Aug 2, 2012 - research on the effects of Utz Certification on coffee farmers in Kenya, which took me all the way to places with names as Embu and Kiriaini, ...
Maintaining a sustainable livelihood: An analysis of the effects of Utz Certification on market access, risk reduction and livelihood strategies of Kenyan coffee farmers
Mirjam Schoonhoven-Speijer
Pictures from top left to bottom right (by Mirjam Schoonhoven, January – April 2011, Kenya) Coffee trees; Road sign of Rianjagi (Utz) cooperative, Embu; Farmers sorting mbuni, at Kithungururu cooperative, Embu; Road sign of Kangunu (Utz) cooperative, Mathioya; Drying tables at Kamagogo cooperative, Mathioya.
Maintaining a sustainable livelihood: An analysis of the effects of Utz Certification on market access, risk reduction and livelihood strategies of Kenyan coffee farmers
Master Thesis Mirjam Schoonhoven-Speijer (s0239151) Supervisor: Prof. Dr. Ruerd Ruben Second reader: Dr. Luuk van Kempen Research Master Social and Cultural Science Radboud University Nijmegen August 2012
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Preface and Acknowledgements 2th of August 2012, Leerdam, the Netherlands Dear reader, I am quite relieved that I can finally write this preface to the Thesis with which I conclude my Research Master Social and Cultural Science at the Radboud University Nijmegen. It has been quite a journey, especially during the last months in which I deemed it possible to combine finishing my Thesis with starting a career in the development sector. With this research, I hope that I can make a contribution to that sector, especially in the field of certification and the entrance of farmers in the global market. A few years ago, when my interest for these certification schemes developed, Fair Trade still seemed to be the main player in the field. Nowadays, many other labels are occurring, such as Utz Certified. I very much enjoyed doing research on the effects of Utz Certification on coffee farmers in Kenya, which took me all the way to places with names as Embu and Kiriaini, and which gave me insights in the livelihoods of the members of the cooperatives Rianjagi, Kithungururu, Kangunu and Kamagogo. My four months of fieldwork were an incredible experience, and I would especially like to thank Eveline Dijkdrenth for experiencing it with me, the highs and lows. I also want to thank my enumerators, Ann Mwita, Alvin Munene, Ann Nyakio and Kamanu Ng’anga, their assistance in gathering all the data has been of immense value. Last but not least I want to thank all farmers for their time of sitting through a lengthy questionnaire, interviews and group discussions. Together they gave me a wealth of information, without which this research would not have been possible. This research would also not be here without the support of my supervisor, Pr. Dr. Ruerd Ruben. He introduced me to Solidaridad to be able to do my research among Utz certified cooperatives. But before that, during my bachelor’s, he introduced me to the complexities farmers are facing in maintaining a rural livelihood in the course ‘development: policy and practice’ and urged me to do optional courses at Wageningen University. My interest in agriculture, rural livelihoods and their inclusion in value chains has been strong since. I want to thank Ruerd Ruben for these opportunities, and for his never waning support throughout the whole process of finishing this thesis. This thesis concludes my Research Master. It has been great to be part of such a challenging Master program and I am very glad that I had the opportunity to participate in it, together with my classmates, some of whom have become good friends. To all of you, thanks very much for a great time. Special thanks go however to Jesper Rözer. I am very grateful for all the time he put into reviewing the chapters of this research. Even this preface must end, so last but not least I want to thank my friends and family for being there for me through all these years. I hope I finally have more time for them now that this thesis is finished. The final thanks are for Stefan. Because he deserves them.
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Executive Summary Since the turn of the century, there has been a renewed focus on agriculture and rural development, as it remains the best opportunity for 500 million households, representing an estimated 1.5 to 2 billion people worldwide, to work their way out of poverty. To make development through agriculture happen, farmers should be able to market their products at local or global markets. These markets represent opportunities for income generation, professionalization and diversification; however, risks such as price uncertainties, and the requirements and high standards of international markets might raise barriers for new, small-scale, producers to enter them. One way of investing in the entry of small-scale farmers is by the use of certification schemes, such as Fair Trade and Utz Certified. These programs offer farmers assistance in producing and selling their products through the improvement of coffee practices, and above market prices. This is thought to stimulate farmers in production of better quality and better quantity coffee. However, questions about these standards remain. Do small-scale producers really benefit, and under what conditions? Do they only benefit in terms of increased production and income, or also in terms of enhanced resilience and risk reduction? We argue that farmers can only sustainably profit from inclusion in international markets, if this inclusion reduces their vulnerability and enhances their opportunities for effective income strategies leading out of poverty. In this research we therefore examined the extent to which Utz Certified attributes to these aspects of farmers livelihoods: vulnerability reduction and effective income strategies. The research was done among coffee farmers in Kenya, who are organised in cooperatives. We included two Utz-certified cooperatives, and compared them with two neighbouring noncertified cooperatives which were not involved in any certification scheme, but have further similar characteristics as certified farmers. The two groups of cooperatives were located in two regions: Embu and Mathioya, both located in central Kenya. These regions differ from each other in agroecological aspects and local market opportunities. This provides the opportunity to study contextual differences and their influences on livelihood strategies as well. We formulated 4 hypotheses to elaborate the effects of Utz Certification on the livelihood of coffee farmers in Kenya. We argue that Utz Certification reduces vulnerability in several ways, directly and indirectly. They do so through intervening in the services offered by producer organisations. These interventions lead directly to a higher and better quality harvest, for which farmers receive a higher price (hypothesis 1). This has the indirect effect that farmers perceive their cooperative as a more reliable partner (hypothesis 2). We expect this to have effects in the reduction of vulnerability of farmers. Market related shocks are reduced, which makes farmers better able to cope with non-market related risks and shocks (hypothesis 3). We argue that this could ultimately lead to a shift in livelihood strategies (hypothesis 4). Our results indicate that the conditions under which cooperatives operate are important for a successful implementation of certification schemes in cooperatives. We confirmed that the combination of technical assistance and higher prices that Utz Certification is offering to farmers is indeed important. Higher yields and better coffee quality do however also depend on a transparency and efficiency within the management of a producer organisation, and a good functioning input supply system. The assessed certified cooperatives scored less convincingly on these factors. However, overall, we conclude that Utz Certification indeed plays an important role in this successful inclusion of smallholders in value chains. The relevance of inclusion differs however for several contexts. Inclusion seems to bring the highest gains for coffee farmers in areas where farmers are stronger depending on coffee, and have less other livelihood options available. Engagement in global value chains in such areas is however also of high risk; farmers, who are not able to succeed, fall below the poverty threshold. Farmers in areas with stronger local market opportunities are less depending on coffee. Coffee is however still an important part of their income spectrum, and coffee revenues can be used to engage in other, viable non-farm activities.
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Table of Contents TABLE OF CONTENTS ........................................................................................................................................ 6 INTRODUCTION .............................................................................................................................................. 10 CHAPTER 1: THEORETICAL FRAMEWORK........................................................................................................ 16 1.1 THE SUSTAINABLE LIVELIHOOD FRAMEWORK .............................................................................................. 16 1.1.2 Assets, capabilities, activities and strategies ............................................................................... 16 1.1.3 Constraints or drivers: context and institutions ........................................................................... 18 1.2 MARKET CONSTRAINTS AND OTHER SHOCKS, SHAPING RISK PERCEPTIONS .......................................................... 19 1.3 THE ROLE OF INSTITUTIONS .................................................................................................................... 21 1.3.1 Producer organisations .............................................................................................................. 22 1.3.2 Utz Certification ......................................................................................................................... 24 1.4 UTZ CERTIFICATION AND SUSTAINABILITY – FORMULATING HYPOTHESES ........................................................... 25 1.4.1 Direct effects of Utz Certification – higher harvest ...................................................................... 26 1.4.2 Indirect effects of Utz Certification – more trust and loyalty… ..................................................... 26 1.4.3 …leading to vulnerability reduction. ............................................................................................ 26 1.4.4 Maintaining a sustainable livelihood – more sustainable strategies ............................................ 27 CHAPTER 2 – CONTEXT: KENYA, COFFEE AND COOPERATIVES ........................................................................ 28 2.1 KENYA: ECONOMY AND COFFEE PRODUCTION ............................................................................................ 28 2.1.1 Kenya’s economy ....................................................................................................................... 28 2.1.2 Coffee and coffee cooperatives................................................................................................... 28 2.2 RESEARCH LOCATIONS AND UNITS OF ANALYSIS ........................................................................................... 31 2.3 THE DISTRICTS EMBU AND MURANGA ...................................................................................................... 32 2.3.1 Demographic figures Embu and Muranga district ....................................................................... 32 2.3.2 Agro-ecological conditions Embu and Mathioya ......................................................................... 33 2.3.3 Comparing districts: sample Mathioya and Embu ....................................................................... 34 2.3.4 Concluding remarks on contextual differences ............................................................................ 36 2.4 THE COOPERATIVES ............................................................................................................................. 37 2.4.1 Cooperatives in the Embu district: Rianjagi and Kithungururu ..................................................... 37 2.4.2 Cooperatives in the Mathioya district: Kangunu and Kamagogo.................................................. 40 CHAPTER 3: MATERIAL AND METHODS .......................................................................................................... 44 3.1 RESEARCH UNITS OF ANALYSIS ................................................................................................................ 44 3.2 USED METHODS AND SAMPLING ............................................................................................................ 44 3.2.1 Interviews at the institutional level and training enumerators ..................................................... 44 3.2.2 Risk Mapping ............................................................................................................................. 45 3.2.3 Survey ........................................................................................................................................ 46 3.2.4 Risk Game .................................................................................................................................. 47 3.2.5 Interviews .................................................................................................................................. 49 3.3 TRIANGULATION, RELIABILITY, VALIDITY.................................................................................................... 50 3.4 DATA ANALYSIS .................................................................................................................................. 51 CHAPTER 4: RESULTS ...................................................................................................................................... 54 4.1 EXPLAINING THE PROBABILITY FOR CERTIFICATION ....................................................................................... 54 4.2 DIRECT EFFECTS – UTZ CERTIFICATION AND PRODUCTION .............................................................................. 57 4.2.1 Production function Embu region ............................................................................................... 57
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4.2.2 Production function Mathioya region ......................................................................................... 62 4.2.3 What cooperatives should do ..................................................................................................... 67 4.3 INDIRECT EFFECTS – TRUST AND LOYALTY................................................................................................... 68 4.3.1 Performance, collective action and trust Embu ........................................................................... 68 4.3.2 Performance, collective action and trust Mathioya ..................................................................... 71 4.4 INDIRECT EFFECTS –RISK PERCEPTION AND VULNERABILITY REDUCTION .............................................................. 73 4.4.1 Shocks and risk perception in the Embu region............................................................................ 73 4.4.2 Shocks and risk perception in Mathioya region ........................................................................... 77 4.5 MAINTAINING A LIVELIHOOD - CHOICES IN LIVELIHOOD STRATEGIES .................................................................. 80 4.5.1 Livelihood strategies of Embu farmers ........................................................................................ 80 4.5.2 Livelihood strategies of Mathioya farmers .................................................................................. 83 5. CONCLUSION .............................................................................................................................................. 86 5.1 HYPOTHESES AND RESEARCH QUESTION .................................................................................................... 87 5.1.1 Hypothesis 1, a higher harvest.................................................................................................... 87 5.2.2 Hypothesis 2, the producer organization as a reliable partner ..................................................... 88 5.2.3 Hypothesis 3, reducing vulnerability ........................................................................................... 89 5.2.4 Hypothesis 4, shifting to more sustainable strategies .................................................................. 89 5.2 CONTRIBUTION TO THEORY: NEW INSIGHTS AND REMAINING QUESTIONS ........................................................... 91 5.3 IMPLICATIONS FOR POLICY AND PRACTICE OF UTZ CERTIFIED .......................................................................... 93 REFERENCES ................................................................................................................................................... 94 APPENDIX....................................................................................................................................................... 98 APPENDIX 1: OVERVIEW OF VARIABLES USED .......................................................................................................... 98 APPENDIX 2: OPERATIONALISATION OF VARIABLES BASED ON SIMPLE INDEXES ............................................................... 100 APPENDIX 3: OPERATIONALISATION OF VARIABLES BASED ON FACTOR ANALYSIS ............................................................. 100 APPENDIX 4: ADDITIONAL TABLES TO CHAPTER 4 - RESULTS ..................................................................................... 102 APPENDIX 5: INTERVIEW GUIDES USED DURING STRUCTURED INTERVIEWS .................................................................... 104 APPENDIX 6: LIST OF CODES USED OF ANALYSIS OF STRUCTURED INTERVIEWS ................................................................ 108 APPENDIX 7: INSTRUCTIONS RISK GAME .............................................................................................................. 112 APPENDIX 8: LIST OF CODES USED FOR ANALYSIS OF OPEN QUESTIONS RISK GAME .......................................................... 115 APPENDIX 9: INSTRUCTIONS PARTICIPATORY RISK MAPPING ...................................................................................... 116 APPENDIX 10: HOUSEHOLD SURVEY KENYA 2011 ................................................................................................. 120
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List of figures and tables Figures Figure 1.1 Figure 2.1 Figure 2.2 Figure 2.3 Figure 4.1 Figure 4.2 Figure 4.3 Figure 4.4
Schematic representation of hypotheses Coffee yield per hectare Kenya, since 1960 Kenyan coffee calendar Location of cooperatives Severity and incidence coffee shocks, Embu Severity and incidence other (idiosyncratic) shocks, Embu Severity and incidence coffee shocks, Mathioya Severity and incidence other (idiosyncratic) shocks, Mathioya
25 30 30 31 74 74 77 77
Livelihood strategies by market and natural resource potential Demographic statistics Embu and Muranga (2002) Comparing characteristics of both districts, Embu and Mathioya Production figures for both Rianjagi (Utz) and Kithungururu, past five years Comparing characteristics cooperatives, Embu Production figures for both Kangunu (Utz) and Kamagogo, past five years Comparaing characteristics cooperatives, Mathioya Choices, payoffs, risk aversion classes, and expected values Descriptives model 1, both regions Model 1 – probability of becoming an Utz Certified farmer (logistic regression) Characteristics concerning coffee productivity, Embu Model 2 – production function, Embu (OLS regression) Characteristics concerning coffee productivity, Mathioya Model 2 – production function, Mathioya (OLS regression) Descriptives model 3, attitudes towards the cooperative, Embu Model 3 – trust in cooperative and loyalty towards cooperative Embu (OLS regression) Descriptives model 3, attitudes towards the cooperative, Mathioya Model 3 – trust in cooperative and loyalty towards the cooperative, Mathioya (OLS regression) Descriptives model 4, risk attitudes of farmers, Embu Model 4 – explaining risk perceptions, Embu (2SLS regression) Descriptives model 4, risk attitudes of farmers, Mathioya Model 4 – explaining risk perceptions, Mathioya (2SLS regression) Income diversifcation, Embu Correlations between income and income shares, Embu Choices in investments, Embu Income diversifcation, Mathioya Correlations between income and income shares, Mathioya Choices in investments, Mathioya Overview of hypotheses and their outcomes
27 32 34 37 38 40 41 48 55 55 57 59 62 64 69 70 71 72
Tables Table 1.1 Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 2.5 Table 2.6 Table 3.1 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 4.5 Table 4.6 Table 4.7 Table 4.8 Table 4.9 Table 4.10 Table 4.11 Table 4.12 Table 4.13 Table 4.14 Table 4.15 Table 4.16 Table 4.17 Table 4.18 Table 4.19 Table 4.20 Table 5.1
Abbreviations CIDIN Centre for International Development Issues Nijmegen CL Choose Lottery CSR Corporate social responsibility FT Fair Trade KPCU Kenya Planters Co-operative Union Ksh Kenyan Shilling NGO Non-governmental organisation
OLS
Ordinary Least Squares
PO PRM SAP SL VC 2SLS
Producer organisation Participatory risk mapping Structural Adjustment Programs Sustainable livelihoods Value chain Two stage least square
75 76 79 79 80 81 81 83 83 84 87
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Introduction ‘Small-scale farms are a source of hope and despair. Despair because over half a billion people who live below the international poverty line of $1.25 a day depend on these farms for their livelihoods; and a source of hope because small-scale farms are seen as the way to achieve food security for a world population that is expected to grow to 9 billion people by 2050’ (Greijn, 2012). Small-scale farmers are back on the agenda of development studies and practice. Since the turn of the century, there has been a renewed focus on agriculture and rural development, as it remains the best opportunity for 500 million households, representing an estimated 1.5 to 2 billion people worldwide, to work their way out of poverty (World Bank, 2007; Hazell et al., 2010). It is especially so since farmers can play an important role in securing food supply for a fast growing world population, expected to increase to 9 billion by 2050. However, to make development through agriculture happen, farmers should be able to market their products at local or global markets. These markets represent opportunities for income generation, professionalization and diversification; on the other hand, risks such as price uncertainties, and the requirements and high standards of international markets might raise barriers for new, small-scale, producers to enter them (Seville et al., 2011). Investing in the supply of smallscale producers delivering commercially-viable products presents several challenges to international commodity companies as well. Inadequate infrastructure, lack of skilled producers, and dependency on favourable weather complicate the consistent quality of products that formal markets require (World Bank, 2007). These challenges make companies biased towards obtaining their supply from larger farmers who are seen as being more reliable and consistent suppliers. On the other hand, working with small-scale farmers might be attractive as well. Smallholder production could offer cost advantages for the delivery of labour-intensive commodities that require strong quality supervision (Ruben et al, 2006). Companies can thus secure supplies while enhancing their reputation through corporate social responsibility (CSR) programs. Many nongovernmental organisations (NGOs) focus on connecting small-scale producers to regional and global markets, often together with companies. One way of investing in the supply of small-scale farmers is by the use of labels, such as Utz Certified. This so-called ‘third-party certification’ is a way of simplifying the engagement of large companies with smallholders and ethical procurements. Best practice standards, credible communication with customers, and improved production for smallholders are offered. However, questions about these standards remain. Do small-scale producers really benefit, and under what conditions? Do they only benefit in terms of increased production and income, or also in terms of enhanced resilience and risk reduction? These issues are central to this research.
Agriculture back on the development agenda The attention for agriculture and rural development has waxed and waned in the development discourse during the 20th century. During the early days of development cooperation in the 1950s, agriculture was not considered central to economic growth. This changed in the 1960 and 1970s, as it became clear that a slow-growing agricultural sector was threatening overall country development, with food becoming scarce while rural populations remained in poverty. The following two decades 10
the focus shifted again away from agriculture, to economic liberalization politics (which were in turn often disastrous for agricultural development), and a stronger focus on social issues such as gender, health and education (Wiggins and Kirsten, 2010). Since the turn of the new century there has been however a growing recognition among developing country governments and donors of the multiple roles of agriculture for development. A sharp jump in commitments to agriculture was witnessed during the 2007-2008 food crisis, reflected by spiking cereal prices at the world market (World Bank, 2007; Borras Jr, 2009; Byerlee et al., 2009; Wiggins and Kirsten, 2010; Dethier and Effenberger, 2012). Especially the convergence of the food crisis with various other on-going crises – financial, energy and environmental – has put ‘the nexus between rural development and development in general back onto the centre stage of theoretical, policy and political agendas in the world today’ (Borras Jr, 2009:6). Agriculture has been labelled a vital development tool for achieving millennium development goal one, which calls for halving the share of people suffering from extreme poverty and hunger by 2015 (World Bank, 2007). Due to the convergence of several crises, the renewed interest in agriculture is widely shared. Developing country governments want to stabilize national food security and balance economic growth, while agri-businesses want to secure their supplies and develop new markets (Vorley et al., 2012). Many NGOs work in between these two actors, trying to link small-scale farmers to businesses, while influencing government policies.
Small-scale farmers: opportunities and constraints Poverty remains largely a rural phenomenon: three-fourths of the world’s poor, around 800 million people, live and work in the country side on 1 dollar per day (Borras Jr, 2009). The majority of them have small farms, being farms of less than 2 ha.1 Development of these small farmers by participation in formal markets is assumed to benefit them in many ways. Improved access to markets, increased returns and improved income security are the most obvious and visible benefits. Other benefits are more complex of nature, such as improvements in organisational capacity, and the reduction of vulnerability and risks. All these factors together can improve the wellbeing and livelihoods of farmers, and their long-term prospects of reducing the poverty they live in (Seville et al., 2011). To integrate farmers in agriculture supply chains that supply markets is however difficult. Chains link together producers, traders and processors from developing countries with retailers and consumers in urban centres and in developed countries. To cope with food safety and health demands, new procedures and practices for organizing food supply networks emerged. Product quality and process standards leads to complex contractual arrangements and an increasing degree of vertical integration. Producers can therefore only be included if measures are taken to enhance product quality (Ruben et al., 2006). However, the capacity of small farmers to produce higher quality products is often limited under the contemporary circumstances. Market opportunities for many farmers are restricted by market imperfections such as variable production inputs, fragmentary and inadequate market information and high transaction costs. Sometimes markets might be missing at all. Imperfections are especially profound in rural areas due to underdeveloped transport and communication infrastructures (World Bank, 2001; Shiferaw et al., 2008). Nevertheless, the connection to the market 1
We focus here on poor households being producers, while they can also be included in markets as wage labourers, or as providers of services to a supply chain. Raising productivity and income of small farmers reflects on these categories as well, by creating employment on farms, pushing down food prices and stimulating the rural nonfarm economy (OECD, 2006; in Wiggins and Kirsten, 2010).
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entails risks as well. Price uncertainties, product denial and contract breach have discouraging effect on smallholder farmers. Farmers can only make the required investment to improve delivery frequency and quality when they can be relatively certain regarding available market outlets (Fafchamps, 2004). In addition to market imperfections and instabilities farmers face, peasants’ insecurity is increased by non-market related shocks. These include climate variations, low social and economic status, a high incidence of health problems and bad politics at the state level. All these factors together hinder small-scale farmers in taking advantages of possible but high-risk opportunities, and instead investing in low but stable returns is often preferred. Overcoming these risks is one of the main conditions for overcoming poverty (Fafchamps, 2003; Dercon, 2008).
Overcoming constraints through social standards Policies focusing on bridging the gap between local economic development and global value chain integration should focus on ways of providing more opportunities to invest and find sustainable pathways out of poverty. In addition, protection from the worst effects of shocks and constraints should be provided. This asks for the emergence of new institutional and organisational networks that enable producers in developing countries to meet business requirements and trade standards in a sustainable way (Ruben et al, 2006). One form of organizing farmers is through fair trade labels and certification initiatives, which are growing rapidly. These seek to enhance market integration of farmers, and the social and environmental sustainability of production processes (Raynolds et al., 2007). A value chain approach is used, by reinforcing the access of small-scale farmers to and linkages with (inter)national market chain. This is achieved by improving their production and delivery conditions, and linking them to networks of traders, processors and retailers (Muradian and Pelupessy, 2005; in: Ruben and Zuniga, 2011). These measures are thought to indirectly reduce vulnerability as well, through the certainty provided by contracts. Several circumstances gave rise to the development of certification initiatives. The most important are the decline of state regulation concerning environmental and social conditions of production, and increasing globalization of the production of commodities. The decline of state regulation is due to a spread of neo-liberal policies, by which government regulations were undermined and risks increased (Raynolds et al., 2007). It can be argued that NGOs are filling this gap, by regulating marketing, social and environmental conditions through the enforcement of standards. Secondly, increasing globalization of the production of commodities has let to inherent unfair trading relations for smallholders and poor farmers. Upstream actors such as traders and wholesalers exert economic power over the entire chain, while producers do not capture the added value of their products. Thus, economic and environmental risks are shifted down the chain to smallscale farmers (Raynolds et al., 2007). Certifying small scale farmers, individually or through their producer organisations, is thought to help them to restore power balances, and reduce vulnerability. Third-party certification is one of the four types of certification initiatives that are distinguished.2 Third-party certification is corporate independent and has a strong consumer appeal
2 Gereffi et al. (2001) distinguish between four types of certification initiatives. First-party certification are the rules and reports on compliance by a single firm. Second-party certification involves an industry or trade association fashioning a code of conduct and implementing reporting mechanisms. Third-party certification involves an external group, often an NGO, imposing its rules and compliance methods onto a particular firm or industry. Lastly, fourth-party certification
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and market position. It is characterized by: ‘Participatory structures, clear standards, and credible verification systems’ (Raynolds et al, 2006:149). Several well-established third-party certifications can be recognized. The first, the Fair Trade label, was launched some twenty years ago with an emphasis on voluntary standards which promote equitable market access of coffee small-holder cooperatives. Fair trade guarantees a minimum selling price and provides a (joint) premium for community development programs. Newer initiatives such as Utz Certified and Rainforest Alliance emphasize private, or business-to-business, standards under which coffee is bought under market-conform conditions. Farmer’s income is supported through dynamic efficiency gains. Private standards are often favoured over voluntary standards by large commodity companies, as companies argue that the output price support on which fair trade is based, may favour production inefficiencies (Ruben and Zuniga, 2011). In this research, we focus on one of these private standard initiatives: Utz Certified.
UTZ Certification - the way to a sustainable livelihood? The aim of Utz Certified is to assure that products with an Utz Certified label are produced responsible, according to social and environmental standards. Utz Certified offers farmers assistance in producing and selling their coffee. This stimulates farmers to supply better quality coffee, which is rewarded by offering them above market prices (Farnworth and Goodman, 2006). Higher prices come about because traders offer higher prices for the better quality coffee. Consumers are also willing to pay a higher price for certified products. They relate a label such as Utz Certified to their concerns about small-farmer livelihoods, food safety and environmental sustainability concerns. Utz Certified thus promotes farmer participation in the global market through higher production and higher prices, market access, and positive publicity (Raynolds et al., 2007). Utz Certified hereby follows a market-based approach, which focuses on creating synergies between actors along the value chain, by which the position of the farmer within a value chain is strengthened. Critiques however emphasize that a market-based approach might be too much focused on export markets. Too little emphasis is laid on improving the capacity of producers and their organisations, thereby reducing their vulnerability (Vorley et al., 2012). Nonetheless, the mission of Utz Certified suggests that it goes beyond a market-based approach, and includes vulnerability reduction. The mission of Utz Certified is to ‘ensure that farm production is really going to be sustainable. Sustainability is about managing risk, generating value and ensuring the long term viability of enterprises and the societies they operate in’ (utzcertified.org, May 2012).This emphasis on sustainable farm production includes risk management and long term viability of income strategies. As argued above, reducing market constraints and non-market related shocks is a crucial aspect for the inclusion of small farmers in world markets. In this research, we will therefore examine to which extent Utz Certification attributes to vulnerability reduction and effective incomes strategies of smallholder farmers. We will examine whether being Utz Certified reduces market constraints as well as non-market related shocks and stresses, and how the two influence each other. We use the sustainable livelihoods (SL) framework (Chambers and Conway, 1992) to examine the extent to which Utz Certified contributes to vulnerability reduction. We do so because the definition of sustainability used by Utz Certified closely resonates with the definition given by the SL framework, which states: ‘a livelihood is sustainable which can cope with and recover from stress
involves government or multilateral agencies listing environmental, labour, and human rights principles for companies to follow.
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and shocks [managing risks], maintain or enhance its capabilities and assets [generating value], and provide sustainable livelihood opportunities for the next generation [ensuring long term viability]’ (Chambers and Conway, 1992:6). We focus mainly on generating value and managing risks, the first being related to market constraints, and the second to non-market related shocks and stresses. The sustainable livelihoods framework was defined by Chambers and Conway in their renown IDS Discussion Paper in 1992 (Chambers and Conway, 1992). It has been central to rural development thinking and practice in the past decade. The SL framework marked a much more holistic and inclusive perspective on well-being and livelihoods. The SL approach was able to link to economic theory through a mutual emphasis of institutions. New institutional economics as well as the SL approach emphasized the idea of institutions and organisations mediating livelihood strategies and pathways. A new emphasis was also put on sustainability, which became a central policy concern following the UN conference on environment and development in Rio in 1992. An advantage of the SL framework is that it adapts a multi-dimensional definition of poverty by focusing on how people make a living, the choices they make, the way how people gain access to resources and how they handle them. A disadvantage is however, that the SL framework focuses too much on the micro, household level, and much less on macro-linkages on regional or international level. It is argued that the SL approach has not adequately engaged with processes of economic globalization (Scoones and Wolmer, 2003). Challenges around politics, dynamics, knowledge and scale should be included in the framework for an integrated analysis of smallholder’s livelihoods and their integration in value chains (Scoones, 2009). In this research we particularly include issues around scale and knowledge. Scale emphasizes the importance of linking place-based analysis of farmers with broader processes of globalization and international trade. This includes how particular forms of globalization, such as private standards and associated processes of production, create livelihood opportunities for farmers. The knowledge perspective concerns, among others, the question whether inclusion in vibrant international markets should always be the ideal to strive for. We add to this argument by stressing that inclusion in markets should go together with a reduction of market constraints and non-market shocks. We link the sustainable livelihoods framework to value chain (VC) theory two examine aspects mentioned above. Value chain theory pays attention to the distribution of value-added throughout the supply chain, amongst different agents. It helps to explain the distribution of benefits, particularly income, to those participating in the global economy. The focus is not just on the efficiency of production in the chain, but also on those forces which determine the successful participation of particular groups of producers in final markets (Kaplinsky and Morris, 2001; Ruben et al., 2006). In our research, we focus on how Utz Certification influences the distribution of benefits to farmers participating in producer organisations through the enforcement of contracts. The research also contributes to the research and knowledge on the impact of private standards on small-farmers households and livelihood strategies. Many studies assessing the impact of standards only focus on outputs (e.g. higher prices, training activities) rather than on outcomes (e.g. higher outcomes, new skills) or livelihood impacts (changes in material wealth, social well-being and empowerment) (Nelson and Pound, 2009). The studies that have been performed on standards, mainly examine the effects of Fair Trade. Much less substantial research is done on new (private) standards such as fair trade (Ruben and Zuniga, 2011). This focus on Utz Certified is interesting as it, compared to fair trade, stronger based on a market-based approach. We therefore focus on outputs as well as on outcome effects of Utz Certified, and then especially outcomes concerning risk reduction and changes in risk behaviour. We thereby examine risk from several angles; we will use 14
objective and subjective risk measures, as well as measuring risk behaviour. Some exploration is also done towards impact of Utz Certification by examining income strategies. We define income strategies using the framework of Dorward and others (2010), who operationalize three types of strategies: hanging in, stepping up, and stepping out. More knowledge on how farmers manage risk in formal markets is necessary in theory development on inclusion of farmers in value chains (Seville et al., 2011). Stefan Dercon (2008) also argues that research on risk and its consequences should go beyond the rural settings and typical rural risks it has focused on, but should include ‘institutional risks’ as well. We define institutional risks are those risks that are related to the enforcement of institutions, such as private standards and producer organisations. In the research, we focus on coffee farmers in Kenya. Coffee is characterized by Southern production and Northern consumption, and thus constitutes a main link connecting less developed countries to global markets. It is grown in more than 60 developing countries by around 25 million farmers, the majority of which are smallholders (Bitzer et al., 2008:271). In addition, the sector is characterized by many challenges at production level, such as poor working conditions, environmental degradation and biodiversity decline (IBID). We therefore consider coffee a good example commodity for the issues at stake in this research. Moreover, Kenya is an interesting case since it saw quite some shifts in market arrangements for coffee in the past. Coffee production in Kenya is organized via producer organisations (cooperatives), and since the new cooperatives act of 1998, the government no longer had any policy making jurisdiction over the economic activities of cooperatives. This led to increasing levels of mismanagement, corruption and opportunism (Mude, 2006). This fits in the argumentation that social standards take over some government functions, and it is therefore interesting to examine how Utz Certification influences coffee farmers, as well as the organisation of coffee cooperatives in Kenya. The above leads to the formulation of our main research question as follows: ‘to what extend does Utz Certification attribute to vulnerability reduction and stimulate effective income strategies of farmers?’ The research is hereafter structured as follows. In chapter one, we will describe the theory underlying the research question, and operationalize it by the use of the SL framework and concepts related to value chain theory. Chapter two contextualizes the research by describing coffee farming in Kenya and the regions in which the research is conducted. Chapter three gives an overview of the methods and types of data used for answering the research question. In Chapter four, the results of the research will be described. Finally, in chapter 5 we will summarize and discuss our main findings and conclude.
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Chapter 1: Theoretical Framework In this chapter we discuss the theoretical framework that lays the basis for examining whether Utz Certified attributes to vulnerability reduction and effective income strategies. The outline of the chapter is as follows: we start with an explanation of the sustainable livelihoods framework and its several aspects, being assets, capabilities and activities, and the relation between livelihoods and institutions. Thereafter we pay more attention to the risks and shocks farmers are facing, and how the combination of producer organisations and Utz Certification help farmers to overcome these. We do so by the use of value chain theory and related concepts. We conclude with the operationalization of our theory and the deduction of hypotheses from the SL framework, so to test our research question.
1.1
The sustainable livelihood framework
The SL framework focuses on how people make a living, on the way in which people gain access to resources and how they handle them. This leads to activities required for a means of living. The SL framework hereby adapts a multi-dimensional poverty definition, going beyond single measures such as income and production. Being poor means the experience of deprivations of income as well as education, health, consumption, and power (Hulme and Sheperd, 2003). The SL framework thus offers the possibility to analyse a livelihood in a broad and holistic way. In short, a livelihood ‘comprises the capabilities, assets (stores, resources, claims and access) and activities required for a means of living’ (Chambers and Conway, 1992:6). The definition of a livelihood can be at different hierarchical levels3, for sake of brevity and clarity however, we use the household as the unit of analysis in this research. The household is defined as ‘each family member who stayed within the household for a period of at least one month for the last twelve months. Together the household members have a shared income and shared expenditures’.4
1.1.2 Assets, capabilities, activities and strategies A livelihood is the means of gaining and securing a living through the use of assets, capabilities and activities. Assets and capabilities are closely related, and together shape the set of activities a household is engaged in. Scoones (1998) defines assets as the livelihood resources, or the ‘capital base’, from which different productive streams are derived by which livelihoods are constructed. This capital base can be subdivided in five types of capital (DFID, 1999), namely: 1. Physical capital: the basic infrastructure (transport, shelter, water, energy and communications) and the production equipment and means that enable people to pursue livelihoods. 2. Natural capital: the natural resource stocks (land, soil, water, biodiversity, environmental resources) from which resource flows and services useful for livelihoods are derived. 3. Economic or financial capital: the financial resources available to people which provide them with different livelihood options (cash, credit, savings, regular remittances or pensions).
3
The most commonly used is the household level, but the individual or intrahousehold level are also used, for instance to examine the wellbeing and access of especially women and children, which may be inferior to that of others. 4 Adapted from the following survey: (Tegemeo institute, 2009)
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4. Social capital: the social resources (networks, memberships of groups, relationships of trust, access to wider institutions of society) upon which people draw when pursuing different livelihood strategies. 5. Human capital: the skills, knowledge ability to labour and good health and physical capability, important for the successful pursuit of different livelihood strategies. Human capital determines the abilities of a person to pursue a livelihood strategy, because human capital is required in order to make use of any of the four other types of assets. Chambers and Conway (1992) define human capital with capabilities. The concept of capabilities was introduced by Amartya Sen. It denotes a person’s opportunities and abilities to generate valuable outcomes, taking into account relevant personal characteristics and external factors. The concept of capability thereby comprises both the processes that allow freedom of actions and decisions, and the actual opportunities that people have (Sen, 1999). This emphasis on processes and opportunities implies that capabilities are not only reactive, such as the capability to respond to adverse changes in conditions; they are also proactive. This includes gaining access to and the use of assets, services and information, and exploiting new conditions and resources (Chambers and Conway, 1992). Development, then, is about developing the capabilities of people by increasing options available to them, by which people can maximize their assets to choose the way of life they value (Kabeer, 1999). Assets and capabilities together shape the opportunity set of activities for the livelihood strategies of the household (Ellis, 2000; in Mutenje et al., 2010). Activities might include the following: cultivation of crops, reciprocal and/or wage labour, trading, providing transport services or other developing other businesses. They variously provide the following ‘outputs’: food, cash, and other goods to satisfy a wide variety of human needs. Some of these outputs are consumed immediately, others go into short or long-term storage, to be consumed later or to be invested in other assets (Chambers and Conway, 1992). Rural farmers, our object of research, have specific characteristics which influence the activities they employ and related outputs. They are defined as peasants, being farm household who ‘derive their livelihoods mainly from agriculture, utilize mainly family labour in farm production, and are characterized by partial engagement in input and output markets which are often imperfect and incomplete’ (Ellis, 1998:13). The main activities of peasants thus are the cultivation of subsistence and cash crops, for which land is the main determining asset. Land forms the basis of their livelihoods and the availability of land puts an important restriction on the outputs derived from crop cultivation. More land means more possibilities for the diversification of activities towards cash crops, such as the cultivation of coffee, next to subsistence farming. In addition, to be able to participate in and benefit from cash crop value chains, peasants should have access to the right assets, and the ability to use these assets effectively (Seville et al., 2011). Next to on-farm activities such as the production of cash crops and income derived from livestock, income is derived from diversification towards off-farm activities as well. The involvement in either on- or off-farm activities such as wage labour or business development depends on capabilities of the household such as family labour available, and the level of education of household members. The degree of access to assets strongly influences the ability to engage in more profitable (non-farm) activities. A livelihood strategy depends on how activities and outputs are combined towards a strategy. Agricultural intensification/extensification and livelihood diversification are combinations of activities defined as livelihood strategies (Scoones, 1998:9): 17
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Intensification/extensification leads to gaining more of a livelihood from agriculture. The main input variables influencing intensification are considered labour and capital inputs (Ray, 1998). Two strategies for increasing productivity are distinguished in empirical studies: a) a labour-led intensification based on intensive use of (family) labour to increase production, and b) capital-led intensification, where farmers augment their production by combining labour with capital inputs , mainly fertilizers and irrigation (Boserup, 1965; and Reardon et al., 1996; in Ruben, 2008:160). Extensification implies that more land is put under cultivation. Diversification is a shift to a range of off-farm income earning activities. This can be an active choice (or opportunity) to invest in diversification for accumulation and reinvestment. It can also be aimed at coping with temporary adversity or a permanent adaptation of livelihood activities, when other options are failing to provide a livelihood. Migration can either be voluntary or involuntary, and leads you to move away and seek a livelihood elsewhere, temporarily or permanently.
