdum 2012). The PAC were also in line with the IIRSA goals of improving railway, highway, and energy infrastructure to strengthening axes of integration and development. By way of illustration, the bauxite mining of Juruti, the regional expansion of soybean agriculture, as well as the series of large hydropower dams proposed to the Tapajos River are examples of the projects under the PAC’s heading (Zhouri 2010). The state-owned Brazilian Economic and Social Development

Bank (BNDES) was the main financing agent of the PAC agenda. In 2002, the Bank

created the BNDESPAR (BNDES Participação S/A) to manage its participation as a partner in the capital of state and private companies in various sectors such as pulp and paper, beef, civil construction and engineering, oil and gas, mining, and others. They have also created a specific funding line for PAC infrastructure projects (Verdum 2012).

The PAC II, implemented between 2011 and 2014, in the government of Dilma

Rousseff, aims to continue the portfolio of projects of the PAC 1. In 2012, a five-year horizon plan linked to the PNLT was elaborated, focusing on agribusiness, mining, manufacturing, services and commerce. The PAC works have then followed the same rational. To promote the territorial and logistic integration of Brazil with the

"sparsely” populated neighbor’s South American countries, the PNLT adopts the same nomenclature used by IIRSA, establishing the “continental integration and development vectors.” This suggests that the PNLT, as well as the PAC and the IIRSA itself, are not mere infrastructure projects portfolios, but a broader, long-term development strategy

(Verdum 2012). Besides incorporating the projects started in the first phase of the

Program and not completed, PAC II directs investments to road, waterway and rail infrastructure seeking to stimulate the integration of different modalities of transportation

(e.g., road, rail, waterway, air). The difference is that the PAC II portfolio included new projects with more immediate social outreach, such as urbanization on slums, water treatment and sewage, environmental sanitation, construction of day care and basic health facilities, and sports, culture and leisure facilities (Verdum 2012).

Conclusions: Development Outcomes and the Urgent Call for Sustainable Alternatives

Development efforts have successfully integrated Amazonia with Brazil’s national economy, consequently the region today is one of the world’s most productive cattle

and meat packing areas, with vast mineral wealth ever more accessible with the advance of roads, and it has an emerging industrial sector that could become dominate if the multimodal transportation network planned under IIRSA comes to fruition

(Simmons et al. 2018). All signs indicate that the new Bolsonaro administration has the intention to fast track the mega infrastructure projects despite the threats to indigenous peoples and their homelands and the substantial environmental harms likely to result

(Simmons et al. 2018; Walker and Simmons 2018). In contrast to these success, the social development indicators have declined while violent conflict directed at traditional populations and peasant farmers have intensified, despite development plans that promised social welfare improvements. Finally, the environmental impacts from development have been dire, and despite a short-lived decline in deforestation in the first years of the 21st century, rates are once again on the rise. Environmental concerns continue that deforestation will reach the tipping point with negative consequences for the global environment and, as a result, scholars, environmental and social advocates, and international donors are urgently seeking sustainable development alternatives.

The sustainable alternative agenda proposed for the Amazon comprises a multifunctional combination of activities considered appropriate and compatible with the

Amazon conservation, such as NTFP extraction and trade, agroforestry, community forest management, biodiversity certified fair-trade agreements, ecotourism, crafts, and payments for environmental services, all strategies that aim to conserve the forest while also creating cash income opportunities for small farmers through niche markets. One of the overarching goals of this alternative strategy is to make the Amazon rainforest more valuable left standing than its potential value as timber, pasture, and other more

destructive activities (Becker 2004; Bicalho and Hoefle 2015). Consequently, the extraction and commercialization of NTFPs as a way to improve rural livelihoods and conserve standing forest has gained traction in Amazonian development policies, supported mainly by international donors (Arnold and Ruiz Pérez 2001; Guedes et al.

2012; Homma 2018; World Bank 2009). Proponents assert that the sustainable production of NTFPs is a viable way to mitigate and adapt to climate change by reducing tropical forest deforestation, while at the same time providing income opportunities for rural poor communities (Hagen 2014b; Neumann and Hirsch

2000;Brites and Morsello 2016; Morsello et al. 2012). According to this perspective, a wide variety of forest products including palm oils, seeds, medicinal plants, roots, barks, branches, leaves, fruits, flowers, and others, can be produced sustainably for growing markets (Perz 2004; Shanley, Pierce, and Laird 2006). It has been also claimed that the commercialization of NTFPs is also one of the few available sustainable options to overcome poverty, in both forested areas and marginally degraded agricultural lands

(Scherr, White, and Kaimowitz 2002).

Nevertheless, there has been much criticism and research with a less optimistic view concerning the presumed links between NTFP commercialization, conservation and social welfare outcomes. Opponents highlight that the production of NTFP is insufficient to promote conservation, alleviate poverty and increase the income of small farmers (Browder 1992; Dove 1993; Godoy and Bawa 1993; Homma 1992). To provide greater context to the thesis research, Chapter 3 that follows will explore the scholarship on NTFP extraction, providing the key debates and challenges for these approaches, as well strategies that have been implemented to increase successful outcomes.

CHAPTER 3 NON TIMBER FOREST PRODUCTS

The Rise of Forest Extractivism

Extractivism in the Amazon has remained on the agenda of global academic discussions since the emergence of the Brazilian rubber tappers movement, headed by

Chico Mendes (1944-1988). After his assassination in 1988, extractivism gained momentum with the creation of Extractive Reserves (RESEX), taking a prominent position in the debate about the future of the Amazon. In this context, Brazilian rubber tappers, whose visibility was insignificant even during the rubber boom, resurfaced in the 1980s, as Drummond and De Souza (2016) puts it, “with a new identity of environmentalists,” advocating the advantages of the primarily extractive use of the

Amazonian forests on the grounds that low extraction does not entail deforestation. The

RESEX, forest areas populated by traditional communities awarded long-term common usufruct right to natural resources, aids the maintenance of extractive activities and local communities reliant on forest resources for their livelihood (Allegretti 1989; Gomes,

Vadjunec, and Perz 2012). These protected areas, that have been promoted as a policy tool for linking forest conservation with recognition of the economic value of forests, support the use of the land and natural resources serving as an innovative strategy for regularization of collective land rights, favoring the conservation of forest areas and extractive and sustainable use of natural resources (Drummond and De Souza 2016;

Gomes, Vadjunec, and Perz 2012).

From the heart of this movement, there was sharp criticism towards the development strategies adopted by the Brazilian government in the Amazon region, comprising the road and hydropower construction, expansion of cattle ranching, and

incentives for migration, which were considered threats for the integrity of Amazon rainforest ecosystems (Perz 2004; Schmink and Wood 1984; Simmons et al. 2007). It was in this context that the rubber tappers' movement gained momentum, with rubber tappers advocating a low-impact development model that supposedly would not result in more deforestation and environmental degradation. The emerging enthusiasm with

NTFP low scale extractivism seemed to be germane to tropical forest conservation and rural development. The global environmental justice movement -- strongly influenced by activists and scientists from developed countries, eager for an alternative to cope with deforestation and climatic change threats -- had a pivotal role to the extractivism rebound (Arnold and Ruiz Pérez 2001; Drummond and De Souza 2016; Homma 2018).

As an alternative to the current mainstream development model, community management of NTFP resources and agro-forestry gained great notoriety as a potentially solution to reconcile conservation and development conflicting goals (Ros- tonen and Wiersum 2003a). Proponents of NTFP approach argue that sustainable harvesting and commercial production of forest products is a potential way to mitigate and adapt to climate change by reducing deforestation, while also provide income opportunities for poor small farmers living in vicinity areas where useful species of interest occurs (Morsello et al. 2012b; Ros-tonen & Wiersum, 2003b). Additional benefits are provision of safety net functions (Brites and Morsello 2017; Jaramillo-

Giraldo et al. 2017; Morsello et al. 2012b; Pattanayak and Sills 2001; Rizek and

Morsello 2012; Scherr, White, and Kaimowitz 2002), “natural insurance” to rural producers living on inaccessible, remote forested areas (Albers and Robinson 2013;

Hagen 2014b; Morsello 2006b; Pattanayak and Sills 2001), as well "cultural appropriateness” (Perz 2004).

Extractivism, however, is inherently limited to the resource stock base upon which the harvesters depend and therefore the economic potential is often limited. It is also strongly influenced by rural-urban coupling and inherent urbanization forces, which increases the opportunity cost of rural labor severely, reducing rural labor productivity in work related to extractivism and family agriculture (Homma 2012b). Thus, some researchers have reflected that traditional seringueiros livelihoods are actually at risk of obsolescence in the Amazon, since rubber production has substantially decreased by the last twenty years affected by the decrease in the rural population and urbanization processes. Rubber market supply chains, for example, have not achieved intended results, even after heavy incentives and governmental subsidies (Homma 2012b;

Jaramillo-Giraldo et al. 2017). Consequently, associations of rubber tappers involved in policy programs focused on the community management of “socio-biodiverse” resources, even though substantial in the state of Acre, cannot presently ensure that the livelihoods of traditional rubber tappers will be maintained for more time yet through rubber extractivism alone (Gomes et al. 2012; Jaramillo-Giraldo et al. 2017; Salisbury and Schmink 2007).

The Onset of NTFP as a Sustainable Development Approach

Non-timber forest products are an important aspect of forests and forest use, playing a significant role in rural livelihoods, particularly for the poorest populations

(Neumann and Hirsch 2000; Ros-tonen and Wiersum 2003b; Shackleton and Pandey

2014). Rural households can access NTFP from a variety of sources including extractive reserves, state forests, ‘de facto’ open access forests, private farm forests,

and surrounding woodland areas around community villages (Albers and Robinson

2013a; Bank 2004; Canalez 2009; Veríssimo and Amaral 1996). Several raw materials with substantial local non-cash benefits and economic uses are collected or extracted from tropical forests worldwide. It is estimated that tropical forests provide subsistence crops and income to approximately 1.6 billion people mostly in developing countries

(Albers and Robinson 2013; Bank 2004; IUCN 2015).

Forest resources have been divided into two basic categories: (1) timber products, and (2) non-timber forest products (NTFP), including a broad array of fruits, seeds, oils, resins, fibers and vines. Normally associated to traditional knowledge and populations, forest products also can considered non-material cultural ecosystem services system (FAO 1999; Gomes et al. 2012; Jaramillo-Giraldo et al. 2017). Forest services for regional and global climate balance (e.g., hydrological cycles and carbon sequestration) are also often considered as non-wood products (FAO 1999). Genetic resources, in turn, are regarded as a category of NTFP as well (Calderon 2013a;

Constanza et al. 2017; Myers 1988a; Shanley et al. 2002; Veiga and Zacareli 2014). In general, NTFP include those occurring in natural forests as well as species that are managed, planted or domesticated in forest farms, forestry schemes, and even from areas outside forests (FAO 1999).

The wide range of different interests in NTFP’s uses give rise to different definitions, encompassing an extensive list of terminologies and distinct forest products.

Specifically, it is considered NTFP any and every natural resource extracted from the forest except timber (Neumann and Hirsch 2000). Sometimes also referred as “minor forest products”, “wild products”, “non-wood forest products”, “secondary forest

products”, or “by-products of forests,” the definition of NTFP is not very straightforward, and involves considerable debate concerning the scale of production, if harvest occurs in wild forest or in plantations, among other ambiguities (Shackleton et al. 2011; Wahlén

2017). Regardless, the most recognized and widely accepted terminology, NTFP comprises fruits, oils, and resins (Myers 1988; Shanley et al. 2002). In this study, I adopt the concept that considers NTFP all products of biological origin other than wood, extracted from natural or planted forests, including leaves, fruits, fibers, nuts, straws, seeds, oils, resins, gums, rubbers and medicinal plants (Calderon 2013; FAO 1999;

Shackleton et al. 2011).

The commercialization of NTFP has been advocated as a conservation strategy because it serves as an alternative form of income generation for rural communities with a smaller environmental impact if compared with other land uses, such as logging or conversion of woodlands into zones destined for agriculture and livestock. The activity has been regarded as a form of sustainable exploitation because, in most situations, it does not involve the removal of tree species (IPEA 2016). The extraction of NTFP is crucial for rural communities’ welfare because it can enhance incomes in seasons of crop loss or low earnings. Moreover, wood products also have non-monetary value for agrarian families: even if forest products are not traded, they nevertheless can be consumed, so saving money that would otherwise be spent on other needs such as foodstuff, medication or construction materials, for instance. In these cases, forest products function particularly as safety nets, alleviating poverty and smoothing family’s overall expenses during bad agricultural seasons (Pattanayak and Sills 2001). It is important to note that studies have pointed out the links between forest clearing,

expansion of agriculture, and decline in safety net for poor populations. Consequently, in spite of the reduction of safety net and subsistence activity associated with NTFP, the production has become increasingly more market-oriented (Sunderlin et al. 2005;

Wiersum et al. 2014).

Early Economic Studies of NTFP

The literature addressing NTFP production, “working forests” and “productive conservation” flourished in the 1990s and 2000s (Nepstad and Schwartzman 1992;

Neumann and Hirsch 2000). The influential study by Peters, Gentry, and Mendelsohn

(1989) was the first to raise awareness of the economic potential of NTFP. This study reflects the early enthusiasm about NTFP as an approach for conservation and development (Sheil and Wunder 2002), and becomes a classic of the literature of non- timber forest products (Bolwig et al. 2014; Gereffi and Fernandez-Stark 2011).

In their seminal article, Peters et al. (1989) set out to assess the value of one- hectare plot in the village of Mishana, 30 km from Iquitos, in Peruvian Amazonia. The authors cataloged the existing species within the single plot and calculated production and market values according to standard monthly prices (Jaramillo-Giraldo et al. 2017;

Sheil and Wunder 2002). They compared the profits derived from two kinds of forest goods, fruits and latex, and from timber plantations and cattle ranching, and concluded that NTFP had a higher value and that profits from a sustainable forest use exceed those from forest conversion. The findings suggested that tropical forests are worth more than has been presumed and that the actual returns got from timber are smaller in comparison to those of NTFP. Furthermore, the total net revenues achieved by the sustainable harvesting of NTFP are two to three times higher than those resulting from forest conversion.

In particular, the gross annual value per hectare value for NTFP trade come to

US$ 700. A net present value (NPV) of fruits and latex only was calculated at

US$6,330, for the single hectare after deducting labor and transport cost, total ten times more than the income derived from timber, and over two times from other land uses values (Peters et al. 1989; Sheil and Wunder 2002). Assuming that NTFP could be harvested yearly, the authors concluded that the plot generates a much higher income with the extraction of NTFP instead of logging and other land uses. Another advantage considered was that the production of NTFP does not harm biodiversity; the activity neither impact species’ genetic and biological diversity, nor the provision of environmental services by surrounding forest ecosystems (Ros-tonen and Wiersum

2003a).

In this context, it was believed that the commercial extraction of NTFP causes minimal forest degradation, which led to many conservation advocates promoting the establishment of extractive reserves (Nepstad and Schwartzman 1992). Furthermore, domestication could enhance biodiversity conservation by reducing pressures for the exploitation of natural forests (Leakey and Izac 1996). This aspect raises a debate about the potential of environmental degradation in wild forest areas that could be stimulated by increasing production of NTFP to satisfy a higher demand (Homma

2012b). Many writers have later challenged Peter’s claim about the poteconcerning ntial of NTFP production as a sustainable solution for forest conservation and poverty alleviation (Belcher 2005; Kusters et al. 2006), pouring cold water over the earlier optimism with NTFP (Arnold and Pérez 2001; Belcher 2005; Kusters et al. 2006; Sheil and Wunder 2002). These studies suggested the production of NTFP is far less

economically valuable than plantations or cattle ranching (Cavendish 2002). Later studies have additionally shown that NTFP production can barely meet both conservation and development goals (Arnold and Pérez 2001; Kusters et al. 2006;

Morsello and Adger 2006; Rizek 2010; Ros-Tonen and Wiersum 2005). Even though, presently there is still no consensus on the ability of NTFP production to deliver outcomes related to both conservation and development in rural forest areas. As can be seen, literature confirming that production and trade of NTFP leads to improved family farmers’ livelihoods without necessarily compromising the forest environment continues to be inconclusive (Arnold and Pérez 2001; Kusters et al. 2006; Morsello and Adger

2006; Morsello 2006a; Neumann and Hirsch 2000; Rizek and Morsello 2012) .

Income Derived from NTFP

In the last four decades, there have been many efforts to determine the significance of income derived from NTFP extraction in small farmer’s households, and despite considerable research, their influence and contribution to household income remain invisible (Cavendish, 2002; Shackleton & Pandey, 2014a; Sills et al. 2011).

Several evaluation methods have been used in different contexts, thus making the proper comparison across studies and regions impractical (Cavendish 2002; Wahlén

2017).

Deriving household incomes from NTFP-based production is key to designing policy aimed at improving small farmer’s income and welfare, as well as forest conservation, which will evaluate the trade-offs of forgone extractive activities over perhaps more profitable activities (Cavendish 2002). Reliable information on NTFP contributions to small farmer's income is also crucial for improving multifunctional forest management mechanisms in synergy with NTFP-centered value chains (IPEA 2016)

comprising necessary processing stages within producer's communities. This is considered the main viable approach to enhancing small farmers remuneration and forest-based livelihoods, and therefore, reconcile social and economic development with conservation (Carvalho Ribeiro et al. 2018).

An enduring debate on the feasibility of the commercializing NTFP revolves around how significant the activity’s income is. Critics of low impact extractivism highlight that the typical small incomes, and the extensive areas used for the collection or extraction of forest products, are suited only for a light, sparse community at a particularly modest income level, if not within poverty standards. These limitations, associated with the absence of land tenure and competition with other activities, enables conditions favoring the extractivism of subsistence (Drummond and De Souza

2016). As a result, NTFP offer little hope for boosting small farmer’s incomes, unless there is an established market. Regarding poverty alleviation, the potential of NTFP has also proven to be limited. First, the poorest and most marginalized small producers are the ones typically involved in NTFP collection. These live in poor conditions, and only in a few exceptional cases is the sale of NTFP capable of providing a significant contribution to rural livelihoods, notably in situations where high value, niche commodities together with other forest-based activities can supplement each other or can be joined with farming (le Polain de Waroux and Lambin 2013; Ros-tonen and

Wiersum 2003b). In such case, conditions to improve NTFP-based income may be enhanced by domestication of high value species and its integration in farming systems

(Ros-Totem and Wiersum 2003b).

When it comes to raising income earned from NTFP linked to a multifunctional production system, contemporary cases have highlighted that an expanding cattle economy presents tempting possibilities for increasing incomes. The cattle economy is well above the usual mix of more sustainable economic activities, which suggests that another more effective way to forge new forest-livelihood options for small farmers is urgent (Carvalho Ribeiro et al. 2018). This is critical not only for local communities, but also to conservation interests as they face the intensifying environmental threat associated with industrialization and climate change at global scale.

In general terms, it is believed that at the household level, the contribution of

NTFP ranges from 10 to 60% of household income, depending on different national, economic, and cultural contexts (Shackleton & Pandey, 2014a; Wahlén, 2017). Other accounts, specify that the contribution of the commercialization of NTFP to total household income varies from less than 5% to over 90%, depending on the degree of engagement and specialization of the activity, in addition to seasonal variations

(Angelsen et al., 2014; Morsello et al., 2012; Shackleton & Pandey, 2014a; Shackleton et al. 2008). The failure to properly assess the importance of NTFP-derived income and other forest-based sources of income, such as ecosystems services, gives a

“misleading picture of forest-livelihoods and an inadequate basis for policy design” especially if focused on poverty reduction and rural social welfare (Angelsen et al. 2014;

Wahlén 2017). These cases support the view that the profitability of forest products market is poorly understood, undervalued and most often neglected (Costa, 2012;

Shackleton & Pandey, 2014a; Wahlén, 2017).

In Brazil, the variety of commercial NTFP is substantial, but still economically invisible. The Brazilian Institute of Geography and Statistics (IBGE), the Brazilian statistics office, annually releases a dataset entitled Production of Plant Extraction and

Silviculture (PEVS) reporting the production of 32 different forest products. No other official statistics report with a complete list of forest products exist in Brazil. Current data, for instance, do not explicitly uncover the commercialized extractive species sourced to cosmetic industries in national and international markets as murumuru, pataua, breu branco, priprioca, jambu. This lack of tracking, besides preventing an accurate valuation of products, could also mean products are being sold cheaply or gains are leaking along the productive chain. This is a critical piece of information to poverty alleviation and land use policy design to opposing other more harmful to the environmental land uses (Shackleton & Pandey, 2014b; Shanley & Stockdale, 2008).

Most of the literature addressing the economy of NTFP in the Amazon are case- specific studies (Sheil and Wunder 2002; Soares Filho et al. 2017). Due to the great variability in estimation methods, scarcity of reliable data, diversified biological and socioeconomic aspects and dynamics found in different areas of the Amazon region, the economic importance of NTFP presents a high degree of uncertainty (Cavendish 2002;

Gram 2001; Soares Filho et al. 2017). When data is available, it is mostly restricted to a few organizations promoting forest-wise projects, that is, actors interested in the development and continued funding of such initiatives. Consequently, there are reports and other publications informed by these data, but since they have been analyzed by organizations with stakes conflict of interest are possible. In such a case, data collection may focus more on meeting the informational needs, and the agenda of the group

rather than achieving a broader understanding of both the ecological and economic realities and needs of local people. Aside from the ambiguity of the NTFP's ability to meet conflicting conservation and social development goals, some argue strongly that despite their alleged capacity to ensure biodiversity conservation while enhancing forest-livelihoods, NTFP-derived incomes are not suitable to compete against other economically useful exploitation, mainly because collection and trade are primarily seasonal, complementary in essence, unfeasible to sustain rural families in the long- term (Albers & Robinson, 2013b; Schroth et al. 2004; Gomes et al. 2012). Even though this view is hotly debated.

The Decline of Forest Extractvism

Payments for Ecosystems Services (PES), as part of the REDD+ program, and other alternative policy measures emphasizing multi-functional forest uses, are newer strategies aiming at increasing the small farmer’s income. This new multi-functionality contemplates a combination of community-based agroforestry, and other “green” alternatives including crafts, tourism, and environmental service payments in contrast to other conventional, degradation prone land uses. The approach also considers that the mix of agricultural and non-agricultural activities, traditionally practiced by small peasants, including hunting, fishing, and farming activities, are not able to overcome subsistence or poverty levels. The multi-functional combination of NTFP commercialization, ecotourism, and PES is then advocated as a workable alternative for generating additional income for rural small farmers living near conservation reserves or in buffer areas without cause more damage to the Amazonian biodiversity (Bicalho and

Hoefle 2015; Gomes et al. 2012; Hagen 2014b).

CHAPTER 4 COMPANY-COMMUNITY PARTNERSHIPS

While a variety of definitions of the term partnerships have been suggested, this paper will use the interpretation presented by Mayers (2000) who first unraveling the terms “partnership,” “company,” and “community” in the context of commercial relationships between multinational companies and family farmer’s communities.

“Companies” may be defined as business-oriented enterprises that sell goods or services for making a profit, ranging from large private corporations to small companies.

The term “communities,” in turn, can be described as a group of people living in the same place including small farmers and community-level units of social organization such as local farmers’ associations or cooperatives. While “partnerships,” in the context of the present study, can be traced back as the varied formal or informal market- oriented relationships established by the above parties on the expectation of mutual benefits, which may also involve third parties performing a range of roles (Mayers 2000, p. 33-34).

In the literature, company-community partnerships between NTFP small producers and multi-sector companies refer to emerging market-based strategies to increase forest-livelihoods, mainly because these big multinational companies are thought to provide to smallholders information, technology, and access to a variety of global value chains. They also allow the payment of higher prices for “premium products,” increase access to social programs, and bear costs with organic and

Fairtrade certification, which add value to products (Almeida, 2013; Duchelle et al. 2014;

Mayers, 2000; Morsello, 2006a).

Induced by the growing demand for sustainable forest-based natural or organic products associated with niche markets for the cosmetic, perfumery, pharmaceutical, and food sectors, these market-oriented partnerships between private companies, indigenous communities or small-farmers aim at improving the community’s livelihoods through income-generating activities based on the sustainable extraction or collection of forest products. These agreements serve to supply companies with valuable natural products, presumably benefitting all partners involved (IFAD, 2013; Mayers &

Vermeulen, 2012b; Morsello et al. 2012; Otsuki, 2011; Porro et al. 2015). Partners, be they powerful multinationals, cooperatives, collective small enterprises, or even a family farmer are all motivated by profit to some degree (Mayers 2000).

Pioneer Partnerships with Global Cosmetic Companies

Earlier descriptive studies exploring formal and informal partnerships between multinational corporations and small-farmers in the Amazon emerged in the mid-1990s.

The pioneering partnership involved the British cosmetics company The Body Shop, the

Kayapo Indians, and the cooperative Amazon Coop, a community-based enterprise organized to supply Brazil nut and other raw materials to the company. Since the partnership of The Body Shop with the Kayapo’s indigenous people was one of the pioneers corporate-community deals, it is valid to lay down some aspects on how the strategy unfold so to have an understanding on whether or not the practices and tactics used by the company in the past have changed over time or have persisted alike.

The Body Shop Partnership

The story began when the head of The Body Shop, Anita Roddick, attended in

1989 the mass rally organized by Kayapós Indians to oppose a series of mega hydropower dam projects planned for the Xingu River. Over a thousand Indians

including the Kayapós and other allied tribes, along with riverbank dwellers and small farmers, took part in the big protest. The protest of the Kayapós against the hydropower of Kararaô (today, the so-called Belo Monte dam) gained great international notoriety after the British singer Sting became involved with the cause1. Keen to align the image of her company with the Kayapós people and their charismatic leader, Paulinho

Payakan, Roddick proposed to partnership with A’ukre community for the extraction and sale of Brazil nut oil to be used in a hair product. In 1990 the company started the pilot partnership. The Kayapós were then the first Indigenous group to engage in the earliest wave of “green capitalist” enterprises based on the sustainable production of NTFP in the Amazon (T. S. Turner 1995).

Blending social justice, environmentalism and indigenous empowerment discourse the Body Shop-Amazon Coop agreement seemed like a great deal for the indigenous communities especially the prospects of reducing their dependence on government funding. The idea was to provide the indigenous peoples an income- generating alternative to collusion with mining and logging firms without radical transformations in their physical, natural, and cultural environment and identities. The agreement emphasized the need for moving beyond subsistence extractivism by incorporating and intensifying other land uses, including the use of abandoned areas for agroforestry systems (Burke, 2010; Morsello, 2006a; Turner, 1995). The plan was to prove that through the market-based partnership, economically, socially, and politically marginalized communities could benefit from economic globalization resulting in “fair

1 https://www.internationalrivers.org/resources/amazon-indians-rally-to-oppose-xingu-dams-in-may- journalists-invited-3829; https://www.nationalgeographic.com/magazine/2014/01/kayapo-courage/).

globalization” But despite having added material benefits to indigenous communities involved,. it was also noticed that these partnerships might result in problems and benefits, and that indigenous communities became more vulnerable and dependent on the company and other outsiders, revealing power imbalances and ineffective state policies (Burke, 2010; Morsello, 2006a; Turner, 1995).

Other Partnerships Experiences

Named “Rainforest Harvest” by its prominent theoretician and practitioner, Jason

Clay (former director of Cultural Survival Enterprises, current senior vice-president of the WWF, and executive director of the Market Institute), partnerships of local

Amazonian communities with the private sector for the “harvest” and commercialization of forest-based products extracted from the Amazonian biodiversity is considered economically profitable through the sustainable production of marketable NTFP.

According to this view, the “market solution” is the only realistic path for saving the

Amazon from other destructive land uses adopted by migrants, cattle ranchers, loggers, and miners (T. S. Turner 1995). However, according to Mayers (2000), this idea of “only realistic way” does not seem to be the best way to frame such partnerships, since these initiatives are typically “context-specific, risk-sharing agreements.” In this case, there are not a “right” only way or most appropriate single model of partnerships, because factors such as regulatory and policy environment, market conditions, stakeholders involved, credit availability, land tenure situation, and land type play each crucial roles in shaping a company-community partnership. Veiga et al. (2016) endorse this view and stress that these arrangements are not new by any means among multinational companies; but, a apparent aspect is that has attracted attention is the fact these agreements have

become central for many global cosmetic and pharmaceutical corporations more recently.

Almeida (2013), highlights that the professor at the University of São Paulo,

Carla Morsello, has been developing research addressing the relationship between cosmetic companies and local communities in the Amazon with the publication of several articles and guidance on master’s and doctoral studies. Figueiredo (2005), for example, investigated the effect of partnerships between local communities and the company Natura on income. The study, focused on the commercialization of vegetable oils in the Extractive Reserve of the Juruá (AM), suggests that there was a decrease in the time allotted for hunting and fishing, due to the increase in the time set aside for subsistence collection and agriculture. For the author, the effects of the partnership have negative implications for conservation, as NTFP commercialization does not lead to a reduction of the impact of subsistence and commercial agriculture. The study also shows that the effort allocated to agriculture into forested areas continues and that the agreement has also attracted new residents to the community, increasing impacts on forest resources. Despite these issues, Figueiredo (2005) concludes the trade of NTFP through partnerships with the company is one of the most promising alternatives among the possible alternatives, and that adjustments in the existing partnerships might minimize some negative impacts. Mich (2007), in turn, addresses two commercial partnerships with Baniwa and Yawanawá indigenous groups involved in agreements with the multinational companies Tok & Stok (furniture) in Amazonas, and Aveda

(cosmetic), in Acre. The research highlights the need for more support of the State in the operationalization of such partnerships and commercialization of forest products to

ensure indigenous and local communities rights and interests. Results also emphasize the importance of the State and civil society organizations for formulating and enforcing policies concerning these partnerships. The replacement of the State by private corporations in essential functions intrinsic to these partnerships might be problematic, leading to internal conflicts, community dependence, and social inequalities.

For Almeida (2013), the relations between companies and communities are often unilateral, based in asymmetric power relations and unmatched company discourse and practice. The researcher documented the conflict between the company Natura and rural communities involving the use of priprioca (Cyperus articulatus) and "breu branco" in the state of Pará. According to the study, the company used community traditional knowledge in the creation of a new perfume. The company did not admitted the use in the first place, but later confirmed that had indeed accessed the genetic patrimony of priprioca species found in the community surroundings areas. The company was required by law to pay the respective benefit-sharing to the community (Almeida 2013).

Almeida (2013) also points out that production intensification may bring out potentially negative implications for communities because they are abandoning activities that guarantee their food security. Some other authors claim that this partnership approach is basically a fusion of free-market liberalism with environmentalism and advocacy “on behalf” of the forest environment, indigenous cultures, and forest peoples (Igoe and

Brockington 2007; MacDonald 2010; T. S. Turner 1995).

The book "Esverdeando a Amazônia: Comunidades e Empresas em Busca de

Práticas para Negócios Sustentáveis” (Greening the Amazon: Communities and

Businesses in Search of Sustainable Business Practices), by Anderson and Clay

(2002), is considered one of first empirical work addressing the topic. The authors discuss pioneering partnerships between multinational companies and local and traditional communities in Protected Areas and Indigenous Lands. They explore common obstacles and benefits. The obstacles pointed out include the irregular supplying, dependence on subsidies, bureaucracy in the export process, inadequate transportation infrastructure, and difficulties in implementing quality control of production, as well as lack of training of the producers involved in the management of the forest enterprises (Anderson and Clay 2002). They also mentioned that all the partnerships studied had a strong motivation on increasing producers' income, in addition to other direct and indirect benefits, but for them the main limitations are more related to the “geography” of the activities especially involving the dispersion of natural resources and the high transportation costs associated. The authors, illustrate, for example, that approximately 90 percent of new businesses in developed countries end up failing in only three years, warning that starting a bio-enterprise in the Amazon may be risky. Even so, they emphasize that more and more multinational corporations and local communities have chosen to face these problems in a "courageous and creative” ways (p. 22).

The groups of producers represented in the book include indigenous, rubber tappers, "caboclos," settlers and urban residents, who produce in isolated areas of the forest. Typically, these producers are organized in associations or cooperatives working in partnership with national or international companies. Among the pioneering partnerships reviewed, as it could not be otherwise, they addressed the classic agreement between The Body Shop and the Kayapós in the Xingu River and the

partnerships between Aveda Corporation with Yawanawas in the Acre state for the production of urucum in exchange for the use of the image of the indigenous in the marketing campaign of the product. The main goals of these pioneering partnerships were to ensure the supply of raw materials promptly, while maintaining the traditional practices of those indigenous communities. In the conclusion, Anderson and Clay point out that the communities came to realize that significant added value is incorporated in the product after the sale, realizing that if the local communities could process the raw materials or even produce finished products locally they would likely capture more income derived from the product itself plus the value added on top of the raw materials.

This premise has gradually becoming a trend now after almost 25 years of the first partnership. Finally, the authors suggest that the implementation of community- company partnerships is a critical factor that determine the success for the commercialization of forest products as part of a joint solution which may benefits both, companies and communities involved. Subsequent studies, largely in the same vein, emphasized partnership characteristics and their effects on local communities (Morsello

2006a; Scherr, White, and Kaimowitz 2003; Vermeulen, Nawir, and Mayers 2003), reflecting an initial skepticism regarding their feasibility and outcomes by stressing common traps and limitations (Costa 2012; Morsello 2006a; Vermeulen et al. 2003).

Further partnerships have divided opinions. The idea of the “Rainforest Harvest,” for example, received a good deal of compelling criticism. The human rights defender Stephen Corry (1994:37) highlighted inherent flaws carried out by these partnerships. Generally, they incite community dependence, not empowerment, and consist of just one more case of an exploitative labor relation in which an international

company exert control over a powerless workforce, in a way quite similar to colonial trade. In this respect, Corry contends that these partnerships do not represent an innovation as has being proclaimed by the companies, but instead an old colonialism.

