Mar 1, 2017 - during the Ugandan CLE in 2016, as well as two case studies from participating cooperatives. ... means of democratic voting and internal committees (WOUTERSE AND .... intermediaries' and state that they should be visible to the end-users of innovation, ...... Missouri Department of Agricultural Economics.
HUMBOLDT-UNIVERSITÄT ZU BERLIN Faculty of Life Sciences Albrecht Daniel Thaer Institute for Agricultural and Horticultural Sciences
Agricultural Cooperatives as Knowledge Intermediaries The Case of Uganda
Master thesis in the study program ‘Integrated Natural Resource Management’ Submitted by Marianne Helena Poot (567636)
Gothenburg, March 1, 2017
First examiner: Prof. Dr. Markus Hanisch, Humboldt-Universität zu Berlin, Division of Economics of Agricultural Cooperatives Second examiner: Dr. Gian Nicola Francesconi, Technical Centre for Agricultural and Rural Cooperation (CTA)
ii
Acknowledgements I would first like to thank my supervisors, professor Markus Hanisch from the Division of Economics of Agricultural Cooperatives at Humboldt University Berlin and Nicola Francesconi from CTA Wageningen, for all the support and feedback on my thesis and for making my internship at CIAT Uganda possible. I would also like to express my gratitude to GIZ for the financial support for my stay in Uganda. I would like to thank Bernardo Manzano Lepe, the CLE team, and all my former colleagues at CIAT Uganda for the great collaboration and sharing your ideas and knowledge. I would like to thank Malte Müller, Gerlinde Behrendt, and all other team members of the Division of Economics of Agricultural Cooperatives, for their help and feedback sessions. I am grateful to all farmers, cooperative leaders and managers who joined the CLE and who participated in the interviews for making the EDC project a success and for their input for my research. Last, I would like to thank Jelmer, my family, and my friends for all their support and love.
iii
Abstract Agricultural cooperatives are considered possible knowledge intermediaries, who can bridge smallholder farmers to suppliers of innovation. Based on the literature on agricultural cooperatives and agricultural innovation systems, it is expected that their functioning is influenced by their relations with stakeholders, their organizational design and start-up incentives. The main objective of this master thesis is to combine the previous seemingly unrelated literature on cooperative studies and knowledge intermediation, and to strengthen this with additional quantitative and qualitative data. What are the main factors influencing the performance of agricultural cooperatives as knowledge intermediaries? This question is answered using quantitative data for logistic regression of 99 Ugandan agricultural cooperatives and qualitative data from two case studies. Knowledge intermediation is measured as the provision of extension, training, and advocacy services combined with collective investments in paid staff. Backwards selection and likelihood ratio tests are used to find the best fitting regression models. Marginal and discrete changes in predicted probability are used to interpret the effects on knowledge service provision. The logistic regression outputs show a significant and positive effect of relations with NGOs and proximity to public extension on the provision of knowledge services. Initiative for establishment by an outsider and membership of a national farmer union both have negative significant effects on knowledge service provision. Cooperative size, age of the cooperative, and training on management skills are significantly positively correlated with knowledge services. The results are clear indicators that the AIS of cooperatives at time of establishment is influencing the knowledge services that they provide later in their life cycle. This implies that relations between cooperatives and public extension and NGOs should be supported and improved, without initiating establishment. Public extension needs a broader coverage in remote areas in Uganda to improve cooperatives’ role as knowledge intermediaries. The influence of a variety of stakeholders and types of relations on the functioning of cooperatives should be further researched, especially the role of apex organization deserves critical examination. It is concluded that Ugandan cooperatives show how they can serve the important role as knowledge intermediaries with internal structures for knowledge transfer. In this light, the potential of cooperatives in developing countries for farm data management is a salient topic worth further investigating.
iv
CONTENT List of Tables ..................................................................................................................... vi List of Figures ................................................................................................................... vii List of Abbreviations ....................................................................................................... viii 1
2
INTRODUCTION ........................................................................................................ 1 1.1
Problem statement ................................................................................................. 1
1.2
Literature gap ........................................................................................................ 1
1.3
Research objectives and research questions .......................................................... 2
1.4
Context of the research .......................................................................................... 4
1.5
Outline ................................................................................................................... 4
THEORETICAL FRAMEWORK ................................................................................ 5 2.1
Agricultural cooperatives and technological change ............................................ 5
2.1.1 Agricultural cooperatives: concept definition ................................................. 5 2.1.2 Agricultural cooperatives as a key factor in technology adoption .................. 5 2.2
Agricultural innovation systems............................................................................ 7
2.2.1 Innovation: concept definition ........................................................................ 7 2.2.2 From traditional extension to agricultural innovation systems ....................... 8 2.2.3 Agricultural cooperatives as knowledge and innovation intermediaries ........ 9 2.2.4 Factors influencing knowledge and innovation intermediation .................... 11 2.3
Internal dynamics of agricultural cooperatives ................................................... 16
2.3.1 The cooperative life cycle framework ........................................................... 16 2.3.2 New generation cooperatives ........................................................................ 18 2.3.3 Factors influencing performance of agricultural cooperatives ...................... 19 2.4
Study background: agricultural cooperatives in Uganda .................................... 27
2.5
Conceptual model ................................................................................................ 29
2.5.1 Definition of the conceptual model ............................................................... 29 2.5.2 Expected outcomes ........................................................................................ 31 3
METHODOLOGY ..................................................................................................... 32 3.1
Methods for data collection ................................................................................. 32
3.1.1 Quantitative data collection: surveys ............................................................ 32
v 3.1.2 Qualitative data collection: case studies........................................................ 33 3.2
Operationalization of the variables ..................................................................... 34
3.3
Methods for data analysis .................................................................................... 37
3.3.1 Descriptive statistics ...................................................................................... 37 3.3.2 Regression analysis ....................................................................................... 37 4
RESULTS ................................................................................................................... 40 4.1
Descriptive statistics ............................................................................................ 40
4.2
Logistic regression .............................................................................................. 43
4.2.1 Extension, information and advisory services. ............................................. 44 4.2.2 Training and demonstration services............................................................. 47 4.2.3 Advocacy for policies and programs ............................................................. 49 4.3
Results from case studies .................................................................................... 51
4.3.1 Mukono District Farmers Association .......................................................... 51 4.3.2 Kalangala Oil Palm Growers Association ..................................................... 53 5
DISCUSSION ............................................................................................................. 56 5.1
Discussion of the main results............................................................................. 56
5.1.1 Descriptives ................................................................................................... 56 5.1.2 Discussion of the regression results .............................................................. 57 5.2
Limitations of the research .................................................................................. 62
5.2.1 Conceptual model .......................................................................................... 62 5.2.2 Data sample ................................................................................................... 62 5.2.3 Data analysis ................................................................................................. 64 5.3 6
Reflection on research questions and objectives ................................................. 65
CONCLUSIONS ........................................................................................................ 69
DECLARATION OF ORIGINALITY ............................................................................. 71 REFERENCES ................................................................................................................. 72 APPENDICES .................................................................................................................. 81 Appendix A. Survey questions ..................................................................................... 81 Appendix B. Case study interviews .............................................................................. 83 Appendix C. Additional tables and figures ................................................................... 85
vi
List of Tables Table 2.1 Overview of empirical qualitative studies analyzing cooperatives and intermediary organizations in the AIS........................................................................................................ 12 Table 2.2 Overview of empirical quantitative studies about factors influencing the performance of agricultural cooperatives................................................................................................... 20 Table 3.1 Operationalization of the outcome variables ................................................................ 35 Table 3.2 Operationalization of the explanatory variables ........................................................... 36 Table 4.1 Descriptives of the knowledge services provided by Ugandan cooperatives ............... 40 Table 4.2 Combinations of knowledge services provided by Ugandan cooperatives .................. 41 Table 4.3 Descriptive statistics of the continuous explanatory variables ..................................... 41 Table 4.4 Descriptive statistics of the binary explanatory variables ............................................ 42 Table 4.5 Results from logistic regression: Extension services .................................................... 46 Table 4.6 Results from logistic regression: Training and demonstration ..................................... 48 Table 4.7 Results from logistic regression: Advocacy services ................................................... 50 Table 4.8 Functions of knowledge intermediation provided by MDFA and KOPGA ................. 55 Table A.1 Overview of selected question from the CLE Uganda 2016 survey ............................ 81 Table C.1 Chi square test for extension and training services ...................................................... 85 Table C.2 Chi square test for collective marketing at the start and in 2016 ................................. 85
vii
List of Figures Figure 2.1 The innovation intermediation framework. ................................................................. 11 Figure 2.2 The cooperative life cycle. .......................................................................................... 17 Figure 2.3 Characteristics, incentives and relations in the AIS at establishment that are expected to influence the performance of agricultural cooperatives as knowledge intermediaries. .... 30 Figure C.1 Histogram of the size of Ugandan agricultural cooperatives. ..................................... 85 Figure C.2 Histogram of the size of smaller cooperatives with up to 150 members. ................... 86 Figure C.3 Histogram of the age in years of Ugandan agricultural cooperatives. ........................ 86
viii
List of Abbreviations ACE
Area cooperative enterprise
AIS
Agricultural innovation system
AKIS
Agricultural knowledge and innovation system
CGIAR
Consultative Group on International Agricultural Research
CIAT
International Center for Tropical Agriculture
CLE
Cooperative leadership event
CSA
Climate smart agriculture
EAFF
Eastern Africa Farmers Federation
EDC
Enhancing development through cooperatives
FAO
Food and Agriculture Organization of the United Nations
FBO
Farmer-based organization
FO
Farmer organization
GDP
Gross domestic product
GoU
Government of Uganda
GISC
Grower Information Services Cooperative
ICA
International Cooperative Alliance
IFAD
International Fund for Agricultural Development
KOPGA
Kalangala Oil Palm Growers Association
KOPGT
Kalangala Oil Palm Growers Trust
MAAIF
Ministry of Agriculture, Animal Industry and Fishery
MDFA
Mukono District Farmers’ Association
MTIC
Ministry of Trade, Industry and Cooperatives
NAADS
National agricultural advisory services
NARO
National Agriculture Research Organization
NARS
National agricultural research system
NCBA CLUSA
National Cooperative Business Association CLUSA International
NGC
New generation cooperative
NGO
Non-governmental organization
OLS
Ordinary least squares regression
OPUL
Uganda Oil Palm Limited
ix PMG
Producer marketing group
RPO
Rural producer organization
SACCO
Savings and credit cooperative organization
SPO
Smallholder producer organization
SSA
Sub-Saharan Africa
UCA
Uganda Cooperative Alliance
UCLA
University of California, Los Angeles
UGX
Ugandan Shilling
UNFFE
Uganda National Farmers Federation
USAID
United States Agency for International Development
VIF
Variance inflation factors
1
1 INTRODUCTION 1.1
Problem statement
Poverty reduction in the global south has little chance to succeed without improving agricultural production in developing countries, which is characterized by a large share of smallholder farmers who are affected by low levels of technology use, harsh climatic circumstances and environmental degradation (ABEBAW AND HAILE, 2013; FISCHER AND QAIM, 2011). The spread of technological innovation is seen as one of the most effective ways to improve agricultural production and increase yield levels. Despite attempts to boost agricultural production, in regions such as Sub-Saharan Africa (SSA), agricultural development and technical efficiency still lag behind (MEKONNEN, SPIELMAN, FONSAH AND DORFMAN, 2015). Why do many smallholder farmers fail to innovate? As BERDEGUÉ AND ESCOBAR (2001) point out, the poorest farmers are often not the ones who are the early adopters of innovations and therefore do not benefit of new technology. Although smallholder farmers do have the incentives to innovate, they often lack the means, such as capital and access to credit. Besides, they might have not enough information to innovate, such as knowledge of the risks and benefits of innovation, or they might not know which innovation best addresses their needs (FEDER, JUST AND ZILBERMAN, 1982).
1.2
Literature gap
Collective action in the form of agricultural cooperatives is considered as an important way to enhance development and alleviate poverty in rural areas in SSA (BERNARD AND SPIELMAN, 2009; GFRAS, 2015). Cooperatives are defined by the International Cooperative Alliance (ICA) as “autonomous associations of persons united voluntarily to meet their common economic, social, and cultural needs and aspirations through a jointly owned and democratically-controlled enterprise” (2016, para. 1). Agricultural cooperatives are thus groups of farmers that coordinate their actions and collectively invest in order to receive benefits for the whole group (ORTMANN AND KING,
2007). Common goals of agricultural cooperatives include better prices for members’
output, access to knowledge and technology, and business development (SHIFERAW, OBARE AND MURICHO, 2006). Common activities of agricultural cooperatives include collective land management, collective marketing, bargaining prices for inputs like fertilizers and seeds, and
2 making collective investments in assets such as warehouses, processing facilities or transport (KWAPONG, 2012; GYAU, FRANZEL, CHIATOH, NIMINO AND OWUSU, 2014). ABATE, FRANCESCONI AND GETNET (2014), ABEBAW AND HAILE (2013) and KOLADE AND HARPHAM (2014) found that cooperative membership is a key factor influencing the uptake of agricultural technologies by smallholder farmers in developing countries. First, they provide services that facilitate the adoption of innovations, such as providing agro-chemicals and other inputs, collective marketing, and providing (access to) financial services. Second, by bringing farmers together, cooperatives can facilitate the spread of knowledge and information. They can connect farmers to other actors and information sources and can provide the network that give farmers access to the providers of knowledge and innovation (KWAPONG
AND
HANISCH, 2013;
BERNARD AND SPIELMAN, 2009; FISCHER AND QAIM, 2011). This is in line with the literature on agricultural innovation systems (AIS), where innovation is a result of interaction between different stakeholders in a system (SPIELMAN, 2005; BERDEGUÉ
AND
ESCOBAR, 2001). Cooperatives are
being identified as possible ‘intermediaries’ of innovations, bridging the demand side (smallholder farmers) to suppliers of innovation, such as researchers, government, the private sector and NGO’s (RAJALAHTI, JANSSEN AND PEHU, 2008; YANG, KLERKX AND LEEUWIS, 2014). YANG ET AL. (2014) were the first to do research to the functioning of agricultural cooperatives as innovation intermediaries in China. Knowledge services are the core part of innovation intermediation, which is referred to as ‘knowledge intermediation’. In the context of AISs, only limited attention is given to the factors that determine the performance of cooperatives in service provision. The literature on agricultural cooperatives in developing countries shows however that their performance is highly variable (FRANCESCONI AND WOUTERSE, 2015A; FRANCESCONI, COOK AND LIVINGSTON, 2015). African cooperatives often face problems as elite-capturing and side-selling, which can lead to a lack of financial means to sustain service provision to members over time (FRANCESCONI ET AL.,
2015; SATGAR AND WILLIAMS, 2008). Many of these problems can be attributed not only to
the institutional context in which cooperatives operate, but also to the organizational design during their establishment phase (COOK AND BURESS, 2009; WOUTERSE AND FRANCESCONI, 2016).
1.3
Research objectives and research questions
The previous section outlined the importance of knowledge services for smallholder farmers in developing countries and the role of agricultural cooperatives. Still, in the research on
3 the performance of agricultural cooperatives, the aspect of knowledge and information services received much less attention than the supporting services such as marketing, finance, and input supply that cooperatives provide. The purpose of this master thesis is to fill this gap, focusing on the knowledge part of innovation intermediation. The different functions of knowledge intermediation are hereby understood as knowledge services. This thesis aims to:
develop a quantitative framework to measure the performance of agricultural cooperatives in providing knowledge services;
gain more insight in the provision of knowledge services by agricultural cooperatives in developing countries;
identify which factors influence the performance of Ugandan agricultural cooperatives as knowledge intermediaries. These objectives are addressed by the following research questions:
(1) Which factors can be found from the literature on agricultural cooperatives and agricultural innovation systems that influence the performance of agricultural cooperatives as knowledge intermediaries? (2) Which functions of knowledge intermediation do Ugandan agricultural cooperatives fulfill? (3) Which organizational characteristics of agricultural cooperatives influence their performance as knowledge intermediaries? Based on a literature review, a quantitative framework is developed to measure the performance of cooperatives in providing knowledge services, complementing the theory on agricultural innovation systems and innovation intermediation with theory on the organizational design and performance of cooperatives. This framework is applied to Uganda, a developing country in SSA, to identify how the organizational design at establishment has effect on the longterm performance of cooperatives. Identifying these factors might guide researchers and policy makers how to improve the governance of agricultural cooperatives in order to stimulate the diffusion of agricultural innovation.