1.1.3 Constraints or drivers: context and institutions The ability to follow a certain livelihood strategy is, next to a combination of assets and capabilities, shaped by the context of a household. The context comprises policy settings, history, agro ecology, and socio-economic conditions (Scoones, 1998). The agro ecology of a certain context determines for instance in which crops a farmer can specialize. Moreover, the choice for certain off-farm activities can be profitable due to socio-economic circumstances. The link between a context and livelihoods are institutions: they are the structures and processes that mediate the complex process of achieving a sustainable livelihood. Institutions are broadly defined as the ‘rules of the game that define the incentives and sanctions affecting people’s behaviour’ (Dorward et al., 2005:2). They are to be distinguished from organisations, which are players who set the rules of the game. Several institutions together form institutional arrangements, which are sets of rules and structures that govern particular contracts, and the context within which contracts are governed (Shiferaw et al., 2008). Power relations are thus embedded within institutional forms. Institutions are also dynamic, as they are being shaped and reshaped over time. Understanding institutional processes allows the identification of restrictions, barriers and opportunities or gateways to sustainable livelihoods. Institutions affect the way people gain access to assets and thus influence the composition of livelihood strategies (Chambers and Conway, 1992). This means that institutions mediate the ability to carry out strategies and achieve certain outcomes (Scoones and Wolmer, 2003). Livelihood outcomes are in the SL framework determined as the improvement of well-being and capabilities, the reduction of poverty, and the enhancement of vulnerability and resilience of a household (IBID). So far we described the several concepts that together shape the livelihoods framework: within a particular context, a combination of livelihood resources (e.g. assets and capabilities) results in the ability to follow a combination of livelihood strategies, with certain outcomes. Institutions thereby mediate the ability to carry out strategies and achieve certain outcomes (Scoones, 1998). We find this combination of concepts useful as it provides clear building blocks of the complexity of a livelihood. We have however several remarks. Firstly, outcomes are not very specific defined by the livelihood framework, while it is important to distinguish between several types and levels of outcomes, especially when measuring impact. Impact is defined as ‘the systematic analysis of lasting 18
or significant changes – positive or negative, intended or not – in people’s lives brought about by a given action or series of actions’ (Roche, 1999; in Nelson and Pound, 2009:4). The different levels at which impact can be measured are outputs (higher prices, training activities), outcomes (e.g. higher incomes, or new skills), and livelihood impact (changes in material wealth, social wellbeing and empowerment) (IBID). Outcomes as defined by the SL framework thus resemble livelihood impact when the above definition is used. For sake of clarity, we choose to use the output-outcome-impact scheme of impact measurement, since it gives the possibility to measure the effects of Utz Certification more precise. Secondly, much depends on why households make choices to use a combination of livelihood resources for certain strategies. In our opinion, the livelihood framework does not provide enough clarity as to how this aspect is to be assessed. A second concept that needs further explanation in this respect is the mediating ability of institutions. As is explained in the introduction, current institutional arrangements are often of a complex nature, especially if it concerns arrangements in a global commodity supply chains. Kenyan coffee farmers are included in global supply chains, and how they make choices is thereby influenced by these arrangements. We therefore go deeper into these questions in the next two paragraphs. We thereby draw on theories of risk perception and risk behaviour to explain why certain choices are made. Thereafter, we explain how institutions enforced by producer organisations and Utz Certification influence (perceived) risks and mediate choices.
1.2
Market constraints and other shocks, shaping risk perceptions
Peasant farmers have to make decisions concerning production and consumption. As a producer, the household chooses levels of inputs and outputs that satisfy the producer’s technology, and maximize profits. As a consumer, the household also chooses levels of consumption, which is maximized under constraints of income (Janvry et al., 1991). The outcome of production decisions influences consumption decisions and vice versa, since farmers produce cash as well as subsistence crops. Investing in a cash crop for instance, might result in the need to buy food if food crops are replaced by cash crops. In this research we focus mainly on decisions on the production side of the household, since we focus on a cash crop. We take however the influence of consumption into account as well. The choice to be involved in markets contains a continuous tension between the risky advantages of market participation and the conservation of a non-market basis for survival (Ellis, 1998; World Bank, 2001). Market participation is risky since the markets in which peasants are engaged, are often imperfect and incomplete (see Text Box 1). Market deficiencies are especially profound in rural areas due to underdeveloped transport and communication infrastructures. The process of exchanging cash crops is undermined due to high transaction costs and the absence of institutions that help coordinate marketing functions or that link producers to markets. Peasants are thus vulnerable to production risks (such as the weather, pests, and sickness) and market risks (such as price volatilities, delays in payment, breach of contracts) which are systemic to agriculture (Dorward et al., 2005).
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Textbox 1: Economic conditions faced by the peasant farm family - Capital markets are fragmentary or non-existent - Variable production inputs may be erratically available or unavailable, their quality may vary and access to them may involve formal or informal systems of rationing - Market information is poor, erratic, fragmentary and incomplete, and there is high cost for the farm household in acquiring information beyond the immediate confines of village or community - Markets and communications in general are not well integrated, and depending on place and infrastructure there are varying degrees of isolation between local communities, regions and the more developed segments of the national economy (Source: Ellis, 1993:12)
In addition to market imperfections and instabilities, peasants’ insecurity might be increased by climate variations, low social and economic status and bad politics at the state level leading to natural disasters, conflicts, political instability and health risks (Dercon, 2008). Vulnerability brought on by these shocks influences, together with the assets a household possesses, livelihood activities and the scale of outcomes (Ellis, 1998). Farmers often remain in a diversified set of more risk-averse activities, since diversification has important consumption smoothing, risk management and productive functions (Dorward et al., 2001).The objective pursued with a livelihood is thus not only maximizing income and consumption, but at the same time managing risks and thereby avoiding vulnerability (Ellis, 1998). In general, two types of shocks can be distinguished, namely stochastic shocks and idiosyncratic shocks. Idiosyncratic shocks are the ones that people experience individually; these are related to household characteristics. Stochastic shocks are those experienced by everyone, such as a high dependency on the weather, and high prices of household goods (Dercon, 2002; Fafchamps, 2003). Whether you are dealing with the consequences of idiosyncratic or stochastic shocks requires different approaches. Idiosyncratic shocks, for example, can be insured within the community since not everyone is affected. The risks can thus be shared, which is not possible in the case of a stochastic shock. They are faced by a whole region and cannot be overcome by communal insurance (Dercon, 2002). Other features important are the frequency and the intensity of shocks, as well as the persistence of their impact. Persisting shocks are much more difficult to cope with, since households need to recover from shocks time after time (Hulme and Sheperd, 2003). Risk is defined by Smith, Barrett et al (2000) as: ‘uncertain consequences, and in particular exposure to potentially unfavourable circumstances, or the possibility of incurring nontrivial losses’. Risk refers to the possibility that something unfavourable might occur. The interpretation of this possibility is subjective: it is not a probability objectively calculated on past events, but it is based on the farmer’s subjective interpretation of events. This because people’s behaviour is not only influenced by measurable, objective risks that they face, but also, or even more, by their subjective perceptions of risks and the possible consequences of different events (Doss et al., 2008). Subjective risk perception is a valuable concept as it incorporates multiple facets of risk: the understanding of objective risks and the expectations about the farmer’s personal exposure to risks (Smith et al., 2000). The combination of experienced shocks and risk perception about coming shocks leads to the choices farmers make in strategies to mitigate (ex-ante) or cope (ex-post) with adverse events if they occur. The former are called risk management decisions and resemble livelihood strategies, such as the diversification of activities and assets, migration, and specialization into low-risk activities. Ex-
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post coping refers to self-insurance via precautionary savings or the use of informal community or network-based risk-sharing arrangements (Fafchamps, 2003). 5 People who are persistently prone to a variety of shocks have a greater risk on becoming chronic poor due to the significant capability deprivations they experience. Shocks affect their assets base, and their individual production and consumption behaviour. A low return to assets caused by the repetitive experience of shocks is likely to lead to risk aversion, and in its turn influences the ability to manage risks and shocks (Mosley and Verschoor, 2005). The chronic poor are thus thought to stay in poverty, since their priority becomes more and more to minimize vulnerability to shocks as much as possible. They become trapped below a critical threshold of wealth that people have difficult time crossing from below (Barrett, 2005). People who are above this threshold are able to grow toward a high-productive steady state, while those below the threshold sink toward lowproductivity subsistence equilibrium. To deal with poverty traps, Chris Barrett suggests two types of policies. Safety nets are required to effectively block the descent of people into chronic poverty. Examples are emergency feeding programs, and crop insurance. Next, cargo nets need to be in place to help the chronic poor find ways out of poverty, by overcoming the structural forces that otherwise would keep them down (IBID). Choices for certain strategies, in combination with the right types of safety and cargo nets are of thus main importance for development. They determine whether households can apply effective strategies out of poverty. An effective strategy leads to the accumulation of sufficient productive assets, so that the household can earn surplus above and beyond the immediate consumption needs. Accumulated surplus might enable continued accumulation and steady growth (Barrett, 2005:55). Little et al (2001) define three types of variables that are important in choices for accumulation or investment strategies6: a) initial conditions, b) local response and c) opportunity variables. Conditional variables include system-level phenomena which are the same for everyone in an area. They indicate whether conditions are conducive for certain strategies, such as the fertility of the soil, climate and the availability of land. Local response variables cover the individual and household characteristics such as wealth differentiation, gender, age and other social factors. Opportunity variables help explain the types of risk strategies available. These include the distance to markets, available services and infrastructure, education. Local response variables are important in facilitating or constraining strategies, while opportunity variables can favour certain strategies (IBID).
1.3
The role of institutions
A lack of institutions is often thought to be the reason why market opportunities for peasants are limited. Institutional arrangements can provide the following functions to markets: transmitting of information; mediate transactions; reduce transaction costs; facilitate the transfer and enforcement of property rights and contracts; and manage the degree of competition (Rodrik, 2000; World Bank, 2001). Institutions thus provide opportunity variables for the poor, as they have the ability to make market systems more inclusive and integrated. In addition, they can help farmers to manage risks from market exchange, increase efficiency, and raise returns. Two of these enhancing market functions in rural areas will be discussed in more detail below: agricultural producer organisations 5
Marcel Fafchamps (2003) argues that this distinction cannot be made this rigorously. He rather proposes a separation between strategies that reduce risk itself (preventive) and strategies that insulate welfare from risk (curative).
Their focus is specifically on diversification, but since diversification is a way of risk coping, we argue that their model can be applied here as well. 6
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and Utz Certification. Producer organisations address market constraints in the local market context, whereas third-party certification is able to address issues in the broader, international context.
1.3.1 Producer organisations The renewed attention given to agriculture and its role in poverty reduction has led to the recognition that organisational structures, such as producer organisations, can improve the efficiency of agricultural marketing (Bijman and Wollni, 2008). An agricultural producer organisation (PO) is defined as a ‘formal, voluntary membership organisation set up for the economic benefit of agricultural producers by providing these producers with services that support the farming activities, such as bargaining with customers, providing inputs, providing technical assistance, providing processing and marketing services’ (Bijman and Wollni, 2008:4). A PO is thus an association established to promote the interest of their members. Members are farmers who each have their own farm, and produce the same cash crop. The main goal of the PO is to provide services that support producers in their farm activities, including the marketing of their products. The benefits of being member of a PO are several. POs invest in the knowledge improvement and production factors of their farmers. Thus, they are able to aggregate surplus output of higher quality and reaching economies of scale (Milford, 2004; World Bank, 2007; Blandon et al., 2009). Next, possibly the greatest advantage of POs is the collective marketing of this harvest which can reduce the transaction costs of individual farmers in several ways. The costs of marketing are spread, and a PO has enhanced abilities to negotiate for better prices, thus improving the market power of many individual farmers (Dorward et al., 2005). Through these measures sales are likely to become more stable, leading to a more stable income through the mutual insurance of otherwise uninsured risks (Key and Runsten 1999). Being a member of a cooperative does not only have benefits, but farmers face costs as well. Examples of these are a membership fee, specific requirements for production, and compulsory cooperative meetings. These costs are voluntarily applied. Due to low managerial capacity, however, also involuntarily costs might occur, such as a delay of payment, and insufficient provision of technical and commercial assistance (Milford, 2004). Other governance issues that can occur are elite capture, legal restrictions and exclusion of the poor (World Bank, 2001; Mude, 2006). These slow down growth and expansion, and lead to high transaction costs. Barham and Chitemi (2009; in Seville et al., 2011) argue that producer organisation need strong internal institutions, strong group functioning activities and a good asset base to improve their market situation and take advantage of market opportunities. POs can thus offer smallholders several ways to overcome market constraints, but only under certain conditions. The group functioning of POs depends on several underlying dynamics, the most important being collective action and trust. Groups organized through collective action, enable individuals to empower themselves and increase benefits from market transactions. Collective action is therefore widely recognized as a positive force for rural development. The strength of the group’s resources, knowledge and efforts are combined to reach a goal shared by everyone, such as the marketing of coffee. Getting together with others can allow individuals to better cope with risk, particularly when other institutions do not provide sufficient insurance against risk (Place et al., 2004). The member’s active participation is of great importance for the successful functioning of the cooperative. However, production takes place at each individual farm. It is therefore difficult for the POs management, as well as for individual members, to control the production practices of others. Problems arise when not all members participate in the creation of its benefits, but free-ride on the 22
work of others without contributing to the provisions the PO is offering. Free-riding occurs when there is a discrepancy between the optimal situation for the individual and the collective. The economic optimum for all is the situation in which all farmers participate in producing good quality coffee of high quantity. The optimal situation for an individual is however where all the others provide the goods, while the individual free rides (Milford, 2004). This may encourage the underproduction (inefficient production) of the public good, in this case coffee (Olson 1965; in Ostrom, 2003). For coffee producers, free-riding happens for instance when part of the farmers invest strongly in the quality of their coffee, thereby lifting the average quality of coffee. Farmers, who did not invest much, will then still receive the benefits of higher prices received due to the better coffee quality. Another problem related to collective action and free-riding is the cost of control (Milford, 2004). A cooperative is a democratic organisation in which every member has an equal say. All members together control the management. If the management of a cooperative generates less profit than the profit that could potentially be reached, the costs are paid by the members. However, since these costs are shared, the incentive for an individual farmer is not high enough to participate actively in changing this situation. A problem related to the democratic management of the organisation, is the way in which the objectives of the cooperative are chosen. Many objectives cannot be simultaneously enforced, so conflicts will arise as strategies have to be chosen for democratically. For instance on pricing of services to members, the distribution of profits, etc. (IBID). Interpersonal trust is a condition that facilitates collective action. ‘Interpersonal trust is a psychological state comprising the intention to accept vulnerability to the actions of another party, based upon the expectation that the other will perform a particular action that is important to you’ (Six, 2007). Trust thus has an instrumental value in helping to reduce risks and transaction costs of relationships (Williamson, 2000). Formal means such as institutions can manage risks, but this might be too difficult or too expensive. They can never completely eliminate relational risk, and hence some degree of trust is always needed (Nooteboom, 2007).Especially when formal institutions are failing to meet local information or market needs, the exchange of knowledge through trust is important for meeting cooperation and collective action. Research shows that these characteristics of trust strengthen bonds between individuals, facilitate information exchange, and enables risk taking by business people (Williamson, 1979; Murphy, 2002). Trust and actions must mutually reinforce each other. Trust and the performance of the cooperative thus influence each other; the opportunistic behaviour described above for instance limits trust in each other, and thus limits collective action. Elinor Ostrom (2003) therefore describes trust as the core link between networks and collective action and the most relevant factor to provide voluntary cooperative action. It is enhanced when individuals are trustworthy and networked with one another within institutions that reward honest behaviour. On the other hand, lower trust in the cooperative may lead to alterations in risk management and attitudes, because there is less trust in the risk managing capacities of the cooperative. High or low trust in a person or an institution is reinforced by someone’s dealings with that person or institution. To value if the other party is trustworthy, people prefer to depend on past dealings with the other party. Especially if a relation is entered into with an economic motivation, trustworthiness is important, so not to discourage future transactions. Economic relations are overlaid with social content that carry strong expectations of trust and absent from opportunism (Granovetter, 1985).
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1.3.2 Utz Certification International trading relations are often considered inherently unfair for smallholders and poor farmers. Producers do not capture the added value of the product they produce; and governance structures are organized in such a way that upstream actors exert economic control over the entire chain. Due to this, economic and environmental risks are shifted down the chain to small-scale farmers (Farnworth and Goodman, 2006).Third-party certification such as Utz Certified promotes positive economic, social and environmental benefits, which are thought to have positive implications for the sustainability of the well-being of peasant producers (Raynolds et al., 2007). Utz Certified was founded by two business partners, a coffee grower and a coffee roaster, under the name Utz Kapeh, meaning ‘good coffee’ in the Mayan language Quiché. Since the market launch in 2002, Utz Certified has grown to be one of the leading sustainable coffee programs worldwide, and has also developed models for cocoa and tea. This extensive growth of Utz Certified is fuelled by their focus on mainstream markets, and their integration of lower quality coffee. Utz certified pursues a CSR approach, focusing on mainstream roasters and brands such as Ahold and Nestlé, and seeks to ensure that products are grown with respect for producers and environment. The CSR approach of Utz Certified entails the following: prices are not regulated, but instead the quality and quantity of coffee is raised through which farmers generally receive a modest premium; standards are laid out in a code of conduct; extensive record keeping and custody documentation is required by producers; and they rely on existing labour and environmental regulations (Raynolds et al., 2007). With this approach, Utz Certified differs from Fair Trade (FT). FT empowers producers by focusing on farmer organisations and paying a premium, while Utz stimulates producers by guaranteeing minimum requirements in the mainstream coffee industry (IBID). The expected benefits from such a program include several: the strengthening of farmer organisations in terms of good governance and increased efficiency in provision of technical as well as commercial services. The greater accessibility of farmers to these services leads to higher productivity, higher producer prices and higher enterprise and farm incomes. Consequently, farmers might be able to make greater investments on-farm and/or in other activities that improve the welfare of household members (Tegemeo institute, 2009). Contracts define the rules and obligations for establishing cooperation between Utz Certified and the cooperative. They are used as an instrument to improve product quality and enforce permanent supply (Ruben et al., 2006). For Utz Certified, contracts reduce monitoring costs and are especially preferred in markets with high-quality demand, such as coffee. For farmers, the contractual arrangement reduces price uncertainty. Saenz and Ruben (2004) showed that the existence of a contract reduces uncertainty for the producer, enables investments in land improvements, and better crop management. Product quality is also further reinforced by institutional variables like technical assistance and delivery frequency. Contracts require however trust building, so to guarantee loyalty and reduce opportunistic behaviour. The effectiveness in contract compliance between different agents involved in a supply chain leads to the reduction of transaction costs and risks (Sheldon, 1996; in Ruben et al., 2006). The reduction of risks is important, since the engagement of smallholder farmers in international supply chains is seriously constraint by risk motives. Price uncertainties, product denial and contract breach have discouraging effect on smallholder farmers. Farmers can only make the required investment to improve delivery frequency and quality when they can be relatively certain regarding available market outlets (Fafchamps, 2004). 24
1.4
Utz Certification and Sustainability – formulating hypotheses
UTZ certified wants to assure buyers of their coffee responsible production according to economic, social and environmental standards. In return, they offer coffee cooperatives assistance in producing and selling their coffee, giving them access to an international network of support programs of buyers and development organisations, and technical assistance (UTZ Certified, 2008). Utz certified thus claims to ensure sustainable farm production, by which they define sustainable as ‘managing risks, generating value, and ensuring long term viability of enterprises and the societies they operate in’. Within the scope of this research, we focus on the extent to which Utz Certified attributes to ‘managing risks’ – coping and recovering from stresses and shocks -, and ‘generating value’ – maintaining or enhancing its capabilities and assets. The ability to cope and recover from stress and shocks is central to the definition of a sustainable livelihood. Those who are unable to cope or adapt are inevitably vulnerable and unlikely to achieve sustainable livelihoods (Scoones, 1998). We argue that the degree of vulnerability reduction due to Utz Certification depends on several features of the producer organisation and the way it is influenced by Utz Certified. It depends on the performance of the cooperative, and especially the way in which the cooperative reduces market imperfections, transaction costs and provides services which enable farmers to reach scale. On the other hand, as explained above, the cooperative management might provide stresses due to managerial problems, and cooperative members might contribute to the risk aversion of farmers by free-riding and a lack of collective action. Utz Certified adds to the resilience of farmers by enforcing contracts which provide the security of prices and extension services. We argue that Utz Certification thus influences the sustainability of livelihoods in several ways, indirectly and directly. They do so through intervening in the services offered by producer organisations. These interventions lead directly to a higher and better quality harvest, for which farmers receive a higher price (hypothesis 1). This has the indirect effect that farmers perceive their cooperative as a more reliable partner (hypothesis 2). We expect this to have effects in the reduction of vulnerability of farmers. Market related shocks are reduced, which makes farmers better able to cope with non-market related risks and shocks (hypothesis 3). We argue that this could ultimately lead to a shift in livelihood strategies (hypothesis 4). Figure 1.1 shows schematically how these hypotheses are related to each other Figure 1.1: Schematic representation of hypotheses
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1.4.1 Direct effects of Utz Certification – higher harvest Our first hypothesis concerns the direct effects of Utz certification, or the outputs. We expect Utz Certified farmers to have a higher harvest, which is achieved through the improvement of extension services, and the raise of coffee prices (Hypothesis 1). We expect both to lead to a higher harvest, through capital-led intensification. Households participating in value chains can only benefit from their participation if they have the ability to use and accumulate certain assets effectively. In the case of coffee farmers, the production of coffee of good quality and high quantity earns farmers a higher harvest and, if they receive a good price for their harvest, benefits from their participation in the coffee value chain. Extension services are often already in place but the successes of these are mixed, some have high returns and others have only negligible success, and extension officers often lack accountability (World Bank, 2007). An important guarantee for the accumulation of the assets to upgrade quality and quantity of coffee is the perceived support farmers receive from their organisation (Ruben, 2008). We thus expect that the differences between certified and non-certified farmers will especially be on the quality of extension services received.
1.4.2 Indirect effects of Utz Certification – more trust and loyalty… A cooperative is a collective, based on the fact that farmers are given an opportunity to produce and collectively sell differentiated and value-added products. This allows producers to capture a greater share of the price paid by final consumers. To participate in high-value markets, collective action is a necessary condition to manage costs and facilitate participation (Ruben, 2008). In the presence of trust, a farmer can realize an action with the confidence that other farmers will do what they are supposed to do. Trust has a great influence on the willingness to participate in a cooperative (Blandon et al., 2009). Through trust in each other and the cooperative as an organisation, risks can be shared. We argue that Utz-certified farmers have more trust in their cooperative and its members (H2a) because it is a prerequisite for working effectively together towards higher outputs. In addition, farmers are more loyal to their cooperative (H2b).
1.4.3 …leading to vulnerability reduction. Hereafter, we argue that higher trust and more loyalty towards the cooperative, lead to less risk averse behaviour. Due to trust and loyalty, individual risks are partially shared. Risk sharing is based on solidarity systems and is a form of mutual insurance (Fafchamps, 2003). Risks are shared due to the collective sale of outputs, and the collective distribution of inputs, knowledge and tools. Michael Carter (1987) argues that collective activity is especially desirable for farmers with a small parcel of land. If small holder farmers would operate on their own, they are more likely to be disadvantaged in inputs, outputs, credit markets and prices, as well as seasonal shocks. They have a smaller ability to withstand bad years of production. A good functioning cooperative assures a good, collective derived, income, which can form the basis for further individual (household) development (Ruben, 2008). On the other hand, there is the risk that the cooperative does not deliver on these factors. Smallholders might then withdraw into safer production strategies which minimize financial exposure, but which lessen average productivity (Carter, 1987).
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1.4.4 Maintaining a sustainable livelihood – more sustainable strategies Finally, we argue that all these effects could influence livelihood strategies in a positive way. The higher income out of coffee, combined with a less risk averse attitude due to vulnerability reduction make that we expect Utz Certified farmers to make more optimal choice in income strategies. Dorward et al. (2010) identified three broad types of livelihood strategies, with three corresponding types of asset or activity contribution to livelihood strategies: a) Hanging in: whereby assets are held and activities are engaged in to maintain livelihood levels, often in the face of adverse socio-economic circumstances b) Stepping up: whereby current activities are engaged in, with investments in assets to expand these activities, in order to increase production and income to improve livelihoods (an example might be the accumulation of productive dairy livestock). c) Stepping out: whereby existing activities are engaged in to accumulate assets which in time can then provide a base or ‘launch pad’ for moving into different activities that have initial investment requirements leading to higher and/or more stable returns. For example, the accumulation of livestock as savings which can then be sold to finance children’s education (investing in the next generation), buying of vehicles, or fund migration. This scheme gives a simple way of analysing the multidimensionality of poverty, and has the following two propositions: People aspire both to maintain welfare and to enhance it In trying to advance welfare, people can attempt to expand their existing activities and/or move into new ones. It recognises the importance of current livelihoods, but it also directs attention beyond those livelihoods to consider wider and long-term aspirations of stepping out. Due to the positive effects of certification, we expect that farmers who are Utz Certified are possibly more engaged in stepping up or stepping out activities, whereas non-certified farmers will be stronger engaged in hanging in activities. A livelihood is always obtained within a certain context. Table 1.1 gives a simple overview of how natural resources and local market opportunities constrain or enable livelihood strategies. Table 1.1: Livelihood strategies by market and natural resource potential Status Poor Naturalresource potential
Low
High
Less poor Poor Less poor
Local market opportunities High/Dynamic Hang in (more local non-farm based) Step out (migrate) Step out (local non-farm) Hang in (farm / subsistence) Hang in (farm and non-farm) Step out (migrate) Step out (local non-farm) Step up (exports) Step up (local market) Low/stagnant Hang in (subsistence )
Source Dorward et al. (2010)
Kenyan coffee farmers deal with several, overlapping, contexts: the household, the coffee cooperative, and this broader agro-ecological and economic context. In the next chapter, we will sketch this context accordingly, so to be able to test our hypotheses while taking this contextual information into account.
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Chapter 2 – Context: Kenya, coffee and cooperatives We start our analysis with giving an overview of the economic, political, social and agro-ecological context in which the research was done. We firstly focus on Kenya’s overall economy and Kenya’s cooperative history. Thereafter the choice of cooperatives is described, and the local economic and agro-ecological contexts of the four selected coffee cooperatives. We conclude with the features of the cooperatives themselves. The descriptions below are based on literature, interviews with representatives from the cooperatives, interviews with cooperative members and the survey.
2.1
Kenya: Economy and coffee production
2.1.1 Kenya’s economy Kenya ranks 143 on the Human Development Index of 2011, and is thereby listed under countries with low human development. Kenya has an estimated population of 35.5 million people, which has tripled over the past 30 years. This increased pressure on the country’s resources. Kenya’s economy is for a great part based on agriculture. The percentage of the population living in rural areas is 78 per cent and they rely on agriculture for most of their income: 71 per cent of the total population of Kenya is economically active in agriculture. The share of agricultural exports of total exports is rather high: 53.4%, which makes Kenya the 12th country in the world with the highest agricultural export share of total exports. The three main agricultural export products are: Black tea - 14,3 per cent of total exports, cut flowers - 13,8 per cent, and none-roasted coffee - 5,9 per cent (World Bank, 2011). In addition, the agricultural share of the GDP is 25.4% per cent in 2009 (IFAD, 2007). Nearly half of the total population is living below the poverty line, and 85% of this group lives in rural areas (World Bank, 2011). Among the main causes of poverty are according to IFAD (2007) the following: low agricultural productivity and poor marketing; insecurity, leading to losses of property; unemployment and low wages; and a lack of capital to facilitate self-employment. In addition, women are more vulnerable to poverty due to unequal access to social services and economic assets.
2.1.2 Coffee and coffee cooperatives Almost all coffee produced in Kenya is exported; only 1 or 2 per cent is consumed locally. Coffee is an important source of employment in rural Kenya, since it is an organisation intensive crop enterprise (Tegemeo institute, 2009). In the organisation of the coffee sector, two types of farmers can be recognized: 40 per cent of the coffee farming is done by large scale farm estates, whereas the other 60 per cent of the coffee is grown by small-scale farm households. Small-scale farmers are organized in coffee cooperatives through which they sell their coffee. Estates have a higher coffee harvest per hectare at 509.9 kg/ha, whereas cooperatives harvest on average 265.8 kg/ha. Box 3.1 gives an overview of the organisation of the Kenyan coffee industry.
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Textbox 2.1: structure of Kenyan coffee industry The coffee producers are mainly small-scale farmers with farms of less than 5 acres while the estates have farms of over 5 acres. The small-scale farmers form cooperative societies who market and distribute their produce. The co-operatives and the estates send their produce to commercial millers for milling and grading. The commercial millers then send the graded coffee to marketing agents who prepare, classify the coffee, prepare catalogues and put a reserve price. The sample of the coffee is then sent ten days before the weekly auction to the buyers/dealers for evaluation before the actual auction day. The licensed coffee dealers buy coffee at the auction for export and for local roasting for the domestic market. The major players in the coffee industry in Kenya are Kenya Planters Co-operative Union (KPCU), which is involved in almost all the processes of coffee production and marketing. KPCU currently holds a packer’s license, a warehouse license, a coffee auction license, management agent certificate and a miller’s license. The Coffee Board of Kenya (CBK) is the other major player in the industry and is involved in licensing and regulating the sector. CBK is an arm of the government and falls under the Ministry of Agriculture. Source: Export Processing Zones Authority, 2005.
Before independence, cooperatives were used by colonial powers to group rural producers by their export commodity, so to collect commodities such as coffee cost-effectively. After independence, cooperatives were given an essential role for the development of rural areas. They were granted a monopoly in the supply and marketing of their product, but in exchange the government had a key role in regulating activities for cooperatives, and thus held strict supervision. Cooperatives were thus initiated and directed from above by the government. Therefore, they may lack common bonds and mutual trust which are characteristics of a ‘bottom-up’ process of the formation of a producer organisation (Develtere et al., 2008). In addition, farmers must market their coffee through a cooperative, and have to deliver their produce to an organisation close to their home. This might exempt them from being efficient, as there are no longer constraints that force them to maximize their benefits to cooperation. An important motivation for organisation, to attain optimal scale, thereby loses its meaning (Mude, 2006). In the late 1980s, Structural Adjustment Programs (SAPS) let to the withdrawal of the State and the liberalization of state-controlled cooperatives. Therefore, the 1966 cooperative societies act was replaced by a new act in 1997, which granted the cooperatives internal self-rule, so to become a more autonomous, self-managed and sustainable cooperative movement (Develtere et al., 2008). However, since the regulation role of the state was not yet replaced by another institution, cooperatives were left without a regulatory mechanism. Freedom was abused immediately, which led to massive corruption, political opportunism and mismanagement, including the coffee cooperatives (Mude, 2006). Figure 2.1 shows the decline in coffee production since the late 1989s due to these policies. In 2004, a new cooperative act was introduced which installed a commissioner for cooperative development, which created an improved framework for the development and growth of cooperatives. Coffee production has slowly declined over the last two decades while the area under coffee has been more or less maintained, thus leading to a decline in coffee productivity. Recent figures forecast however a slight increase in productivity, but the challenges to smallholder coffee farmers remain high: they have competition from other enterprises, high costs and low returns. At the cooperative level, the utilization of pulping facilities is below capacity (21%) and the cost of pulping and overheads are therefore high compared with coffee estates (USDA, 2011).
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Figure 2.1 - Coffee yield per hectare Kenya, since 1960 (Source: FAOSTAT)
coffee yield in kg/ha
10000 9000 8000 7000 6000 5000 4000 3000 2000 1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
years
Most of the coffee in Kenya is grown in the regions around the slopes of Mt Kenya: Kiambu, Kirinyaga, Muranga, Nyeri, Ruiru and Thika. In Kenya, coffee is grown in areas with an altitude between 1200 and 2200 meters above sea level. Mean favourable temperatures for coffee growing lie between 18oC during the night and 22oC during the day (Wintgens, 2009). Low temperatures favour coffee berry disease, while high temperatures can lead to other problems such as defective fruit set. Rainfall is the most important restrictive factor for coffee growing. The annual rainfall needed for Arabica coffee is between 1400 and 2000 millimetres of rainfall. In addition, two or three months with little or no rain are necessary to induce the flowering of the crop. Kenya has a bi-modal rainfall pattern with two periods of drought and subsequent rains, and thus two periods of flowering of the coffee crop and subsequent harvesting. The main crop flowers at the start of the long rains in March/April, which contributes 55% of the total crop. The flowering which precedes the short rains takes place in September/October (IBID). Figure 2.2 gives an overview of the Kenyan coffee calendar. Figure 2.2 - Kenyan coffee calendar
Source: Tegemeo Institute 2010
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2.2
Research locations and units of analysis
This research is part of a larger evaluation program on the impact of several third-party certification programs on smallholder farmers in Kenya, conducted by the Centre for International Development Issues Nijmegen (CIDIN). Four Kenyan coffee cooperatives where selected for the research; two cooperatives who received Utz Certification (treatment groups) and two cooperatives not participating in the Utz program (control groups). Solidaridad, the non-governmental organisation (NGO) funding Utz certification programs for smallholder farmers, was instrumental in the choice of the following treatment groups: Rianjagi7, situated in the Embu district, and Kangunu, located in the Mathioya district (see Figure 2.3: Mathioya is located south of Nyeri). We searched ourselves for possible cooperatives in the same districts that would serve as a control group. This was achieved through discussions with the governmental cooperative district officers of the district, as well as discussions with the management of the Utz certified cooperatives. We aimed for control cooperatives located close to the treatment groups to ensure similar agro-ecological circumstances and a similar socio-economic context. In addition, we preferred cooperatives with similar characteristics in Figure 2.3: location of cooperatives terms of the number wet mills, membership and governance structures. In this way otherwise identical farm-households were selected, so that differences in outcomes can be attributed to certification. This type of comparison is based on the following type of impact assessment, the with/without appraisal: ‘addressing differences in behaviour and responses between the target group and a control group, the latter being composed of otherwise identical individuals. The development of the control group provides a counterfactual to the results reached by the target group’ (Ruben, 2008:23). We thus make a comparison of the farm household level impact of Utz Certification between farmers involved in Utz Certification compared to similar farmers delivering to the conventional market. The control group selected for Rianjagi cooperative in the Embu district is Kithungururu. Both are of similar size in terms of membership, have one wet mill and are closely located to each other. In addition, both cooperatives split from the same larger cooperative (Kapingazi) in 1997. The control group chosen for Kangunu cooperative in Mathioya district is Kamagogo. Kangunu is a single factory cooperative8, whereas Kamagogo is one factory being part of a four wet mill cooperative, Kiru. Kamagogo is quite smaller than Kangunu with 756 members, compared to the 1750 members of Kangunu. It was however not possible to compare Kangunu with another single factory cooperative in the district, since only one other coffee cooperative with a single wet mill existed. However, this wet mill had a much lower coffee production and had different growing conditions since it was on other altitude levels. Kamagogo however is bordering Kangunu, and therefore growing conditions for farmers included in both cooperatives are similar.
7
The choice for Rianjagi was an interesting case concerning gender for the research of Eveline Dijkdrenth. Kangunu was chosen since it was one of the first coffee cooperatives to become Utz Certified in Kenya, and therefore an interesting case for Solidaridad to consider. 8 A wet mill is called a factory as well. A single factory cooperative is thus a cooperative with only one wet mill.
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2.3
The districts Embu and Muranga
The following paragraphs will discuss the districts in which the cooperatives are located, Embu and Muranga. The Embu district is situated in the Eastern province, but borders the Central Province in the west. The district is divided into five administrative divisions, namely Central, Kyeni, Manyatta, Nembure and Runyenjes. The selected cooperatives are both situated in the Central division, which includes Embu town. Muranga district is one of the seven districts in the Central Province. There are four administrative divisions, of which the cooperatives are located in Mathioya division.
2.3.1 Demographic figures Embu and Muranga district Table 2.1 shows the demographic figures for both Embu district and Muranga district. The total Embu district occupies a total area of 729.4 km2, of which the Central division is only 70.6 km2. The population density of Central division is relatively high: there are 783 persons living per square km, which is mainly due to the inclusion of Embu town. Embu town services as the provincial headquarters of the Eastern Province, and is also the district headquarters of Embu District. It is thus a town with a lot of political and economic activity. Muranga district is with 930 km larger than Embu district. Mathioya, the district in which the two cooperatives are included, has a population density of 500 people per km2. Population pressure in Mathioya district is thus lower than in Central Embu district. Table 2.1: Demographic statistics Embu and Muranga (2002) Embu 2
total area (km )
% of total population
729,4
total population
290312
Area of cooperatives district (km2)
70,6
Muranga
% of total population
930 2
398 per km
350303
377 per km2
220,8 2
per km
500
per km2
Population of cooperatives district
783
female/male ratio
100:96
primary school pop (6-13 yrs)
59439
20,5%
88005
25,1%
sec school pop (14-17 yrs)
30850
10,6%
40415
11,5%
organisation force 15-64 yrs
168053
57,9%
184541
52,7%
dependency ratio
100:42
Population growth rate
1,7%
rural population 2002
232175
80,0%
278980
79,6%
urban population 2002
63801
22,0%
69324
19,8%
total nr of households
67406
average hh size
4
nr of female headed hh
16740
100:90
100:47 0,2%
84900 4,1 24.9%
16980
20%
absolute poverty (rural&urban)
56%
39%
income from agriculture
60%
60%
income from rural self-employment
10,40%
10%
wage employment
20,30%
5%
urban self-employment
7,50%
n.a.
nr of unemployed
88260
30,4%
73290
20,9%
Sources: district strategic plans of Embu and Muranga 2005-2010 (NCPD, 2005a; NCPD, 2005b)
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The female population is higher than the male population for both districts. This is due to a high migration of males to other districts and towns in search of employment and business opportunities. Labour force, educational levels and dependency ratios are also quite similar for both districts. Both comprise almost half of the population. The population growth rate for Embu is higher than for Muranga, which makes demographic pressure for the Embu district even higher in the future. The number of people living in absolute poverty (e.g. having less than one dollar per day to spend) is much higher in Embu: 56% of the population in 2001, against 39% in Muranga. Poverty figures have declined to 36.6% in 2006 in Embu, and 28.5% in Muranga9 (Kenya open data). The main causes of poverty in Embu include the following: poor access to water, inadequate infrastructure, persistent drought, unemployment of the youth, inaccessibility to credit facilities, and diseases such as HIV/AIDS (NCPD, 2005b). Unemployment is especially high due to and population pressure. The poor are mostly those sections of the population comprised by the landless, destitute, single mothers and slum dwellers. For Muranga, the following factors are mentioned to cause poverty: inaccessibility to health services, food security, inadequate potable water, inadequate shelter, and inaccessibility of education. The people hardest hit by poverty are women, unemployed youth, widows and orphans, neglected retired old people, street children and those living in the marginal areas of the district (NCPD, 2005a). Income rates from agriculture are the same for both districts. In Embu district, however, more people are involved in wage employment. Total unemployment rates are higher for Embu than for Mathioya. For Mathioya, the collapse of the coffee and tea factories is specifically mentioned as a factor leading to unemployment (NCPD, 2005a).