He further also confronts that the income from the sustainable production of forest products can never approach the far greater profits obtained from logging or mining, for example. Therefore, it cannot be considered a realistic alternative as a source of income for most forest people. In this case, ecologically sustainable production will tend to be regarded as a supplement rather than a substitute to ecologically destructive forms of extraction, and in this sense, again it cannot be regarded as an incentive for conserving forest ecosystems, because the other destructive activities simply continue to be practiced in parallel (p. 115). Another substantive problem pointed out by Corry and other scholars is the proportion of total forest inputs that make up the total income, which have been minuscule. Besides that, there is not enough evidence that these sort of agreements helps conserve forests or that at least empowers forest inhabitants. He concludes that the belief that these partnerships constitute economically significant incentives to save the rainforest is just a wishful thinking that remains nothing more than mere marketing hype. For this author, all the narratives around the ideal of a sustainable partnerships are just a marketing strategy carried out by profit-oriented companies, which have nothing to do with the real needs or the threatened rights of the local communities, or even with the threats directed to natural environments increasingly trapped by development (Corry 1994: 37).

To understand the nuances of the complex partnerships between global companies and small farmer’s communities and a multi-actor network engaged around such an innovative “conservation-and-market-based NTFP” agreement, it is necessary to shed light on the local scale and the unequal power relations that unfold from company-community relationships. Despite NTFPs have been initially conceptualized as

"sustainable commodities," key to a promising strategy to reduce deforestation and alleviate poverty, it is in fact unable to eradicate poverty, despite some undeniable beneficial effects (Costa 2012; Carla Morsello 2006a; Vermeulen, Nawir, and Mayers

2003). It is true that commercial NTFP-centered agreements have promoted capacity building, management support and quality control (Anderson and Clay, 2002;

Vermeulen et al., 2003; Morsello, 2006; McAfee 1999), by interpreting, training and

“enforcing” policies and regulations concerning biodiversity conservation and quality standards for compliance with industrial supplying (Veiga et al. 2016; McAfee 1999). It is also true that these agreements serve to foment horizontal and vertical collaborations among diverse actors engaged in the production chain to improve production and commercialization (Morsello 2006; Veiga et al. 2015; Futemma et al. 2016).

The flagship of these collaborative actions is the structuring of cooperatives or associations that integrate the productive process (Veiga et al. 2016; McAfee 1999).

Proponents of partnerships between small farmers and the private sector regard these partnerships as mechanisms that help poor farmers gain entry in otherwise inaccessible markets, supply and value chains, and networks. According to this perceiving, the rural landscape is changing with new actors and demands having to cope with a fragile environment, climate change, and decreasing natural resources, besides ensuring

decent incomes. This way, the partnerships and value chains, for instance, influence the development of policies focusing on the demands of all actors involved, protecting natural resources, and the access to knowledge, research, finance, and technology.

Through the cooperatives, family farmers can, for example, access resources and markets not accessible to individual producers acting in isolation. Cooperatives can likewise turn farmers’ produce more accessible to diverse private business along diversified networks. In other words, public-private partnerships can help farmers to scale up their market-centered initiatives (entrepreneurship) with the commercialization of diversified products and services, such as fruits, nuts, oilseeds, handcraft, and touristic activities, among others. However, to be successful, small farmers must be organized to meet quantity and quality requirements and regulations applicable to the market in viewing. The cooperatives then are seeing as an excellent tool for implementing connections to markets and building up capacity (IFAD 2013).

NTFP and Sustainable Global Value Chains of Biodiversity Products

Despite their alleged capacity to ensure biodiversity conservation while enhancing traditional livelihoods, NTFP derived incomes are not suitable to withstand economically useful exploitation (Schroth et al., 2004; Shone and Caviglia-Harris, 2006;

Gomes et al., 2012; Albers and Robinson, 2013). After four decades of efforts to make

NTFP production more viable to achieve forest conservation goals and raise small producer’s earnings, the outcomes have been mixed. Some blame the narrow potential of management of timber and NTFP for combining conservation purposes with local profit generation (Shone and Caviglia-Harris, 2006), while others advocate for the practice and have identified factors to enhance NTFP value chains (MMA, 2009; Nunes et al., 2012; WWF, 2014).

A cosmetic global value chain generally comprises the segments that form the path of the natural raw materials, stretching from its initial extraction or collection in forested areas, to the final consumer in global, high-end markets. NTFP-based- cosmetic production chains broadly involve: (1) small farmers; (2) the cooperative; (3)

“intermediary” processing companies; (4) the industry, wholesalers, retail companies and (5) the end consumers. Sales commonly take place in at least three different stages in which value is added. First, during NTFPs trade with the “intermediary” processing companies which holds the most know-how in supplying natural products to large cosmetic industries. This step comprises the trade of low value-added raw materials extracted or collected from natural or planted forested areas by the productive communities in most cases organized into cooperatives. Second, when the

“intermediary” company responsible for processing chemical compounds based on the raw materials (e.g., vegetable oil, clays, butters, complex assets, organic extracts, and natural exfoliating particles) sells the inputs to leading manufacturers industries. Third, with the sale of manufactured cosmetic products to final consumers.

Beyond these common steps, is worth mentioning that the “intermediary” companies make the raw materials more valuable, therefore more expensive, because of the cost of organic certification, of hiring professionals prepared to deal with biodiversity and local development, and of ongoing research and development of new essences derived from unpublished, unknown or unexplored plant species. The gain can amount to up to 20% higher than ordinary products (Boechat and Almeida 2015).

Some processing steps may take place locally, but their addition to the product's final price is, as a rule, very limited. In this case, the productive communities act only in

the initial stage of the chain. Furthermore, small producers do not take in any stake over the remuneration of the profitable actors involved in the "downstream" of the chain

(Drummond and Sousa, 2015: 19). As forest extractors or collectors are at the beginning of the chains, they generally do not benefit from the later stages (Drummond and De Souza 2016; Neumann and Hirsch 2000; Veiga et al. 2015). To put it another way, as small farmers typically are the first actors in the chain’s pathway, the profit captured by them is often limited given the low value-added to the supply chain in the stage they participate. Thus, since the forest extractors act only in the initial stage of the chain, they are rewarded exclusively for their “minimal” participation in the chain (Bolwig et al., 2014; Drummond and De Souza 2016).

In fact, the geography of these chain networks is usually extended, quite dispersed and connects both, national and international markets in different production stages. In fact, the manufacturing of cosmetics has led to a broad network of diversified actors, such as small family producers, independents or organized in cooperatives, micro and small companies, technology-based incubators, bio-chemical companies, research centers, universities, governmental and non-governmental organizations, certifying entities and final markets and consumers as llustrated next.

Figure 4-1. NTFP into cosmetic chains (elaborated by the author).

The natural inputs, for example, most of the times derives from remote areas where forest resources are available. In some cases, cooperatives have performed necessary processing steps, such as extracting essential oils, which add value to the input still inside the community vicinities, that is, at the local level. However, this is the not rule but the exception. In most cases, the added value takes place in other nearby localities or distant regions, predominantly urban centers. In this way, extractive economies remain "simple" and "isolated" (Drummond and De Souza 2016). Some more “progressive” industries, like Natura and Symrise, however, have installed industrial plants near the origin site of forest inputs, possibly to obtain advantages from reduced transportation costs, but they capitalize the added value locally with image and reputation dividends.

Figure 4-2. NTFP actor’s interactions (elaborated by the author).

Another aspect of the value chain is that large international industries usually outsource several stages of the production process with specialized firms – notably located outside the Amazon region, in Southeast Brazil. As such, most of their commercial relationships are with other multinational processing companies that have

technology and equipment to pre-process natural inputs and are responsible for buying the products directly in the communities, after they processing and selling the assets to the name-branded companies, such as Natura, Aveda, L'Oréal, among others. Critical reflections on that “intermediary” role, commonly performed by large processing companies, have been documented in the literature. Costa (2012), in a study about a partnership for jaborandi supplying for pharmaceutical industries, addressed the participation of the processing company Cognis, headquartered in Manaus, that refines essential oils before they are sold to leading multinational companies Natura and

Beraca. Figueiredo and Morsello (2006) and Rizek (2006) in discussing the partnership between Natura and communities of the Extractive Reserve of middle Juruá, involving the sourcing of andiroba oils and murumuru, also identified the role of “intermediaries” companies within community-company partnerships. Some practitioners also acknowledge the presence of “neo” intermediaries’ agents as contemporary

"middleman" (Andréia Pinto, director executive and research of Imazon, personal communication, May 2017).

From the business perspective, however, these processing companies are not

“intermediaries,” but a specialized partner providing processing inputs, such as natural and organic certified vegetable oils, clays, butter, bio scrubs, and other ingredients obtained from local extractive communities, contributing to local development and environmental conservation. “As the leading industry needs products that strictly meets technical specifications concerning quality and health standards for regulatory compliance, there is no way one can get a forest-based ingredient to directly transform it into a high quality finished product that meets all the ANVISA, the National Health

Surveillance Agency (Agência Nacional de Vigilância Sanitária, the governmental regulatory body for personal hygiene, perfumery and cosmetics) requirements, and also all applicable laws, even to export,” explain the Natura’s manager (Mauro Costa, personal communication, 2017). He notes, however, that the company prefers to buy the ingredients directly from the supplying communities, to add value in the beginning of the chain. Then, he says that there are now already eight communities with a small

“agro-industry” structure functioning, providing semi-processed inputs directly to the company.

Many new treaties, policy recommendations, and work plans have underlined the potential effects of the connection between poor people or regions with global markets.

Since the mid-1990s, specific literature addressing international value chains has emerged, reinforcing also the importance of these “chains” as a methodological device to advance our understanding on how multinational companies and forest areas in developing countries are increasingly linked into global markets (Bolwig et al. 2008). A few value chain studies, however, have uncovered the value chain’s effect on the environment, poverty, and gender (Bolwig et al. 2014). Beyond the standard “chain” analysis, new approaches integrating horizontal and vertical aspects that affect small farmer’s poverty and sustainability have been proposed. However, global chain studies have not been concerned with the opportunities and limitations for fruitful integration of small farmers living in marginal areas with the global market (Bolwig et al. 2008).

Currently, there is the underlying assumption that development and poverty alleviation will be achievable only through economic development, business sector investment, and global value chains participation. This view is reproduced in the portfolios of many

donors and development agencies (e.g. IUCN, USAID), suggesting that the integration of small farmers participation in global markets is likely to result in positive impact

(Shacketon 2007).

Coe (2012) assesses Global Production Networks (GPN) and provides a framework for explaining the interrelation and the uneven development of the world economy. He notes that there are two competing approaches to the global production network approach: the Global Commodity Chains (GCC) and Global Value

Chains (GVC), as discussed above. The three approaches, however, are all similar because they concentrate on the global inter-organizational relationships that the production of goods and services are embedded. To explain the patterns and structures of development in places like the Amazon is challenging. I agree that these three frameworks (i.e., GCC, GVC, and GPN) are analytically limited, especially to appraise the “anatomy” of the community-company partnerships for NTFP production in the

Amazon. Therefore, I am persuaded by my advisor to explore Actor-Network Theory

(ANT) as a heuristic device. However, a complete discussion and application of this framework, in this case, is beyond the scope of this study.

Cooperatives

Cooperatives comprise collaborative activities among diverse actors involved in a mutual effort designed for accomplishing common objectives. Initial studies affirm that individuals are expected to enter cooperatives only under external influence combined with particular benefits. Other researchers contend that the creation of cooperatives for collective effort may take place even without external drives or other additional incentives (Bromley 1992; Ostrom 1990; Wade 1988). The literature though agrees that proper institutional provisions are a vital feature of a successful cooperative (Futemma,

Castro, and Brondizio 2016). Companies have also provided financial resources to support communities by providing basic infrastructure, access to technology and certification costs ( Mayers and Vermeulen 2012; Morsello 2006b; Scherr, White, and

Kaimowitz 2003)., enhancing the chances of small-farmers’ entrepreneurship to succeed. Processing, for example, adds value to the products within the community, also reducing the urgency to sell the products and favoring the collection or extraction of larger volumes, which might increase returns (Belcher and Schreckenberg 2007;

Morsello et al. 2012c). Companies often create a functional institutional arrangement at the local level (Veiga et al. 2015). Given these points, we speculate that these efforts combined may enhance social capital among the communities in the long run. On the other hand, the partnerships between communities and corporations have produced uneven economic benefits. Companies may make decent profits from the sustainable products, usually returning little of their revenues to sourcing communities (Veiga et al.

2015), not to mention the gains they receive in reputational and venture capital.

Local population generally has obtained aid of governmental and non- governmental organizations (NGOs) to overcome difficulties of implementing community resource management and marketing of forest products (Morsello, 2004; Costa 2012).

Conservation actors (e.g., NGOs and development agencies) have actually become the company's “right-hand” in multiple fronts of company-community partnerships. These actors too often play a significant role as “brokers” or mediators of these agreements

(McAfee, 1999a, 1999b). As such, they usually adapt their agenda and navigate to attend to both markets demands and environmental problems. In this context, these conservation entities function either as sources of or as vehicles which spread the new

globalist discourse, a post-neoliberal “environmental-economic” paradigm in which nature is considered as a global currency (McAfee 1999a, 1999b).

The growth of market-based conservation agreements produced a boom in the commodification of nature and new global markets for biodiversity-based products are flourishing. Although earlier alliances were more focused on traditional extractive sectors, today they are more related to the extraction of value from raw materials from biodiversity, which at least does not require the physical removal of “nature.” For that reason, some conservationists’ groups began to argue that partnerships with business sector may be a realistic pathway to achieve challenging biodiversity conservation goals. In this regard, cooperatives and “socially responsible” companies are pointed as possible “fixes” to the socioeconomic and ecological exploitation of transnational capitalism (Costa, 2012).

In contrast, the agreements may also increase ecological and socioeconomic impacts and risks to communities including the possibility of simplifying ecosystems by selection and increasing the frequency of species of interest for commercialization, creating dependency on companies and other external actors (Clay 1992; Morsello

2006; Costa 2012), induce reduction in income due to exclusive rights to commercialization linked to improvements implemented in the factory yard to equip processing (Scherr et al. 2003; Morsello 2006), or direct communities to other more destructive land uses by the supplementary capital made available (Mayers and

Vermeulen 2002; Morsello 2006).

Other possible socio-economic impacts and risks associated with corporate communities can be greater vulnerability to market fluctuations, the emergence of social

inequalities after entering the market, changes in traditional value systems of internal disputes and problems in the traditional organization of the communities (Rizek and

Morsello 2008; Figueiredo; Costa 2012).

From the business side, partnerships can: (1) provide access to forest resources at a competitive cost (Scherr et al., 2003); (2) improve organizational capacity; (3) foster innovation and workers’ performance; and (4) allow the diversification of portfolios through access to socio-environmentally sensible sources (Mayers and Vermeulen,

2002). The most significant benefit, however, involves improving the reputation of the company and its trademarks (Utting, 2001). Despite these potential benefits to companies, managing company-community agreements is troublesome and costly

(Wollenberg, 1998; Anderson and Clay 2002).

Socio-Biodiversity Sourcing Partnerships in the Amazon: Current Scenarios

A new wave of studies exploring the phenomenon of corporate-community partnerships with a focus on the “upgrading” aspect to enhance NTFPs productive chain is emerging. This recent literature presents ambiguous conclusions with respect to the relationships between forest conservation, market based NTFPs production, income generation and poverty reduction. Many of these studies have been conducted by researchers linked to organizations responsible for developing projects focused on

NTFP production development, either over contracts with governmental and multilateral agencies or private companies (Clark et al. 2011; Ingran et al. 2014; Dicken et al. 2001;

Bolwig, 2008).

Cosmetics, pharmaceuticals and horticulture are important niche markets for

Amazonian extraction products. Examples of popular crops in this market are Açaí

(Euterpe oleracea Mart.), Taperebá (Spondias mombin L.), murici [Byrsonima crassifolia

(L) HBK], tucumã (Astrocarium vulgare Mart.), Pupunha (Bactris gasipaes HBK), andiroba (Carapa guianensis Aublet), murumuru (Astrocaryum murumuru), copaiba

[Copaifera langsdor i (Desf.) Kuntze] and uxi [(Endopleura uchi (Huber) Cuatrehouses]

(Homma, 2011; Drummond and Sousa, 2015:30).

Recently cosmetics corporations and the national and global bioindustries have recently gained a large load of visibility due to the development of products obtained from the purportedly sustainable use of the Amazonian natural resources. These businesses use native plants to produce essences, and other compounds for cosmetics, vaccines, pharmaceuticals ingredients formulated from socio-biodiversity resources.

Some examples are the increasing purchase and use of Brazil nut oils (Bertholettia excelsa Kunth), andiroba (Carapa guianensis Aublet), murumuru (Astrocaryum murumuru) and copaiba [Copaifera langsdor i (Desf.) Kuntze]. Production and marketing of these inputs occur within the partnerships between the cooperatives of extractive communities and companies of the sector, which have been progressively in demand for "natural" products associated with more "sustainable" and socially responsible manufacturing practices. In general, this market comprises several sectors

(e.g., cosmetics, personal hygiene, pharmaceutical, food) and products, such as essential oils, medicinal plants, fibers and resins (Drummond and Sousa, 2015: 24).

These extractive economies “leak out" economically transformed natural economic values exchanged specially in "cosmopolitan" developed economies (Bunker 1985;

Drummond and Sousa, 2015: 34).

An example of this sort of partnership has risen since 2000 between Natura

(private multinational Brazilian cosmetic company) and the Mixed Cooperative of

Producers and Extractivists of Rio Iratapuru-Comaru - COMARU, in Amapá state. The cooperative was created in 1992 to enhance the standard of living of the rural and remote community of the São Francisco village. This partnership was the first one to implement the criterion of benefit sharing with the involved communities focused on the supplying contract of breu branco, Brazil nut oils, andiroba, and copaiba, among other products to produce cosmetics. The early trade of Brazil nut oil by the cooperative took place in 2003. The company Cognis do Brasil is the largest purchaser of this production.

This company is still responsible for the oil refining, after which the processed input is sold to Natura. The Forest Stewardship Council handles the certification of the Brazil nut collection areas (Le Tourneau and Greissing 2010; Almeida 2013; Drummond and

Sousa, 2015).

The variety of primary inputs presently produced in Brazil in general is substantial, but somehow invisible. The Brazilian Institute of Geography and Statistics -

IBGE annually produces a study entitled Production of Plant Extraction and Silviculture -

PEVS. The purpose is to update the volume and importance of extractive commodities in Brazil. This database advances to the understanding of the production scenarios and the economic feasibility of extractivism. The Amazon Region is responsible for the production of a considerable part of the products of the extractivism, such as Açaí and

Brazil nuts, as well as other typical oilseeds and fruits of the Amazon region. Recent data reveal that an increasing number of products and species have been collected and marketed. The IBGE released data from the Municipal Agricultural Survey indicates that the value of Açaí production exceeded R$ 5 billion in 2017, almost a third of the value of the national production of coffee beans. At the same time, there is a trend of retraction

in the production of some species such as the Brazil nut, which had a sharp drop in production and revenue touching the 70% mark in 2017 (SIDRA/IBGE, 2017). Although there are these data from IBGE,2 no other official statistics with a complete list of

NTFP normally commercialized exists. The lack of data for many forest products hinders an accurate perception of economic value of this market.

The statistics that currently exist, for instance, do not clear detect extractive species that have been marketed by the recent demands for inputs to supply cosmetic industries in the national and international markets, as it has been the case of the murumuru, breu branco, priprioca, jambu, among others.

Figure 4-3. IBGE data on annual production of acai (elaborated by the author).

2 In the item “Oilseeds” from the SIDRA/IBGE data the following oilseeds are presented: babassu (almond), copaiba (oil), cumaru (almond), licuri, oiticica, pequi (almond), tucum (almond). Products that do fit this classification are identified as “other.” See link for more details: https://sidra.ibge.gov.br/pesquisa/pevs/quadros/brasil/2017

Figure 4-4. IBGE data on annual production of oilseeds (elaborated by the author).

The lack or inconsistency of data concerning the many NTFP prevent a more accurate notion of their productivity. This is a critical piece of information needed to analyze the economic benefits of these products as opposed to other land uses

(Shanley and Stockdale, 2008: 5). Likewise, this information is a crucial factor also for the management of protected woodlands as well as for the improvement of strategies designed to make productive chains more effective (IPEA, 2016).

Since the 1990s, several cosmetics that used plants of Amazonian biodiversity were introduced into the market. Some of these products have presented dubious conservation and preservation goals, like what Zimmerer would call “just-so-stories”

(Zimmerer 2007). Company-community partnerships for NTFPs production in the

Brazilian Amazon encompass a variety of products and sectors, such as cosmetics, pharmaceuticals, food and the automobile industry. Multinational companies engaged in these agreements vary in revenues and origin. The cosmetics industry is the leader among these sectors, due to the widespread trend of adoption of natural sourcing,

vegetable-based products, and corporate social responsibility practices (Morsello 2009).

Partnerships for sustainable production of NTFPs to supply the cosmetic industry chain are progressively increasing in the Brazilian Amazon (Miguel, 2007; Becker 2001;

Brondizio 2011; Makishi 2015; Nobre 2018).

Biodiversity-based product chains have attracted attention not only of cosmetics industry, but also from the processing industry, interested in exploring biodiversity inputs and supplying the growing demand for functional and exotic forest products valued by preservation principles and fair-trade relations, especially in developed countries

(Makishi, 2015). Technological advances such as the identification of active compounds and processing capacity corroborate to the growing valorization of biodiversity inputs that, linked with calls for social and environmental responsibility and natural resources conservation, create competitive advantages for this industry. In this way, the coordination of socio-biodiversity or agrobiodiversity chains can provide access to rare and difficult to replicate or substitute biological resources, as it is the case of cosmetics ingredients extracted from the agrobiodiversity (Makishi 2016).

These partnerships involve small farmer communities and multinational companies, which have had an increased demand for products and inputs associated with nature and more sustainable practices (Drummond and De Souza 2016;Morsello

2006a). Notably, in the case of Brazilian Amazon, the appeal of the rainforests and its traditional peoples, have stimulated the partnerships focused on a diverse set of

“sustainable” forest commodities, such as medicinal plants, fibers and resins and essential oils (Drummond and De Souza 2016; Miguel 2007; Morsello 2006a). However, despite the fanfare some cosmetic industries and processing partners have given to

hese partnerships (Mayers and Vermeulen 2012; Morsello 2006; Turner 1995), the studies addressing the effect of the commercialization of NTFPs on forest communities and forests environments are still inconsistent, ambiguous or inconclusive (Brazilian

Forest Service 2013). Overall, evidence suggests that the effect of these partnerships on communities, companies, and forests are mixed. Morsello (2006) observes that some scholars indicate that these partnerships are, likewise, a means to enhance forest

“de facto” management by rural supplier communities, which benefit not small producers alone, but also multinational enterprises, service provider firms, and the environment. Others, however, have been more skeptical, pointing out several issues and traps associated with these agreements, including companies' excessive control, overharvesting and depletion of forest resources, and interference with the social organization of the community (Homma 2018; Mayers 2000; Morsello 2006a). For some other authors, partnerships can contradictorily provide both benefits and damage to the environment, communities, and the companies involved. Although, the negative impacts are stronger at the community level.

Despite some studies claim there is plenty evidences that company-community partnerships associated with production of NTFP may lead to better results than commercialization without the agreement (Brazilian Forest Service 2013), little is known about the effects of these partnerships in the Brazilian Amazon and scholars have called for additional research directions addressing community forestry as part of a more extensive economic system (Morsello 2006). Public institutions and civil society organizations play pivotal roles in partnerships’ outcomes, for their diversified actions in several strategic fronts comprising brokering, promotion, and assessment. As Morsello

(2004b) puts it remoteness, forest diversity and cultural factors entangle these arrangement's context, provoking different results among corporate-community partnerships along the Amazon. Thus, one of the big challenges associated with such partnerships in Amazon region is to untangle the factors influencing partnership’s failure or success.

The landscape of extraction and commercialization of NTFP has also been dramatically changed by globalization and the related forces that operate in production and consumption markets worldwide. Similarly, forest tenure changes have been trailed by global social, political, economic, and cultural changes, leading companies to take on new strategies to attend to the growing demand for natural forest products, practices and outcomes more aligned with best practices of social-environmental responsibility

(Morsello 2006).

The partnerships involve complex alliances of actors with conflicting interests and distinct and supplementary roles, including large corporations and Amazonia forest communities, supply chain service providers, research centers, universities, certifiers, development agencies and public institutions especially the ones in charge of research, technology development and environmental conservation (Almeida 2013; Miguel 2007;

Morsello and Adger 2006). The current situation is that despite the assumed potential to both protect biodiversity and support traditional livelihoods, NTFP market values are not enough to sustain economically viable, large-scale exploitation (Hoefle 2016).

Now, after roughly 30 years of the advent of the alternative approach focused on

NTFP studies have demonstrated that socio-economic and environmental effects of this alternative market vary greatly case by case (Albers and Robinson 2013a; Arnold and

Ruiz Pérez 2001; Belcher and Schreckenberg 2007; Morsello 2004; Rizek 2010). The literature has suggested, for example, that there is a “trade-off” between communities’ well-being and forest conservation, and better outcomes for conservation result in worse proceeds in terms of improvement in the communities (Kusters et al. 2006). By all means, forest products market has not been able to substantially reduce poverty levels in the communities (Morsello 2006c; Rizek 2010; Ros-Tonen 2000).

CHAPTER 5 RESEARCH METHODOLOGY

Theoretical Framework

Political Ecology (PE)

The study uses a poststructuralist Political Ecology (PE) frame to examine the effects of global production chains of NTFP on rural livelihoods and tropical forest conservation in the Brazilian Amazon. This frame integrate concerns and insights of ecology and political economy (Blaikie and Brookfield 1987; Rocheleau 2008). In geography, Political Ecology lenses addresses environmental issues beyond land change providing a critical context to economic issues that have a spatial pattern. PE is also concerned about sustainability, social, economic and political vulnerability, and persistent dire poverty (Simmons 2004; Turner and Robbins 2008). A more recent wave of political ecology focuses on coupled human-environment system and social- ecological system, covering the connections between these two subsystems, hence making possible a more comprehensive, thorough analysis. Issues of rural communities’ adaptation to climate change; global commodity chains and other drivers of globalization -- and their hierarchical forces interacting across spatial scales - influencing socioeconomic well-being, inequality, underdevelopment, local-to-regional land-use decisions, ecological degradation and ecosystem services are examples of problems tackled by political ecologists (Simmons 2004; Turner and Robbins 2008). A geography-centered PE thus furnishes an analytical interpretative structure that allow this study to focus on the linkages between local NTFP production and global and national economic forces driving regional and local land use choices changes within the

Amazonian “politicized environment” (Blaikie and Brookfield 1987; Burchardt and Dietz

2014).

PE argue that actors and institutions interact across scales through different power relations which determine “winners” and “losers” (Blaikie and Brookfield 1987).

Contemporary political ecologists asserts that deforestation cannot be solved solely by policies creating protected reserves only, and that understanding the very causes pushing environmental degradation is key. They emphasize that it is common to attribute to small farmers the blemish for deforestation and forest degradation, contending the reasons why poor small farmers have been pushed to forest frontiers - carrying over deforestation - is what needs to be addressed. In the case of deforestation, peasants might do so for several reasons. First, they are physically pushed out to new frontiers because capitalized farmers usually buy better lands closer to urban centers. Second, remote undocumented lands are more likely to generate property rights, and deforestation is precisely one of the means to secure the land tenure. PE scholars then have suggested local level solutions targeting these marginalized actors. A presumable solution for forest clearing would be empowering peasants by providing extractive areas for communitarian use, and support for rural workers union, local associations, and cooperatives, to mitigate power unbalances and inequalities.

According to this theoretical framework, a peasant farmer is a family farmer who depends on natural resources and maintains limited relations to outside markets

(Caldas et al. 2007). Precisely, the family farmer makes up a social category comprising the rural poor (landed and landless), who typically derives its traditional or

multifunctional livelihoods from small-scale agriculture, extractive activities, and sometimes off-farm labor (Pereira, Simmons, and Walker 2016). They may be linked to the market system through relationships, or when some of their family members engage in wage labor (Chayanov 1991). Hence, peasant households may be market-oriented or not. More likely, they may be some combination of both (Pereira, Simmons, and Walker

2016). Because of their predominant cultural and socioeconomic characteristics, scholars agree that small peasant farmers do not perfectly fit into the conventional binary classifications of social development which considers families purely autarkies versus integrated into the capitalist market (Brondizio et al. 2009). In fact, rural smallholders in the Amazon are no longer autarchic peasants living in unexploited remote areas. They are now entirely engaged in the global economy (Pereira,

Simmons, and Walker 2016). But, distant parts of the world experience the effects of the global economy differently. How the dynamics of the capitalist world-economy unfold in these remote and rural communities must be then examined.

Most of the work addressing peasant household are context-oriented, survey- based, committed to interpreting interconnections between land uses, land cover and household economics, as well as traditional market factors, such as transportation costs. Demographic characteristics, such as family size, length of family residence on the property, and accessibility to external markets, are also remarkable explanatory variables in studies adopting this frame in Amazonian research sites (Brondizio and

Moran 2008; Perz and Walker 2002; Walker et al. 2002).

Therefore, in this thesis, I apply a Political Ecology frame “optimized” by the so- called New Economic Geography (NEG) and Proper Economic Geography (PEG) to

capture the influence of global environmental governance – comprising governments, civil society groups, community associations, social movements, multilateral organizations, and private corporate actors – and NTFP supply chains and production networks on the income of small farmers in the Brazilian Amazon region.

Economic Geography

Economic Geography is a subdiscipline of Human Geography concerned with defining and explaining different places and spaces where economic activities occur

(Gregory et al. 2011). The prime interest of economic geographers is the spatial distribution of production and the use, and transportation of natural resources, goods, and services, and their repercussions on the environment, as well the structure, and dynamics of the global and regional economy (Stutz and Warf 2012).

Methodologically, the discipline has evolved around two fundamental paradigms of knowledge production: (1) Theory Building - characterized by qualitative methods, specific, synthetic, and power influenced narratives; and (2) Description and Synthesis – oriented to quantitative methods, comprehensive, analytical, objective facts and truths.

In 1950, studies with an emphasis on Economic Geography shifted attention from

Areal differentiation to Spatial Analysis and Regional Science, in the descriptive side of the paradigmatic spectrum. From the 1970s, debate involving Radical Geography gained prominence, turning the pendulum to the Theory Building side. Beginning in

1980 onwards there was again a paradigm change back to the Descriptive and

Synthesis approach, with the expansion of studies based on Post-Structuralist approaches, linked with the investigation of Post-Fordism with the development of globalization studies. This period is so-called the “cultural turn” in the geography scholarship. In the 1990s, a new research agenda focusing on Global Production

Networks (GPN) and Global Value Chains (GVC) emerged, until the succeeding advance of New Economic Geography (NEG) connected with Spatial Econometrics modeling techniques. Fujita and Krugman (2004:139) explain NEG as a body of research initially deriving from international trade theory which seeks to investigate the development of an increased range of economic agglomeration (or accumulation) in geographic location or space. Specifically, the realm of spatial econometrics is characterized as the assemblage of approaches that deal with the peculiarities induced by space in the statistical analysis of regional science models (Anselin

1988:7). Recently, there has been a flourishing interest in new analytical and modeling methods, even though there is no comprehensive, up-to-date literature discussing the collection of methodological approaches available straightforwardly (Anselin 1999).

Current methods applied by economic geographers include Geographic Information

Systems (GIS), mathematical models, and also qualitative assessments based on interviews and fieldwork (Stutz and Warf 2012).

Spatial Econometric Theory

Applied work in Economic Geography and Regional Science relies heavily on spatial data linked to points in space. Applied econometric regression models in regional science depend on cross-sectional spatial data formed by observations collected about points in space, such as households, towns, regions or states. Two major complications arise when data carries a locational factor: (1) spatial dependence, and (2) spatial heterogeneity (LeSage 2014).

Although ordinary least squares (OLS) would be the common statistical estimation method for this analysis, the “cross-sectional” nature of the data presents the potential problem of spatial dependence. Spatial dependence results from a lack of

independence among cross-sectional units caused by (1) spill-over effects between units of observation, and (2) the presence of direct influence of neighboring units of observation. In the presence of spatial correlation due to direct influence of spatial neighbors, OLS estimates will likely be inconsistent (Anselin 1988a).

Traditional econometrics methods have generally ignored these two issues that violate the Gauss-Markov assumptions adopted in conventional regression modeling.

These issues rises the need for alternative estimation methods. Similarly, spatial heterogeneity violates the Gauss-Markov theory that a single linear relationship is constant across sample observations. If relationship differs across the spatial data sample, alternative estimation methods are required to represent this variety well, and make appropriate interpretations (Lesage 1998).

(OLS) regression generates the best possible unbiased coefficient estimates for any linear estimation procedure. Failing to consider the assumptions can lead to unreliable estimations. Another justification for the use of specialized methods of spatial regression analysis (spatial econometrics) is the presence of spatial heterogeneity

(LeSage 2014).

Spatial dependence refers to the fact that values detected at one place depend on the values of neighboring observations at close places. These reciprocal relationships violate the assumption considering observations from one place as independent of other observations, which is a precondition of conventional regression procedures. Conventional statistical methods have infringed the Gauss-Markov theorem assumptions adopted in regression modeling. These assumptions specify that when a linear regression fulfills the Gauss-Markov premises, an ordinary least squares (OLS) regression generates the best possible unbiased coefficient estimates for any linear estimation procedure. By contrast, failing to consider the assumptions can lead to incorrect estimations. Spatial externalities then play a vital role in the recent development of “spatial thinking” in the social sciences (Anselin 2003b; Goodchild et al.

2000). Empirical verification of the strength and scope of such spatial externalities calls for the specification and assessment of spatial econometric models (Anselin 2003).

Spatial Dependence

Spatial dependence refers to the fact that values detected at one place depend on the values of neighboring observations at close places. These reciprocal relationships violate the assumption observations from one place are independent of other observations which is a precondition of conventional regression procedures. To put it another way, standard econometrics methods have infringed the Gauss-Markov theorem assumptions adopted in regression modeling. These assumptions specify that when a linear regression fulfills the Gauss-Markov premises, an OLS regression generates the best possible unbiased coefficient estimates for any linear estimation procedure. By contrast, failing to consider the assumptions can lead to incorrect estimations. Another justification for the use of specialized methods of spatial

regression analysis (spatial econometrics) is the presence of spatial heterogeneity

(LeSage 2014).