4
1.4
Context of the research
The research for this master thesis has been conducted during an internship of the author at the International Center of Tropical Agriculture (CIAT) in Uganda, in the context of the project ‘Enhancing Development through Cooperatives’ (EDC). CIAT is part of the Consultative Group on International Agricultural Research (CGIAR). One of CGIARs strategies is improving agricultural production by upscaling technologies and improving smallholder farmers’ livelihoods (CGIAR, 2016). This includes the EDC project of CIAT, which aims at “promoting the development of commercially viable, socially inclusive and environmentally sustainable cooperative agribusiness” (EDC, 2016, para. 1). The project conducts research - organizational diagnostics of cooperatives as well as impact evaluations - and organizes outreach and networking activities. Those activities include ‘cooperative leadership events’ (CLEs) throughout Africa where managers and chosen leaders from African agricultural cooperatives receive training and coaching, and have the opportunity to exchange knowledge with other cooperatives, researchers, non-governmental organizations (NGOs) and policy makers. This master thesis uses data collected during the Ugandan CLE in 2016, as well as two case studies from participating cooperatives.
1.5
Outline
The remainder of this thesis is organized as follows: Section two provides the theoretical framework for this research by reviewing the literature to explore the potential role of cooperatives in AISs and to identify factors that influence the performance of cooperatives in terms of knowledge services. This section ends with answering the first research question by presenting the conceptual model for the empirical part of this thesis. Section three shows how the variables that are identified in the conceptual model will be measured. Besides, methods for data collection and data analysis will be explained. Section four presents the results. The second research question will be addressed using descriptive statistics as well as findings from the two case studies. The third research question will be addressed using inductive statistical analysis, illustrated with examples from the case studies. These results will be discussed in section five. Section six concludes by answering the remaining research questions and giving recommendations for further action and research.
5
2 THEORETICAL FRAMEWORK 2.1
Agricultural cooperatives and technological change 2.1.1 Agricultural cooperatives: concept definition
Farmer organizations or cooperatives are a formalized way in which rural farmers engage in collective action (GYAU ET AL., 2014). They are owned by the farmers that make use of the services of the cooperative. In contrast to investor owned firms, cooperatives (patron-owned firms) have a property-rights regime that gives the power of decision-making to its users, for example by means of democratic voting and internal committees (WOUTERSE
AND
FRANCESCONI, 2016).
Agricultural cooperatives can provide benefits to their users by improving their bargaining power towards large buyers (FISCHER AND QAIM, 2011). They are able to reduce risks that farmers face, for example by providing risk-reducing technologies or by storage facilities to deal with price fluctuations. Moreover, cooperatives can enjoy economies of scale by collectively accessing markets, for example buying inputs at lower prices. They can also reduce transaction costs in accessing services such as getting information about markets and technology (KWAPONG, 2012; FRANCESCONI AND WOUTERSE, 2015A). Agricultural cooperatives can provide a variety of services to their members, such as input supply or market access. In return for services, members often have to contribute financially to the cooperative, for example by membership fees or to pay a part of their income generated through the coop. These contributions are used to cover operational costs of running the coop, but can also be used for collective investments (ICA, 2016; FRANCESCONI AND
WOUTERSE, 2015A). In this research, the concept of ‘cooperative’ refers to all kinds of
agricultural cooperatives, including farmer associations but excluding rural savings and credit associations (SACCOs).
2.1.2 Agricultural cooperatives as a key factor in technology adoption Besides market failures, the low level of technology use is seen as one of the major factors hindering agricultural development in SSA, with many farmers facing constraints in adopting new technologies or innovations (BERDEGUÉ AND ESCOBAR, 2001; GYAU ET AL., 2014). GIANNAKAS AND FULTON (2005)
argue that cooperatives have specific characteristics that gives them relative
advantages for the generation and spread of innovation in society. According to SMITH (1994), the
6 organizational structure of cooperatives has advantages over investor-owned firms in industries where embedded knowledge plays an important role. GUINNANE (2001) found that member control and embedded-knowledge in German credit cooperatives reduced the information asymmetry between agents and principals compared to regular credit organizations. For this reason, GUINNANE calls cooperatives ‘information machines’ (p.366). In current literature on adoption of agricultural technologies in developing countries (KNOWLER
AND
BRADSHAW, 2007; LESTRELIN
ET AL.,
2012; UDDIN, BOKELMANN
AND
ENTSMINGER, 2014), cooperatives are found to be a factor with a positive influence on technology uptake by farmers. FISCHER AND QAIM (2011) analyzed the role of agricultural cooperatives in innovation adoption among smallholder banana farmers in Kenya, using quantitative household survey data. They found that cooperative membership is positively correlated with the adoption of tissue culture techniques, use of agro-chemicals, and improved farm management practices. According to the authors, these are “clear indications that collective action can spur innovation through promoting efficient information flows” (FISCHER AND QAIM, 2011, p. 1266). KOLADE
AND
HARPHAM (2014) analyzed the effect of cooperative membership on the
uptake of technological innovations in Nigeria. They found that members of agricultural cooperatives where more likely to adopt tractors, high-yield seed varieties, and agro-chemicals than non-members, with cooperative membership having a stronger influence on technology uptake than access to land, education and gender. They argue that cooperatives function as platforms for the spread of agricultural innovations. It is easier to reach farmers when they are already grouped in cooperatives than to reach individual farmer households, for example for the purpose of extension and for private firms to sell inputs and innovations. However, there could be other reasons why farmers are both innovative and a member of a cooperative. ABATE ET AL. (2014) and ABEBAW AND HAILE (2013) try to correct for the influence of other factors by using propensity score matching. ABATE ET AL. (2014) analyzed the influence of cooperative membership on technical efficiency of farmers by comparing groups of cooperative members with groups of non-members with similar characteristics. They found that members are on average five percent more efficient than non-members. Because of the matching method, they conclude that cooperative membership is the cause of increased efficiency, rather than the other way around. Their explanation is that cooperatives better serve farmers’ interests by providing
7 more efficient technologies, as well as by inputs and support services such as information and extension, leading to a higher output produced by cooperative members. ABEBAW
AND
HAILE
(2013) also looked at the influence that cooperatives have on the adoption of technologies by farmers and found that adoption rates of fertilizers and pesticides were nine to ten percent higher for cooperative members. The effect is even stronger for households who are less accessible in terms of infrastructure and for illiterate households. This suggests that cooperatives play a role in the access to inputs and technology and contribute to overcoming communication barriers.
2.2
Agricultural innovation systems
2.2.1 Innovation: concept definition The previous section showed that agricultural cooperatives can potentially play an important role in technology uptake, a concept closely related to innovation in the agricultural sector. Innovation is not just the adoption of a new practice or technology, but “any new knowledge introduced into and utilized in an economic or social process” (SPIELMAN, 2005, p.12). In the context of smallholder farmers, this means that the technology itself doesn’t have to be new, but merely new to the farmer who adopts it. Moreover, innovation does not necessarily have to be a product (like fertilizers or improved seeds), but can also be organizational or institutional, for example a new learning method (SPIELMAN, DAVIS, MEGASH AND AYELE, 2011). Probably the most remarkable example of diffusion of agricultural innovation is the Green Revolution: The large change in agricultural production in several developing countries from the 1940s to the 1970s, starting in Mexico and achieving high increased yields of staple crops in many Asian countries as well, including China and India (FAO, 1996). Key factors in the large success of the Green Revolution were large public investment in the agricultural research sector, supplying farmers with improved varieties, fertilizers and improved practices, supported by the necessary institutional changes and policies (PINGALI, 2012). Despite large successes elsewhere, in other regions such as SSA, the diffusion path of the Green Revolution cannot be observed. This gave rise to a vast amount of research on how to bring about technological change in SSA (SPIELMAN, 2005).
8
2.2.2 From traditional extension to agricultural innovation systems Inspired by the Green Revolution, the common way to disseminate agricultural knowledge and technologies was largely government-driven and top-down. Governments established ‘national agricultural research systems’ (NARS) with centralized research centers producing knowledge to disseminate through local extension offices down to the farmer level (SPIELMAN, EKBOIR, DAVIS
AND
OCHIENG, 2008). This NARS model for innovation diffusion has been
criticized due to its linearity, and a system perspective to look at knowledge dissemination became more widespread during the 1980s. Where the NARS model focuses at taking away barriers, mainly lack of finance, labor, land, and inputs (FEDER ET AL., 1982), the system perspective also looks at the context that shapes or takes away these constraints. Innovation is the result of interactions between stakeholders in the wider knowledge system. An example of the system approach is the ‘agricultural knowledge and information system’ (AKIS) to facilitate mutual information-sharing, knowledge generation and learning (BERDEGUÉ
AND
ESCOBAR, 2001;
SPIELMAN, 2005; SPIELMAN ET AL., 2011). However, criticism includes AKIS’ strong focus on the public sector as well as the lack of institutional and historical perspective. The concept of the ‘agricultural innovation system’ (AIS) is a broader and more inclusive approach than the NARS and AKIS. In contrast to AKIS, the traditional knowledge providers are not necessarily considered to be the dominant actors in the AIS (COOKE, 2001; KLERKX
AND
LEEUWIS, 2008A; SPIELMAN, 2005). It incorporates the actions of all public and private stakeholders as well as the institutional environment that determines the interaction between them (MEKONNEN ET AL., 2015). The AIS perspective can be used as a guide to analyze the innovation system in a specific region, country or sector. By identifying the connections and understanding the relations between the actors in the different domains, and hence the flows of information between them, efforts can be undertaken to improve the capacity of specific actors to learn and to work on strengthening relations between stakeholders. AISs consist of different domains with actors involved in the process of developing, introducing or using new knowledge. First, there is the demand domain with the end users of agricultural innovation. Then there is the education and research domain, where transferable knowledge for innovation is produced by organizations such as research institutes, universities, but also private companies. In the AIS, examples of knowledge are farm management practices
9 such as crop rotation or the crop calendar, improved seed varieties, irrigation systems, but also new learning processes such as the concept of farmer field schools (RAJALAHTI ET AL., 2008). Often, knowledge and information is exchanged between farmers and suppliers through other organizations; this happens in the intermediary domain, which consists of extension workers, NGOs, consultants, and farmer organizations (REES
ET AL.,
2007; RAJALAHTI
ET AL.,
2008).
Farmers can also be producers of knowledge, therefore knowledge being transferred through the intermediary domain are two-way or multi-lateral flows (SMEDLUND, 2006). Last, the AIS comprises supporting structures for innovation, such as education, infrastructure, and access to finance (RAJAHLATI ET AL., 2008). Examples of instruments based on the AIS perspective include stakeholder forums, participatory research projects, and the integration of experts from the education and research domain into networks with stakeholders from other innovation domains (SPIELMAN ET AL., 2008).
2.2.3 Agricultural cooperatives as knowledge and innovation intermediaries One of the roles of agricultural cooperatives in the AIS lies in the intermediary domain, bridging demand and supply of innovation. Market imperfections cause ‘structural holes’ in the network, creating a need for brokerage (MAZZAROL, REBOUD, CLARK, SIMMONS AND MAMOUNIMIMNIOS, 2013). Cooperatives have the capacity to bridge this gap for farmers by giving them access to information, knowledge and markets which would otherwise be difficult or impossible to access individually. The activities of innovation intermediaries range from creating awareness, to searching for new innovations, information brokerage, finding and matching partners, contracting, and supporting in marketing the outcomes (HOWELLS, 2006; SMEDLUND, 2006). Another aspect of innovation intermediation is the transformation of codified knowledge from the education and research domain as well as tacit knowledge from farmers (RAJALAHTI ET AL., 2008). KLERKX
AND
LEEUWIS (2008A; 2008B; 2009) developed the concept of ‘innovation
intermediaries’ and state that they should be visible to the end-users of innovation, have access to sources of relevant information and knowledge, and have credibility in the perception of the endusers and sources of innovation. They have to be able to respond timely to the needs of end-uses, and they should complement the particular weaknesses of the end-users of innovation. Therefore, it is important that intermediary organizations have a neutral position in the AIS. KLERKX, HALL AND
LEEUWIS (2009) see innovation intermediation as an upcoming activity in the AIS in
10 developing countries and see different types of organizations taking up this role, incusing NGOs, donors, researcher institutes, government organizations and farmer cooperatives. These organizations often have other major functions and perform intermediation as side-activities. This might impede their neutrality as well as the durability of providing intermediary services. YANG ET AL. (2014) were the first to focus solely on agricultural cooperatives as innovation intermediaries, finding them in a suitable position to match innovations to the demands and needs of their members, creating synergies by combining innovation intermediation with other activities such as input supply and marketing. They created a framework (figure 2.1) for agricultural cooperatives as innovation intermediaries, based on theories on AIS and innovation intermediation. In this explorative study, they did case studies on three cooperatives in China who were being identified to have played an important role in the introduction of an innovation among their members. They found that the core part of innovation intermediation is knowledge intermediation, which includes not only knowledge transfer and the facilitation of knowledge use, but also the facilitation of knowledge production. Besides, other supportive services are necessary to create the necessary conditions for innovation, such as linkages with other actors in the AIS and access to inputs (YANG ET AL., 2014; WENNINK AND HEEMSKERK, 2006). Interestingly, they found all functions among their selected cooperatives, although none of them had all functions at the same time. The three functions of knowledge intermediation are: (1) Articulation and voicing the needs and demands of users of innovation. This means recognizing problems and needs of farmers, and translating and communicating these to other actors who can supply relevant knowledge, training or technologies. (2) Provide members with information for problem solving, and responding to the needs of members. This is what YANG ET AL. (2014) call the classic extension and advisory services. (3) Engaging and supporting actors (including farmers and researchers) in joint knowledge production. For example, by bringing actors together and organizing sessions for ‘demandled-research’ where projects are based on the identified needs of the farmers. Besides, they identified three supporting functions that cooperatives can have: Creating a vision on the new technology or innovation, including creating awareness and managing the expectations of all actors involved; creating and managing a network with actors from different
11 domains in the AIS; and facilitation of learning processes and participating in learning processes. This implicates support by creating the right conditions for different types of learning methods. Figure 2.1 The innovation intermediation framework.
Note. Reprinted from YANG ET AL. (2014, p.116).
2.2.4 Factors influencing knowledge and innovation intermediation Knowledge and innovation intermediaries are important central components in AISs and strengthening these organizations is necessary to enhance the sharing of knowledge in order to support innovation. (SPIELMAN ET AL., 2008). EKBOIR (2012) recommends training to improve the innovative capacity of cooperatives. For innovation intermediaries in general, sufficient internal or external financial funds are important. For cooperatives, member commitment, a federated structure and initiative at establishment are mentioned as important factors influencing their functioning (SPIELMAN ET AL., 2011). However, these articles don’t build on empirical evidence, but on experience of the authors or on reviewing other articles. Therefore, a selection of six articles from the AIS literature, based on empirical data, has been made to find out which factors from the innovation system are important for agricultural cooperatives to fulfil their role as intermediaries. Table 2.1 gives an overview of the six selected articles. Included are the research objectives and methodology, as well as the factors that the articles identify as having influence on how cooperatives and intermediary organizations function in the AIS.
12 Table 2.1 Overview of empirical qualitative studies analyzing cooperatives and intermediary organizations in the AIS.
Article
Factors & relation with innovation intermediation
Research objective
Methods
HUSSEIN, 2001
Aims at identifying the attributes of agricultural cooperatives that are characterized as being successful in linking farmers to service provision.
16 case studies in the Gambia, Ghana, Cameroon, Burkina Faso, and Guinea. Semi-structured interviews with policy-makers, cooperatives, and farmers. Stakeholder workshops at local, regional and national level.
- Size of cooperative + - Federated structure + - Internal income sources + - External income sources + - Collective marketing + - Training on technical and management issues + - Open membership + - Clear decision-making rules +
WENNINK AND HEEMSKERK, 2006
Aims to identify the functions of agricultural cooperatives in the AIS.
5 case studies in Benin, Rwanda, and Tanzania. Key informants identified by multi-stakeholder groups. Data collected by semi-structured interviews and group discussions.
- Type of cooperative: commercial, marketing or service +/- Formal contracts + - Collective marketing + - Financial basis +
KILELU, KLERKX, LEEUWIS AND HALL, 2011
Aims at identifying innovation intermediaries in the Kenyan AIS and inventorying the functions they fulfil.
Explorative case study of the AIS of dairy, horticulture and maize in Kenya. 22 intermediary organizations are analyzed, using semi-structured interviews with key informants.