2.3.2 Agro-ecological conditions Embu and Mathioya The highlands of Embu, where the cooperatives are located, have an altitude between 1400 and 2000 meters and are used for cash-crop– tea and coffee – and food-crop production. Coffee and tea have been introduced since 1958 in the district, which intensified land use, and led to a higher demand for food crops due to increasing population. At the beginning of the 21th century, the landscape is saturated, with a dense population and an intensified combined cash-crop and foodcrop system (Imbernon, 1999). The average rainfall lies between 1000-2000 mm (in the year 2000), which is a bit lower than rainfall needed for coffee growing (1400-2000 mm). Average rainfall for Mathioya North (the area where the cooperatives are located) lies between 1400 and 1600 mm. The area has a deeply dissected topography and is well drained by several rivers. The average farm holdings are very small, with some households occupying less than an acre of land. Crop diversification of farms is limited due to unsuitable weather patterns, and flatter areas more suitable for crop production are limited. Mathioya division houses Kiriaini market centre, which is a hive of business activities and flourishing commerce. A major environmental problem is soil erosion, since most of the land in the district is hilly with sparse vegetation. Some landslides occurred on steep slopes. Due to erosion, soil and water conservation measures are inadequate. The current average production levels of coffee for the whole Muranga district are 400 kg per ha, which could be elevated to 1000 kg per ha, if quantity and quality were improved (NEMA, 2006).
9
Figures derived from ‘Kenya open data’, which is an open government data portal making demographic, economic and expenditure data available (opendata.go.ke).
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2.3.3 Comparing districts: sample Mathioya and Embu After describing both districts in which the cooperatives are situated, we will now compare the two districts based on our own data. Table 2.2 shows these figures for both districts. To compare averages, a t-test was used. The N for Embu is 101, and for Mathioya 85. For some variables, however, data was not available for all respondents. If so, the lower N is included in the most left column per district. If we look at the household characteristics, several things stand out: Embu has a higher percentage of males in the household, the average age of the household is lower, the level of education is higher, and housing is better. Embu farmers are however situated further away from the nearest market. Table 2.2: Comparing characteristics of both districts, Embu and Mathioya Embu Mean
N=101 S.D.
Mathioya Mean
N=85 S.D.
t-test
4,17 0,88 0,51 50,38 33,28 10,55 9,59 100 13,51 100 0,62 5,21 6,73 4,10
1,738 0,325 0,202 14,000 13,659 4,258 2,975 3,492 0,225 2,415 1,038 1,292
4,47 0,71 0,41 59,12 38,39 6,71 7,35 11,09 0,60 3,91 6,04 3,79
2,212 0,458 0,233 12,342 16,875 4,440 3,190 4,532 0,272 2,381 1,443 1,489
n.s. *** *** *** ** *** *** *** n.s. *** *** *
2,13 61652,92 68975,45 344,79 331,84
1,950 144163,832 68450,155 279,292 277,009
1,66 21106,94 64581,83 246,39 239,09
1,140 19751,436 92837,456 226,978 227,529
** *** n.s. *** ***
99
0,65 16167,72 0,89 5036,09 21535,39 0,61 6560,80 0,93 4386,70 16820,34
0,478 23984,651 0,313 4616,484 26402,924 0,489 18099,050 0,255 6353,425 31854,231
0,76 13377,35 0,95 6703,47 20381,94 0,64 83 6926,17 0,95 80 5353,97 76 14320,42
0,427 23104,450 0,213 5412,015 25561,192 0,484 21484,260 0,213 5081,468 17687,592
** n.s. * ** n.s. n.s. n.s. n.s. n.s. n.s.
86
985,20 3,87 39106,42 56938,99 30047,85
958,894 4,410 41158,084 76652,171 59268,802
1278,76 5,90 49606,50 37143,91 79 5904,89
1343,245 5,833 55202,549 59752,633 10097,595
** *** * ** ***
N Household characteristics household size gender of head (1=male) Gender ratio of household ( % male) age of head mean age of household education of head (yrs) mean education of household (yrs) max education of household (yrs) dependency ratio of household (% working) distance to nearest market (km) housing (rank) nr of rooms in the house (excl kitchen) Wealth acres owned today value of assets owned today (ksh) value of livestock owned today (ksh) Total nr of coffee trees nr of mature coffee trees Inputs coffee and other Hired labour for coffee production (%) costs of hiring labour (ksh) Spent money on fertilizer for coffee (%) costs of fertilizer for coffee (ksh) total input costs coffee hired labour for crop production (%) costs of hiring labour crops (ksh) spent money on fertilizer for crops (%) costs of fertilizer for crops (ksh) total input costs crops Productivity and sales coffee harvest, berrie and mbuni (kg) coffee harvest, kgs of coffee per tree gross income out of coffee sold livestock production (milk, eggs, in ksh) crop sales (macadamia, bananas, etc, in ksh)
N
34
Incomes and expenditures income: rented out land (ksh) 881,19 7791,389 income: employment (ksh) 41919,80 121390,581 income: remittances (ksh) 3334,65 11400,100 gross farm income (coffee, livestock products) 96045,41 89344,718 gross farm income (coffee, livestock prod, crops) 86 124369,47 125770,379 gross non-farm income 46135,64 121387,135 gross total income (without crops) 141229,86 170744,230 gross total income 86 173398,54 203897,93 coffee share of income (without crops) 101 0,44 0,340 livestock share of income (without crops) 101 0,34 0,316 employment share of income (without crops) 101 0,19 0,280 migration share of income (without crops) 101 0,04 0,127 coffee share of income (incl crops) 86 0,32 0,340 livestock share of income (incl crops) 89 0,27 0,316 employment share of income (incl crops) 97 0,18 0,280 migration share of income (incl crops) 95 0,03 0,127 Crop sales share of income 86 0,17 0,203 household member applied for credit (%) 0,43 0,497 household member had savings account (%) 0,84 0,367 total consumption per year 93 243117,96 173457,357 total consumption per year without clothing, hh goods, 99 177806,77 135913,399 health Perceptions perception of economy vs. 5 years to come 1,33 0,736 (1=better 2=same 3=worse) perception of economy vs. 5 years ago 1,50 0,867 (1=better 2=same 3=worse) perception of coffee profits (1=loss 2=equal 3=profit) 100 2,42 0,878 * = α < 0,10; ** = α < 0,05; *** = α < 0,01. Source: own household survey, 2011
84 755,95 9821,18 84 1401,98 86750,41 79 88345,27 83 12241,76 83 97873,45 78 98007,47 84 0,61 84 0,28 85 0,08 84 0,03 78 0,55 79 0,26 83 0,06 83 0,02 78 0,09 0,46 0,72 57 226114,81 74 185572,76
4213,071 32671,730 2974,435 86361,281 80892,347 33213,846 92889,459 84957,317 0,331 0,299 0,169 0,073 0,331 0,299 0,169 0,073 0,167 0,501 0,478 175383,399 161244,548
n.s. *** * n.s. ** *** ** *** *** n.s. *** n.s. *** n.s. *** n.s. *** n.s. ** n.s. n.s.
78 1,13
0,466
**
84 1,39
0,776
n.s.
2,10
0,965
***
Concerning the wealth of the household, Embu farmers are richer in terms of their land size, the number of coffee trees, and the value of their assets. Both districts have an equally high value of their livestock. The higher number of coffee trees and higher asset levels in Embu do not, however, lead to higher investments in coffee. If we look at inputs, Mathioya farmers have significantly more expenditures in coffee production for fertilizers, and a higher percentage of farmers hire in labour. The inputs for other crops in the field do not differ significantly. The higher input costs for Mathioya farmers result in higher yields out of coffee for Mathioya farmers compared to Embu and a higher production per tree. Embu farmers, however, have higher returns from their other farm activities being livestock production (i.e. selling milk and eggs) and the sale of crops.10 Next to that, their income out of employment is on average around 32,000 ksh higher per year than for Mathioya farmers. The income out of remittances is also slightly higher, but the share of remittances to the total income is not very substantial. The total income for Embu farmers is significantly higher than for Mathioya farmers, and the income composition differs substantially; Mathioya farmers are more dependent on coffee farming than Embu farmers. For Mathioya farmers, coffee farming comprises on average 63% of their income, while for Embu farmers it is only 44%. This 10
Unfortunately, there are quite some missings on these variables, which might distort the comparing of averages. However, if we look at the percentage of farmers that sold their crops, there is still a significant difference: 86.1% of the farmers in Embu sold their crops, while only 55.3% of Mathioya farmers sold other crops than coffee.
35
is compensated by the higher income share of employment for Embu farmers. Despite differences in income patterns, consumption patterns do not differ significantly for both districts 11. However, almost 40% more farmers have a savings account in Embu district. Lastly we describe attitudes. Embu farmers are slightly more pessimistic about the economic future, but they are more optimistic about their profits out of coffee. The first might be due to the high population pressure described in the first part of the chapter. The attitudes towards the coffee cooperatives do differ much concerning the technical services received from the cooperative: Embu farmers are more negative about these than Mathioya farmers. The attitudes towards commercial services do not differ significantly. If we consider trust, Embu farmers have on average more trust in their cooperatives, but less in the cooperative’s members, and they are more risk averse than Mathioya farmers.
2.3.4 Concluding remarks on contextual differences The two regions, Embu and Mathioya, show interesting differences. The agro-ecological conditions for growing coffee seem slightly better for Mathioya than for Embu. Mathioya district is situated on a more favourable altitude, with better rainfall patterns, but differences are not that large. Mathioya district is, when focusing on socio-economic circumstances, a district with less absolute poverty than Embu, lower population pressure and less unemployment. However, the coffee farmers included in our survey show a different pattern: Embu farmers are richer than Mathioya farmers in terms of absolute income and wealth. In Embu district, coffee farmers are thus in the richer segment of the population, while in Mathioya region, coffee farmers are less well off. These economic differences are reflected in the income strategies displayed by coffee farmers in the two regions. In Mathioya, investments done in coffee are higher, which results in higher gross income out of coffee and a larger income share of coffee. Embu farmers invest less in coffee, but the lower income share of coffee is compensated by a higher income share of employment. Farmers in Embu thus seem to diversify their incomes more towards off-farm earning activities. Since the absolute income and wealth is higher than in Mathioya region, this seems to be an active choice to invest in diversification for accumulation or reinvestments. Farmers in Mathioya, on the other hand invest in the intensification of their strategies, focusing mainly on coffee. This can be explained by the fact that in Embu region, there are more possibilities to diversify in the local market. Embu town is quite large and has a lot of economic activity. The cooperatives in Mathioya region, however, are located closely to Kiriaini, which is a small town with little economic activity. In terms of the livelihood strategies presented by Dorward (2010), both regions seems to be having a good natural-resource potential for growing coffee; the potential of Mathioya being slightly higher than Embu. The local market opportunities for Mathioya farmers are however low, while they are higher for Embu farmers. This leads to the following options for farmers in Mathioya region, either hanging in through subsistence farming, or stepping up in exporting their coffee crops. Choices for Embu farmers are broader, they can either hang in with farm and non-farm activities, step up to the local market or export markets; or step out to local non-farming activities. This broader set of choices seems to be reflected in the broader range of income activities of Embu farmers. They are therefore less depending on coffee for livelihood opportunities, while Mathioya farmers are much stronger depending on coffee as the most viable opportunity. In the results chapter, we will examine the influence of Utz Certification on these available livelihood strategies.
11
This might however be due to the poor state of consumption figures of the survey.
36
2.4
The cooperatives
2.4.1 Cooperatives in the Embu district: Rianjagi and Kithungururu In the Embu district the cooperatives Rianjagi and Kithungururu were selected for the research. The official name for Rianjagi is Rianjagi Cooperative Society Limited. Rianjagi is located around 8km north of Embu town situated on a main tarmac road. The cooperative has been founded in 1997, in which year it split from the larger cooperative Kapingazi. This was, according to the chairman of Rianjagi, due to massive mismanagement in this organisation. The splitting coincides with the new cooperative act of 1997 which gave cooperatives more self-control and indeed let to mismanagement (see also paragraph 2.2.2). At the moment, Rianjagi has a total of 1,519 members of which 1,037 are active members (68%), with a cooperative board consisting out of three men and two women. Rianjagi received Utz Certification in 2007, and has been certified since. The wet mill is located at an altitude of 1600m. Kithungururu Farmers’ cooperative society limited is located 6 km North-West of Embu town, and is less easily accessible than Rianjagi, since it is not located near a tarmac road. The wet mill is located slightly lower than Rianjagi, at 1532m. This cooperative was founded in 2001, and also splitted from the coffee cooperative Kapingazi, due to mismanagement. Kithungururu has a total of 1,811 members, of which 1,262 are active members (70%). Kithungururu has one factory, but during the time of research, a second factory was being built. The board of the cooperative consists of five men. Kithungururu was at the time of research not involved in any certification scheme. They were however starting a collaboration with Technoserve,12 and in 2011 some samples of coffee were bought by Beanthere, a fair trade coffee company based in South-Africa. Table 2.3: Production figures for both Rianjagi (Utz) and Kithunguru, past five years year
total mbrs
Rianjagi (Utz) 05-06 1282 06-07 1292 071310 13 08 08-09 1442 09-10 1502 Kithungururu 05-06 1538 06-07 1589 07-08 1629 08-09 1697 09-10 1811
share capital (1000 ksh)
Production (1000 kgs)
kgs per mbr
Sales (1000 Ksh)
Payments (1000 Ksh)
payment (ksh/kg)
payment (% of sales)
256 258 262
563 600 264
439.56 464.59 202.29
18,205 15,461 10,746
13,623 10,795 8,267
20.95 18 33
82.39 77.58 89.67
288 305
775 857
537.83 571.00
30,879 50,324
23,601 38,154
33 50
91.57 75.82
208 286 298 309 326
781 752 299 705 643
508.44 473.42 183.58 415.81 355.40
25,363 20,893 10,414 30,295 39,755
20,321 16,237 8,926 23,798 31,444
28 22 30 34 47
80.12 86.98 95.41 87.15 85.28
Source: Data received at cooperatives, February 2011
12
Technoserve is an organisation that helps entrepreneurial men and women in poor areas of the developing world to build businesses that create income, opportunity and economic growth for their families, their communities and their countries (www.technoserve.org). 13 The 07-08 season saw very bad weather conditions, which is why production dropped significantly for both cooperatives during this season.
37
Table 2.3 shows the production figures for the last five years for both cooperatives. Share capital shows the capital the cooperatives have in savings, which is similar for both cooperatives. This money is used to pay staff, and maintenance of the factory and the grounds. Production in kilograms gives the amount of coffee (berries and dried coffee) that have been sold in total to the middle man, before the coffee went to the auction. Coffee production of Kithungururu was in earlier years higher than production of Rianjagi farmers. Since certification (in 2007), production of Rianjagi has been increasing. This increase in production is an increase of production per member, and similarly the income of sales has increased, as well as the rate of payment to the farmers. The payment rate per kilogram of both cooperatives does however not differ much. Table 2.4 compares the two cooperatives on the same survey figures with which the regions were compared. Farmers in Embu district do not differ much on household characteristics. The household size of Rianjagi farmers is on average larger, but the gender ratio, as well as average age and education levels are similar. Kithungururu farmers live significantly further away from the nearest markets than Rianjagi farmers. Farmers do also not differ significantly concerning their wealth, only Rianjagi farmers have invested on average 17,000 ksh more in livestock. Farm inputs do also not differ much, but Kithungururu farmers have a surprisingly higher coffee harvest, which will be further discussed in chapter four. Incomes out of coffee are therefore higher for Kithungururu farmers. However, the total income of both farmer groups does not differ significantly, but the income composition is different. Rianjagi farmers derive more income out of the sale of livestock and livestock products, and crops; while Kithungururu farmers derive a greater share of their income out of coffee and employment. The farmers in Embu district do thus not differ much on household characteristics. The largest differences are seen in activities chosen: Rianjagi farmers focus more on the sale of livestock and livestock products, while Kithungururu farmers derive a higher income share out of employment. We will analyse these differences further in the results chapter. Table 2.4: Comparing characteristics cooperatives, Embu N Household Characteristics household size gender of head (1=male) genderratio of household (% male) age of head mean age of household education of head (yrs) mean education of household (yrs) max education of household (yrs) dependency ratio of household (% working) distance to nearest market (km) housing (rank) nr of rooms in the house (excl kitchen) Wealth acres owned today acres used for coffee total nr of coffee trees nr of mature coffee trees value of assets owned today (ksh) value of livestock owned today (ksh)
Rianjagi, Utz (N=52) Mean S.D.
N
Kithungururu (N=49) Mean S.D.
4,42 0,90 0,51 50,86 34,11 10,42 9,42 51 13,12 51 0,61 4,85 6,77 3,98
1.829 0,298 0,202 12,393 13,093 3,195 2,725 2,819 0,226 2,359 1,022 1,093
3,90 0,86 0,51 49,88 32,40 10,69 9,77 13,92 0,63 5,59 6,69 4,22
2,03 48 0,61 324,60 304,37 62413,75 77416,73
2,044 2,24 0,509 46 0,65 231,434 366,22 223,220 361,00 143175,972 60845,51 78522,333 60017,35
1.610 0,354 0,205 15,640 14,318 5,185 3,238 4,066 0,226 2,440 1,065 1,476
t-test *
*
1,861 0,479 323,556 324,399 146685,015 55236,495 *
38
Inputs coffee and other hired labour for coffee production (%) 0,67 0,474 costs of hiring labour (ksh) 16659,42 25156,772 spent money on fertilizer for coffee (%) 0,94 0,235 costs of fertilizer for coffee (ksh) 5184,37 4923,775 total input costs coffee 22099,83 27801,216 hired labour for crop production (%) 0,62 0,491 costs of hiring labour crops (ksh) 7645,19 22872,006 spent money on fertilizer for crops (%) 0,92 0,269 costs of fertilizer for crops (ksh) 4060,08 4257,221 total input costs crops 51 20215,18 40485,767 Productivity and sales coffee harvest, berrie and mbuni (kg) 809,67 827,786 coffee harvest, kgs of coffee per tree 3,32 4,125 gross income out of coffee 29527,82 31146,066 sold livestock production (milk, eggs, in ksh) 67736,06 84437,692 crop sales (macadamia, bananas, etc, in ksh) 44 32908,75 62213,286 income and expenditures income: selling livestock (ksh) 16129,837 30786,434 income: rented out land (ksh) 1711,539 10843,515 income: employment (ksh) 24467,31 84942,534 income: remittances (ksh) 6150,00 15336,468 gross farm income (coffee, livestock products) 97263,87 93052,666 gross farm income (coffee, livestock products, crops) 44 123794,70 135809,019 gross non-farm income 32328,85 86522,807 gross total income (without crops) 127881,72 134023,378 gross total income 43 159074,25 169967,347 coffee share of income 0,40 0,336 livestock share of income 0,42 0,347 employment share of income 0,12 0,207 migration share of income 0,06 0,171 coffee share of income (incl crops) 44 0,27 0,215 livestock share of income (incl crops) 45 0,32 0,310 employment share of income (incl crops) 51 0,11 0,190 migration share of income (incl crops) 48 0,06 0,177 crop sale share of income (incl crops) 44 0,20 0,222 household member applied for credit (%) 0,33 0,474 household member had savings account (%) 0,81 0,398 total consumption per year 47 261160,21 160323,774 total consumption per year without clothing, hh goods, 51 190230,78 119352,480 health Perceptions perception of economy vs. 5 years to come 1,35 0,764 (1=better 2=same 3=worse) perception of economy vs. 5 years ago 1,46 0,851 (1=better 2=same 3=worse) perception of coffee profits (1=loss 2=equal 3=profit) 51 2,35 0,890 * = α < 0,10; ** = α < 0,05; *** = α < 0,01. Source: own data, household survey 2011
0,63 15645,92 0,84 4878,74 20936,39 0,61 5410,02 0,94 4733,31 48 13213,33
0,487 22923,974 0,373 4311,839 25107,687 0,492 11135,493 0,242 8037,472 18607,181
1171,48 4,45 49271,47 45480,87 42 27050,71
1057,696 4,666 47891,102 66370,879 56615,654
42
42
42 44 46 47 42
46 48
12833,537 0 60440,82 346,94 94752,34 124971,62 60787,76 155540,09 188404,95 0,48 0,25 0,27 0,01 0,36 0,22 0,24 0,01 0,14 0,53 0,88 224683,48 164606,25
29315,668 0 149509,152 1774,249 86179,318 115971,020 149391,507 203100,142 235460,750 0,344 0,255 0,326 0,024 0,270 0,240 0,304 0,009 0,181 0,504 0,331 185875,902 151714,543
1,31
0,713
1,53
0,892
2,49
0,869
**
** * *** **
n.a. * ***
*** *** ** ** *** *** ** * **
39
2.4.2 Cooperatives in the Mathioya district: Kangunu and Kamagogo Kangunu Cooperative (Utz) is located around 3,5km south-west of Kiriani, situated on a main tarmac road. The cooperative has been founded in 1998, in which year it also split from a larger cooperative. Again the split was due to massive mismanagement in the organisation. The board members of Kangunu mentioned that this came about due to the period of government reforms and the new cooperative act. At the moment, Kangunu has a total of 1,770 members, of which there are 1360 active members. The altitude of the wetmill is at 1,724m. The cooperative board consists out of five men. Kangunu received Utz Certification in 2006, and was one of the first cooperatives in Kenya to be Utz Certified. Kamagogo factory is located slightly closer to Kiriaini, 2.3 km south-west of the town. It is not located on the tarmac road and is therefore more difficult to access. Kamagogo is part of the four factory cooperative Kiru, which was founded in 1993. The cooperative has been in place since. Kiru factory has a total of 3837 members (2330 active), of which Kamagogo consists of 756 members. The number of active and dormant members is unknown. The altitude of the wetmill is 1,820m, so slightly higher than Kangunu’s wetmill. The factory board consists out of five men. Table 2.5 shows the main figures of the cooperative. For Kamagogo, unfortunately only the figures of the last season were available. The total figures for production and sales are not comparable, since Kamagogo is a much smaller factory in terms of members. The kilograms produced per member in the last season are however lower for Kamagogo, as well as the rate of payment per kilogram. Table 2.5: Production figures for both Kangunu (Utz) and Kamagogo, past five years Year
active mbrs Kangunu (Utz) 05-06 ? 06-07 1320 07-08 1341 08-09 1341 09-10 1360 Kamagogo 09-10 756
Production (in 1000 kgs)
Kgs per member
Sales (1000 ksh)
Payment (1000 ksh)
Payment (ksh/kg)
Payment (% of sales)
594 880 432 902 723
? 667.16 332.08 672.96 529.07
18,712 27,501 16,860 37,372 47,327
13,651 22,024 12,331 31,766 40,219
22.9 25.0 28.55 35.2 55.65
80 80 80 85 85
339
448.67
992
920,803.1
40.10
80?
Source: data received at cooperatives, February 2011 Table 2.6 gives an overview of a first comparison between Kamagogo and Kangunu (Utz) farmers. Farmers do not differ on household characteristics such as age and education; Kamagogo farmers are only located closer to the market, while the housing of Kangunu farmers is significantly better. Kangunu farmers also have higher wealth levels than their counterparts; their average value of livestock is significantly higher, as well as the value of assets (although not significantly). Kangunu (Utz) farmers invest more in coffee, which pays of in a significantly higher yield and income out of coffee. In comparison, Kamagogo farmers derive on average a higher income out of the sale of other crops. The other sources of income such as employment and the sale of livestock (products) do not differ significantly from each other. Kangunu did however make more investments of livestock, as the value of livestock is higher for Kangunu (Utz) farmers.
40
Table 2.6: Comparing characteristics cooperatives, Mathioya N Household Characteristics household size gender of head (1=male) genderratio of household (% male) age of head mean age of household education of head (yrs) mean education of household (yrs) max education of household (yrs) dependency ratio of household (% working) distance to nearest market (km) housing (rank) nr of rooms in the house (excl kitchen) Wealth acres owned today acres used for coffee total nr of coffee trees nr of mature coffee trees value of assets owned today (ksh) value of livestock owned today (ksh) Inputs coffee and other hired labour for coffee production (%) costs of hiring labour (ksh) spent money on fertilizer for coffee (%) costs of fertilizer for coffee (ksh) total input costs coffee hired labour for crop production (%) costs of hiring labour crops (ksh) spent money on fertilizer for crops (%) costs of fertilizer for crops (ksh) total input costs crops Productivity and sales coffee harvest, berrie and mbuni (kg) coffee harvest, kgs of coffee per tree gross income out of coffee sold livestock production (milk, eggs, in ksh) crop sales (macadamia, bananas, etc, in ksh) income and expenditures income: selling livestock (ksh) income: rented out land (ksh) income: employment (ksh) income: remittances (ksh) gross farm income (without crops) gross farm income (coffee, crops, livestock) gross non-farm income gross total income (without crops) gross total income coffee share of income livestock share of income employment share of income migration share of income
Kangunu (N=43) Mean S.D. 4,64 0,71 0,43 59,74 39,80 6,76 7,50 11,33 0,60 5,21 6,36 3,86
2,497 0,457 0,217 11,340 17,218 4,653 3,257 4,432 0,283 2,590 1,479 1,441
N
Kamagogo (N=43) Mean S.D. 4,30 0,70 0,40 58,51 37,01 6,65 7,20 10,86 0,61 2,64 5,72 3,72
1,909 0,465 0,248 13,355 16,619 4,276 3,156 4,668 0,264 1,197 1,351 1,548
t-test
*** **
1,71 36 0,61 259,05 251,07 23539,17 83680,04
1,207 1,60 0,409 35 0,41 115,459 234,02 115,735 227,40 22687,040 18731,28 121597,998 45927,76
1,081 0,282 299,513 300,300 16312,498 45635,091
0,83 12210,48 0,98 8284,06 20743,47 0,64 40 6330,75 0,98 40 5328,86 38 15492,00
0,377 23165,425 0,154 6326,793 26191,355 0,485 17500,436 0,154 5455,151 21948,296
0,70 14517,09 0,93 5159,63 20028,82 0,63 7480,06 0,93 40 5379,09 38 13148,85
0,465 23261,123 0,258 3816,558 25235,623 0,489 24821,891 0,258 4748,131 12231,523
*
1541,21 6,42 68309,70 40662,31 39 4109,74
1123,721 4,030 58209,610 50515,468 6618,653
1022,40 5,40 31338,26 33707,33 40 7655,15
1496,584 7,187 45776,359 68009,034 12444,879
** n.s. *** n.s. *
10743,95 41 0,00 9309,52 41 1399,17 108972,01 39 110476,27 40 11209,15 40 120006,11 38 119075,80 41 0,67 41 0,27 0,04 41 0,01
16589,445 0,000 41325,884 3321,608 84994,907 82031,468 42109,986 91609,757 87247,771 0,300 0,257 0,159 0,040
10645,78 1476,74 10320,93 1404,65 65045,59 40 66767,55 13202,33 76771,17 40 77992,55 0,54 0,29 0,11 0,04
21503,388 5829,883 21614,816 2641,244 82995,754 74586,909 22477,486 90137,715 78646,382 0,362 0,343 0,180 0,093
*
**
***
*** *** ** **
*
41
coffee share of income (incl crops) livestock share of income (incl crops) employment share of income (incl crops) migration share of income (incl crops) crop sale share of income (incl crops) household member applied for credit (%) household member had savings account (%) total consumption per year total consumption per year without clothing, hh goods, health Perceptions perception of economy vs. 5 years to come (1=better 2=same 3=worse) perception of economy vs. 5 years ago (1=better 2=same 3=worse) perception of coffee profits (1=loss 2=equal 3=profit) satisfaction with technical services satisfaction with commercial services trust in cooperative trust in members of cooperative risk attitude * = α < 0,10; ** = α < 0,05; *** = α < 0,01
38 39 41 41 38
0,64 0,26 0,03 0,01 0,05 1,52 1,31 24 240600,17
0,270 0,253 0,139 0,033 0,079 0,505 0,517 212096,373
40 40 42 42 40
0,46 0,27 0,09 0,03 0,13 1,56 1,26 33 215580,00
0,332 ** 0,341 0,148 0,082 * 0,203 ** 0,502 0,441 145685,082
34 200232,47 185157,319 40 173112,00 138963,321
39 1,03
0,160
39 1,23
0,627
**
41 1,15 2,33 0,54 0,25 -0,11 0,14 4,52
0,478 0,928 0,233 0,758 0,972 0,976 0,969
1,63 42 1,86 0,02 -0,41 -0,22 0,64 4,93
0,926 0,952 0,963 1,163 1,192 0,773 0,258
*** ** *** *** ** ***
42
43
Chapter 3: Material and Methods The following chapter discusses the methods used for the collection and analysis of our data. Firstly, the selection of the locations and units of analysis is described, followed by a detailed discussion of the sampling and research methods applied for our data collection. Thereafter, an explanation of the way in which triangulation has been applied. The chapter will conclude with a short description of the way in which the data is analysed.
3.1
Research units of analysis
We use three levels of analysis in this research: the institutional level, the household level and the individual level. The institutional level was mainly important in the first phase of the research, to gain more insight in the organisational structures of the cooperative, the total production, the assistance provided by the cooperative and, in case of the treatment groups, the application of Utz Certification. This information was gained through interviews at the board level of cooperatives, and with staff members of Solidaridad in the regional office in Nairobi. The main focus of the research is on the household level and the individual farmers’ level. The household level is of main importance for information concerning coffee production and other income and outcome flows, and asset levels. This and other data was collected through the survey. Individual level data is important for examining attitudes concerning the cooperative, risk and trust. Individual level data was collected through the survey, as well as the risk game and semi-structured interviews. The following paragraph discusses the above mentioned methods in more detail.
3.2
Used Methods and Sampling
In this paragraph we will discuss the methods in the chronological order in which they were applied in the field. A certain method was concluded in both regions before implementing the following method. For instance, risk mapping was first done in Embu, subsequently in Mathioya region, after which we started surveys in Embu, etc. If applicable, we explain the used sampling techniques as well. The research was conducted from January to April 2011 on location in Kenya.
3.2.1 Interviews at the institutional level and training enumerators The research started out with a number of unstructured interviews (Bernard, 2002:205) with staff members and the co-director of the regional East Africa office of Solidaridad in Nairobi. Topics discussed were the history of Utz Certification, and the process of selecting cooperatives to become Utz Certified. In addition, a total of four enumerators received extensive training during this period. The enumerators had the following tasks: translation between the local languages14 and English during risk mapping, risk game and interviews; conducting the survey; and explaining risk mapping and the risk games. During the training they were informed of the research objective, the main research questions and how these related to the research methods, in order to gain a clear idea of the goal of the research methods used. Thereafter they were trained in conducting risk mapping, the survey, risk game and interviews. Instructions were repeated during fieldwork just before a research
14
Ki-Embu for Embu district, Ki-kuyu for Mathioya district, mixed with Swahili.
44
method was actually applied. In addition, evaluation and feedback took place during the research, especially while conducting the survey. In February we started with data collection at the cooperatives. We informed board members of the cooperatives about the goal of the research and the several research methods applied so to be clear about our activities at the cooperatives in the coming months. In addition, semi-structured interviews15 were held with board members of all four cooperatives. This gave us insights in the main figures of the cooperative concerning production and members, the organisational structure of the cooperative, goals set for the cooperative, and the provision of technical assistance to its members.16 To increase reliability, we interviewed the chairman of each cooperative, as well as the manager of the cooperative and if possible a few more board members.
3.2.2 Risk Mapping We used participatory risk mapping (PRM) to get insight in the experienced and perceived risks among cooperative members on individual, household and cooperative level. The participatory group method was used as a complementary pilot research before the survey is conducted. During group discussions risks were identified, ranked ordered in terms of severity, and it was discussed how participants solve these risks17. The risks mentioned by the groups were subsequently used in the survey, so to get responses on a larger scale. Participatory Risk Mapping was developed by Smith et al. (2000) to get more insights in the subjective risk perceptions and the occurrence of a particular risk. The data gathered through risk mapping shows the relative importance of problems perceived by individual people in the cooperatives (Quinn et al., 2003; Tschakert, 2007). It for instance shows the importance of problems concerning coffee, compared to other problems. We chose to conduct PRM early in the research, since it gives us an overview of shocks and risks as perceived by farmers early in the process of data collection. It is thus possible to apply this information in subsequent methods such as the survey18 and the individual interviews. PRM is a group method, which is chosen since it allows people to respond informally in a group discussion and to discuss items among themselves. It is an easy way of identifying differences of opinion as well as areas of consensus within a group (Pratt and Loizos, 2003). Through discussion, common (or stochastic) shocks might be separated from idiosyncratic shocks. In addition, it is a useful way to gather a lot of information in a short time. Risk mapping involves a three-stage system of questions about risks. Respondents were firstly asked to identify the risks they face when providing for themselves and their household. The questions were open-ended, as not to influence the cited risks, the number of risks mentioned, and the order of importance. We asked one of the group members to write down the risks on cards. In this way the group members felt more comfortable to mention problems. Informants could list as many or as few risks as they wished. In listing the risks, respondents were encouraged to discuss these among themselves and to decide which risks were major risks encountered by their community and/or cooperative. During the first risk mapping exercise, we decided to split the risks in two groups: risks concerning coffee and risks concerning other (household) problems.
15
See appendix 5 for the questionnaire used. For the complete interview guide used in these interviews, see appendix 5. 17 See appendix 9 for instructions for enumerators used during the PRM. 18 Smith et al. (2000) used the results of the PRM they conducted as well in a subsequent study, so to examine risk behaviour of pastoralists in Northern Kenya (Doss et al. 2008). 16
45
The second step contained rank ordering the risks that were identified in the first step. Hereby, a simple, ordinal scheme was used, running from the most severe risks to the least serious. Risks that were thought equivalent were noted as equivalent, respondents were not forced to choose between these risks. Respondents had to come to an agreement about the rank order, and were asked whether they all agreed on the proposed order. In the last phase, informants were asked how they would solve each of the risks, if they had not already addressed this. However, the last phase was not exercised at every cooperative, due to time constraints. At each cooperative, we used a stratified random sample. The group size of group methods is preferably between 6 and 10. We therefore sampled a group of 10 people at each cooperative, which is the maximum size. In case of non-response, we still had enough respondents left to participate in the study. There was however no case of non-response, although in most cases it took quite some time before all group members were there. We therefore started with a smaller group, and then explained the game individually to farmers who came later. The PRM was done with four groups, one at every cooperative, so to take into account variation among cooperatives. The sampling was done stratified since it contained farmers out of different areas of the cooperative, and therefore included farmers coming from the total area of the cooperative.
3.2.3 Survey We conducted a survey to gather most data on the household level, which was done through single farm visit interviews. A survey aims to give ‘systematic, representative and reliable information’ about the research population, and is especially useful for obtaining factual or attitudinal information (Pratt and Loizos, 2003). These two characteristics of survey research cover the data we want to collect by using a survey. The survey was designed as follows: questions were based on literature and the questionnaire used by Tegemeo Institute and CIDIN (Kamau et al., 2010). A few staff members of Tegemeo institute reviewed our questionnaire, and improvements were made based on discussing the context of the research with the enumerators. Secondly, all attitudinal statements were translated from English to either Ki-embu or Kikuyu in close cooperation with the enumerators. The pilot questionnaire then available was field-tested in order to detect all sources of ambiguity and confusion, and to further train the enumerators in administering the questionnaire. While testing the survey, the researcher was present to answer questions and monitor the process. After this process of improving and adjusting the survey, the structured questionnaire was administered to the respondents by enumerators. Respondents were mainly the household head, or the spouse to the household head. A farm household was defined as ‘each family member who stayed within the household for a period of at least one month for the last twelve months. Together the household members have a shared income and shared expenditures’ (Kamau et al., 2010). We collected data on the following issues19: Household characteristics (age, gender, education, size, migration, employment characteristics) Farm household characteristics (farm size, access to markets and other services) Coffee production and marketing characteristics General agricultural production characteristics Investments in the on-farm and off-farm activities Household consumption, saving and investment characteristics 19
See appendix 10 for the full questionnaire.
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Individual perceptions (benefits of the coffee cooperative, attitudes towards the cooperative, risk and trust) The data collection covered coffee production and marketing activities for the 2010 coffee calendar year i.e. the period from September 2009 to August 2010. A survey generates a description of a wider population without actually talking to every individual in the population, provided that all in the population have an equal probability of being included in the sample (Thomas et al., 1998). To ensure this equal probability, we used a stratified systematic sample from the population of each cooperative. A sampling frame was prepared for each of the cooperatives included in the study and included all members from one wet mill affiliated to the cooperative.20 The sample was divided in strata by the cooperative. The certified cooperatives are divided in villages, and their non-certified counterparts were divided in election areas, which form the strata of the sample. In this way we secured that members from areas further away from the cooperative grounds are also included in the sample. Per strata, the households were selected systematic: each nth farmer of the strata was selected. The minimum sample size to meet the central limit theorem 21 is 60 households per cooperative or 240 households in total. This is when a confidence interval of 10% is used, and the sample size is corrected for the average population size.22 In the case of non-response, farmers were replaced by interviewing a neighbouring farmer. We are aware of the fact that this might lead to bias, but this chance is relatively small since sampling was already done stratified for each area of the coffee cooperation. In a few cases replacement was necessary. Each questionnaire took an average of 1,5 to 2,5 hours to complete. The total number of surveys completed is 21823: 56 for Kangunu, Kithungururu and Rianjagi each, and 50 for Kamagogo. The total amount is lower than the required 240. Unfortunately it was not possible to expand the total number of surveys due to time and money constraints.
3.2.4 Risk Game The aim of conducting a behavioural field experiment was to examine attitudes towards risk. We chose to conduct a risk game in the form of a Choose Lottery (CL) experiment.24 In a CL experiment ‘participants are presented with a series of lotteries and they are asked to pick one from a list which controls for the probability of winning a large prize (all determined by the toss of a coin) but varies 20
In the case of Rianjagi, Kithungururu and Kangunu, this implied the whole cooperative since these are all societies with one wet-mill. In case of Kamagogo, the choice was made to sample for only one wet-mill. 21 The central limit theorem is met when 1) the mean and the standard deviation of the sample means will usually approximate the true mean and standard deviation of the population, 2) the distribution of sample means will approximate a normal distribution (Bernard 2002:167). 22 The formula used for estimating a sample size when estimating proportions in a large population is: n = 2 2 z (P)*(Q)/(confidence interval) , whereby P is a prior estimate of the proportion we want to measure. Since we have no prior estimate, P is set to 0.5. Q is 1-P, thus also 0.5. To correct for the finite population size, we used the following formula (from Cochran 1977): n’= n/(1+ (n/N)), where N is the size of the total population (Bernard 2002). 23 Unfortunately, this number is lower than the minimum sample size, this is due to the fact that we calculated the minimum sample size after conducting the surveys. In the analysis of the data, we allow for this small sample size by using a larger confidence interval. 24 Another type of lottery experiment is the Accept/Reject type, developed by Holt & Laury (2002). The Accept/Reject Lottery (ARL) involves more complex gambles and more choices, whereas the Choose Lottery (CL) involves fewer gambles and always an equal choice. Since we will be conducting the experiment in a developing context among small scale farmers in Kenya, the experimental design should be as straightforward as possible (Cardenas & Carpenter, 2008:329). We therefore prefer using a Choose Lottery experiment.