Spatial Heterogeneity

The term spatial heterogeneity is associated with the systematic variation of relationships over space, which represents a problem for conventional regression models which requires a constant relationship for every point of the data sample

(Lesage 1999; LeSage 2014).The occurrence of these two spatial effects problems (i.e., spatial dependence and spatial heterogeneity) violate classical regression analysis assumptions. Therefore, specialized methods of spatial regression must be implemented to avoid biased inferences and inaccurate inferences (Anselin 1988;

Lesage 1999). These problems of simultaneous spatial dependence and heterogeneity, which regularly arises in cross-sectional spatial data, motivates the applied spatial econometric techniques used in this thesis. The estimation modeling approach adopted here have becoming a cornerstone in econometric studies since such models not simply can deal with spatial effects problems appropriately, but also can handling spatial spillovers and omitted explanatory variables, aspects also present in spatial dependent data (LeSage and Pace 2009).

Point observations, in general, may include characteristics such as household incomes, education attainment, engagement in social activities, and a varied array of other attributes. Geographic Information Systems (GIS) capabilities make geocoding viable, enabling the conversion of descriptive factors, such as a name of a place, an address, or a pair of coordinates, to a location point on the earth's surface, therefore incorporating geographic traces necessary for mapping or spatial analysis (LeSage and

Pace 2009).

Spatial Econometric Models

Several new spatial regressions models have been developed to handle spatial effects problems including maximum likelihood, Bayesian, spatial Durbin model, robust

Bayesian, semi-parametric, to name a few. There is a host of methods to choose from as each model involves a variety of robust statistical features. Defining the most appropriate method from the plethora of alternative modeling procedures possible seems a daunting task. Fortunately, some principles exist to determine the best estimation technique, the one that fits both, data aspects and research goals (LeSage

2014). The existence of the spatial effects justifies the advancement of a specialized branch in spatial econometrics methodology (Anselin 2003a).

The family of models that have been labeled SAC, SAR, SEM and SDM were popularized by Anselin (1988b) work, and most of the literature on statistical testing of alternative model specifications has built on this family of models (LeSage

2014). Anselin (1988b) provides a relatively complete treatment of these models from a maximum likelihood perspective (Lesage 1999). The most general statement of a spatial autoregressive model is expressed mathematically in the following form (1):

푦 = 휌푊1 푦 + 푋훽 + 푢 (5-1)

푢 = 휆푊2푢 + 휀

Where y contains an n x 1 vector of cross-sectional dependent variables and X represents a nxk matrix of explanatory variables. 푊1 and 푊2 are known n x n spatial weight matrices, usually containing contiguity relations or functions of nearest distance

(Lesage 1999b).

For a spatial weights matrix analogous to the first-order contiguity, each of the powers contains a higher order of contiguity (closeness), implementing rings of a usually greater distance around each location, connecting every location to every other one (Anselin 2003b).

Weight Matrix

The variance-covariance matrix correlates every location with every other location in the structure, but closer locations more so, following Tobler’s (1979) first law

(Anselin 2003b). The matrix W is the spatial weight matrix that comprises non-zero elements 푊푖푗 if observations j and i are neighbors and zero otherwise (LeSage and

Pace 2014).

There is near-universal agreement that coefficients and inferences from spatial regression models are sensitive to the specifications used for the spatial weight structure in these models. LeSage and Pace (2014). find a little theoretical basis for this held belief and conclude this myth may have arisen from past applied work which misinterpreted the model coefficients as if they were partial derivatives, or used misspecified models. A complete specification of the main models is beyond the scope of this study, so I will only briefly describe the models implemented in this study, namely: SAR, SEM, SDM, and their respective alternative Gibbs sampling Bayesian models: SAR_g, SEM_g and SDM_g.

The Mixed Autoregressive-Regressive Model (SAR)

The SAR model adopts a matrix X of explanatory variables with a similar specification to that used in frequentist multivariate OLS regression model, and takes the following form:

푦 = 휌푊푦 + 푋훽 + 휀 (5-2)

Where y consist of an n x 1 vector of dependent variable and in the right-hand side, the parameter ρ is a coefficient on the spatially lagged dependent variable,

Wy, X retains the matrix of explanatory variables, and W represents the spatial weights matrix, and the parameter vector β depicts the significance of the explanatory variables in the outcome variable y. The model is called a spatial autoregressive model or mixed regressive model on the grounds that it integrates the conventional multiple regression model with a spatially lagged dependent variable. In the SAR model, the parameters to be estimated are the common regression parameters α, β, σ in addition to the parameter ρ.

The Spatial Autoregressive Error Model (SEM)

The spatial errors model carries the spatial dependence in the disturbances as show below:

푦 = 푋훽 + 푢 (5-3)

푢 = 휆푊푢 + 휀

The vector y accommodate the dependent variable and X means the typical matrix explanatory variables. W portrays the spatial weight matrix, and the parameter lambda λ is a coefficient on the spatially correlated errors. The specification of β express the effect of the set of explanatory variables X on variation in y, the outcome variable.

The Spatial Durbin Model (SDM)

Another component of the family of spatial regression models is the SDM model, numerically expressed as indicated below:

푦 = 휌푊푦 + 푋훽1 + 푊푋훽2 + 휀 (5-4)

Where y carries the dependent variable and X the set of explanatory variables linked to a parameter β1. W embody the spatial weight matrix and the parameter ρ is a coefficient on the spatial lag of the outcome variable. An auxiliary set of explanatory variables is then accommodated to the model by composing a spatial lag of the explanatory variables using the matrix product WX, including parameter β2. This bundle of explanatory variables is set up as averages from neighboring observations.

The standard taxonomy of spatial autoregressive lag and error models implemented in spatial econometrics leaves out other possibilities for mechanisms through which phenomena at a given locality influence actors and properties at another locality (Anselin 1988b, 2003b). Thereby, the SDM model is considered a more robust model. LeSage and Pace (2009) document the robustness and useful superior statistical features of the SDM model specification in applied modeling situations.

Bayesian Spatial Autoregressive Models

The maximum likelihood estimation methods presented previously are based on the assumption that the underlying disturbance process involved in specifying the model is normally distributed. When data holds spatial heteroscedastic the implementation of a

Bayesian heteroscedastic regression model is more appropriate. Bayesian methods do not encounter the same degrees of freedom constraints, because the model can rely on an informative prior and posterior distribution. The posterior distribution for the parameters is obtained by using a recent methodology known as Markov Chain Monte

Carlo (MCMC), called Gibbs sampling.

The Heteroscedastic Bayesian Linear Models

The implementation of Gibbs samplers for the spatial autoregressive variants of

SAR, SEM and SDM models is straightforward due to computational capabilities which enables a simulation of the parameters needed to be estimated. The robust Bayesian estimates provide a better basis for statistical inference.

A question that continually arises is which model specification holds the most appropriate model? The alternative models can be considered as extending, nesting one another. A particularly recommended judgment is to turn down negative spatial autocorrelation measurements for either ρ or λ (rho and lambda parameters of spatial autocorrelation), as these reveal that neighboring observations exhibit more different relationships than distant ones, a result counter to intuition.

Quantification of Location Points

One of the ways to quantify location points in a set of data is knowing the latitude- longitude coordinates associated with spatial data observations. This information allows one to calculate distances between points. Near observations should reflect a greater degree of spatial dependence than those more distant from each other.

This suggests that the strength of spatial dependence between observations should decline when distance between observations increase. Distance may also be important for models involving spatially heterogeneous relationships. If the relationship varies over space, near observations should exhibit similar relationships and vice-versa (Anselin

1988b).

An important aspect of this is the definition of what is meant by neighbors, typically carried out through specification of a spatial weight matrix. Spatially lagged variables can be included for the dependent variable (leading to so-called spatial lag

models), explanatory variables (spatial cross-regressive models) and error terms

(spatial error models), as well as combinations of these, yielding a rich array of spatially explicit models (For instance, see Anselin (2003b), which describes spatial econometric model specifications that incorporate spatial externalities in different ways).

Exploratory Spatial Data Analysis (ESDA)

Methods for visualization of spatial data has been called exploratory spatial data analysis, or ESDA. As specified by Anselin (1999), ESDA is a set of techniques to describe, visualize and interpret spatial distributions and spot spatial patterns, outliers, and clusters of spatial association, or hot spots; and suggest spatial treatments. ESDA is subset of exploratory data analysis or EDA, barring a focus on the particular characteristics of geographical data. Positive values for Moran’s I indicate positive spatial autocorrelation (clustering), while negative values suggest spatial outliers.

Data Preparation and Exploratory Data Analysis

Data preparation is considered one the most challenging tasks in data analysis

(Florax and Vlist 2003). First, I detected that the dataset had missing values. A reasonable approach to tackle this issue is exclude the observations with incomplete datasets. So, before proceed with any analysis, I deleted all observations with missing data, procedure also called list wise deletion.

Prior to handle more formal statistical analysis, I begun the process of exploration, visualization and interpretation of the essential characteristics of the dataset by checking to scatter plots, histograms and descriptive statistics to determine the appropriate method for statistical analysis. In this initial exploratory step, I used the open-source program R because it presents state-of-art graphics capabilities that helped me to visualize patterns and anomalies in the data better, as well as understand

and interpret the dataset as a whole. In this initial check, I was specifically looking at the functional relationship between the dependent variable 푦 “total income” and the array of possible explanatory variables X that could exert an influence on the variable of interest, total income.

Figure 5-1. Regression total income versus explanatory variables

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Figure 5-1. Continued.

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Figure 5-2. Regression total income versus explanatory variables by membership status

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Figure 5-2. Continued.

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Figure 5-2. Continued.

Figure 5-3. Explanatory variables by location.

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Figure 5-3. Continued.

In visualizing the shape of the scatter plots and box plots, center tendency and spread of numerical distributions of each potential exploratory variable I detected an

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unusual pattern in the data distribution. The histograms plots for the total income y, for example, showed that the distribution of these variables was positively highly skewed to the right.

Figure 5-4. Histograms variables

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Figure 5-4. Continued.

This distribution is due to asymmetric monetary income level obtained by the peasant households, which is often very small on average, given that income in rural areas in the Amazon region is often close to zero (Min and Agresti 2002; Morsello

2009). Gujarati (2004) and Sills and Abt (2003) advice that highly positive skewed variables, such as income, have to be directly transformed into log-scale before proceeding with further analysis. I am aware that logarithmic transformations can make the distribution more symmetric and easier to model. Transformation of skewed variables, as income, into logarithmic form is advisable and widely used to treat not only

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problems with normality but also outliers. This technique is yet the most common course of action among all transformation techniques to regression models (Gujarati 2004).

However, to simplify the interpretation in this preliminary study, I opted for using the

“pure” number, presenting the results of the final “best fit.”

Figure 5-5. Relationships between Total Income, Proportion of Income from NTFP, Years of Education, Length of years on Property, Membership status, Age, Family Size, Distance to the near river, Distance to the near road, Distance to the capital (market).

From these graphs, I observed the central tendency of the data to identiy the typical value. In analyzing the mean and median of total income, I was seeking to decide whether reporting the mean or median of the data. As can be seeing from the figures above, the data have a skewed shape instead of a bell curve. When data

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presents a bell curve shape, these can take the mean as the central value because the data distribution is close to having a normally distributed data.

In contrast, when data has big outliers and considerable variation, which is the case of the present data, the mean is not the best value to be reported and base the analysis. Outliers influence and augment the mean, so this parameter may not represent the typical income of all households in the study site, because the mean may be higher than it really is. Because the median value is usually more immune to outliers

- as compared to mean and mode parameters - I decided to report the mean and median as a central tendency or typical value to base the analysis.

During this exploration step, I also detected a substantial proportion of natural zeros for specific income sources not related to missing data. These large frequencies of natural zeros reflect the situation that the poorest small producers make close to nothing associated with a specific season or activity (i.e., agriculture, fishing, NTFP extraction or collection). In such cases, the producer’s income is unstable and vary depending on the availability of a natural resource stock or a proper season. For instance, fishing, a priori, cannot occur in area without rivers or streams. Açai is mostly produced in floodplains. Then, earnings from fishing or Açaí production in communities without the specific natural environment will likely be zero. The excessive proportion of zeros likewise can be attributed to the heterogeneity (or differences) between communities and municipalities studied. They belong to different regions and, therefore, present distinct socioeconomic and environmental profiles in some respects. With this preliminary exploratory analysis, I determine the main characteristics of the data: vast frequencies of zeros and skewed distribution. These data types, common in ecological

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studies on species and income levels, are usually unbalanced and challenging to modeling. Fortunately, appropriate statistical approaches have been developed to deal with excessive zeros and patchily distributed data, such as using logarithmic or Box-Cox transformation, conventional poison and logistic regressions, especially for normalizing the dependent variable, among others (Gujarati 2004; Min and Agresti 2002).

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CHAPTER 6 RESEARCH DESIGN AND METHODS

Studies addressing well-being and poverty is often dominated by large scale quantitative data and panel surveys suitable to not predominant trends. In some cases, this quantitative information is analyzed in a highly socially and economically decontextualized way (Bevan 2004; Bolwig et al., 2008). Thereby, some scholars have been calling recently for such surveys to be complemented by qualitative information

(Shepherd 2007). Attempts to do this are often limited and still operate within an

“econometric imaginary” that, according to some critics, disregards the key role played by social process and social relations. Based on these calls, I sought to develop an mixed methods research approach that combines ‘the best of both worlds,’ since the integration of qualitative and quantitative methods provides a more complete understanding of the research problem than either approach alone, because the collection of both data offset the weakness of each form of knowledge source (Creswell

2014).

Then, in this thesis I specifically use an explanatory sequential mixed method design in which an econometric-based case study is conducted to test the effect of

NTFP commercialization and membership in partnerships for supplying cosmetic industries on the gross total income of small farmers in the Brazilian Amazon. In the sequence, a qualitative portion of the study is conducted to help to explain the quantitative econometric statistical results, by allowing to cross-check the plausibility of the statistical outcomes.

Put another way, I use multiple modes of analytical assessment combining both context and detail, through explanatory sequential mixed methods. In the qualitative part

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of the study, I first conduct the literature review on NTFP, and community-company deals, surveying published data covering experiences and key lessons learned of community forestry and community-company partnership with an emphasis on the

NFTP extraction and trade focused on the cosmetic industry. The literature review is then augmented by in depth key informant interviews (by phone, Skype and email) with knowledgeable individuals, including academic researchers and practitioners, officials from governmental agencies, community leaders, and staff from NGOs and corporate executives involved in the commercialization of NTFP in partnership with cosmetic companies, with knowledge on policies affecting these initiatives to provide their perceptions and additional contextual information to back up the study discussion and interpretation of the results. The literature review and in-depth interviews data thus provide a interpretative context, helping to develop and situate a complex picture of the issue at hand, beyond inform and gauge the particularities of the statistical analysis in the econometric-based quantitative portion of the study, which is detailed next.

Study Site

The data1 used in this study were drawn from an extensive household survey, conducted in remote villages of the municipalities of Anajás, Augusto Corrêa, Bragança,

Breves (Furo do Gil), Igarapé-Miri, Tomé-Açu and Salvaterra, located in the northeast of

Pará state, in the Brazilian Amazon. The villages studied present different levels of exposure to the NTFP market. The Figure 1 shows the map of the study site.

1 Data were collected in January 2014 and June 2016 through a collaborative research by interdisciplinary scholars and graduate students at the Columbia University (New York/USA) and the University of Sao Paulo (USP/Brazil) focusing on the effects of oilseed harvest on small farmers livelihoods in detriment of other income source activities performed in the study site.

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Figure 6-1. Study Site

Fieldwork included administering a two page semi-structured survey gathering information regarding main land-uses (i.e., extraction or collection of NTFPs, agriculture, logging, fishing and non-farming activities), demographic and socioeconomic household characteristics (i.e., membership in cooperatives, family size, years of residence on property, age of the household head and years of education).

Table 6-1. Total Income by Municipality (R$) Median Median Median Mean family Mean annual income per Municipality annual income per member income family income month member Anajás 5 22,963 20,920 1,743 349

Breves 5 32,553 20,512 1,709 341

Igarapé-Miri 4 49,776 47,340 3,945 986

Tomé-Açu 4 69,977 46,800 3,900 975

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The research site encompasses the poorest municipalities of the Amazon region, with the lowest Human Development Index (HDI) in Brazil. The analysis turns to the effect of the company Beraca, whose lead the governance of NTFP supply chains and biodiversity sourcing to “big name-branded” cosmetic, pharmaceutical and food industries. Beraca is a global company that provides biodiversity products to other major cosmetics corporations such as Natura, Avon, L'Occitane, The Body Shop, L'Oréal, among others. The company claims it has developed the social technology of partnerships with local communities within this sector through business incentives, fomenting the creation of associations or cooperatives to supply big corporations.

Beraca adopts a model of supply chain management. Currently, Beraca leads supply chains with approximately 150 rural, remote communities sourcing several assets of biodiversity.

Participants

The study site is inhabited by peasant 2communities as described by Brondizio et al. (2009), consisting of poor small-scale farmers “neglect in the process of development of the Amazon and conservation policies,” which generally totalizing rural populations as “traditional communities” within the context of small scale production systems, ignoring specifics social and economic needs (Brondizio et al. 2009). These rural communities, many originally from northeastern Brazil, rely on a mixture of subsistence activities including fishing, hunting, and extraction of forest products, agriculture, and slash-and-burn cultivation.

2 In this study, peasant communities are referred interchangeably as small producers as designed in the peasant social system.

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The term peasant relates to family farmers (farming households) which primarily function as a collective unit of production and reproduction. Peasants are yet distinguished by direct access to their means of production in the land, by the prevalent use of family labor. Traditional Amazonian peasants are also conceptualized as a diverse population engaged in a continuum of mixed economic activities (see Nugent

1993: 97; Pace 2004). The continuum may include extraction products for domestic and world markets (e.g., timber, palm hearts, cacao, etc.), subsistence agriculture (e.g., manioc, corn, rice, beans, husbandry, fishing, for both, subsistence and commercial), hunting, wage labor employment (e.g., sawmills, cattle ranches) (Pace 2004: 233). The connection of peasants with the world economy is limited as they are subordinate to different external forces and actors in larger political economies holding political and economic power. Their livelihood strategies are influenced by an array of factors including natural resources access and quality, labor availability, education levels, market integration and historical, cultural, socioeconomic, institutional and ecological contexts.

Sampling Methods

The full dataset, including 497 observations total, comprise originally the income composition of the small farmers with different levels of market exposure. The surveys collected detailed data on income which allow an assessment of the importance of the combination of various agronomic activities performed by the remote rural communities to generate income, in the form of a multi-functional family farm livelihood approach. In detail, the data encompasses information concerning household earnings, composed of all agricultural and non-agricultural income activities that comprise the total income of the rural families. Specifically, their overall income is obtained from vegetal NTFP

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extraction and transformation into oils to be processed by cosmetics industry; commercialization of agriculture produce (i.e., pepper, cocoa, farinha, cupuacu, passionfruit, etc.); other extractive based income (i.e., wood, palm heart, fishing, crab, shrimp); livestock (i.e., raised chicken); besides wage labor off farms and governmental transfer payments (i.e., pension, Bolsa Familia, Green Grants). The analysis of these different income sources allows us to assess the particular proportional significance of these activities for the overall household income.

Figure 6-2. Percentual Production

The activities with the highest proportional contribution for income generation, by order of importance, are acai and pepper, accounting for about 22% of the total income value each. The other forms of income (wage income), corresponding to 11% of the total; pension, responding for 10% of the total; and Farinha accounts for 9% of total income. NTFP represents only an average of 3% of the total income of all municipalities of the study. The production of NTFP is more expressive in the cities of Augusto Correia

(13%), Anajas (8%) and Salvaterra (7%). Breves and Braganca presented the same

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proportion of 4% each. In general, the literature has shown a proportion range of 0 to

9%. We speculate that the differences and greater reliance on NTFP production in those cities might be attributable to the lack of other markets, which does not apply to the towns of Tome Acu and Igarape Miri that have both consolidated markets for pepper

(responding for 81% of the total income) and acai (representing 82% of the families earnings), respectively, being these activities greatly more proportional preponderant than any other. The income generated by passiflora, cocoa, cupuacu, fishing, chicken, crab, shrimp, defeso and Bolsa Verde subsidies, in its turn, have minuscule proportions with figures around 1% of the household’s total income that compose the dataset.

During the fieldwork, a quasi-experiment method using spontaneously formed groups (e.g. villages) was organized to produce variation on the influence of business intervention in distinct communities, allowing comparison between similar and non- equivalent groups. Two separate groups of small farmers were examined: (1) small producers engaged in partnerships with the chemical/cosmetic industries that gather and commercialize NTFP (i.e. oilseeds), and (2) small producers that do not gather nor commercialize NTFP, and, therefore, are not involved in the partnerships with the chemical/cosmetic companies. To put it another way, the fieldwork targeted a group that receive the influence of the business intervention, that is, the families that collect oilseeds, and a group that does not collect NTFP and are not influenced by direct business partnerships. This way, it was possible to compare the groups sampled exploring the effect of the commercial NTFP production and engagement in partnerships and the absence of those specific market incentives on the farmer’s household income.

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However, despite diversity in income level reflected in the data sampled, in which some cities are making more money (i.e., Tomé-Açu and Igarapé-Miri), than others (i.e.,

Anajás and Breves), the municipalities studied also share important similarities in the general level of poverty and welfare.

Table 6-2. Descriptive Statistics Income Source Activities (N=286) Min 25th median mean 75th Max quartile quartile Total 1800 11292 20512 33061 40404 293844

NTFP income 0 0 0 0.04 0.0431 0.5263

Education 0 0 5 5.12 7 19

Property 1 20 33 32 45 80

Membership 0 0 0 0.47 1 1

Age 18 33 42 43 54 81

Family size 1 4 5 5.38 7 17

Distance near river 0 1118 7127 9247 8224 56986

Distance near road 318 17305 6882 59889 10012 116747

Distance capital 77062 113554 170507 168021 205716 257734 (market)

Income from NTFP

After the exploratory analysis of the data I assessed the descriptive statistics of the socio-economic characteristics of households in the full sample. Most of the household heads have an average of 44 years. In terms of education, the average for the household head was five years of formal education. On average, each household comprises five people, with a length on 33 years in the same property. From the total sample, 48 percent of households have reported the status of member in the

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cooperatives linked to the partnerships with the companies, while 51 percent declared themselves as non-members of the cooperative.

The mix of economic activities performed in each locality varies widely among the municipalities. Annual household income data analysis shows that Açaí provides the highest income followed by income from pepper Figure 6-3 below.

Mean percent that came from NTFP is 3% and there is some variation between the cities. The production of NTFP seems to be more interesting in some communities than in others. The main land-use and diversified income-generating activities performed by family farmers in each municipality are detailed in Figure 6-3.

Figure 6-3. Income source activities

The highest proportion of income derived from NTFPs production was recorded in the city of Anajás (8%). Breves displayed the 4%. Small producers from the municipalities of Tomé-Açu and Igarapé-Miri do not reported production of NTFP. I speculate that the differences between the cities and the greater reliance on NTFP extraction in the city of Anajás was due to the effect of the influence of the business incentives together with the lack of other markets, which does not apply to the municipalities of Tomé-Açu and Igarapé-Miri which have consolidated markets for

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“pepper” (responding for 76% of the total income) and “Açaí” (representing 82% of the familie’s economic rent and earnings), respectively. Açaí profits also vary enormously across the cities and clearly stands out as the most profitable crop.

Econometric Multiple Regression Analysis Approach

To test the relationship between total income, the engagement in NTFP production and the effect of membership in cooperatives associated with the partnerships and market incentives for NTFP commercialization, I implemented a host of multi-variate regressions models, including the classic frequentist approach, such as the ordinary least square (OLS) regression, besides conventional spatial econometric methods (e.g. SEM, SAR and SDM), in addition to alternative Bayesian models. These methods were chosen because of the nature of the dataset and because in implementing these different model specifications I could check the results across more conventional methods to more robust spatial models.

First, I conceptualize an econometric model approach incorporating the primarily three central variables of interest to the empirical strategy designed in this study, namely the “total income” and “membership in cooperatives” and “proportion of income from NTFP.” Since it is believed these last two variables might be affecting the outcome, these are the main explanatory variables to assess the response variable “total income.”

The “total income” is influenced by changes in the “percent income derived from

NTFP” and in the status of “membership” in agreements with cooperatives implemented to support the commercialization of NTFP. The model specification also comprises control variables that potentially influence the small farmer’s income, such as “years of education,” “length of residence on property,” “age of the household head,” and “family size,” based on theoretical positions found in studies on peasant and household

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economics. In addition to these typical explanatory variables, distance variables such as

“distance to nearest road”, “distance to nearest river” and “distance to the market” were also included in the model, as stated below:

푌 = 훽0 + 훽1 × (푝푒푟푐푒푛푡 𝑖푛푐표푚푒 푓푟표푚 푁푇퐹푃) + 훽2 × (푦푒푎푟푠 표푓푒푑푢푐푎푡𝑖표푛) + 훽3 × (푙푒푛푔푡ℎ 표푓 푟푒푠𝑖푑푒푛푐푒 표푛 푝푟표푝푒푟푡푦) + 훽4 (5-4) × (푚푒푚푏푒푟푠ℎ𝑖푝 𝑖푛 푐표표푝푒푟푎푡𝑖푣푒푠) + 훽5 × (푎푔푒 표푓 푡ℎ푒 ℎ푒푎푑) + 훽6 × (푓푎푚𝑖푙푦 푠𝑖푧푒) + 훽7 × (푑𝑖푠푡푎푛푐푒 푡표 푛푒푎푟푒푠푡 푟𝑖푣푒푟) + 훽8 × (푑𝑖푠푡푎푛푐푒 푡표 푛푒푎푟푒푠푡 푟표푎푑) + 훽9 × (푑𝑖푠푡푎푛푐푒 푡표 푚푎푟푘푒푡) + 휀

Where 푌 accommodate the dependent variable “total income” and X means the conventional matrix of explanatory variables as indicated into the parentheses. The β indicate the effect of the set of explanatory variables X on the variation in Y, the outcome variable. The 휀 express the error term of the regression. The parameter 훽표 embody the intercept and it has no particular meaning as an independent term in the regression model.

Subset Sample

The full dataset comprises seven municipalities and 497 observations total.

However, some technical aspects limited the sample size ultimately used in this study.

The data for the influential variable “property” (length of residence on property) was missing for three municipalities (i.e., Salvaterra, Bragança and Augusto Corrêa). As missing data can reduce the statistical power of a statistical analysis by producing biased or inaccurate estimates, the solution adopted to solve this problem was to delete these observations from the matrix dataset. A subset of the data was then created comprising four municipalities: (1) Breves (2) Tomé-Açu, (3) Igarapé-Miri, and (4)

Anajás. The exclusion of observations did not jeopardize the analysis though, given that even with the reduction of a good chunk of the dataset, a sample size of 286

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observations was left. This sample size is sufficiently large and statistically significant to make correct inferences and confidently produce reliable results.

Hypotheses and Research Questions

Based on the relevant literature, this study will test the following two central hypothesis:

1. Small producers engaged in NTFP production make more income.

2. Membership in cooperatives associated with community-company partnerships is significant and results in higher income.

The null hypothesis is that the commercialization of NTFP does not influence overall income significantly (or the production of NTFP does not have a significant effect on the household overall income). These hypotheses build on the following research questions:

 What does extent ad conditions NTFP production and trade seems to be a viable approach to provide long-term sustainability for small farmers in the Brazilian Amazon?

 Are commercial NTFP production linked to partnerships with cosmetic industry a viable alternative for peasant farmers in the Amazon?

 To what extent and in what ways NTFP production and trade seems to be a viable economic approach to provide long-term sustainability for small farmers in the Brazilian Amazon?

 How has the engagement in cooperatives sourcing NTFP products to cosmetic companies impacted the livelihoods of the small producers and environmental conservation?

Data Generating Process and Variables Definition

The outcome ‘Total Income’ is a continuous variable created by the sum of all earning-income activities the small producers perform to generate income. These activities include: commercialization of NTFP (i.e., oilseeds); mix of annual agriculture produces (i.e., Açaí, pepper, cocoa, manioc flour (farinha), Cupuaçu and Passionfruit;);

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wood harvesting-based income (i.e., wood, palm heart, sawmill); livestock (i.e., raised chicken); besides fishing, crab, shrimp, wage labor off farms, and governmental transfer payments (including pension, “Bolsa Familia”, “Green Grants” and “Seguro Defeso”).

Similar explanatory variables were used on previous work exploring the relationship between forest resources, market oriented NTFP economics, and land use, including theoretical developments concerning small producer’s households (Caldas et al. 2007;

Perz, Walker, and Caldas 2006a; Simmons 2002). In general, household income, family size, years of schooling, and attachment to markets, have been shown to be significant to increase total income. I also added other factors which are hypothesized to impact households’ net revenues, such as the distance to roads, rivers, and main consumer market, distance to capital. The correlation between the defined set of exploratory variables considered likely to be correlated with the independent variable ‘total income’ was tested, and the results are presented in Figure 6-4.

Figure 6-4 Correlational Matrix.

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Distance Variables

Theoretical models have depicted distance to roads, to market and to forest areas as influential factors for commercialization of NTFP (Sills and Abt 2003).

Empirical studies of NTFP extraction also have described distance as a key parameter in measuring the overall sum of NTFP extracted (Albers and Robinson

2013a). Some studies have attested empirically that households with better access to woodlands and highways collect more fuelwood. Progressively, studies are including distance variables in econometric tests addressing NTFP. While some analysts affirm that the distance is an influential parameter for the analysis of the NTFP market, others, however, assert that, on the contrary, distance appears not have any bearing effect on the commercialization of NTFP (Sills and Abt 2003).

Coordinates Points

As the original data set did not contain the geographic coordinates of the household’s location and having spatially referenced data is an essential prerequisite to run the spatial models, I had to create a spatial referenced version of the dataset. The approach I used to georeferencing the data was to locate the communities using Google

Maps, finding the longitude and latitude point coordinates. Next, I converted the Keyhole

Markup Language (KML) format to Esri Layer (KML to Layer) using ArcMap. Then, I created distance variables using a series of ArcToolbox tools in Esri ModelBuilder.

ModelBuilder is an application of ArcGIS for creating geoprocessing workflows. The workflow created utilized spatial joins, centroid calculations, distance, and data management tools, as showed in the Figure 6-5. The result of the workflow was the appending of a distance measurement for the cities from the major road and river networks, or in other words distance to access transport to external globalized markets.

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Figure 6-5. Model Builder Workflow to create distance variables from the major road, river and capital networks.

Given that much of the empirical deforestation literature finds that road access and distance to cities correlates to lower levels of forest cover (Sills and Abt 2003), I speculate that these distance parameters from roads, rivers and cities might also have an effect on the dynamics of commercialization of NTFP linked to the partnerships with cosmetic industries, which ultimately led to the creation and inclusion of three additional distance variables in the modelling estimation, namely the “distance to rivers”, “distance to roads” and “distance to capital” (Belem), representing the connection with the market.

Specifically, to create these variables, I used the Proximity toolset “Near,” in the Toolbox of ArcMap, which calculates distance and proximity information between features and the closest near feature in another layer. With this toolset, the distance between any two features is estimated as the shortened distance between the two points, that is, where the two features are closest to each other.

During the formulation of the model, I considered aggregate some variables in groups, however, despite being a common approach for income values, it may produce biased or unreliable results (Caldas et al. 2007; Walker et al. 2002). Thus, I have chosen to use disaggregated variables and include in the model only the explanatory variables of prime interest, such as the continuous variable “proportion of NTFP derived income” and the binary variable “membership in cooperatives”, as opposed to aggregate the variety of earning-income activities the small producers perform to

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generate income and include these aggregate variables into the model. The data generating process used to illustrate the model approach was then performed by linking different methods such as household survey data, GIS tools for geo-referencing the dataset and to specify “distance” variables, and statistical DGP methods applied to each estimation model implemented in the study.

Main Exploratory and Dummy Variables

As I was particularly interested in analyzing if being a member of the cooperative tied to the partnerships with the cosmetic companies influence the household income, I aggregated first category 0 (non-member) and category 2 (non-member, non-collector).

So, the final “membership in co-ops” variable was coded as “1” for “member” and “0” for

“non-member” forming a dummy categorical variable. Since I was also interested in the effect of the NTFP production on total income, I have created a new exploratory variable, "proportion of NTFP income," computed by dividing the income value obtained from NTFP commercialization by the total income.