- Lack of funding -
HELLIN, 2012
Identifying the role of collective action in providing extension to farmers. Finding characteristics of network brokering.
2 case studies in Peru and Mexico. Qualitative value chain mapping and interviews with farmers and other stakeholders.
- Leadership skills + - External support (capacity building) + - External support by government (financial) +
13
Article
Factors & relation with innovation intermediation
Research objective
Methods
ORTIZ ET AL., 2013
To better understand the functioning of the AIS in 4 countries, to identify the actors and their relations, and to find opportunities to strengthen the AIS.
Case studies in Bolivia, Ethiopia, Peru and Uganda. Ten participatory workshops for rapid appraisal were performed with representatives from the government, farmers and farmer organizations (FOs), and the nonprofit sector.
- Member commitment + - Participation in research projects with government and non-profit sector + - Limited interactions between domains in the AIS -
YANG, KLERKX AND LEEUWIS, 2014
Aims to understand the functioning of agricultural cooperatives as innovation intermediation. Looks at how the positioning of the coop in the AIS influence their functioning as intermediaries.
Case studies of 3 cooperatives in China, selected for their role in implementing innovation. Data collection by means of semi-structured interviews with members, management, and related organizations involved in the innovation.
- Operation level +/- Fund-raising capacity + - Legitimacy (trust in coop and among other actors) + - Linkages with extension + - External support + - Formal market linkages (contracts) + - Familiarity with scientific language of leaders +
Contains a framework for analyzing the functioning of innovation intermediation, meant for qualitative research.
14 2.2.4.1 Size According to HUSSEIN (2001), second-tier organizations are more successful in advocating for better services by establishing formal relationships with service providers and finding financial sources, while smaller agricultural cooperatives perform less well in need articulation to service providers. In China, on the contrary, it was the smaller, community-based cooperative who performed better in aggregating and articulating needs of members, due to their strong ties to farmers (YANG
ET AL.,
2014). Smaller cooperatives benefit from integration into a union in
management and linkages to finance and services, while unions need close linkages to villagebased groups to learn about the needs of their members (YANG ET AL., 2014; HUSSEIN, 2001). 2.2.4.2 Financial management Market access is a trigger factor for farmers to innovate (WENNINK 2006). All agricultural cooperatives in YANG
ET AL.
AND
HEEMSKERK,
(2014) had established important market
linkages with supermarkets to sell their produce. Moreover, they introduced the innovation with help of financial funding from external stakeholders. Also HUSSEIN (2001) points out that cooperatives should be organized around successful economic activities, like marketing. Besides, for service provision, cooperatives should be able to access internal or external funds. Especially marketing cooperatives have the resources for the provision of knowledge services to their members (WENNINK AND HEEMSKERK). However, they see extension as the responsibility of other actors and often do not exert their power to direct services to what their members need. Serviceoriented cooperatives see extension as their responsibility but often lack financial resources. 2.2.4.3 Decision-making mechanism Based on sixteen case studies on how to strengthen cooperatives in linking farmers to service provision, HUSSEIN (2001) recommends clear and acceptable decision-making rules, preferably aligned with national regulations. Combined with member participation, this enhances member commitment and trust within the cooperative. Especially when leaders and members have diverging interests, lack of member participation can give rise to conflicts (YANG ET AL., 2014). 2.2.4.4 Leadership HELLIN (2012) found that farmers with an intermediary function in innovation systems had strong leadership skills: creating trust and motivation among farmers and their organizational skills
15 were important factors for successful dissemination of agricultural knowledge. According to YANG ET AL. (2014), one
of the strengths in the innovative cooperatives in their study was the presence
of knowledgeable members and leaders in the cooperative. They were able to translate innovation into practices that farmers can apply. Those key persons in the cooperative had both extensive practical experience in farming and knowledge of the scientific language. This suggests that both experience and education are important for innovation intermediation, because cooperatives can then function as a translator between sources of tacit knowledge and codified knowledge. 2.2.4.5 Network Connections to other actors facilitate access to innovation and information and knowledge flows to and from farmers. In the research by ORTIZ ET AL. (2013), Ugandan cooperatives’ strong presence in the AIS can be explained by their involvement in participatory research projects with governmental and non-profit organizations. The three innovative agricultural cooperatives in China all had close relations with extension services and research institutes. The personal connections of cooperative leaders gave them access to new technologies and knowledge sources. However, these relations were merely between the leaders and other actors. Cooperatives act often as an in-between, without direct linkages between farmers and other actors (YANG ET AL. 2014). Contrary, ORTIZ ET AL. (2013) state that strong linkages with actors from one domain while having weak connections with other domain also hinders the access to innovation. 2.2.4.6 External support Networking can lead to external support, which can be beneficial, for example as in YANG ET AL.
(2014), where cooperatives received funding for implementing innovations. External
funding might be necessary to realize investment when there is limited financial capital or access to credit. Any support should be demanded by the recipient, not initiated by the donor organization, according to WANYAMA (2014). However, government support in the form of subsidized prices or fixed output prices can provide security and stability but at the same time hinders incentives to innovate, as HELLIN (2012) observed in Mexican maize cooperatives. On the other hand, external support in the form of training to leaders and management is recommended to strengthen the role of cooperatives in the AIS, to improve service provision to their members and to help leaders better voice farmers’ demands (HUSSEIN, 2001; WENNINK AND HEEMSKERK, 2006; ORTIZ ET AL., 2013).
16
2.3
Internal dynamics of agricultural cooperatives
Section 2.2 discussed how agricultural cooperatives can contribute to the adoption of innovation by smallholder farmers as innovation intermediaries and reviewed the literature to identify factors influencing the performance of cooperatives as knowledge and innovation intermediaries. Research shows that the performance of African cooperatives is highly variable (FRANCESCONI
AND
WOUTERSE, 2015A; FRANCESCONI
ET AL.,
2015), which underlines the
necessity of organizational capacity building in the AIS. The goal of this section is to strengthen the theoretical framework by YANG
ET AL.
(2014) with the state of the art literature on the
performance of agricultural cooperatives.
2.3.1 The cooperative life cycle framework Agricultural cooperatives are generally seen as potentially strong organizations with benefits for smallholders (FRANCESCONI
ET AL.,
2015). In practice, however, it is difficult to
organize collective action amongst groups of farmers and cooperatives face challenges in defining rules and regulations of their organization and in maintaining members’ trust, commitment and compliance (GYAU ET AL., 2014). The ‘cooperative life cycle framework’ (COOK AND BURRESS, 2009), shows the business cycle that cooperatives move through during their life and associates common complications with different stages in the life cycle. Most importantly, the framework suggests that many problems that cooperatives face later on in their life cycle are the result of (and can be prevented by) the definition of objectives and rules at their start-up phase. The framework can be used by leaders of cooperatives to recognize and foresee these problems in order to take appropriate actions to alleviate or prevent them (COOK AND BURRESS, 2009; FRANCESCONI AND RUBEN, 2008). The cooperative life cycle (figure 2.2) consists of five stages: 1) economic justification, 2) the organizational design of the cooperative, 3) growth, glory, and heterogeneity, 4) recognition and introspection, and 5) choice. The different stages are related to the health of the cooperative, which increases or decreases depending on the moment in their life cycle. Health refers to the performance of agricultural cooperatives in general, as defined by the organizations themselves, including both economic and social objectives (COOK AND BURRESS, 2009).
17 Figure 2.2 The cooperative life cycle.
Note. Adapted from COOK AND BURRESS (2009, p.3). Phase 1: economic justification. In this phase, farmers decide to engage in collective action to reach common goals. Justifications can range from rotation schemes to reduction of transaction costs by dealing collectively with output buyers and bargaining better prices. Phase 2: Organizational design. This comprises setting up the constitution and the by-laws of the cooperative. These rules affect the division of property-rights for the members regarding decision-making and residual rents, and how trust and commitment can be secured in the future. The degree of heterogeneity of members’ preferences at this phase is usually low as their interests often are aligned, but a common problem is future heterogeneity when membership has increased and higher interests are at stake. Phase 3: Growth, glory and heterogeneity. After establishment, agricultural cooperatives enter a phase of growth, either in membership or in investments. This often implies a growth in heterogeneity of members’ preferences regarding investment, service demand or risk preferences. It is in this phase that the implications of constitutional choices have consequences. For example, open membership can result in a large growth in membership when the joining the cooperative is profitable. However, when this leads to oversupply of certain products, selling prices decrease, diminishing farmers’ incentives to sell through the coop (FRANCESCONI
AND
RUBEN, 2014).
Increased heterogeneity in members’ preferences can lead to risk-reducing investment, but also to higher costs in decision-making and running the organization. When collective investments are not made, the cooperative will have problems to cover those increased operational costs (FRANCESCONI AND WOUTERSE, 2015A).
18 Phase 4: Recognition and introspection. This is the phase where problems due to growth and increased heterogeneity are recognized. Phase 5: Choice. After organizational problems are recognized, agricultural cooperatives can choose what to do next. They can tinker (adjusting the organization within the existing rules), reinvent (by changing their constitutions or bylaws), change into a different type of organization (such as an investor-owned firm), and last, cooperatives can either cease to exist or become dormant (COOK AND BURRESS, 2009).
2.3.2 New generation cooperatives The solution to the usual problems that agricultural cooperatives experience during their life cycle is the promotion of the new generation cooperative (NGC). The combination of open membership and unlimited growth with unclear property rights give rise to problems such as freeriding, trust issues and the horizon problem. The NGC has an organizational structure designed to reduce these problems: They are “a defined membership organization requiring an up-front equity investment in equity shares possessing both tradeable and appreciable properties. Investment in the cooperative is based on a member's anticipated level of patronage and all members adhere to a legally binding uniform marketing agreement” (COOK AND ILIOPOULOS, 1999, p.529). Delivery rights imply not only rights, but also obligations to members. If farmers are not able to meet that demand, they should purchase the produce in the same amount as stated in the contract on the free market and then deliver to the cooperative. This ensures that the cooperative can be trusted in the delivery to a buyer. Next to a patronage system based on delivery rights, a NGC has a shareholding mechanism in order to finance collective investments. Members can thus invest in a collective good (for example storage facility) by buying the amount of shares proportional to their expected use of the facility. These shares can be traded among members. This prevents members from free-riding on collective investments and therefore takes away trust constraints for members to invest. It also solves the horizon-problem because shares are redeemable, reducing members’ insecurity in investing in long-term project (FRANCESCONI AND RUBEN, 2008; COOK AND ILIOPOULOS, 1999). Last, a NGC has closed membership. New members have to make an up-front investment, and members leaving the cooperative should be facilitated. This mechanism selects committed members. At the same time, the presence of delivery-rights and equity shares does not change the ‘one member, one vote’ principle for issues such as
19 leadership elections. Different from traditional cooperatives, NGCs have a hired manager who is responsible for the daily operations of the organization. Members and their representatives in the executive committee monitor and control the management (COOK AND ILIOPOULOS, 1999; HARRIS, STEFANSON AND FULTON, 1996).
2.3.3 Factors influencing performance of agricultural cooperatives The theory on the cooperative life cycle and NGCs already identified factors of the internal design of cooperatives, especially those at establishment, influencing their health later in their life cycle. To complement this literature, this sections reviews nine articles to identify factors influencing the performance of cooperatives. All articles are quantitative empirical studies about agricultural cooperatives in the Sub-Saharan region. In the review, different dependent variables to measure the functioning of agricultural cooperatives are found. Generally, one can distinguish between ‘performance’ and ‘health’. Where performance in most cases refers to financial performance, such as collective marketing, access to finance, or total output (AMPAIRE, MACHETHE 2009; FRANCESCONI
AND
AND
BIRACHI, 2013; BERNARD
AND
SPIELMAN,
RUBEN, 2008), health adopts a broader interpretation, including
economic as well as social objectives (FRANCESONI
AND
WOUTERSE, 2015A; WOUTERSE
AND
FRANCESCONI, 2016). Social performance is in these studies operationalized as collective investments, which can be physical assets as well as human resources. This aligns in turn with other interpretations of economic performance: in the research by (SHIFERAW
ET AL.,
2006;
FRANCESCONI AND WOUTERSE, 2015B), collective investment indicates economic performance. This suggest that economic and social performance are closely related, being two sides of the same coin: one side referring to success in terms of patronage delivery, and the other to success in investing a part of farmers’ patronage for service provision other than collective marketing. In this context, it seems plausible to assume that the same factors influencing the performance and health of cooperatives might be applicable for the framework by YANG ET AL., (2014), to analyze if they affect the performance of cooperatives in terms of service provision for innovation. Table 2.2 gives an overview of the nine selected articles. Included are the research objectives, how the dependent variables are measured, and methodology. The right column contains the factors identified in the articles. Also the relation with dependent variable is indicated using a plus (+) or minus (-), for respectively positive and negative relations.
20 Table 2.2 Overview of empirical quantitative studies about factors influencing the performance of agricultural cooperatives. Article SHIFERAW, OBARE AND MURICHO, 2006
FRANCESCONI AND RUBEN, 2008
Research objective
Dependent variable(s)
To analyze which factors determine effectiveness of producer marketing groups (PMGs), and to understand the relation between collective action and performance.
Performance: effectiveness in market functions, measured by assets and volume produced, standardized per capita.
To identify the characteristics influencing the performance of agricultural cooperatives
Performance in terms of marketing.
BERNARD AND Aims to investigate if the SPIELMAN, rural poor benefit from 2009 agricultural cooperatives. Besides, aims at analyzing the relation between inclusiveness and performance of agricultural cooperatives.
Collective action is measured by indicators regarding member participation and members’ patronage.
Measured by the probability if the coop is engaged in collective marketing. Performance: collective marketing. Measured by the probability if the agricultural cooperative sold any of its members’ output.
Methods Data collection: Structured interviews with 400 households, semi-structured interviews at village and PMG level with leaders and farmers from 10 PMGs in Kenya. Data analysis: descriptive and correlation statistics. Data collection: Structured interviews with the boards of 206 Ethiopian agricultural cooperatives.
Factors & relation to DV - Number of elections + - Attendance of meetings + - Initial start-up capital + - Membership fee + - Number of villages - Distance to markets - Group size - Lack of credit - Low business skills - Age - Farmer initiative + - Region
Data analysis: Probit regression Data collection: Structured interviews from 7186 households and 205 agricultural cooperatives in Ethiopia (unrelated). Data analysis: Logistic regression.
- External support (training) + - Ext. financial support + - Member heterogeneity + - Size (in membership) – - Trade-off between inclusiveness, participatory decision-making, and performance.
21 Article
Research objective
WAMBUGU, OKELLO AND NYIKAL, 2009
To identify the effect of social capital (inclusiveness and member participation) on the economic performance of agricultural cooperatives.
Performance: value of collective marketing.
Aims to analyze the relation between social capital and the financial performance of agricultural cooperatives.
Financial performance, operationalized by access to finance and deliveries of coffee production.
RUBEN AND HERAS, 2012
Dependent variable(s)
Measured by the value of output sold through the coop/ total value of members’ output.
Methods Data collection: Structured interviews, 225 household surveys from 45 agricultural cooperatives in Kenya. Data analysis: Ordinary Least Squares (OLS) regression.
Data collection: Open interviews with leaders from 5 cooperatives in Ethiopia. Structured interviews with 500 member farmers. Data analysis: factor analysis and analysis of variance
AMPAIRE, MECHETHE AND BIRACHI, 2013 AMPAIRE AND MACHETHE, 2012
To identify the factors that influence the effectiveness of rural producer organizations (RPOs) in linking members to markets.
Effectiveness: linking their members to markets. Measured by the percentage of member selling produce through the cooperative.
Data collection: Focus group discussions with 62 second-tier RPOs and structured interviews with 1377 farmers in Uganda. Data analysis: OLS regression.
Factors & relation to DV - Member heterogeneity + - Democr. decision-making + - Group solidarity + - Attendance of meetings + - Age of leader +/- Size (membership) + - Distance to tarmac road + - Presence of by-laws + - Trust - Age of cooperative - Distance to main road: positively related to social cohesion. - Social cohesion: positively associated with a higher score on financial performance. - Democratic governance + - Size (number of members) + - Share of female members + - Size of board – - Training leaders – - Internal practices – - Bulking distance –
22 Article FRANCESONI AND WOUTERSE, 2015A
FRANCESONI AND WOUTERSE, 2015B
WOUTERSE AND FRANCESCONI,
2016
Research objective
Dependent variable(s)
Methods
- External support - Growth in membership +
To test the validity of the cooperative life cycle framework and to analyze the relation between the health and performance of farmer-based organizations (FBOs).