47
the high and low pay-outs and, in doing so, the expected payoff. Dependent on how risk averse a participant is, he or she should trade off expected return for less variability’ (Cardenas and Carpenter, 2008:326).25 To make sure that the risks in the experiment were taken to be real-life risks, and were not interpreted as gambles, we conducted a framed field experiment. This experimental design ‘emphasizes the identification of a naturally occurring setting in which one can control for experience in the way that it is accumulated in the field’ (Harrison and List, 2004:1022). We framed the gambles in such a way that they represented the local context and farmers thus perceived the experiment to be a realistic situation related to their perceived risks. We did so by using the price a farmer might receive for the harvest of one of his or her coffee trees in the coming year. This price resembled a two days income, which creates the necessary incentive for participants to take the game seriously, instead of perceiving it as a gamble (Cardenas and Carpenter, 2008). The choices we presented (see Table 3.1) to the participants ranged from risk averse to risk neutral26. Since the expected value of each consecutive choice is higher, individuals ‘purchase’ higher expected value at the cost of a higher standard deviation. Choice 5 has the same expected return as choice 6, here only the variance is higher. In this way only risk-neutral or risk-preferring individuals will make the step from 5 to 6 (Binswanger, 1980). The experiment does not imply that people can lose money, because then you would be measuring the impact of cash or budget constraints, instead of attitudes towards risks (Binswanger, 1980). The worst possible outcome is thus a zero gain. Sampling was done as follows. The target population were the 50(56) people per cooperative who participated in the survey. At the end of each survey, we mentioned to the household that we were playing a game a few weeks later and we asked them if they would like to participate. We thus had a list of people who would like to participate which is the sampling frame from which we will select the participants for the risk experiment. 27 The sample size for the experiment was 100 small farmer households, 25 of each cooperative, which is 50 per cent of each cooperative. Table 3.1: Choices, payoffs, risk aversion classes, and expected values28 Choices 1 2 3 4 5 6
Option 1 = green (p=50%) 100 KSh/10 kg coffee 190 KSh/10 kg coffee 240 KSh/10 kg coffee 300 KSh/10 kg coffee 380 KSh/10 kg coffee 400 KSh/10 kg coffee
Option 2 = red (p=50%) 100 KSh/10 kg coffee 90 KSh/10 kg coffee 80 KSh/10 kg coffee 60 KSh/10 kg coffee 20 KSh/10 kg coffee 0 KSh/10 kg coffee
RA Class Extreme Severe Intermediate Moderate Slight-neutral Neutral-negative
Expected Value 100 KSh 140 KSh 160 KSh 180 KSh 200 KSh 200 KSh
The game was conducted at the cooperative itself; respondents received a small amount for travel expenses when showing up. Two enumerators both assisted in conducting the game29. The first made sure that the respondents who already played the game did not speak to the ones who were to conduct the game. In this way cross talk was avoided, which is a point of concern in conducting field experiments because this might influence the way people conduct the experiment (Cardenas and 25
The first to conduct a Choose Lottery in a developmental setting has been Binswanger (1980), with a risk experiment among Indian farmers, and this has been repeated often since (see for instance Wik & Holden, 1998; Nielsen, 2001; Barr, 2003; Harrison et al., 2005). 26 See also appendix 7 for the actual work sheet used during the execution of the risk game. 27 We are aware of the fact that this contains a certain bias, since only the farmers willing to participate will be playing. 28 Based on the worksheet of Barr (2003); values used by Barr are framed to the local Kenyan context of coffee farmers. 29 See appendix 7 for instructions of the risk game.
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Carpenter, 2008). The other enumerator explained the game to the farmer and translated comments the farmer made. Both enumerators were trained on how to conduct the game extensively. Carrying out the game lasted one and a half day per cooperative. Each person was taken in, one at a time. They were then explained how the game works, by showing them the different options and payoffs. To improve the understanding of the game and to take possible illiteracy into account, we choose to follow a simple procedure, and include the use of signs and posters. We then gave some examples of selected bets and the respective payoffs related to that. The player could ask questions and was given some more examples if necessary, until the player understood the game. The player decided which of the six options to choose, and that game was played. The game was only played once with each person and directly afterwards the money was paid. After playing the game, we discussed with the farmer why he made that particular choice. In addition, we asked what a farmer would do with several hypothetical amounts of money. We used the data to compare it with the outcomes of the five hypothetical statements on risk behaviour in the survey, i.e. stated risk perception. Similarities between actual risk behaviour and stated risk perception can be seen as triangulation of the data on risk behaviour, and increases the internal validity of the findings. Differences between actual risk behaviour and stated risk perception, on the other hand, might advert to under- or overestimation of farmers’ own risk behaviour. As a consequence, this indicates that some caution is needed when drawing conclusions on stated risk perception in the overarching research.
3.2.5 Interviews At the end of the research, we interviewed six farmers per cooperative, using a semi-structured questionnaire. Semi-structured interviews are especially valuable to answer ‘why’ or ‘how’ research questions, since answers to such questions are often too complex to answer with predefined survey options. Instead, issues can be explored with informants in a much more flexible way, by the use of supplementary questions (Thomas et al., 1998). Through semi-structured interviews, questions about why people think or act the way they do can be explored. The topics explored in the interviews were attitudes towards the cooperative, and issues related to risks. The questionnaire was a guideline for discussing these topics, while it was still possible to probe or improvise on answers given in the interviews. The questionnaire is included in appendix 10. Since a large part of the interview emphasizes risk attitudes and choices related to risks, the selection of farmers was based on the risk game. A sample of farmers was selected that included farmers ranging from extreme risk averse to almost risk neutral. Farmers were already asked at the risk game if they were willing to participate in one part of the research. The interviews with farmers mostly occurred on the terrain of the cooperative, whereby attention was given to the privacy of the farmers. In some instances farmers were visited at their homes. The interviews were undertaken with one researcher and one enumerator. All interviews were recorded, so to have all primary information available after the interview. The interviews were transcribed as soon as possible after the interview. If needed, the enumerator was asked for clarification in the transcription process.
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3.3
Triangulation, Reliability, Validity
Triangulation refers to the use of multiple sources, methods and types of information to test and modify one’s understanding of a given problem or situation; and to cross-check and validate data and information to limit biases (Thomas et al., 1998; Mikkelsen, 2005). Several types of triangulation can be distinguished, namely methodological, investigator, theory and person triangulation. The last refers to the comparison of reactions at several levels of analysis, namely a) the individual level, and b) the interactive level among groups. Person triangulation is used by risk mapping, since risk mapping has been done in group discussions and on the household level by the use of the survey. Investigator triangulation is established by discussing the research with supervisors and enumerators, as well as fellow researcher in the field. Theory triangulation has been applied by composing the theoretical framework out of different theories. However, triangulation mostly involves methodological triangulation, or between-method triangulation, when different methods are used in relation to the same object of study. Using different research techniques provides additional scope for triangulation by providing alternative ways of visualizing and interrogating social activity (Thomas et al., 1998). Applying triangulation enhances the reliability – when repeated observations using the same instrument under identical conditions produce similar results - and validity – the extent to which the data collection instruments measure what they purport to measure - of the research (Mikkelsen, 2005). We will now discuss which measures were taken to ensure the validity and reliability of the research, and which problems were encountered in doing so. A first issue which might cause problems with validity is if people feel comfortable enough to answer questions honestly. To reduce biased answers to a minimum, the following measures were taken. The survey was conducted at people’s homes, so that they were in familiar surroundings and more honest about their answers. An additional advantage is that items that concern the whole household, such as coffee production and employment, can be cross-checked with other household members. A disadvantage of being at home is that farmers might be interrupted by visitors or household sores. Especially with the methods that took place at the grounds of the cooperative (risk mapping, risk game and the interviews) we emphasized several times that the information given by the respondent was anonymous and was not going to be submitted to the cooperative. During risk mapping, we asked one of the group members to write down the problems, so that participants would feel free to speak about their problems. According to the enumerators, they were indeed fairly honest in the answers given. In addition, we made clear to board or staff members of the cooperative that they were not to participate in the group discussions.30 In one instance, at the risk mapping at Kangunu, one of the farmers refused to discuss household problems with the group. Since this was obstructing an open discussion, we decided to discuss only those problems related to coffee and the cooperative. The main way of improving reliability is assuring that all enumerators interpret the research goal and methods in the same way. This was especially of importance with the survey, were three to four enumerators conducted surveys simultaneously. This was accomplished by testing the survey extensively. In this way enumerators became more comfortable with the survey, and the researcher had the chance to further improve questions. Secondly, all surveys were examined for non-response and ambiguities at the end of each day. If needed, enumerators called respondents for clarification. 30
This was necessary, since at two cooperatives board and staff members had understood the method risk mapping as if the researcher was going to teach information to the respondents, and they wanted to be part of that as well.
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Besides, a few times during the execution of the survey, researcher and enumerators discussed all survey questions again to make sure that everyone was still asking all questions in the same way. The coffee harvest of the farmers was cross-checked with figures of the cooperative for improving internal validity. Unfortunately, this information was not available for all farmers. The reliability of the data might be hampered as well due to language issues. The research was conducted in two areas were two different local languages were spoken, Ki-embu and Kikuyu, in addition to Swahili. Of the enumerators, one was fluent in both, while the other two were both better at one of the local languages. Therefore, if possible, an enumerator was chosen who was best at the language of a certain area. For instance, the explanation of the risk game in Embu was done by the enumerator being best at Kiembu, while for Mathioya the enumerator who was best at Kikuyu was mainly used. This did not raise very serious issues however, since Ki-embu and Kikuyu are rather similar to each other. The semi-structured interviews were done with only one researcher, for which the researcher was used who was fluent at both local languages. The main reason was however that he was most precise in filling in questionnaires and he showed the most at home in the research topics and methods. Considering the risk game, there were some problems with understanding the game. Some of the respondents were illiterate and did not understand the worksheet, even after thorough explanation by the enumerator. We then just gave them money for appearing at the game, and still discussed the extra questions. Unfortunately the response rates were hereby lowered. At Rianjagi, due to miscommunication farmers were not invited and had to be invited at the last moment. Therefore, only 20 farmers participated in the game instead of 20.
3.4
Data Analysis
The first data collected in the field were the interviews at board level and risk mapping. Interviews at board level were processed immediately, mostly verbatim. The list of risks that were the result of the risk mapping was used in the survey. Together with the enumerators, a list of 35 risks was reduced to 22 risks, whereby overlapping categories were combined to one risk. Hereby we chose to separate risks related to coffee and risks not-related to coffee, so as to have a detailed overview of risks related to coffee growing and selling practices. Therefore, some risks still have overlap, such as ‘the availability of inputs for coffee’ and ‘the availability of inputs for other crops than coffee’. The survey data were processed by the use of the statistical data analysis program SPSS 18.0 for Windows, so to prepare raw data for analysis. This was simultaneously a control to ensure that all respondents had answered the full questionnaire. If not, enumerators were to call respondents or visit them again, so to complete the questionnaire. In addition, data from the other methods, the risk game and interviews, were processed in the field as much as possible. For the risk game this was necessary since information from the game, together with the survey, was used as selection criteria for in-depth interviews in the last phase of the research. Back from the field, extensive data cleaning took place by controlling the survey for discrepancies. After that, in-depth data analysis was performed by the use of the data analysis and statistical software packages SPSS and STATA . The quantitative data were analysed by performing a wide range of statistical tests, notably independent sample t-test, factor analysis and multiple regression. The validity and reliability of the data of the data was ensured by performing tests such as Cronbach’s Alpha and Chow tests.
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The ultimate aim of the data analysis was to test our hypotheses concerning the effect of participation in Utz Certification on a series of dependent variables: yield, trust in the cooperative, loyalty to the cooperative, risk index all risks, risk index coffee risks, coffee part of income31. We started with performing a t-test for equality of means so to determine whether the performance of the sub-groups did significantly differ or not. We compared means between the two regions (see Chapter 2, context), as well as a comparison of means between treatment and control group within each region. The first comparison was done so to take into account the influence of context in our analysis. The first regression model run is a logistic regression model where the possibility of being Utz Certified is tested against independent variables that are thought not to be influenced by membership of a cooperative. In this way we test whether treatment and control groups are comparable to another. After establishing this, we ran an ordinary least square (OLS) regression to get more insight in the effects of Utz Certification on the coffee yield of farmers. This is followed by a two stage least squares (2SLS) regression, analysing the effects of trust, loyalty and risks occurrence on each other. A 2SLS is used, since loyalty and trust are endogenous variables for the model explaining risk occurrence (Wooldridge, 2008). In most models, the influences of personal and household characteristics such as age, education, household size and wealth in terms of assets) were taken into account as control variables. In addition, several dependent variables were tested for the statistical significance of their relationship with independent variables, most of them being indexes. These are the following: asset value, technical assistance, monetary benefits, performance of the cooperative, institutional trust in the cooperative, trust coffee production of other members, loyalty to the cooperative, all risks, and coffee risks. All indexes but the first (asset value) came about by the use of principal component analysis. The indexes ‘technical assistance’ and ‘monetary benefits’ are two indexes based on one principal component analysis; the index scores are based on the two components that came out of this analysis. The operationalization of these variables is included in appendix 3. The results from the games were correlated with comparable results from the survey, such as risk attitudes and trust, so to test the relationship between stated and actual behaviour. Lastly, qualitative information collected during the risk mapping, survey and risk game, as well as the content of in-depth interviews, was used to supplement statistical analysis, as well as to confirm or contradict results from analyses. In this way triangulation was guaranteed.
31
See appendix 1, 2 and 3 for the operationalisation of variables.
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53
Chapter 4: Results We argue that social standards, such as Utz Certified, have a positive influence on farmers affiliated with a certified cooperative. Our hypothesis is that the influence is in such a way that their coffee production and livelihoods become more sustainable, due to the reduction of vulnerability. This chapter gives more insights in these effects: are the effects of certification actually positive, and in what way do they have effect on the livelihoods of rural farmers? We will discuss the results of our analyses in the following way. The first model looks into the determinants of becoming an Utz Certified farmer. We will do so to establish whether the farmers of the treatment and control cooperatives are actually comparable to one another. Hereafter, the hypotheses will be tested in model 2, starting with the direct effects of Utz Certification on farmers. Model 3 and 4 concern the supposed behavioural effects of Utz Certification, with an explicit focus on risk attitudes and trust. The chapter concludes with model 5, an exploration of income strategies of Utz Certified and non-certified farmers.
4.1
Explaining the probability for certification
We start to examine the extent to which farmers of the treatment and control cooperatives are comparable to one another. Therefore, we compare the probabilities of being in the treatment or control group for several variables (Model 1). For this purpose we use a set of pre-treatment variables and exogenous characteristics of the household. These characteristics are not likely to be influenced by participation in an Utz Certified cooperative. They therefore give information about the extent to which farmers in both the treatment and the control group differ from each other. If they differ, we have to be careful in the – coming – comparison of the control and the treatment group and have to statistically control for the differences. The used pre-treatment variables are the following: the land owned by the household (hh) in acres, and the distance to the nearest market. Exogenous characteristics used are gender, age and level of education of the head of the household (head hh), and the household size. The descriptives of these variables are presented in Table 4.1 for both the Embu and Mathioya region. Whether the figures of Utz certified farmers and the other farmers differ significantly is tested using independent T-tests. Because of the small sample size, significance levels smaller than 0.10 are regarded as significant. In the Embu region, the households of the two cooperatives only differ significantly concerning their household size, and their distance to the local market. Rianjagi (Utz) farmers live closer to the market than Kithungururu farmers do, and have a slightly larger household. The other exogenous variables do not show significant differences, and neither does the pre-treatment variable land size. For Mathioya region, the farmers show similar household characteristics. The two cooperatives only deviate from each other concerning the distance to the local market: Kangunu (Utz) farmers are on average 2.5 kilometres further away from the market than are Kamagogo farmers.
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Table 4.1: Descriptives model 1, both regions Rianjagi Utz (N=52) Mean S.D.
Kithungururu (N=49) Mean S.D.
Kangunu Utz (N=42) Mean S.D.
gender head hh (% male) age head hh (yrs) education head hh (yrs) household size (no) land owned by hh (acs) asset value (1000 ksh)
0.904 50.860 10.423 4.423 2.025 62.414
0.298 12.393 3.195 1.829 2.044 143.176
0.857 49.878 10.694 3.898 2.238 60.845
0.354 15.640 5.185 1.610 1.861 146.685
0.714 59.738 6.762 4.643 1.714 23.539
0.457 11.340 4.653 2.497 1.207 22.687
0.698 58.511 6.651 4.302 1.601 18.731
0.465 13.355 4.276 1.909 1.081 16.312
distance to market (km)
4.850
2.359
5.590
2.440
5.214
2.590
2.640
1.197
*
*
Kamagogo (N=43) Mean S.D.
* = α< 0,10; ** = α< 0,05; *** = α< 0,01 (source: own household survey 2011)
Table 4.2 shows the outcomes of the probability model for becoming an Utz Certified farmer for both regions. We first start with discussing the results for the Embu region. The probability of becoming an Utz Certified farmer is determined by the average age of the household, the land owned by the household and the distance to the local market. Households with a higher average age are more likely to be a member of an Utz Certified cooperative, and this effect gradually increases by age. Secondly, poorer households in terms of initial land owned are less likely to be participating in an Utz Certified cooperative. Furthermore, households located closer to the local market are more likely to be involved in an Utz Certified cooperative. Table 4.2: Model 1 - Probability of becoming an Utz Certified farmer (logistic regression) Embu Constant gender hh (% male) age hh (yrs) age2 hh education hh (yrs) household size (no) land owned by hh (acres, log) value assets (ksh, log) distance to the market (km) Chi2 Nagelkerke pseudo R2 Chow test
Mathioya
B
S.E.
Exp(B)
-7.220
3.747
.001
.459
.707
1.583
.292 -.003 .008 .153 -.603 -.045 -.176
.133 .001 .059 .140 .335 .225 .097
1.339 .997 1.008 1.166 .547 .956 .839
14.420 .177 27.000
** ***
** ** **
** **
B
S.E.
Exp(B)
-12.993
7.671
.000
.503
.726
1.653
.342 -.003 .028 -.112 .374 -.109 .935
.255 .002 .085 .165 .429 .277 .214
1.408 .997 1.028 .894 1.453 .897 2.547
39.136 .492 26.030
** *
***
*** ***
Dependent = Utz y/n; * = α< 0,10; ** = α< 0,05; *** = α< 0,01(source: household survey 2011)
The results for Mathioya region show fewer significant differences between farmers. Again, household heads with a higher average age are more likely to become Utz Certified. In addition, households located further away of the local market are significantly more likely to become a member of an Utz Certified cooperative. For both regions, the distance to the market is a determining factor. This might be due to the selection criteria Solidaridad applies when considering
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***
the certification of cooperatives. However, this did not seem the case: according to Solidaridad, the closeness to the market is a coincidence and had nothing to do with the choice for the wetmill.32 In addition, we performed a Chow test to establish whether the different regions have different predictors for Utz Certification, and should thus be treated ad separate functions (Ruben 2008). The Chow test is significant, revealing that regressions must indeed be conducted separately for the two regions. By the use of semi-structured interviews, we questioned the farmers concerning their reasons for joining their current cooperative. Most reasons mentioned were the same for both the treatment and the control group. The main reason to become a member of the cooperative is a fairly practical one: the cooperative located most close to their home is chosen. In the Embu region, more than half of Rianjagi (Utz) as well as Kithungururu farmers mentioned this reason. By some farmers, the economic aspect of marketing their produce was pointed out: joining a cooperative is necessary for the marketing of coffee. The comments made by Rianjagi (Utz) and Kithungururu (control) farmers differed concerning quality aspects of the cooperative. For Rianjagi, only one farmer mentions a quality aspect of the cooperative: he felt that Rianjagi is the best cooperative around. Kithungururu was more often associated with these quality aspects by its members. One farmer specifically pointed out that the management of Kithungururu is better than another factory more closely situated to her farm, while two more farmers mentioned that their cooperative is the best cooperative in the area. One farmer added that the cooperative has benefits such as the provision of inputs. Turning to Mathioya, the farmers were less diverse in their answers on the question why they joined this cooperative. Again, for both cooperatives, most farmers pointed out that the cooperative they are a member of is just situated near their home, and some note that it is not possible to sell coffee on their own. For Kangunu, only once a quality aspect is mentioned, namely that the cooperative pays well. Quality aspects were not mentioned at all by Kamagogo farmers. The fact that practical reasons are mentioned more than quality aspects of the cooperative might be due to government policies. For Kenyan coffee farmers, it is imposed by the government that they sell their coffee through a local cooperative. In addition, farmers have to process berries within 24 hours of harvest. With limited access to transportation, they are thus forced to work with close-by cooperatives (Mude, 2006). This might limit the benefit maximization function of a cooperative, since the very motivation for economic organization, to attain optimal scale, loses its meaning (IBID). Concluding, it seems possible for the households to compare them in the coming regression analyses. The differences found between households will be taken into account in analysing the coming models. In addition, we trust that triangulating statistical outcomes with qualitative data is sufficient to enable a reliable analysis on the effects of certification.
32
Solidaridad gave the following reasons for choosing a cooperative: 1) there should be no internal leadership wrangles within the management of the cooperative. 2) Farmers and the management of the cooperative should be willing to participate so to ensure the success of the implementation of the project. 3) The dry mill to which the cooperative brings its coffee needs to be Utz Certified as well, or if not so, the cooperative needs to be willing to change to another dry mill. 4) There needs to be a trader interested in the particular coffee of the cooperative, and the trader needs to be a willing partner of Solidaridad and ready to link with a certified or specialty market.
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4.2
Direct effects – Utz certification and production
We continue with establishing whether there are differences in productivity of the treatment and control groups. The main input variables influencing agricultural productivity are considered labour and capital inputs (Ray, 1998). Two strategies for increasing productivity are distinguished in empirical studies: a) a labour-led intensification based on intensive use of (family) labour to increase production, and b) capital-led intensification, where farmers augment their production by combining labour with capital inputs , mainly fertilizers and irrigation (Boserup, 1965; and Reardon et al., 1996; in Ruben, 2008:160). The second strategy is the considered the most promising, since it guarantees improvements in yields and labour returns, while the first strategy can lead to an over-use of labour and thus declining marginal returns. We expect Utz Certified farmers to increase their productivity with capital-led intensification, due to their improved access to input and output markets, and improved provision of technical knowledge about improvements in coffee production. Both will enable farmers to reach higher returns (Ruben, 2008:159).We measure productivity using the Cobb-Douglas function, which follows the following formula: Y = a.Kb * Lc. Y=yield, K=capital inputs and L=labour inputs; a, b, and c are coefficients. This implies that we use an OLS regression with coffee yield as the dependent variable, and capital inputs as well as labour inputs as explanatory variables. For capital inputs we use a combination of the total value of assets the household has, as well as the money spent on fertilizer. For labour inputs we include the money spent on labour, as well as the percentage of farmers that hired in labour. In addition, we want to measure the influence the cooperative has on coffee yields. We do so by including two proxies, one for the technical assistance farmers receive, and one for monetary benefits. Together they represent the direct influence Utz Certification ought to have on a farmer’s yield. Lastly, we control for household characteristics, being the average age and level of education of the household, as well as the household size and the gender ratio.
4.2.1 Production function Embu region Table 4.3 describes the characteristics concerning coffee production for Rianjagi and Kithungururu.33 We will discuss differences in inputs and technical information received by the cooperative, and the outputs, respectively. The differences in inputs between the Utz certified farmers and the control group farmers are minimal. The average number of mature coffee trees is slightly but not significantly lower for Rianjagi farmers. Utz Certified farmers however have a significantly higher number of young coffee trees. The average number of family members working in the coffee field is around two people for both cooperatives. More Rianjagi (Utz) farmers spent money on fertilizers, but the money spent on fertilizer per farmer is equal for both groups, as are expenditures on other factors. This leads to the total inputs costs being similar for both groups. The technical information received by farmers is significantly higher for Rianjagi (Utz) than it is for Kithungururu farmers. 97% of the farmers received training, whereas only 51% of the controlgroup farmers received training at their cooperative in the last four years. In addition, Rianjagi farmers are more content with the technical assistance they received: they score significantly higher on the index technical assistance. The averages for the index monetary benefits do however not significantly differ from each other. Farmers are equally content with the price they receive for their crop.
33
Unfortunately, we were not able to provide data for the used acres in coffee production due to too much missings on this variable. The same holds for Mathioya.
57
The outputs realized by the above described inputs and technical assistance show some interesting results (Table 4.3). Kithungururu (non-Utz) farmers have an absolute higher coffee harvest than Rianjagi (Utz) farmers. The harvest per tree of Kithungururu farmers is also slightly higher, with significance on the 10 per cent level. This difference in output results in a higher absolute revenue from and a higher profit out of coffee for Kithungururu (non-Utz) farmers. This effect is however not significant when we consider the profit per coffee tree. The figures in Table 4.3 give not much explanation of why the outputs of Kithungururu farmers are higher than Rianjagi farmers, except for the difference found in the number of young trees, which is significantly higher for Rianjagi farmers. When examining this more closely, it appears that 25% of Rianjagi farmers have non-fruit bearing trees, against 8.2% of Kithungururu farmers. This translates to an average of 12% of Rianjagi coffee trees being young trees, while for Kithungururu the same average is 3,6% of the trees. Rianjagi farmers thus have invested more than Kithungururu farmers in young trees. Investments done in terms of fertilizer and labour do not yet show results in terms of harvest and revenue, because they are not fruit bearing yet. This is probably the main explanation of why the outputs for Kithungururu farmers are higher than for Rianjagi(Utz) farmers. Table 4.3: Characteristics concerning coffee productivity, Embu Rianjagi (Utz)
Kithungururu
Mean
S.D.
Mean
S.D.
Input total no of coffee trees
324.60
231.43
366.22
323.56
no of young trees (0-3 yrs) no of fruit bearing trees (> 3 yrs) Workforce in farm hh (no) Ratio of total workforce hh (%) hired labour, % Use of fertilizer, % money spent on hired labour (1000 ksh) money spent on fertilizer (1000 ksh)
20.23 304.37 2.00 0.70 0.67 0.94 16.66 5.18
53.99 223.22 1.08 0.28 0.47 0.24 25.16 4.92
5.22 361.00 1.90 0.76 0.63 0.84 15.65 4.88
23.36 324.40 0.92 0.26 0.49 0.37 22.92 4.31
0.26
0.51
0.41
0.96
5.44
5.08
5.29
4.75
0.96 0.40 -0.04
0.19 0.53 1.02
0.490 -0.915 0.146
0.51 1.23 0.96
Output total harvest berries and mbuni (kgs) 809.67 827.79 kgs of coffee per fruit bearing tree 3.32 4.13 gross coffee revenue(1000 ksh) 40.48 41.39 profit out of coffee (1000 ksh) 35.04 38.85 profit per coffee tree (1000 ksh) 0.145 0.197 farmers perception of coffee turnover 2.35 0.89 (1=loss, 2=equal, 3=profit) * = α< 0,10; ** = α< 0,05; *** = α< 0,01(source: household survey 2011)
1171.48 4.45 55.06 49.77 0.189 2.49
1057.70 4.67 49.71 47.14 0.206 0.87
money spent on seeds, machinery, sacks (1000 ksh) total inputs costs, except labour (1000 ksh)
t-test
**
**
Technical assistance Attending training program (%) Technical assistance (index) Monetary benefits (index)
*** ***
** * * **
58
Table 4.4 shows the results of the production function for Rianjagi and Kithungururu. Model 2a was run with only the control variables, which shows significant effects for the gender ratio, the level of education of the household and the land size of the household. The last two effects however diminish when our explanatory variables are included in model 2b and 2c. Model 2b was run with the inputs in percentages of farmers who used inputs or hired in labour. Model 2c shows the results when inputs are taken into account as costs (in Ksh). 34 The explained variance of model 2c is slightly higher than model 2a which implies that the inputs as cost have as significant influence. The household variables that are of significant importance in coffee production are the following: the gender ratio of the household, and the wealth of the household in terms of land, assets, and livestock. The gender ratio is negatively significant for both models: production is higher if a household has more women. The research of Eveline Dijkdrenth (2011) 35 signified that in the Embu region, women do most of the work in the coffee field. They maintain the coffee trees during the season, while men often only help out during harvesting. The quality of the harvest thus depends for a large part on the labour of women. The wealth of the household, as indicated by the combined value of assets and livestock gives a positive effect, as well as the land owned by the household. Table 4.4: Model 2 - production function, Embu (OLS regression) Model 2a: control variables B S.E. Sig Constant Household characteristics Gender ratio (% male) Mean age hh (yrs) Mean education hh (yrs) Household size (no of people) Area owned by hh (acres, log) Assets &livestock value (ksh, log) Inputs and assistance Hired labour (%) Use of fertilizer (%) Total input costs, incl. labour (ksh) Cooperative characteristics Monetary benefits (index) Technical assistance (index) Utz Certified (1=yes) F-value Adjusted R-square Chow test
5.862
0.587
***
-0.572 0.009 0.050 0.046 0.333
0.439 0.009 0.031 0.063 0.120
*
-0.408 6.137 0.531
0.182 ***
** ***
**
model 2b: inputs in % B S.E. Sig. 3.195
1.082
***
-0.536 0.008 0.015 0.038 0.155 0.230
0.418 0.008 0.030 0.062 0.122 0.092
*
0.310 0.499
0.192 0.295
* **
-0.098 0.007 -0.578
0.090 0.093 0.208
* ***
***
model 2c: inputs in ksh B S.E. Sig. 2.988
0.978
***
-0.552 0.007 0.007 0.042 0.094 0.195
0.390 0.008 0.028 0.465 0.115 0.085
*
0.158
0.037
***
-0.132 0.025 -0.628
0.083 0.086 0.195
**
5.382 0.632
***
7.910 0.684
***
10.40
***
11.91
***
* ***
***
Dependent: harvest in kg (log). * = α< 0,10; ** = α< 0,05; *** = α< 0,01(source: household survey 2011)
34
We used a log transformation of the input costs; therefore it was not possible to consider labour and fertilizer separately, since the distribution of the separate variables is not normal due to the peak at the zero. By running two models, the effects of labour and fertilizer can be examined separately as well. 35 A master student Development Studies of the Radboud University who did her research at the same cooperatives in Embu.
59
Farmers, who have the means to do so, invest more in coffee which leads to a higher yield. The physical capital of the household is thus of more importance in coffee production than human capital. Age and household size are not of influence on coffee production, and education only if we do not control for inputs, assistance and cooperative characteristics. There can be several reasons why capabilities are of minor influence on coffee production in the Embu region. Firstly, 60% of the farmers hire in labour, which makes part of the coffee production depending on the capabilities of this labour force, instead of the own capabilities. In addition, in the Embu region, farmers are on average for 40% of their income depending on their coffee production; other important sources of income are livestock (products), the sales of crops and other employment (see Chapter 2). This might why be there is a less clear dependency between the households capabilities and coffee production. The inputs and assistance variables show a positive significant effect in model 2a and model 2b. A higher expenditure per farmer on labour and other inputs result in a higher production. These results are in line with the theory on production functions; farmers reach higher returns by investing more in inputs and labour. Production functions for both Rianjagi and Kithungururu are thus based on capital-led intensification. The indicators used for the influence of the cooperative show interesting results. The dummy for certification is negative and strongly significant, thus indicating that members of the Utz Certified cooperative are more likely to have a lower yield. This is in line with the differences in outputs we found in Table 4.3, and is probably caused by more investments done in trees that do not yet bear fruit. The index for monetary benefits is only negatively significant in model 2b. This indicates that – controlled for the total input costs – farmers who have a higher yield, do feel that they do not benefit from that with higher prices. It might be that farmers with a higher yield are less satisfied with the price they receive, since for them there is more at stake if prices are low. The index indicating satisfaction with technical assistance is not of influence on the production function. We also performed a chow-test, which is significant. This indicates that the production functions of the farmers of the two cooperatives are differing significantly from each other. We therefore also ran model 2 for each cooperative separately (see Appendix 4.1 and 4.2). It is noticeable that the main effects still are significant: wealth in terms of assets influences the harvest positively, as does hiring labour and expenditures on inputs such as fertilizer. Thus, the main variables explaining harvest for both cooperatives are their initial wealth in terms of assets and land, and how much money they spent on labour and inputs. Technical assistance is significant for the Rianjagi production function, but not for the Kithungururu production function. This reflects the results presented in Table 4.3; Utz-certified receive more training and are more positive about the technical assistance they receive, which results in a higher harvest. Secondly, the index indicating monetary benefits remains negatively significant, but only for Rianjagi farmers and only in model 2c. Being Utz Certified thus might lead to positive results for Rianjagi farmers, as one of the factors explaining their harvest is the training they received. The influences of the cooperative on the yield of farmers were also discussed during the interviews. Starting with technical assistance, the results we found so far for Rianjagi were reflected during the interviews. Farmers at Rianjagi were quite positive about the education they received and most deem it adequate. Education includes several education days per year, where farmers are taught 60
how to spray, how to apply fertilizer and how to prune their trees. Farmers noted that education days are held more often and are more extensive since the cooperative became Utz Certified. In addition, an Utz certified cooperative has promoter farmers, who are cooperative members spread throughout the cooperative area. They are instructed to educate farmers and to examine whether farmers are following the right procedures. These promoter farmers are appreciated, and are seen as another improvement that came about due to the certification of the cooperative. One farmer remarked that sufficient education remains important in the future, as new coffee diseases are coming up due to climate change for instance. The interviews done at Kithungururu showed mixed results. Three out of the eight farmers claimed that they did not have education days, and that knowledge sharing does not take place. Field trips are organized, however the farmers that attend these trips are usually the same, richer, farmers who do not share the information they receive. Other farmers however mentioned that there are education days at which an agricultural officer of the government teaches them coffee practices. Lastly, two farmers remarked that ‘education alone is not enough, if farmers do not have the capability to apply what is educated, for instance the right inputs, how to apply the education you receive?’ The provision of inputs was an issue quite often debated during interviews. At all cooperatives (also in Mathioya area) the way of providing inputs is organised in more or less the same way: At the beginning of a season, farmers can apply for fertilizers and pesticides that are provided by the cooperative. They can, however, only apply up to a maximum that is determined by the farmer’s harvest of last year. The costs of fertilizers and pesticides you applied for will be deducted from your coffee pay-out, once you harvested your coffee. Farmers explained that this system has the disadvantage that if, due to rainfall, pests or other causes, your harvest was lower than expected, the amount of inputs you can apply for in the next year will also be lower. This might eventually lead into a downward spiral, since farmers have less fertilizer and pesticides to apply in the coming season. The main reason why this system is in place is that farmers might apply for more inputs than they eventually can afford. There were also cases mentioned were farmers did not use the inputs on their own land, but sold it to others. Both might leave the cooperative with a debt since farmers buy their inputs on credit, which is something being avoided by this system. We discussed this system with the farmers at all cooperatives. At all cooperatives, farmers are either in favour of this system, or propose two alternatives; the maximum should be determined by reviewing a couple of years, instead of only the last year; or the maximum should be determined by the number of trees a farmer has. Rianjagi farmers were mostly in favour of a system where the maximum is determined by reviewing a couple of years back, and one farmer saw a role for promoter farmers there. The main reason mentioned to prefer this system was to prevent the cooperative from debts, because, as one farmer remarked, ‘you cannot order something which your kilos cannot pay. You order what your kilos can pay’. Most farmers agree that they know when and how to apply inputs. For some farmers inputs are not always enough. However, all farmers mentioned that they have the ability to buy the inputs elsewhere with other money sources. One female farmer explained that she does not order her inputs through the cooperative at all, so to have ‘more money in her hand’; i.e. to receive her coffee payment without deductions. Also at Kithunguru, most farmers would also prefer a system where not only last year is taken into account to determine the amounts of inputs you can apply for. Three farmers complained about delays in inputs, one farmer gave the example that his coffee was already attacked by pests due to the delay in input provision. Delays are especially troublesome if a farmer has no other means of 61
buying inputs than on credit by the cooperative. When farmers were asked about improvements that could be made in the cooperative, two things were mentioned most; improvements in the way inputs were provided, and in coffee prices. Lastly, we discussed farmer’s satisfaction with the price they receive for their coffee. Kithungururu (non-Utz) farmers – the farmers with the highest coffee production - are not entirely satisfied with the price they receive for their coffee. Some farmers explained that their coffee is sold at a good price, but they have never been ahead of other cooperatives neighbouring them. On the other hand, one farmer noted that you cannot do otherwise but be satisfied with the pay-out you receive, since an individual farmer does not know the prices at the market. At Rianjagi (Utz), sentiments about the price they receive for their coffee are similar, although farmers were not uniform in their answers. Some argue that Rianjagi pays well, while others also remark that other cooperatives surrounding Rianjagi pay higher prices for coffee, which was supported by our data. Discussion For the Embu region, household characteristics such as gender and wealth seem to be of most importance to increase inputs and yield, and the right application of fertilizers and pesticides. The Utz-certified cooperative has a positive influence on the harvest of its farmers through the technical assistance they receive. From interviews it appeared that farmers appreciate the technical assistance they receive. This does however not yet pay off in a higher harvest than the control group, which might be due to two reasons. Firstly, Rianjagi was certified in 2007, and the survey was done over the 2009-2010 season. It might thus be too early to already see effects of certification in terms of improved harvest. Secondly, Rianjagi farmers invested significantly more in young trees than Kithungururu farmers. These yield at the moment lower outcomes, and this might be another reason why their harvest is not (yet) higher than that of Kithungururu farmers. Thirdly, and alternatively, unobserved effects might play a role; for instance, more effective investments in the provision of inputs by the cooperative.
4.2.2 Production function Mathioya region Table 4.5 shows the descriptives concerning productivity for Mathioya region. Here the differences between the certified and non-certified cooperative are more profound. Both the control and the treatment group have a similar number of trees. They also do not differ on the nr of people of the household working on the farm. Almost all farmers of both cooperatives spent money on fertilizers, but the average amount of money spent on fertilizer per farmer is significantly higher for Kangunu farmers; per year a Kangunu (Utz) farmer spends around 3300 Ksh more on fertilizers than a member of Kamagogo. In addition, 84 per cent of the Utz Certified farmers spent money on hiring in labour, while just 70 per cent of the control group farmers do so. In total, Kangunu farmers spend more money on inputs and labour. The farmers of both cooperatives also differ significantly on the assistance they receive of their cooperative: The Utz Certified farmers received significantly more training, are more satisfied with the technical assistance received, and with their monetary benefits. This and the significantly higher outputs realized by Kangunu (Utz) farmers shows that the certification program thus seems to have a strong positive effect on the coffee productivity of its farmers.