Empirical Statistical Modeling Approach

To test the hypotheses, I implemented a battery of regression estimation methods modelling the dependent variable “total income” (푌) as a function of explanatory variables of interest (푋), comprising the (1) “proportion of income derived from NTFP,” (2) and the status of “membership” in the partnerships for cosmetic industry supplying (coded 1 for member and 0 for non-member, meaning whether or not the small farmer is engaged in NTFP production through the cooperative). Control variables as “age of the household”, “family size”, “years of education”, and “length of residence on property,” which are the main demographic and socioeconomic characteristics of the households at our disposal, were also included in the model. In

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addition, variables associated with distance were also including “distance to the nearest river,” “distance to nearest road,” and “distance to the capital.” forming together the complete set of explanatory variables (푋) of this study. These variables were chosen based on the literature showing the influence of household characteristics in income sources and formation (Caldas et al. 2007; Guedes et al. 2012; Kaimowitz and

Angelsen 1998; Perz, Walker, and Caldas 2006), comprising life cycle location (i.e., age of household head, length of residency in the community and years of education attainment of the household head), land use strategy (i.e., proportion of the income in

NTFP trade and other land uses), accessibility (i.e., distance to village center, distance to nearest city and nearest river), and community-based networks (i.e., membership to local cooperative). After careful consideration, nine variables were selected for the exploratory analysis, as shown in Table 6-3:

Table 6-3. Dependent and Exploratory Variables Variable Description 1. Total income (dependent variable) Total income; sum of all income sources 2. Proportion of income from NTFP Proportion of income derived from NTFP 3. Years of education Schooling years 4. Length residence on property Length of residence in the property Status of membership coded as member =1 or non- 5. Membership in partnership member = 0 6. Age of the household head Age of head of the household (> 18) 7. Family size Number of family members Distance to river (calculated by proximity tool “near” 8. Distance nearest River in ArcMap) Distance to roads (calculated by proximity tool “near” 9. Distance nearest Road in ArcMap) Distance to capital Belem (calculated by proximity 10. Distance nearest Main Market tool “near” in ArcMap)

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Descriptive Statistics

The descriptive statistics for the subset sample show that the mean annual income is BRL 33.061 (8,768 USD). Total average income per month was BRL 2.755 per month (730 USD), which represents a mean individual income of BRL 551.00

(146.13 USD) monthly, considering the average of five individuals per household

(income per capita). The monthly mean income equivalent (divided by 12 months of the year) is consistent with the average rural monthly individual household income of BRL

484 (147.61 USD) in Para state, in the Brazilian Amazon. The average proportional income obtained from NTFP production, which adds up to the total revenue obtained yearly by the producer, represents 3% of the total household income. Most of the residents live on the same property for about 32 year’s average. The mean producer's age is 43, and the households have an average of five family members. Descriptive statistics for the outcome and explanatory variables in the cross-section subsample are presented in Table 6-4:

Table 6-4. Dependent and Exploratory Variables (n=286) Variable Mean Median Std. Dev. Min Max

Total income 33061.32 20512 39209.77 1800 293844 Proportion of NTFP 0.03 0.00 0.07 0 0.52 Years of Education 5.12 5.00 4.17 0 19 Years on the Property 32.4 33.00 17.83 1 80 Membership in Co-ops 0.47 0.00 0.50 0 1 Age of the Head 43.63 42.50 14.11 18 81 Family Size 5.38 5.00 2.60 1 17 Distance near River 9247.26 7127.88 12137.96 0 56986.62 Distance near Road 59889.59 68825.28 40545.77 318.49 116747.3 Distance to capital 168021.03 170507 53123 77062.17 257734.42

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Regression models (OLS and spatial) were then developed to account for inter- household variations in NTFP production, income, similar set of independent variables was used across regression models capturing the effects of differential land use. The analytical approach adopted paid particular attention to statistical issues affecting regression analysis efficiency, including spatial autocorrelation. As for spatial autocorrelation, there are abundant evidence that small farmers make contagious decisions inspired by actions taken by their neighbors which could lead in the present context to spatial relationships (Walker and Solecki 1999). The presence of such links leads to erroneous results if conventional approaches to regression analysis are undertaken (Caldas et al. 2007). To ensure consistency and efficient estimates, I test for spatial autocorrelation, since performing OLS regression in the presence of spatial autocorrelation may result in biased, inefficient, or even inconsistent coefficient estimates (Florax & Vlist, 2003). The following section discusses the research design and methodology used in this thesis.

Bayesian Statistics

In Bayesian statistics is a method in which one's inferences about parameters or hypotheses are updated as evidence accumulates. Bayes’ rule is used to transform prior probabilities into posterior probabilities. In this study, the in-depth discussion about the underlying perspective and theory of the Bayesian paradigm is outside the scope.

So, I want mention only the main aspects of the Bayesian methods to explain the fundamentals used in implementing Bayesian modelling and analyses. To test for spatial dependence or autocorrelation and define the most suitable model to the data, I followed the rationale suggested by LeSage and Pace (2009) with some adaptations, as shown in Figure 6-6.

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Figure 6-6. Adapted from Lesage and Pace Simplified Approach for Spatial Models. Courtesy of author.

In this study, I have implemented all spatial econometric methods in MATLAB software from the Math Works Inc, using the Econometrics Toolbox library set up by

Lesage and Pace, available at http://www.econ.utoledo.edu. For the OLS Regressions I

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also used MATLAB, in conjunction to R for exploratory data analysis and data visualization.

Data Analysis

Following the rationale shown in the Figure 6-6 above, I first implemented an ordinary least squares (OLS) model regressing the variable “total income”, which is our independent variable, on all explanatory variables shown in Table 5. Results of the

OLS-1 regression are shown in Table 7. It appears that “income from NTFP,” “years of education,” “age,” “family size,” “distance to roads,” and “distance to market” do not have a significant impact on the household “total income” as indicated by higher p- values obtained from the regression. The coefficient estimates for “membership in cooperatives” and “distance to river” though came out positive and significant which gave us the base to conclude that they positively and significantly affect the “total income”, while the probability for the “property” indicates a borderline significance (p- value = 0.07). These OLS regression estimates are in line with prior assumptions, nevertheless, the risk of spatial dependence in cross-sectional data motivated the need to assert the reliability of the OLS estimates by detecting spatial data autocorrelation and heterogeneity. As most agricultural data are commonly spatially auto correlated, failing account for spatial dependence results in spurious estimates (Kao and Bera

2013).

To empirically determine spatial autocorrelation in the data, I specified weights matrices (W), to fit the conventional Spatial Error (SEM) and Spatial Lag (SAR) regression models to detect if spatial autocorrelation issues are in evidence in the data, considering that the parameters vary as a function of latitude and longitude. After adding longitude and latitude coordinates to the dataset and create the weights

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matrices, I estimated SEM and SAR models. SEM denotes the Spatial Error Model with the spatial dependence parameter in the error term, whereas SAR stands for the Spatial

Autoregressive model with spatial lag dependence.

Both SAR and SEM model estimates indicated spatial correlation with level of significance greater than the threshold 0.05, as showed by the spatial parameters: rho =

-0.41<0.05 and lambda =0.46<0.05. Thus, following the Lesage and Pace (2009) approach, I next run the SDM models, which indicated no spatial correlation though

(rho=-0.17>0.05). Different spatial weights matrices were created and tested in the sequence as well. However, all SDM models produced with weights matrices (i.e., W2,

W4, W8) indicated no spatial dependence issue: rho= - 0.14>0.05; rho=-0.23>0.05; rho= - 0.25>0.05.

Next, I set out the Bayesian alternative models (SAR_g, SEM_g, and SDM_g).

Both SAR_g and SEM_g showed no spatial dependence, as per their respective autocorrelation parameters (rho=-0.11 >0.05 and lambda=0.08S >0.05). SDM_g model stated a “suggestive” result with a rho parameter -0.45 and 0.06S (>0.05) which is borderline. However, because of both negative R2 of -0.16 and rho coefficient, I eliminated this model, after having the same pattern of imprecise results in estimating new SDM models with different W matrices, that is, rho parameter negative, although positive and significance, in the case of the models SDM_g W2(rho=-0.03 and p- value=0.02**), SDM_g W4 (rho= - 0.61 and p-value = 0.01***), SDM_g W8 (rho= - 0.53 and p-value = 0.07S).

Regarding these outcomes, I assumed SDM_g specification model did not fit well the data set or vice-versa. Then, I excluded SDM_g models from next trials. Still, under

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the realm of the Bayesian models, I tested different spatial weights matrix for SAR_g and SEM_g (W2, W4, W8) and each model using different W generated ‘rho’ and

‘lambda’ parameters no significant for spatial dependence. Overall, the results following show consistent estimations across all fitted models and variables. The coefficient magnitude varies, and interesting results turned up from the simulations. I summarize the results in the tables below, coded to indicate statistically significance.

Table 6-5. OLS, SAR and SEM model comparisons (n=286) SAR1 (p-value) SEM1 (p-value) Variable OLS-1 (p-value) W matrix W matrix

Constant 43268.44 43319.73 41551.58 (0.00) (0.00) (0.00) Propntfp 13691.50 0 23241.26 20727.60 (0.65) (0.44) (0.49) Yearsedu -99.49 -1.14 20.91 (0.86) (0.99) (0.97) Property 245.21 266.18 269.23 (0.07**) (0.04**) (0.04**) Membership 9395.08 11710.30 9699.07 (0.02**) (0.00***) (0.02**) Age -23.46 -43.05 -24.52 (0.90) (0.81) (0.89) Familysize 249.20 319.92 301.98 (0.77) (0.00***) (0.71) D-rivers 0.87 0.98 1.04 (0.00***) (0.00***) (0.00***) D-roads -0.13 -0.11 -0.12 (0.38) (0.46) (0.43) D-town -0.13 -0.15 -0.14 (0.23) (0.16) (0.20) Rho/Lambda -0.41 0.46 N/A (0.00) (0.01) 푅2 0.2728 0.2939 0.2861 Log- -3282.279 -3283.95 N/A Likelihood

Notes: P-value statistics are given in parentheses. **p ≤ 0.05 (significant) ;***p ≤ 0.01 (highly significant); s p ≤ 0.10 (suggestive)

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Table 6-6. SDM model comparisons (n=286) SDM(p-value) SDM (p-value) SDM (p-value) SDM (p-value) Variable W matrix W2 matrix W4 matrix W6 matrix

Constant 42044.70 42844.79 43407.12 44376.69 (0.00) (0.00) (0.00) (0.00) Propntfp 16680.80 26411.01 26264.80 18415.24 (0.59) (0.44) (0.39) (0.55) Yearsedu -34.87 -13.42 -67.52 -111.58 (0.95) (0.98) (0.90) (0.84) Property 296.65 287.83 290.26 300.13 (0.02**) (0.03**) (0.02**) (0.02**) Membership 10148.10 10837.31 10723.22 10710.98 (0.01***) (0.01***) (0.70) (0.01***) Age -63.74 -75.22 -72.25 -93.88 (0.73) (0.68) (0.70) (0.62) Familysize 341.01 291.70 268.54 370.98 (0.68) (0.72) (0.74) (0.65) D-rivers 1.18 1.25 1.22 1.19 (0.00***) (0.00***) (0.00***) (0.00***) D-roads -0.10 -0.06 -0.07 -0.09 (0.49) (0.66) (0.62) (0.56) D-town -0.15 -0.17 -0.16 -0.16 (0.18) (0.13) (0.14) (0.14) W-Propntfp -98446.81 18477.16 -10262.50 -204047.60 (0.00***) (0.87) (0.00***) (0.00***) W-Yearsedu -2697.88 -259.73 -1602.58 6170.79 (0.54) (0.90) (0.58) (-0.31) W-Property -363.25 -0.63.79 -600.94 -355.83 (0.82) (0.91) (0.61) (0.86) W-Membership 42145.71 27297.80 36573.69 6199.33 (0.55) (0.45) (0.45) (0.96) W-Age 287.03 -28.54 560.73 -1130.29 (0.88) (0.97) (0.75) (0.64) W-Familysize 1182.40 769.39 3864.70 13022.24 (0.85) (0.83) (0.46) (0.25) W-D-rivers -0.35 -0.92 -0.68 0.38 (0.75) (0.27) (0.52) (0.88) W-D-roads 0.08 -0.44 -0.35 -0.18 (0.91) (0.44) (0.54) (0.89)

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Table 6-6. Continued SDM(p-value) SDM (p-value) SDM (p-value) SDM (p-value) Variable W matrix W2 matrix W4 matrix W6 matrix

W-D-town -0.15 0.03 -0.13 0.17 (0.71) (0.92) (0.74) (0.85) Rho -0.17 -0.14 -0.23 -0.25 (0.50) (0.42) (0.36) (0.51) 푅2 0.3042 0.3064 0.3070 0.3067 Log-Likelihood -3279.05 -3278.61 -3278.43 -3278.62

Notes: P-value statistics are given in parentheses. **p ≤ 0.05 (significant) ;***p ≤ 0.01 (highly significant); s p ≤ 0.10 (suggestive).

Next, in Table 6-7, I report results obtained from the Bayesian variants models.

Table 6-7. SAR_g and SEM_g model comparisons (n=286)

SAR_g SEM_g Variable W matrix W matrix Constant 39522.32 41299.71 (0.00) (0.00) Propntfp 21675.64 20171.06 (0.10s) (0.19) Yearsedu 447.64 195.49 (0.07*) (0.33) Property 135.94 194.84 (0.03**) (0.03**) Membership 6130.56 6518.79 (0.00***) (0.02**) Age 66.61 33.41 (0.25) (0.40) Familysize 603.19 436.29 (0.08*) (0.25) D-rivers 0.31 0.55 (0.01***) (0.00***) D-roads -0.00 -0.07 (0.49) (0.27) D-town -0.18 -0.16 (0.00***) (0.03**) Rho/Lambda -0.11 0.08 (0.11) (0.35) 푅2 0.1990 0.2510 Notes: P-value statistics are given in parentheses. **p ≤ 0.05 (significant) ;***p ≤ 0.01 (highly significant); s p ≤ 0.10 (suggestive)

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The Bayesian SDM_g models with matrices W, W2, W4 and W8 all produced estimates very similar about spatial autocorrelation, but with a negative ‘rho’ parameter, as summarized in the Table 10. Despite the negative spatial autocorrelation, the corresponding estimated p-values came out significant and positive. Here, I had to decide between ignore the spatial parameter and fit OLS regression straightaway or insist on the spatial model. In the conventional linear regression, there is a straightforward interpretation of estimated coefficients; while in spatial regression models, a change in the explanatory variable in one place may influence not only its own region but also the nearby areas, creating another impact on the initial region.

From previous empirical studies, negative spatial dependence may emerge from some economic interactions, such as negative spillovers or strong competitions among places. Different spatial model specifications can also influence the sign of the spatial dependence parameter. Based on these preceding discussions, negative spatial dependence has a different interpretation for inference

Table 6-8. Bayesian SDM_g model comparisons (n=286) SDM_g SDM_g SDM_g SDM_g Variable W matrix W2 matrix W4 matrix W8 matrix

Constant 40178.63 (0.00) 40990.28 (0.00) 42024.38 (0.00) 41753.16 (0.00) Propntfp 20479.11 (0.12) 25081.36 (0.08*) 29162.86 (0.05*) 21464.90 (0.12) Yearsedu 380.19 (0.13) 384.01 (0.12) 364.91 (0.14) 290.12 (0.20) Property 136.56 (0.02**) 130.53 (0.04**) 130.40 (0.03**) 147.49 (0.02**) Membership 5740.66 (0.00***) 6092.94 5949.27 (0.00***) 5912.26 (0.00***) (0.00***) Age 62.95 (0.26) 60.49 (0.28) 56.17 (0.28) 31.34 (0.38) Familysize 551.39 (0.10*) 560.64 (0.10*) 486.85 (0.13) 489.87 (0.13) D-rivers 0.31 (0.02**) 0.34 (0.02**) 0.35 (0.01***) 0.35 (0.01****)

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Table 6-8. Continued. SDM_g SDM_g SDM_g SDM_g Variable W matrix W2 matrix W4 matrix W8 matrix D-roads -0.00 (0.49) 0.03 (0.33) 0.03 (0.33) 0.01 (0.44) D-town -0.18 (0.00***) -0.20 (0.00***) -0.20 (0.00***) -0.19 (0.00***) W-Propntfp -113334.05 (0.17) 16331.24 (0.39) 75570.69 (0.25) -92590.84 (0.32) W-Yearsedu -2825.16 (0.16) -143.58 (0.45) -206.89 (0.45) -1457.91 (0.35) W-Property -771.45 (0.22) -8.92 (0.47) -390.89 (0.28) 306.58 (0.38) W- 46026.22 (0.16) 19032.25 (0.18) 26719.95 (0.17) -27508.40 (0.34) Membership W-Age 1302.49 (0.15) 144.08 (0.39) 511.79 (0.30) -625.43 (0.33) W-Familysize -2448.68 (0.25) -343.21 (0.43) 2267.62 (0.21) 6749.73 (0.14) W-D-rivers 0.23 (0.37) -0.20 (0.32) -0.12 (0.42) -0.50 (0.38) W-R-roads -0.08 (0.42) -0.55 (0.05**) -0.66 (0.04**) -0.70 (0.18) W-R-town -0.08 (0.37) 0.14 (0.24) 0.05 (0.42) 0.44 (0.21) Rho -0.48 (0.05) -0.36 (0.02) -0.61 (0.01) -0.53 (0.07) 푅2 -0.1609 -0.1598 -0.1564 -0.1533

Notes: P-value statistics are given in parentheses. **p ≤ 0.05 (significant) ;***p ≤ 0.01 (highly significant); s p ≤ 0.10 (suggestive)

From the Bayesian “SAR_g” models, all three models using different W matrix did not produce significant estimates for p-value and rho, as noted in Table 6-9.

Table 6-9. Bayesian SAR_g model comparisons (n=286) SAR_g SAR_g SAR_g Variable W2 matrix W4 matrix W8 matrix Constant 24362.28 (0.00) 39003.83 (0.00) 38853.56 (0.00) Propntfp 20836.69 (0.10s) 20507.37 (0.11) 21334.97 (0.10s) Yearsedu 487.94 (0.06*) 473.18 (0.07*) 458.39 (0.08*) Property 131.48 (0.03**) 131.84 (0.03**) 137.37 (0.03**) Membership 5982.69 (0.00***) 6138.20 (0.00***) 6027.11 (0.00***) Age 79.01 (0.21) 74.31 (0.22) 67.53 (0.24) Familysize 604.57 (0.08*) 615.82 (0.07*) 588.66 (0.09*)

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Table 6-9. Continued. SAR_g SAR_g SAR_g Variable W2 matrix W4 matrix W8 matrix D-rivers 0.31 (0.01***) 0.31 (0.01***) 0.32 (0.01***) D-town -0.18 (0.00***) -0.18 (0.00***) -0.17 (0.00***) D-roads -0.00 (0.48) -0.00 (0.47) -0.00 (0.44) D-town -0.18 (0.00***) -0.18 (0.00***) -0.17 (0.00***) Rho/Lambda -0.11 (0.10) -0.13 (0.10) -0.11 (0.14) 푅2 0.1983 0.1987 0.1998

Notes: P-value statistics are given in parentheses. **p ≤ 0.05 (significant) ;***p ≤ 0.01 (highly significant); s p ≤ 0.10 (suggestive)

The Bayesian “SEM_g” models with matrices W2, W4, and W8 displayed very similar estimates, as noted in Table 6-10.

Table 6-10 -Bayesian SEM_g model comparisons (n=286) SEM_g SEM _g SEM _g Variable W2 matrix W4 matrix W8 matrix

Constant 41514.90 (0.00) 40798.18 (0.00) 41184.80 (0.00) Propntfp 20050.77 (0.20) 21268.15 (0.18) 20606.12 (0.19) Yearsedu 170.68 (0.35) 207.43 (0.33) 188.92 (0.34) Property 195.06 (0.03**) 198.75 (0.02**) 199.60 (0.03**) Membership 6643.71 (0.02**) 6722.27 (0.02**) 6638.38 (0.02**) Age 28.50 (0.43) 26.99 (0.42) 26.07 (0.43) Familysize 433.97 (0.24) 444.18 (0.24) 445.73 (0.23) D-rivers 0.55 (0.00***) 0.57 (0.00***) 0.58 (0.00***) D-roads -0.06 (0.29) -0.06 (0.27) -0.07 (0.26) D-town -0.16 (0.03**) -0.16 (0.03**) -0.15 (0.03**) Rho/Lambda 0.03 (0.43) -0.08 (0.33) 0.19 (0.26) 푅2 0.2497 0.2515 0.2548

Notes: P-value statistics are given in parentheses. **p ≤ 0.05 (significant) ;***p ≤ 0.01 (highly significant); s p ≤ 0.10 (suggestive)

Next, I implemented the model assessment to choose the “best fit” candidate model. Results are shown in Table 6.11.

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Models Assessment

Table 6-11. Bayesian Models Assessment (SAR_g, SEM_g and SDM_g models) SAR SAR SAR SDM SDM SDM SEM SEM SEM SEM SAR SDM W2 W4 W8 W2 W4 W8 W2 W4 W8

0.00 0.00 0.00 0.00 0.07 0.00 0.01 0.91 0.00 0.00 0.00 0.00

The model assessment tool indicates the model SDM_gW8 as the ‘best fit’ model

(close to 1). However, considering that the 푅2 is negative, I excluded that option assuming it as ill-conditioned option. Next, I checked the results for the SAR_g and

SEM_g models. SAR_g model was indicated as the most efficient.

Table 6-12. Bayesian Models Assessment (SAR_g and SEM_g) SAR SARW2 SARW4 SARW8 SEM SEMW2 SEMW4 SEMW6

0.33 0.30 0.16 0.19 0.00 0.00 0.00 0.00

The results suggest that the data does not have the problem of spatial autocorrelation. In absence of spatial autocorrelation, the OLS estimates may be accurate. Thus, based on the estimated produced by the OLS model I can say that the

“total income” is not correlated with itself in space. Explicitly, the SAR model is indicating that the income in one household does not affect the income in a neighbor household, while the lack of spatial autocorrelation in the error term, detected by the

SEM model, effectively suggest that the error terms are not spatially auto-correlated.

That typically means that there is no unknown underlying variable spatially auto correlated affecting the model. Therefore, the data do not have spatial issues. Then, based on the results produced by the various spatial models implemented, I have substantial evidence to stick to OLS regression models, following Anselin and Lesage

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and Pace approach. Henceforth, I run ordinary least squares (OLS) regressions as detailed in following Table 6-13.

Table 6-13. OLS model comparisons (n=286) Variable OLS 1 OLS 2 OLS 3 OLS 4 OLS 5 Constant 43268.44 31099.14 30914.84 30679.92 31086.35 (0.00) (0.00) (0.00) (0.00) (0.00) Propntfp 13691.50 7713.49 7788.71 7798.98 7761.56 (0.65) (0.80) (0.79) (0.79) (0.79) Yearsedu -99.49 (0.86) -32.77 (0.95) -27.39 (0.95) Dropped Dropped Property 245.21 255.35 253.89 255.87 256.87 (0.07**) (0.06**) (0.03**) (0.02**) (0.02**) Membership 9395.08 9109.53 9107.61 9110.47 9168.67 (0.02**) (0.03**) (0.03**) (0.03**) (0.02**) Age -23.46 (0.90) -4.39 (098) Dropped Dropped Dropped Familysize 249.20 106.54 105.45(0.90) 110.53 (0.89) Dropped (0.77) (0.89) D-rivers 0.87 0.79 0.79 0.79 0.78 (0.00***) (0.00***) (0.00***) (0.00***) (0.00***) Droads -0.13 -0.30 -0.30 -0.30 -0.30 (0.38) (0.00***) (0.00***) (0.00***) (0.00***) D-town -0.13 (0.23) Dropped Dropped Dropped Dropped 푅2 0.2728 0.2690 0.2690 0.2690 0.2689 Root MSE 33978 34005 33943 33943 33823 Log-lik -3484.697 -3385.44 -3330.24 -3330.24 -3385.45 AIC 6789.39 6788.88 6786.88 6786.88 6782.90

Notes: P-value statistics are given in parentheses. **p ≤ 0.05 (significant) ;***p ≤ 0.01 (highly significant); s p ≤ 0.10 (suggestive)

For the estimation of our “best fit” OLS model, I started building a model with highly correlated variables, by including one by one variable keeping an eye on individual betas and R2. I dropped the ones who did not make much addition to the R2 and were also not significant. As the R2 is of particular importance for comparing models performance, with all the possible combinations, I shortlisted four models (i.e.,

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OLS, OLS2, OLS3, and OLS4) as my first best candidate models. In the OLS models, the R2 was coming out ranging from 0.272 to 0.268, which might be considered “decent” values for a social science field parameter. To pick the best model, I additionally calculated the root mean square error (RMSE), which is a standard measure of accuracy based on errors. RMSE indicates the absolute fit of the model to the data, that is, how close the data points are to the models predicted values. I also calculated AIC

(Akaike Information Criterion) and Bayesian Information Criterion (AIC and BIC, respectively). Based on these measures, the best candidate model selected for this first study was the model OLS 4.

In some contexts, a high R-squared is not relevant. For example, when the interest is in the relationship between variables, not in prediction, the R-square value is less important. Thus, this study seeks to estimate the effects of proportion of NTFP income, memberships in communities, age, years of schooling, among others, on total income, by assessing the relationship between those variables and the total income. No one would expect that only these limited set of factors explains a high percentage of the variation in total household income, as net income is affected by many other factors.

Thus, an R-squared in the range of 0.27 to 0.26 seems reasonable. Then, we compare the RMSE “choice” with the AIC (Akaike Information Criterion) and BIC (Bayesian information criterion) parameters.

As the models are not nested, RMSE is another good measure of how accurately the model predicts the response and is the most important criterion for fit if the main purpose of the model is prediction. In any case, lower values of RMSE indicate better fit.

The literature is quite vague about how to assess and select the best fit model, so the

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line of action I took in this study based on an integration of (Anselin 1988b; Lesage

1999b) approaches was to produce the likelihoods and compare the coefficients, including R-squared and likelihoods observing the extent those coefficients are similar or dissimilar in each model estimated. Furthermore, the best measure of model fit depends on the study objectives, and more than one are often useful. Based on the measures below, our best candidate models so far is the OLS 5 model.

Table 6-14. Measures of fit Methods OLS OLS 2 OLS 3 OLS 4 OLS 5

푅2 0.2728 0.2690 0.2690 0.2690 0.2689 Root MSE 33978 34005 33943 33883 33823 Log-lik -3484.69 -3385.44 -3385.44 -3385.44 -3385.45 AIC 6789.39 6788.88 6786.88 6784.88 6782.90 BIC 6789.39 6821.78 6816.12 6810.47 6804.83

Turning now to the diagnostic plots to examine residuals, fitted values, Cook’s distance, and leverage to assess revealing unexplained patterns in the data that shall contribute to the analysis improving the inference. So, after determining the “best fit model,” I run built-in diagnostic plots for linear regression analysis in R (see Figure 6-7).

Figure 6-7. Diagnostic plots for linear regression analysis in R.

It can be seen from the Residuals vs. Fitted graph that residuals appears do not meet the regression assumptions very well. This plot also helps to detect

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influential outliers in linear regression analysis. Normal Q-Q plot reveals that points blast off upward from the left to right of the straight dashed line indicating the presence of outliers in the dataset. Looking at the Scale-Location plot, heteroscedasticity is apparent, since points seems not equally spread in the horizontal line. Looking at the outlier’s values at the upper left of the Leverage plot, those appear inside the red dashed line, called Cook’s distance. Generally, when observations are outside of the

Cook’s distance, the observations are influential to the regression results and exclude those might alter the results or improve the model. As illustrated in the residuals vs.

Leverage plot, the residuals do not appear to have higher Cook’s distance, meaning exclude those cases will not alter the model much. The plots reveal the influential cases as #81, 77, and 105. Inspecting those cases in the dataset to identify which municipality they represent, I found out that it was Tomé-Açu, just the town that stand apart from others of the study site because it presents a more mature agroforestry production system comprising mosaics of diverse land uses (i.e., agriculture, fruits, forest products, cattle ranching, and hardwood) linked to national and global chains. Such market connectivity has been favorable to higher income in that location, which could be singled out by the extreme values for total income.

In such a case, Robust Regression is advisable in any case in which OLS regression is applied. The robust specification also yields better accuracies over OLS and is resourceful when no compelling reasons to exclude outliers from the data exists since these outliers are not measurement errors, but extreme values. To be in the safe side, I further implemented robust regression, but as expected, not a substantial difference in the parameters was found. Hence, I kept the results from the OLS model.

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The thesis does not engage with the impact of “robustification” on the estimates or regression diagnostics, which are beyond the scope of this thesis. Then, due to practical constraints, I will focus on the analysis and discussion of the standard “best fit" OLS 5 model results.

Statistical Regression Models Results

The results obtained from the OLS 5 regression model, as shown in Table 15, show that the overall magnitude’s inferences of the high significant variables presented no large discrepancies between the coefficients of the least squares and the alternative spatial models’ variants. It is apparent from the Tables previously presented that all models indicate virtually the same variables as the most significant, namely “property,”

“membership in co-ops,” “distance to rivers” and “distance to roads.” The OLS 5 model, specifically, produces regression coefficients positive for β1 parameters of the

“proportion of NTFP” variable (+7761, p>0.05) with an insignificant p-value, meaning that the NTFP proportional income has any bearing whatsoever in the household net income. The coefficients of the variable “property” exhibit a statistically significant effect on the outcome variable, given the p-values 0.02**<0.05. At the 95% confidence level, a p-value lower than the 0.05 threshold would hint that the amount of years living in the same property contributes significantly to household income. Interpreting the estimates supplied by the OLS 5 model, it can be inferred that for each added year residing in the same property the farmer might raise his/her profit in about 256 BRL.

The “membership in co-ops” dummy variable is the most robust variable of all explanatory variables of the models estimated in this study. The probability measures t- value and p-value produced for this variable indicate positive high significance

(0.02**<0.05) in all five OLS models implemented, as well as across the previous spatial

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models estimated before. In this case, the variable bears very small p-values meaning they are likely substantial to income improvement. Interpreting the coefficients results, it can be inferred that small farmers that are members of local cooperatives are likely to have an average 9395,08 BRL more annual income that those producers who are not members of collective forest management organization. Likewise, it can be implied that member of cooperatives engaged in the commercialization of NTFP might make more income on average than non-members.

There was also a highly significant positive relationship between “Total Income” and

“distance from the nearest river” variable given its p-value (0.00***<0.05) for all OLS models estimations, but with a lower magnitude of 0.78. From this value, it can be stated that for each additional meter close to the river, the small producer can make an extra income of 0.78 cents. The influence of “distance from the nearest road” which imply more accessibility to markets was also assessed, and came out highly significant, although with an also a very low and negative coefficient of -0.30 (0.00***<0.05).

Increasing distances from nearest roads represent constraints on accessibility, which influences the farmer’s household’s income.

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CHAPTER 7 DISCUSSION

Statistical Regression

The results of the econometric regression models indicate that the data appears not have the problem of spatial autocorrelation and in the absence of spatial autocorrelation, the OLS regression model estimates are accurate. Thus, it can be said that the total income is not correlated with itself in space. Explicitly, the SAR model seems to suggest that the income in one household does not affect the income in a nearby household, while the lack of spatial autocorrelation in the error term, as detected by the SEM model, suggests that the error terms are not spatially autocorrelated as well. This typically means that no unknown underlying variable is spatially autocorrelated and affects the model. Therefore, based on the results produced by the various estimation methods implemented in this study showing the absence of spatial issues in the data, I have strong evidence to stick to OLS regression models, under both

Anselin and Lesage and Pace approaches.

Key findings based on the OLS regression are that the coefficients on the exploratory variables “memberships in co-ops,” “years on property,” “distance to near rivers” and “distance to near roads” are positive and significant, suggesting that households are likely to make more income when a unit value in these significant explanatory variables increases, ceteris paribus. This result supports our central hypothesis that households in which small producers are engaged in the cooperatives tied to the partnerships between cosmetic companies are likely to make more income and that income derived from NTFP extraction seems does not have a substantial effect in the household’s gross total income.

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Exploring the extent of NTFP income generation contribution and other socioeconomic factors influencing small farmer’s decision making and livelihoods is crucial in designing any development and conservation initiative. Carefully selected scholars' arguments and key informant interviews, containing opinions, knowledge, and feelings about the topic, support the results above from a qualitative standpoint, as detailed in the thematic discussion sessions below.

Multifunctional Production

In the Amazon, rural communities have been characterized by inadequate social capital, low human capital, and high transaction cost due to the story of boom-and-bust economic cycles. Development policies and institutional assistance have also failed to enhance welfare (Futemma, Castro, and Brondizio 2016). In the meantime, residents are embedded in distinct regional contexts and geographies and are also subject to various external forces, since globalization, as the advanced global integration of dispersed activities, affords a wider set of new activities pathways for agriculture, livelihoods, resource use, and environmental conservation (Zimmerer 2007).

The subsistence practices adopted by these forest dwellers are affected natural resources availability, quality and proximity, cultural, historical and institutional conditions, labor opportunity, literacy levels, and market connection (Zeidemann 2012).

Those factors also affect forest-based and non-forest-based household income and the paucity of this knowledge, when carrying out and managing public policies intended to preserve forested areas can jeopardize their ultimate targets to safeguard the forest.

The diversification in small-scale rural production is an essential element in this discussion because some have interpreted it as a business and domestic practice which deals with a hybrid model where residents combine a mix of livelihood ‘strategies to

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diversify their sources of income, either own or collective, to take advantage of more than one economic opportunity ensuring a minimal level of social well-being to the household level (Guedes et al. 2012b; Makishi 2015b). Diversification has been the then the best choice for sustenance found in such communities, which face a variety of risks ranging from floods and droughts (Morsello et al. 2014; Perz et al. 2015) to the wide price fluctuations that characterize underdeveloped markets (Morsello et al. 2014) and other prevailing structural restraints to rural areas(Guedes et al. 2012b). It directly relates welfare to the capacity to benefit small producers, or household, in the form of income (Makishi 2015a; Reardon et al. 2009). Moreover, income is a crucial variable often used in studies of diversification of the rural production, but other benefits, as subsistence, could be still examined (Makishi 2015a).

The sustainable insertion of rural production invokes interest in a large variety of aspects of great interest to development policies such as environmental changes, poverty reduction, food and nutritional security, establishing manpower in rural economic activities in the countryside, efficiency of public spending in public subsidies, satisfactory economic relations, and the conservation of biodiversity and traditional way of life (Makishi 2015a).

Multifunctionality or diversification of production is seen as a land use strategy for conservation and sustainable use of biodiversity, because it enhances the environmental services of floodplain forests and their biodiversity rather than degrade the natural resources (Pinedo-Vasquez et al. 2011). Some suggest that the diversification of rural production serves as an alternative not only to poverty in the countryside but also as an opportunity for income stabilization. From this viewpoint, the

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matters would not be in increasing the income of rural populations, but in supporting a well-being level which could be reached from various sources of income. This understanding has gained adepts, and diversification strategies for rural production have grown into a target of policies in many developing countries (Makishi 2015a;

Reardon et al. 2009).