Health is defined by:
Objective is to analyze the role of internal dynamics and external support on collective investments of farmer-based organizations (FBOs).
Performance: collective investments in either capital assets or human resources.
Data collection: Structured and open interviews with board members from 500 FBOs in Measured by the probability if the Ghana. FBO has done any collective Data analysis: OLS and investments. instrumental variable regression.
- Heterogeneity in members’ risk preferences - Population density – - External support (financial) –
Objective is to define the determinants of cooperative health and analyze the relation between health and economic performance of smallholder producer organizations (SPOs).
Health is determined by collective Data collection: Structured selling, growth in membership interviews from board and investment, and side-selling. members of 253 SPOs in Ethiopia, Senegal, and Malawi. Performance is measured by the
- Age of coop +/- Shares sold to members + - Education leaders + - Distance to market + - Size at establishment – - Established with external support –
- the degree of member heterogeneity in risk preferences, and - the probability if the FBO has done collective investments.
probability if the SPO has done collective investments.
Data collection: Structured and open interviews and ‘risky dictator games’ with board members from 500 FBOs in Ghana.
Factors & relation to DV
Data analysis: Cluster and correlation analysis.
Data analysis: Factor analysis and regression analysis.
23 2.3.3.1 Size Growth in investment and membership are correlated with an increase in cooperative health (COOK AND BURRESS, 2009). Intuitively, a healthy or well-performing cooperative is very likely to attract more members. Also, many scholars found the size as well as the growth of membership to have a positive effect on health and performance. Large membership implies economies of scale and more financial means for investment and service provision (WAMBUGU ET AL., 2009; AMPAIRE ET AL.,
2013; SHIFERAW ET AL., 2006; WOUTERSE
AND
FRANCESCONI, 2016; FRANCESCONI AND
WOUTERSE, 2015A). BERNARD AND SPIELMAN (2009) and SHIFERAW ET AL. (2006) found group size to be negatively correlated to performance. For large sizes, the cost of operating the organization might outweigh the benefits. The relation between size and performance of cooperatives can be non-linear, with the most optimal form being a medium-sized organization. 2.3.3.2 Age WAMBUGU
ET AL.
performance. WOUTERSE
(2009) found the age of cooperatives negatively correlated to
AND
FRANCESCONI (2016) on the contrary, found a positive relation
between age and health. The relation with health and performance might depend on the phase of the life cycle. One can expect an increase in health as the coop gets older and advances along the life cycle, but health might decrease after problems such as side-selling might impair collective marketing. This is supported by the research of FRANCESCONI AND RUBEN (2008), who found that the probability that cooperatives are engaged in collective marketing decreases as they get older. 2.3.3.3 Financial management Members have to make up-front investments in the form of delivery rights and they can buy shares for investments they want to use (HARRIS ET AL., 1996; COOK AND ILIOPOULOS, 1996). These characteristics are positively related to economic performance and health. Cooperatives with by-laws that obligate members to sell through the cooperative performed on average better than those without (WAMBUGU ET AL., 2009). Start-up capital and membership fees (SHIFERAW ET AL., 2006) and selling shares to members (WOUTERSE
AND
FRANCESCONI, 2016) are positively
correlated with performance. Cooperatives with a strong financial base are more profit-oriented, better able to attract external funds, and are more often able to make investments in physical assets and staff or services AMPAIRE ET AL. (2013).
24 2.3.3.4 Member heterogeneity WAMBUGU ET AL. (2009) found that cooperatives with heterogeneous members showed better performance. When heterogeneity in preferences leads to more risk-reducing investment, this has a positive effect on health (FRANCESCONI
AND
WOUTERSE, 2015A). However,
heterogeneous members have less common interests (WAMBUGU ET AL., 2009). FRANCESCONI AND WOUTERSE (2015B)
for example, found that heterogeneity in risk preferences was negatively
associated with collective investments in Ghanaian cooperatives. This strongly indicates that internal trust is important to hold the cooperative together, and to ensure that farmers show commitment by investing in assets and services (RUBEN AND HERAS, 2012; AMPAIRE ET AL., 2013). 2.3.3.5 Decision-making mechanisms Member commitment can also be enhanced by democratic and participatory decisionmaking processes. RUBEN AND HERAS (2012) state that trust alone is not enough for performance. Instead, agricultural cooperatives need clearly defined property rights, internal control mechanisms and benefits for members to provide patronage. Several indicators of democratic and participatory decision-making are found to be positively related with performance including internal committees, high attendance of members at meetings, and regular general meetings and elections (AMPAIRE ET AL., 2013; SHIFERAW ET AL., 2006; WAMBUGU ET AL., 2009). Attendance of meetings suggest that cooperatives with members who participate well perform better due to information sharing about production and marketing skills (WAMBUGU ET AL., 2009). BERNARD AND SPIELMAN (2009) on the contrary, found no relevant correlation between member participation
and economic performance. Their study on Ethiopian cooperatives showed a trade-off between inclusiveness, participatory decision-making, and performance. 2.3.3.6 Purpose WOUTERSE AND FRANCESCONI (2016) found that agricultural cooperatives established with an offensive purpose (such as value addition and marketing) were less healthy. Instead, cooperatives with a defensive purpose (accessing services, benefiting from economies of scale), are found to be healthier. Their explanation is that cooperatives with an offensive purpose might focus too much on growth, while losing attention for internal cohesion between members. This contradicts previous studies from FRANCESCONI AND WOUTERSE (2015A), where cooperatives with an offensive or economic objective are healthier than those with a defensive purpose. The
25 difference in findings between the articles can possibly be explained by differences in indicators for health. 2.3.3.7 Initiative at establishment Agricultural cooperatives founded by its own members are healthier and more likely to engage in commercial activities (WOUTERSE WOUTERSE, 2015A; FRANCESCONI
AND
AND
FRANCESCONI, 2016; FRANCESCONI
AND
RUBEN, 2008). “Top-down interventions tend to attract
opportunistic and subsistence farmers, eager to extract subsidies rather than embark in economic activities. Cooperatives established by the spontaneous initiative of farmers are instead more likely to aim for commercial objectives” (FRANCESCONI AND RUBEN, 2008, p.117). Although government interference might have an initial beneficial effect on collective marketing, cooperatives established by the initiative of farmers themselves sustain this activity over a longer time (FRANCESCONI AND RUBEN, 2008). Most problematic for cooperatives, however, is when they are established to attract donor money of in response of government plans. This reduces solidarity and commitment by farmers to invest in the cooperative (RUBEN AND HERAS, 2012; BERNARD AND SPIELMAN, 2009; FRANCESCONI AND RUBEN, 2008). FRANCESCONI AND WOUTERSE (2015A) found in Ghana many cooperatives who were non-active, waiting for financial impulses of external organizations to become operative again. In Ethiopia, cooperatives who had strong government intervention were more likely to face issues of corruption and mistrust among members (FRANCESCONI AND RUBEN, 2008; RUBEN AND HERAS, 2012). 2.3.3.8 External support The review showed mixed effects of external supports to agricultural cooperatives. Often, partner organizations provide training and advice, not only on technical matters but also on financial management, leadership and managerial skills. In Ethiopia, financial support and training on management skills at time of establishment have a significant positive effect on the performance of cooperatives (BERNARD
AND
SPIELMAN, 2009). Other studies (such as WOUTERSE
AND
FRANCESCONI, 2016; FRANCESCONI AND WOUTERSE, 2015A) state that support from donors can be counterproductive and hampering autonomous development. (FRANCESCONI
AND
WOUTERSE
(2015B) found that participation of cooperatives in Ghana in an external government support program is negatively associated with collective investment. This strongly suggests that the government program crowded out collective investments.
26 2.3.3.9 Leadership FRANCESCONI
AND
RUBEN (2008) identify poor managerial skills as a reason why
agricultural cooperatives fail to sustain collective marketing. Traditional African leaders might have strong legitimacy among members, but at the same time they might lack education and the experience to lead a large business organization. WOUTERSE AND FRANCESCONI (2016) found that the education level of the leader has a positive and significant effect on health, because this person is in charge with organizational change. Alternatively, a paid manager with appropriate education and experience is a possible solution to the leadership problem (FRANCESCONI AND RUBEN, 2008). Although counterintuitive, AMPAIRE ET AL. (2013) found that the presence of more leaders who received leadership or management training has a negative effect on commercial performance. The given explanation is that training can lead to stricter control on regulations, demotivating or hampering members to sell through the cooperative. WOUTERSE AND FRANCESCONI (2016) found that cooperatives with a female chairperson were found to be less healthy. A possible reason is that female leaders focus more on social performance and monitoring than on economic performance, which might result in higher costs for the organization. 2.3.3.10
Location in the innovation network
In Kenya, performance of agricultural cooperatives decreases when their distance to markets increases. This indicates that proximity to markets improve market access and information sharing, which in turn leads to better performance in terms of sold output. Proximity to other actors in the network, such as input suppliers, output buyers and service providers, can improve coordination with them (SHIFERAW ET AL., 2006). Also FRANCESCONI AND WOUTERSE (2015B) found distance to markets negatively correlated with collective investment, although this result is not significant. Contrary, the negative correlation WAMBUGU ET AL. (2009) found between distance to the main road and output sold through the coop indicates that members are more inclined to sell through the coop when alternative marketing options are more difficult to access. Accordingly, WOUTERSE
AND
FRANCESCONI (2016) also found a positive association between distance to
markets and health of cooperatives. FRANCESCONI AND WOUTERSE (2015B) found that the size of the nearest town is negatively associated with collective investments. These results indicate that lack of opportunities, such as in less urbanized areas, could be a trigger for successful collective action.
27
2.4
Study background: agricultural cooperatives in Uganda
Uganda is a land-locked country located around the equator in the Nile basin in Eastern Africa. Although rainfall patterns vary in every region, most parts of the country experience regular and vast amounts of rainfall, except for the dryer north. Around 85 percent of Uganda’s population lives in rural areas, with the agricultural sector accounting for approximately 37 percent of GDP (PWC UGANDA, 2016). Although poverty rates have been decreasing, a quarter of the population still lived from less than $1.25 per day in 2012. Around 41 percent of the available land is cultivated. The most important products produced in Uganda are coffee, tea and cotton for export, and beans, matooke, wheat, maize, groundnuts, sweet potatoes, cassava, fish, and livestock mainly for subsistence and the domestic market (USAID 2013; IFAD 2016). Ugandan smallholder farmers face challenges in production due to rainfall variability, lack of agricultural inputs and technology use, limited access to infrastructure for transport, weak market linkages, and constraints to accessing financial services (IFAD, 2016; USAID, 2013). From the 1930s to the 1960s, agricultural cooperatives in Uganda enjoyed a blossoming period, where cooperative organizations experienced rapid growth in membership and successfully increased farmers’ income. Their performance after independence declined due to government interference and corruption, but cooperatives were the only way for farmers to sell their produce during this period. In the early 1990s, cooperatives were starting their recovery when the government liberalized the markets after a political unstable period. The cooperative sector found themselves in a new market situation in which they were not able to compete with traders, to which farmers were now allowed to sell their produce (KWAPONG
AND
KORUGYENDO, 2010; HILL,
BERNARD AND DEWINA, 2008). Traders offered higher prices and immediate payment. Therefore, sales though collective marketing dropped dramatically during the following decade (HILL ET AL., 2008; MTIC, 2011) and many cooperatives did not survive (KWAPONG AND KORUGYENDO, 2010). The cooperatives that did survive after liberalization could so because they managed to find a market for their members’ produce and to obtain financial support from external organizations (both government and donors). Other characteristics were good leadership, a strong asset base and strong member commitment (KWAPONG, 2012). Since the market liberalization, the Government of Ugandan (GoU) and the Ugandan Cooperative Alliance (UCA) initiated a reformation of the cooperative sector with the objective to
28 make cooperatives viable organizations in the new market situation. These reforms included the so-called ‘tripartite’ cooperative model (KWAPONG, 2012). Cooperatives were supported to form hierarchies, second-tier cooperatives formed by farmer groups or farmer associations (AMPAIRE ET AL., 2013).
Farmers usually bulk their produce in first-tier organizations but marketing is done
by second-tier cooperatives (KWAPONG, 2012; AMPAIRE
ET AL.,
2013). The tripartite model
introduced Area Cooperative Enterprises (ACE), which are second-tier organizations consisting of smaller farmer groups. ACEs were motivated to engage in the collective marketing of at least three different crops to reduce production and marketing risks. The tripartite model has an integrated approach where ACEs are linked to SACCOs to provide financial services to farmers. ACEs have an elected executive board as well as a manager with a higher education degree. They have democratic decision-making mechanisms such as internal committees and work on capacity building and technical training to improve production (KWAPONG, 2012; AMPAIRE ET AL., 2013). In 2001, the GoU established the national agricultural advisory services (NAADS) program (KWAPONG, 2012). This program was meant to established a demand-driven extension system, and operated through farmer groups, which formed platforms for farmers to get into contact with extension officers of the GoU. The NAADS programs also implies that government extension and cooperatives work together on providing training and extension, with cooperatives providing those services that are not provided by the NAADS. Information and training is given to ‘model farmers’, who in turn pass knowledge to other farmers living in the same parish. Also inputs are provided through the ACEs, with the demands of farmers channeled through the primary organizations to the ACEs, who connect to private input suppliers. Inputs can be bought by cash or on credit through SACCOs (KWAPONG, 2012; MSEMWAKELI, 2012). During the first phase of NAADS, a large amount of new farmer groups has established and many of them are part of a second-tier cooperative organization. Besides NAADS, also the traditional top-down extension services are operational in Uganda (KWAPONG, 2012). In 2011, 10,746 cooperative societies were registered at the Ministry of Trade, Industry and Cooperatives, of which 55 percent were marketing or production cooperatives (MTIC 2011). Extension in Uganda is being offered by the government, apex organizations, private input suppliers, and NGOs and donor organizations. Uganda’s cooperative apex organizations are the UCA and the Uganda National Farmer Federation (UNFFE), the latter being part of the Eastern African Farmer Federation (EAFF).
29
2.5
Conceptual model
2.5.1 Definition of the conceptual model The first research question of this master thesis asks which factors can be found from the literature on agricultural cooperatives and AISs that influence the performance of agricultural cooperatives as knowledge intermediaries. The answer can be found in figure 2.3, which is the conceptual model for this master thesis. Based on a review of empirical qualitative and quantitative peer-reviewed articles, the model shows those variables that are expected to influence performance of cooperatives as knowledge intermediaries. The model of YANG ET AL. (2014) (figure 2.1) shows knowledge intermediation as the three basic functions of innovation intermediation, which can be interpreted as knowledge services that cooperatives provide to their members. The question remains, however, how to use the concept of performance in terms of knowledge intermediation for this research. Literature on health and performance does not provide indicators to measure performance regarding to knowledge services. Instead, collective investment is an important indicator of cooperative health, either in physical assets or in human resources. Collective investment is also an indicator of performance in terms of social objectives, which might include knowledge services as well. For the provision of knowledge services, cooperatives need to invest in staff rather than in physical assets. The conceptual model in figure 2.3 shows how performance as knowledge intermediary in this master thesis is conceptualized as the combination of (i) knowledge service provision and (ii) collective investment in human resources (paid employees). The left side shows the organizational characteristics at establishment that are expected to influence the current performance of agricultural cooperatives as knowledge intermediaries. The literature review provides numerous potential variables to explain the performance of agricultural cooperatives as knowledge intermediaries. To limit the scope of the research, choices for including and excluding variables had to be made. Five categories of characteristics are chosen. Financial management of the cooperative is split up in collective selling of members’ output, a share-holding mechanism and membership fees. Participatory decision-making includes mechanisms to voice members’ demands and concerns as well as the frequency of general meetings. Leadership is split up between the education level and the gender composition of the
30 executive board. Three incentives at establishment are included: initiative for establishment can be either taken by farmers themselves or by an external organization. External support can be either financial support (grants or donations) or support in the form of training on management skills. Connections with other actors in the AIS is limited only to being a member of an apex organization and the relations with NGOs. Besides, some control variables are included in the model as well. Size refers to the current amount of members, while age is the amount of years the cooperative has been operational. The distance to the capital is chosen, because many organizations in the AIS are located here, such as the government and NGOs. Because of the research focus, it is expected that not the distance to markets but the distance to the nearest public extension office influences if cooperatives provide knowledge services to members. Figure 2.3 Characteristics, incentives and relations in the AIS at establishment that are expected to influence the performance of agricultural cooperatives as knowledge intermediaries.