62
Table 4.5: Characteristics concerning coffee productivity, Mathioya Kangunu (Utz)
Kamagogo
Mean
S.D.
Mean
S.D.
total no of coffee trees no of young trees (0-3 yrs)
259.05 7.98
115.46 22.98
234.02 6.63
299.51 17.48
no of fruit bearing trees (> 3 yrs) Workforce on farm hh (no)
251.07 2.02
115.74 1.18
227.39 1.88
300.30 0.88
0.77 0.83 0.98 12.21 8.28 0.25 8.53
0.27 0.38 0.15 23.16 6.33 0.41 6.44
0.75 0.70 0.93 14.52 5.16 0.35 5.51
0.25 0.47 0.26 23.26 3.82 0.76 3.92
1.00 0.54 0.25
0.00 0.23 0.76
0.86 0.02 -0.41
0.35 0.96 1.16
1541.21
1123.72
1022.40
1496.58
kgs of coffee per fruit bearing tree 6.42 4.03 gross coffee revenue (1000 ksh) 85.77 62.54 profit out of coffee (1000 ksh) 77.24 59.62 profit per coffee tree (1000 khs) 0.32 0.22 farmers perception of profit 2.33 0.93 (1=loss, 2=equal, 3=profit) * = α< 0,10; ** = α< 0,05; *** = α< 0,01 (source: household survey 2011)
5.40 41.00 35.49 0.18 1.86
7.19 60.01 59.24 0.28 0.952
t-test
Inputs
Ratio of total workforce hh (%) Hired labour, % Use of fertilizer, % money spent on hired labour (1000 ksh) money spent on fertilizer (1000 ksh) money spent on seeds, machinery (1000 ksh) total inputs costs, except labour (1000 ksh) Technical Assistance Attending training program (%) Technical assistance (index)36 Monetary benefits (index)37 Outputs total harvest berries and mbuni (kgs)
*
*** *** *** *** *** ** *** *** *** **
Kangunu farmers have, on average, 500 kilograms more berries harvest than Kamagogo farmers, which translates in almost a kg more per tree. Kangunu farmers also have a higher total profit and profit per coffee tree. The higher input costs Kangunu farmers have, are thus paying off in a higher profit. The farmers of both cooperatives might be differing in terms of production function. We look further into this by analysing the production function in model 2. Table 4.6 gives the results of the OLS regression with coffee yield as dependent. The dummy for Utz Certification is positive: Utz Certified farmers are indeed more likely to have a higher yield than their counterparts. Household factors leading to a higher yield are the following: the average age of the household, and the household size. If the mean age of the household is higher, and the household is larger, the coffee yield increases. These effects are strong, since they do not fade out when the explanatory variables are introduced in model 2b and 2c. Family labour thus seems to be of more importance in this region than in Embu region. This is also shown by the type of labour that is hired. 36
Index based on the following questions: if a farmer has attended a training program (%), if a farmer learned better coffee practices (%); and statements: I am provided with sufficient fertilizers and pesticides (1-5), I am provided with sufficient knowledge to improve the quality of my coffee (1-5). 37 Index based on the following questions: if a farmer received higher coffee prices (%), if a farmer’s coffee productivity increased (5), and if a farmer’s household income increased; and statements: I am provided with sufficient fertilizer and pesticides (1-5).
63
In Mathioya, farmers of Kangunu and Kamagogo sparely hire full-time labour; an average of 1 per cent of the hired labour is full-time labour (as opposed to seasonal labour).For Rianjagi and Kithungururu the averages for hiring full time labour lay between 10 and 20 per cent of the farmers. This might be explained by contextual factors. In Mathioya region there is less unemployment in comparison with Embu region (see chapter 2). This makes labour less abundant ant thus more expensive than in Embu. Table 4.6: Model 2 - production function, Mathioya (OLS regression) Model 2: Control variables B S.E. Sig. Constant Household characteristics Gender ratio (% men) mean age hh (yrs) mean education hh (yrs) Household size (nr of people) area owned by hh (acres, log) assets &livestock value (ksh, log) Inputs and assistance Hired labour (%) Use of fertilizer (%) total input costs, incl. labour (ksh) Cooperative characteristics Monetary benefits (index) Technical assistance (index) Utz Certified (1=yes)
4.695
.492
-.455 .027 .035 .141 .189
.380 .007 .028 .053 .121
F-value Adjusted R-square
6.596 .580
Chow-test
.551
.170 ***
***
*** *** *
***
B
Model 2a: inputs in % S.E. Sig. 2.877
1.001
-0.431 0.023 0.008 0.145 0.130 0.126
0.379 0.007 0.032 0.05 0.112 0.109
0.514 0.510
0.013 0.409
***
*** ***
B
Model 2b: inputs in ksh S.E. 2.904
0.912
***
-0.326 0.021 0.020 0.115 0.113 0.044
0.352 0.006 0.028 0.046 0.104 0.104
0.200
0.045
***
0.045 0.069 0.361
0.084 0.126 0.165
**
*** ***
**
0.123 0.173 0.255
0.087 0.133 0.175
* * *
6.741 0.712
***
9.495 0.752
***
3.58
***
4.75
***
Dependent is harvest in kg (log). * = α< 0,10; ** = α< 0,05; *** = α< 0,01 (source: household survey 2011)
Age shows a positive effect; older farmers report more harvest outcome. The effect of age might be explained by the fact that younger farmers are less interested in coffee farming and have less knowledge and experience in farming. In interviews this was several times mentioned as a problem in the area. One farmer mentioned that educated youth do not want to work on farms anymore; instead they prefer to set up businesses or a career job. Wealth of the farmer in acres or assets is not of significant influence on the farmers yield. For Mathioya region, capabilities and household labour are thus of a stronger influence on the coffee harvests than are assets. The production function is thus signified by a labour-led intensification. We now turn to discussing the effects of inputs and assistance of the cooperative. Since the chow-test is again significant for this model, we ran the two models for the two cooperatives separately as well (see appendix 4.1 and 4.2). It then appears that the total asset value is positively significant for Utz Certified (Kangunu) farmers. Utz Certified farmers with a higher level of physical capital have more capital to invest in inputs, resulting in a higher yield. This capital is especially invested in hiring labour, which has a significant effect in Kangunu’s production function. Kamagogo farmers have a significantly higher yield if they spend money on fertilizer. 64
Turning to the cooperative characteristics, the index scores for technical assistance and monetary benefits are positively significant at the 10 per cent level. However, these turn insignificant when controlled for the total inputs (Model 2c). Technical assistance and monetary benefits thus influence the choice of farmers to invest in extra labour or fertilizer, but less the amount of money spend on these. Again, we examined these index scores for the separate cooperatives as well (appendix 4.1 and 4.2). The proxy for monetary benefits is positively significant for Kangunu (Utz) farmers, whereas the proxy for technical assistance is negative, but not significant. For the Utz-certified farmers especially the monetary benefits are of influence on having a higher harvest. This might point at the fact that farmers value the monetary benefits of their cooperative especially if they have enough knowledge and resources to invest in their coffee (see also Table 4.5). In other words, the Utz Certified cooperative Kangunu provides farmers with a successful combination of knowledge, inputs and a higher price. Other research confirms that this combination is important in improving yields ((Barham et al., 2011). This while for Kamagogo, technical assistance and price received are significantly lower. These factors do therefore not play a role of importance on the harvest of farmers. The interlinkages between technical assistance, inputs and payment were confirmed during the interviews. Starting with technical assistance, the interviews validate the distinction in education between the two cooperatives. At Kangunu (Utz certified), half of the farmers consider the education useful and adequate. Farmers considered education, and the promoter farmers, one of the improvements brought about by Utz Certification. It is also appreciated that Kangunu has hired an agricultural extension officer who goes round the farms. One farmer described it as follows: ‘since he came into the picture, farmers are doing their best to produce high quality coffee’. This extension officer was hired for one year with a fund provided by Solidaridad. After that, Kangunu decided to keep him hired. He is now coordinating the promoter farmers at Kangunu, and is their spokesperson at the management committee. One remark made is that there are field trips made, however only the management committee and promoter farmers are invited, while this information does not get back to the individual farmers. One farmer argued that you can always have more knowledge, but you also need the capabilities to implement it to your farm to see results. At Kamagogo, it is mentioned that there are one or two education days per year. One farmer considered the education good, but most farmers however do not consider it useful. Several reasons were provided: the knowledge taught is not satisfactory and the frequency of education days not high enough. This is in line with our theory that extension services have mixed success if they are not supported by certification schemes (World Bank, 2007). One farmer we interviewed was also a member at Kangunu, and according to him the frequency of education days is higher there. Another large difference in favour of Kangunu is that he has access to an agricultural extension officer there. One farmer mentioned that the use of the education days is not high since ‘they have been having that kind of knowledge over the years, yet the prices of coffee they are receiving are low, and are getting poorer and poorer’. Regarding the system of supplying inputs, several Kangunu farmers consider it unfair, especially for those farmers who do not have the means to buy inputs themselves. On the other hand, it was argued that it is necessary to have some restrictions on how much you borrow with your money; ‘it limits you from overtaking things’ one farmer explained. Several farmers argued to change the system to one where the number of coffee trees is taken into account. This has the advantage 65
that the cooperative can set a target as in how much you should produce per coffee tree, and can then provide the inputs accordingly. Farmers also see a role for the agricultural officer. He can follow you up on whether you are applying enough inputs, or if you need to request more. One farmer mentioned that she prefers to buy inputs herself in order to have a bigger ‘lump sum’ at the pay-out of coffee money. No farmer complained that the inputs were not enough, some farmers needed more input during the season, but they were able to buy that by their own means. Kamagogo farmers, however, were less optimistic about the provision of inputs. Some farmers mentioned that they applied the little that they received and were not able to use more. On the other hand, other farmers had the means to buy extra from other sources. The arguments about what system to use do not much differ from the ones mentioned by Kangunu farmers. One farmer remarks that ‘looking more than one year back might be too optimistic, and you might end up with too many inputs which you can’t repay, because your harvest is too low’. Lastly, we compare both cooperatives based on their payment systems. Kangunu farmers were overall quite satisfied with the price they receive for their coffee. The farmers that were satisfied were so due to the fact that the payment came rather fast this year.38 Some farmers argued that the payment of Kangunu is the highest in the area. The other half of the farmers reasoned that they were satisfied with their payments; however, they feel that their coffee should be paid a higher price considering the inputs and work they put into their coffee. The management should for instance be investigating whether the miller they bring their coffee to gives the best prices. This satisfaction with payments is also one of the improvements mentioned by farmers that came about since the certification of their cooperative. At Kamagogo, the control group, things looked quite different. The argument used at Rianjagi (Utz) resonates, namely that farmers are only satisfied because they have ‘no other option but to accept the price the miller offers’. This is because ‘you can’t sell your own coffee as an individual farmer’. Another farmer added that ‘you have to be content with the pay-out you receive, since you can’t eat coffee’. Most farmers are negative about their payment: other cooperatives pay their farmers at higher prices (5 farmers); there is a delay of payment (4 farmers), and money ‘gets lost’ between the miller and the farmer (1 farmer). An explanation given for the last two reasons is that the management board usually keeps their money longer on the cooperative’s bank account, so to earn the rent, while farmers are waiting for their money. Discussion Concluding we can say that in Mathioya region, treatment and control group farmers differ more profoundly than they do in Embu region. In Mathioya region, farmers are stronger depending on their households for farm labour. In addition, Kangunu farmers also have the means to invest in coffee, more than Kamagogo farmers. If needed, Kangunu farmers thus have a larger ‘buffer’, or have more possibilities to spread their risks due to their asset levels. However, they might also have less need to, since they receive better and more training, are more satisfied about their input provision, and the higher price they receive for their coffee stimulates them in their coffee production.
38
Kangunu was the only cooperative of the four examined cooperatives in this study that was already giving payment to their farmers in March/April 2011(which was the money received from the August/December season).
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4.2.3 What cooperatives should do We conclude this paragraph with an analysis of the question what the cooperative should do, according to the farmers, to produce coffee of high quantity and quality. This gives further insights in what is considered important to farmers, and how the cooperatives are performing momentarily. Rianjagi(Utz) farmers are quite positive about the fact that the cooperative stimulates farmers to produce coffee of high quality. Farmers opinions about ways of how this is stimulated are quite fragmented: By giving inputs (2 farmers), on the right timing; by the introduction of promoter farmers (2 farmers); due to education, although it is the farmers who are left with the task of applying whatever has been taught (2 farmers); and higher coffee payments (1 farmer). Kithungururu farmers are more negative than their Rianjagi neighbours. Four out of eight farmers mention that the cooperative does nothing, or not enough to stimulate a good quality coffee production. On the other hand, three farmers mention education as a way of how the cooperative stimulates farmers to produce good quality coffee. When asked how the cooperative should stimulate good quality coffee production, four farmers mention that good coffee prices are very important. ‘Prices are the most essential thing to improve productivity. If prices are good, it will always give the farmer moral work to work more in his shamba’. While at the moment, coffee payments are poor which is why farmers opt to abandon their coffee and take up other farm activities. Kangunu (Utz) farmers are merely positive about the efforts of their cooperative to stimulate good quality coffee production. The main efforts mentioned are the agricultural extension officer hired by the cooperative, and the high coffee payment. The agricultural extension officer goes round the farms to make sure good quality coffee is produced. Secondly, coffee prices encourage farmers very much to put more efforts into their coffee. ‘You are motivated to practice better farming methods, so to get more kilograms per tree’. Two farmers had the interesting remark that they think that the agricultural officer or the general committee should get the power to take action to those farmers who do not tend to their coffee. These farmers should either be blocked from the cooperative, or should be helped to improve their coffee. At the end of the day the farmers with the best coffee share their income with those farmers who do not tend to their coffee. High quality coffee production is least stimulated at Kamagogo cooperative. 5 farmers think that the measures taken by the cooperative are not sufficient: education is not sufficient, the inputs available are not enough and coffee prices are poor. Most farmers give up on coffee production because of these reasons, due to which coffee prices stay poor. Or, even if education is useful, farmers are not able to implement what they are taught since you need money to apply fertilizers. The farmer who distributes coffee to Kangunu as well as Kamagogo explains that the higher prices at Kangunu brought about competition between farmers, which is good for the coffee quality. Since the certification, that is when changes developed between Kangunu and Kamagogo. The following things are suggested: committee members should be setting examples, now they are not even delivering coffee of high quality. Also, education should improve, for instance by showing the knowledge in the field, and inputs should be delivered on time, so that farmers do not have to pick it on credit.
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4.3
Indirect effects – trust and loyalty
In this paragraph we examine more closely the influence of the cooperative itself on farmers. Do cooperatives become more reliable organisations due to Utz Certification? We do so by analysing trust and loyalty attitudes towards the cooperative, as these are important determinants of the functioning and performance of a producer organization. In the survey, trust was measured with a set of 8 statements based on a Likert scale39. Factor analyses showed that these items are directed to two dimensions of social trust; one for institutional trust, or trust in the cooperative as a whole, and the second representing the trust farmers have in their cooperative members (see appendix 3). For the assessment of collective action and the loyalty towards the cooperative, 5 statements gave further insights. Loyalty is here defined as the loyalty of a farmer in selling coffee to the cooperative. Collective action can be defined as ‘action taken by a group (either directly or on its behalf through an organisation) in pursuit of members’ perceived shared interest’. (Meinzen-Dick et al., 2004). The main shared interest of coffee farmers organised in a cooperative is to market their coffee for a good price. An important motivation therefore is the price they receive for their coffee; a lower price might urge them into selling their coffee through other channels. OLS regression is used to explain the trust and loyalty farmers have towards their cooperative. The following explanatory variables are included: the coffee harvest of farmers, the received technical assistance, monetary benefits, whether farmers perceive corruption in their cooperative, and the trust they have in the members of their cooperative. We included technical assistance and monetary benefits as proxies for the performance of the cooperative. We expect that these positively influence the trust farmers have in their cooperative. In addition, as control variables we included the average level of age and education of the household, the gender ratio, and the level of assets of the household.
4.3.1 Performance, collective action and trust Embu Table 4.7 shows the descriptives of our dependent variables and our main explanatory variables. For the Embu region, farmers do not differ in their opinion on the performance of the cooperative; both are equally satisfied with the way the cooperative performs in terms of efficiency and profits. If we, however, look specifically into corruption, then Rianjagi farmers perceive more corruption in their cooperative than the non-Utz certified farmers do. Nonetheless, Rianjagi farmers have on average the same levels of trust in their cooperative as Kithungururu farmers have. In addition, Rianjagi farmers score higher on the proxy for loyalty to the cooperative, as well as the statements on selling to another party. Rianjagi farmers are thus less inclined to sell to another party than Kithungururu farmers. Concerning the individual level of trust between farmers, Rianjagi farmers have more trust in the coffee production of the members of their cooperative than do Kithungururu farmers. This is especially due to a higher score on the following statement ‘I trust that members of my cooperative do everything they can to produce coffee of high quantity and quality’. A reason for these levels of trust might be the improvements that have been made in coffee maintaining since the Utz certification in 2007. Farmers explained during the interviews that these improvements increased their trust in the performance of other farmers. Utz Certification might thus reduce free-riding behaviour. 39
See appendix 3 for an overview of these statements.
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Table 4.7: Descriptives model 3, attitudes towards the cooperative, Embu Rianjagi (Utz) (N=52) Mean S.D.
Kithungururu (N=49) Mean S.D. t-test
Performance of the coop (index) Corruption (statement, 1=none, 5=much corruption)
.132 3.077
1.048 1.557
.188 2.184
.793 1.302 ***
Institutional trust in the coop (index) Trust coffee production of other members (index) Loyalty to the coop (index) Sell to other party for 50 ksh per kilo bag (%) Sell to other party for 60 ksh per kilo bag (%) Sell to other party for 70 ksh per kilo bag (%) Sell to other party for 80 ksh per kilo bag (%) Sell to other party for 100 ksh per kilo bag (%) Sell to other party for 120 ksh per kilo bag (%) Sell to other party for a higher amount than 120 ksh(%)
.106 -.161 .336 .058 .077 .115 .173 .346 .442 .538
1.000 .937 .689 .235 .269 .323 .382 .480 .502 .503
.149 -.585 -.338 .082 .122 .204 .286 .408 .469 .551
.892 1.020 ** 1.150 *** .277 .331 .407 .456 * .497 .504 .503
* = α< 0,10; ** = α< 0,05; *** = α< 0,01 (source: own household survey 2011)
Table 4.8 present the results of the OLS-regression with the proxies for loyalty and trust as dependent variables. We start with discussing the OLS-regression for the dependent variable trust. Household characteristics are of little influence on the trust farmers have in their cooperative, except for age; trust increases with age, which is in line with previous research (Sutter and Kocher, 2007). A context specific explanation might be that elder farmers have more trust in selling their coffee through a coffee cooperative, due to their experiences in the past. Quite some elder farmers referred back to the time when coffee was really a good earner, and that those times might return in the future. Wealth of the household, in terms of the asset value, is of significant influence on trust in the cooperative, as well as the coffee harvest. Farmers with a higher level of assets have less trust in their cooperative, while the opposite appears for farmers with a higher coffee yield. This latter can be explained by the fact that a higher yield is caused by the benefits farmers received from the cooperative. Farmers who are wealthier in terms of assets have less trust in the cooperative. Technical assistance is not of influence on trust, monetary benefits however are; higher perceived monetary benefits lead to higher levels of trust. This confirms the importance of the price farmers receive for their coffee. Trust in the members of the cooperative also positively influence trust, while corruption correlates negatively with trust. The dummy for Utz Certification is, while controlling for all the above effects, not significant. This is in line with Table 4.7 which showed that levels of trust between Rianjagi (Utz) and Kithungururu farmers do not differ much. Other variables, such as wealth and trust in cooperative members, are thus of greater importance. We discussed the trust farmers have in their management also with farmers. The issue of corruption at Rianjagi (Utz) came up in a few in interviews; though in other instances it was not mentioned at all. The latter might be due to the fact that farmers were reluctant to discuss such delicate issues. Farmers, who mentioned corruption, were especially pointing at the secretary
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manager and the bookkeeper.40 These are the ones in charge of the cooperative funds. One farmer explained that the previous secretary manager was looking out for farmers. The current one however seemed to be more focused on (his own) money. Some farmers noted that he drives a very fancy car. We discussed this issue also with staff members at the Solidaridad head office in Nairobi, and they recognized that certification might sometimes lead to higher levels of corruption, due to the fact that the cooperative receives higher prices for their coffee. Kithungururu (Non-Utz) farmers however praise their current management committee, which is an improvement in comparison with their former committees. Before, there was more corruption and prices were low. Especially the chairman received some positive remarks; one farmer mentioned that ‘since he is in charge, there has been a constant increase in the coffee payment rates’. The cost of controlling the management thus seems especially problematic for Rianjagi farmers. Loyalty towards the cooperative is only explained by indexes related to the cooperative, as none of the household characteristics are significant. The technical assistance and monetary benefits received by the cooperative have a positive significant influence on a farmer’s loyalty towards the cooperative. This is in line with the findings of Saenz and Ruben (2004), who found that loyalty is influenced by non-price factors such as technical assistance, as well as price factors. Corruption negatively influences loyalty, while Utz certification has a positive effect; Utz Certified farmers are more loyal towards their cooperative than their counterparts. Table 4.8: Model 3 - trust in cooperative and loyalty towards cooperative Embu (OLS regression) Model 3A - Dep: trust cooperative Model 3B - Dep: loyalty to cooperative B Constant Household characteristics gender hh head (1=male) age hh head (years) max educ hh (years)
.610
S.E. Sig.
B
S.E. Sig.
.819
-.082
.892
-.202 .013 -.022
.255 .006 ** .024
.118 .008 -.019
.278 .007 .026
total value assets in ksh (log) total coffee harvest in kg (log) Cooperative characteristics technical assistance (index) monetary benefits (index) trust in coop. Members (index)
-.150 .200
.081 ** .098 **
-.047 .105
.088 .107
-.051 .286 .230
.089 .083 *** .088 ***
.195 .115 .180
.097 ** .090 * .095 **
corruption Utz certified (1=yes)
-.183 .193
.059 *** .211
-.201 .551
.064 *** .229 ***
adj. R-square F-value
.326 5.799 ***
.313 4.793 ***
* = α< 0,10; ** = α< 0,05; *** = α< 0,01(source: household survey 2011)
The main reason for loyalty towards the cooperative mentioned in the interviews, for Rianjagi as well as Kithungururu farmers, is that another selling to another party would not offer the farmer as many
40
While executing the research, we also noticed ourselves that the bookkeeper and secretary manager were quite suspicious about how we were proceeding with our research, and tried to control the way in which the research was executed.
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benefits. These are for instance the provision of inputs, the security of a good price for your coffee and of a long term relationship with the cooperative. One Kithungururu farmer explained that a lower performance of the cooperative leads to less loyalty towards the coffee cooperative. This might be explaining why Utz-certification has a positive influence on loyalty; Kithungururu (non-Utz) farmers receive fewer benefits in terms of technical assistance by their cooperative than Utz-certified farmers do. We can conclude that for the Embu region, Utz Certification especially influences the loyalty of farmers towards the cooperative, but less the trust farmers have in their cooperative. Rianjagi farmers have higher levels of trust in their cooperative members, but experience also higher levels of corruption. Therefore the trust Utz Certified farmers have in their cooperative is not lifted above the levels of trust of non-certified farmers. Loyalty however is strongly influenced by cooperative characteristics, and is stronger among Utz Certified farmers. This difference between loyalty and trust in the cooperative might be due to the focus of Utz Certification programs. Their main focus is on improving the coffee practices of farmers through technical assistance, and less through improvements in the management (Raynolds et al., 2007). Technical assistance is indeed influencing farmer’s loyalty towards the cooperative. Managerial improvements have however not occurred; it even seems that levels of corruption even have risen since Utz Certification.
4.3.2 Performance, collective action and trust Mathioya Table 4.9 shows the results for Mathioya region. The differences between the two cooperatives are stronger in this region. Members of Kangunu (Utz) cooperative consider the performance of their cooperative higher and the corruption in the cooperative lower than their counterparts. The trust in the cooperative, however, does not significantly differ between both cooperative. The trust between members of the cooperative is higher for Kamagogo (Non-Utz) members than Kangunu farmers, which is opposite than our findings in Embu region. Table 4.9: descriptives model 3, attitudes towards the cooperative, Mathioya Kangunu (Utz) Kamagogo (N=42) (N=43) Mean S.D. Mean S.D. t-test Performance of the coop (index) Corruption (statement, 1=none, 5=much corruption) Institutional trust in the coop (index) Trust in the members of the coop (index) Loyalty to the coop (index) Sell to other party for 50 ksh per kilo bag (%) Sell to other party for 60 ksh per kilo bag (%) Sell to other party for 70 ksh per kilo bag (%) Sell to other party for 80 ksh per kilo bag (%) Sell to other party for 100 ksh per kilo bag (%) Sell to other party for 120 ksh per kilo bag (%) Sell to other party for a higher amount than 120 ksh(%)
0,147 2,452 -0,107 0,141 0,176 0,024
0,855 1,400 0,972 0,976 0,979 0,154
-0,527 3,419 -0,225 0,645 0,029 0,116
1,193 1,651 1,192 0,773 1,004 0,324
*** ***
0,048 0,071 0,095 0,238 0,310 0,429
0,216 0,261 0,297 0,431 0,468 0,501
0,186 0,233 0,279 0,581 0,628 0,698
0,394 0,427 0,454 0,499 0,489 0,465
** ** ** *** *** ***
*** **
* = α< 0,10; ** = α< 0,05; *** = α< 0,01(source: household survey 2011)
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An explanation can be found that at Kangunu, free-riding does seem to occur. During the interviews, Kangunu farmers showed some critique to their cooperative members, especially to the ones producing, in their view, coffee of less quality and quantity. Several farmers explained that there had been a debate going on within the cooperative about splitting the coffee harvest in two groups. One farmer explained that she felt bitter about those farmers who do not tend to their coffee, because ‘at the end of the day my coffee will be mixed with the coffee of those farmers who do not tend to their coffee’. Farmers with less quality coffee are thought to lower the price of those farmers who bring coffee of higher quality, which is a typical free-riders problem. There had been an election about this issue during a general meeting, but most of the farmers voted against a separation. For Kamagogo farmers, the situation seems quite different. Most farmers appear to be on the same side. They understand from each other that farmers are demoralized by the low prices they receive for their coffee. They feel that most farmers do the best they can; they have little money to reinvest in their coffee as farmers have to pay school fees and farm other crops as well. This might explain why trust in members is higher at Kamagogo, but the loyalty towards the cooperative is much lower than for Kangunu (Utz) farmers. Table 4.10 shows the results of the OLS regression with trust in the cooperative and loyalty towards the cooperative as dependent variables. Trust in the cooperative is not explained by household characteristics. The total coffee harvest is negatively related to trust; farmers with a lower harvest have higher levels of trust in their cooperative. Technical assistance and monetary benefits are both having a positive significant influence on trust in the cooperative, as does trust in its members. Lastly, corruption has a negative influence. In both models, there appears to be no significant difference in trust for Utz certified or non-certified farmers. The model for loyalty has a very low explained variance; only trust in the cooperative members is positively explaining loyalty. The loyalty farmers have is thus for the most part mediated by the trust they have in their members. Table 4.10: Model 3 - trust in cooperative and loyalty towards cooperative Mathioya (OLS regression)
Constant gender hh head (1=male)
Model 3A Dep: trust in cooperative B S.E. Sig. 1,56 1,15 * -0,14 0,26
age hh head (years) max educ hh (years) total value assets in ksh (log) total coffee harvest in kg (log) technical assistance (index) monetary benefits (index)
-0,01 -0,03 0,09 -,194 ,368 ,254
trust in coop. Members (index) corruption Utz certified (1=yes)
,317 -,197 -,121
Mathioya
0,01 0,03 0,11 ,148 * ,163 ** ,118 ** ,129 *** ,070 *** ,261
Model 3B Dep: loyalty to cooperative B S.E. Sig. 1,202 1,218 -,243 ,271 -,006 ,033 -,086 -,015 ,054 ,066
,010 ,030 ,117 ,158 ,173 ,125
,228 -,074 ,132
,137 ** ,074 ,278
Adj. R-square 0,246 -0,24 F-value 3,736 *** ,802 n.s * = α< 0,10; ** = α< 0,05; *** = α< 0,01; Source: own household survey 2011
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4.4
Indirect effects –risk perception and vulnerability reduction
We expect that trust and loyalty influence the risk perception of members of a cooperative. Risks are shared due to the collective sale of outputs, and especially the contract with Utz Certified might give farmers a higher sense of security and a reduction of vulnerability. In this part we examine this hypothesis by the use of a two stage least square (2SLS) regression. We use a 2SLS regression because the model we use involves endogeneity (Wooldridge, 2008). We want to explain the risk perception of farmers with, among others, the trust and loyalty of farmers towards the cooperative. Trust and loyalty are however endogenous in this model. They are explanatory variables, but are jointly determined with our dependent variable because we use the same control variables in both models. We therefore have to control for the correlation of the variables trust and loyalty with the error term of the model. We do so by using the following variables as instrumental variables for trust and loyalty: technical assistance, monetary benefits, trust in the cooperative members, and corruption. We expect that these variables influence the experience of risks only indirectly, via trust and loyalty towards the cooperative. Before analysing the explanatory variables for risk perception, we give more insights in risk perception, experience and behaviour of farmers. The main measure we used to get more insights in the shocks farmers face, and how they assess these, is participatory risk mapping (PRM) (Smith et al., 2000). PRM gives simultaneously insights in the understanding farmers have of objective risk occurrence –incidence-, and the expectations about the farmer’s personal exposure to risks severity.
4.4.1 Shocks and risk perception in the Embu region The outcomes of the PRM done at Rianjagi (Utz) and Kithungururu is shown in Figure 4.1 and 4.2. For sake of clarity we split the overview of coffee related shocks and other related shocks into two figures. They are however related: the index of the shocks is based on single ranking farmers exercised for all shocks together. On the X-axis runs the incidence of a shock from low incidence (0) to high incidence (1), while the Y-axis represents the severity of a shock, running from high severity (1) to low severity (2). Farmers do not differ that much on the risk perception of risks related to coffee. Most risks have a low incidence (meaning that they were mentioned by less than 50% of the farmers). They differ however in severity. For Rianjagi farmers, the problems with highest severity are poor payment and a delay of payment, the provision of inputs, and leadership and transparency in the cooperative. Kithungururu farmers are also concerned about payment, and inputs, but less about issues concerning leadership and transparency. This confirms our earlier results that Rianjagi farmers are more content about the technical assistance they receive, but they are less satisfied with the management of the cooperative, for instance the levels of corruption of the cooperative. Figure 4.2 shows the non-coffee related shocks for both cooperative. Here, the payment of school fees, the availability of food and money stand out as the main problems. These problems have a higher incidence for Kithungururu farmers, although their severity is experienced stronger by Rianjagi (Utz) farmers.
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Figure 4.1: Severity and incidence coffee shocks, Embu 2,00 low incidence – low severity
high incidence – low severity
R: knowledge
1,50
K: knowledge K: transport R: transport K: transparency K: leadership R: transparency R: climate K: delay pay R: leadership R: inputs R: delay pay K: climate K: poor pay K: inputs
Rianjagi C
Kithungururu C
R: poor pay
low incidence – high severity
high incidence – high severity
1,00 0,00
0,50
1,00
Source: household survey 2011
Figure 4.2: Severity and incidence other (idiosyncratic) shocks, Embu 2,00
low incidence – low severity
high incidence – low severity
K: knowledge R: knowledge R: water R: inputs
R: markets
K: inputs K: markets R: jobs K: jobs K: water K: school fees K: food R: school fees
1,50
Rianjagi Kithungururu
R: food
K: money R: money low incidence – high severity
high incidence – high severity
1,00
0,00
0,50
1,00
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We asked farmers to explain why they were facing the main shocks they listed. The answers explained that the shock with highest incidence and severity, a lack of money, is quite interrelated with coffee farming. For Rianjagi farmers, their lack of money is mainly related to the coffee revenues. Several reasons are mentioned why these revenues are low. One farmer blames the ‘coffee industry in general’, which is doing much less well than it did several years ago. Others mention the low coffee production of either themselves or members of the same cooperative. One remark was being made about the frequency of coffee payment. Farmers receive their coffee pay-out only once or twice a year. If this farmer were to receive the pay-out monthly, the lack of money would not be there. An advantage of receiving a lump sum on the other hand, is that the bank makes fewer charges. Only one farmer specifically mentioned the rate per kilogram being to blame. She also sells her coffee to a neighbouring coffee cooperative, and there they pay her more per kilogram. High prices of household goods are mentioned as a stress that further complicates things, and which is something caused by the national government. At Kithungururu, three out of four farmers mentioned poor coffee payments as a cause for their lack of money. With ‘poor coffee payments’ there is referred to a low absolute income received from coffee, not necessarily the low price rates received for their coffee. This is caused by several factors: bad (general) coffee prices, which is something the cooperative cannot influence; other farmers not having the knowledge or financial capability to tend to their coffee; a lower harvest due to climate change; a lack of inputs provided by the society because they are not given without limits. Another farmer also mentioned low inputs as a problem, which is something the cooperative should provide (including tools such as wheel barrows and spraying pumps), even if it means that members have to contribute some little amount. This lack of coffee money than leads to household problems, which shows the interrelatedness of market constraints and non-market related shocks. One farmer explained: ‘you might have been planning for expenses with your coffee money, but if it does not meet all your expenses, which is when household problems come in’. A factor contributing to this is the recent rise of the costs of living and household goods, since the government is taxing heavily on household goods. Shocks are thus caused by the interplay of contextual factors, such as the prices of household goods and general coffee prices, and factors within the cooperative. The factors within the cooperative are either caused by the cooperative management, or by fellow farmers. Table 4.11: Descriptives model 4, risk attitudes of farmers, Embu
Risk index all shocks (index) Risk index coffee shocks (index) Outcome risk game (1, risk averse to 6, risk prone)
Rianjagi (Utz)
Kithungururu
Mean
S.D.
Mean
0.076 0.150 2.889
1.091 1.047 1.183
0.447 -0.246 3.789
S.D. t-test 1.152 * 0.673 ** 1.273 **
* = α< 0,10; ** = α< 0,05; *** = α< 0,01 N for risk game: Rianjagi= 18; Kithungururu= 19 (source: household survey 2011)
Table 4.11 shows the descriptives of several risk measures we use. The indexes below are based on the shocks shown in Figure 4.1 and 4.2. On average, farmers of Kithungururu (non-Utz) score higher on the joint risk index than Rianjagi (Utz) farmers. In other words, they are more concerned about the incidence and severity of a range of future shocks. If we, however, focus on risks concerning
75
coffee, Rianjagi farmers score higher than the farmers of Kithungururu. Rianjagi farmers are thus stronger concerned about (coffee) market constraints, but significantly less about other shocks. These worries about coming risks are associated with the outcomes of the game risk behaviour. Utz Certified farmers are less willing to take risks than their counterfactuals. This might be explained by the fact that Utz Certified farmers are more worried about coffee shocks than the control group farmers. The risk game was namely framed in such a way that it specifically concerns the willingness of farmers to take risks in their coffee growing practices (see appendix 7). This is not confirming our hypothesis that Utz Certified farmers are less risk averse. The analysis of variables explaining the score of farmers on both risk indexes are shown in Table 4.12. Of the household characteristics, the most important indicators are the highest level of education within the household, and the value of assets and livestock. Farmers with a higher level of physical and/or human assets are less worried about coming shocks. This is in line with our theory that physical and human assets are important in the level of security farmer’s experience. They help farmers to cope with and recover from shocks (Hulme and Sheperd, 2003). In addition to these explanatory variables at the household level, variables at the cooperative level are of main importance, for both the index of coffee risks, as well as all risks listed. The level of trust in the cooperative and loyalty towards the cooperative are both strong negatively significant in all models. Higher levels of trust in the cooperative lead to a less negative perception of future shocks, and the same holds for stronger loyalty towards the cooperative. Risk sharing within the cooperative is thus an important way of reducing risks related to the cooperative, as well as other household shocks, which confirms research of Carter (1987) and Fafchamps (2003). Table 4.12: Model 4 - explaining risk perceptions, Embu (2SLS regression) 2SLS Embu
Model 4A Dep: all risks B S.E. Sig.
Model 4B Model 4C Dep: all risks Dep: coffee risks B S.E. Sig. B S.E. Sig.
Model 4D Dep: coffee risks B S.E. Sig.
Constant Household characteristics gender hh head (1=male) age hh head (years) max educ hh (years) total asset value(ksh, log) total coffee harvest (kg, log) Cooperative characteristics trust in cooperative (index) loyalty to cooperative (index) Utz certified (1=yes)
3,142 ,952 *** 2,729 ,994 *** 1,382 ,691 **
1,111 ,787 *
,212 -,011 -,030 -,253 ,106
,403 -,013 -,015 -,060 -,048
-,433 ,210 **
-,241 ,230
,317
Adj. Rsquare
,193
,144
,309
,323 ,008 * ,031 ,101 *** ,128
,362 -,013 -,023 -,204 ,051
,329 ,008 * ,031 ,103 ** ,129
-,396 ,114 ***
,236 -,009 -,026 -,116 ,035
,234 ,006 * ,022 ,073 * ,093
,261 * ,007 ** ,025 ,081 ,102
-,447 ,083 *** -,285 ,114 ***
-,184 ,091 ** ,152 **
SEE 1,009 1,039 ,733 F-value 4,378 *** 3,381 *** 7,339 *** * = α< 0,10; ** = α< 0,05; *** = α< 0,01(source: household survey 2011)
,436
,182 ***
,129 ,823 3,091 ***
The influence of Utz Certification is however not confirming our expectations. The influence of Utz Certification is negatively significant in model 3A, indicating that Utz Certified farmers are less risk averse when it concerns all risks. This effect however only appears in the model controlled for trust in the cooperative. If we control for loyalty, Utz Certified is not significant anymore. Utz 76
Certified has however a positive parameter in model 3C and 3D. Utz certified farmers are thus more risk averse when it concerns risks within coffee. With these results, our theory is confirmed that more trust and more loyalty towards the cooperative negatively influence the risk incidence and severity of farmers. A producer organization is thus an important factor reducing risks of small-scale coffee farmers. However, our hypothesis that this especially holds for certified farmers, is partly rejected. Utz Certified farmers do experience less shocks than non-certified farmers, but this gives only significant effects in one model. However, they experience more risks related to coffee farming, which is not as expected.