In addition, rural family production has found opportunities in market sectors where scale is not necessarily a competitive determining factor, as it is the case of the production of organic goods, socio-biodiversity products and other natural inputs derived from agroforestry systems. In such cases, the integration of rural family production by industrial sectors becomes strategic especially when a “plantation” does not represent a viable option (Makishi 2015a), as it is the case of NTFP found in the

Amazon rainforest which constitutes particular niche markets. From this perspective, the insertion of diversified rural production denotes a rich research agenda dedicated to studies on new mechanisms in agroindustry systems and tropical conservation.

The environmental preservation agenda has also been directed to the assumption that the economic exploitation of NTFP can provide an alternative to the generation of work and income in the forest communities, valuing and preserving biodiversity. Advocates of the non-timber forest products capacity to conciliate the dual goal of providing welfare for small farmers and preserving the Amazon forests motivated the Brazilian government to request studies exploring the benefits of

NTFP linked to economic and industrial development. These studies subsidized the implementation of a number of public policies and work programs with the purpose to

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broaden the knowledge and stimulate the use of Amazonian forest products in the cosmetic productive chain and other sectors.

For instance, the structuring project named “Forest Based Cosmetics of the

Amazon” (Cosméticos de Base Florestal da Amazonia), executed by the Brazilian

Service in Support of Micro and Small Companies (Sebrae), was developed with the purpose of developing normative, technological and market knowledge about the sustainable and productive business opportunities associated with the forest-based and non-timber forest-based cosmetics chain. The project sought to mapping and disseminate knowledge about industrial innovation processes to the quality of raw material and use of equipment that improve the quality of the products at a low cost, boosting local cosmetic production of small and medium-sized enterprises (SMEs) of the Amazon region. The “Cosmetic Valley”, a global leading center for resources in perfumery and cosmetics implemented in 1970s in France, inspired this project. The pole was implemented as a collaborative territorial arrangement of small cosmetic industries in the south of the country. Stimulated by public policies, it involved the promotion of technological development and regional innovation, which consolidated the

French cosmetic industry growth as a competitive cluster, gaining the world market control of these sector (Sebrae, n.d).

Another policy initiative was the National Socio-Biodiversity Product Chain

(PNPSB), a policy launched in 2009 bringing together efforts of the federal government, representatives of civil society and the private sector concerning the conservation and sustainable use of biodiversity inputs providing an alternative income for rural communities, through access to credit policies, technical assistance and rural extension,

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markets and marketing instruments and the minimum price guarantee policy (Ministério do Meio Ambiente et al. 2009). The widespread idea is that the exploitation of NTFP represents an alternative for the generation of work and income among rural communities, serving at the same time as a mechanism to fix manpower in the rural areas, preserving biodiversity, while also promoting socioeconomic inclusion. It is also believed that the preservation of biodiversity is strongly linked to the generation and maintenance of levels of social well-being in the form of income and subsistence in extractive or agroextractivist communities. In Amazonian communities, the welfare levels are usually linked to activities for subsistence and trade in local markets, including production and extraction of traditional foods, such as cassava flour, açai, animal husbandry, fishing and hunting. The structuring of the so-called socio- biodiversity chain then seeks to take into account these diversified livelihood strategies

(Makishi 2015a). These socio-biodiversity chains have then attracted the attention of the processing industry, interested in exploring biodiversity inputs and meeting the growing demand, notably in developed countries, for functional and exotic natural products valued by environmental preservation principles and fairer trade relations.

The four municipalities that compose the research site of this study, namely

Anajás, Breves, Igarape-Miri, and Tomé-Açu, are situated in the northeast of the Pará state. These cities have local particularities associated with their connections to the market and the maturity level of these relations and networks. But despite some specificities the rural areas of the four cities, especially the smallholder livelihoods, have dealing for more the 40 decades with comparable issues on poverty level, land rights, resource dependence, lacking basic needs, rural credit, essential infrastructure,

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technological assistance, and other socioeconomic limited conditions, suggesting the propagated sustainable development, if improved, has not being internalized, above all the spatially isolated residents living in rural and remote areas.

The history of regional occupation and colonization phases has been thought of as a key factor to understand the contemporary dynamics of place’s land use and cover change, so next I will briefly present a few lines concerning the municipalities’ profile.

Tomé-Açu

Figure 7-1 Location of the municipality of Tomé-Açu-PA, highlighting the infrastructure

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The city of Tomé-Açu is situated in the Northeastern Meso-region of Pará

(Eastern Amazon), 250 km away from Belém, capital of Pará state. Broadly, its soils are classified as Dystrophic Yellow Latosol. In the American classification, latosol is referred to as “Oxisols” (yellow and red-yellow latosols), a soil poor in quality (fertility and nutrients) and rich in iron and aluminum, common in tropical areas with high rainfall. The representative vegetation is the Low Plateau Dense Forest, fairly modified throughout the years, with secondary jungle patches or capoeiras. The most important hydrological and natural patrimony is the Acará-Mirim River and its tributaries.

In the past two decades, the Human Development Index (HDI) in Tomé-Açu has shown a gradual increase from 0.347 (1991) and 0.438 (2000) to 0.586 (2010), although the town still faces rural poverty, limited roads in some areas, lack of basic sanitation, inadequate public health services, disconnected markets, among other deficiencies and limitations common to Amazonian cities. The dynamics of its social, economic and cultural life revolves around agriculture, one of the most important sources of income for the local population. Two main groups of smallholder farmers make up the Tomé-Açu local population: (1) Japanese immigrants (Nikkei community) and their descendants and (2) Small farmers or peasant colonos families. Although the history of Tomé-Açu is mostly associated with Japanese immigration and its ethnic identity, this group today is a minority in the town, being the small farmers the majority nowadays. The city also houses several traditional communities, including indigenous peoples (i.e., Tembé people), Afro-Brazilian descendants, known as quilombolas communities, and riverine populations (E. S. Brondizio et al. 2009; Futemma, Castro, and Brondizio 2016).

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Generally, Tomé-Açu is recognized for its profitable agriculture strategy focused on the supplying of raw inputs to produce fruit pulp and seed oil, which are exported to other parts of Brazil and overseas (e.g., USA and Japan). Connections with external markets are not new. Between the mid-1940s and mid-1980s, the town was a leading black pepper global producer. Significant profits from the trade of pepper in the national and international markets afforded to the cooperative the condition to re-invest in its own development (Piekielek 2010). Since the 1980s, the "black gold" fields gave room to Agroforestry Systems (Brondizio 2012; Futemma, Castro, and Brondizio

2016;Homma 2004).

Agroforestry systems combine the intercropping of food, perennial and annual crops with tree species (Simmons et al. 2002). This land use system intends to economically take advantage of the land every month of the year, as well as recover forests and degraded areas and its ecological functions. Trees, for instance, are intercropped to cope with erosion and restore soil fertility (Nobre and Nobre 2019).

Besides managing herbaceous, arboreal, and permanent agricultural areas, some SAFs also integrating animals in the same unit, resulting in a diversified management system practiced along the entire year, reducing monoculture plantations risks (Piekielek 2010).

The agricultural cooperative, the Cooperativa Agrícola Mista de Tomé-Açu

(Tomé-Açu Mixed Agricultural Cooperative - CAMTA), created by a small community of

Japanese immigrants in Tomé-Açu, is considered a successful case of agroforestry, cooperativism and sustainable farming in the Amazon. Created about 80 years ago,

CAMTA and its producer’s members have been fruitful in overcoming successive

“booms and busts” cycles and ecological changes, including plant diseases and

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plagues. They have developed along the way a variety of strategies to buffer against these negative changes. Known as SAFTA (Agroforestry System of Tomé-Açu), the

SAF of Tomé-Açu was designed by Nikkei family farmers and it is considered unique in the Amazon region. According to Homma (2018), the small producers of Tomé-Açu exhibit a "mimicry" with the Japanese producers who arrived in 1929. As such, they have adopted on a reduced scale the activities developed by Japanese farmers and their descendants (Japanese Brazilians).

Significant profits from the trade of pepper in the national and international markets afforded to the cooperative the condition to re-invest in its own development and so feedback it to the community. To achieve global markets, Nikkei farmers used technology to increase productivity and compliance with global sanitary and commercial regulations. Today, members of the cooperative work in the processing of a variety of fruit pulp, including açai, passionfruit, cocoa almond, cupuaçu and its by- products for industrial supplying, including perfume, cosmetic and pharmaceutical industries (Homma 2011). Recently, they also engaged with the introduction of oil palm plantations in collaboration with large companies such as Agropalma (oil palm monoculture), Biopalma (dendê monoculture) in addition to the partnerships with cosmetic companies like Natura and Beraca (cosmetic, pharmaceutical and food/dietary products). The fruits processed by the cooperative come either from the traditional crop extractivism or through SAFs (Ipea 2016). Based on an entrepreneurial logic or mindset, the production is processed locally within the community and oriented to national and international markets. Both, the expansion of oil palm (Elaeis guineensis) cultivation in large-scale (i.e., agribusiness systems) and small-scale (i.e., contract-

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farming systems) has thrived with strong governmental support (Futemma, Castro, and

Brondizio 2016; Homma 2018). With such an intensive approach, they expanded production and revenues. To handle this growing farming arrangement, Nikkei farmers oftentimes hire rural peasants. Later, some workers leave the Nikkei properties to produce by their own after learning agroforestry techniques; others although continue working in the Japanese descendants’ farmers, still reach financial conditions to endeavor their own small production based in local “experiments” (Futemma, Castro, and Brondizio 2016).

The cosmetic company Natura, together with partners such as Embrapa

(Brazilian Company for Agricultural Research) and Finep (Financier of Studies and

Projects - a Brazilian public company promoting science, technology, and innovation), implemented a pilot project in 2007 that tested the replacement of palm monoculture by an agroforestry system (SAF). They cultivated palm oil associated with other plant species and obtained significantly higher production of palm oil than in the traditional monoculture system. One more benefit highlighted by the company was the environmental benefits brought about with the natural barrier against pests created by the diversity of plant species. Since then, Natura has supported the expansion of agroforestry systems in Tomé-Açu. The company asserts that SAFs reconcile small farmer’s livelihoods with the provision of Ecosystem Services (ES). ES include the furnishing of timber, food, fiber, fuel, new biodiversity input products, biological regulation, nutrient cycling, climate and air quality, ecosystem regulation of diseases, regulation of fires, cultural services, among other services (Millennium Ecosystem

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Assessment 2005a). Besides the more efficient production of palm oil, the adoption of

SAFs still allows carbon sequestration and avoids deforestation (Natura 2016).

Due to the success of the SAFs in Tomé-Açu, other collective organizations have arisen in the town and neighbor’s cities. An important example is the Associação dos

Produtores e Produtoras Rurais da Agricultura Familiar do Município de Tomé-Açu

(Apprafamta). The cooperative is situated in an area where the native predominant forest is the Lowland Ombrophilous Dense Forest, and its extension specifically comprises 67 ha of SAF, 75 ha for natural regeneration and 152 ha of native forest

(FGV 2014). By way of illustration, a partnership between small farmers affiliated with the Cooperative with Beraca has helped small farmers to benefit from income generated with the sale of cupuaçu (Theobroma grandiflorum) fruits and oilseeds cultivated in organic farming, using an agroforestry production system, in addition to fruit pulp, meeting the demand and standards of industries located in São Paulo willing to buy organically certified products (Beraca 2008). The sale fruit pulp turned an additional product to the sale of black pepper, until then the major product cultivated in the town

(Beraca 2008). It is interesting to note that somehow in addition of being a point of support for the processing and marketing of forest products, the cooperative also play a role in shaping a diversified production through more sustainable agricultural practices, as well as fostering local development through training offered by the companies’ partners (Homma 2018).

Recently, in 2018, an agroindustry for fruit processing was inaugurated in the

Cooperative. The plant was built through agreements with governmental actors focused on implementing projects in regions that greatly need to improve the quality of life,

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encouraging local family farming. Most of the families living in the city live on family agriculture, and the action, as reported in the press, seeks to improve access to technology, technical assistance, and marketing networks, so that more value can be added to local production. The “agro-factory” has a capacity for 20 people each shift, pulping the fruits and freezing it in plastic packages of 1kg, ready for the sale. Cocoa, cupuaçu, acerola and passion fruit pulp are the main products for this line. The cooperative was fully regularized with the support of government agents and partner companies. Part of the production should go to local markets and school feeding programs (Pará Rural 2018).

The agroforestry arrangement of Tomé-Açu is considered unique to the Amazon region. Despite the SAFs in Tomé-Açu, the other municipalities of the study site have similar levels of poverty, indicating that the wealth generated is not being internalized in the municipalities (Futemma, Castro, and Brondizio 2016; Homma 2018).

Despite the prominence of SAFTAs in Tomé-Açu, today its land cover and use is a mosaic of agricultural production systems comprising forest fragments in different successional stages besides pastures; less sustainable land-use practices such as slash-and-burn subsistence farming (cassava); logging; reduced livestock; pepper and palm monoculture plantations; diverse types of SAFTAs. Close to the former Japanese settlements is practiced modern agriculture, with farmers organized in associations, adopting agricultural inputs and SAFs. Near the urban zones and also in more remote spaces, with difficult access or closer to river streams is observed the traditional cultivation of subsistence carried out by riverine communities. Fields with monoculture production undertaken by large companies happen closer to the roads. Remnants

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forested areas are spatially distributed throughout the town extension irregularly, but with a greater density in the southern part of the city (Braga 2017; Homma et al. 2018).

Among the leading products are cassava roots, palm oil, lumber (i.e., mahogany trees, standing cattle and coconut.) (Piekielek 2010). Although most of the inhabitants practice small agriculture, there is no absolute poverty, since government transfers (e.g., Bolsa

Família; Bolsa Verde; Seguro Defeso; pensions) have complemented the income from farming activities, increasing the total income of the households (Homma et al. 2018).

Igarapé-Miri

Figure 7-2. Location of the municipality of Igarapé-Miri, highlighting the infrastructure

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The city was formed in the banks of the igarapé of the same name, where there was a national hardwoods factory marketed in Belém. Portuguese families who dominated the timber market have enriched. In 1821, they excavated a navigation channel to facilitate trade because the natural route had obstructed. This new navigation channel was influential for the expansion of the region. The soils of the town are formed predominantly by oxisols. Little areas remain with the primitive forest cover. Currently, the city is mostly covered by secondary forests, interspersed with agricultural crops. In the lowland areas, there is typical vegetation of hydrophilic species

(water), broad-leaved (broadleaf), interspersed with palm trees, especially açaí. The major river of Igarapé-Miri is the Meruú River (Fapespa 2016).

From the eighteenth century onwards the communities of Igarapé-Miri had their primary source of sustenance linked to the extraction of NTFP and timber. From the

1980s, with the decrease of the local sugarcane production, the impact on fisheries caused by the construction of the Tucurui hydroelectric dam, and the spread of firms extracting palm heart, imperiling the açai production, many changes occurred in the city. Several peasants’ farmers decided to migrate to neighbor areas.

Community leaders then persuaded a group to come back to the city to start an alternative production strategy, supported by an Italian church, an NGO and the local university and local government allies. So, they instructed those farmers’ agroforestry practices for açai management and prepared them to trade Açaí abroad (Siqueira and

Brondizio 2014).

Over the past years, the Brazilian Amazon the açaí fruit (Euterpe oleracea) agroforestry economy has stood out, shifting from regional staple food to a national and

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international food. Recently, IBGE released data from the Municipal Agricultural Survey, which indicates that the value of Açaí production exceeded R$ 5 billion in 2017, almost a third of the value of the national production of coffee beans. Despite generating millions of “reais” yearly, the açai economy has not created the supposed local socioeconomic development and small farmer‘s welfare, mainly due to the lack of local transformation industries (Siqueira and Brondizio 2014).

Concerning these partnerships, the literature has shown that certain types of communities are more propitious to succeed in market-oriented initiatives than others, but the specific factors that influence the success or failure of those market incentives needs more in-depth empirical investigation. With the currently expansion of primary processing within the communities, the earnings of the small producers involved in the cooperativist strategy might increase. We speculate that companies also save on labor work costs, since all work performed by the family farmers within the cooperatives is

"informal," linked to the "entrepreneurial" character of each producer, and the leading company accrues any extra labor cost related to the processing step. The same cannot be said when the processing work is all done by a specialized firm since because in general these companies labor force is formal, and all associated extra labor costs are naturally passed on to the chain lead company through the total costs paid for the service provided. We speculate in the same direction concern transportation costs. The extra costs assumed by the lead company for accessing the natural products in remote areas are passed on to some actor along the chain. The transportation costs may also be offset by tangible and intangible image and reputational capital gains acquired by industries and retailers who control the market. These players use compelling narratives

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associating their business and portfolio image with the sustainable natural products extracted directly from the exotic and “fantastic” Amazonian biodiversity, from

“traditional” local communities.

Breves (Furo do Gil)

Figure 7-3. Location of the municipality of Breves, highlighting the infrastructure

Breves holds a total of ten rural settlements under the Agroextractive Settlement

Project (Projeto de Assentamento Agro-Extrativista - PAE) model, being: (1) PAE Ilha

Mututi (326 residents, created in 2010); (2) PAE Ilha Aranai (159 residents, created in

2010); (3) PAE Ilha Ituquara (197 residents, created in 2009); (4) PAE Ilha Limăo (355 residents, created in 2009); (5) PAE Ilha Buiussu (123 residents, created in 2009); (6)

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PAE Ilha dos Macacos (1043 residents, created in 2008); (7) PAE Ilha Aturiá (140 residents, created in 2008); (8) PAE Ilha Pracaxi (250 residents, created in 2009); (9)

PAE Ilha Santo Amaro II (255 residents, created in 2009); and (10) PAE Ilha Japichaua

(221 residents, 2010), totaling 3,069 smallholdings. Implemented by INCRA, the

National Institute for Colonization and Agrarian Reform, these settlements are similar the Extractive Reserve (ER), also called Resex in Brazil. The federal government holds the land tenure, and settlers are granted access to land on a provisional basis via Use

Concession Contract (Contrato de Concessão de Uso) to live in the area. They may still obtain the titles of the domain, instruments that transfer the rural property to the beneficiary, after the provision period. According to Law 8.629/93 that creates the PAE, this settlement model is intended for economic and sustainable extractive activities in resource-rich forested areas. The size of rural properties varies giving the city. In

Breves a unitary area of 1 (one) fiscal module is equivalent to 70 hectares, classified as smallholdings (see http://www.incra.gov.br/assentamentoscriacao). Regarding aptitude for agriculture the soil of the territory of Breves and considered regular the good with medium to high fertility, flat topography and gently undulated throughout the municipality, with flood risks in some areas.

Marajó’s APA occupies a large part of the municipality’s territory. Among the infrastructure works planned for the region, a waterway is planned as part of Plano

Nacional de Logistica 2025. However, there is no forecast of other works of great impact, such as gas, pipeline, railroad, hydropower plan or new roads. There is also no requirement for active mineral research or activity in operation. There is a hypothesis that the area shelters the greater aquatic biodiversity of igapó flooded forest (i.e.,

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blackwater-flooded forests) and várzea forest (i.e., whitewater-flooded forest), which is little known, so the area is an extremely high priority for conservation. Among the activities practiced in the municipality, is the sustainable use of natural resources, but there is the impact of buffalo cattle that needs to be monitored. There are traditional extractive populations in the region. The area near the Ituquara RESEX, there has been the predatory removal of wood, besides the illegal extraction and sale of palm heart and predatory fishing. It is estimated that approximately 250 families are involved in oilseeds and fruit collection activities in Breves (Makishi et al. 2016).

Anajás

Figure 7-4. Location of the municipality of Anajás, highlighting the infrastructure

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Most part of the territory of Anajás is part of the APA of the Marajó Archipelago.

Other conservation areas are also nearby, such as Mapuá Resex1, Terra Grande

Pracuuba Resex, and INCRA Settlements, PAE Baixo Anajás I (147 residents, create in

2009); PAE Baixo Anajás II (91 residents, create in 2009); Japichaua, Ilha dos

Macacos, PAE Bom Samaritano, PAE Ilha Charapucu, PAE Ilha do Corre, PAE Ilha

Purure. Companies of the cosmetic sector, in general, have signed partnerships with communities living in or near areas of Extractive Reserves (RESEX) or Sustainable

Development Reserves (RDS), when the plans for the use and management of natural resources allow local communities who live within the protected area to commercialize non-timber forest products.

Private Company Beraca

Beraca Sabará Chemicals and Ingredients S.A is a Brazilian company that provides technologies and natural materials for different industries including the cosmetics. Besides the headquarter in Sao Paulo, the corporation has other five units in

Brazil, one in France and two in the United States. Beraca, originally a family enterprise, passed through many changes since its foundation in 1956. In 2001, Beraca acquired the microenterprise “Brasmazon” founded by professors from the University of Para.

Brasmazon was a start-up business in the Technology-Based Companies Incubation

Program of the University of Pará (PIEBT) (Programa de Incubação de Empresas de

Base Tecnológica da Universidade Federal do Pará (PIEBT), a pioneering initiative

1 “Extractive reserves” seeks to reconcile the protection and conservation of forests with their sustainable economic use for the benefit of local populations. However, in the long run, the viability of the Resex may require that income from the sale of NTFP be complemented by other revenue transfer mechanisms to compensate the forest stewards for the environmental and social benefits they help to conserve and produce (Hall 2002).

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supporting the establishment of technology-based companies focused on the economic potential of the Amazonian biodiversity (Miguel 2007). This merger has become a significant turning point in the company’s history. From that acquisition on, Beraca defined sustainability as one of its primary business, developing the technology of extraction and processing of natural, organic ingredients for cosmetics industries

(Boechat and Almeida 2015). In 2015, the Switzerland based supplier of specialty chemicals Clariant, one of the world’s preeminent chemical industries, acquired 30% share of the company. Today, Beraca, of the Sabará Group and Clariant, is specialized in the supplying of raw materials for the cosmetics, pharmaceutical, and personal hygiene industries. The company leads the global supplier market of innovative natural ingredients obtained from the Amazon rainforest. One illustration of the innovatory products Beraca provides is the newly created silicone made from tucumã fruit. The

Tucumã Butter is a 100% natural ingredient for skin care products, alternative to man- made chemical compounds. Beraca claims that the Tucumã Butter2 is also socially sustainable. Still, according to the company, collecting tucumã fruit helps provide added income for over 90 Brazilian small farmers’ families.

Beraca funded an Impact Assessment study in areas where it maintains partnerships with extractive communities, to measure the impacts of the company’s intervention on the communities involved. The assessment was developed by the

2 See the links below for more details: http://biofuelsdigest.com/nuudigest/2018/04/10/brazils-beraca-to-launch-silicone-alternative-made-from- tucuma-fruit/ http://www.clariant.com/pt/Corporate/News/2015/01/Clariant-to-establish-strategic-alliance-with-Beraca- getting-30-of-its-shares-of-Health-Personal-Ca

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University of Sao Paulo (USP) and Columbia University. Professor Joao Paulo Veiga, from USP, who’s coordinated the study, explain that the activities occur in areas of extractive reserves and sustainable development (RESEX and RDS), besides settlement projects (SPs) designed to support family farming and private areas. The research assessment specifically was carried out mainly in private areas, in the vicinities of public forested areas, where families owned land, although without the final title, but with some level of property right defined or in the process of regularization.

According to the first version of the impact assessment study, the company-community partnerships between Beraca and local communities has resulted in a positive impact on the communities, with a "substitution effect" in the composition of the family farm income besides positive environmental externalities, especially in more remote communities. “In the communities of the Marajo area, for example, is was observed that when the families have a more significant income opportunity with oilseed collection, they are likely to stop cutting wood activities,” notes Professor Joao Paulo Veiga,

Professor and Researcher, which coordinated a research in the area (Personal communication, 2017). He recalls that during the field work it was found that some families were living in a dramatic situation of semi-slavery, for contracting excessive debts with landowners where they collect palm hearts.

In this type of community, the impact of the fruits and oily seeds harvesting is high because the activity can replace the sale of palm hearts extracted from Açaí of their income source activities, freeing the small producers from the slavery as well as reducing the wild hunting. Although the income generated is relatively small compared to what the producers can actually earn with other activities at their disposal, the collection of NTFPs produces a significant impact especially when poverty is extreme and liquidity is critical for subsistence (Personal communication, 2017).

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As to the cases when communities live close to urban centers, with a much more varied range of opportunities, the incentive to collect oilseeds is apparently much smaller. The data shows the income extractive activities is number 5 in importance, so it does not go to resolve the problem of income sufficiency in rural areas. But it does provide a safety net. The study concludes that the NTFP production, specifically oilseeds collection, replaces the wood cutting in the localities studied. It also indicates that collecting forest products might be an interesting activity, but in general, it does not generate any significant change concerning income and well-being for the families involved in the activity (Boechat and Almeida 2015). In practical terms, if taken into consideration what the company does and what local impact it brings to the communities one can see positive aspects. However, from a more comprehensive standpoint, comprising sustainable local development the current evidences suggest an insufficient impact, especially regarding well-being, since the income does not present a significant change.

Other Experiences

The Brazilian multinational cosmetic industry Natura was one of the pioneer companies to undertake partnerships with rural communities in the Amazon linked to the cosmetic and perfumery sector. Natura’s early agreement was signed 19 years ago, in the year 2000, with the Iratapuru community, at Amapá state, North part of the

Amazon region. The company organized the first partnership with the contribution of a partner firm liable for acquiring the raw materials directly from the associations.

However, the arrangement did not work well in practice. To overcome the difficulties found in the initial agreement, Natura developed its current model of business relationship with local communities prioritizing the supply coming from family farming

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and extractive community cooperatives rather than processing firms (Personal communication, 2017).

To operationalize the partnerships, the company had to create specific areas composed of professionals with an appropriate profile to deal with the demands and realities of the company-community partnerships. Today the relationship and supplying of socio-biodiversity inputs are managed by a specific internal area responsible for the commercial and institutional relationship between the company and the supplier communities. These changes are all so recent that until 2000, for example, the company did not have a specific area dedicated to the purchasing of natural forest inputs. A general procurement area was liable for the negotiation with the communities.

In mid-2005 and 2006, the company created the area of management and relationship with communities but focusing restrictedly on the institutional relationship rather than on the commercial relationship with the supplier communities. The area dedicated to biodiversity was established in 2012, to oversee the relationship between Natura and communities, formed by professionals who came from NGOs and government agencies and have already had experience with the relationship with extractive communities, as observed the company’s manager. Only in 2014, Natura took another step towards its new organizational model, merging the areas of biodiversity and relationship in one, forming the Socio-Biodiversity Relations and Supply Management, whose primary responsibility is to define the strategy for supplying biodiversity inputs (Company manager, Personal communication, 2017).

The company also decided not to acquire a specific area or to maintain a partnership with a large farmer or even another company, prioritizing business directly

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with the extractive communities. Today Natura work with 33 communities that supply forest inputs in Brazil, representing roughly 2,500 families engaged in the supplying of various raw forest ingredients to the company. He adds, however, that in "some" specific cases, partner companies process some products for Natura because "it is not feasible to verticalize upright everything” (Company manager, Personal communication,

2017). With this strategy, the company sought to focus on the business and the relationship between Natura and the supplier communities. In this model, the relationship is direct with Natura holding, and not with its institute or a partner firm. "In this model, Natura is doing business with the community, and as in all commercial relationship, the business has to be good for both sides," stress the company manager

(Personal communication, 2017).

Throughout almost 20 years of partnership with extractive communities in

Amazonia, Natura has gained experience in the relationship with supplier communities.

For the company manager, this relationship is a commercial relationship rather than a partnership per se, as is common in the literature about the company-community partnerships. He states there are many difficulties and challenges associated with the partnerships, but the company has accumulated knowledge.

We learned that we should always prioritize the direct contact and relationship. That was a valuable lesson learned for Natura. At first, we believed this relationship could be outsourced, but it did not work well in practice.

Recalls the company manager, complementing that currently Natura negotiates directly with the communities, and as in all commercial relation, “the transaction has to be positive for both parties,” says. He also accounts that a facet that usually is thoroughly discussed with the family farmers is the production demand-expectation. In

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this regard, the company specifies a demand anticipated for a three-year period, which might fluctuate. In the manager’s words:

A finished product may or may not pick up a satisfactory acceptance in the retail market, and if the shoppers do not like or do not buy the product, its demand will naturally fall. The company, in this case, will not buy a natural product without a true demand, which otherwise it would create a paternalistic relationship, etails the company manager (Personal communication, 2017).

These agreements hold many challenges concerning the necessary assistance the supplier communities need to thrive. There is, for example, an idea that seems to be unanimous among the companies of the cosmetic sector: the dependence of communities should be avoided. So, today, most companies seem to be aware of the importance of do not create a paternalistic arrangement. Thus, in general, most companies of the cosmetic sector have sought to establish a commercial relation far from a condescending, paternalistic approach.

Leading companies like Natura, Beraca, and L'oreal, for instance, have made investments in the communities as a form of incentive for the partnerships by providing training programs for capacity building, and also some necessary machinery to boost cooperative's productivity, so that local producers can be processing raw materials within the villages, thus selling a value-added product. In some cases, partners companies still have helped in the improvement of the regional infrastructure, likewise, to make the trade viable.

Effect of Partnerships on Communities’ Welfare

Evaluating the impact of these partnerships on communities is still a challenge.

Natura, for example, has adopted the social progress indicators as a measuring of progress in the communities involved. For Natura's manager, trying to measure the

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results of these business interventions is not an easy task. "There is always an increase in income because many of the species acquired serve as a complement to the income of the families. For example, in some regions, the Açaí is the primary income. However,

Açaí is only produced in the second half of the year, then in the first semester, the communities work with the production of andibora, murumuru, ucuuba and other species of interest, so they have another income-generating alternative comprising the entire year,” explains.

Income Average

The commercialization of forest products marginally increases the income of the communities, but its character is intrinsically supplementary and seasonal. In some cases, though it may represent the main income of some places. For example, in

Igarapé-Miri the collection of Açaí is the primary income source. However, the Açaí only produces in the second half of the year and in the first semester the communities have the alternative to work with the production of andiroba, murumuru, ucuuba and other species of interest if they engage in the forest products market. In this way, the commercialization of NTFP assumes an essential role of complementing the income at a time when it is difficult for the producers having another income-generating alternative that does not harm the environment. To optimize the profits of the producers, some companies say they work with the concept of “basket of articles” to foment communities to diversify production. This way, as the manager of Natura explains, even if a company stops selling a particular product, whenever the community solely produces a single crop, the company is likely to end the commercial relationship since it would no longer have reasons to continue the agreement without completing the demand. However, if the community brings in over one forest produce, then it has more chances for a lasting

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business relation with this market. In rare or specific cases though companies maintain partnerships with a community that provides a unique product (Mauro Costa, Natura

Eco-Relations manager, Personal communication, 2017).

Intensive Agroforestry Systems

Professor Joao Paulo Veiga, from the University of Sao Paulo, whose has been doing research in the area and coordinated the data collection used in this study, recognize that changes in the socioeconomic profile of the small producers occur more substantially to species incorporated into intensive agroforestry systems, like the Açaí, for example. In these agroforestry arrangements, he notes that an intensive market- oriented activity replaces the subsistence practices, generating more clear transformations on income and well-being.

Price Negotiation

Natura works with the concept of fair trade, when the purchase prices are suggested and negotiated with communities through open spreadsheets. The company has also certifications such as the international accreditation UEBT (Ethical Union for

BioTrade) attesting good practices in the business-forest-community relationship. The manager tell that the company has established, in partnership with UEBT, the verification procedures for its production chains, auditing the company internally. An autonomous third party subsequently audits the chains too and a number of other internal mechanisms for management of the entire process are also in place to make sure the company is in line with the principles of Ethical BioTrade, acting under the legislation, and the company‘ standards and corporate values (Mauro Costa, Natura

Eco-Relations manager, Personal communication, 2017).

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Leonardo Pacheco, Manager of Social and Environmental Management of the

Brazilian Ministry of Environment (MMA), actively participated in the first pilot negotiations to the implementation of a partnerships with extractive communities of the

Medio Jurua region, in his capacity of former reserve manager.

I was able to follow up how the relationship evolved from the very beginning, which had a quite ‘aggressive’ character, and how the relationship has changed over time" recalls. Pacheco recalls that when the company was going to negotiate the price with the communities generally a team of about nine people, including lawyers, pharmacist, biologist, administrator, forestry engineer, all dressing suits, ties, briefcase in hand, laptops.

That was somehow ‘scary’ to the villagers. They got off the airplane directly to negotiate prices with the small farmers, with a very firm, aggressive posture.

Leonardo believes that the company did not seem to understand well the particularities of the commercialization with extractivist, rural communities involving volatile production dynamics (Leonardo Pacheco, Manager of Social and Environmental

Management of the Brazilian Ministry of Environment (MMA), personal communication,

2017).

I remember the first year the company bought five tons. The second year approximately 10 tons. In the third, they demanded 40 tons. But the community only could extract about 20 tons of oil. In the following year, the company surprisingly requested only three tons. The change from 40 to 3 tons was displeasing because the producers worked hard to meet a presumed higher demand that year. By that time there was an exclusive market safeguard, and the cooperative could not trade the production with another buyer, the small farmers had to keep the production anyway.

(Leonardo Pacheco, Manager of Social and Environmental Management of the

Brazilian Ministry of Environment (MMA), personal communication, 2017). The government agent also recalls that, as the RESEX manager, he interceded with the

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company, speaking precisely with the manager that was brother of one of the “creators” of extractive reserves model in Brazil.

I personally called him and said the demanded amount was unacceptable. They ended up buying some more. The company lacked of ‘sympathy’ to the fact that the team did not have anthropologists, sociologists or other social science specialists with a sense of the local dynamics. There was nevertheless a lack of appreciation in general. Today, I believe the company learned how ‘to make’ relationships with communities (Manager of Social and Environmental Management of the Brazilian Ministry of Environment, Personal communication, 2017).

Transport Challenges

Transport in the Amazon is complex and constitutes an important challenge to most commercial activities within the region.

A trip from a village near the Jurua River to the nearest city by boat lasts at least 2 to 3 days. After the town of Caruari to Manaus there are another seven days. Following the trip from Manaus to Belem, it picks up another 7 to 8 days to arrive at the Natura’s Manufacturing Plant, so called “Ecopark,”in Benevides, urban area of Belem. The entire pathway can sum up an average of 45 days, considering that all environmental issues have gone smoothly, which is not always the case since frequently boats are becoming stuck on sandbanks, for example (Company manager, Personal communication, 2017).