31 Although identified as an important factor in the review, having a manager or not is not included as an explanatory variable. The reason is that there is too much overlap with one indicator of the outcome variables: Having paid staff could in many cases have the same meaning as having a manager, since a manager is a paid staff member. Membership rules are identified in the literature review but not explicitly included in the conceptual model because some of them have overlap with financial management, such as membership fees and share-holding mechanisms. Also member heterogeneity is not included in the model. Heterogeneity in preferences increases as cooperatives grow larger and is therefore left out to avoid multicollinearity. Heterogeneity in membership can also be conceptualized as diversity or inclusiveness of the cooperative. This is partly covered by the gender composition of the board. A separate variable for inclusiveness is not included to keep the model simple. Also trust and internal coherence are identified in the review to be important factors influencing the performance of agricultural cooperatives over time. However, those are likely to be influenced by other factors in the model, most likely decisionmaking rules and leadership. Besides, they are difficult to measure at the organizational level. Therefore, those variables are not included in the model either.
2.5.2 Expected outcomes The second research question asks which functions of knowledge intermediation Ugandan agricultural cooperatives fulfill. Ugandan agricultural cooperatives are expected to provide three possible knowledge services but are not expected to provide all three services simultaneously. This will be analyzed by descriptive quantitative analysis, showing descriptive statistics of the three identified knowledge services among Ugandan cooperatives in the data sample. Qualitative data from two case studies will be used to provide examples how these services are provided in practice. The third research question asks which organizational characteristics of agricultural cooperatives influence their performance as knowledge intermediaries. The financial management, decision-making mechanism, leadership, external incentives and the network of cooperatives at time of establishment are expected to influence their current performance in knowledge service provision. These possible causal relations will be tested with regression analysis. The two case studies will provide additional information on if and how two selected agricultural cooperatives in Uganda function as knowledge intermediaries and provide more detailed insights in the causal relations underlying the conceptual model.
32
3 METHODOLOGY 3.1
Methods for data collection
3.1.1 Quantitative data collection: surveys Quantitative data is collected during the Cooperative Leadership Event (CLE) which took place from May 2 to May 6, 2016 at the National Agriculture Research Organization (NARO) station in Kawanda, Uganda. The CLE is organized by CIAT in collaboration with ten local partner organizations. Those organizations are UCA, UNFEE, KCCA, IFAD, USADF, NCBA-CLUSA, ACDI-VOCA, Global Communities, ACAV, and several institutes of the CGIAR consortium. They include two national farmer unions, a Kampala local government institute working with urban agricultural cooperatives, research institutes, and local and international non-profit organizations, all well-experienced in their support to agricultural cooperatives in the country. In Uganda, a comprehensive and complete overview or database with all operational agricultural cooperatives is missing, which challenges random sampling. This is illustrated by the cooperatives in the sample, of which only 63 percent are registered with any official central institution. To compensate for this information gap, project partners were asked to invite a number of cooperatives that they perceive as representative for the cooperatives they work with. The EDC project therewith strives to make a first step towards a representative database of agricultural cooperatives in Uganda (FRANCESCONI, 2017, personal communication). The event was attended by 100 managers and chosen leaders from 99 agricultural cooperatives and farmer associations, resulting in a sample of 99 observations. The sample contains a great diversity of agricultural cooperatives, including first-tier as well as second-tier organizations. The cooperatives in the sample are involved in the production of a diverse range of agricultural products: 58 percent produce cereals, 28 percent roots and tubers, 22 percent oil seeds, 20 percent pulses, and 20 percent cash crops such as coffee, tea, cotton, or palm oil. The sample represents all four districts of Uganda, with cooperatives from 56 out of all 112 districts in Uganda. 19.2 percent of the cooperatives in the sample is located in the central region, 33.3 percent in the western region, 30.3 percent in the northern region, and 17.2 percent in the eastern region.
33 Data collection took place on the first day of the event and was executed by the CLE team, consisting of ten staff members, including CGIAR researchers, students from Makarere University in Kampala and students from Humboldt University Berlin. The structured surveys consist of multiple choice questions as well as open questions for numerical data such as distances and prices. Besides general information about the participants and their cooperatives, the survey contains questions about objectives, service provision, membership and growth. Importantly, it also contains questions about the organizational design of the cooperatives, including questions about decision-making, the executive board, management, internal committees, marketing and distribution of services, benefits and shares. Because the participants filled in the questionnaires themselves, they had received the survey in advance in order to prepare the detailed questions about their cooperative. The EDC team assisted the participants in filling in the survey when necessary and checked all surveys twice for gaps and inconsistencies.
3.1.2 Qualitative data collection: case studies Two case studies on agricultural cooperatives participating in the CLE were conducted with the objective to better understand the role agricultural cooperatives in Uganda have in knowledge intermediation. Case studies can provide detailed information on causal relations which might be lacking in surveys. Two cooperatives from the CLE were selected because of their involvement in innovation. Data has been collected by means of semi-structured interviews with key informants, with the purpose of finding out which functions of knowledge intermediation the cooperatives fulfilled and to identify key factors that lead to successes or challenges in knowledge intermediation. The interviews for the Mukono case study were done by the author of this thesis and those for the Kalangala case study were done in collaboration with two other CLE team members. The interviews with all key informants were based on the framework by YANG ET AL. (2014) and structured around the following topic list:
Introduction and implementation of the innovation, organizations involved in the process;
Involvement of the cooperative in learning processes for the innovation;
The vision of the cooperative on the innovation;
Relations with other organizations/actors in the AIS;
The services the cooperative provides to their members;
The services other organizations/actors provide to the cooperative’s members.
34 3.1.2.1 Case study 1: Mukono District Farmers Association The Mukono District Farmers Association (MDFA) is a multi-purpose district cooperative with around 5,000 members, located in Mukono, Mukono District. The MDFA has a federated structure with three levels. The cooperative is not engaged in collective marketing. The innovation in this case study is ‘climate smart agriculture’ (CSA), introduced to MDFA farmers in 2014 by UNFFE and EAFF. CSA is a new approach to tackle climate change while at the same time improve agricultural production of smallholder farmers. Five semi-structured interviews were held with six key informants: three farmers (two of which were ‘model farmers’), the manager of the cooperative, the chairperson of the executive board, and one extension officer of MDFA. 3.1.2.2 Case study 2: Kalangala Oil Palm Growers Association The Kalangala Oil Palm Growers Association (KOPGA) is located on Bugala island in Kalangala District. KOPGA is established to give farmers a voice in the local oil palm network. It focuses on one crop (oil palm). KOPGA has a federated structure and around 1,700 members. The innovation in this case study is oil palm production. Kalangala is the first location in Uganda were the production of palm oil has been introduced. This case study is especially relevant for CIAT and the EDC project because a follow-up project is planned with the objective to transform KOPGA into a new generation cooperative. Twelve semi-structured interviews with fourteen key informants have been held, including four farmers, two local government representatives, two members of the executive board of KOPGA, three managers of Kalangala Oil Palm Growers Trust (KOPGT), two KOPGT field officers, and the manager of Uganda Oil Palm Limited (OPUL).
3.2
Operationalization of the variables
The model for innovation intermediation from YANG
ET AL.
(2014) is a qualitative
framework used for case studies and therefore had to be converted into measurable variables that can be used for statistical analysis. As can be seen in the conceptual model (figure 2.3), the outcome variable ‘knowledge intermediation’ is conceptualized as the provision of knowledge services combined with collective investment in staff. The service provision corresponds to the three functions of knowledge intermediation and can be measured as services provided by cooperatives. The CLE questionnaire includes questions that ask about the services the cooperatives provide to their members, as well as questions about their personnel costs. All
35 questions from the questionnaire that are used in this master thesis are collected in table A.1 in Appendix A. Because knowledge intermediation consists of three functions, it is split up in three separate outcome variables, each of them having two indicators: (i) providing the knowledge service (yes or no) and (ii) having paid staff (yes or no), that both have to be fulfilled. Table 3.1 shows all variables for knowledge intermediation, how they are measured, the survey question references and type of variable. Table 3.1 Operationalization of the outcome variables. Variable
Function of knowledge intermediation
Measured as
Survey questions
Type of variable
extension
Supplying information for Provides extension/ problem solving and advisory/ information responding to users’ needs services and has paid employees (1 = yes)
A17, C18b
Dummy
training
Engaging and supporting actors in joint knowledge production
Provides training/ demonstration services to members and has paid employees (1 = yes)
A17, C18b
Dummy
advocacy
Articulating and voicing demand of users’ needs
Advocates for agricultural policies and programs on behalf of farmers and has paid employees (1 = yes)
A17, C18b
Dummy
All explanatory variables are summarized in table 3.2. The table shows the variable names, how they are measured, the corresponding CLE survey questions (see table A.1 in Appendix A), the type of variable (continuous or dummy) and the expected effect on the three outcome variables of knowledge intermediation. Some of the variables that originally have continuous values are converted into binary variables (dummy’s). For the variable marketing, the percentage of members selling through the cooperative was computed from the survey data. However, checking preliminary correlations, this variable had no relation with any of the three outcome variables. One reason could be that the values 0 and 100 are both overrepresented in the data sample. Conversion into a binary variable revealed the correlation with the outcome variables. The same is done for the variable committee (presence of internal committees for members). Based on AMPAIRE ET AL. (2013), the amount of committees is converted in to a dummy, representing if the cooperative has at least two committees.
36 Table 3.2 Operationalization of the explanatory variables. Variable
Measured as
Survey questions
Type of variable
Exp. effect
size_log
Log of the size (in number of members)
C1b
Contin.
+/-
age_log
Log of the age (in years)
ID2b
Contin.
-
Kampala
Distance to Kampala (in km)
01
Contin.
+
public_ext
Distance to nearest extension office (in km)
07
Contin.
+
marketing
Cooperative provided collective marketing at establishment (1 = yes)
A14
Dummy
+
share_log
Log of the value of shares per member (in UGX)
B84, B85
Contin.
+
fee
Annual membership fee at establishment (in 1,000 UGX)
B4
Contin.
+
committee
Having at least two committees at establishment (1 = yes)
B53
Dummy
+
meeting
Amount of general meetings during first year after establishment
B6
Contin.
+
education
% of executive board members with higher education degree at establishment
B11, B23
Contin.
+
gender
% of women in executive board at establishment
B11, B13
Contin.
-
initiative
Initiative for establishment by external organization or by founding members (1 = external organization)
A4
Dummy
-
grant
Received grant/donation for establishment (1 = yes)
A20
Dummy
-
mgm_skills
Board received training on management/financial issues (1 = yes)
B32
Dummy
+
apex
Member of a union or federation at establishment (1 = yes)
038
Dummy
+
NGOs
Amount of NGOs the coop dealt with at establishment
040
Contin.
+
For some of the variables, outliers had to be removed. One answer about the size of the cooperative was unrealistically high. Distances to the nearest extension office higher than 140 kilometers were dropped, after comparing their answers with cooperatives from the same district
37 or town. The variable meeting is based on the data from a multiple answer-question. The data represent the frequency of general meetings during the first year after establishment. The multiple choice options are converted into actual numbers. Answer option ‘three to five meetings per year’ is converted into the average of four. A disadvantage is that only the amounts of zero, one, two, four, six, and twelve meetings per year are included. When analyzing the results, it should be taken into account that the respondents had to choose the value closest to reality instead of the real number of meetings. For three variables, the natural logarithm of the original value is taken, creating size_log, age_log and share_log. The reason is that the values of those variables were very right-skewed: with a large range with numerous low values and few very high values. The logarithm brings the distribution of the variables closer to normal and improves the relation with the outcome variable; they are thus expected to improve the fit of the regression models. The main disadvantage of logged variables is the difficulty in interpreting the results (FENG ET AL., 2014).
3.3
Methods for data analysis 3.3.1 Descriptive statistics
Descriptive statistics will be used to answer the second research question: which knowledge services Ugandan agricultural cooperatives provide. For continuous variables, the mean, standard deviation, and distribution are relevant data to display. Histograms will be made to check the distribution of the continuous variables. For binary variables, the distribution of respondents that do and that do not provide the services is relevant, as well as the incidence of combinations of providing different knowledge services. Stata has an option to make a two-way or three-way table, showing how many observations there are for all possible combinations of the three knowledge services.
3.3.2 Regression analysis 3.3.2.1 Logistic regression The third research question asks which organizational characteristics of agricultural cooperatives influence their performance as knowledge intermediaries. This will be answered by logistic regression analysis, which assumes a causal effect of the values of the explanatory variables on the outcome variable. A basic regression equation looks like:
38 𝑦 = 𝛽0 + 𝛽𝑖 𝑥𝑖 + 𝛽𝑗 𝑥𝑗 … + 𝛽𝑡 𝑥𝑡 where y is the outcome variable, x1 the explanatory variable, and β1 the coefficient of the explanatory variable. β0 represents a constant. When predicting y for a given value of x in a sample of N observations, there will usually be variance in the different values for y between the observations. The differences between the predicted y and the observed y in the data sample are called deviations. In an ordinary least square regression (OLS), the goal is to determine the coefficients for all explanatory variables that minimize the squared variance of y for all observations in the sample (KOHLER AND KREUTER, 2008). Because all three outcome variables in this master thesis are binary, a logistic regression would be the obvious choice (KOHLER KREUTER, 2008; LONG
AND
FREESE, 2001; RABE-HESKETH
AND
AND
EVERITT, 2004). According to
ANGRIST AND PISCHKE (2008), also OLS regression will give a robust outcome for marginal effects on a binary outcome. However, OLS requires absence of heteroscedasticity, normality of residuals, and a linear relation between the explanatory and outcome variables. A model with binary outcome variables will not meet these requirements (LONG AND FREESE, 2001). Therefore, a generalized regression model such as the logistic regression is more robust in such cases where the strict requirements of OLS are not met. In logistic regression, y represents the likelihood that y equals one for a given value of x1. Similar to OLS, the equation contains a constant as well as coefficients for every x. Because the logistic regression follows a logistic function that is difficult to interpret, the equation can be converted into an exponential function, where P represents the likelihood that y equals one: 𝑃(𝑦𝑖 ) = 1/(1 + 𝑒 −(𝛽𝑖 +𝛽𝑗,𝑖 +𝛽𝑘,𝑖 … +𝛽𝑡,𝑖 ) ) As in the OLS regression equation, β0 represents a constant, and β1 the coefficient for variable x1. The e is the exponential function which is retrieved from converting the logarithmic function (KOHLER AND REUTER, 2008; NEWSOM, 2015). The coefficient β1 in the regression output indicates how much the logit of the outcome variable changes after x1 increases with one unit, all other variables held constant. This outcome is difficult to interpret, therefore Stata can compute the marginal effect, which is the effect of a change in x on the probability that the outcome variable equals one (LONG AND FREESE, 2001). The logistic regression produces a Pseudo R2. This cannot be interpreted as the R2 in logistic regression (which is the explained variability by the model) and
39 has to interpreted with caution. Stata uses the McFadden’s R2. A higher pseudo R2 corresponds to a higher likelihood and hence a better fit (LONG AND FREESE, 2001; UCLA, 2016A). 3.3.2.2 Likelihood ratio test It should be noted that the models for each outcome variable with all sixteen explanatory variables are initial models for the analysis. Those sixteen explanatory variables can be seen as a toolkit rather than the final model. The model fit and predictability can be improved by creating a series of reduced models, which are tested with the ‘likelihood ratio test’. This is a post-estimation test that tests if the reduced variables have a significant effect on the outcome variable. To do so, first the estimates of both the full model and the reduced model have to be saved, using the Stata command ‘estimates store’ (LONG AND FREESE, 2010; STATACORP, 2009; KOHLER AND REUTER, 2008). For the reduced model, the same observations as in the full model should be used, this should be included in the Stata command as well. The output of the test will provide a p value: the probability of the computed outcome given the null hypothesis. The null hypothesis is that the value of the coefficient of the reduced variables equals zero (implying the reduced model is correct). If this probability is below the 0.05 threshold, the chance that the outcome occurs under the null hypothesis so small that this hypothesis can be rejected. This means that the reduced model is not a better fit, because it leaves out variables with a significant effect on the outcome variables (LONG AND FREESE, 2001; BEIN, 2013). 3.3.2.3 Diagnostics for logistic regression A series of diagnostics has to be performed to check if the data is suitable for logistic regression. First, the outcome variables should be binary. Second, the explanatory variables are either continuous or nominal. Third, no relations between the observations are allowed, which can be tested with the ‘Durbin Watson test’ in Stata using a time series variable. Fourth, multicollinearity between explanatory variables is not allowed. This can be tested with correlation tables and by calculating the ‘variance inflation factors’ (VIF) (UCLA, 2016B). Fifth, all continuous explanatory variables should have a linear relation with the outcome variables. For logistic regression, the residuals should be checked for an s-shaped curve between the zero and one lines with the ‘lowess smoothing’ function (KOHLER AND REUTER, 2008). Finally, absence of significant outliers, high leverage points or highly influential points is required (UCLA, 2016B).