4.4.2 Shocks and risk perception in Mathioya region Figures 4.3 and 4.4 give an overview of the incidence and severity of shocks at Kangunu and Kamagogo. Here, the control group (Kamagogo) experiences both more concerns about coffee shocks, as well as other shocks not related to coffee. Most coffee related shocks especially differ from each other in severity, since most shocks are placed in an incidence between 0 and 0.5, but almost all risks listed by Kamagogo have a higher severity than the risks listed by Kangunu (Utz). Of these the most outstanding are the poor payments as well as the delay in payments, a lack of leadership and transparency, and a lack of inputs. For Kangunu, the most severe shocks are a lack of knowledge and a lack of inputs. This confirms our findings so far about the differences between these cooperatives. Figure 4.3: Severity and incidence coffee shocks, Mathioya 2,00 low incidence, low severity
high incidence, low severity
Ka: delay pay Km: knowledge Ka: leadership Ka: transparency Ka: climate
Ka: infrastructure
Km: infrastructure
Ka: lack of inputs Ka: knowledge Km: climate
1,50
Kamagogo C
Km: inputs Km:transparency
Km: delay pay Km: poor pay
Kangunu C
Km: leadership
Ka: poor pay high incidence, high severity
low incidence, high severity
1,00 0,00
0,50
1,00
Source: household survey 2011
77
Turning to Figure 4.4, we find that the main concerns for farmers from both cooperatives are a lack of money, difficulties to pay school fees, a lack of food, and inputs. Figure 4.4: Severity and incidence other (idiosyncratic) shocks, Mathioya 2,00 low incidence, low severity
high incidence, low severity
Ka: knowledge Km: knowledge Km: markets Ka: lack of jobs
Ka: markets Km: water 1,50
Km: lack of jobs Ka: water
Ka: inputs Ka: school fees
Kangunu
Km: inputs
Kamagogo
Km: food Ka: food
Km: school fees Ka: money Km: money low incidence, high severity
1,00 0,00
high incidence, high severity
0,50
1,00
Source: household survey 2011
The explanations of how risks are related to each other are more or less similar to the explanations given in Embu region: a lack of money is strongly related to the coffee harvest. Farmers in Mathioya appear however to be stronger dependent on their coffee farm. Especially some farmers of Kamagogo (non-Utz) explained that they have not much space left in their land to grow other crops than coffee. If coffee incomes fail, farmers thus face the problem that they might be lacking incomes to buy food. Their stronger dependency on coffee than farmers in Embu region leads to stronger challenges to match production and income patterns with consumption and investments, especially if coffee incomes are low. The risk indexes based on the shocks presented in Figure 4.3 and 4.4 do indeed significantly differ from each other. Utz Certified farmers in Mathioya score higher on the risk index representing all risks jointly, as well as the index specifically presenting coffee risks. Utz Certified farmers are thus less risk averse concerning coffee risks, as well as the other issues presented in Figure 4.4. These risk perceptions were confirmed during the risk game. Kamagogo farmers were on average more likely to choose more risk-averse options than Kangunu (Utz) farmers did. This can be explained by Chris Barrett’s (2005) theory on thresholds. It seems that Kamagogo farmers are ‘below the threshold’ of assets and capabilities that is required to grow toward a high productive steady-state. Instead, they are struggling with choosing between making investments in consumption or in the production of coffee. For Kangunu farmers, Utz Certification appears to work 78
as a cargo net being in place, which helps them to find ways out of poverty, and overcome structural forces such as market constraints. Table 4.13: Descriptives model 4, risk attitudes of farmers, Mathioya Kangunu (Utz) index of all risks index of all coffee risks Risk behaviour: Risk game (1-6)
Kamagogo
Mean
S.D.
Mean
-0,563 -0,485 3,737
0,352 0,234 1,327
0,163 0,573 2,905
S.D. t-test 0,990 *** 1,354 *** 1,758 *
* = α< 0,10; ** = α< 0,05; *** = α< 0,01(source: household survey 2011) N of the risk game: Kangunu 19; Kamagogo 21
Table 4.14 shows our results for model 4A to 4D, explaining the risk index for Mathioya region. A remarkable difference with the model explaining risk aversion in Embu is that household characteristics are of more influence, while wealth in asset value and coffee harvests are not significantly influencing risk occurrence. Age and education are both positively significance, implying that risk occurrence and severity is higher for farmers with a higher age and a higher level of education. Education is measured as the highest level of education achieved by a household member. It might therefore point at the fact that these household had to invest more in school fees, while paying school fees is one of the main problems listed (see figure 4.4). We expected a higher level of education, and therefore a higher level of capabilities in the households, to lead to lower levels of vulnerability. Paying school fees might however be a risk in itself. On the other hand, it is not in line with other findings. Stefan Dercon (2008) argues that not finishing school is often done as a coping strategy, but leads to a reduction of capabilities in the future. Table 4.14: Model 4, explaining risk perceptions, Mathioya (2SLS regression) 2SLS Mathioya Constant gender hh head (1=male) age hh head (years) max educ hh (years) total asset value (ksh, log) total coffee harvest (kg, log) trust in cooperative (index) loyalty to cooperative (index) Utz certified (1=yes)
Model 4A Dep: all risks B S.E. Sig. ,013 -,120 ,015 ,028 -,039 -,101 -,340
,696 ,164 ,006 *** ,018 * ,071 ,093 ,065 ***
Model 4B Dep: all risks B S.E. Sig. ,347 -,133 ,017 ,048 -,107 -,090
,784 ,186 ,007 *** ,020 *** ,079 * ,105
Model 4C Dep: coffee risks B S.E. Sig. -,464 -,001 ,011 ,014 ,034 -,030 -,533
,880 ,207 ,008 * ,022 ,090 ,118 ,082 ***
-,177 ,079 ** -,642 ,149 ***
-,668 ,168 ***
-,998 ,188 ***
Adj. Rsquare ,427 ,269 ,489 SEE ,626 ,707 ,792 F-value 9,940 *** 5,423 *** 12,470 *** * = α< 0,10; ** = α< 0,05; *** = α< 0,01 (source: household survey 2011)
Model 4D Dep: coffee risks B S.E. Sig. ,049 -,018 ,014 ,044 -,072 -,014
1,055 ,249 ,009 * ,027 ** ,107 ,141
-,258
,107 ***
-1,042
,226 ***
,264 ,951 5,297 ***
The proxies for trust in the cooperative and loyalty towards the cooperative are negatively significant, meaning that higher trust in the cooperative and higher loyalty towards the cooperative again both lead to lower risk occurrence and severity. This confirms our earlier findings for Embu 79
region. In addition, being Utz Certified is now for all models negatively significant. Utz Certified farmers thus experience fewer risks and shocks than their counterparts, while we controlled for household and cooperative characteristics. These findings are in line with our hypothesis, and contrary to the findings for Rianjagi (Utz Embu).
4.5
Maintaining a livelihood - choices in livelihood strategies
In paragraph 2.3.4 of the context, we already explored the different sets of livelihood strategies available to farmers in Embu and Mathioya. We concluded that the natural-resource potential for Mathioya farmers is higher; however Embu farmers have better local market opportunities. The following options are therefore available for Mathioya farmers: they can either hang in through subsistence farming and some selling of cash crops to the local market in Kiriaini, or they can step up through the export of coffee. Embu farmers have more options available: they can hang in with farm and non-farm activities, step up to the local market or the export markets; or step out to local nonfarming activities. In this paragraph, we explore the livelihood strategies applied by farmers further and compare Utz and non-Utz certified farmers.
4.5.1 Livelihood strategies of Embu farmers Table 4.15 shows the total income of farmers, as well as the share of income farmers derive out of several activities. The total income of Rianjagi farmers is slightly, although not significantly, lower than that of their non-certified counterparts. The income is however derived out of significantly different income sources between the farmers of both cooperatives. The income share of Embu farmers is dominated by coffee, the sale of livestock and other cash crops. Kithungururu farmers have a higher share of income out of coffee, but a significantly lower share in livestock, and a higher share out of employment. Utz certified farmers thus seem to diversify more in on-farm activities such as other cash crops than coffee, and the off-farm sale of livestock products. Their counterparts are slightly stronger specialized in coffee, and diversify stronger to non-farm activities. Table 4.15: Income diversification, Embu Excluding crops
Including crops
Rianjagi (Utz)
Kithungururu
Mean
Mean
S.D.
S.D. t-test
Total income (in 1000 ksh) (coffee, livestock, employment, migration) 127.88 134.02
Rianjagi (Utz) N=43 Mean S.D.
Mean
Kithungururu N=42 S.D. t-test
(including crops)
155.40
203.10
159.07
169.97
188.40 235.46
Part of income: Coffee (%)
0,40
0,33
0,48
0,34
0,27
0,22
0,36
0,29 **
Sale of livestock products (%)
0,42
0,35
0,25
0,26 ***
0,32
0,31
0,22
0,32 ***
Employment (%)
0,12
0,21
0,27
0,33 ***
0,11
0,19
0,24
0,19 ***
Migration and remittances (%)
0,06
0,17
0,01
0,02 **
0,06
0,17
0,01
0,09 **
0,20
0.22
0.14
0.18 *
Crops (bananas, macadamia, etc, %) * = α< 0,10; ** = α< 0,05; *** = α< 0,01(source: household survey 2011) % farm work of employment: R 15%, KI 10%; KA 25%, KM 40%
If we examine these strategies more closely, it appears that 73.3% of the households of Kithungururu have someone working outside the own farm, whereas this percentage is 53.8% for Rianjagi (Utz)
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farmers (see appendix 4.3). The main types of employment are the following: salary earning, a business, and farm labour at another farm than the own farm. The share of income out of livestock for Rianjagi farmers is higher than for Kithungururu farmers. This difference is mainly due to a difference in the sale of livestock products (see also table 2.4, Chapter 2). The value of the livestock currently owned by Rianjagi farmers is also significantly higher than for Kithungururu farmers: the average value of livestock for Rianjagi farmers lays around 78,000 ksh, while for Kithungururu farmers this lies around 60,000 ksh. We also examined the correlations between the total income and these several activities, of which the results are shown in Table 4.16. For both cooperatives, a lower total income correlates with a higher share of income out of coffee. Farmers with a lower income appear to have less opportunities to invest their coffee revenues in anything else but coffee. On the other hand, farmers with higher revenues do not invest their surplus back into coffee, but rather diversify to other activities. For Rianjagi farmers, surplus appears to be invested mainly in livestock, as this gives a positive correlation with the total income of farmers. Kithungururu farmers do not show a particular pattern for the investment of their surplus. Table 4.16: Correlations between income and income shares, Embu Part of income (%): Total income Rianjagi Kithungururu
coffee
livestock
employment
-,464 *** -,339 **
,323 ** ,132 n.s.
,264 n.s. ,251 n.s.
migration -,109 n.s. -,121 n.s.
* = α< 0,10; ** = α< 0,05; *** = α< 0,01(source: household survey 2011)
During the risk game, we asked farmers what they would do if they were to receive a large amount of money (the height of the amount was not specified). The answer to this question gives more insight in the prospects farmers see for certain livelihood activities, investments and strategies. Table 4.16 lists the answers farmers gave for Embu region. Table 4.17: Choices in investments, Embu invest in coffee invest in farm - buying a cow
Rianjagi Kithungururu (N=18) (N=19) 50.0 88.2 33.3
27.8
invest in farm - other livestock than cow
0.0
5.6
invest in farm - cash crops other than coffee
5.6
11.1
invest in business: rental houses, shop, matatu
5.6
27.8
buy extra land
16.7
5.6
save money at the bank
11.1
5.6
pay off debts
11.1
5.6
pay cash workers
16.7
0.0
pay off school fees
16.7
16.7
invest in basic needs such as family, food, clothes
16.7
22.2
invest in luxury: car, house
5.6
0.0
help out others
5.6
5.6
Source: household survey 2011
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Kithungururu farmers do indeed invest more in coffee than Rianjagi farmers. Almost 90% of the respondents listed coffee as one of the activities they want to invest in. Of Embu farmers, only half of the farmers listed coffee. The future investments preferred for livestock are similar for both Rianjagi and Kithungururu. A cow is seen to have several advantages, it gives milk which can be used in the household and can be sold. In addition, its manure can be used on the (coffee) field. Another remarkable difference is that Rianjagi farmers seem to invest more outstanding debts, such as the payment of cash workers, and of school fees. It might be that their higher levels of risk aversion concerning coffee lead them to pay off debts first, before heavily investing in new projects. Another issue which might give us more insigt in choices for livelihood strategies, is the question why farmers opt to stay in coffee growing. We discussed this with farmers during the interviews. Reasons given at both cooperatives do not differ much from each other. Almost all farmers emphasized the fact that they spread their incomes, and coffee is one of these incomes with a specific function; namely that it comes in one large amount once or twice per year. Since it is a lumpsum, you can budget for large investments with the money, such as school fees. Other sources such as milk and bananas come more often in smaller amounts, and have other, smaller uses such as daily expenses. One Kithungururu farmer remarked that, if it would be coming in smaller amounts, there would be a larger possibility to misuse the money. The reasons mentioned in the Embu area resound the fact that there farmers are less depending on coffee, but more on other crops as well. The reason why farmers prefer mixed farming, is because it is a necessity to spread their risks and incomes. ‘Money from coffee comes once a year, than at another moment you receive money for the macadamia nuts you are growing, after that maize will have grown (etc)’ a farmer remarked. In this way income is spread throughout the year, which makes it possible to cover consumption requirements throughout the year. One farmer remarked that the income derived from one source is not more reliable than the other: ‘all are unpredictable; you just rely on God as a farmer’. They thus prefer to stay in mixed farming, because, if one income source fails, another one will be there to help you out. If we summarize this discussion on livelihood strategies in Embu, there do not appear to be major differences between the two cooperatives. Both might be characterized as having stepping-out livelihood strategies. Rianjagi farmers appear to use their coffee surplus to invest in livestock and livestock products. The combination of crops and livestock production is one of the predominant forms of agriculture in the developing world. Livestock provides power to cultivate land, manure to fertilize soil, while crop residues can be used to feed livestock. Income from livestock can buffer for low crop yields in dry years. It thus offers opportunities to sustainably increasing production by raising productivity and increasing resource efficiency (Herrero et al., 2010). Utz Certified farmers might be more risk averse towards investments in coffee production, due to the mismanagement at their cooperative. This appears from results of models on risk perception as well as the outcomes of the risk game. Kithungururu farmers are less risk averse towards investing in their coffee fields. This might explain why Kithungururu farmers spread their investments more between their coffee production and business development. More qualitative research would however be needed to fine tune choices in livelihood strategies, within a category such as stepping-out.
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4.5.2 Livelihood strategies of Mathioya farmers The total income of farmers in Mathioya is lower than that of farmers in Embu. In Mathioya, farmers are in general stronger specialised in coffee, and Utz Certified farmers are more so than non-certified farmers. Utz Certified farmers derive on average 60% of their income out of coffee, whereas for Kamagogo farmers this is 40%. The income out of the sale of livestock products is not significantly different for both cooperatives, the income out of employment is however higher for Kamagogo farmers. Table 4.18: Income diversification, Mathioya Kangunu (Utz) Mean
S.D.
Kamagogo Mean
Kangunu (Utz)
S.D. t-test
Mean
Kamagogo S.D. Mean
S.D. t-test
Total income (in 1000 ksh) (coffee, livestock, employment, migration) 120.01
91.61
76.77
90.14 **
119.08 87.25 77.99
78.65 **
Coffee (%)
0,60
0,27
0,45
0,29 ***
0.64
0.27
0.46
0.33 **
Sale of livestock products (%)
0,35
0,24
0,40
0,32
0.26
0.25
0.27
0.34
Employment (%)
0,04
0,16
0,11
0,19 **
0.03
0.14
0.09
0.15
Migration and remittances (%)
0,01
0,04
0,04
0,09 **
0.01
0.03
0.03
0.08 *
0.05
0.08
0.13
0.20 **
Part of income:
Crop sales (bananas, macadamia etc) * = α< 0,10; ** = α< 0,05; *** = α< 0,01(source: household survey 2011) % farm work of employment: KA 25%, KM 40%
If we compare the employment activities in which farmers are engaged in (see appendix 4.3), Kangunu farmers are engaged in either salary earning jobs, small businesses or farm labour, the first being the smallest category. Kamagogo farmers are mainly deriving income from employment out of sales and businesses, and farm labour at other farms than their own. Salary earning jobs have a much smaller share of their employment activities. Kangunu farmers thus have better capabilities to do higher educated jobs. Table 4.19 shows the correlations between total income and the shares of income. The same pattern emerges as in Embu: farmers with a lower income are stronger specialised in coffee. This resembles findings from Ruben et al. (2008) who showed that a too strong specialisation in coffee makes farmers too dependent on coffee, which might lead to deprivation. Farmers with higher incomes have a higher income share from livestock products; farmers in Mathioya thus seem to invest their coffee revenues mainly in livestock. Table 4.19: Correlations between income and income shares, Mathioya Part of income (%): Total income Kangunu Kamagogo
coffee
livestock
employment
migration
-,380 **
,281 **
,243 n.s
-,061 n.s.
-,271 **
,297 **
,046 n.s.
-,261 **
* = α< 0,10; ** = α< 0,05; *** = α< 0,01(source: household survey 2011)
Table 4.20 gives an overview of the preferences of Kangunu and Kamagogo farmers if they were to receive a large amount of money. All farmers would return their investments back to coffee, which clearly shows the strong dependency of farmers in Mathioya on coffee. Farmers do not much differ on the investments they would like to make in on- and off-farm activities. The main difference shown is that Kamagogo farmers prefer to invest more money in basic consumption categories such as 83
school fees, food and clothes. Kangunu farmers are apparently more able to invest stronger in ‘luxury’ items such as buying a car or making improvements to their houses. Table 4.20: Choices in investments, Mathioya invest in current coffee shamba invest in farm - buying a cow invest in farm - other livestock than cow invest in farm - cash crops other than coffee
Kangunu
Kamagogo
100.0
100.0
58.8
53.3
5.9
6.7
0.0
6.7
23.5
33.3
0.0
6.7
11.8
6.7
pay off debts
0.0
0.0
pay cash workers
0.0
0.0
pay off school fees
17.6
26.7
invest in basic needs such as family, food, clothes
29.4
40.0
invest in luxury: car, house
23.5
13.3
5.9
0.0
invest in business: rental houses, shop, matatu buy extra land save money at the bank
help out others Source: household survey 2011
Lastly, we again discuss the reasons of why farmers are staying in coffee growin as one of their main activities. Kangunu (Utz) farmers are slightly more optimistic about their reasons for staying in coffee than Kamagogo farmers. Several farmers call coffee a profitable earner: it is a bigger earner than the food crops a farmer would otherwise make space to grow; it brings a lump sum which you can budget for, and use for larger expenditures. One farmer remarked that the climate and soil around the area are just suitable for coffee, which thus constraints farmers to coffee. Other farmers are a bit less optimistic; they see disadvantages such as variations in climate, but still they stay in coffee because they expect to earn something. It all depends on the payment, one farmer concludes; ‘it is still there, even when there is no good payment, so it depends on the payment, but if the payment is good, you’ll find people rushing there’. Half of Kamagogo’s (non-Utz) farmers stay in coffee because it is a profitable earner to them. It is for instance a better earner than tea, and it is the crop of which they get their main income. An advantage of coffee, compared to tea, is that it is possible to intercrop with other crops such as potatoes and beans. One farmer noted that this is only under the condition of being able to tend to her coffee in the right manner. Then it could even bring enough money to do some other projects, however she is often constraint by a lack of knowledge and inputs. The other half of the Kamagogo farmers is slightly more negative about coffee. They stay in coffee because they ‘hope for the best’, although the money is at the moment not enough to make a living. This is partly because it was once a good earner, and it is difficult to uproot your coffee stems and switch to another crop altogether. It also depends whether the land a farmer has is large enough to grow other crops as well. If we summarize this discussion on livelihood strategies for Mathioya region, it seems that Kangunu farmers are able to step up, and invest in their coffee practices which raises their income and gives them the opportunity to invest in other activities such as livestock. Kamagogo farmers do however seem to be hanging in mostly with their strategies.
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85
5. Conclusion Since the turn of the century, there has been a renewed focus on agriculture and rural development, as it remains the best opportunity for 500 million households, representing an estimated 1.5 to 2 billion people worldwide, to work their way out of poverty. To make development through agriculture happen, farmers should be able to market their products at local or global markets. These markets represent opportunities for income generation, professionalization and diversification; however, risks such as price uncertainties, and the requirements and high standards of international markets might raise barriers for new, small-scale, producers to enter them. One way of investing in the entry of small-scale farmers is by the use of certification schemes, such as Fair Trade and Utz Certified. These programs offer farmers assistance in producing and selling their products through the improvement of coffee practices, and above market prices. This is thought to stimulate farmers in production of better quality and better quantity coffee. However, questions about these standards remain. Do small-scale producers really benefit, and under what conditions? Do they only benefit in terms of increased production and income, or also in terms of enhanced resilience and risk reduction? We argue that farmers can only sustainably profit from inclusion in international markets, if this inclusion reduces their vulnerability and enhances their opportunities for effective income strategies leading out of poverty. In this research we therefore examined the extent to which Utz Certified attributes to these aspects of farmers livelihoods: vulnerability reduction and effective income strategies. Farmers face many constraints and shocks, which are either related to the market – market imperfections such as variable production inputs, inadequate market information and high transaction costs – or nonmarket related – climate variations, health problems and bad politics. Vulnerability reduction is thus an essential condition for small-scale farmers to come to effective income strategies. Our main research question is therefore as follows: to what extend does Utz Certification attribute to the vulnerability reduction and stimulate effective income strategies of farmers? To answer this research question we investigated outputs, outcome and impact of Utz Certification. The research was done among coffee farmers in Kenya, who are organised in cooperatives. We included two Utz-certified cooperatives, and compared them with two neighbouring noncertified cooperatives which were not involved in any certification scheme, but have further similar characteristics as certified farmers. The two groups of cooperatives were located in two regions: Embu and Mathioya, both located in central Kenya. These regions differ from each other in agroecological aspects and local market opportunities. This provides the opportunity to study contextual differences and their influences on livelihood strategies as well. The following section discusses the outcomes of the hypotheses. Thereafter, we relate these outcomes to existing theories, discussing new insights and remaining questions. The last section elaborates on implications for policy and practice of Utz Certification.
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5.1
Hypotheses and research question
We formulated 4 hypotheses to elaborate the effects of Utz Certification on the livelihood of coffee farmers in Kenya. We argue that Utz Certification reduces vulnerability in several ways, directly and indirectly. They do so through intervening in the services offered by producer organisations. These interventions lead directly to a higher and better quality harvest, for which farmers receive a higher price (hypothesis 1). This has the indirect effect that farmers perceive their cooperative as a more reliable partner (hypothesis 2). We expect this to have effects in the reduction of vulnerability of farmers. Market related shocks are reduced, which makes farmers better able to cope with nonmarket related risks and shocks (hypothesis 3). We argue that this could ultimately lead to a shift in livelihood strategies (hypothesis 4). Table 5.1 shows these hypotheses and their outcomes per region. Table 5.1: Overview of hypotheses and their outcomes Utz-certified compared to non-certified farmers: 1. have a higher harvest, because a. they receive better extension services, b. and receive higher prices for their coffee. 2. get to see their organization as a reliable partner, because a. they have more trust in their cooperative, b. and are more loyal towards their cooperative. 3. are less risk-averse, 4. and sooner shift to more sustainable strategies. +=confirmed, -=rejected, ±=partly confirmed
Embu + ± + ± -
Mathioya + + + ± + + +
Outcomes per regions might differ due to differences in context, as described in chapter 2. The main differences are as follows: the natural-resource potential to grow coffee is slightly higher for Mathioya farmers; Embu farmers have however better local market opportunities. The following livelihood options are therefore available for Mathioya farmers: they can either hang in through subsistence farming and some selling of cash crops to the local market in Kiriaini, or they can step up through the export of coffee. Embu farmers have more options available: they can hang in with farm and non-farm activities, step up to the local market or the export markets; or step out to local nonfarming activities. We compared basic descriptives for both regions. These showed that farmers in Mathioya are indeed stronger depending on coffee than in Embu region. Mathioya farmers derive on average 60% of their income from coffee, whereas this is 40% of the income for farmers in Embu. These differences in livelihood strategies available might influence the effects of Utz Certification on vulnerability reduction and livelihood strategies. We will return to these effects when discussing the last hypothesis. To test our hypotheses we combined quantitative and qualitative methods: we held a survey under 95 Utz and 92 non-certified farmers, conducted in depth-interviews under 14 Utz and 15 noncertified farmers, and conducted a risk game under 45 Utz and 48 non-certified farmers. We will now go through the outcomes of the subsequent hypotheses. For each hypothesis, first the results of Embu region will be discussed, and thereafter the results for Mathioya.
5.1.1 Hypothesis 1, a higher harvest Hypothesis 1 which predicts that Utz Certification leads to a higher harvest, had to be rejected for the Utz Certified cooperative in Embu; they were not having significantly higher coffee yields. There might be several reasons for this. Firstly, farmers see good technical assistance, a good system of input provision and a high coffee price as main conditions stimulating a high harvest. The importance 87
of these aspects is confirmed by other research (Ruben, 2008; Barham and Weber, 2012). The Utz certified cooperative in Embu did however not stand out on all these three aspects, compared to the control group. The system of providing inputs, as well as the price received per kilogram coffee, did not differ much from the counterpart cooperative. Prices improved due to Utz Certification, but did not significantly stand out above prices received at the control cooperative. Farmers of both cooperatives are only partly satisfied with the input provision system, and both suggest that prices received for their coffee might be higher. Other research shows that these factors are indeed important in stimulating farmers for growth in coffee yields. Utz Certified farmers in Embu only receive better technical assistance than their counterparts, and this assistance was indeed of significant influence on their production function. Another reason why Utz Certified farmers might face a lower harvest is because they were having a significant higher number of young trees at the moment. Their efforts in the coffee field might thus pay off in the future, but are not visible yet. Other factors explaining production in Embu were a higher initial wealth in terms of assets and land. Farmers of both cooperatives thus showed to practice capital-led intensification in increasing yields. Utz Certified farmers in Mathioya region do have a higher harvest than their counterparts. At this cooperative, the above mentioned conditions are better in place: a good educational system, combined with a more or less satisfactory input system and high prices received for their crops. Farmers agreed that especially higher prices were very stimulating to produce coffee of high quality and quantity. Certified farmers in Mathioya thus confirm the ‘philosophy’ behind Utz Certified, that higher prices stimulate production efficiency of farmers. Good education and sufficient inputs remain however important conditions. Their counterparts received lower prices for their crops, and did not have a good functioning education scheme. The input system was not considered satisfactory by farmers of both the Utz Certified cooperative and the control group. Most Utz Certified farmers however had the means to buy inputs from other sources, if inputs from the cooperative were not sufficient. Initial wealth is thus of importance for higher production.
5.2.2 Hypothesis 2, the producer organization as a reliable partner We can conclude that for the Embu region, Utz Certification especially positively influences the loyalty of farmers towards the cooperative. Certified farmers did however not differ from their counterparts in trust towards the cooperatives; they even experience higher levels of corruption. Corruption might be caused by the improvement in prices due to Utz Certification. Some members of the management of the cooperative appeared to take advantage of this and were enriching themselves. Certified farmers were thus experiencing difficulties with controlling their management, which is an aspect of cooperatives found more often (Milford, 2004). Certified farmers do however have stronger trust in the fact that their cooperative members produce coffee of high quality and quantity. These differences between loyalty and trust in the cooperative might be explained by the focus of Utz Certification programs. Their main focus is on improving coffee practices of farmers through technical assistance, and less through structural improvements in the management of cooperatives (Raynolds et al., 2007). In Mathioya region, outcomes on these hypotheses are similar. Utz Certified farmers are more loyal towards their cooperative, but we did not find differences on the levels of trust in the cooperative. The explanation for the latter is slightly different than for the Embu region. The management of the cooperative is according to the farmers less characterized by corruption than the management of the control cooperative. Farmers have however significantly lower levels of trust in their cooperative members. Lower trust in cooperative members seems to be caused by free-riding. 88
Farmers who were doing well in coffee production were complaining about the low coffee quality of other farmers. They perceived that this lowered the price they received for their coffee. Certified farmers were thus not achieving the economic optimal in the production of their product due to freeriders. Still, they received good prices for their coffee, which were higher than those of the control cooperative. We can thus conclude that Utz certification make farmers more loyal towards the cooperative, through benefits such as technical assistance. On the other hand, Utz might be influencing trust in a negative way: In Embu region, certification seems to be causing corruption among the management, while it appears to initiate free-riding in Mathioya region.
5.2.3 Hypothesis 3, reducing vulnerability Loyalty and trust towards the cooperative were both important in explaining the risk perception of farmers: higher levels of trust and loyalty lead to the reduction of risk aversion. Utz Certification in Embu has however a partially positive influence on risk perceptions; overall, risk aversion is lower among Utz Certified farmers. Risk aversion related to coffee shocks is however higher for Utz Certified farmers, and especially so if we control for trust towards the cooperative. These results were confirmed by the results of the risk game: farmers of the control cooperative chose on average less risk averse options than Utz Certified farmers did. This might be explained by the lower confidence Utz Certified farmers in Embu have in their management, and is in line with our theory; trust has an instrumental value in helping to reduce risks and transaction costs of market relationships (Williamson, 2000). The asset value of the household was, in addition to trust and loyalty, important in explaining risk aversion of farmers. Farmers with a higher initial wealth were less risk averse. Results for Utz Certified farmers in Mathioya region are according to our hypothesis: they are less risk averse than non-certified farmers, and this holds for coffee shocks specifically, as well as shocks in general. In Mathioya these results were also confirmed by the outcomes of the risk game. Loyalty and trust towards the cooperative were again of positive influence in the reduction of risks. This confirms our theory that producer organisations can have an important risk sharing function (Carter, 1987; Fafchamps, 2004), and that Utz Certification has the possibilities to attribute to this. The wealth of the household was less important in explaining risk aversion than in the Embu region. Instead, a higher age and higher level of education influenced risk aversion negatively. We discussed with farmers in both regions how the experience of (coffee) market-related shocks and perceptions about non-market risks were related to each other. This gave similar outcomes for both regions. Farmers in both regions explained that price certainty of coffee is of main importance in reducing risk perceptions. Farmers are for an important part of their income depending on coffee, and shocks or reductions in their coffee payment lead to consumption problems in terms of buying food, other household goods, and paying school fees. Vulnerability to these shocks is thus caused by the interplay of contextual factors, such as prices of household goods and general coffee prices, and factors within the cooperative. Utz Certification only leads to a reduction of this vulnerability if farmers trust and loyalty towards the cooperative are enhanced. Certification therefore seems to have the strongest effects on vulnerability reduction in Mathioya.
5.2.4 Hypothesis 4, shifting to more sustainable strategies Our final hypothesis, which concerns a shift to more sustainable livelihood strategies, has to be rejected for Embu farmers. We explained before that contextual - agro-ecological, and socio89
economic - factors for farmers in Embu are such that they have several options available; hanging in with farm and non-farm activities, step up to the local market or export markets; or step out to local non-farming activities. Farmers of both cooperatives in Embu appear to be engaged in local non-farm activities, and their total income is not differing from each other. Their livelihood strategies are only partially concentrated on coffee; and this concentration is lower for certified farmers (27%) than for non-certified farmers (36%). That certified farmers are less engaged in coffee might be caused by their higher coffee-related risk aversion. Farmers of both cooperatives are engaged in the sale of other farm crops, livestock products and employment. Utz-certified farmer are stronger engaged to the sale of livestock products and of other cash crops, whereas non-certified farmers specialized stronger in employment, especially business development. Both strategies can be interpreted as ‘stepping-out’ of farm activities and engaging in local markets. Farmers seem to be able to do so due to investing the surplus derived from coffee into these activities. We can conclude that the engagement in international markets is an opportunity to invest in local market activities for both groups of farmers; and opportunities are not stronger for Utz Certified farmers. Farmers explained that they prefer this package of mixed activities, as it makes sure that they have a sustainable income flow throughout the year. In Mathioya, the situation is quite different. Farmers face similar or even better agroecological circumstances than in Embu. Their local market opportunities are however severely limited compared to Embu. Farmers are therefore much stronger depending on their farm activities, and engagement in international coffee markets is of stronger importance for a sustainable livelihood than for farmers in Embu region. Our hypothesis concerning a shift towards sustainable livelihood strategies is therefore confirmed in Mathioya region. Utz-certified farmers are able to ‘step-up’ towards international markets, and their total income is significantly higher than that of non-certified farmers. Non-certified farmers are clearly hanging in. They face the difficulties of being engaged in an international market without proper support. Because of their low returns on investments in coffee, they have difficulties matching their production and income patterns with necessary investments in consumption. Non-certified farmers are, without support, not able to overcome their poverty thresholds (Barrett, 2005), while certified farmers are. Does Utz Certified attribute to vulnerability reduction and effective income strategies? For Embu, a region with good local market opportunities, Utz Certification does not significantly influence the investigated aspects of a farmer’s livelihood. Certified farmers receive better technical assistance and are more loyal to their cooperative. Prices received for their coffee are however not higher, neither are their coffee yields, and certified farmers experience more corruption in their cooperatives. This seems to result in more risk-aversion towards investments in coffee farming. In other words, coffee market vulnerabilities are not significantly reduced. Income strategies of certified farmers are however not influenced strongly by these effects of Utz Certification. Probably because farmers have enough options in livelihood activities, and are still able to invest their coffee revenues into valuable non-farm activities. The asset value of the household indeed appears plays an important role vulnerability reduction and investment choices. The results in Embu region thus lead us to conclude that Utz Certification does not have a clear positive influence on vulnerability reduction and income strategies, but this is neither a clear negative effect. Utz Certification seems to have a more positive influence on their farmers in Mathioya region. Due to certification, institutional arrangements for certified farmers are more favourable than these of their counterparts. Utz-certified farmers benefit from technical assistance as well as 90
higher prices, which translates to higher yields. The management of the certified cooperative seems to be doing quite well; farmers are satisfied with transparency and efficiency of their management, and show more loyalty towards their cooperative. This leads to a reduction of coffee market vulnerability for certified farmers in Mathioya, as well as a reduction in non-market related shocks. Contextual circumstances are such that Utz certified farmers in Mathioya are much stronger depending on the international coffee market for a sustainable livelihood. This strong dependence on coffee production, and a good ‘roll-out’ of the certification program makes that certified farmers in Mathioya are able to shift to more sustainable livelihood strategies. Our results indicate that the conditions under which cooperatives operate are important for a successful implementation of certification schemes in cooperatives. We confirmed that the combination of technical assistance and higher prices that Utz Certification is offering to farmers is indeed important. Higher yields and better coffee quality do however also depend on transparency and efficiency within the management of a producer organisation, and a good functioning input supply system. The assessed certified cooperatives, especially the one in Embu, scored less convincingly on these factors. However, overall, we conclude that Utz Certification indeed can play an important role in this successful inclusion of smallholders in value chains. The relevance of inclusion differs however for several contexts. Farmers in areas with strong local market opportunities are less depending on coffee. Coffee is however still an important part of their income spectrum and coffee revenues can be used to engage in other, viable non-farm activities. Inclusion in global value chains seems to bring the highest gains for coffee farmers in areas where farmers are stronger depending on coffee, and have less other livelihood options available. Engagement in global value chains in such areas is however also of high risk; farmers, who are not included in global value chains successfully, face difficulties in making ends meet.
5.2
Contribution to theory: new insights and remaining questions
Our research findings emphasise the importance of a good understanding of the influences of contexts and institutions on rural livelihoods. Context and institutions are in the livelihoods framework acknowledged as important factors influencing livelihoods. It is however not clearly operationalized how they exactly do so. Our theoretical framework gives better insights in how local livelihoods are linked to (international) markets through for instance producer organizations and Utz Certification. We thus make a theoretical connection between the livelihoods framework and value chain theory. A good understanding of the influence of the global economy on local economies and economic choices becomes more and more important due to the renewed focus of development policy and practice on agriculture. Our research offers several implications for research on value chains, concerning the inclusion of small scale farmers in value chains. Inclusion in value chains is complex and is not always favourable for small-scale farmers (Seville et al., 2011). Our findings give more insights in the conditions under which inclusion can be favourable. Inclusion is favourable for farmers who are strongly depending on an export crop; but only if farmers receive sufficient support from the cooperative through which they sell their crop. If these support systems are not there, which was the case for our control group in Mathioya, farmers face severe struggles in maintaining their livelihood. This also shows the risks of inclusion in value chains with high quality standards. Farmers face the risk of being locked into a production activity which is not profitable if market constraints are severe. In that case, inclusion seems to lead to risk averse choices and being trapped in chronic poverty. 91
Inclusion is also favourable for farmers with enough opportunities in local markets, such as coffee farmers in Embu. For them it appears to be profitable to be partially engaged in international markets, which can boost their investments in local non-farm activities. It seems that farmers thus willingly choose to only partially engage in value chains. Our research gives insights in favourable and less favourable conditions for inclusion; more in-depth research is however needed in the choicemaking of farmers. Producer organisations and certification schemes are important institutions mediating the access of farmers to these international markets. Our research confirms former research that producer organisations are indeed important in reducing market vulnerability (f.i. Milford, 2004; Blandon et al, 2009). On the other hand, we also found (small) evidence of the fact that producer organisations can constrain access to market through corruption and free-riding (f.i. Mude, 2006; Barham and Chitemi, 2009). Utz Certification schemes appear particularly successful if they offer a complete package of technical assistance, higher prices and input supply, which should be executed by an efficient and transparent management committee. Of these, input supply appeared to be the least successfully organised, while a good input supply system is very important for boosting production (Mude, 2006). Our findings show that this is especially a problem for poorer farmers, who do not have the means to buy inputs elsewhere if the supply system of their cooperative is failing. This points at another problem of inclusion in value chains; inclusion might especially be favourable for farmers with initial wealth. Further research should focus on the inclusion of poorer farmers who are most dependent on support systems such as POs and certification schemes. With these insights in the application of Utz Certification, we add to contribute to theories on certification schemes. Most research on certification focuses on Fair Trade schemes, while little research has been done so far on private standards such as Utz Certified (IBID; Ruben and Zuniga, 2011). In addition, studies that focus on evaluating the impact of certification mainly focus on outputs and outcome levels (Nelson and Pound, 2009). With our research, we contribute to a better understanding of outcome, as well as impact levels of certification schemes. We did so by examining vulnerability reduction within markets as well as outside the markets, and income strategies. Our findings show that the successful reduction of market imperfections due to Utz Certification reduces vulnerability in other non-market shocks as well. Our findings are however still quite broad, therefore more research might be needed to more closely examine these effects. We examined income strategies by applying the livelihood strategies scheme developed by Dorward and others (2010). To our knowledge, our study is one of the first to apply this scheme empirically. It appeared to be a very useful outline as it served to explain variances caused by contextual differences. This context analysis appeared to be crucial in explaining our research findings. Our examination of livelihood strategies could have been more thorough, as we did not control for causal relationships between variables with regression analysis. Further research done in that way could give stronger underpinning of our findings. The scheme provided by Dorward and colleagues did however not provide specific clarity on strategies. For example, farmers in Embu region appear to be developing stepping-out activities, but in different ways. We encourage further research to examine what that means for possibilities of sustainable livelihoods. Our research leaves open questions on other strands of theory and research as well. Regarding to livelihood strategies, we examined mainly risk management strategies, and laid less emphasis on coping strategies such as saving, credit and risk sharing. For theory on certification schemes as well as inclusion in value chains, it would be very interesting to examine these parts of vulnerability reduction as well. We also focused mainly on the production side of the household, and 92
less on consumption patterns. One recurring theme in our research findings was for instance the preference farmers have for a ‘lump sum pay-out’, because they use this large amount for specific consumption investments such as education. More research might be done on these relations. A last issue we want to emphasize is that Utz Certified is still a relatively ‘young’ label. The certified cooperatives in Mathioya and Embu were at the time of research certified for respectively 5 and 4 years. If more research is done a few years from now, certification schemes might be stronger internalized, which might have other outcomes. It might than also be possible to do longitudinal research and examine changes over time. Longitudinal research could give more insights in longerterm effects of certification schemes, and whether shifts in livelihood strategies are sustainable over a longer time period.