Location

Collection or extraction areas are not solely located in remote or isolated districts.

There is no such rule. A large portion of the supplying Natura takes in, for example, coming from villages situated in the northeast area of the state of Para, specifically from the cities of Abaetetuba, Igarapé-Miri, and Cametá situated near Belem with access through the Transamazon highway. Likewise, partnerships with more inaccessible, remote communities, such as those in the Middle Jurua, in the state of Amazonas may occur. In such locations, social movements and organizations are expected to be more active and influential, since this region represents the birthplace of the rubber tappers'

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national movement. So, no single strategy of working in more isolated communities exists. On the contrary, the best opportunity for companies happens when the supplier community is closer. According to the Natura’s manager, what’s matters most is the natural resource availability (Mauro Costa, Natura Eco-Relations manager, Personal communication, 2017).

Amounted Traded

The amount of NTFP commercialized varies conforming to company's demand and application and also communities' supplying capacity. Sometimes, companies have a higher demand for species as Brazil nut, murumuru, andiroba, and cacao. In such cases, several communities provide these inputs to different companies in the sector.

Natura, for example, receives inputs from these species of greatest demand from a group of six communities. Currently, villages in the state of Rondônia, Amapá and Mato

Grosso, for example, supply Brazil nut acquired by Natura (Mauro Costa, Natura Eco-

Relations manager, Personal communication, 2017).

Unfeasible Extractivism of NTFP?

In most cases, companies work with a part of the plant that does not require the suppression of the entire tree, except in the case of the cultivation of aromatics, as is the case of the priprioca, when the producers have to farm and harvest the tree species.

Presently, the species most demanded by the cosmetic industry are primarily the walnuts and fruits (Mauro Costa, Natura Eco-Relations manager, Personal communication, 2017).

Some practitioners believe that plant extractivism becomes unfeasible when the land uses changes.

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The disappearance of the Brazil nut trees in the state of Para, for example, was not brought about by a rise in Brazil nut's demand, but an effect of the spread of livestock in the region points out Mauro Costa, manager of Natura (Personal personal communication, 2017).

For other informants, it is important to emphasize in this regard that far from the romantic idea of extractive activities reinforced by a constellation of different interests, the extractivism itself is essentially a laborious and dangerous work.

Small producers who works with extractivism logically does not want their son or daughter doing the same activity, except in other conditions, earning more, with EPI’s, and more dignity. Actually, I can surely say that what most rural families want to see is their children in school, being able to leave the field, having a better life that they did not have, says Leonardo Pacheco, Manager of Social and Environmental Management of the Brazilian Ministry of Environment (MMA), Personal communication, 2017).

The MMA manager also pointed out that changes in the bureaucracy of some policies created to benefit rural populations are necessary. Some of the supposed benefits represents a burden that a family farmer cannot always take part. An illustration is the process of the RGP, general fishing registry that is the governmental allowance due to a closed fishing season. To get this income supplement, the small producers must present invoices demonstrating they sell fish inputs regularly. However, the traditional fisher does not have such invoices since he trades directly to intermediaries most of the time. Therefore, however adequate a policy looks on paper, they must be applied in practice; otherwise, it will not have adherence in real conditions supporting the small producers, that are artisanal fishers too, ensuring an annual subsistence income through a combination of low impact activities. Forging a dialog between what is decided in Brasilia by policymakers and what takes effect on the ground is thus indispensable if one wishes to push or support the NTFP “solution.” He

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also notes the consequences of the present political and economic Brazil deadlock on programs directed on small producers and extractivism of NTFP.

The budget vanished when there was still much to be worked out to make the activities more viable, such as to organize production chains, develop up the scale, among others. Those steps were expected in a second moment of the socio-biodiversity plans, but they now are discontinued, reveals Leonardo Pacheco, Manager of Social and Environmental Management of the Brazilian Ministry of Environment (MMA), Personal communication, 2017).

Sustainability and Threats

A number of authors have noted that NTFP has encountered serious problems related to production costs, scale of production, limited markets, and harvesting seasonality. Roads expansion and market integration will decisively affect all local communities because it will facilitate invest with lower transportation costs and short-term economic gains, such as ranching and agriculture, notably soybeans (Souza

2014). Concerning the threats that infrastructure construction, deforestation, and environmental degradation represents to the Amazon region, companies in the cosmetics sector, in general, have been in favor of a "new" model of economy linked to the concept of standing forest, this at least in their marketing rhetoric.

Natura intends to be a vector for the strengthening of sustainable directions. With this purpose, the company has been investing in capacity building within the communities and the improvement of local infrastructure, says the Natura’s manager, Mauro Costa, Personal communication, 2017).

Competition with Other Economic Activities

Economic models of deforestation have indicated that deforestation is higher when the area is more accessible, prices are higher for crops and hardwood, off-farm earnings opportunities are smaller, and long distance commerce opportunities are higher (Chomitz et al. 2008; Duchelle 2009; Kaimowitz and Angelsen 1998; Pfaff 1999).

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Revenues linked with different land uses usually affect small producers’ choices about whether to continue standing forest or transform it for agricultural, pasture or other purposes (Chomitz et al. 2008; Duchelle 2009).

Many agree that with adequate support NTFP and other alternative activities could compete with mainstream economic activities. Otavio Nogueira, a consultant of

GIZ, for example, argue that competition among activities is influenced by several factors including lack of adequate technical assistance, low value-added products, in case of timber, barriers with licensing procedures, and a series of other aspects that make competition unfair for socio-biodiversity products.

Many extractivists are in the Amazon two or three generations because of the rubber cycle, and they know well how to work with the forest. However, because of many bottlenecks, they ended up giving up of extractivism and shifting to other more profitable activities, usually most degrading, as extensive cattle raising" (Otavio Nogueira, Technical Advisor to GIZ (Deutsche Gesellschaft für Internationale Zusammenarbeit - German Society for International Cooperation, Personal communication, 2017).

The advisor also adds that it has commonly been assumed that the problem with

NTFPs is their often low value, which he refutes:

The problem is not the value of forest products, but the difficulty that the small farmer face for making multiple uses of the forest, for adding value for their production, connecting with market, and other factors that together influencing the producer's decision to shift to other activities.

He complements that in the case of Acre, at RESEX Chico Mendes, for example, more and more forest extractivists are surrendering to livestock production.

This choice is not wrong. In fact, according to the rules of the extractive reserves, they may produce a certain amount of cattle, because the cattle have more liquidity, and it is easier to produce because there is not much variation, and already has a market with consolidated demand. That weight in the small producer's decision.

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The point is then not the lowest value producers can obtain from NTFPs, but instead it is the low value that they can produce under the current conditions.Otavio Nogueira, Technical Advisor to GIZ (Deutsche Gesellschaft für Internationale Zusammenarbeit - German Society for International Cooperation, Personal communication, 2017).

In this sense, regarding the competition between productive activities, which is commonly observed, the specialist argues that the choice is not so much the profitability of the activity, as it has commonly been addressed, but a consequence of the lack of adequate mechanisms to ensure the sustainability of the activity.

This idea applies even to both extractivism and family agriculture, and for every decision in the rural environment, because, unlike what has been defended in some studies, to a certain extent impregnated by prejudice and distant from rural and forest realities, the producer does not practice more agriculture or extractivism because they are satisfied with the low income of the family grant, or simply wants to take advantage of government programs (Deutsche Gesellschaft für Internationale Zusammenarbeit - German Society for International Cooperation, Personal communication, 2017).

According to him, the small farmers give up or do not have the right opportunities to insist on NTFP production because of adequate technical assistance is lacking, as well as social organization, and the whole constellation of bottlenecks including disconnection with the market, logistical difficulty, poor access to education, water, energy, credit for machinery, among others. In addition, he says that in some cases in the Chico Mendes Reserve, producers working with Brazilnut, rubber, and timber were actually obtaining a higher income than what they got from cattle.

For the Natura manager, the crux of the matter is that interested actors shall employ more ‘energy’ in joint efforts aimed to enhance the socio-biodiversity-based industry in the Amazon. He highlights that today Brazil is considered the world's food basket concerning agribusiness. However, he reminds, the agri-sector received more than 30 years of massive investments, comprising not only financial resources and tax

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incentives, but also research investments focused on developing genetic materials, more efficient production systems, and other measures. Concerning the socio- biodiversity market, policies, information, and technology to support its development still lack. In this perspective, he stresses that the private sector alone cannot change the whole juncture and that the active and efficient involvement of local governments and other interested actors are vital for promoting programs and policies to stimulate and strengthen the biodiversity-based segment of the economy (Mauro Costa, Natura Eco-

Relations manager, Personal communication, 2017).

For Pacheco, the challenge of the NTFP’s competition with more profitable activities calls for a rigorous study.

The unbalanced competition with other economic activities is very patent. Cattle ranching, for example, is highly liquid and its dispute with Brazil nut is unfair, to say the least. Hardly an extractive product will hit the ranching. There is yet a critical need to diversify the production. For a long time the government concentrated efforts particularly on Brazil nut and other production chains individually, when in reality the people do not live off a single product, none of them live (Leonardo Pacheco, Manager of Social and Environmental Management of the Brazilian Ministry of Environment (MMA), personal communication, 2017).

The families who in the past lived off based on one product alone was because they were under the yoke of the exploitation of a large landowner restricting the small producers to engage in other activities. This situation was common in 1920, 1940, when debt peonage, a distinct form of slavery, was more common. Particularly during this period, a single extractive activity was practiced by peasant households (Leonardo

Pacheco, Manager of Social and Environmental Management of the Brazilian Ministry of

Environment (MMA), Personal communication, 2017).

Octavio Nogueira, technical consultant for GIZ (Deutsche Gesellschaft für

Internationale Zusammenarbeit – Sociedade Alemã para a Cooperação Internacional),

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one of the main financing sources of the Amazon Fund, points out that all plants can become domesticated.

There are already some successful experiences in agroforestry systems with some level of domestication. Acai, for example, has been domesticated in plantations that replace and overlap the acai of extractivism. Planting, monoculture or agroforestry systems, and extractivism must go together. There are vast areas producing a head of cattle every five hectares in the Amazon. Monoculture systems planted in consortium systems could compete with cattle. A range of oilseeds, such as andiroba, pataua, murumuru, ucuuba, and others can be introduced for oil production, enabling a more close structure to the forest, so the small producer has gained the conditions of having a continuous plantation of several species throughout the year.

For the smallholder this can be an interesting alternative, since it does not prevent a smaller area from being dedicated to livestock, which can also be done in a consortium with forest species.

If we could create agro-industrial poles in several Amazon regions, facilitating, for example, Anvisa’s health legislation, producers could be able to process forest products that would improve product’s quality with greater added value (Deutsche Gesellschaft für Internationale Zusammenarbeit - German Society for International Cooperation, Personal communication, 2017).

Public Policy

Some studies have indicated any conservation policy can probably fail without acknowledging the role of NTFP to local Amazonian communities. As such, many have advocated collective resource governance in the Amazon to balance local livelihoods and conservation goals (Souza 2014). Brazil has already taken some necessary steps to strengthening the NTFP market, such as the National Socio-Biodiversity Plan, but there is yet much to be done. As Leonardo Pacheco, Manager of Social and

Environmental Management of the Brazilian Ministry of Environment (MMA) notes,

Brazil has been moving from a time when there was no public policy focused on the

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extractive peoples, for a time where several disconnected policies exist and need to be articulated, better integrated.

The purchasing policies for forest products are currently under the responsibility of Conab. Technical assistance is under the management of INCRA. Green Grant is under the responsibility of the Ministry of Environment. Pronatec is under MEC leadership. The crucial question now is how to orchestrate all these efforts, how to integrate these actions, Leonardo Pacheco, Manager of Social and Environmental Management of the Brazilian Ministry of Environment (MMA), personal communication, 2017).

For Otavio Nogueira, Technical Advisor from GIZ, public policies focused on enhancing NTFP chains in the Amazon have had a low implementation in practical terms.

Among the policies that are being articulated, he cites: Planafe, Pronatec Extractivist, Bolsa Verde, a program of income transfer conditioned to environmental conservation with payment for ecosystems services (PES) in a more straightforward way. If the producer living in the area has made a sustainable use, and there was no deforestation and no misuse, the producer is paid, details Otavio Nogueira, Technical Advisor to GIZ - Deutsche Gesellschaft für Internationale Zusammenarbeit - German Society for International Cooperation (Personal communication, 2017).

Companies that trade socio-biodiversity resources appears not to have, for example, any specific added tax benefits or other incentives substantial incentives like the cattle ranching received. In the Manaus Free Zone, the industry might have tax subsidies for electronics, but it does not have a particular incentive for Amazonian socio-biodiversity products, for instance.

Natura's decision to employ socio-biodiversity assets in its products, for example, was a company bet based on a business strategy alone, which demanded a substantial investment not associated to any benefit or governmental incentive, says Mauro Costa, from Natura (Personal communication, 2017).

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Some other initiatives and discussions at the state and national level are nevertheless going on. In Para, for example, the state government has focused on the need to establish a group comprising all companies that work with biodiversity within the state. Moreover, studies addressing the cosmetic production chains have been developed, but up to now, there is nothing concrete to boost NTFP-centered businesses or stimulate a 'green' economy in the Amazon.

In this regard, land tenure reform is also seen as an important tool to curb deforestation rates and improve land management in the region, and the Amazon Fund

(http://www.fundoamazonia.gov.br) has allocated more than 1 billion dollars to enforcing deforestation, titling landholders and regularizing property rights. Even so, the relationship between deforestation and land tenure reform is not straightforward from either an economic or a policy perspective. There is much fear that both state-led and so-called “direct action” efforts to grant property rights may be perpetuating a system of perverse incentives for land grabbing and deforestation in anticipation of titling programs; a land tenure policy where claims to land are legalized ex-post will continue to incentivize deforestation unless major changes are made to titling programs

(Simmons et al. 2010).

Selecting Zones for Extraction or Collection

The first step to develop a forest product is to identify the location where the forest asset occurs. Second is to verify the existence of cooperatives or associations in the area. In general, based on Union for Ethical BioTrade (UEBT) guidelines and verification system, companies evaluate good practices, labor issues, and other criteria to assess if establishing a commercial partnership in a determined community would be feasible or not. When viability is identified, a plan to improve the social organization is

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elaborated. Thus, the primary criterion for the implementation of a commercial partnership is having the natural forest resource in the locality, both the species that the company is currently working and too certain species that the company prospects to explore in the future (Mauro Costa, from Natura, Personal communication, 2017).

Survey of Areas where the Species of Interest Occurs

According to a cosmetic private company, the survey of the locations where the species of interest naturally occur is done mainly through contact with partners such as

NGOs, trade unions, employees, and research in bibliographical surveys, and other secondary sources.

Benefits Sharing

To a certain degree, the corporations involved in partnerships with local communities are following the same model taken up by the pioneering companies like

The Body Shop. The novel aspect is that now the companies must comply with a new legislation addressing biodiversity extraction and benefit sharing. From the business standpoint, the new legal framework of biodiversity production in force, when compared to the previous provisional measures, has brought more legal clarity to the question of benefit sharing.

Companies are currently in the phase of adjustment and implementation of the system created by the Ministry of Environment to expedite the approval process with the CGEN, details Mauro Costa, cosmetic private company, Personal communication, 2017).

Remote Areas

It is not of today that the natural areas of occurrence of NTFP have been suffering from the pressure of development projects. Extensive livestock displaced most of these areas in the Southeastern Para, for example. With the arrival of massive

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infrastructure projects, such as the Grande Carajás mining, and the construction of large hydropower dams, railways, roads and other associated groundwork, areas of extractivism of NTFP were “migrating” to other places, becoming then more concentrated in remote areas where it is still easier to found biodiversity resources than in the areas degraded by urbanization. Moreover, the first partnerships were created in very remote, isolated communities, located far from urban centers. The impact of these partnerships is much more significant in these remote communities, claim Beraca.

In Anajás, for instance, one of the main subsistence activities is the hunting game, notably rodents, and there is a severe food security problem associated with animal protein limitation contrasting with environmental conservation. It is pricey and too far away for the inhabitants going to the towns to buy food, so they typically access animal protein by hunting wild animals such as cotia, tatu, paca, monkey, sloth, and other game. They also cut Açaí heart palm for food. In these further secluded communities, the tradeoff between environmental preservation and food security is more apparent (Professor and researcher, Personal communication, 2017, male).

Some landless families survive in a dramatic semi-enslaved situation after accumulated excessive debts with the large landowners where they collect palm hearts.

In such poor communities, the impact of oilseeds trade is higher because it can replace the palm hearts as an income source, freeing the small farmers from the analogous slavery situation, also reducing wild animals hunting. Thus, although the income generated is relatively small compared to what they could earn with other activities at their disposal, the collection of NTFP produces a significant impact. Here, the net offset is essential. When communities live closer to urban centers the incentive to collect oilseeds is much smaller because they have other economic opportunities.

The study site displays a valuable illustration of how market forces and globalization bring about social, economic and environmental changes. For example,

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the oil of Patauá (Oenocarpus bataua), extracted in a rural village in Anajás from a palm tree native to the Amazon rainforest, which produces edible fruits rich in high quality oil, is used in the manufacture of luxury cosmetic products, sold all over the world, but bearing an effect in local and regional markets.

Diversification

Today, small producers in one season break Brazilnuts, in another grazing, in the sequence, go to the “garimpo,” so in fact, peasant farmers perform a series of activities throughout the year allowing them to make a reasonable income to survive. Some question the usefulness of such an approach.

We need to stop looking at this so-called ‘product basket’ admitting that it can only have forest products. Households may have cattle too. Why not? One can farm, fishing, hunting, all together,” defends. He also reflects that policy will only make a real impact in the lives of poor families when policymakers start to look at their livelihoods with a ‘less conservationist look’ (Leonardo Pacheco, Manager of Social and Environmental Management of the Brazilian Ministry of Environment (MMA), personal communication, 2017).

As discussed throughout the study, one of the most important issues is how to run these partnerships, a topic explored by several surveys over more than 20 years without a conclusive answer yet. This indicates the need for a differentiated look by the government to investments directed to conservation and capacity building of local actors in the Amazon. In the wake of more institutional and financial support, a meaningful counterpart would be the creation of jobs and training for professionals within the region, not only concerning productive forest production, but also training of skilled professionals to work with sustainable development and conservation in Amazonia in leadership positions. The empirical knowledge of local people linked to the professional skill set improvements could potentially represent a qualitative jump, since these people

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could begin already "running" because they are knowledgeable about particularities of the Amazon. Also, the learning curve for local professionals is potentially much smaller than for an outsider. This has also to do with the fact that at the center of this global discussion it is important to create job opportunities for local people, who are never invited to effectively have a voice in these discussions concerning their own destinies. If one intends to implement a biotechnology-based economy which may be interesting given the premises discussed, it is paramount that local universities, the people of the region, be involved without the coward mantle of the meritocracy's excuse. The knowledge generated and accumulated over more than 25 years of the global environmental agenda headed by transnational NGOs and their national strategic arms have a lot to gain if they extend the opportunities for young local students favoring equal conditions in the disputes with young professionals from other regions of the south, notably from the southeast.

Sustainable value chains contributing to socioeconomic development, environmental conservation and mitigation and adaptation to climate change in the

Amazon. As the activities to promote and encourage value chains of sustainable products in the Amazon, such as the production of noble oils and other biodiversity products, partnerships should continue to be the flagship of this sustainable emerging environmental agenda. For these partnerships to achieve their expected goals in the context of the global agenda with a focus on climate change, the public policy needs to be carefully tailored. Here, some believe that the participation of corporations as allies has a preponderant weight because only companies can cope with more harmful investments to the environment facing other economic sectors less aligned with the

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environmental agenda. Since it is the business sector who actually controls the governmental agenda, having these "environmentally friendly" companies alongside at least reinforces the "barricade" necessary to contain the advance of more destructive activities, by defending their business interests in the congressional benches. The key is to integrate their regulatory agendas and bargaining power in the governmental plan directed to protected areas and territorial planning, to foment a favorable conjuncture for the development of bio-products, if that is the new paradigm Brazil has to follow as defended by Nobre and Nobre (2019) more recently, and also Bertha Becker in a number of accounts (see, for example, a collection of her works in As Amazônias De

Bertha K. Becker - Ensaios Sobre Geografia e Sociedade Na Região Amazônica

(Becker 2015; Becker 2009).

Socioeconomic Context

Rural areas in the Amazon have been characterized by low social capital, low human capital, and high transaction cost from a continuum history of “boom-and-bust” economic cycles, successive failures of development programs and inadequate institutional support (Futemma, Castro, and Brondizio 2016). The production of NTFP in areas close to forest ecosystems may indirectly stimulate conservation, which is particularly important in the conservation units surrounding areas since these buffer zones requires alternative land uses. However, small farmers engaged in NTFP for subsistence or market supplying have facing increasing challenges posed by the competition for natural resources.

Their livelihoods are increasingly pressured, on the one hand, by conflicting actions implemented by divergent social actors with stakes in the area and by illegal loggers and local elites due to the unequal power relations (Porro et al. 2015).

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According to previous studies in the same geographical area, company- community partnerships has a positive impact on the local communities, with a

"substitution effect." It is claimed that the sustainable production of NTFPs (oilseeds) replaced the cutting of the wood in the remote communities studied. That is because when families have a higher income opportunity with oilseeds collection, they tend to stop predatory logging to complement their income (Beraca 2016; Makishi 2016; Veiga,

Makishi, Zacareli, et al. 2016).

In this sense, looking strictly for what the company generally does and its local impact, we can see positive aspects bring about by these partnerships. However, looking at the big picture, incorporating the issue of sustainable conservation and local development in a broad term, the impact of the NTFP production might seem very limited in terms of significant change, especially in terms of well-being. In other words, in general, the activity of collecting forest products proves to be an interesting activity, but it does not generate a significant change in terms of income and well-being for the families involved. Where there have been significant changes in the pattern of land uses and socioeconomic profile is the case in which there is the intensification of the production through agroforestry systems, as it is the case of the açaí, for example.

Cooperatives

Cooperatives may be described as an enterprise organized as an association of members with the desire of improving their shared economic interest.

Cooperatives are influential in agricultural or farming sector and engage independent, family producers whose market connections and influence are fragmented. By creating cooperatives, producers pursue to enhance their negotiation position with

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corporate actors. Recently, these institutions have grown into a staple aspect of ‘Fair

Trade’ initiatives supported by western development agencies (Johnston et al. 2005).

L'Oréal, the biggest beauty brand in the world, together with Beraca, the German cooperation agency GIZ, and the NGO Caritas supported the creation of cooperatives in

NTFP supplier communities. They signed in 2012 a contract in Rio +20 to improve extractive communities in Brazil. L’Oréal, in its webpage, underlines that corporate contributions have helped enhancing the quality of life of 50 residents, which has one of the smallest HDI (Human Development Index) in Brazil. The company funded the acquisition of solar panels for the village health center together with partners. During the inauguration of the cooperative, the community pastor emphasized that with solar energy the community can now keep the refrigerator on to conserve the snake antivenom and provide care to people bitten by snakes, which is very common in the village (L’oreal, 2015) 3. River or unpaved road make the access to the village. People and production that need to be transported are carried out by waterways. The transport of oilseeds, collected through the partnership is done by freighter trucks contracted by the company (Boechat and Almeida 2015).

3 L'Oréal global webpage: https://www.lorealparisusa.com/about-loreal-paris/overview.aspx

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CHAPTER 8 CONCLUSION

This thesis addressed the extraction of NTFP in the Amazon, with an emphasis on company-community partnerships linked to the multinational cosmetic industry. The objectives of the study were to critically evaluate (1) how income generated from market-oriented NTFP commercialization impacts small farmers’ livelihoods; and (2) whether membership in a cooperative linked to corporation- community agreements is a factor in improved livelihood.

To this end, I successfully employed a spatial econometric modelling approach using household level data to assess if commercialization of NTFP and participation in partnerships between small farmers and cosmetic companies results in statistically significant increases in overall household incomes. To estimate this relationship a host of conventional and spatial regression models were employed, as OLS, SAR, SEM,

SDM, as well as alternative Bayesian models. This preliminary exploration enabled me to test innovative empirical strategies for specifying a spatial Bayesian model to be refined and implemented during my Ph.D. research.

Many studies have emphasized the importance of accounting for spatial dependence; however, for the case of growing market driven NTFP extraction linked to business incentives and partnerships with multinational cosmetic corporations, few studies have actually tried to explicitly operationalize spatial regression econometric models in empirical studies addressing such emerging niche markets in the Amazon.

LeSage and Pace (2014) note that explanatory variables applied in spatial regression models usually reveal significant spatial dependence. Respectively, they observe that county, census tract, and block group variables measuring age, race,

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income, employment, educational attainment, and other socioeconomic indicators that typically exhibit spatial dependence. This thesis presented a preliminary exploration of these relations in the case of the cosmetic industry and community partnerships and outlined a spatial econometric modeling approach focused on the effect of these business arrangements on the total income at the household level.

This study successfully implemented varied spatial regression models that contributed positively to the initial task of making reliable inferences about the relationships explored, advancing our knowledge on the effects of these market-based strategies on farmer’s livelihoods. I employed a spatial econometric modelling approach using household level data to assess if engagement in NTFP commercialization and membership in corporate-community partnerships results in statistically significant increases in overall household’s incomes. Ordinary least squares regression was used to estimate “total income” and explanatory variables, controlling for socioeconomic characteristics.

Empirical findings will be much scrutinized, but we could draw two conclusions:

(1) NTFP production is a source of additional income for rural households, but it does not have any bearing on the increase on total income. (2) Membership in cooperatives linked to the company-community partnerships is the most robust variable with stable, consistent results along with all models estimated.

The foremost overall conclusion is that membership in cooperatives appears to be influential to increase total income at the household level. The statistically significant results for membership seem to indicate that members engaged in the social

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organizations are more likely to make more income than those who do not engage in the cooperatives.

The aim of this study was to assess if engagement in NTFP commercialization and membership in corporate-community partnerships results in statistically significant increases in overall household’s incomes. One of the more significant findings to emerge from this study is that membership in cooperatives linked to company- community partnerships for the market-oriented production of NTFP appears to be influential to increase total income at the household level. In particular, the statistically significant results for the “membership in cooperative” variable seem to indicate that members engaged in the social organizations are more likely to make more income than those who do not engage in the cooperatives and partnerships with the industry.

This research provides mixed results when it comes to the value of NTFP extraction as a sustainable development alternative. While the econometric results show there is not a statistically significant relationship between engagement in NTFP extraction and total household income, they do suggest that membership in cooperatives tied to corporate-community agreements do have statistically significant effects with increases in total income. Overall, key informants concur, suggesting that extraction of NTFP alone is not adequate to alleviate rural poverty, but instead programs must invest in long term partnerships between cosmetic corporations (i.e.,

Natura, Beraca) and community cooperatives. The role of NTFP in conservation is performed by a series of intertwined actions aimed at increasing the income of the families as to support and influence them to not shift to other more harmful to the environment activities. These actions combine the payment for the sale of the NTFP

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itself, and the promotion of the cooperative businesses, including certification of the production, besides benefit-sharing for the manufactured products commercialized and sometimes the payment for carbon sequestration based on the avoided deforestation on their lands linked to carbon credits market.

The influence of “distance to road” seems to imply more accessibility to markets was also assessed, and came out highly significant, although with an also a very low and negative coefficient of -0.30 (0.00<0.05). Increasing distances from nearest roads represent constraints on accessibility, which influences the farmer’s household’s income since membership in cooperatives appears to be influential for increasing total income at the household level. The significant negative results for “distance to road” suggest that income is more likely to decrease near the roads, possibly because residents have more economic alternatives; it also can be reflex of limited forested areas for exploration of forest products, since areas near roads are typically more degraded, urbanized areas. This outcome also appears to be most likely linked to market limitation access in that area. In turn, the statistically significant positive results for “distance to rivers” seem to indicate that a household’s income is more likely to increase near rivers. A likely explanation is the occurrence of species of interest in "várzea” (i.e., floodplain) ecosystems.

The modeling approach implemented in this study will be used as a point of departure for future simulations and preparation of fieldwork trials, in the same study site and other distinct Amazonian states for comparison purposes. The Acre and

Maranhão states are possible candidates. The former due to the established

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extractivism economy, and the latter because of the notably growing corporate- community partnerships for NTFP extraction linked to brand-named companies.

Rural areas in the Amazon have been characterized by low social capital, low human capital, and high transaction cost, as a consequences of a past of “boom-and- bust” economic cycles, successive failures of development programs, and inadequate institutional support. The municipalities of the study site do not fall outside this rule.

NTFP production linked to cosmetic industry is not a “win-win” solution as it has been promoted for some interest groups. Therefore, it cannot be a solution for environment conservation and development, especially because it does not equally compete with other more profitable activities. The intensification of production seems to not resolve the problem of income insufficiency in rural areas either. But it does provide a safety net for both the most impoverished and vulnerable people, and the forest, in keeping the areas protected as long they have this forestry reminiscences still standing. The activity seems to be more viable in remote areas where biodiversity still exists, opportunities of income are scarce, and cost of opportunities are higher.

Market-based approaches, despite being problematic in some ways, are much keener and similar to community-based conservation, in which some actors organize people to work for collective objectives. This is different than merely receiving a

‘humiliating’ handout payment of a “Bolsa Floresta,” or other form of payment for ecosystems services with the same insignificant monetary range. These partnerships also seem to be positive from the perspective of self-confidence and appreciation.

Furthermore, the cooperatives have had a positive effect on the community in many ways.

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Most of the problems impoverished communities face in the Amazon is that the structure of the institutions and existing policies are not well aligned to let the resident’s benefit from the work they can and know do. All too often, the needs and expectations of local communities are missed or overlooked in the momentum of several policies.

Historically, these policies have been influenced by developmentalism, neoliberalism, and environmentalism agendas and their social constructed ideological discourses, resulting in the perpetuation of poverty and inequality in the region. The preceding also results in the subordination of the local communities formed by native groups, peasants, riverine people, agricultural colonists, urban dwellers, and migrants descendants, whose destinies have been in the hands of powerful ruling social groups, especially international and national economic, political, and academic elites.

These partnerships are essentially a neoliberal and market-based alternative with some known flaws, and exploitative vices. However, as of now, we have seen no other alternative for impoverished people. So, we believe these market-oriented arrangements sometimes can work for the better and improve people’s lives and well- being because at the end of the day, the problem people in the Amazon are facing is urgent, they are poor, and they must feed their families today. If this solution dissatisfies, what would be an alternative?

I conclude with a reflection inspired by a brief conversation I had with Arun

Agrawal (April 2019, personal communication), which seems to synthesize the deadlock poor communities face given the lack of opportunities for livelihood increase. He illustrated it is impossible to live without market, especially today. Every community, every society, even those deemed primitive have had some sort of market-based

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relations and have benefitted from some type of exchange or barter. The critical question then is not whether the market is "good or bad,” but what kind of market can help the most vulnerable or marginalized, and what market does the opposite and only benefits those in power.

Research Contribution

This thesis insights and findings aims to contribute to expand the knowledge base on development and conservation in the Brazilian Amazonia, addressing growing partnerships between local communities and multinational companies with an emphasis on NTFP extraction and supplying of global markets. Findings provided an opportunity to advance the understanding of the context of NTFP markets linked to cosmetic industries in the Amazon, providing insights for policy initiatives intended to enhance these relationships.

Limitations

Data used in this thesis were collected using a self-administered questionnaire written in Portuguese; the study sample presented some absences concerning the name of villages and also lack coordinate points for the villages and households interviewed. Therefore, coordinates were collected from afar, using Google Earth. This measure is not exact, but it serves as a good baseline for our intended research agenda at this step.

Future Research

This multivariate analysis of a household income extends previous research on determinants of income of smallholders producing NTFP in partnership with global cosmetic companies. Future empirical research on NTFP income and partnerships with cosmetic companies is needed to generalize the results covering more places of the

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"various" Amazonias, stretching from Acre, and the agricultural frontier that competes with extractivism; Maranhão, where the extractivism with babassu production is still substantial in some respects and projects with other species are being implemented as well by getting advantage of the poverty condition in many rural areas; the Purus River area, where a lot of Brazil nut and syringe has been produced; and in the Marajo region, where a footprint more focused on the açaí, besides the extractivism in marine reserves are taking place as well. These examples need more investigation to empirically evaluate how the NTFP contributes to the small-farmers income and in which extent the activity is complemented by other activities, such as cattle raising, agriculture, mineral extraction, logging, and other, are still missing. Only with this broad knowledge, without

‘fetishisms,’ would be possible to propose effective public policies, besides assess if the current policies are firing in the right direction.

The exploratory pilot model implemented in this thesis has a potential to facilitate future empirical research, improving our knowledge and understanding on the topic.

Thus, the model is being refined in ongoing research. Sensitive and extensive data collection, such as income, is difficult to obtain and sometimes impractical for fieldwork in a vast area, due tight time and budget. Using the modeling approach specified in this study, further research, less expensive in terms of costs and far more comprehensive in scope, can be done with faster and accurate results taking advantage of spatial and

Bayesian statistics approaches.

The modeling approach implemented in this thesis, focused on examining to which extent and under what conditions the production of NTFP and engagement in

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cooperatives supported by business incentives can work, enhancing local income and conservation, should be refined during my doctorate.

What is the relationship between these NTFP production agreements and tropical deforestation? What is the prospect for these small farmers given the expansion of mega-infrastructure projects in the Amazon? Will these scattered initiatives be enough to reduce deforestation, as well as increase income? I am hoping to be able to answer these questions soon.