40
4 RESULTS 4.1
Descriptive statistics
This section presents the results of the descriptive statistics. The data sample contains 99 agricultural cooperatives from Uganda, represented by their managers or chosen leaders. 44.4 percent of the cooperatives in the sample said to provide extension services to their members. 48.5 percent provides training and demonstration services, and 30.3 percent advocate for better policies and programs on behalf of their members (see table 4.1). Table 4.1 Descriptives of the knowledge services provided by Ugandan cooperatives. Outcome Variable
N.
No (0)
Yes (1)
extension
Provides extension/advisory services and has paid staff
99
55.6%
44.4%
training
Provides training/demonstration and has paid staff
99
51.5%
48.5%
advocacy
Advocates for policies/programs on behalf of farmers and has paid staff
99
69.7%
30.3%
Table 4.2 shows how many agricultural cooperatives provide the three services combined. The table shows that there is a large overlap between cooperatives that provide the three different services, with 46 percent not providing any knowledge services at all. 40 of the cooperatives in the sample that provide extension also provide training, of which 22 also provide advocacy. This makes the share of cooperatives that provide all three services almost 25 percent. Some combinations of services show a very high correlation. The first are cooperatives who do not provide any services at all. Also extension and training show correlation: Most cooperatives that do not provide extension (extension = 0), do not provide training services either (47/55). Of those cooperatives that do provide extension, most of them also provide training and demonstration services (40/44). A crosstab of both variables with a simple chi square test shows that their correlation is significant on the 0.01 level (see table C.1, appendix C).
41 Table 4.2 Combinations of knowledge services provided by Ugandan cooperatives. extension (0)
extension (1)
advocacy (0)
advocacy (1)
advocacy (0)
advocacy (1)
Total:
training (0)
46
1
1
3
51
training (1)
4
4
18
22
48
Total:
50
5
19
25
99
Table 4.3 shows the descriptive statistics for the continuous explanatory variables. Given are the minimum and maximum values, the mean, and standard deviation. Table 4.4 shows the descriptive statistics for the binary variables. The table shows the share of cooperatives that do have that characteristic (= 1) and that do not have that characteristic (= 0). All explanatory variables have enough variation to use them for further analysis. Table 4.3 Descriptive statistics of the continuous explanatory variables. Explanatory variable
Min-max
Mean
Std. Dev.
size_log
Log of the size in amount of members
2.71 – 9.47
5.96
1.98
age_log
Log of the age in years
0 – 4.14
1.68
1.14
Kampala
Distance to Kampala (in km)
0 - 700
300.16
165.75
public_ext
Distance to nearest extension office (in km)
0 - 54
7.63
4.35
share_log
Log of the value of shares per member (in UGX)
0 – 11.23
4.18
4.35
fee
Annual membership fee at establishment (in 1000 UGX)
0 – 200
18.83
33.96
meeting
Amount of general meetings during first year after establishment
0 - 12
3.20
3.62
education
% of executive board members with higher education degree at establishment
0 – 77.78
17.48
20.25
gender
% of women in executive board at establishment
0 – 100
36.58
23.85
NGOs
Amount of NGOs the coop dealt with at establishment
0-8
1.30
1.39
42 Table 4.4 Descriptive statistics of the binary explanatory variables. Explanatory variable
Value = 0
Value = 1
marketing
Cooperative provided collective marketing at establishment (1 = yes)
28.28%
71.72%
committee
Having at least two committees at establishment (1 = yes)
57.73%
42.27%
initiative
Initiative for establishment by external organization or by founding members (1 = external organization)
57.14%
42.86%
grant
Received a grant or donation for establishment (1 = yes)
57.14%
42.86%
mgm_skills
Board received training on management/financial issues (1 = yes)
33.67%
66.33%
apex
Member of a union or federation at establishment (1 = yes)
59.18%
40.82%
Control variables. As can be seen in the histograms of the size of agricultural cooperatives in figures C.1 and C.2 (appendix C), there is a large variety in the amount of members of the cooperatives in the sample, with the data being strongly right-skewed. The mean value of size is 1819.7 members with a standard deviation of 3081.2. However, one third of all cooperatives in the data sample have less than 100 members. Also the age of the cooperatives in the sample is rightskewed, with cooperatives from 0 up to 63 years. The mean is 10.16 years, with a standard deviation of 13.72. The graph in figure C.3 (appendix C) shows that half of the cooperatives have been operational for less than five years. Last, the average distance to Kampala is 400 km and the average distance to the nearest public extension office is 7.6 km. Financial management. Most agricultural cooperatives in the sample did collective marketing at the start (72 percent). The average share value per cooperative member is 7,139 UGX, with values ranging from 0 to 75,301 UGX. The annual fees varied highly from 0 to 200.000 UGX. 32 percent of the cooperatives in the sample did not levy any annual fees at al. Participatory decision-making. On average, the cooperatives in the data sample had three general meetings for their members during the first year after establishment. 42 percent of the cooperatives in the sample had at least two internal committees at the start.
43 Leadership. On average, 37 percent of the board members at the start of the agricultural cooperatives were women. The cooperatives in the sample had an average of 17 percent of board members with a higher education degree at establishment. 40 percent, however, had no board members with a higher education degree at all. External incentives. 57 percent of the agricultural cooperatives in the sample were founded by initiative of a farmer and 43 percent were founded by initiative of an external organization. 43 percent of the cooperatives received a grant or donation to establish itself. In 66 percent of the cooperatives, one or more board members received training on management skills. Network. On average, agricultural cooperatives had relations with one or two NGOs at establishment. While one quarter of them had no relations with NGOs at all, some of them dealt with up to eight NGOs at time of establishment. 41 percent of all cooperatives in the sample were member of an apex organization (farmer union or farmer federation) at establishment.
4.2
Logistic regression
This section shows the results from the logistic regression. First, the outcomes of the diagnostics are described. After that, the results of the logistic regression of all three outcome variables will be shown in separate tables. The binary nature of the outcome variables makes a logistic regression the most suitable regression analysis. The data for the explanatory variables are chosen and converted for their suitability for logistic regression, which requires binary or continuous explanatory variables. The Durbin Watson test showed that no serial correlation can be assumed for the explanatory variables and all of the three outcome variables. The p-values of 0.601 (for extension), 0.631 (for training) and 0.090 (for advocacy) are too high to reject the null-hypotheses that there is no serial correlation. The correlations between all explanatory variables are reasonably low enough to reject the assumption of multicollinearity. The highest correlations exist between size_log and age_log (0.55), Kampala and meeting (0.46), and age_log and meeting (0.46). The VIF values confirm the rejection of multicollinearity, with VIFs ranging from 1.17 to 2.47. The linearity of the variables is not perfect. Transforming size, age and share into their log-values greatly improved their linearity, shown by s-shaped curves computed by the lowess smoothing function. Kampala and public_ext do not show linearity at all, and the other continuous variables only weakly. Outliers,
44 extreme and unrealistic data are removed. For all the three regression models, the explanatory variable share_log is removed from the full model, because it causes a distorting effect on the coefficient of some of the other variables, especially in the model of outcome variable extension. It disguised the effect of NGOs and it reduced the coefficient of age_log dramatically. Removing share_log makes the effect of age_log more coherent with the effect that it has on the other outcome variables. The reason of this effect of share_log is unknown. The variable has no notifiable correlation with any of the other variables, nor has it with the outcome variables.
4.2.1 Extension, information and advisory services Table 4.5 shows the output of both the full model as well as the best fitting reduced model of the logistic regression for extension. The columns on the left side show the coefficients, standard error and significance level for every explanatory variable in the full model. The right side columns show the coefficients, standard error, the significance level and the predicted marginal effect of the explanatory variables in the best fitting reduced model. Below that, the number of observations, the pseudo R2 and the significance level for the full model and for the reduced model are given. First, the full model for extension was run with the Stata command ‘logit’, using all fifteen explanatory variables. The model performance is good, with a significance lower than 0.01. After running the full model, a series of reduced models were made. For the reduced models, one or more explanatory variables were eliminated every time, and tested while nested in the full model, using the likelihood ratio test in Stata (command ‘lrtest’). For simplicity, only the best fit is shown in table 4.5. After the best fitting reduced model was found, the marginal changes in probability for the explanatory variables in the reduced model were computed with ‘predicted values’, shown in the lower part of the table. They show the probability that the outcome variable equals 1, given a certain value of an explanatory variable, all other variables held at their mean. Included are the discrete changes in probability after the explanatory variables change from their minimum to their maximum value. For NGOs, the table also shows the change in probability when the variable changes from 0 to 1 NGOs. For binary variables, 0 and 1 are the minimum and maximum value. Under the reduced model, the number of observations increases from 74 to 85, while the significance level remains the same. The Pseudo R2 increases from 0.545 to 0.547, which indicates that the reduced model is a slightly better fit to the data. When comparing both models, the reduced model shows the same variables to have a significant effect: size_log, age_log, public_ext,
45 initiative, apex, and NGOs. However, omitting the other variables from the model slightly changes the coefficient of the significant variables, but not their direction, and their significance levels increase in all cases. The results show positive and significant relations (p < 0.01) between the logged size and extension and between the logged age and extension. Because the logged values don’t allow for easy interpretation, the changed probability between the minimum and maximum values provide additional information. The difference in probability that cooperatives provide extension between the smallest and largest cooperatives is 0.87. The the difference in probability to provide extension between the youngest and the oldest cooperatives is 0.87. The variable public_ext has a negative and significant correlation (p < 0.05) with provision of extension services. On average, every kilometer further from the nearest extension office decreases the probability that agricultural cooperatives provide extension by 0.023, while the maximum distance in the model reduces the probability to almost 0. Initiative by an external organization has a negative and significant coefficient (p < 0.05). The predicted values show that agricultural cooperatives have a probability to provide extension of 0.55 when establishment is initatied by a farmer, compared to a probability of 0.16 when initiative is taken by an outsider, all other variables held at their mean. Being a member of a federation at establishment has a negative coefficient (p < 0.10), and decreases the probability that cooperatives provide extension from 0.50 to 0.18, all other variables held at their mean. Relations with NGOs, on the other hand, has a positive and significant marginal effect (p < 0.01), with a large change in probability (0.86) between 0 and 8 relations with NGOs. Having relations with 1 instead of 0 NGOs at the start increases the probability of extension services by 0.16. Collective marketing, annual fees, amount of general meetings, and management training have positive but non-significant coefficients. Distance to Kampala, having at least two committees and financial support have a negative but non-significant coefficient. The coefficients of education and gender composition of the board are too small to be relevant and are not signicant either.
46 Table 4.5 Results from logistic regression: Extension services. Full model extension Coefficient*
P
Reduced model extension Coefficient*
Marg. effect
P
size_log
1.037 (0.407)
0.011
0.834 (0.295)
0.191
0.005
age_log
1.250 (0.498)
0.012
1.297 (0.422)
0.297
0.002
Kampala
-0.003 (0.003)
0.384
public_ext
-0.118 (0.050)
0.019
-0.102 (0.041)
-0.023
0.013
marketing
0.162 (1.179)
0.890
fee
0.008 (0.011)
0.490
-0.579 (1.019)
0.570
0.168 (0.168)
0.317
education
-0.001 (0.040)
0.974
gender
-0.006 (0.018)
0.732
initiative
-2.508 (1.136)
0.027
-1.836 (0.817)
-0.421
0.025
grant
-0.185 (0.831)
0.823
0.645 (0.831)
0.506
-1.823 (1.072)
0.089
-1.487 (0.347)
-0.341
0.071
1.193 (0.495)
0.016
0.976 (0.347)
0.224
0.005
-7.654 (3.174)
0.016
-6.796 (2.146)
committee meeting
mgm_skills apex NGOs Constant
74
N. of observations Pseudo R2
0.545
0.002
85 0.000
0.547
0.000
Likelihood ratio test (assumption: reduced model nested in full model) LR chi2(9) = 3.00
Prob > chi2 = 0.9641
size_log
age_log
public_ext
initiative
apex
NGOs
x(min)
0.033
0.064
0.543
0.551
0.500
0.138
x(max)
0.907
0.937
0.005
0.164
0.184
0.998
Difference
0.874
0.873
-0.538
-0.387
-0.315
0.860
Predicted values
x(0)
0.138
x(1)
0.297
Difference
0.160
*Standard errors are in parentheses
47
4.2.2 Training and demonstration services Table 4.6 shows the results of both the full and the best fitting reduced logistic regression model for training and demonstration services. Under the reduced model, the number of observations increases from 74 to 91. The Pseudo R2 increases from 0.399 to 0.423, which indicates that the reduced model is a better fit for the data of training compared to the full model. The reduced model improves the significance of age_log and NGOs slightly, bringing them under the significance level of 0.05. Under the reduced model, the coefficients of size_log, age_log and NGOs have changed a bit, but their directions remain the same. Size_log has a positive and significant relation (p < 0.01) with extension. The probability that cooperatives provide training and demonstrations increases with 0.84 between the lowest and highest amounts in membership. The results for both age_log and NGOs indicate a positive and significant effect (p < 0.05) on training and demonstration provision. The effect on the outcome variable for the logged age is less strong than the effect of the logged size, with an increase in probability of 0.58 between the youngest and oldest cooperatives. Having relations with 1 more NGO at the start increases the probability of service provision with 0.16, while the cooperatives with the maximum amount of relations (8 NGOs) have a probability to provide training and extension of almost 1. The coefficients of the distance to Kampala, distance to extension, annual fees and gender composition of the board are non-significant and too small to be relevant. Marketing, internal committees, and gender composition and education level of the board have negative, nonsignificant coefficients. Initiative by an external organization and financial support also have negative non-significant coefficients. Management training has a positive but non-significant coefficient.
48 Table 4.6 Results from logistic regression: Training and demonstration. Full model training Coefficient*
P
Reduced model training Coefficient*
Marg. effect
P
size_log
0.880 (0.301) 0.003
0.751 (0.204)
0.188
0.000
age_log
0.612 (0.376) 0.104
0.645 (0.299)
0.161
0.031
Kampala
-0.003 (0.003) 0.264
public_ext
0.004 (0.044) 0.918
marketing
-0.378 (0.928) 0.684
0.801 (0.408) 0.050
0.654 (0.315)
0.164
0.038
-5.717 (2.129) 0.007
-6.447 (1.381)
fee committee meeting
0.001 (0.008) 0.916 -0.849 (0.850) 0.318 0.052 (0.133) 0.694
education
-0.007 (0.019) 0.723
gender
-0.000 (0.017) 0.993
initiative
-0.576 (0.764) 0.451
grant
-0.250 (0.733) 0.733
mgm_skills apex NGOs Constant N. of observations Pseudo R2
0.845 (0.769) 0.272 -1.053 (0.834) 0.206
74 0.399
0.000
91 0.000
0.423
0.000
Likelihood ratio test (assumption: reduced model nested in full model) LR chi2(12) = 5.93
Prob > chi2 = 0.9195
size_log
age_log
NGOs
x(min)
0.074
0.254
0.303
x(max)
0.927
0.831
0.988
Difference
0.835
0.577
0.685
Predicted values
x(0)
0.303
x(1)
0.456
Difference
0.153
*Standard errors are in parentheses
49
4.2.3 Advocacy for policies and programs Table 4.7 shows the results of the full model as well as the best fitting reduced model for logistic regression for advocacy services. The full model has a significance level < 0.05. Under the reduced model, the number of observations increase from 74 to 94. The Pseudo R2 decreases from 0.316 to 0.279. This means the reduced model fits less well than the full model, although the predictability improves (p < 0.01). Omitting the variables proved to be successful using the likelihood ratio test, but unlike the models for extension and training, the fit did not improve. For this reason, Kampala is left in the reduced model, because omitting it would further decrease the fit, without improving the predictability any further. Both the size in membership and management training have a positive significant correlation (p < 0.05) with advocacy. The effect of size on advocacy is less strong than its effect on the other two outcome variables. Still, the probability of advocacy services increases with 0.50 between the minimum and maximum amount of members. Having received management training corresponds to an increase of probability to provide advocacy services of 0.28. The probability that agricultural cooperatives provide advocacy is 0.38 when mgm_skills equals 1, all other variables held at their mean. Relations with NGOs have a positive and significant relation (p < 0.1) with advocacy, which is revealed in the reduced model. The effect is less strong when the amount of relations of NGOs changes from 0 to 1 (change in probability of 0.06), compared to the marginal effect (change in probability of 0.12). The results of age of cooperatives show no significant or relevant effect on advocacy services. The coefficients of the distance to Kampala, distance to nearest extension office, annual fees, and education level and gender composition of the board are so small that they have no relevance. Collective marketing, having at least two internal committees, initiative by an eternal organization and financial support have negative non-significant coefficients. Regular meetings and being member of a union or federation at the start both have positive but non-significant coefficients.