5.3
Implications for policy and practice of Utz Certified
Our research findings emphasize the need of a holistic approach in certification. We confirmed the fact that higher prices are indeed a strong incentive to stimulate farmers to produce coffee of higher quantity and quality (Ruben and Zuniga, 2011; Barham and Weber, 2012). However, farmers are only able to do so if two other conditions are in place: a good system of providing fertilizers and pesticides, and sufficient training on coffee growing practices. For future policy development, we would suggest that it is especially important to look into ways in which inputs are provided. Farmers at all cooperatives suggested that these systems should be improved. The way in which input supply is currently organised especially disadvantages poorer farmers. They depend on this system for their inputs and do not have the means to buy extra inputs if they need to. Certification schemes might thus favour especially richer farmers, who have the capacity to buy inputs themselves, if these are not structurally provided by the cooperative. We therefore advice examining these systems critically; together with the cooperative management ways should be found for improving them. Our research also showed to possible perverse effects of Certification, both caused by the higher prices received at the cooperative. Among one of the certified cooperatives (Rianjagi, Embu), management seemed to become more corrupt due to the higher revenues of the cooperative. The other certified cooperative (Kangunu, Mathioya) was one were the management appeared to be functioning very well, but farmers were having problems with the free-riding behaviour of others. Free-riding was also related to coffee prices. There is a group of farmers who has the capabilities and assets to pick up training and inputs, and produce high quality coffee. They are however starting to get concerned about the free-riding of other members, who profit from higher prices, but do not put enough efforts into their own coffee production. This is a serious issue as it might eventually lead to demoralization of the farmers who are producing well, and thus to a lower productivity overall. These two issues would have the following policy implications. Firstly, it is important to examine the efficiency and transparency of the cooperative management, as this clearly influences practices of farmers. If farmers experience more security within their cooperative, they are more inclined to invest in their coffee practices. Secondly, it is important that a certification scheme engages all farmers to produce high quality coffee, and has the means to keep them engaged over time. Lastly, our research also showed that contextual factors are of main importance for the successful implementation of Utz Certification. We therefore would advise Utz Certified to take these into account as well. Based on our research, it might be worthwhile to pay more attention to those regions in which farmers are stronger dependent on coffee for sustainable livelihood strategies. 93
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Tegemeo institute (2009). Impact assessment of fair and responsible trade in coffee - Kenya household survey November 2009. Nairobi, Egerton University. Thomas, A., J. Chataway and M. Wuyts (1998). Finding out Fast: investigative skills for policy and development. London, SAGE publications. Tschakert, P. (2007). Views from the vulnerable: Understanding climatic and other stressors in the Sahel. Global Environmental Change 17: 381-396. USDA (2011). Kenya Coffee Annual Report. S. Diaby and C. N. Kamau, USDA Foreign Agricultural Service Vorley, B., E. Pozo-Vergnes, del, C. Gribnau, B. Ghose and D. Muñoz (2012). Making markets work for smallholders? Capacity.org - a gateway for capacity development. Leiden, Contactivity bv. Wiggins, S. and J. Kirsten (2010). the Future of Small Farms. World Development 38(10): 1341-1348. Williamson, O. E. (2000). The New Institutional Economics: Taking Stock, Looking Ahead. Journal of Economic Literature 38(3): 595-613. Wintgens, J. N., Ed. (2009). Coffee: growing, processing, sustainability production – a guidebook for growers, processors, traders and researchers. Weinheim, WILEY-VCH. Wooldridge, J. M. (2008). Chapter 15: Instrumental variables and two stage least squares. Introductory Econometrics: a modern approach. J. M. Wooldridge. Mason, USA, SouthWestern Cengage Learning. World Bank (2001). Building institutions for markets. World Development Report 2002. Washington DC, World Bank. World Bank (2007). Agriculture for Development. World Development Report 2008. Washington DC, World Bank. World Bank (2011). Africa Development Indicators 2011. Washington, World Bank.
97
Appendix Appendix 1: overview of variables used 1 2 3 4 5 6 7 8 9 10 11 12
13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
30 31 32 33 34 35 36 37
variable household characteristics household size gender hh head gender ratio of household age head hh mean age hh education head hh mean education hh max education hh dependency ratio hh distance to market Utz certified wealth of the household housing roofing material wall material floor material nr of rooms in the house land owned by hh asset value livestock value coffee productivity workforce in farm hh ratio of total workforce hh no of young trees no of fruit bearing trees total no of coffee trees hired labour
description of variable
unit
total number of people belonging to the household gender of the head of the household (0=female, 1=male) ratio of males/total size of household age of the household head average age of all household members highest level of education of the household head average level of education of all household members highest level of education achieved within the household percentage of household members working (either on-farm or off-farm) the distance to the nearest market is the household member of an Utz Certified cooperative (0=no, 1=yes)
no binominal percentage years years years years years percentage Km binominal
total score on 'roofing material', 'wall material', and 'floor material' 1=grass, 2=iron sheets, 3=tiles 1=mud, 2=iron sheets, 3=wood, 4=bricks, 5=plaster 1=earth, 2=wood, 3=cement, 4=tiles the total number of rooms a house has (excluding the kitchen) total size of the land owned by the household value of all assets owned by the household value of all assets owned by the household
no no no no no acres Ksh Ksh
total number of household members who work on the family farm Ratio of workforce in farm hh/total number of household members the total number of young coffee trees (age3) owned by household 19+20 did the household hire in labour for working in the coffee field (0=no, 1=yes) did the household use fertilizer in the coffee field (0=no, 1=yes) the amount of money spent on hiring labour the amount of money spent on buying fertilizer the amount of money spent on seeds, machinery, sacks needed in coffee production 25+26 total coffee harvest of the household during the 2009-2010 season 28/21
No percentage No No No binominal
use of fertilizer money spent on hired labour money spent on fertilizer money spent on seeds, machinery, sacks total input costs, except labour total harvest kgs of coffee per fruit bearing tree household income and consumption sold livestock production income out of selling livestock products (milk and eggs) crop sales sale out of selling crops (macadamia, bananas, etc) gross coffee revenue total amount of money earned with coffee harvest profit out of coffee 32-27 profit per coffee tree 33/21 income: selling livestock (ksh) total household income out of selling livestock (2010) income: rented out land (ksh) total household income out of renting out land (2010) income: employment (ksh) total household income out of employment (2010)
binominal Ksh Ksh Ksh Ksh kgs no
Ksh Ksh ksh ksh ksh ksh ksh ksh
98
38 39 40 41 42 43 44 45 46 46
47 48
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
64 65 66
income: remittances (ksh) total income
total household income out of remittances (2010) the income the household derived out of coffee, sale of livestock products, employment and migration total income including crops the income the household derived out of coffee, sale of livestock products, employment and migration, and the sale of cash crops coffee (%) the share of income out of coffee of the total income sale of livestock products (%) the share of the sale of livestock products of the total income employment (%) the share of income out of employment of the total income migration and remittances (%) the share of income out of migration of the total income crops (%) the share of income out of the sale of cash crops of the total income total consumption per year money spent on clothing, household goods, health etc Perceptions of coffee production/cooperative farmers perception of coffee if I consider the expenses and profits in my coffee fields, I am 1) losing profit money in coffee farming; 2) having equal expenses and profits; 3) making money in coffee farming perception of economy vs. 5 if you compare your expected economic situation within 5 years, with your years to come current situation, you expect it to be 1) better, 2) same, 3) worse perception of economy vs. 5 if you compare your economic situation of five years ago, with your current years ago situation, you perceive your current situation to be 1) better, 2) same, 3) worse training program did the respondent attend a training program in the last 5 years? (0=no, 1=yes) index technical assistance index based on factor analysis, see appendix 3 index monetary benefits index based on factor analysis, see appendix 3 performance of the cooperative index based on factor analysis, see appendix 3 corruption (statement) The cooperative is characterized by corruption (1-5) institutional trust in the coop index based on factor analysis, see appendix 3 trust coffee production of other index based on factor analysis, see appendix 3 members loyalty to the coop index based on factor analysis, see appendix 3 loyalty to the coop index based on factor analysis, see appendix 3 Sell to other party for 50 ksh If another party would offer you 50ksh per kilo coffee, would you sell your per kilo bag (%) coffee to this party? (0=no, 1=yes) Sell to other party for 60 ksh If another party would offer you 60ksh per kilo coffee, would you sell your per kilo bag (%) coffee to this party? (0=no, 1=yes) Sell to other party for 70 ksh If another party would offer you 70ksh per kilo coffee, would you sell your per kilo bag (%) coffee to this party? (0=no, 1=yes) Sell to other party for 80 ksh If another party would offer you 80ksh per kilo coffee, would you sell your per kilo bag (%) coffee to this party? (0=no, 1=yes) Sell to other party for 100 ksh If another party would offer you 100ksh per kilo coffee, would you sell your per kilo bag (%) coffee to this party? (0=no, 1=yes) Sell to other party for 120 ksh If another party would offer you 120ksh per kilo coffee, would you sell your per kilo bag (%) coffee to this party? (0=no, 1=yes) Sell to other party for a higher If another party would offer you morethan 120ksh per kilo coffee, would amount than 120 ksh(%) you sell your coffee to this party? (0=no, 1=yes) Risk perceptions risk index all shocks index based on factor analysis, see appendix 3 risk index coffee shocks index based on factor analysis, see appendix 3 outcome risk game option chosen during the risk game, see also appendix 7
Ksh ksh ksh percentage percentage percentage percentage percentage ksh ratio
ratio ratio
binominal no no no no No No No No binominal binominal binominal binominal binominal binominal binominal
no no no
99
Appendix 2: operationalisation of variables based on simple indexes Assets
Livestock
Crops
Consumption
wheel barrow bull cart bicycle spade machete fork hoe spraying pump water tank
cow bull calf goat sheep pigs chickens rabbits
macadamia bananas tealeaves avocado trees cabbage cassava passionfruit tomatoes
food animal feed and/or medicines clothing and shoes household goods health care/medicines education renting in land ceremonies transport
radio tv
yams mangoes
phone solar panels
grass potatoes
shovel
sugarcane sweetpotatoes beans maize
Appendix 3: operationalisation of variables based on factor analysis All indexes came about by the use of principal component analysis. The indexes ‘technical assistance’ and ‘monetary benefits’ are two indexes based on one principal component analysis; the index scores are based on the two components that came out of this analysis. Index technical Assistance
Rianjagi (Utz) N=52 Mean S.D.
Kithungururu N=49 Mean S.D.
Kangunu (Utz) N=42 Mean S.D.
Kamagogo N=43 Mean S.D.
0.962
0.194
0.490
0.505
ttest ***
1.000
0.000
0.860
0.351
ttest ***
0.942
0.235
0.510
0.505
**
0.976
0.154
0.744
0.441
***
3.846
1.513
2.388
1.483
***
4.190
1.153
2.442
1.722
***
4.538
1.019
2.959
1.695
***
4.786
0.470
3.953
1.558
***
Benefits: higher coffee prices, yes/no
0.827
0.382
0.939
0.242
**
0.976
0.154
0.628
0.489
***
Benefits: Increase in coffee productivity, yes/no Benefits: Increase in household income, yes/no Benefits: I am provided with sufficient fertilizers and pesticides Index trust in the cooperative, CA=0,795
0.615
0.491
0.571
0.500
0.690
0.468
0.488
0.506
**
0.750
0.437
0.735
0.446
0.810
0.397
0.628
0.489
**
3.519
1.590
3.510
1.431
3.762
1.376
2.977
1.640
***
I trust the management committee of the cooperative (1-5) I trust the rules of my cooperative (1-5)
4.115
1.367
4.347
0.903
3.786
1.423
3.442
1.777
4.673
0.678
4.633
0.809
4.571
0.801
4.698
0.773
I trust the information provided by my cooperative (1-5) I trust the staff members of the cooperative (1-5)
4.327
1.080
4.122
1.184
3.952
1.188
3.977
1.488
4.327
1.133
4.408
0.864
4.357
1.078
4.372
1.310
CA of combined index technical assistance and monetary benefits: 0,670 Have you attended a training program through your cooperative in the last 4 years Benefits: learn better coffee practices, yes/no Benefits: I am provided with sufficient technical assistance (1-5) Benefits: I am provided with sufficient knowledge to improve the quality of my coffee (1-5) Index monetary benefits
100
I am sure that the cooperative will sell my coffee at a good price (1-5) Index trust in the cooperative's members, CA=0,637
4.115
1.199
Mean
S.D.
Mean
S.D.
I trust the members of my cooperative (1-5)
4.231
1.131
4.163
1.067
I trust that the members of my cooperative do everything they can to produce coffee of high quantity and quality (1-5) I trust that members only bring coffee to the factory that they grew themselves (1-5) Index Loyalty, CA=0,626
4.115
0.983
3.245
1.331
2.808
1.621
2.531
1.401
I intend to continue the relationship with the cooperative (1-5) It is most likely that I leave when better opportunities appear (1-5) If an individual buyer offers me a 10% better price than the cooperative, I will sell to him (1-5) I am more interested in the price of my coffee than on the relation with my cooperative (1-5) I sell to temporary buyers that might not buy again from me (1-5) Index Performance, CA=0,809
4.846
0.364
4.551
0.738
2.808
1.815
3.163
1.650
1.885
1.542
2.469
1.672
3.942
1.474
3.531
1.231
0.757
The cooperative is an efficient organisation 4.308 (1-5) The cooperative is a profitable organisation 4.212 (1-5) The cooperative is an organisation that 4.212 reacts efficiently in the face of events (1-5) I am very satisfied with the cooperative's 4.000 overall performance (1-5) Index coffee risks, CA=0,738 poor coffee payment 0,041 delay of payment of coffee 0,065 lack of farm inputs for coffee 0,071 lack of knowledge and agricultural skills 0,013 (coffee) lack of good leadership of cooperative 0,071 lack of transparency at the cooperative 0,083 Index all risks, CA=0,682 (coffee risks also included) lack of money 0,280 inability to pay school fees for children 0,070 lack of jobs for educated youth 0,143 food shortages 0,145 lack of water for domestic use 0,038 lack of farm inputs (other than coffee) 0,048 lack of knowledge and agricultural skills on 0,030 farming (other than coffee) lack of markets and low prices for farm 0,075 produce (other than coffee)
Rianjagi
4.286
0.979
4.000
Kithungururu
1.126
Kangunu
1.517
*
Kamagogo
Mean
S.D.
Mean
S.D.
4.167
1.167
4.651
0.923
ttest **
3.905
1.303
4.349
1.232
*
4.214
1.279
4.767
0.812
***
4.667
0.786
4.721
0.797
2.929
1.813
3.581
1.803
*
**
2.190
1.565
2.977
1.970
**
1.582
*
4.381
1.081
4.256
1.329
1.918
1.412
***
1.238
0.726
1.302
1.036
1.147
4.408
0.840
4.048
1.147
3.605
1.650
*
1.226
4.226
0.986
4.048
1.209
3.674
1.358
*
1.405
4.102
1.195
4.357
1.032
3.302
1.670
***
1.343
4.204
1.040
4.357
1.032
3.488
1.502
***
0,078 0,101 0,105 0,032
0,061 0,073 0,126 0,035
0,110 0,120
0,044 0,075 * 0,027 0,055 ***
0,010 0,026 0,007 0,020
0,094 0,133 *** 0,106 0,143 ***
0,256 0,110 0,159 0,168 0,068 0,077 0,057
0,484 0,044 0,151 0,204 0,109 0,155 0,024
0,249 0,093 0,148 0,262 0,025 0,134 0,007
0,376 0,089 0,128 0,141 0,104 0,116 0,013
0,109
0,161 0,166 ***
0,099 0,109 0,158 0,064
0,319 0,076 0,161 0,204 0,145 0,157 0,048
ttest
3.558
***
***
n.s. n.s. ** **
*** * n.s. * *** *** n.s.
0,006 0,000 0,035 0,000
0,022 0,002 0,069 0,000
0,267 0,135 0,169 0,219 0,052 0,168 0,020
0,010 0,026
0,014 0,063 0,167 0,004
0,041 0,104 0,187 0,016
0,301 0,126 0,149 0,167 0,128 0,147 0,034
n.s. *** *** **
** n.s. n.s. *** *** n.s. n.s.
0,004 0,014 *
101
Appendix 4: Additional tables to Chapter 4 - Results 4.1 - Model 2A, analyses per cooperative Rianjagi (Utz)
Kithungururu
B
B
S.E. sig.
Kangunu (Utz)
S.E. sig.
B
Kamagogo
S.E. sig.
1,989 1,834 -,514 ,644 ,011 ,013 ,054 ,052
3,225 1,636 ** -,403 ,602 ,005 ,013 ,000 ,039
household size (no of people) area owned by hh (acs, log) asset & livestock value (ksh, log) hired labour % spent money on fertilizer % monetary benefits (factor)
,052 ,085 ,198 ,578 ,663 -,101
,096 ,183 ,139 * ,289 ** ,614 ,139
,079 ,241 ,224 ,093 ,607 -,124
,099 ,207 ,143 * ,296 ,372 * ,136
,099 ,051 ,350 ,621 ,306 ,261
,048 ** ,129 ,127 *** ,243 *** ,580 ,145 **
,228 ,208 -,116 ,409 ,896 ,028
,111 ** ,190 ,182 ,343 ,618 * ,137
technical assistance (factor)
,314
,242 *
-,049
,113
-,341
,414
,147
,181
,635 2,771 **
* ** ** *
S.E. sig.
(Constant) gender ratio (% male) mean age hh (yrs) mean education hh (yrs)
Adj Rsquare F-value
2,125 1,315 -,904 ,443 ,015 ,008 -,042 ,033
B
4,004 1,634 ** -,438 ,702 ,034 ,013 *** ,056 ,052
,637 2,600 **
,777 4,737 ***
,676 2,607 **
Rianjagi (Utz)
Kithungururu
Kangunu (Utz)
Kamagogo
B
B
4.2 - Model 2B, analysis per cooperative
S.E. Sig.
(Constant) gender ratio (% male) mean age hh (yrs)
,976 -,504 ,014
1,545 ,586 ,011
mean education hh (yrs) household size (no of people) area owned by hh (acs, log) asset & livestock value (ksh, log) total input costs, incl labour (ksh) monetary benefits (factor) technical assistance (factor)
,037 ,107 -,069 ,134 ,290 -,207 ,273
Adj Rsquare F-value
,702 4,526 ***
,048 ,087 ,172 ,128 ,080 *** ,127 * ,218 *
S.E. Sig.
B
S.E. Sig.
B
S.E. Sig.
3,225 1,511 ** -,469 ,555 ,005 ,011
1,114 1,247 -,368 ,435 ,015 ,007 **
4,555 1,440 *** -,439 ,614 ,031 ,011 ***
-,009 ,059 ,152 ,207 ,121 -,119 -,015
-,014 ,078 -,028 ,250 ,287 ,090 -,403
,061 ,188 ,199 -,206 ,205 -,041 ,055
,037 ,089 ,184 ,128 * ,043 *** ,123 ,105
,684 3,804 ***
,035 ,045 ** ,126 ,142 ** ,108 *** ,152 ,406
,778 5,468 ***
,045 * ,098 ** ,163 ,161 ,056 *** ,119 ,160
,750 4,565 ***
102
4.3 - Overview of off-farm employment Rianjagi (Utz) Kithungururu % of households with people working outside own farm of people working outside own farm, occupation: salary earner such as teacher, policeman etc. sales/business such as butcher, carpentry, clothes farm labourer at another farm than family farm general kiosk owner transport construction trading farm products other, specify
Kangunu (Utz)
Kamagogo
53,8
73,3
34,3
41,7
N=36 36,11 16,67 13,89 2,78 13,89 5,56 2,78 8,33
N=48 31,25 35,42 10,42 2,08 2,08 4,17 2,08 12,50
N=18 22,22 33,33 33,33 0,00 0,00 5,56 5,56 0,00
N=23 8,70 43,48 34,78 4,35 0,00 0,00 8,70 0,00
103
Appendix 5: interview guides used during structured interviews 5.1 – Interview guide Cooperative Board Goal of the interview, gaining: -
Practical information on the cooperative: how many members, assets, etc. organisational information on the cooperative: how is the cooperative structured knowledge on perspectives of the board on goals set for the cooperative
Make sure that these goals are reached at the end of the interview! Introduction -
-
Thank you for participating in this interview, and the research as a whole Explanation of the research: part of a research is conducted by our university, in cooperation with Solidaridad. Solidaridad is an organisation which works on creating fair and sustainable supply chains for all kinds of products, including coffee. Short explanation of the topics of our separate researches: I am focusing on risks the coffee farmers of the cooperative are facing. A risk they face for example might be that farmers get paid for their coffee a few months after harvesting, why they might be in need of money earlier.
Cooperative organisation -
When was the cooperative established? How many persons are member of the cooperative? (Female/Male) How do farmers become members of the cooperative? What is the organisational structure of the cooperative? What kind of committees exists? How often do you have meetings with the cooperative board and the members? How often does the general assembly meet? And how many members attend your general assemblies, on average?
-
Are there other kind of assemblies as well, how often are they held and with what purpose? How many women participate in the committees and/or in the cooperative board?
-
Does the cooperative have any laws or statements? What is the difference between active membership and total membership?
Practical information - What kind of trainings or services do you offer to your members? For instance technical training, education, transport, marketing of goods, credit How are these received by your members? -
Can members make suggestions for trainings or services? What kind of assets does the cooperative possess? For instance truck, car, warehouse, grainer, how many wetmills 104
-
-
Does the cooperative have access to any credit? If yes, from which organisation? If yes, how much credit is borrowed? If yes, for what purpose is the credit used? How is the money used that the board can invest in the cooperative, over the last year? (so the 20% of the price)
Perspective on the functioning of the cooperative - Does the cooperative face any problems? What kind of problems? -
Does the cooperative have any solutions to these problems? What do you think are the main risks/problems faced by farmers? What is/can be the role of the cooperative in helping farmers overcoming these problems?
Production and finances - How much coffee (berry and mbuni) did you buy from the members in the last season (2010) and in the last year (2009)? - What are the price-ranges you offered to your members in the last harvesting season? -
What was the average time farmers had to wait between weighing of the coffee and payment, what the longest? (of the last harvesting period)
-
How would you think about giving the farmers their payment in phases? How much coffee (berry/mbuni) did you sell to your purchasers (partners) in the last harvesting season? What was the price your purchasers offered you for the processed coffee/kilo sold? Is the cooperative a certified one? If so, what kind of label? (Fair trade, Organic, Utz or other?) If so, what where the criteria to become certified? How is the certification renewed (through which process, and how often)?
-
Do you receive a premium for the certificated coffee? How do you spend the premium received and how do you decide on spending it?
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5.2 - Interview guide members of cooperative Topic 1: Cooperative in general - Why did you become a member of this cooperative? - What is your opinion on the performance of the cooperative (look up answers given in survey!), concerning: o The management of the cooperative Functioning of the board Functioning of staff members / nr of staff members o Governance of the cooperative Nr of meetings per year Efficiency of meetings Whether you have a voice at meetings o Coffee payment Price of the coffee. according to the figures of the cooperative, the payout of 2009 is … in the survey, you answered … why do you think this differs? Are you satisfied with the pay-out you receive at the factory? (NB: Price, and delay of payment) Advance payment / possibility of receiving credit (credit: how exactly are you provided credit through the cooperative?) o Technical assistance Education / knowledge sharing Provision of fertilizer and pesticides (receiving inputs according to your kgs of last year, do you know when to apply which fertilizer and pesticides?) If positive, ask why, if negative, ask for specific problems - If you could suggest three improvements the factory has to make (on any of the above subjects), which improvements would you suggest? - Are there any problems within the cooperative? - About trusting other farmers: look up those three questions. o Does the way you think other farmers treat their coffee, influence your decisions in tending to your coffee? o In what way does the cooperative stimulate that all farmers produce coffee of high quantity and quality? (are you satisfied with that, or does it need improvement?) - Loyalty to cooperative: only ask if they would sell to other parties than the cooperative. Why? Topic 2: UTZ Certification of the cooperative (Only Rianjagi and Kangunu) - Survey: aware of certification and receiving benefits from that: o If aware, and benefits: which benefits? o If aware and no benefits: why not? o If not aware, explain further - Which features of the cooperative have changed due to certification, regarding o the management level o the factory (organisation, maintenance) o farmers (e.g. especially in the form of training, promotor farmers) 106
Topic 3: Coping with risks - You listed the following problems regarding coffee farming: What are those problems caused by? (by your coffee cooperative / other factors?) - You listed the following problems regarding other farming and the household. What are those problems caused by? - What are ways in which you try to solve them? o NB: Include here asking for which other sources of income a farmer has, next to coffee o Why do you choose for these strategies, instead of others? - What is the impact of those problems (f.i.: did you have to make adjustments in incomegenerating activities, are you unable to do certain things, like buy enough inputs, pay school fees, medicines) - Risk sharing: If you face a problem / crisis, can you rely on other people? o How do you rely on them o How are you related to them - Risk sharing: Do other people ask you for help when they have problems? o How are you related to them o How do they rely on you -
Last question: Why do you stay in coffee growing?
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Appendix 6: list of codes used of analysis of structured interviews Cover term
Included term
Explanation
UTZ Certification
awareness (A)
not aware (A-NA) not aware but makes sense (A-NAS) aware (A-A) higher coffee payments (I-CP) production (I-Pr) promotor farmers (I-PF) resources: spraying pump (I-SP) resources: improvements at factory (I-Fa) knowledge (I-K) inputs (i.e. fertilizers and pesticides) (I-I) agricultural officer (I-AO) transparency (I-Tr) efficiency (I-Eff)
Improvements (I)
lack of improvements (LI)
management (I-Ma) money (in general) (I-Mo) added value (I-AV) resources such as wheel barrow, spraying pump (Re) coffee payments (LI-CP) management (LI-Ma) knowledge (LI-K)
membership
reason (R)
near home (R-NH) Infrastructure (R-Inf) two societies (R-TS) marketing coffee (R-MC) Paying well (R-PW) just because (R-JB) best cooperative around (R-BS) Inherited the share (R-Inh)
management
management committee (MC) Staff members (SM)
are doing well (W) trust members (TM) corruption (C) number is enough (NE) number is too many (NM) number is too low (NL) performing better than before (PB) are doing okay, but things could improve (OI) not satisfied (NS)
payment
satisfied, no (N)
Other coops pay higher (N-OH) delay of payment (N-DP) too low coffee quality (N-CQ) high quality coffee (N-HQ) market price is too low (N-MP) high costs at factory (N-HC)
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education
raise payment through (RP) satisfied, yes (Y)
education (RP-Ed) dependent on market price (Y-DM) take other costs into account (Y-OC) no delay of payment (Y-NDP) prices improved (Y-PI) highest compared to other coops (HP) no competition (Y-NC)
offered by cooperative, useful (U) offered by cooperative, not useful (NU) offered by cooperative, neutral (N)
general education (G) agricultural officer, of government (AOG) agricultural officer, employed by cooperative (AOS) promotion of products (PP) promotor farmers (PF) field trips (FT) education days (ED) training by committee member (CM)
what is expected (Ex)
stimulate to produce high quantity and quality
How others threat coffee
sharing knowledge among farmers (SK) booklets (Bo) increase coffee quality (Ex-CQ) information on coffee growing practices (Ex-GP)
improvement wanted (IW)
more education days (ED) inputs at the right time (In)
how it is stimulated (S)
inputs (In)
how it should be stimulated (Sh)
promotor farmers (PF) high coffee payment (CP) education (Ed) Agricultural officer of the cooperative (AOS) the cooperative does nothing (No) committee member (CM) action on bad farmers (BF) competition between farmers (CBF)
Good quality of other (GQ)
example to follow (FE)
Bad quality of other (BQ)
does not influence own coffee maintenance (NI)
general quality of other (Q)
influence on coffee payment (ICP)
General opinion on state of coffee farming (GO)
most usually tend to their coffee (TC) most usualy don't tend to their coffee (DTC) possibility to learn from other (PL) demoralizes (De) set an example for others (SE)
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improvements (Im)
higher coffee payment (CP) better coffee quality (CQ) the cooperative in general (SG) Management (committee) (Ma) secretary manager (SM) provision of inputs (In) provision of credit (PC) none (No)
provision of inputs (PIn)
general (G)
delay y/n (DY) (DN) has enough knowledge to apply y/n (Kn-y) has enough inputs to apply y/n (Inp-y) supply from the cooperative is sufficient y/n (SS-y) buys inputs somewhere else (SE) way of ordering inputs (OI) supply from the cooperative is on time y/n (T-y)
preferred system of provision (SP)
according to harvest of last year (HLY) according to av. harvest of several years (HSY) according to coffee stems (CS) according to a examination of your coffee (EC)
why prefer this system
misuse of inputs (Mis) unable to pay-back (UnPay) variation of harvest over seasons (Var)
risks - causes
poor coffee payment (PCP) High prices of households goods (HPHG)
Cooperative in general (Soc) inputs (In)
delay of coffee payments (DCP)
government (Gov)
Lack of money (LM) general household problems (HP)
Low (general) coffee prices (LowCP) capability of farmers (CoF)
Lack of farm inputs (LFIn) Lack of water for irrigation (LWI)
Global warming (GW) OP - Oil prices
Lack of leadership (LL) Climate Change (CC)
Depending on one crop (DepOC) frequency of pay-out (FPO)
lack of jobs for youth (LJY) Food Shortages (Fsh) Lack of Land (Land) Lack of health (LH) lack of education (Ledu) poor infrastructure (PI)
low coffee harvest (CH) jobs are not interesting (NI) inheritance system
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risk-coping
why diversification (WD)
Inherited too few coffee trees (Inh) (risk of) low coffee payment (LCP) spreading of risks (SR) types of diversification (TD) - for sale Bananas (B) y/n (SY/SN) Macadamia (M) cow (milk) (C) horticultural crops (also beans/potatoes) (HC) chicken (Ch) Fish (F) Maize (Mz) Advocados (A) Animals (An) business (Bus) Labour (Lab) Nappier Grass (Nap) No other options (NO) brewing alcohol (BA) Passion Fruit (PF) Tea ratio coffee/other types (RC)
Use of coffee money (UCM) considers only coffee (OC) considers increase in coffee trees (ICT) problems with diversification (PD)
stay in coffee
why coffee (WC)
reasons to uproot coffee (UC)
number coffee is less than other income flows (Less) coffee is more than other income flows (More) Education (E) yes/no (Y/N) yes/no (Y/N) no room to increase coffee due to the size of land (IC) / Size of land in general (SL) scarcity of land to buy or rent (SL) no money to diversify (NM) it is a profitable earner (compared to other crops) (PE) it comes in lumpsum (LS) hoping for better days, instead of uprooting (BD) earns enough if it is tended in the right way (TRW) there is no need for uprooting as the farm is big enough (FB) possibility to intercrop with other crops (IC) fits best into the agro-ecological circumstances of this area (AEC) there is no better option (NBO) it is reliable, in the sense that it is always there (Re) spreading of risks (SR) if the other crop is a better earner (BE)
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Appendix 7: instructions risk game Worksheet Choice nr 1 2 3 4 5 6
Good year (green) 100 190 240 300 380 400
Bad year (red) 100 90 80 60 20 0
Instructions and framing Before the game Thank you taking the time to come today. Let us introduce ourselves, my name is Mirjam and this is Alvin/Kamanu. You participated in my research by answering the survey, and today’s event is also part of this same research. Before we begin, I would like to make some general comments about what we are doing here today and explain the rules that we must follow. We will be playing a game with money. Whatever money you win will be yours to keep and take home. I will be supplying the money. You should understand that this is not my own money, but it is given to me by the University of Nijmegen in the Netherlands to use for research. If at any time you find that this is something that you do not wish to participate in for any reason, you are of course free to leave whether we have started the game or not. You will then however only receive a compensation for travelling. When you are finished with the game, it is very important that you do not speak to the persons who are still waiting to play the game. This is very important, because otherwise we will not be able to play the game with them. During the game You see here a worksheet that shows six different options (labeled 1-6). These options represent different prices you might receive for the harvest of one of your coffee trees in the coming year. Depending on whether the weather is good or bad, and whether you applied the right amount of fertilizer and pesticides, you either have a good harvest from this tree, or a bad harvest. I will ask you to choose one of these six options, the one that you think offers you the best outcome for the coming year. After you have chosen an option, we will play the game to see whether the year was good or bad. This will be done by choosing a card. If you pick the red card, it has been a bad year, and you will receive the price for the harvest of one coffee tree that is mentioned under a bad harvest. But if you pick the green card, it has been a good year. You will then receive the price for the harvest of one coffee tree that is mentioned for the good harvest. Because we use two cards, you have equal chances on a bad harvest or a good harvest. We will now give a few examples: For instance, you choose option 4, where you will receive 300 ksh if you have a good year, but where you receive 60 ksh if you have a bad year. If you then pick the green card, you will receive 300 ksh. If you pick the red card, you will receive 60 ksh. To summarize, you will receive the working sheet with six different options. You have to decide which option you prefer. We will then select whether it has been a good year or a bad year by picking a card. If it is green, you will receive the money mentioned under the good harvest; if it is red, you will receive the amount of money mentioned under the bad harvest. Note that different people may make different choices for the same decision. We are interested in your preferences, i.e., which options you prefer, so please think carefully about your decisions.
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Questions after game (if there is enough time): Why did you choose this option, what were your considerations? Regarding to you, which factors determine whether you have a good or bad harvest, next to the weather and inputs? Imagine, your harvest is really good, and coffee prices are really good, and you earn 100.000 ksh with the coffee. What would you do with the money? (save it, make certain investments, use it to help out other people?) Imagine, your harvest is really bad, and you earn almost nothing with your coffee, f.i. 5000. What would you do to make sure you have enough money? (sell livestock, get credit, ask family/friends/neighbours for help?)
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Worksheet Risk Game
Nr
year: GOOD
BAD
1
100 ksh 100 ksh
2
190 ksh
90 ksh
3
240 ksh
80 ksh
4
300 ksh
60 ksh
5
380 ksh
20 ksh
6
400 ksh
0 ksh 114
Appendix 8: list of codes used for analysis of open questions risk game Cover term risk game, high income
Included term investments (Inv)
Explanation invest in current coffee shamba (CC) invest in new coffee trees CT (by buying extra land CTL) farm - cow (F-cow) farm - chicken or other (F-Ch/Oth) farm - cash crops (F-cc) business (Bus) - rental houses (RH) business (Bus) - shop (Sh) business (Bus) - matatu (MT) business (Bus) - buying and selling crops (C) buy extra land (in general, not specifically for coffee) (Land) farm - irrigation system (Irr)
savings (Sav) open cases (OC)
debts (D) cash workers (CW) School Fees (SF)
basic needs (BN)
development of the house (DH) help out family (HF) food (F) clothes (Cl) car (Car) household goods in general (HG)
No savings (NS)
investments brings profits, savings do not (NoPr) useless to put money in the bank (UseL)
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Appendix 9: instructions participatory risk mapping Goal: getting insight in the experienced and perceived risks among cooperative members on individual, household and cooperative level. This will be done by identifying risks, rank order these in terms of severity, and getting insights in how participants solve these risks. The risks mentioned will subsequently be used in the survey, so to get response on a larger scale. The participatory method is thus used as a complementary pilot research before the survey is conducted. Used method: Participatory Risk Mapping (PRM), as presented by Smith et al. (2000). According to them, experienced and perceived risks can be measured in two ways, through a ‘subjectivist’ or a ‘frequentist’ approach. The frequentist approach is an objective measure, and focuses for instance the incidence or severity of undesirable events such as a disappointing harvest, price shocks, and water shortage. A problem with this approach might be that the emphasis does not lie on the risks that respondents consider most important. A subjectivist approach, however, focuses on the subjects’ perceptions and preferences, allowing for variations among otherwise identical subjects in their assessment of a particular risk. The participatory risk mapping is developed for getting insight in these subjective perceptions and preferences. I will therefore start my field research with asking people about the risks they experience by using a PRM. Subsequently, I will integrate these results into the questionnaire. Smith et al. (2000) used the results of the PRM they conducted as well in a subsequent study, so to examine risk behaviour of pastoralists in Northern Kenya (Smith et al., 2001). The PRM is a group method, which is chosen since it allows people to respond informally in a group discussion and to discuss items among themselves (ref: Choosing research methods). Through discussion, common (or stochastic) shocks might be separated from idiosyncratic shocks. In addition, it is a useful way to gather a lot of information in a short time. Concerning the homogeneity of the group, it is argued that the group should be more or less homogeneously, in order for people to feel comfortable and to be able to speak freely. Method Design41: PRM involves a three-stage system of questions about risks. Respondents are firstly asked to identify risks, secondly to rank order these risks by severity, and lastly they will be asked how they use to solve each of the risks (or group of risks). I will now go through each of these steps one by one. 1. Identifying risks Respondents are asked to identify the risks that they face when providing for themselves and their household. Informants can list as many or as few risks as they wish. The questions are open-ended, as not to influence the cited risks, the number of risks mentioned, and the order of importance. In listing the risks, respondents will be encouraged to discuss these among themselves and to decide which risks are major risks encountered by their community and/or cooperative. In this way, individual risks can be separated from the risks faced as a group. Note that instead of risks, the words problems, worries, stress, threats can be used as well, so that the questions are as open-ended and non-leading as possible. In wrapping up this stage, the researcher will try to categorize those risks (together with the respondents) that represent the same concept. 2. Rank order the risks The second step contains rank ordering the risks that were identified in the first steps. Hereby, a simple, ordinal scheme is used, running from the most severe risks to the least serious. This will be done by the use of an H-diagram, and group members can discuss the placements of the risks. Risks that are thought equivalent will be noted as equivalent, respondents will not be forced to choose
41
The design is based on Smith et al. (2001), Quinn (2003) and Tschakert (2007). The last two both implemented the design of Smith et al. and introduced some minor changes.