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APPENDIX A EXPLORATORY DATA ANALYSIS

NTFP and membership in company-community partnerships with Global Cosmetic Industry in Amazonia

March 2018 - Aghane Antunes

Contents . Non Bayesian SAR, SEM and SDM Models . Load data . Dependent variable - total income . Latitude and Longitude Coordinates . Weight Matrix W . Spatial weights matrices . Exploratory variables matrix . x (all) . OLS model . Heteroscedastic Model set up . Spatial dependence tests . Moran's I-statistic for spatial correlation in residuals . LM error for spatial correlation in residuals . LM sar for spatial correlation in SAR model residuals . SAR model 1 . SEM model 2 . SDM model 3 . SDM W2 model 4 . SDM W4 model 5 . SDM W8 model 6 . Bayesian Models - SAR, SEM and SDM Models . SAR_g model W model 1 . SEM_g model W model 2 . SDM_g model W model 3 . SDM_g model W2 model 4 . SDM_g model W4 model 5 . SDM_g model W8 model 6 . SAR_g model W2 model 7 . SAR_g model W4 model 8 . SAR_g model W6 model 9 . SEM_g model W2 model 10 . SEM_g model W4 model 11 . SEM_g model W8 model 12 . Model Assessment SAR_g, SEM_g and SDM_g (all) . Model Assessment SAR_g and SEM_g Non Bayesian SAR, SEM and SDM Models Load data

load('subset.mat'); % 286 observations [n, k] = size(Subset); Dependent variable - total income

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y=Subset(:,15);

Latitude and Longitude Coordinates long=Subset(:,13); % Longitude Coordinate latt=Subset(:,14); % Latitude Coordinate Weight Matrix W

[W1,W,W3]=xy2cont(latt,long); % contiguity matrix from x-y coordinates ndraw=5000; nomit=2000;

Warning: Duplicate data points have been detected and removed.

Some point indices will not be referenced by the triangulation.

Spatial weights matrices spatial weight matrix (standardized, row-sums = 1)

W2 = make_nnw(long,latt,2); % create W-matrix based on nearest 2 neighbors W4 = make_nnw(long,latt,4); % create W-matrix based on nearest 4 neighbors W8 = make_nnw(long,latt,8); % create W-matrix based on nearest 8 neighbors Exploratory variables matrix x (all) x=[ones(n,1) Subset(:,7:12),Subset(:,16:18)]; % supply a constant term in the first column of X nvar=11; vnames= char('total','constant','propntfp','yearsedu','property','membership','age',... 'familysize','drivers','droads','dtown'); OLS model resols1=ols(y,x);prt(resols1,vnames);

% plot the predicted and residuals plt(resols1);

Ordinary Least-squares Estimates

Dependent Variable = total

R-squared = 0.2728

Rbar-squared = 0.2491 sigma^2 = 1154488951.3176

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Durbin-Watson = 1.9434

Nobs, Nvars = 286, 10

***************************************************************

Variable Coefficient t-statistic t-probability constant 43268.444636 2.760569 0.006157 propntfp 13691.500927 0.441074 0.659505 yearsedu -99.490236 -0.172905 0.862852 property 245.217338 1.795217 0.073713 membership 9395.083829 2.228355 0.026663 age -23.468555 -0.121851 0.903106 familysize 249.200074 0.291198 0.771118 drivers 0.872215 3.912972 0.000115 droads -0.136816 -0.869156 0.385517 dtown -0.135654 -1.199183 0.231485

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Heteroscedastic Model set up info.rval = 4;

Spatial dependence tests Moran's I-statistic for spatial correlation in residuals resmoran = moran(y,x,W); prt(resmoran);

Moran I-test for spatial correlation in residuals

Moran I 0.02159600

Moran I-statistic 1.82495992

Marginal Probability 0.06800708 mean -0.00199774

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standard deviation 0.01292836

LM error for spatial correlation in residuals reslmerror = lmerror(y,x,W); prt(reslmerror);

LM error tests for spatial correlation in residuals

LM value 2.47157783

Marginal Probability 0.11592150 chi(1) .01 value 17.61100000

LM sar for spatial correlation in SAR model residuals reslmsar = lmsar(y,x,W,W); prt(reslmsar);

LM error tests for spatial correlation in SAR model residuals

LM value 0.00003581

Marginal Probability 0.99522555 chi(1) .01 value 6.63500000

SAR model 1 ressar=sar(y,x,W);prt(ressar,vnames);plt(ressar);

Spatial autoregressive Model Estimates

Dependent Variable = total

R-squared = 0.2939

Rbar-squared = 0.2708

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sigma^2 = 1083572726.6701

Nobs, Nvars = 286, 10 log-likelihood = -3282.279

# of iterations = 17 min and max rho = -1.0000, 1.0000 total time in secs = 0.1870 time for x-impacts = 0.1710

# draws x-impacts = 1000

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30

***************************************************************

Variable Coefficient Asymptot t-stat z-probability constant 43319.732612 2.852619 0.004336 propntfp 23241.264529 0.768129 0.442411 yearsedu -1.147907 -0.002054 0.998361 property 266.180918 2.007886 0.044655 membership 11710.307871 2.828989 0.004670 age -43.056438 -0.230636 0.817598 familysize 319.920203 0.385731 0.699696 drivers 0.985470 4.431313 0.000009 droads -0.111998 -0.733229 0.463419 dtown -0.152571 -1.390411 0.164404 rho -0.418955 -2.791588 0.005245

Direct Coefficient t-stat t-prob lower 01 upper 99

206

propntfp 23920.211604 0.797881 0.425601 -56334.370550 99057.921221 yearsedu -3.643452 -0.006391 0.994906 -1690.154981 1457.716288 property 271.433898 2.119091 0.034945 -83.198031 611.402479 membership 11590.779488 2.750917 0.006322 1253.401677 22135.313897 age -45.979166 -0.241661 0.809216 -518.464405 424.116197 familysize 337.951227 0.409144 0.682740 -1651.438591 2581.992668 drivers 0.978545 4.234461 0.000031 0.412805 1.640059 droads -0.124798 -0.786970 0.431951 -0.510328 0.317466 dtown -0.144139 -1.278572 0.202084 -0.459049 0.146736

Indirect Coefficient t-stat t-prob lower 01 upper 99 propntfp -7040.757596 -0.754421 0.451217 -35493.104423 15346.248472 yearsedu -5.018436 -0.030103 0.976006 -511.411046 402.925220 property -78.349143 -1.728911 0.084904 -212.420103 19.695014 membership -3358.769143 -2.017139 0.044615 -8010.643953 170.989973 age 13.125368 0.230010 0.818249 -147.986577 173.609226 familysize -97.310421 -0.396996 0.691667 -812.207560 468.484610 drivers -0.282370 -2.461767 0.014416 -0.626921 0.003626 droads 0.034429 0.713533 0.476098 -0.103240 0.187422 dtown 0.041738 1.142485 0.254208 -0.055373 0.158110

Total Coefficient t-stat t-prob lower 01 upper 99 propntfp 16879.454007 0.793788 0.427977 -41701.749956 69518.366312 yearsedu -8.661888 -0.020899 0.983340 -1200.294429 1036.159041 property 193.084755 2.082423 0.038192 -68.151204 440.091324 membership 8232.010345 2.724290 0.006841 911.134761 16377.563470 age -32.853799 -0.240067 0.810450 -397.464809 314.070630

207

familysize 240.640806 0.402940 0.687294 -1276.979841 1970.837094 drivers 0.696176 4.168474 0.000041 0.293245 1.156692 droads -0.090369 -0.786538 0.432204 -0.409618 0.217715 dtown -0.102401 -1.276430 0.202839 -0.332388 0.099344

SEM model 2 ressem=sem(y,x,W);prt(ressem,vnames);plt(ressem);

Spatial error Model Estimates

Dependent Variable = total

R-squared = 0.2861

Rbar-squared = 0.2628 sigma^2 = 1093771308.1651 log-likelihood = -3283.9414

208

Nobs, Nvars = 286, 10

# iterations = 0 min and max rho = -0.9900, 0.9900 total time in secs = 0.0620 time for optimiz = 0.0320 time for t-stats = 0.0150

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30

***************************************************************

Variable Coefficient Asymptot t-stat z-probability constant 41551.586326 2.708399 0.006761 propntfp 20727.608936 0.685497 0.493030 yearsedu 20.911098 0.037320 0.970230 property 269.233089 2.021559 0.043222 membership 9699.078563 2.283401 0.022407 age -24.520636 -0.131739 0.895191 familysize 301.982098 0.363165 0.716482 drivers 1.042264 4.589604 0.000004 droads -0.122557 -0.776527 0.437438 dtown -0.145023 -1.279455 0.200737 lambda 0.460000 2.466607 0.013640

209

SDM model 3 ressdm=sdm(y,x,W);prt(ressdm,vnames);plt(ressdm);

Spatial Durbin model

Dependent Variable = total

R-squared = 0.3042

Rbar-squared = 0.2573 sigma^2 = 1063555136.7476 log-likelihood = -3279.0581

Nobs, Nvars = 286, 19

# iterations = 17 min and max rho = -1.0000, 1.0000 total time in secs = 0.2500 time for lndet = 0.0160

210

time for t-stats = 0.0150 time for x-impacts = 0.2190

# draws used = 1000

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30

***************************************************************

Variable Coefficient Asymptot t-stat z-probability constant 42044.705433 2.743457 0.006080 propntfp 16680.807373 0.524518 0.599918 yearsedu -34.877405 -0.061991 0.950570 property 296.652401 2.235383 0.025392 membership 10148.101464 2.368231 0.017873 age -63.744735 -0.340878 0.733195 familysize 341.014257 0.410869 0.681169 drivers 1.183580 5.017977 0.000001 droads -0.109165 -0.685368 0.493112 dtown -0.151125 -1.323756 0.185584

W-propntfp -98446.813432 -41.994246 0.000000

W-yearsedu -2697.888396 -0.608531 0.542835

W-property -363.252476 -0.221627 0.824604

W-membership 42145.719009 0.591265 0.554343

W-age 287.032745 0.147523 0.882720

W-familysize 1182.400767 0.184489 0.853630

W-drivers -0.356589 -0.317441 0.750909

W-droads 0.080426 0.110441 0.912060

211

W-dtown -0.158365 -0.368402 0.712573 rho -0.174981 -0.662328 0.507761

Direct Coefficient t-stat t-prob lower 01 upper 99 propntfp 18347.422421 0.587160 0.557559 -66177.539008 99142.713105 yearsedu -28.617028 -0.052878 0.957866 -1418.484320 1358.333111 property 298.480897 2.257383 0.024738 -66.612499 628.881702 membership 10042.065034 2.320390 0.021023 -1157.264442 21340.129963 age -60.915725 -0.317786 0.750879 -608.574976 455.000572 familysize 353.354219 0.429306 0.668024 -1771.779143 2311.567104 drivers 1.188638 4.928788 0.000001 0.540173 1.779695 droads -0.112369 -0.707856 0.479611 -0.477450 0.296433 dtown -0.148479 -1.299665 0.194762 -0.445308 0.126878

Indirect Coefficient t-stat t-prob lower 01 upper 99 propntfp -91265.213755 -4.272691 0.000026 -187586.581032 -44265.057754 yearsedu -2473.518538 -0.541829 0.588358 -19424.841342 8550.843631 property -393.645206 -0.244133 0.807303 -5070.056421 3791.160101 membership 35858.020406 0.521000 0.602770 -146006.981652 243883.302779 age 274.889026 0.148055 0.882404 -4711.750072 5534.371175 familysize 684.646273 0.111958 0.910936 -17647.032086 18433.714791 drivers -0.473617 -0.433872 0.664709 -3.549088 2.750633 droads 0.087419 0.120371 0.904274 -2.161344 2.077212 dtown -0.116879 -0.282428 0.777819 -1.397183 0.928294

Total Coefficient t-stat t-prob lower 01 upper 99

212

propntfp -72917.791334 -2.167439 0.031026 -194035.878750 -756.277496 yearsedu -2502.135566 -0.543344 0.587316 -20158.836573 8305.550080 property -95.164309 -0.059102 0.952912 -4734.699408 4128.711571 membership 45900.085441 0.667408 0.505050 -139241.299712 256911.901001 age 213.973301 0.114276 0.909099 -4636.398829 5556.480694 familysize 1038.000492 0.167490 0.867103 -17354.269229 18834.009780 drivers 0.715021 0.655482 0.512684 -2.397640 4.064979 droads -0.024950 -0.035547 0.971668 -2.317572 1.899912 dtown -0.265358 -0.654381 0.513392 -1.545827 0.761652

SDM W2 model 4 ressdm2=sdm(y,x,W2);prt(ressdm2,vnames); plt(ressdm2);

Spatial Durbin model

213

Dependent Variable = total

R-squared = 0.3064

Rbar-squared = 0.2596 sigma^2 = 1059712838.8651 log-likelihood = -3278.6169

Nobs, Nvars = 286, 19

# iterations = 10 min and max rho = -1.0000, 1.0000 total time in secs = 0.2500 time for x-impacts = 0.2340

# draws used = 1000

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30

***************************************************************

Variable Coefficient Asymptot t-stat z-probability constant 42844.795302 2.763698 0.005715 propntfp 26411.011598 0.769652 0.441506 yearsedu -13.421615 -0.023799 0.981013 property 287.837930 2.168150 0.030147 membership 10837.319221 2.534430 0.011263 age -75.221849 -0.398899 0.689968 familysize 291.706277 0.349875 0.726432 drivers 1.259020 5.207928 0.000000 droads -0.068926 -0.429564 0.667513 dtown -0.172177 -1.499683 0.133697

214

W-propntfp 18477.164355 0.156789 0.875411

W-yearsedu -259.731519 -0.121310 0.903446

W-property -63.794633 -0.105900 0.915662

W-membership 27297.809041 0.744667 0.456473

W-age -28.549147 -0.030857 0.975384

W-familysize 769.390692 0.212602 0.831638

W-drivers -0.927188 -1.092504 0.274612

W-droads -0.449263 -0.764431 0.444610

W-dtown 0.031482 0.094501 0.924711 rho -0.140990 -0.795221 0.426485

Direct Coefficient t-stat t-prob lower 01 upper 99 propntfp 26168.729414 0.760590 0.447528 -63193.756346 117293.445413 yearsedu -22.282524 -0.039638 0.968409 -1425.652323 1477.286385 property 283.926508 2.038410 0.042429 -100.833258 620.516919 membership 10814.352486 2.563580 0.010871 -292.932618 21373.825232 age -74.713980 -0.386608 0.699334 -594.914823 418.878529 familysize 330.550878 0.396937 0.691710 -1673.103673 2586.552220 drivers 1.261950 5.286239 0.000000 0.663786 1.838724 droads -0.076628 -0.467812 0.640275 -0.522227 0.340641 dtown -0.165317 -1.413155 0.158698 -0.477333 0.149701

Indirect Coefficient t-stat t-prob lower 01 upper 99 propntfp 10429.509026 0.098516 0.921591 -270376.112564 291040.493922 yearsedu -330.119252 -0.169666 0.865393 -7000.664540 4239.359052 property -83.197775 -0.152377 0.878997 -1773.071363 1256.426031

215

membership 22794.031577 0.684793 0.494029 -58466.452370 115649.285558 age -13.586972 -0.016417 0.986913 -2728.154830 2026.737486 familysize 694.801403 0.208315 0.835131 -7314.931520 9356.231797 drivers -0.967320 -1.258560 0.209216 -3.048831 1.160753 droads -0.374539 -0.706519 0.480441 -1.898298 0.881259 dtown 0.037718 0.121822 0.903126 -0.748816 0.816360

Total Coefficient t-stat t-prob lower 01 upper 99 propntfp 36598.238440 0.297886 0.766006 -288059.552774 365225.509111 yearsedu -352.401776 -0.172172 0.863424 -6695.507214 4585.618037 property 200.728733 0.366048 0.714600 -1460.050497 1621.166323 membership 33608.384063 1.001731 0.317320 -52998.460748 129442.390250 age -88.300952 -0.102088 0.918758 -2662.055494 1989.337331 familysize 1025.352281 0.306047 0.759791 -7542.776367 9606.809811 drivers 0.294630 0.389020 0.697551 -1.653603 2.299298 droads -0.451168 -0.898095 0.369891 -1.966462 0.667726 dtown -0.127598 -0.427701 0.669191 -0.861044 0.650166

216

SDM W4 model 5 ressdm3=sdm(y,x,W4,info);prt(ressdm3,vnames); plt(ressdm3);

Spatial Durbin model

Dependent Variable = total

R-squared = 0.3070

Rbar-squared = 0.2602 sigma^2 = 1057795919.9380 log-likelihood = -3278.4343

Nobs, Nvars = 286, 19

# iterations = 11 min and max rho = -1.0000, 1.0000 total time in secs = 0.2500 time for x-impacts = 0.2350

217

# draws used = 1000

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30

***************************************************************

Variable Coefficient Asymptot t-stat z-probability constant 43407.128553 2.800379 0.005104 propntfp 26264.800799 0.851918 0.394260 yearsedu -67.528754 -0.119426 0.904938 property 290.262498 2.188734 0.028616 membership 10723.221465 2.515665 0.011881 age -72.258981 -0.382592 0.702022 familysize 268.541989 0.321397 0.747909 drivers 1.227443 5.169270 0.000000 droads -0.079139 -0.491704 0.622929 dtown -0.168739 -1.467824 0.142152

W-propntfp 10262.507427 4.353974 0.000013

W-yearsedu -1602.582677 -0.542470 0.587495

W-property -600.944032 -0.509269 0.610564

W-membership 36573.695796 0.739623 0.459529

W-age 560.732469 0.313280 0.754068

W-familysize 3864.706663 0.731611 0.464406

W-drivers -0.687622 -0.632813 0.526856

W-droads -0.353025 -0.598823 0.549291

W-dtown -0.137501 -0.320987 0.748220 rho -0.239964 -0.900909 0.367637

218

Direct Coefficient t-stat t-prob lower 01 upper 99 propntfp 29176.362886 0.569862 0.569219 -51079.475419 110631.407827 yearsedu 226.715277 0.024426 0.980530 -1606.178444 1396.938906 property 324.665333 0.288899 0.772868 -90.927262 630.960944 membership 12896.838837 0.168559 0.866263 -106.450656 21489.281911 age -56.546110 -0.139904 0.888834 -571.375192 460.540981 familysize 565.825623 0.057754 0.953985 -2001.377215 2721.585806 drivers 1.286261 0.904467 0.366509 0.655710 1.950078 droads -0.058041 -0.151613 0.879599 -0.537836 0.362527 dtown -0.221124 -0.151254 0.879882 -0.504981 0.138776

Indirect Coefficient t-stat t-prob lower 01 upper 99 propntfp 112236.310938 0.034200 0.972742 -32195.728182 40943.979942 yearsedu 21449.634402 0.028955 0.976921 -9307.947320 5048.140175 property 2214.544577 0.024919 0.980137 -3765.570932 2086.353386 membership 222693.881839 0.036425 0.970969 -80778.437078 154475.714052 age 1673.543294 0.057505 0.954183 -3600.117809 5611.483270 familysize 27291.821015 0.034833 0.972237 -8981.724820 16861.195974 drivers 2.837092 0.025052 0.980031 -3.357629 1.815543 droads 0.632910 0.022261 0.982255 -1.686992 1.209097 dtown -3.809725 -0.032587 0.974027 -1.254304 0.868763

Total Coefficient t-stat t-prob lower 01 upper 99 propntfp 141412.673824 0.042565 0.966078 -34477.114694 122265.672811 yearsedu 21676.349680 0.028899 0.976965 -9446.377464 4617.473929

219

property 2539.209910 0.028218 0.977508 -3394.947804 2407.766052 membership 235590.720676 0.038059 0.969667 -72196.067223 163946.324043 age 1616.997184 0.054886 0.956267 -3835.226478 5735.037706 familysize 27857.646638 0.035117 0.972011 -9050.245373 17834.083484 drivers 4.123353 0.035964 0.971336 -2.171530 2.827941 droads 0.574870 0.019977 0.984075 -1.708303 1.087208 dtown -4.030850 -0.034054 0.972858 -1.395830 0.610932

SDM W8 model 6 ressdm4=sdm(y,x,W8,info);prt(ressdm4,vnames); plt(ressdm4);

Spatial Durbin model

Dependent Variable = total

R-squared = 0.3067

220

Rbar-squared = 0.2600 sigma^2 = 1060093486.4265 log-likelihood = -3278.6265

Nobs, Nvars = 286, 19

# iterations = 14 min and max rho = -1.0000, 1.0000 total time in secs = 0.2500 time for x-impacts = 0.2340

# draws used = 1000

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30

***************************************************************

Variable Coefficient Asymptot t-stat z-probability constant 44376.696179 2.874563 0.004046 propntfp 18415.244584 0.592249 0.553684 yearsedu -111.585180 -0.197062 0.843779 property 300.137458 2.235827 0.025363 membership 10710.984803 2.476449 0.013270 age -93.889265 -0.494882 0.620683 familysize 370.988792 0.444227 0.656879 drivers 1.193163 5.094364 0.000000 droads -0.092202 -0.577045 0.563909 dtown -0.164762 -1.444238 0.148672

W-propntfp -204047.602737 -146.653335 0.000000

W-yearsedu -6170.796290 -1.000365 0.317134

221

W-property -355.838967 -0.170261 0.864805

W-membership 6199.336076 0.049045 0.960884

W-age -1130.294253 -0.466373 0.640948

W-familysize 13022.247897 1.142095 0.253414

W-drivers 0.387478 0.139248 0.889254

W-droads -0.185709 -0.137433 0.890689

W-dtown 0.172613 0.187792 0.851040 rho -0.258991 -0.657125 0.511100

Direct Coefficient t-stat t-prob lower 01 upper 99 propntfp -354206078060337600.000000 -0.038066 0.969662 -56148760372747920.000000 92792.424719 yearsedu -7918695857870416.000000 -0.042739 0.965939 -20513963769722.461000 22218644.516463 property 1602629131084991.000000 0.044862 0.964249 -8800784211.541386 1466680499.007444 membership -171062559626819490.000000 -0.032153 0.974373 -23941496866912.766000 8599176520.528118 age -1707968085462045.500000 -0.019725 0.984276 -3866132571610.439500 524.467919 familysize 30732698838253352.000000 0.043520 0.965318 -1974.290951 875910193360510.750000 drivers 2284938277047.505900 0.039158 0.968791 0.477322 10989337206.403679 droads 1415124526539.145500 0.028221 0.977506 -6026242.783329 1523284.043351 dtown 2489291015.348452 0.000201 0.999839 -805753.687027 8337548.632391

Indirect Coefficient t-stat t-prob lower 01 upper 99 propntfp -59247032150926655000.000000 -0.038066 0.969662 -9391841690734219300.000000 -95022.629297 yearsedu -1324537485787316000.000000 -0.042739 0.965939 -3431311731489294.000000 3716457680.272436 property 268067166366372770.000000 0.044862 0.964249 -1472081936925.514600 245327353597.085300 membership -28613142455176315000.000000 -0.032153 0.974373 -4004625340264680.000000 1438357243825.803500

222

age -285686910360916960.000000 -0.019725 0.984276 -646676878136467.250000 83827.704762 familysize 5140570162221938700.000000 0.043520 0.965318 -20772.274437 146510979347112190.000000 drivers 382195055218612.810000 0.039158 0.968791 -44.914050 1838154834285.127700 droads 236703810337066.440000 0.028221 0.977506 -1007992287.086231 254795340.922326 dtown 416376550162.176390 0.000201 0.999839 -134776065.715994 1394597803.640951

Total Coefficient t-stat t-prob lower 01 upper 99 propntfp -59601238228987003000.000000 -0.038066 0.969662 -9447990451106967600.000000 -71829.396783 yearsedu -1332456181645186000.000000 -0.042739 0.965939 -3451825695259016.000000 3738676324.788899 property 269669795497457570.000000 0.044862 0.964249 -1480882721137.056200 246794034096.092650 membership -28784205014803132000.000000 -0.032153 0.974373 -4028566837131591.500000 1446956420346.331800 age -287394878446378820.000000 -0.019725 0.984276 -650543010708077.750000 84297.653217 familysize 5171302861060192300.000000 0.043520 0.965318 -20789.507151 147386889540472700.000000 drivers 384479993495659.500000 0.039158 0.968791 -44.341676 1849144171491.531200 droads 238118934863605.560000 0.028221 0.977506 -1014018529.869560 256318624.965678 dtown 418865841177.612180 0.000201 0.999839 -135581819.403021 1402935352.273342

223

Bayesian Models - SAR, SEM and SDM Models SAR_g model W model 1 resultsarg=sar_g(y,x,W,ndraw,nomit,info);prt(resultsarg,vnames);plt(resultsarg);

Bayesian spatial autoregressive model

Heteroscedastic model

Dependent Variable = total

R-squared = 0.1990

Rbar-squared = 0.1729 mean of sige draws = 243714102.5746 sige, epe/(n-k) = 1271658808.3859 r-value = 4

Nobs, Nvars = 286, 10 ndraws,nomit = 5000, 2000

224

total time in secs = 10.9080 time for lndet = 0.0150 time for sampling = 10.1900

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30 min and max rho = -1.0000, 1.0000

***************************************************************

Posterior Estimates

Variable Coefficient Std Deviation p-level constant 39522.321389 8697.003315 0.000000 propntfp 21675.642368 17230.320167 0.106333 yearsedu 447.644444 314.965113 0.075333 property 135.947804 73.322814 0.032000 membership 6130.566181 2298.641917 0.003333 age 66.610180 103.011994 0.252333 familysize 603.194229 442.017969 0.089667 drivers 0.315494 0.145254 0.013333 droads -0.003286 0.080326 0.490000 dtown -0.185200 0.059269 0.000667 rho -0.119804 0.101375 0.111000

Direct lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -19512.564808 -10758.404656 21687.973155 55792.877403 71096.231498 yearsedu -365.484360 -168.575087 447.897865 1088.213365 1248.703136 property -48.231355 -6.378936 136.023967 281.035964 329.464500

225

membership 249.299727 1641.818799 6134.158250 10795.517042 12164.697185 age -192.175476 -133.379158 66.650251 267.150058 343.372287 familysize -529.949821 -248.836148 603.540455 1451.682128 1788.872824 drivers -0.076234 0.037876 0.315675 0.598825 0.692528 droads -0.222758 -0.156212 -0.003289 0.152678 0.191786 dtown -0.333143 -0.302198 -0.185307 -0.068979 -0.039829

Indirect lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -11833.961269 -8927.376420 -2149.484532 2532.134983 5382.629586 yearsedu -224.271635 -173.781698 -44.080245 46.284289 95.959027 property -60.244224 -46.736561 -13.373763 11.107105 24.971795 membership -2457.417758 -1921.104357 -616.628273 514.547645 1053.378103 age -63.237552 -43.671056 -6.797350 19.696315 33.569791 familysize -302.712500 -235.933361 -60.403745 56.626931 135.984847 drivers -0.127057 -0.103799 -0.031446 0.026209 0.058627 droads -0.034660 -0.022386 0.000313 0.023711 0.034486 dtown -0.033102 -0.014887 0.018616 0.055299 0.066954

Total lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -17799.454564 -9213.438234 19538.488624 50912.161128 66187.687910 yearsedu -338.673503 -150.185089 403.817620 995.093570 1225.109822 property -40.995124 -5.642586 122.650204 260.226178 316.873812 membership 210.928445 1440.842927 5517.529977 9799.049014 11212.742706 age -187.672410 -120.331022 59.852901 239.983023 312.695177 familysize -465.180706 -218.758768 543.136710 1334.439407 1635.877090 drivers -0.065618 0.034361 0.284229 0.553857 0.647667

226

droads -0.194753 -0.142177 -0.002976 0.135584 0.174075 dtown -0.308389 -0.277395 -0.166691 -0.063538 -0.033216

SEM_g model W model 2 resultsemg=sem_g(y,x,W,ndraw,nomit,info);prt(resultsemg,vnames);plt(resultsemg);

Bayesian spatial error model

Heteroscedastic version

Dependent Variable = total

R-squared = 0.2510

Rbar-squared = 0.2266 mean of sige draws = 614033169.1909 r-value = 4

Nobs, Nvars = 286, 10

227

ndraws,nomit = 5000, 2000 total time in secs = 9.3930 time for sampling = 9.3300

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30 metropolis-hastings used for rho min and max lambda = -1.0000, 1.0000

***************************************************************

Posterior Estimates

Variable Coefficient Std Deviation p-level constant 41299.717317 13073.763816 0.001000 propntfp 20171.066674 23622.529577 0.190000 yearsedu 195.492398 475.797266 0.333667 property 194.843968 104.595297 0.030333 membership 6518.791126 3397.947999 0.029333 age 33.412683 153.259704 0.402000 familysize 436.295338 648.332692 0.250000 drivers 0.556010 0.224130 0.005333 droads -0.071315 0.117173 0.271667 dtown -0.160630 0.086456 0.034333 lambda 0.088173 0.299782 0.353667

228

SDM_g model W model 3 resultsdmg=sdm_g(y,x,W,ndraw,nomit,info);prt(resultsdmg,vnames);plt(resultsdmg);

Bayesian Spatial Durbin model

Heteroscedastic model

Dependent Variable = total

R-squared = -0.1609 mean of sige draws = 244735038.9349 sige, epe/(n-k) = 1905156788.5392 r-value = 4

Nobs, Nvars = 286, 20 ndraws,nomit = 5000, 2000 total time in secs = 11.4560 time for sampling = 10.6420

229

time for effects = 0.7510

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30 min and max rho= -1.0000, 1.0000

***************************************************************

Variable Coefficient Std Deviation p-level constant 40178.634885 9044.447260 0.000000 propntfp 20479.113181 18057.511173 0.126667 yearsedu 380.195869 331.074977 0.130333 property 136.567587 73.008555 0.026333 membership 5740.667411 2353.784845 0.009000 age 62.958266 100.353679 0.265000 familysize 551.399892 437.332995 0.108333 drivers 0.317882 0.162196 0.022000 droads -0.000286 0.087297 0.498000 dtown -0.185660 0.064233 0.001000

W-propntfp -113334.051164 120027.438261 0.171333

W-yearsedu -2825.163198 2919.384146 0.166000

W-property -771.454251 1024.655383 0.227000

W-membership 46026.225288 48377.462907 0.167667

W-age 1302.498698 1266.027297 0.154333

W-familysize -2448.681846 3877.710977 0.258333

W-drivers 0.238849 0.717596 0.376000

W-droads -0.085743 0.474402 0.421667

W-dtown -0.081571 0.262375 0.376667

230

rho -0.457825 0.291019 0.069667

Direct lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -25413.331152 -13173.866891 21818.303653 57035.641918 66349.648443 yearsedu -454.881355 -255.629119 412.640002 1048.547416 1300.571971 property -39.554811 4.467266 145.644058 298.699967 347.194590 membership -1195.294185 347.679962 5300.502062 10116.960016 11491.827895 age -200.432449 -146.488229 49.609184 248.865948 309.563877 familysize -549.956447 -272.435823 581.300940 1450.272396 1748.900332 drivers -0.117535 0.006448 0.317510 0.643739 0.765004 droads -0.225649 -0.179697 0.000566 0.173705 0.223194 dtown -0.346701 -0.311632 -0.186116 -0.051371 -0.019751

Indirect lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -376718.115869 -287356.690757 -89457.349287 75414.135254 137494.411885 yearsedu -10383.883821 -7090.716913 -2198.117253 1925.780022 3703.661935 property -3424.763155 -2262.985551 -608.100439 800.557826 1611.801414 membership -75138.161016 -38233.620192 32460.706864 116841.904870 153895.797533 age -1713.996561 -910.712034 941.060783 3115.993694 4503.572717 familysize -10947.774449 -8052.439913 -1955.728478 3783.102792 5933.559228 drivers -1.349353 -0.927366 0.085993 1.219535 1.793395 droads -1.053763 -0.783627 -0.063802 0.671442 0.930166 dtown -0.660265 -0.432390 -0.008144 0.386991 0.553404

Total lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -374353.772256 -275471.015433 -67639.045633 107192.044999 178330.563570

231

yearsedu -9848.391925 -6850.268888 -1785.477251 2396.540531 4083.190182 property -3292.427801 -2111.443936 -462.456381 942.623315 1760.280560 membership -69412.419669 -32332.548857 37761.208926 122445.758775 160217.553123 age -1595.348203 -850.402530 990.669967 3166.061121 4508.688856 familysize -10373.895677 -7534.410036 -1374.427539 4343.436184 6733.511083 drivers -0.944680 -0.587326 0.403503 1.531574 2.137819 droads -0.974542 -0.756778 -0.063236 0.624328 0.914456 dtown -0.838182 -0.601818 -0.194260 0.182807 0.362769

SDM_g model W2 model 4 resultsdmg2=sdm_g(y,x,W2,ndraw,nomit,info);prt(resultsdmg2,vnames);plt(resultsdmg2);

Bayesian Spatial Durbin model

Heteroscedastic model

232

Dependent Variable = total

R-squared = -0.1598 mean of sige draws = 243933617.3502 sige, epe/(n-k) = 1903327128.1247 r-value = 4

Nobs, Nvars = 286, 20 ndraws,nomit = 5000, 2000 total time in secs = 11.4550 time for sampling = 10.6580 time for effects = 0.7340

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30 min and max rho= -1.0000, 1.0000

***************************************************************

Variable Coefficient Std Deviation p-level constant 40990.284465 9372.354075 0.000000 propntfp 25081.364011 18533.693602 0.083333 yearsedu 384.016761 332.785630 0.126333 property 130.534714 73.816430 0.040000 membership 6092.940410 2321.738454 0.005333 age 60.496403 104.080145 0.282667 familysize 560.649387 445.895876 0.102667 drivers 0.349070 0.172010 0.022000 droads 0.032465 0.083577 0.339667 dtown -0.204720 0.063232 0.001000