50 Table 4.7 Results from logistic regression: Advocacy services. Full model advocacy Coefficient*
P
size_log
0.784 (0.307) 0.011
age_log
0.054 (0.444) 0.904
Kampala
-0.003 (0.003) 0.340
public_ext
0.009 (0.035) 0.807
marketing
-0.324 (0.958) 0.735
fee committee meeting
Reduced model advocacy Coefficient*
Marg. effect
0.657 (0.178)
0.117
0.000
-0.003 (0.002)
-0.000
0.123
1.796 (0.703)
0.311
0.012
0.064
0.098
0.012 (0.009) 0.143 -0.170 (0.893) 0.849 0.184 (0.166) 0.270
education
-0.000 (0.017) 0.991
gender
-0.010 (0.015) 0.525
initiative
-0.093 (0.764) 0.903
grant
-0.152 (0.685) 0.824
mgm_skills
2.460 (1.086) 0.024
apex
0.681 (0.828) 0.404
NGOs
0.191 (0.271) 0.481
0.296 (1.418)
-8.084 (2.645) 0.002
-5.938 (1.418)
Constant N. of observations Pseudo R2
74 0.316
0.000
94 0.014
0.279
0.000
Likelihood ratio test (assumption: reduced model nested in full model) LR chi2(11) = 4.81
Prob > chi2 = 0.940
size_log
mgm_skills
NGOs
x(min)
0.110
0.108
0.184
x(max)
0.611
0.383
0.767
Difference
0.501
0.275
0.583
Predicted values
P
x(0)
0.184
x(1)
0.239
Difference
0.055
*Standard errors are in parentheses
51
4.3
Results from case studies
4.3.1 Mukono District Farmers Association 4.3.1.1 General information MDFA is a multipurpose district level farmers’ association of approximately 5,000 members, operational since 1992. They have a federated structure with 125 groups at the parish level, 25 sub-county groups, a chosen executive board, a central management, and technical staff. The MDFA has been established after sensitization by MAAIF, with the purpose to provide services and to empower farmers with knowledge. MDFA is located in the city Mukono and is therefore close to local public extension, NGOs and research institutes (SILVER NGANDA, 23/6/2016; BEN LUBEGA, 28/6/2016), as well as close to the cities Kampala and Jinja, where stakeholders such as ministries, farmer fairs, NARO, NGOs, and farmer unions are located. CSA was introduced by UNFFE and EAFF in 2014, starting as a trial for 200 farmers with current plans to further disseminate these technologies in the district. Climate smart practices implemented by the farmers are soil and water conservation and agro-forestry, such as digging contours, trapping water in the plot, mulching, and planting trees (SILVER NGANDA, 23/6/2016; EMMANUEL, 23/6/2016; RINET NALUGO, 7/7/2016). 4.3.1.2 Functions of knowledge intermediation In the process of implementing the CSA innovations at the farm level, the MDFA showed to serve all three possible functions of knowledge intermediation. The management and executive board have built a broad network with institutes from all sectors, with whom they actively lobby for farmers’ interests. They have regular contact with all groups of stakeholders. For example, they talk with contact persons at the Ministry of Agriculture to create awareness about agroforestry and the problems with deforestation faced by the farmers in Mukono District (SILVER NGANDA, 23/6/2016). The MDFA reaches out to other institutes in order to introduce new technologies, such as the climate smart stove (BEN LUBEGA, 28/6/2016). Moreover, they say to have a good reputation and they are approached by external institutes to be involved in projects to introduce new technologies, as was the case with CSA (SILVER NGANDA, 23/6/2016). All new projects are preceded by demand-driven sessions where external institutes (who initiate a project) are brought together with farmer communities to identify problems and find appropriate solutions (FRED
52 MUKASA, 7/7/2016). The MDFA also organizes study tours, for example to NARO, and they participate in competitions and farmer fairs. These events are important for lobby activities and to get into contact with support institutes to access new projects and to obtain new products (BEN LUBEGA, 28/6/2016; SILVER NGANDA, 23/6/2016). CSA is introduced to the members of MDFA through extension-linked farmers and model farmers. Those farmers were involved in a training program by EAFF, involving presentations as well as learning-by-doing in the form of plot demonstrations. There have been three trainings, each lasting four to seven days (EMMANUEL, 23/6/2016). Extension-linked farmers are a mechanism how MDFA disseminates information to the farmers. Extension-linked farmers receive training twice a year (on average), including topics such as CSA but also value addition (RINET NALUGO, 7/7/2016). They are trained at the main office of MDFA in Mukono and disseminate the knowledge to their own farmer group on a weekly basis, a little bit every time. Those farmers are chosen because of their knowledge of English, teaching skills, and their authority within their own farmer group (SILVER NGANDA, 23/6/2016; RINET NALUGO, 7/7/2016). In the implementation of CSA, the extension-linked farmers involved in the project also functioned as model farmers, each with a demonstration plot for their farmer group (EMMANUEL, 23/6/2016). MDFA has paid extension officers who have the task to visit farmers, advise them about new inputs, and respond to questions. They give information on how to use inputs and the availability of inputs, they give feedback and ask advice to input suppliers, and provide market information as well as technological knowledge (FRED MUKASA, 7/7/2016; SILVER NGANDA, 23/6/2016). The extension officers as well as the management and the executive board also received training about CSA (BEN LUBEGA, 28/6/2016). Besides, MDFA has ‘special interest groups’, each with one ‘contact farmer’. Special interest groups bring farmers together to learn about specific crops (maize, bananas, vegetables and coffee); there are four groups in total. Contact farmers are specialized in those crops and can be contacted by other farmers for advice (SILVER NGANDA, 23/6/2016; FRED MUKASA, 7/7/2016). 4.3.1.3 Factors influencing service provision The MDFA staff is paid by the collective annual fees paid by the farmers. This allows them to have well-educated management and extension officers. Criticism from farmers, however, is that there is too few staff to provide regular extension services to all farmers (RINET NALUGO, 7/7/2016). This is complemented by close collaboration with district extension officers (FRED
53 MUKASA, 7/7/2016). The cooperative is not engaged in collective marketing and has no shareholding mechanism to contribute to service provision. Lack of money for extension is complemented by close collaboration with public extension services of Mukono District (FRED MUKASA, 7/7/2016), as MDFA is an example of the tripartite model introduced in the 1990s in Uganda. Lack of financial means for extension is also substituted by farmer-to-farmer learning processes, and the MDFA managements reaches out to NGOs, apex organizations and research institutes to train farmers on new technologies, which is funded by those institutions rather than by the cooperative. MDFA sees leadership as a key to success in bringing new technologies to farmers (SILVER NGANDA, 23/6/2016; BEN LUBEGA, 28/6/2016). This relates to the good understanding between management and board (trust and division of tasks and activities) (SILVER NGANDA, 23/6/2016) and the connections and knowledge flows from and to farmers (RINET NALUGO, 7/7/2016). Both the manager and the chairperson have a central role in network building and advocating for new projects. They got approached by other organizations as well because they have the reputation to be actively engaged in projects. The personal connections other organizations have with the manager contribute to this (SILVER NGANDA, 23/6/2016).
4.3.2 Kalangala Oil Palm Growers Association 4.3.2.1 General information The production of oil palm in Kalangala started in 2005. Oil palm was a new crop for the farmers on the island. The major project partners in the oil palm production were private company OPUL and the government of Uganda (GoU), who established the Kalangala Oil Palm Growers Trust to support smallholders in the process of establishing and running oil palm production on their own plots (NELSON BASAALIDDE, 30/5/2016; DAMANIK SARADIN, 30/5/2016). KOPGA was established after farmers demanded to be able to exert more influence in the decision-making of the palm oil project. Efforts to establish KOPGA were led by an external organization: IFAD (MARTIN LUGAMBWA, 6/6/2016). Kalangala District is an island group in Lake Victoria, and oneday traveling from Kampala. The district has its own agricultural department and extension system. 4.3.2.2 Functions of knowledge intermediation KOPGA does not serve all functions of knowledge intermediation. The function they do serve is aggregating and voicing the needs of their members. Empowering farmers was the main
54 purpose of establishing KOPGA and they have a federated structure to enable farmers’ concerns, needs and questions to flow to the executive board. From there, they can be communicated to KOPGT, OPUL and other stakeholders in the oil palm project (MARTIN LUGAMBWA, 6/6/2016). One of KOPGAs achievements is the involvement of farmers in the pricing committee, which was a non-transparent process (MARTIN LUGAMBWA, 6/6/2016). Extension services are provided by KOPGT and public extension officers. KOPGA plays a role in mobilizing farmers for training sessions. Also, they communicate information about prices, transportation, loans and information from the KOPGT board meetings. KOPGA has no extension officers and does not provide any technical or production-related information (NELSON BASAALIDDE, 30/5/2016; MARTIN LUGAMBWA, 6/6/2016; CHARLES LUMAGI AND SAVERI SEVAME, 2/6/2016). 4.3.2.3 Factors influencing service provision KOPGT is a temporary institute and for the sustainability of the oil palm project, KOPGA will take over the tasks and responsibilities of KOPGT after the financial support of IFAD ends in 2018. This includes maintaining communication with stakeholders in the network and providing extension services and organizing training (MARTIN LUGAMBWA, 6/6/2016). However, at this time KOPGA does not provide this services at all and faces challenges in taking over these responsibilities (NELSON BASAALIDDE, 30/5/2016). Compared to KOPGT, KOPGA does not have the same capacity in educated management and staff that KOPGT has to provide knowledge services (MARTIN LUGAMBWA, 6/6/2016). The success of KOPGA to fulfill these knowledge services lies in the possibility of taking over the same staff and management after KOPGT ceases to exist (NELSON BASAALIDDE, JOSEPHINE, AND NELSON TEBUKOZZA, 8/6/2016). Besides, KOPGA will need to establish an income generation mechanism. KOPGA used to levy annual fees, but after trust among farmers had been distorted, farmers refused to pay (MARTIN LUGAMBWA, 6/6/2016). An opportunity for income is retaining part of the collective marketing of oil palm fruits to OPUL (NELSON BASAALIDDE, JOSEPHINE,
AND
NELSON TEBUKOZZA, 8/6/2016). Also the
aggregation and voicing of farmers’ demands by KOPGA faces some challenges. Attendance of meetings (of the general meetings as well as extension meetings with KOPGT) has decreased since they do not provide lunch anymore (ENID TKYONGYEIRWE, 2/6/2016, personal conversation). Farmers do not clearly distinguish KOPGA from KOPGT. KOPGA appears to be less visible and farmers do not always know which organization to approach if they have specific needs or questions (CHARLES LUMAGI AND SAVERI SEVAMI, 2/6/2016). Although KOPGA has become more
55 and more involved in the oil palm project, their network in the AIS is not as large and their relation with other actors not as strong as those of KOPGT. The network is maintained through professional (and personal) relations with the manager or chairperson. The network skills of KOPGAs chairperson, however, are limited compared to the KOPGT manager and their network is less extended as well. Contrasting to MDFA, who connects to a variety of stakeholders to access knowledge, technology and funding, KOPGA has the private investor OPUL as its main source for production and technology related knowledge. Table 4.8 Functions of knowledge intermediation provided by MDFA and KOPGA. Mukono District Farmers Association
Kalangala Oil Palm Growers Association
(1) Articulating and voicing demand of users’ needs
Giving farmers a voice is one of the main goals of the MDFA. Farmers are represented by the board of farmer groups to the central board and management. Leaders at the parish level have direct contact with the board and management.
KOPGA is founded with the main objective of giving oil palm farmers a voice in the oil palm production process vis-à-vis the private oil producer and the government institute KOPGT. They represent the farmers in the Manager and chairperson are active project and got integrated in the pricing process. in connecting to other stakeholders in the AIS to link the MDFA to projects such as CSA and lobby at the ministry level for farmers’ interests.
(2) Supplying information for problem solving and responding to users’ needs
MDFA has paid staff (extension officers) who visit farmers and give advice and information about availability of inputs, market information, as well as technological information.
(3) Engaging and supporting actors in joint knowledge production
Organized training sessions for extension-linked farmers. Organized participatory and demand-driven session with NGOs and research organizations with farmers to find solutions for farmers’ problems and to provide knowledge and technology.
KOPGA is involved in mobilizing farmers for training and demonstration sessions, but is not engaged in knowledge production.
56
5 DISCUSSION In this section, the most important results from the descriptives, regression and case studies will be discussed, followed by the limitations of the research regarding the theory, data sampling and data analysis. Last, this section discusses how the research results contribute to addressing the research questions and research objectives of this master thesis.
5.1
Discussion of the main results 5.1.1 Descriptives
The descriptive statistics show that in Uganda all three knowledge services are provided by agricultural cooperatives to their members. As expected, many cooperatives do not provide the whole package of services. Instead, some combinations of services are more likely to be observed than others. The first group contains cooperatives that do not provide any services at all. In the data sample, one can see that the paid staff requirement filters out the 31 smallest cooperatives. A plausible explanation is that a low amount of members does not allow for sufficient income generation from membership fees or marketing. This does not imply that the smallest cooperatives do not provide services at all, because they can also be provided by unpaid members, as can be seen in both case studies. In Kalangala, for example, board members channel concerns and questions of their members and advocate for their interests. This underlines one of the weaknesses of the model: providing a service does not say anything about the quality of the service, nor how many members receive this service. Having paid staff does not necessarily mean that service provision is better, nor can it be known to which activities employees are assigned. In Mukono, interviews with farmers revealed that the cooperative has not enough extension staff to visit all farmers on a regular basis, while extension-linked farmers might be skilled to provide adequate extension to their fellow farmers. The second combination consists of agricultural cooperatives that provide both extension and training. The correlation is significant. This could mean two things: one explanation is that cooperatives tend to provide both services as one ‘service package’. The second explanation is that the respondents of the survey hardly distinguish between the interpretation of both services. In both cases, one could expect that regression will provide similar results for both outcome variables.