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between these risks. Respondents must come to an agreement about the diagram, and will be asked whether they all agree on the proposed order. 3. Solving each risks In the last phase, informants are asked in detail how they use to solve each of the risks, if and why they no longer could, and how they would like to solve them, if they had not already adressed this. Here, it might be useful to ask directional questions to elicit further detail or to pursue related topics. (Example: if women mentioned one way they solve their worry of food shortage is by selling milk or firewood, they are aksed other ways women in their communities make money and which were the best ways to provide for their families). Sampling Method: The group size of group methods is preferably between 6 and 10 (reference). I will therefore sample groups of 10 people, which is the maximum size. In case of non-response, I still have enough respondents left to participate in the study. The PRM will be done with four groups, one of every cooperative, so to take into account variation among cooperatives. The sampling will be done random per cooperative, but will take into account male members as well as female members. This because women might have different risks perceptions, several studies for instance show that women are more risk averse than men (reference). It will thus be a stratified random sample. Instructions for conducting Participatory Risk Mapping Time: approximately 2 to 3 hours Material needed: Large sheets, markers, notecards, and pens Skills: facilitator, observer/note-taker, translator 1) Introduction Researchers start by introducing themselves, and that they are doing this research on behalf of Solidaridad and the CIDIN institute in Nijmegen, the Netherlands. Secondly, the goal and objectives of the discussion is carefully explained. Welcome everyone, thank you for participating in this meeting. Let us first introduce ourselves, my name is Mirjam, and I am conducting this research. These are Alvin and Ann, and they are helping me with the research, especially with translating. I am a master student at a university in the Netherlands, and I am here to conduct research about coffee cooperatives and coffee farmers of the central region in Kenya. This is on behalf of my university, and an NGO called Solidaridad. I will look into the differences between UTZ-certified and non-certified cooperatives and their farmers. I am doing this research at your cooperative, because you are UTZ Certified, and at Kithangururu cooperative, which is not certified. It is important to know that all information that you give will be treated confidentially. The questions I ask are only for my research and my university. I will not send your answers to the cooperative or the government. What are we going to do today? We will have a discussion about risks. I would like to know from you which risks or worries you face when you are providing for you and your households. We are going to make a list of these problems, and then have a discussion with each other about these problems. We will talk about which problems are the most difficult ones, and about ways in which you are used to solve these problems. I want to say again that the information you give will not be send to the cooperative or the government.
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2) Listing risks Start the listing of the risk. This might be introduced by an open question. Try to stimulate the discussion with further open questions, but be aware of the danger of guiding the discussion too much. Make sure that farmers discuss risks among themselves. We will now start with making a list of risks. We will do this in the following way: we will write them all down on these cards. I would like to ask some (at least one man, one woman) of you to write the discussed problems down. So, you can now start discussing the following question: What worries do you have in providing for yourself and your household? Additional questions to help the discussion: o o o o o
What difficulties are you facing in getting a good coffee harvest? What difficulties are you facing in selling your coffee? What difficulties are you facing in other ways of earning income: selling cash crops, finding labour, etc. What worries do you have concerning your children/house/livestock/crops etc? Make sure the following topics are discussed: social, economical, political; individual, household, cooperative.
If people mention a broad category, try to ask further so to understand what specifically the problems are in that category. 3) Reducing mentioned problems into categories Reduce the risks mentioned to categories, by grouping certain risks. Ask the farmers to do so themselves, and carefully record the discussion of why certain risks can be grouped together. On the other hand, try not to make the categories too broad! We are finished with writing down the problems. We will now try to see if certain problems overlap, and try to group them. 4) Ranking of risks Rank the categories. This is done by taking one risk and placing it on the big paper. Subsequent risks are then placed, while comparing these risks with the ones that are already on the diagram. Risks can be labeled in the same category as well, in that case, place them under each other. Discuss the final results with the farmers by reading the results out loud, and ask whether everyone agrees with the chosen order. In the ranking, try to discuss which risks are faced individually, and which are specifically linked to the cooperative and being a member of the cooperative. 5) Risk strategies Discuss per risk which measures farmers take to overcome these risks. Try to refer to past events, and how farmers than dealt with the risks. List these coping mechanisms as well, and refer to them when discussing the subsequent risks, so to get an overview of coping mechanisms. START with the group of individual risks, and then continue with the ‘cooperative’ risks, and see whether strategies can involve group strategies as well. 6) Conclusion Conclude the method by thanking everyone for their participation. 118
Important things to mention to the enumerators: -
It is very important that farmers come with their own perceptions and preferences. Question should thus be as open as possible, and we should try not to give examples, but to ask further questions. There are two tasks for you: one should translate as much as possible, the other one should write down as much as possible (observer). This is especially important in the second part, when people will discuss which risks are the main risks they encounter, and in the third part. With a method like this, not only the outcome is important, but the discussions as well! Please remember that you are not ONLY translators, but you are research assistants as well, and I depend on you on getting the right information.
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Appendix 10: Household survey Kenya 2011 The impact of UTZ certification on risk management of coffee farmers Introduction Hello. First, we would like to thank you that you want to participate in this survey. Your participation is very useful for us. I am [name enumerator] and I am conducting this research in cooperation with Mirjam Speijer. She is a master student at a university in the Netherlands, at the Centre of International Development Issues Nijmegen. She is here to conduct research about coffee cooperatives and coffee farmers of the central region of Kenya. In the next hour and a half, I will pose you some questions. The questions are about you and your family, your farm production and the cooperative. Your participation is completely voluntary, and you do not need to answer any questions you do not wish to answer. All information given by you and your spouse will be treated completely confidential. These questions are for research purposes only, and answers will NOT be reported back to your coffee society. Your participation in answering these questions is very much appreciated. Before we begin, do you have any questions for me? Section A: Household Identifiers Filled in by enumerator A1 Date of interview (day-month-year) _____________________________ A2 Enumerator ______________________________ A3 Respondent(s) Name(s) ________________________________________________ A4 Household ID ___________________________________ A5 Name of the coffee cooperative the respondent is a member of_____________________________________ A6 Household phone number(s) 1___________________________________ 2______________________________________ A7.1 District _________________________ A7.2 Village ___________________________ A8 Is the spouse of the respondent also present _____________________
Section B: Demographic Characteristics of Household Members. B1. We would like to ask you some questions about you and everyone who belongs to your household (= each person who stayed within the household for a period of at least one month for the last 12 months. Together the household members have a shared income and shared expenditures). How many people belong to your household? __________ For each member, can you tell me their… Enumerator: copy name of the respondent from the first page, don’t ask for gender. Ask the respondent if he/she is the head of the household. If not, who is? Be sure to ask for household members who do not currently live in the household! ID
Name
Gender 1=male 2=female B1.2
B1.1
Relation to head of the household B1.3
Age (in years, or year of birth) B1.4
Marital status B1.5
Highest level of formal education completed B1.6
Current occupational status B1.7
Lives currently in household B1.8
1 2 3 4 5 6 7 8 9 10 11 12
B2
Religion of head of household ____________________________________
Relation to head of household B1.3 1. Household Head 2. Spouse 3. Son/Daugther 4. Son/Daughter inlaw 5. Nephew/Niece 6. Father/Mother 7. Grandchild 8. Brother/Sister 9. Other relative, specify 10. Other, specify
Marital status B1.5 1. Single 2. Married 3. Divorced /Separated 4. Widow/ widower
Highest level of formal education B1.6 0=pre school 1= std 1 2= std 2 3= std 3 4= std 4 5= std 5 6= std 6 7= std 7 8= std 8 9= form 1 10= form 2 11= form 3 12=form 4
13=form 5 14=form6 15=college 1 16=college 2 17=college 3 18=college 4 19=univ 1 20=univ 2 21=univ 3 22=univ 4 23= univ 5 and above 24=not yet in school
Current occupational status B1.7 1. Working on family farm 2. Unemployed 3. Pensioned 4. Not yet in school 5. Student/still in school 6. Not yet working 7. Salary earning, such as teacher, policeman, government employee etc. 8. Sales/Business worker, such as carpentry, clothes, etc. 9. Wage earning, temporary jobs 10. Other, specify…….
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Lives currently in the household B1.8 1. Yes, permanently 2. Yes, but has been migrated temporarily for more than 15 days of the last 12 months 3. No, has been migrated infinitely
B3 Wage, salary employment, self-employment/business and remittances B3.1: Were you or any other current member of your household engaged in any money earning activity, besides farming, during the last year (e.g. January 2010 to December 2010)? Yes/no. _________ (if yes, fill in table below. If no, continue with B5). Instruction: this does NOT include money earned through farming activities, f.i. selling of crops, dairy, coffee, and does NOT include household members who migrated infinitely. If engaged in more than one activity, use more than one row. Person name
ID
Activity
B3.1
Is activity a constant earning? Yes/no B3.2
If B3.2 is yes, fill in monthly earning B3.3
If B3.2 is no, classify each month’s gross earnings/sales (0 for no earnings): B3.4 Jan 10
Feb 10
Mar 10
Apr 10
May 10
Jun 10
July 10
Aug 10
Sep 10
Oct 10
Nov 10
Dec 10
Activity B4.1: 1=salary earner such as teacher, policeman etc. (implies constant earnings!) 2=farm labourer at another farm than family farm 3=transport 4=sales/business such as butcher, carpentry, clothes 5=brick making 6=construction 7=general kiosk owner 8 trading farm products 9=Other, specify
B4 Migrated Household members data Please give us some more information about the persons in your household who migrated during the last 12 months, OR any other family member who migrated in the years before. Do they…… Person name
ID (if applicable)
Sends/brings money home regularly (at least three times over last year? (yes or no) B4.1
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if B4.1 is yes: on average how much over the last 12 months? B4.2 Amount Per (day, week, month, year)
Section C Land use and Coffee Inventory The following sections deal with the land you own and the crops you grow. This includes coffee, cash crops, and crops you grow for consumption. C1: Total area of land C1.1 How much land does the household own today in total (acres)? ____________________ C1.2 Have you rented out part of this land during the year 2009 (yes or no)? _______________ C1.3 (Ask only if C1.2 is yes) How much have you rented out on average during the year 2009, in acres? ______________________ C1.4 Have you been renting-in extra land to use during the year 2009 (yes or no)? _______________________ C1.5 (Ask only if C1.4 is yes) how much have you been renting in (in acres) during the year 2009? ______________ C2: Coffee Inventory C2.1 What is the total area of land used for coffee trees in the year 2009 (acres)? ________________________________ C2.2 What is the total number of coffee trees?________________________________ C2.3 What is the number of young trees (0-3 yrs, include seedlings!) _______________ and fruit bearing (> 3 yrs) _______________________ C3: Use of plots, COFFEE. Please tell us about your coffee growing practices during the year 2009, e.g. April-June 2009 and Sept-Dec 2009. Fill in the table for each separate type of coffee grown. Type of coffee: 1=SL27 2=SL28 3=SL34 4=Ruiru 11 5=other, specify
Only ask if growing more than one type of coffee Total area used Total number for type of of mature coffee (acres) plants of this type
Average age of mature coffee trees of this type (code INH if inherited)
C3.1
C3.2
C3.4
C3.3
Fertilizer Do you use fertilizer on this plot? Yes/no C3.5
If yes, which type (include all)
C3.6
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Pesticide Do you use pesticide on this plot? Yes/no C3.7
If yes, which type (include all)
What % of the crop, if any, was lost due to damage in the field?
Main cause of the losses?
C3.8
C3.9
C3.10
C4: Crop harvesting and selling activities, coffee. Please tell us about your coffee harvest and sales during the year 2009, e.g. April-June 2009 and Sept-Dec 2009. Type of coffee
C4.1
Berries
Mbuni
Quantity harvested
Unit
Quantity sold
Unit
Price received per unit
C4.2
C4.3
C4.4
C4.5
C4.6
Sold through other channels than coop (yes/no) C4.7
quantity harvested
Unit
Quantity sold
Unit
Price received per unit
Sold through other channels than coop (yes/no)
C4.8
C4.9
C4.10
C4.11
C4.12
C4.13
C5: Labour used in coffee production C5.1 Did you use hired labour for the coffee production during the year 2009? _______ (yes/no) if no, continue with C6 C5.2 Indicate type of contract (can be more than one!): 1) Seasonal (payment = per day) 2) piecework (payment = per piecework) 3) full-time C5.2A For seasonal labour: 1) how many labourers __________ 2) how many days per labourer __________ 3) indicate payment per day (week/month) ___________ C5.2B For seasonal labour: 1) how many labourers __________ 2) how many days per labourer __________ 3) indicate payment per day (week/month) ___________ C5.2C For seasonal labour: 1) how many labourers __________ 2) how many days per labourer __________ 3) indicate payment per day (week/month) ___________ C5.2D For seasonal labour: 1) how many labourers __________ 2) how many days per labourer __________ 3) indicate payment per day (week/month) ___________ C5.3A For piecework labour: 1) how many labourers_________ 2) how many pieces per labourer__________ 3) indicate payment per piecework ___________________ C5.3B For piecework labour: 1) how many labourers_________ 2) how many pieces per labourer__________ 3) indicate payment per piecework ___________________ C5.4A For full-time labour: 1) how many labourers __________ 2) Indicate payment per labourer ________________ KSH per day/week/month
C6: Expenses on purchased inputs for production of coffee during the year 2009. List all products bought during this period which have been used in the production of coffee. Expenses on: Type C6.1 Quantity C6.2 Unit C6.3 (if applicable) Coffee trees Fertilizer
Pesticides
Renting of machinery Buying of sacks or other storage material Other, specify:
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Price per unit C6.4
Source of input: own coop (y/n) C6.5
Section D Coffee Quality D1: Coffee practices. Please indicate whether you are currently practicing the following on your coffee farm: Enumerator instruction: Indicate whether practicing, ONLY for coffee Production/coffee management practice
Are you practicing this on your farm? 1= yes (all coffee trees) 2=yes (portion of coffee trees) 3=no D.1.1
1 2 3 4 5 6 7 8
If not practicing, give your reason 1= no money to implement 2=not interested 3=not aware of the technology 4=other, specify D1.3
Proper fertilization (in terms of timing and extend applied) Gwikira fertilaiza wega kulingana na mavinda na kana ni mbiganu Proper use of pesticides Utimiri mwega wa ndawa cia kurima kaua davoa cia igunyu Use of new improved (better) varieties, (e.g. Batian) Kurima mimera miceru kana mithemba miceru ta batian Intercropping with trees for shade Kurima mimera vamwe na miti niundu wa kiruru Intercropping with beans or potatoes Kurima vamwe na boco na waru Renewal of coffee trees Kivanda miti miceru ya kauwa Management of coffee trees (e.g. pruning, weeding, watering) Utungati wa kauwa (ta kuruta sakas, kurima, na goikira mai) Deliver of berry immediately after picking Kwendia kauwa wa vindi gakethwa
D2: Profits out of coffee. If you estimate the expenses and profits you make in coffee farming, what applies to your situation: 1) The expenses I have are higher than the profits I make, so I am losing money in coffee farming. 2) The expenses I have and the profits I make are equal. 3) The profits I make are higher than the expenses I make, so I am making money through coffee farming.
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Section E Perception of the cooperative E1: Perception of certification (E1.1 to E1.2: ONLY ask in UTZ Certified cooperatives, thus Rianjagi and Kangunu) E1.1: Are you aware of the UTZ certification of your coffee society? 1) yes 2) no E1.2: Do you think that your household has received any benefits from the certification? 1)yes 2) no E1.3: Have you ever attended a training program through your cooperative? 1) yes 2) no If yes: E1.4: When was the training? Year____________ If yes: E1.5: What type of training was it (more than one answer possible)? 1) Training at the Coffee Research Foundation 2) Education day at the cooperative 3) Visiting farms of another cooperative 4) Visiting an open day at a wet mill 5) Visit of an agricultural extension officer at your farm 5) Other, specify _______________________________________________ If yes: E1.6: how useful was the training? Indicate from 1:not useful at all, to 5:very useful 1 2 3 4 5 If no: E1.7: Why did you not join? (encircle the MOST IMPORTANT reason) 1) Didn’t meet prerequisites 2) Didn’t want to associate with other farmers 3) implies too much effort 4) not invited 5) Other reason ______________________________________________ E2: Household benefits. Has your household benefited in the following services of your coffee society? Benefit: 1 2 3 4 5 6 7
42
1) Yes; 2) No
Learn better coffee practices Kuthoma utugi mwega wa kauwa Higher coffee prices Mbeca mbega cia kauwa Increase in coffee productivity Kwongerereka kwa kauwa Increase household income Kwongerereka kwa mbeca kwi andu a mucii Receiving credit (by aid of the society) Gukovithua Programs of the cooperative for other products than coffee 42 Gutetheka na maundu kumana na kivanda Other, specify cingi, ereithia
For Rianjagi: Milk, macadamian nuts. For Kangunu: bee-keeping, indigenous trees
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E3: Benefits. Please answer the following statements concerning the assistance provided by your society. You can answer the following: strongly disagree, disagree, neutral, agree, strongly agree Statement 1. I am provided with sufficient technical assistance Nindethagua na mathomo meganu 2. I am provided with sufficient fertilizers and pesticides Nivecagwa fatalaiza ,dawa mbiganu 3. I am provided with sufficient knowledge to improve the quality of my coffee Nivecagwa mathomo meganu ma guteithia gukuria kauwa kega 4. I am provided with good access to credit Nivecagwa uteithio mwiganu ki mbeca 5. I am provided with sufficient knowledge about practices that are good for the environment Nimbecagwa mathomo meganu makonii environment
strongly disagree O
disagree
neutral
agree
O
O
O
strongly agree O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
E4: Infrastructure E4.1: What is the distance from your house to your principal coffee plot? _______________ (in m/km) E4.2: What is the distance from your house to the coffee factory you are a member of? _____________________ (in m/km) E4.3: What is the distance from your house to the nearest market (market = place where most of the crops are sold)? _________ (in m/km) Section F Cropping Pattern EXCEPT coffee The following section is about all the other crops you have been growing during the year 2009, for consumption by your household members, consumption by animals, or for selling. F1.1 Did the household have any cropping activity (=production of crops for (animal) food and/or selling) during the year 2009? Yes/no _________ If yes, fill in the details in the table below. Otherwise, continue with section G. F1.2: How many separate fields for growing these crops (gardens, shambas) can be distinguished? __________________ F1.3: What is the area (in acres) of each separate shamba of land used for growing these crops (in acres) 1. ___________________ 4. __________________ 2. ___________________ 5. __________________ 3. ___________________ 6. __________________
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F1.4: Use of plots, OTHER than coffee, SEASONAL. Please tell us about the crops you have grown that are dependent on the seasons, e.g. main season (long rains): April-June 2009 and minor season (short rains): Sept-Dec 2009. If more than one field can be distinguished, ask for the growing practices of all fields and code the fields in the table below. Otherwise, code field nr as N/A. Season
1 2 3 4
Field nr
Crop Code
Type of watering used
Did you use fertilizer on this plot? (y/n)
Did you use pesticide on this plot? y/n
F1.4
F1.41
F1.42
F1.43
F1.44
Harvest
Sales (if no sales, skip) F1.46 Qnt unit
F1.45 Qnt unit
Price of sales per unit
What % of the crop, if any, was lost due to damage in the field
Main cause of the losses?
F1.47
F1.48
F1.49
main minor main minor main minor main minor
F1.5: Use of plots, OTHER than coffee, ANNUAL. Please tell us about the crops you have grown throughout the year during the year 2009. If applicable, use the field codes you have listed at the page before. Field code
Crops Code F1.51
Type of watering used F1.52
Did you use fertilizer on this plot? y/n F1.53
Did you use pesticide on this plot? y/n F1.54
Harvest F1.55 Qnt
Sales unit
1 2 3 4 5 6 7 8
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F1.56 Qnt
Price of sales per unit unit
F1.57
What % of the crop, if any, was lost due to damage in the field F1.58
Main cause of the losses F1.59
F2: Labour used in crop production, other than coffee
F2.1 Did you use hired labour for any cropping activities during the two seasons we just discussed? _______ (yes/no) if no, continue with F3 F2.2 Indicate type of contract (can be more than one!): 1) Seasonal (payment = per day) 2) piecework (payment = per piecework) 3) full-time F2.2A For seasonal labour: 1) how many labourers __________ 2) how many days per labourer __________ 3) indicate payment per day (week/month) ___________ F2.2B For seasonal labour: 1) how many labourers __________ 2) how many days per labourer __________ 3) indicate payment per day (week/month) ___________ F2.2C For seasonal labour: 1) how many labourers __________ 2) how many days per labourer __________ 3) indicate payment per day (week/month) ___________ F2.2D For seasonal labour: 1) how many labourers __________ 2) how many days per labourer __________ 3) indicate payment per day (week/month) ___________ F2.3A For piecework labour: 1) how many labourers_________ 2) how many pieces per labourer__________ 3) indicate payment per piecework ___________________ F2.3B For piecework labour: 1) how many labourers_________ 2) how many pieces per labourer__________ 3) indicate payment per piecework ___________________ F2.4A For full-time labour: 1) how many labourers __________ 2) Indicate payment per labourer ________________ KSH per day/week/month F2.4B For full-time labour: 1) how many labourers __________ 2) Indicate payment per labourer ________________ KSH per day/week/month
F3: Expenses on purchased inputs for production of other crops: 2009 List all products bought during this period which have been used in the production of other crops than coffee. Expenses on:
Crop type F3.1
Fertilizer/pesticide type F3.2
Quantity F3.3
Unit F3.4
Price per unit F3.5
Seeds and plants (specify crop
Fertilizer (specify the crop used for) Pesticides (specify the crop used for) Renting of machinery Trees (OTHER than coffee) Buying of sacks / storage material Other, specify:
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Received at own coop (y/n) F3.6
Section G
Livestock inventory and assets
The following section discusses how many livestock and assets you own. G1: Livestock Has this household kept any livestock for the last 12 months? (yes/no) _____________ if no, continue with G4. We would like to know how many livestock your household has at this moment. How many ….. do you have? Etc. Livestock
Number of items currently owned G1.1
In the last 12 months, how many did you: Purchase G1.2
Sell (also ASK if currently not owned!) G1.3
At what price would you sell one animal at the moment? Consume (= eat at homestead) G1.4
G1.5
Cow Bull/Oxen Calf Goats Sheep Pig Chicken Rabbits Other, specify…….
G2 (Only ask if livestock was sold by the household, see G1.3) Please indicate how much you agree with the following statements. You can answer the following: strongly disagree, disagree, neutral, agree, strongly agree Statement 1 2 3
I sold my livestock because I was in direct need of money Nendirie nyamu ciakwa tondu ninendaga mbeca varivari I sold my livestock because otherwise I could not feed the members of my household Nendirie nyamu ciakwa tondu ndiai na mbeca cia kugurira family irio I sold my livestock to gain some extra savings Nendirie nyamu ciakwa niguo ngie na mbeca cia kigina
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strongly disagree O
disagree
neutral
Agree
O
O
O
strongly agree O
O
O
O
O
O
O
O
O
O
O
G3. Livestock production During the last 12 months, what was your average production of the following products: Product
Average production G3.1
Production used for own consumption G3.3
Production sold G3.4
Qnt
Qnt
Qnt
Unit
period
Unit
period
unit
period
Price received per unit sold G3.5
Product sold through cooperative program yes/no G3.6
Milk Eggs Manure Honey Other, specify_______
G4: Assets. We would like to know how many different assets you have in your household at this moment. How many ….. do you have? Machinery equipment and transport
Number of items currently owned G4.1
In the last 12 months, how many did you purchase ? G4.2
Wheel barrow Bull cart Bicycle Spade Shovel Machete Fork (Jembe) Hoe Spraying pump Water tank Radio Television (mobiel) phone Solar Panels Other…….................................................
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At what price would you sell one unit at the moment? G4.4
G6: Housing G6.1. What type of house does the family live in: (answer by own observation, encircle the one most suitable) 1. 2. 3.
Roofing material:1) grass Wall material: 1) mudded Floor material: 1) earth
2) iron sheet 2) bricks/stones 2) cement
3) tiles 3) iron sheet 3) wood
4) wood 4) tiles
5) plastered
G6.2: How many rooms (bedrooms and living rooms, do not incude kitchen!) does your house have? ___________________ Section H Finances: Consumption, income, credit and savings The following questions deal with your income, consumption and finances. We start with income. H1:Income What was on average your income-composition in the last 12 months? Please indicate this for the following categories: Category
1 2 3 4 5 6 7
Estimate KSh in 2010 (on average)
Farm income Rented out land Employment Remittances (e.g. money received from migrated relatives) Shares of companies (other than the cooperatives) Rentals (houses, rented out land, etc) Other, specify_________________________________ Total amount:
H2: FAMILY'S ECONOMIC SITUATION 1=Better than 2= same as 3=Worse than H2.1 You think that in 5 years your economic situation will be _______ your current situation H2.2 You consider that your current economic situation is ________than 5 years ago
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H3: Consumption These questions are about your expenditures. Could you indicate your expenditures on average during the last 12 months? We will mention the categories per group. How much did you spent on average on ….. per ….. (see category) Expense/Items Estimate (last week/KSh) Estimate (last one Estimate (last 12 H3.1 month/KSh) H3.2 months/KSh) H3.3 HOUSEHOLD 1. Food 2. Animal feed and/or medicines 3. Clothing and shoes 4. Household goods (include phone!) 5. Health care / medicines 6. Education 7. Renting-in land 8. Ceremonies, such as wedding, funeral 9. Transport 10. Other, specify:
H4: Credit H4.1: Did anyone in the household apply for credit over the last twelve months? 1) Yes (if yes, continue with H5); 2) No (if no, continue with H4.2); 3) Don’t know / Don’t want to tell (continue with H6) H4.2: If no, what was the reason for not applying? 1=no security, 2=had outstanding loan, 3=did not require credit, 4=no means to repay, 5=don’t know, 6=other, specify_________________ H5: Specify credit No
Name of household member
Money is used for: H5.1
To whom/where (more than one answer possible, use one line per outstanding debts) H5.2
Amount (Ksh) (mark -98 if person does not want to tell)
Able to pay back in time
H5.3
H5.4
1 2 3 4 Money is used for: 1) Coffee production 2) Agriculture other 3) School fees 4) Medical 5) Business 6) Ceremonial 7) Other (specify)
To whom/where: 1) Relatives 2) Friends 3) Neighbours 4) Commercial bank 5) Money lenders
6) 7) 8) 9)
NGO/Micro finance institution (specify) Own coffee society Other coffee society Other (specify)
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Able to pay back in time 1) Paid in full 2) Unable to pay 3) Still paying 4) Not yet due 5) Other (specify)
H6: Savings H6.1 Has any person in this household been operating a savings account over the last 12 months? 1) Yes (if yes, go to H6.2); 2) No (if no, go to H6.3); 3) Don’t know/Don’t want to tell (Go to Section I) __________________________________ H6.2: Overview of savings Nr.
Name and ID of hh member (see section B)
In which institution is the saving held? H6.21
Frequency of saving into type of account over the last 12 months H6.22
1 2 3 4 5
Institution: 1=Commercial Bank 2=NGO/MFI 3=Coffee Cooperative 4=Family/Friends/Neighbours 5=Village fund 6=In the home 7= ROSCAs 8=Other, specify
Frequency of saving: 1=Daily 2=Weekly 3=Monthly 4=Twice a year 5=Annualy 6=when able 7=other, specify
H6.3: Reason of not having any savings (Do not ask if H6.2 is filled in!) If you never had any savings, what is the reason? 1) 2) 3) 4) 5) 6)
No money to save No nearby bank I don’t know Risk of losing money High bank charges Other (specify)
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Section I: Perceptions on time preferences, risk and trust of the household I1: Risk perceptions about economic situation The following statements are about risks you are willing to take in order to gain income. Taking risks means that you make a choice by which you are uncertain of the outcome. The outcome might be negative or positive. A high risk means that you do not have a small chance of getting a very positive outcome. A low risk means that you have a large chance on a slightly positive outcome. How do you agree with the following statements about risk? You can answer the following: strongly disagree, disagree, neutral, agree, strongly agree Statement 1. You have to take huge risks, to make a lot of money Ungiaga gwicama ndungithondeka mbeca nyiingi 2. I am willing to risk and lose in order to make some profit Nimbitikitie gwicama kana narimwe guta mbia nigetha thondeke mbia nyienge 3. I only invest in something when I am very sure that I will make a good profit Nthukumaga riria mbici ninguthondeka faida mbega 4. Investing in new crops is very risky, I'd rather not do it Kurima mimera miceru ni hatari akava gwikara ndaimumirimu 5. I prefer to invest on something safe with little earnings, instead of investing in something risky, where I can earn a lot but lose everything as well Akava nthukume kindu gitaina adhara na kina faida nini gukira kuthukuma kindu k nginde adhara no ningigia mbeca nyiingi kana ngata indo cionthe.
Strongly Disagree O
Disagree
Neutral
O
O
O
Strongly Agree O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
I2: Time preferences This question is about your preference for receiving money today or in one month’s time. Would you prefer to receive… Would you prefer to receive Check if yes OR would you prefer to receive Check if yes 1 2 3 4 5 6 7 8
1000 Ksh guaranteed today 1000 Ksh guaranteed in one month 1000 Ksh guaranteed today 1200 Ksh guaranteed in one month 1000 Ksh guaranteed today 1400 Ksh guaranteed in one month 1000 Ksh guaranteed today 1600 Ksh guaranteed in one month 1000 Ksh guaranteed today 1800 Ksh guaranteed in one month 1000 Ksh guaranteed today 2000 Ksh guaranteed in one month 1000 Ksh guaranteed today 2200 Ksh guaranteed in one month 1000 Ksh guaranteed today 2400 Ksh guaranteed in one month If respondent has only chosen 1000 Ksh today, which amount would you choose to receive in one month?_______________ Ksh
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Agree
I3: Experiencing of shocks You might be concerned about problems that could happen to your household. We have made a list of concerns people told us about. I am going to read you this list of concerns, and I would like you to tell me which of these… Risk
Poor coffee payment Delay of payments of coffee Lack of farm inputs for coffee (fertilizer, pesticides, manure, seedlings) Lack of knowledge and agricultural skills on coffee farming Poor roads and infrastructure for transportation of coffee to the factory Lack of good leadership/management of the cooperative Lack of transparency at the cooperative Climate change leading to diseases on coffee plants Lack of money Poor health (diseases) Insufficient health facilities and medical care Inability to pay school fees for children Alcohol and drugs (miraa, bhangi) abuse Lack of jobs for educated youth Lack of good housing and electricity High prices of household goods Food shortages Lack of water for domestic use Lack of water for irrigation Lack of farm inputs (pesticides, fertilizer, manure) (other than coffee) Lack of knowledge and agricultural skills on farming (other than coffee) Lack of markets and low prices for farm produce (other than coffee) Lack of land
Occurred to you during the last year? 1)yes 2)no I3.1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
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You are worried that will affect you in the coming months? 1) yes 2) no I3.2
Could you rank the shocks, you are worried that will affect you in the coming months, from the highest worry to the lowest? I3.3
I4: Generalized trust The following statements are about how much you trust other people. This concerns trusting everyone, including strangers. Please indicate how much you would agree with the following statements. You can answer the following: strongly disagree, disagree, neutral, agree, strongly agree How would you agree with the following statements: 1. Generally speaking, most people can be trusted (thus indicating everyone, also strangers) Kwaria kigeneral,andu engi nwametikyo wana aria ndeci 2. Most people would try to take advantage of me, if they got a chance Andu engi nwamagerie kundumira nai makagia kamweke 3. Most of the time, people are only looking out for themselves Maita maria mengi,mundu emenyagirira gike
strongly disagree O
disagree
neutral
agree
O
O
O
strongly agree O
O
O
O
O
O
O
O
O
O
O
Section J: Statements about the coffee society. We conclude the survey with some statements about the coffee society you are a member of. Please remember that all this information is strictly confidential, and that we will not report this back to the management committee or the staff members of the society. J1: Degree of identification with the society: Please answer the following statements concerning how much you identify yourself with your society. You can answer the following: strongly disagree, disagree, neutral, agree, strongly agree. Statement 1. I feel that the society is mine Mbigucaga takwo (kivanda) ino iyakwa 2. I feel very proud to belong to the society Ngoragwo na gikeno gukorwo mumemba wa (kivanda) 3. I feel very committed to the society Ngoragwo ni mwirutiru (kivanda) 4. The society is an efficient organisation (kivanda) ni kiunganio gikinyaniriru 5. The society is a profitable organisation (kivanda) ni iretaga faida kimbeca
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Strongly disagree O
disagree
neutral
agree
O
O
O
strongly agree O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
J2. Loyalty The following statements are about your loyalty to the society. J2.1 How long have you been a member of the cooperative? ____________ (If inherited, ask in which year the membership was inherited) J2.2 Please indicate to which extent you agree with the following statements about loyalty to your society. You can answer the following: strongly disagree, disagree, neutral, agree, strongly agree Statement 1. I will continue the relationship with the cooperative Nwamende ntii na mbere na kiungano giki 2. I will leave the society when better opportunities appear Gukagia maundu mega nwanthie kwingi 3. If an individual buyer offers me a 10% better price than the society, I will sell to him Ngakorwo na maketha mega na mundu binafsi ende kungurira na bei mbegagukira (coop) na gicunji gia ikumi nwa nimwenderie 4. I am more interested in the price of my coffee than on the relation with my society Vata yakwa ni bei ya kaua na tiuria twigucanaga na (coop) 5. I sell to temporary buyers that might not buy again from me Nimendagiria aguri engi onakorwomatikangurira ringi
strongly disagree O
disagree
neutral
agree
O
O
O
strongly agree O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
J2.3: I would sell my coffee to another party than the cooperative if they offer me: 1 2 3 4 5 6
Price offered Yes or No 50 Ksh/kg 60 Ksh/kg 70 Ksh/kg 80 Ksh/kg 100 Ksh/kg 120 Ksh/kg If only answered no, at which price would you sell your coffee to another party? ___________KSh/kg
If they don’t want to sell their coffee to another party at all, indicate shortly why:
J2.4: Have you sold your coffee outside your cooperative during the last two seasons? 1=Yes, 2=No ________________ (if no, continue with J3) J2.5: If you sold coffee outside your cooperative, why did you do it? ______________ 1= I needed cash, 2=got credit from buyer, 3=got advance, 4= made commitment, 5=other, specify.
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J3: Trust We would like to know what you think of trusting the following categories. Please answer the following: strongly disagree, disagree, neutral, agree, strongly agree. Statement 1. I trust the management committee of my society Nimbitikitie memba abundi 2. I trust the members of my society Nimbitikitie memba a kiungano giki 3. I trust the rules of my society Nimbitikitie mawatho ma kiunganio giki 4. I trust the information provided by the society Nimbitikitie maundu maria kiunganio itumenyithagia 5. I trust the staff members of the society Nimbitikitie aruti aria engi a kivanda 6. I am sure that the cooperative will sell my coffee at a good price Nina witikio (coop) ni ikugura kaua gakwa na bei iria twitikanitie 7. The cooperative trusts that I will deliver coffee to them (coop) niitikitie ningumavirira kaua keganu 8. There occurs a lot of corruption in the society to which I sell my coffee (coop) iria mendagiria kaua ina ungumania muno 9. I trust that the members of my society do everything they can to produce coffee of high quantity and quality Nivokete amemba a society mekaga maundu monthe nigetha makurie kauwa kengi na kega 10. I trust that the members only bring coffee to the factory that they grew themselves Nivokete andu aria engi maindaga kauwa karia makuritie o ene 11. The payment I receive for my coffee is not high enough, because other members do not attend to their coffee Marivi maria mavecagwa mati iguru kwigana tondu arimi (memba) matirimaaga kauwa kao wega
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strongly disagree O
disagree
neutral
agree
O
O
O
strongly agree O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
J4: Participation and satisfaction. This last set of statements is about your participation in the society, and your satisfaction with the functioning of the society. Please indicate how much you agree with the following statements, by answering the following: strongly disagree, disagree, neutral, agree, strongly agree. Statement 1. The society is an organisation that reacts efficiently in face of events Kivanda gietu ni giocaga ushukani na ivenya riria kwagia Kaundu 2. I am very satisfied with the society´s overall performance Nimwiganiru na ciiko cia kivanda 3. I attend every meeting of the society Nithiicaga micemanio yonthe ya kivanda 4. When I attend a meeting I never speak up Nathii mucemanio ndiaragia 5. When I attend a meeting, my opinion always matters Nathii micemanio maoni makwa ni mathika girirua 6. When I attend a meeting, my opinion never influences anything Nathii mucemaniori maoni makwa watikoragwa na vata
strongly disagree O
disagree
neutral
agree
O
O
O
strongly agree O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
Thank you for your participation! Would you be willing to participate in some further research? Yes/no
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Additional Comments
141
Crops 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27.
Type of coffee Avocado Bananas Beans Cabbage Carrots Cassava Corn/Maize Cucumber Guava Lemons Macadamia nuts Mangoes Nappier grass Onions Oranges Passion fruit Pineapple Potatoes Sweet potatoes Rice Spinach Sugarcane Tomatoes Trees, commercial Watermelon Yams Other, specify
1. 2. 3. 4. 5.
SL27 SL28 SL34 Ruiru 11 Other, specify
Type of watering
Main cause of losses General coding
1) Rain fed 2) Irrigated (piped) 3) Irrigated (gravity) 4) Other, specify
1) drought 2) flood 3) pest 4) disease 5) animals 6) other, specify
Unit
Period 1. 2. 3. 4.
day week month year
1) 90 kg bag 2) 50 kg bag 3) 25 kg bag 4) 10 kg bag 5) 2 kg bag 6) kgs 7) liter 8) crate 9) numbers 10) gorogoro 11) tones 12) debe 13) grams 14) Wheel barrow 15) oxen/bull cart 16) millimiter
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-98) don’t want to tell -99) don’t know N/A) not applicable 1) yes 2) no
Type of pesticide: 1) chemical 2) organic Type of fertilizer 0=None 1=DAP 2=NPK (20:20:0) 3=NPK (17:17:0) 4=NPK(25:5:+5S) 5=CAN(26:0:0) 6=Foliar feeds 7=NPK(23:23:23) 8=NPK(20:10:10) 9=DAP + CAN
10=NPK(23:23:0) 11=NPK(17:17:17) 12=NPK(18:14:12) 13=NPK(15:15:15) 14=NPK 14:14:20 15=manure 16=ash 17=compost 18=pulp (berrie) 19=Other, specify___