233

W-propntfp 16331.246091 63156.114121 0.394000

W-yearsedu -143.586067 1259.305236 0.455667

W-property -8.923917 371.386596 0.476333

W-membership 19032.257411 21923.684538 0.181000

W-age 144.089873 592.054458 0.391000

W-familysize -343.217007 1929.748799 0.430333

W-drivers -0.209182 0.486139 0.327000

W-droads -0.559479 0.350869 0.051000

W-dtown 0.142710 0.209123 0.245000 rho -0.365688 0.188037 0.027000

Direct lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -21044.964965 -10988.275112 25012.668413 62046.724213 74673.772368 yearsedu -503.083691 -273.364644 389.321767 1023.379737 1261.355786 property -63.016333 -15.130472 131.772550 278.050152 324.947796 membership -414.216032 1213.178617 5835.192355 10341.200620 11700.676158 age -222.534625 -145.408311 58.480795 265.237255 331.395921 familysize -566.415539 -329.304073 570.564085 1465.250595 1721.770440 drivers -0.103390 0.007925 0.355028 0.690947 0.799435 droads -0.178834 -0.129863 0.041699 0.209805 0.257236 dtown -0.368235 -0.331774 -0.208556 -0.078521 -0.043121

Indirect lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -120482.055950 -87224.696180 5932.622645 101580.220912 127556.643254 yearsedu -3015.577391 -2083.587007 -209.096635 1770.635278 2440.211173 property -768.359607 -587.539067 -39.343697 553.250514 780.171736

234

membership -33566.144643 -20466.370626 12940.026034 47489.504455 61505.484098 age -1206.640556 -832.524844 91.471120 957.500538 1221.266065 familysize -4356.500344 -3280.175124 -404.082019 2606.354697 3583.677856 drivers -1.335813 -1.008937 -0.248915 0.544222 0.822147 droads -1.208677 -0.984995 -0.434609 0.088956 0.235038 dtown -0.274682 -0.160068 0.161949 0.496340 0.613588

Total lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -110433.462669 -77185.101462 30945.291059 144980.759181 180253.291167 yearsedu -2733.229235 -1742.890576 180.225132 2153.976381 2995.740536 property -629.757734 -441.477437 92.428854 673.909022 904.726983 membership -25792.920078 -15204.466330 18775.218388 52300.984932 66450.175141 age -1094.328949 -789.058196 149.951915 1027.330104 1323.231916 familysize -3756.336227 -2741.991577 166.482066 3109.805369 4239.523119 drivers -0.916548 -0.600425 0.106113 0.841804 1.148611 droads -1.136182 -0.915914 -0.392911 0.080993 0.227591 dtown -0.452536 -0.347307 -0.046607 0.262394 0.376864

235

SDM_g model W4 model 5 resultsdmg4=sdm_g(y,x,W4,ndraw,nomit,info);prt(resultsdmg4,vnames);plt(resultsdmg4);

Bayesian Spatial Durbin model

Heteroscedastic model

Dependent Variable = total

R-squared = -0.1564 mean of sige draws = 241983943.5425 sige, epe/(n-k) = 1897718647.1415 r-value = 4

Nobs, Nvars = 286, 20 ndraws,nomit = 5000, 2000 total time in secs = 11.4710 time for sampling = 10.6590

236

time for effects = 0.7500

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30 min and max rho= -1.0000, 1.0000

***************************************************************

Variable Coefficient Std Deviation p-level constant 42024.386197 9308.292094 0.000000 propntfp 29162.869572 18639.385610 0.059333 yearsedu 364.913162 344.921090 0.145333 property 130.403530 74.078310 0.038333 membership 5949.274669 2385.350984 0.007000 age 56.176724 103.902607 0.283000 familysize 486.857643 449.635584 0.139333 drivers 0.352996 0.160123 0.012000 droads 0.032606 0.083376 0.337333 dtown -0.206949 0.062143 0.001333

W-propntfp 75570.692142 117940.380966 0.256667

W-yearsedu -206.896184 1760.975903 0.450333

W-property -390.893617 701.835213 0.280667

W-membership 26719.950009 28722.621702 0.172333

W-age 511.790252 1058.944881 0.303667

W-familysize 2267.625129 2916.600213 0.213333

W-drivers -0.124323 0.633216 0.420667

W-droads -0.660069 0.384543 0.046000

W-dtown 0.053077 0.241913 0.422667

237

rho -0.613928 0.238108 0.010333

Direct lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -19938.397821 -7230.757599 28543.648889 63002.781250 79984.024237 yearsedu -547.029416 -338.707405 370.313858 1025.773811 1255.723394 property -46.866531 -8.247422 135.936326 284.043023 327.928489 membership -431.020505 1059.168596 5688.411088 10650.533313 11968.390200 age -216.089030 -158.991262 50.792888 251.845116 326.684861 familysize -693.429513 -442.091335 464.554919 1339.510147 1638.282600 drivers -0.035868 0.033713 0.357221 0.678467 0.796613 droads -0.194303 -0.128994 0.040429 0.204225 0.258404 dtown -0.368132 -0.332698 -0.209219 -0.083068 -0.047816

Indirect lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -176139.597321 -110722.682039 38613.986373 190206.134224 264690.561289 yearsedu -3557.102910 -2515.553004 -264.300025 2016.709484 2686.976325 property -1564.556353 -1230.160386 -301.735687 638.600909 1002.509246 membership -35417.779622 -23026.214800 15142.830647 53780.710571 69601.579015 age -1716.388370 -1142.055335 312.826440 1664.888324 2164.733219 familysize -4010.594682 -2448.746936 1288.079905 5166.707216 7161.491874 drivers -1.381943 -1.072650 -0.211479 0.623024 0.927215 droads -1.197721 -1.001596 -0.441604 0.081998 0.238099 dtown -0.344471 -0.215085 0.111077 0.448217 0.572316

Total lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -166493.299716 -94473.354711 67157.635262 233348.731423 310700.141586

238

yearsedu -3161.775294 -2220.154676 106.013833 2434.927400 3173.069811 property -1393.570472 -1067.950970 -165.799360 758.204263 1157.086288 membership -29457.371510 -16569.513838 20831.241736 58963.060310 75960.896888 age -1640.663522 -1077.301148 363.619328 1724.025047 2190.433421 familysize -3661.521989 -1944.716628 1752.634824 5576.662591 7371.810823 drivers -0.918810 -0.701985 0.145741 0.980007 1.355728 droads -1.129126 -0.909163 -0.401175 0.073104 0.227021 dtown -0.544591 -0.393538 -0.098142 0.206674 0.316260

SDM_g model W8 model 6 resultsdmg8=sdm_g(y,x,W8,ndraw,nomit,info);prt(resultsdmg8,vnames);plt(resultsdmg8);

Warning: Matrix is close to singular or badly scaled. Results may be inaccurate.

RCOND = 2.136207e-16.

239

Warning: Matrix is close to singular or badly scaled. Results may be inaccurate.

RCOND = 2.136207e-16.

Warning: Matrix is close to singular or badly scaled. Results may be inaccurate.

RCOND = 2.152428e-16.

Warning: Matrix is close to singular or badly scaled. Results may be inaccurate.

RCOND = 2.152428e-16.

Bayesian Spatial Durbin model

Heteroscedastic model

Dependent Variable = total

R-squared = -0.1533 mean of sige draws = 245032731.0187 sige, epe/(n-k) = 1892665514.4929 r-value = 4

Nobs, Nvars = 286, 20 ndraws,nomit = 5000, 2000 total time in secs = 11.3930 time for sampling = 10.5960 time for effects = 0.7340

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30 min and max rho= -1.0000, 1.0000

***************************************************************

Variable Coefficient Std Deviation p-level constant 41753.168155 9272.065599 0.000000

240

propntfp 21464.906616 18390.421461 0.120667 yearsedu 290.128914 344.067720 0.200000 property 147.492214 74.650574 0.020667 membership 5912.262608 2451.015674 0.006000 age 31.340377 106.122810 0.384000 familysize 489.870169 435.932746 0.130667 drivers 0.353944 0.161845 0.012333 droads 0.012467 0.083702 0.442667 dtown -0.191361 0.062432 0.001000

W-propntfp -92590.840418 205070.055318 0.320000

W-yearsedu -1457.919344 3851.082854 0.352000

W-property 306.582435 1195.057502 0.387333

W-membership -27508.404282 71717.807695 0.346667

W-age -625.437836 1555.584402 0.338000

W-familysize 6749.739906 6334.771993 0.143333

W-drivers -0.500554 1.710285 0.385333

W-droads -0.704098 0.785883 0.184333

W-dtown 0.442022 0.559512 0.214333 rho -0.539896 0.319379 0.071667

Direct lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -22368.436493 -13179.294006 21954.908426 58356.534927 70595.262035 yearsedu -606.786416 -360.966814 297.528390 974.159131 1184.320445 property -43.441065 4.197805 146.651739 296.229612 342.032630 membership 39.464070 1531.831831 6049.872821 10968.530174 12380.077267 age -240.037338 -170.870849 34.158623 240.575841 304.880506

241

familysize -695.194296 -393.442670 462.790028 1309.847886 1552.879787 drivers -0.059205 0.045404 0.357262 0.680538 0.787279 droads -0.219897 -0.151903 0.015556 0.179545 0.220131 dtown -0.343797 -0.313654 -0.193930 -0.066700 -0.025393

Indirect lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -520325.579883 -361067.651593 -69735.279489 230854.697004 354787.455590 yearsedu -9204.084117 -7005.790145 -1096.533737 4503.541215 6967.855931 property -2243.647076 -1487.115362 170.305632 1972.660010 2944.863426 membership -179959.215625 -129000.783176 -21105.803263 81777.125077 112545.824524 age -3942.065598 -2847.584202 -445.728425 1721.938053 2521.599867 familysize -6534.436461 -3491.545439 4618.639887 15307.310720 23066.667223 drivers -4.193637 -2.896585 -0.458162 1.998881 2.993936 droads -2.533476 -1.745410 -0.497558 0.570528 1.055414 dtown -0.738264 -0.454962 0.365905 1.171885 1.638908

Total lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -511574.299963 -357129.289173 -47780.371063 264614.388317 397014.134437 yearsedu -9042.043434 -6746.161387 -799.005347 4715.179929 7752.038196 property -2144.875847 -1320.122382 316.957371 2102.928924 3164.344476 membership -175441.307667 -123546.395820 -15055.930442 86453.061024 118230.739411 age -3938.428616 -2818.402082 -411.569802 1723.031630 2605.338871 familysize -5897.740190 -3157.651106 5081.429915 15773.850591 23303.875254 drivers -3.835533 -2.609360 -0.100900 2.381867 3.349184 droads -2.463308 -1.719111 -0.482001 0.573022 0.950391 dtown -0.930874 -0.634288 0.171976 1.020496 1.409709

242

SAR_g model W2 model 7 resultsarg2=sar_g(y,x,W2,ndraw,nomit,info);prt(resultsarg2,vnames);plt(resultsarg2);

Bayesian spatial autoregressive model

Heteroscedastic model

Dependent Variable = total

R-squared = 0.1983

Rbar-squared = 0.1722 mean of sige draws = 244143776.6464 sige, epe/(n-k) = 1272660150.2127 r-value = 4

Nobs, Nvars = 286, 10 ndraws,nomit = 5000, 2000

243

total time in secs = 10.8930 time for sampling = 10.2370

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30 min and max rho = -1.0000, 1.0000

***************************************************************

Posterior Estimates

Variable Coefficient Std Deviation p-level constant 38837.694213 9000.276801 0.000000 propntfp 20836.696036 16698.021465 0.103667 yearsedu 487.944673 325.075820 0.061333 property 131.481625 70.929238 0.032000 membership 5982.696045 2269.953085 0.005667 age 79.012619 98.767292 0.211000 familysize 604.576347 431.558641 0.081000 drivers 0.312997 0.150328 0.019000 droads -0.004260 0.078870 0.483333 dtown -0.183990 0.059846 0.001667 rho -0.116560 0.089920 0.105000

Direct lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -20665.570994 -10814.629549 20856.508776 53995.106209 65846.754945 yearsedu -352.356888 -149.204895 488.429666 1141.848040 1372.439982 property -51.112710 -10.641987 131.610495 267.495686 318.845100 membership -234.245832 1566.021240 5988.514349 10309.877436 11702.663814

244

age -161.547264 -111.414695 79.086186 277.088452 346.175159 familysize -496.527803 -286.744615 605.171500 1444.804277 1667.737087 drivers -0.076566 0.017449 0.313305 0.601895 0.681182 droads -0.199806 -0.158164 -0.004263 0.146802 0.184379 dtown -0.336079 -0.300789 -0.184171 -0.067653 -0.028884

Indirect lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -10891.048349 -8150.814054 -2043.895479 2000.336684 4525.273492 yearsedu -231.505724 -174.743984 -49.057892 39.856209 88.033892 property -54.595811 -43.887290 -13.022387 9.840040 24.072176 membership -2161.774157 -1756.533729 -596.593002 373.508457 806.501474 age -50.932301 -40.283058 -7.733178 15.287388 28.054722 familysize -298.900597 -227.693225 -60.762066 54.687562 113.334805 drivers -0.129207 -0.099717 -0.031317 0.020879 0.048443 droads -0.031004 -0.020056 0.000293 0.022104 0.033121 dtown -0.025410 -0.011819 0.018437 0.051051 0.062450

Total lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -19075.746944 -9473.364395 18812.613297 49172.203572 62688.278914 yearsedu -323.170029 -132.725418 439.371774 1050.023375 1239.982135 property -48.832817 -9.446033 118.588107 248.831980 302.485050 membership -197.539885 1361.119878 5391.921347 9320.247553 10905.489965 age -144.332413 -99.610162 71.353009 253.339316 321.307577 familysize -441.840241 -243.725055 544.409434 1303.773870 1547.336780 drivers -0.070151 0.015662 0.281989 0.545210 0.651310 droads -0.178964 -0.143775 -0.003970 0.133191 0.174701

245

dtown -0.313138 -0.273071 -0.165734 -0.060808 -0.024242

SAR_g model W4 model 8 resultsarg4=sar_g(y,x,W4,ndraw,nomit,info);prt(resultsarg4,vnames);plt(resultsarg4);

Bayesian spatial autoregressive model

Heteroscedastic model

Dependent Variable = total

R-squared = 0.1987

Rbar-squared = 0.1726 mean of sige draws = 245322978.0150 sige, epe/(n-k) = 1272040117.9831 r-value = 4

Nobs, Nvars = 286, 10

246

ndraws,nomit = 5000, 2000 total time in secs = 10.8140 time for lndet = 0.0150 time for sampling = 10.1740

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30 min and max rho = -1.0000, 1.0000

***************************************************************

Posterior Estimates

Variable Coefficient Std Deviation p-level constant 39003.837764 8906.449496 0.000000 propntfp 20507.377868 16905.364811 0.110000 yearsedu 473.186120 331.610596 0.072333 property 131.840614 71.736021 0.033000 membership 6138.201384 2334.497361 0.003667 age 74.315779 102.693190 0.225333 familysize 615.829907 434.107145 0.078000 drivers 0.313916 0.144917 0.017333 droads -0.004905 0.080822 0.478667 dtown -0.183586 0.060280 0.000667 rho -0.131488 0.103584 0.105000

Direct lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -20633.004237 -11549.404024 20519.735093 53667.958665 65231.372281 yearsedu -471.897251 -194.907197 473.481394 1113.065935 1342.696517

247

property -48.101734 -7.405150 131.921233 272.702883 322.962440 membership 304.503512 1620.262314 6141.986396 10878.120289 12516.676666 age -190.479518 -132.196504 74.360532 273.911307 347.831175 familysize -555.647821 -255.704086 616.221197 1450.076135 1700.485932 drivers -0.055688 0.031046 0.314104 0.595879 0.687325 droads -0.218619 -0.162486 -0.004909 0.156038 0.206268 dtown -0.350615 -0.302687 -0.183700 -0.067493 -0.034723

Indirect lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -12485.643159 -9053.212854 -2170.158754 2511.743357 4973.660596 yearsedu -276.998928 -194.578073 -52.473230 47.688307 89.541856 property -65.500792 -49.398096 -14.310001 9.741319 24.221717 membership -2447.421560 -1934.169346 -670.197986 459.718914 1072.146676 age -64.053150 -44.642730 -7.912018 21.472616 33.354752 familysize -324.700295 -251.826553 -69.378105 60.463604 117.337807 drivers -0.140222 -0.100755 -0.033585 0.024578 0.063319 droads -0.035419 -0.022984 0.000674 0.025414 0.039342 dtown -0.029589 -0.013616 0.020187 0.057796 0.069282

Total lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -18168.286626 -10562.844133 18349.576340 49741.537374 62059.716244 yearsedu -394.188721 -175.983819 421.008164 997.678502 1204.179060 property -45.053045 -6.439693 117.611233 249.491984 294.993367 membership 297.675039 1422.424516 5471.788410 9944.313397 11235.883034 age -166.074892 -118.401070 66.448514 245.776034 324.549205 familysize -511.802071 -230.071290 546.843092 1312.507200 1564.855119

248

drivers -0.048701 0.026291 0.280519 0.549881 0.655625 droads -0.194193 -0.143685 -0.004235 0.143411 0.189329 dtown -0.326316 -0.273961 -0.163513 -0.058923 -0.031024

SAR_g model W6 model 9 resultsarg8=sar_g(y,x,W8,ndraw,nomit,info);prt(resultsarg8,vnames);plt(resultsarg8);

Bayesian spatial autoregressive model

Heteroscedastic model

Dependent Variable = total

R-squared = 0.1998

Rbar-squared = 0.1737 mean of sige draws = 246501570.3333 sige, epe/(n-k) = 1270370896.1571

249

r-value = 4

Nobs, Nvars = 286, 10 ndraws,nomit = 5000, 2000 total time in secs = 10.7680 time for sampling = 10.1580

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30 min and max rho = -1.0000, 1.0000

***************************************************************

Posterior Estimates

Variable Coefficient Std Deviation p-level constant 38853.568335 8893.036077 0.000000 propntfp 21334.975322 16974.121974 0.104000 yearsedu 458.391559 333.973075 0.085667 property 137.371955 74.263370 0.030667 membership 6027.118975 2306.228514 0.004667 age 67.534516 101.340688 0.246000 familysize 588.669930 441.360729 0.093333 drivers 0.321437 0.144045 0.017333 droads -0.009711 0.080408 0.448667 dtown -0.179434 0.059552 0.001667 rho -0.111756 0.107614 0.148333

Direct lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -19014.507975 -10699.968968 21340.227449 55971.511184 69707.111870

250

yearsedu -390.268026 -177.562564 458.505981 1115.429278 1328.062508 property -49.751744 -5.107072 137.404739 276.343196 325.319408 membership 66.374502 1471.503248 6028.611817 10611.043065 11731.775878 age -201.036858 -127.440478 67.551443 264.833415 327.017902 familysize -586.687625 -313.371208 588.817104 1470.327113 1706.469983 drivers -0.052164 0.027872 0.321517 0.596622 0.689709 droads -0.214732 -0.167225 -0.009714 0.149833 0.193314 dtown -0.334220 -0.295840 -0.179478 -0.063385 -0.024489

Indirect lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -12471.671981 -8799.628844 -1932.791626 2858.263758 6722.916603 yearsedu -247.972157 -182.860459 -43.341030 56.481747 118.427159 property -59.575028 -46.649104 -12.382110 16.926467 35.569396 membership -2331.710492 -1886.638348 -557.084608 665.902363 1389.773169 age -59.228162 -42.148944 -6.373077 20.862615 31.840070 familysize -321.761318 -236.844371 -54.319287 77.090435 158.612069 drivers -0.130049 -0.105216 -0.029576 0.037953 0.073478 droads -0.031517 -0.020360 0.001202 0.024457 0.038209 dtown -0.047178 -0.020405 0.016449 0.053664 0.064664

Total lower 01 lower 05 Coefficient upper 95 upper 99 propntfp -16705.318917 -9609.705941 19407.435823 52413.212219 66648.728295 yearsedu -346.063648 -167.192796 415.164951 1034.763325 1275.955682 property -42.763090 -4.939076 125.022629 262.729230 317.692631 membership 66.374502 1357.872596 5471.527209 9831.358938 11233.370435 age -174.728650 -117.281145 61.178366 240.214658 307.540680

251

familysize -532.104298 -269.987796 534.497816 1342.983536 1671.731793 drivers -0.050452 0.023856 0.291941 0.558925 0.643125 droads -0.198251 -0.152937 -0.008512 0.137383 0.185691 dtown -0.319377 -0.284470 -0.163029 -0.055721 -0.021203

SEM_g model W2 model 10 resultsemg2=sem_g(y,x,W2,ndraw,nomit,info);prt(resultsemg2,vnames);plt(resultsemg2);

Bayesian spatial error model

Heteroscedastic version

Dependent Variable = total

R-squared = 0.2497

Rbar-squared = 0.2252 mean of sige draws = 617764497.7553

252

r-value = 4

Nobs, Nvars = 286, 10 ndraws,nomit = 5000, 2000 total time in secs = 9.3300 time for sampling = 9.2670

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30 metropolis-hastings used for rho min and max lambda = -1.0000, 1.0000

***************************************************************

Posterior Estimates

Variable Coefficient Std Deviation p-level constant 41514.904089 13086.509020 0.001000 propntfp 20050.771695 23963.035584 0.205000 yearsedu 170.685053 481.976078 0.357667 property 195.061150 109.056996 0.034000 membership 6643.714706 3390.302205 0.028333 age 28.508280 151.873679 0.435000 familysize 433.970350 637.516868 0.247667 drivers 0.555838 0.222862 0.004333 droads -0.066672 0.121969 0.293000 dtown -0.162480 0.088848 0.035667 lambda 0.037376 0.181304 0.430000

253

SEM_g model W4 model 11 resultsemg4=sem_g(y,x,W4,ndraw,nomit,info);prt(resultsemg4,vnames);plt(resultsemg4);

Bayesian spatial error model

Heteroscedastic version

Dependent Variable = total

R-squared = 0.2515

Rbar-squared = 0.2271 mean of sige draws = 612025536.7222 r-value = 4

Nobs, Nvars = 286, 10 ndraws,nomit = 5000, 2000 total time in secs = 9.3300 time for sampling = 9.2670

254

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30 metropolis-hastings used for rho min and max lambda = -1.0000, 1.0000

***************************************************************

Posterior Estimates

Variable Coefficient Std Deviation p-level constant 40798.189866 13365.029691 0.001000 propntfp 21268.155754 23758.384138 0.188333 yearsedu 207.432491 472.697276 0.331667 property 198.755709 106.538751 0.029667 membership 6722.273075 3442.879285 0.023667 age 26.999187 156.243563 0.422000 familysize 444.181990 635.511288 0.243333 drivers 0.573912 0.226902 0.004000 droads -0.069386 0.119937 0.275333 dtown -0.160088 0.089132 0.038667 lambda 0.087016 0.254898 0.333667

255

SEM_g model W8 model 12 resultsemg8=sem_g(y,x,W8,ndraw,nomit,info);prt(resultsemg8,vnames);plt(resultsemg8);

Bayesian spatial error model

Heteroscedastic version

Dependent Variable = total

R-squared = 0.2548

Rbar-squared = 0.2305 mean of sige draws = 612265666.9026 r-value = 4

Nobs, Nvars = 286, 10 ndraws,nomit = 5000, 2000 total time in secs = 9.5640 time for lndet = 0.0160

256

time for sampling = 9.5020

Pace and Barry, 1999 MC lndet approximation used order for MC appr = 50 iter for MC appr = 30 metropolis-hastings used for rho min and max lambda = -1.0000, 1.0000

***************************************************************

Posterior Estimates

Variable Coefficient Std Deviation p-level constant 41184.803321 12875.304761 0.001000 propntfp 20606.123018 24159.115871 0.194000 yearsedu 188.925685 480.880333 0.347333 property 199.603048 108.877319 0.035333 membership 6638.382961 3405.610554 0.023000 age 26.077420 151.013386 0.433333 familysize 445.736516 634.153021 0.237000 drivers 0.585224 0.230167 0.002000 droads -0.074439 0.119897 0.260333 dtown -0.159179 0.087889 0.039667 lambda 0.192151 0.332166 0.263333

257

Model Assessment SAR_g, SEM_g and SDM_g (all) probs=model_probs(resultsarg, resultsarg2, resultsarg4, resultsarg8, resultsdmg, ... resultsdmg2, resultsdmg4, resultsdmg8, resultsemg, resultsemg2, resultsemg4, resultsemg8); in.rnames=char('Models','sar','sarw2','sarw4','sarw8','sdm','sdmw2','sdmw4','sdmw8',... 'sem','semw2','semw4','semw8'); mprint(probs,in);

Models sar 0.0000 sarw2 0.0000 sarw4 0.0000 sarw8 0.0000 sdm 0.0703 sdmw2 0.0010 sdmw4 0.0163 sdmw8 0.9124

258

sem 0.0000

semw2 0.0000

semw4 0.0000

semw8 0.0000

Model Assessment SAR_g and SEM_g

probs=model_probs(resultsarg, resultsarg2, resultsarg4, resultsarg8,resultsemg,resultsemg2,resultsemg4,resultsemg8);

in.rnames=char('Models','sar','sarw2','sarw4','sarw8','sem','semw2','semw4','semw8'); mprint(probs,in);

Models

sar 0.3305

sarw2 0.3077

sarw4 0.1675

sarw8 0.1942

sem 0.0000

semw2 0.0000

semw4 0.0000

semw8 0.0000

Published with MATLAB® R2017a

Aghane C. Antunes Published with MATLAB® R2017a 03/26/2018 University of Florida – Department of Geography

Contents

. OLS Parcimonious models . OLS model 1 . OLS model 2 (dtown dropped) . OLS model 3 (dtown and age dropped)

259

. OLS model 4 (dtown, age and yearsedu dropped) . OLS model 5 (dtown, age, yearsedu an family size dropped) OLS Parcimonious models

Exploratory variables matrix x (all)

x=[ones(n,1) Subset(:,7:12),Subset(:,16:18)]; % supply a constant term in the first column of X nvar=11; vnames= char('total','constant','propntfp','yearsedu','property','membership','age',... 'familysize','drivers','droads','dtown'); OLS model 1

resols1=ols(y,x);prt(resols1,vnames);

% plot the predicted and residuals plt(resols1);

Ordinary Least-squares Estimates

Dependent Variable = total

R-squared = 0.2728

Rbar-squared = 0.2491

sigma^2 = 1154488951.3176

Durbin-Watson = 1.9434

Nobs, Nvars = 286, 10

***************************************************************

Variable Coefficient t-statistic t-probability

constant 43268.444636 2.760569 0.006157

propntfp 13691.500927 0.441074 0.659505

yearsedu -99.490236 -0.172905 0.862852

property 245.217338 1.795217 0.073713

membership 9395.083829 2.228355 0.026663

age -23.468555 -0.121851 0.903106

260

familysize 249.200074 0.291198 0.771118 drivers 0.872215 3.912972 0.000115 droads -0.136816 -0.869156 0.385517 dtown -0.135654 -1.199183 0.231485

OLS model 2 (dtown dropped) x2=[ones(n,1) Subset(:,7:12),Subset(:,16:17)]; % supply a constant term in the first column of X nvar2=10; vnames2= char('total','constant','propntfp','yearsedu','property','membership','age',... 'familysize','drivers','droads'); resols2=ols(y,x2);prt(resols2,vnames2);plt(resols2);

Ordinary Least-squares Estimates

Dependent Variable = total

R-squared = 0.2690

261

Rbar-squared = 0.2479 sigma^2 = 1156314630.1998

Durbin-Watson = 1.9404

Nobs, Nvars = 286, 9

***************************************************************

Variable Coefficient t-statistic t-probability constant 31099.141090 2.601460 0.009782 propntfp 7713.495901 0.251560 0.801568 yearsedu -32.773457 -0.057180 0.954443 property 255.351425 1.871517 0.062327 membership 9109.533928 2.162372 0.031445 age -4.398645 -0.022898 0.981748 familysize 105.450218 0.124351 0.901128 drivers 0.792102 3.721850 0.000239 droads -0.308085 -4.651223 0.000005

262

OLS model 3 (dtown and age dropped) x3=[ones(n,1) Subset(:,7:10), Subset(:,12),Subset(:,16:17)]; % supply a constant term in the first column of X nvar3=9; vnames3= char('total','constant','propntfp','yearsedu','property','membership',... 'familysize','drivers','droads'); resols3=ols(y,x3);prt(resols3,vnames3);plt(resols3);

Ordinary Least-squares Estimates

Dependent Variable = total

R-squared = 0.2690

Rbar-squared = 0.2506 sigma^2 = 1152157405.9806

Durbin-Watson = 1.9407

Nobs, Nvars = 286, 8

***************************************************************

263

Variable Coefficient t-statistic t-probability constant 30914.842265 3.503790 0.000534 propntfp 7788.712510 0.255944 0.798183 yearsedu -27.393143 -0.052493 0.958174 property 253.893050 2.107924 0.035933 membership 9107.613666 2.166243 0.031142 familysize 106.540449 0.126062 0.899774 drivers 0.791421 3.762293 0.000205 droads -0.307906 -4.689560 0.000004

OLS model 4 (dtown, age and yearsedu dropped) x4=[ones(n,1) Subset(:,7), Subset(:,9:10), Subset(:,12),Subset(:,16:17)]; % supply a constant term in the first column of X

264

nvar4=8; vnames4= char('total','constant','propntfp','property','membership',... 'familysize','drivers','droads'); resols4=ols(y,x4);prt(resols4,vnames4);plt(resols4);

Ordinary Least-squares Estimates

Dependent Variable = total

R-squared = 0.2690

Rbar-squared = 0.2533 sigma^2 = 1148039188.7249

Durbin-Watson = 1.9399

Nobs, Nvars = 286, 7

***************************************************************

Variable Coefficient t-statistic t-probability constant 30679.923131 4.041895 0.000069 propntfp 7798.982038 0.256746 0.797564 property 255.870524 2.240553 0.025842 membership 9110.479933 2.170991 0.030775 familysize 110.537171 0.131560 0.895427 drivers 0.790480 3.778286 0.000193 droads -0.307646 -4.707425 0.000004

265

OLS model 5 (dtown, age, yearsedu and family size dropped) x5=[ones(n,1) Subset(:,7), Subset(:,9:10), Subset(:,16:17)]; % supply a constant term in the first column of X nvar5=7; vnames5= char('total','constant','propntfp','property','membership',... 'drivers','droads'); resols5=ols(y,x5);prt(resols5,vnames5);plt(resols5);

Ordinary Least-squares Estimates

Dependent Variable = total

R-squared = 0.2689

Rbar-squared = 0.2559 sigma^2 = 1144010014.0385

Durbin-Watson = 1.9403

Nobs, Nvars = 286, 6

***************************************************************

266

Variable Coefficient t-statistic t-probability constant 31086.359484 4.491493 0.000010 propntfp 7761.566645 0.255975 0.798158 property 256.877101 2.258399 0.024690 membership 9168.674626 2.200964 0.028555 drivers 0.789013 3.783287 0.000189 droads -0.305240 -4.873538 0.000002

Published with MATLAB® R2017a

Aghane C. Antunes Published with MATLAB® R2017a 03/26/2018 University of Florida – Department of Geograph

267

APPENDIX B COMPLETE DATASET DESCRIPTION

The complete dataset comprises information on the household total annual income derived from a series of agronomic activities small farmers perform to generate income, in a pattern consistent with a multi-functional livelihood approach, in which peasants combine various “compatible” productive activities to generate cash income

(Hoefle, 2016). The full dataset is comprised of the variables detailed in Table B-1.

Table B-1 Variables and their description for the full data set Variable Description 1 Location Village of the county under study 2 Family size Number of family members (family size) Binary/Dummy variable, coded as: 1 (member) and 2 (non-member and non- 3 Membership collector) related to the small producer involvement in cooperative for NTFP production Income from sale, processing or working on administrative duties associated 4 NTFP with NTFP extraction 5 palmito Income from “palmito” (palm heart extraction) 7 wood Income from wood cutting activity 8 sawmill Income from sawmill activity 9 farinha Income from “Farinha” (cassava/manioc flour) related activity 10 pepper Income from pepper production agriculture 11 Income from activities related to agriculture (trade and services) services 12 Açaí Income from Açaí production 13 passionfruit Income from planted passionfruit production 14 cocoa Income from cocoa production 15 cupuaçu Income from Cupuaçu production 16 fishing Income from fishing activity 17 chicken Income from raising chicken 18 crab Income from crab collection 19 shrimp Income from shrimp production Income from government cash transfers for temporary financial assistance to defeso (fishing 20 artisanal professional fishermen of a minimum wage BRL 937 (284.894 insurance) USD) during fishing closure period Income from “Bolsa Verde” (Green Grant) - government cash transfer designed to support families in extreme poverty living in or near protected 21 Bolsa Verde areas or important wilderness areas, paid as an incentive for communities to continue to use the land they live in a sustainable way. Payment is BRL 300 (91.12 USD), every three months Income from “Bolsa Família” (Family Aid) - government cash transfer 22 Bolsa familia designed to reduce poverty and extreme poverty ranging from BRL 85.00 (26 USD) to BRL 195.00 (60 USD) monthly Rural retirement, disability, and widows’ pension paid through the Brazil’s 23 pension social security 24 other Income from off-farm wages Total annual income determined from the sum of the all economic sources 25 total activities

268

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Almeida, Ruth. 2013. “Empresa Natura S.A, Comunidades Rurais e o Uso de Recursos Naturais Na Amazônia: Uma Análise ‘Do Caso Priprioca’ (Cyperus Articulatus L.) No Estado Do Pará.” Universidade Federal Rural da Amazônia - UFRA.

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BIOGRAPHICAL SKETCH

Aghane Antunes is a graduate student in the Department of Geography at the

University of Florida. She has a bachelor’s degree in Social Communication

(Journalism); a specialization in Corporate Communication, and a certificate in

International Corporate Communication from Syracuse University. Her research interests lie in political and economic geography with a focus on development and environment policy, development and conservation, climate change mitigation, and community forest management. Prior to returning to school for her M.S. and Ph.D. degrees, Aghane worked with Communication, Environmental, Institutional Affairs, and as a senior Government Relations Analyst in multinational companies, specifically in large-scale infrastructure development projects, such as port and railway expansion linked to the mining industry and hydropower dams in the Brazilian Amazon. These professional experiences stimulated her interest in human geography theoretical frames to really understand the underlying causes of poverty and deforestation, particularly the economic and social drivers of change in the Amazon, Brazil, her home country. She is now committed to strengthening her knowledge and expanding her research in order to apply science to shape public policy to benefit marginalized communities.

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