57
5.1.2 Discussion of the regression results 5.1.2.1 Control variables Size. The amount of members of agricultural cooperatives has positive and significant correlations with the probabilities that they provide all three knowledge services. Although logged variables are difficult to interpret, the differences in probability between the minimum and maximum values show there is a very strong effect: namely that larger cooperatives are more likely to provide the three knowledge services to their members. Because size refers to the current amount of members, a causal relation cannot be assumed in this case. According to the literature, this result was expected because cooperatives who grow over time experience increased heterogeneity in members’ preferences. This is an incentive for farmers to reduce risks and to invest in assets or human resources to reduce or cope with those risks. A second explanation is that smaller cooperatives simply do not have enough income to pay for staff members. Alternatively, cooperatives who provide knowledge services might attract more members and hence grow larger. Age. The number of years that cooperatives have been operational has a significant and positive relation with the probability that cooperatives provide extension and the probability that cooperatives provide training and demonstrations. A causal relation can’t be assumed, but these results show that older cooperatives are more likely to provide extension, training and demonstration services and have paid staff. Intuitively, one could assume that older cooperatives have more members and therefore more income to pay for services, but the logistic regression model controls for the influence of size. Instead, the correlation can be explained by the experience and embedded knowledge that cooperatives have built up over the years. This contradicts some articles in the review that found a negative correlation between age and performance. The relation between age and performance might depend on the stage of the life cycle of the cooperative, leading to an alternative explanation: Cooperatives that provide knowledge services and have paid staff might get older because they do better in addressing members’ needs, motivating members to commit to the cooperative for a longer time. Age has no significant relation with providing advocacy services, suggesting advocacy is provided equally by older and younger cooperatives. Distance to nearest extension office. The results show a negative significant relation between the distance to extension offices and extension provision. A causal relation can be assumed, because service provision is unlikely to influence the geographical location of
58 cooperatives. This implies that the closer to the nearest extension office cooperatives are, the larger is the probability they provide extension services to their members and have paid staff. Some of the studies in the literature review (WOUTERSE
AND
FRANCESCONI, 2016; FRANCESCONI
AND
WOUTERSE, 2015B) found that larger distance to markets leads to more provision of certain services, because those markets are more difficult to access. Following this logic, the opposite effect was expected for the distance to extension. However, the results in this master thesis strongly suggest that proximity to public extension stimulates extension service provision to farmers, making knowledge exchange and collaboration easier. Collaboration between cooperatives and public extension has been promoted in Uganda as the tripartite model (KWAPONG, 2012), as in Mukono, where the cooperative staff coordinate their actions with public extension staff to make the services from both institutes coherent and complementary (FRED MUSAKA, 7/7/2016). No significant effects of the distance to Kampala are found on knowledge services provision. This implies that proximity to local institutes is more important for extension provision than to those on the national level. However, in Mukono, proximity to large cities such as Kampala and Jinja enhanced relations in the network of the cooperative, leading to opportunities of knowledge exchange, such as farmer excursions to the NARO research station. 5.1.2.2 Financial management For financial management, no significant effects on any of the outcome variables are found in this research. This is surprising, especially because cooperatives having income from collective marketing or annual fees are expected to be more likely to have paid staff. A possible explanation is the fact that the explanatory variables refer to the time at establishment. A closer look at the dataset shows that collective marketing at establishment might not be a good indicator for collective marketing now. This is illustrated by the fact that 36 percent of cooperatives with no collective marketing at the start did so later in their life cycle, while 23 percent of the collective marketing cooperatives at the start have stopped this activity. Moreover, the data on collective marketing is converted into the binary variable marketing, because this improved the relation with the outcome variables during the diagnostics. However, marketing now does not take into account side-selling or the share of members’ total produce sold through the cooperative. Although the results for marketing and fee are not significant, financial management was found to be of importance for service provision in both case studies. In Mukono, a lack of income generation is
59 perceived by the MDFA as an important barrier for service provision. Collective marketing could then be a way to improve their financial situation. Also in Kalangala, income generation has been a challenge for KOPGA. They started with annual membership fees, but the farmers lost their trust and willingness to pay contributions. When KOPGA has to take over the responsibilities of KOPGT, the most important challenge will be to retain a part of income from oil palm to pay for transportation, human resources, and other services (BEN LUGAMBWA, 6/6/2016). 5.1.2.3 Participatory decision-making The results show no significant effects of participatory decision-making on any of the outcome variables. Possibly, the wrong indicators could have been chosen in the conceptual model. The data on elections showed small variability and were therefore excluded from the regression models. Also, quantitative data on committees and participation rates do not necessarily reflect if members actually have a voice in decision-making. Also the interviews in the case studies did not adequately capture this aspect either. Based on the results of this research, no influence of the amount of committees or attendance rates on knowledge intermediation can be assumed. 5.1.2.4 Leadership No significant relations of any of the leadership variables with the three outcome variables are found. Both the gender composition and education level of the leaders have negative coefficients, but the effect is too small to be of any relevance. Based on these results, one can assume that the gender composition as well as education level of the board at time of establishment have no effect on the probability that cooperatives provide knowledge services. This contradicts the expectations based on the articles by HELLIN (2012) and WOUTERSE AND FRANCESCONI (2016). Possibly, lack of education at the board is substituted by hiring a manager or coordinator, as could be observed in Mukono, where the MDFA hired a coordinator from outside the cooperative who is responsible for managing the daily operations (SILVER NGANDA, 23/6/2016). In Kalangala, stakeholders mentioned the lack of education and experience of the KOPGA board in managing a large organization as one of the main challenges. For KOPGA, hiring an external manager is seen as the best way forward after the IFAD funding has stopped (MARTIN LUGAMBWE, 6/6/2016).
60 5.1.2.5 External incentives Initiative by an external organization. The results show that being established by initiative of an outsider has a significant negative effect on the probability that agricultural cooperatives provide extension and have paid staff. This confirms the expectations, based on articles such as WOUTERSE AND FRANCESCONI (2016), that found cooperatives to be healthier in the long run when the initiative for establishment comes from farmers themselves. Farmer initiative implies an internal motivation to cooperate, while initiative by an external organization might cause the motivation to be related to a support program. Although external support might help cooperatives with their start-up, it can be detrimental for farmers’ commitment on the long run. An alternative explanation is that contact with a support organization might crowd out extension services. The presence of KOPGT, which has both the financial means as well as experienced staff to provide extension and training, makes extension services by KOPGA superfluous. Management training. The results show that training in management skills has a significant positive correlation with advocacy services. A causal relation cannot be derived, because the dataset does not provide information about when the training has been received. Therefore, the following can be concluded: Agricultural cooperatives whose board received management training are at the same time more likely to advocate. This could imply that a higher likelihood of advocacy activities is the result of received training. Alternatively, hand, a plausible explanation could just as well be that board members receive management training as a result of their advocacy activities. In Mukono, the latter was observed: The chairperson and the manager both actively reach out to organizations in order to access projects and training programs, such as introducing climate smart stoves to their farmers (BEN LUBEGA, 28/6/2016; SILVER NGANDA, 23/6/2016). The results show no significant effect of financial support at establishment on the three outcome variables. However, the effect is consistently negative for all three variables. This is consistent with the research by FRANCESCONI AND WOUTERSE (2015A, 2016B), who found that external support might hinder financial autonomy of cooperatives. Still, these findings cannot be confirmed based on the results of this master thesis. 5.1.2.6 Network Membership of union or federation. Being a member of a union or federation at establishment decreased the probability of a cooperative to provide extension services from 0.50
61 to 0.18. Because union membership refers to the situation at establishment, it can be concluded that it has a negative effect on agricultural cooperatives providing extension services and having paid staff, although the significance is moderately convincing (p < 0.1). Union membership has a similar relation with training and demonstration (although not significant), which supports the finding that the variables extension and training show large overlap. Union membership has a positive correlation with advocacy services - although not significant - as expected. The negative effect on extension, however, contradicts the expectations from i.e. YANG
ET AL.
(2014). In
Mukono, being a member of UNFFE was the reason the farmers of the MDFA got involved in the CSA program (SILVER NGANDA, 23/6/2016), which illustrates how contact with an apex organization can be beneficial. The results of this master thesis, however, give rise to concerns about the legitimacy of such umbrella organizations. ACTIONAID (2013) provides examples of corruption in cooperative unions in Uganda, with a long history of government involvement since the 1960s. National unions often have strong relations with the government, such as the MAAIF and MITC, which suggests that apex organization might aim more at gaining influence in the political and institutional context than at in capacity building at the farmer level. Relations with NGOs. The variable NGOs has significant and positive relations with all three outcome variables. Because the data refer to the situation of the cooperatives at time of establishment, a causal effect can be assumed. Therefore, it can be concluded that relations with more NGOs at start-up has a positive effect on the probability that cooperatives provide all three knowledge services and have paid staff. Especially the effects on extension and training and demonstration services are strong. These results confirm what was expected based on articles about AISs that stress the importance of relations within the network of cooperatives (e.g. YANG ET AL., 2014; ORTIZ ET AL., 2013). NGOs can support newly established cooperatives in several ways, including setting up the organization, providing training and advice, and connecting them to markets. NGOs often introduce new production techniques to the farmers in cooperatives, which is knowledge that can spill over to the cooperative staff. The MDFA, for example, has relations with different support organizations – including NGOs - that engage farmers, management and leaders into new farming practices, techniques, and inputs (BEN LUBEGA, 28/6/2016; SILVER NGANDA, 23/6/2016; FRED MUKASA, 7/7/2016). In the case of Kalangala, however, the NGOs operational on the island do are not involved in any activities related to extension or training.
62 Instead, they have reached out to the farmers to warn them from the harmful effect of oil palm production, but they have no collaborative relation with KOPGA (MARTIN LUGAMBWE, 6/6/ 2016).
5.2
Limitations of the research 5.2.1 Conceptual model
An important limitation of the model is that the indicators of service provision simultaneously try to capture aspects of knowledge intermediation and of cooperative performance. Therefore, there is a risk that the outcome variables do not reflect any of them sufficiently. Both performance as well as knowledge intermediation are complex concepts, which can be captured more comprehensively by qualitative methods rather than by quantitative methods. ‘Aggregating and voicing users’ needs’, for example, is operationalized as advocacy services, leaving out how farmers can voice their needs and concerns in the cooperative for simplicity reasons. The strong correlation between the variables extension and training indicate that training probably does not distinguish itself enough from extension. This shows that the model fails to accurately reflect the difference between those two functions of knowledge intermediation. Of course, the conceptual model of this master thesis is an attempt to convert the knowledge part of the qualitative model for innovation intermediation to use it in a quantitative model. Even though in future research the model could be refined, some loss of information is inevitable in the conversion of qualitative to quantitative models. This has to be accepted and ideally be complemented with other sources of data, such as case studies.
5.2.2 Data sample The data collection for this master thesis took place during the four-day CLE in Uganda, where surveys of 99 agricultural cooperatives were collected. To be able to achieve this within this limited time frame, the respondents filled in the questionnaires themselves. This might have led to misinterpretation of the questions and answering questions incorrectly. Assistance at filling in the survey and thorough checks by the EDC team were aimed to prevent this, but despite these measures, some errors were still present in the survey and had to be removed. This might indicate that the dataset still contains undiscovered incorrect answered questions.
63 The moment of data collection challenged the adequacy of the dataset for the research questions in this master thesis. Data collection of both the surveys and the interviews in the case studies took place in May and June 2016, but the final literature review and the conceptual model were only finished some months later. Because of this, important questions necessary for the model might not be incorporated in the interviews. For example, questions about management training at the start are not present in the survey. Besides, information about the characteristics at establishment at the Mukono case study are missing. The total number of observation in the quantitative data sample is 99 agricultural cooperatives. This is a sample large enough to perform regression analysis, although a larger data sample could possibly reveal more significant effects than now have been found. The selection method causes a possible bias in the sample: Many cooperatives in SSA are not operational and it can be assumed that those ‘dormant’ cooperatives are also present in Uganda. These dormant cooperatives are not represented in the sample. However, this bias has obvious merits as well, because it represents the target group of the EDC project. Besides, the sample contains a large variety of cooperatives in terms of region coverage, crops produced, size and age. Another limitation of the data sample is that there are only two case studies. More could have been learned from a larger number of case studies, which could better reflect the diversity of agricultural cooperatives in the quantitative data sample. With more case studies, ideally with interviews that are structured enough to allow for comparison, it would have been possible to look into more detail to differences and similarities in knowledge intermediation between cooperatives with different characteristics, such as size, structure, and specialization. The quantitative data sample contains cooperatives with only zero members and cooperatives who have been operational for less than two years. For these cooperatives, there is little or no difference between time of establishment and time of data collection. Even though regression analysis controls for age and size, the questions referring to establishment represent a different moment in time for every cooperative. This has to be taken into account when interpreting the results, because for cooperatives that are so young, it is less likely to find any effects, simply because they might need more time to set up service provision.
64
5.2.3 Data analysis The conceptual model for this master thesis contains a selected amount of explanatory variables from the literature. Still, a total of 15 explanatory variables is a lot for a regression analysis with 99 observations. Due to missing data, for the full model only 74 observations remain. Running a regression with too many variables for the amount of observations might therefore be biased due to overfitting. Although this can improve the model predictability, there is a risk that redundant variables influence the coefficients of other variables. For this very reason, the variable share_log was removed from the model. The problem of possible overfitting was reduced by backward selection, which left only three to six explanatory variables in the models. Before running the regression analysis, diagnostics were performed to check if the data was fit for logistic regression. Although most assumptions were met, not all explanatory variables had the required relation with the outcome variables. This was partly solved by taking the natural logarithm of the size and the age of cooperatives. This changed the distribution of both variables from strongly right-skewed to one that approaches normality; also the relation with the outcome variables now also approaches linearity. The log-transformation also improved the fit and the predictability of the three logistic regression models. Post-estimation of the minimum and maximum values of the predicted probability of the logged variables partly made up for the difficulties in interpretation. Despite the linearity problem in the data sample, it was still chosen to perform a logistic regression instead of a more generalized model. Interpretation of the coefficients of especially the continuous variables should therefore be done with caution, because these are most accurate when the relation with the outcome variables is linear. When the relation shows a curve, for example, coefficients in a linear model could understate the actual effect, or perhaps not reveal any effect at all. In the future, possibilities for applying other generalized regression models could be explored. Linearity is also desirable for using predicted values to interpret the results of logistic regression. Especially marginal values are most accurate when there is a linear relation. Since this criterion is not met for some of the continuous variables, caution with interpretation is required. For the binary variables, this does not apply. Still, when looking at an important variable as NGOs, the change in predicted probabilities clearly shows an increase in probability when having more
65 relations. Despite the problems with linearity, computing marginal and discrete changes gives much better insight in the effect of variables than the coefficients of logistic regression. The results show that the likelihood ratio test was useful in finding the best fitting reduced model with backward selection. However, using this method is not without its risks. Many variables were omitted without reducing the models’ fit, illustrated by the pseudo R2. This output does not accurately reflect the part of variance that the model explains, but nevertheless does a higher value represents a better fit of the model to the data. For some of the omitted variables in the advocacy regression model, the likelihood ratio test showed they could be removed successfully. Still, the pseudo R2 indicates that the overall fit of the model has decreased. Single variables such as share_log are able to exert large influence on the coefficients of other variables. Omitting such variables might improve the model, especially when analyzing the effect of individual predictors. However, the possibility remains that these kind of variables do have a true interaction with other variables. This is an insecurity that has to be accepted when applying backwards selection to large regression models.
5.3
Reflection on research questions and objectives
The first research question of this master thesis asks which factors can be found from the literature on agricultural cooperatives and AISs that influence the performance of cooperatives as knowledge intermediaries. The literature review identified similarities in factors that are expected to influence knowledge intermediation, but also brought new variables together and therefore the two strands of literature complemented each other. For example, knowledge intermediation is operationalized as the combination of knowledge services with collective investments as a performance indicator. Although the selection of variables potentially leaves out predictors for knowledge intermediation, the first research objective has been fulfilled in the form of the conceptual model in figure 2.3: a framework to measure the performance of cooperatives in providing knowledge services in a quantitative way. The second research question of this master thesis asks which functions of knowledge intermediation Ugandan cooperatives fulfill. This is answered by descriptive statistics and two cases studies. The operationalization of knowledge intermediation in this research has important implications for the results. First, the research does not adequately capture the complexity of the
66 three functions of knowledge intermediation as described by YANG ET AL. (2014). Most important, the outcome variables fail to represent the interactive function of ‘joint knowledge production’ and the variable advocacy fails to represent the ‘aggregation of farmers’ needs’. Second, because of the choice to combine service provision with having paid staff, this research tries to capture a part of what cooperative studies call ‘health’ or ‘performance’. However, this means it leaves out the services provided by cooperatives that do not have staff. The two case studies show how intermediary functions were performed by unpaid members on a regular basis, by a diversity of internal structures for information transfer. The second objective is therefore partly fulfilled: to gain more insight in the provision of knowledge services and the organizational design of cooperatives in developing countries. The descriptive results do show us how many cooperatives do show specific combinations of services, but the most important limitation of this quantitative research is that is does not provides insights in the quality of these services and the effects these services have on their members. Last, a randomized sampling method was not possible in Uganda. Although the sample selection is organized in such a way to represent the variety of cooperatives operational in Uganda, the degree of generalization is therefore limited. The third research question of this master thesis asks which organizational characteristics of agricultural cooperatives influence their performance as knowledge intermediaries. Here, it should be noted that having paid employees is not a sufficient indicator for cooperative performance. The collective investment in human resources does say little about cooperative performance in a broader sense, which can be understood in many ways, depending on the research perspective. Nor does it indicate to which tasks employees are assigned to. Ideally, additional to quantitative data, more qualitative data of a large amount of cooperatives would be needed to be able to make conclusions on how cooperatives ‘perform’ as providers of knowledge services. This having said, it can be concluded that this research does only partly answer the third research question. Still, when cooperatives do provide knowledge services and have paid employees, this can be considered a first necessary step towards fulfilling the functions of knowledge intermediation. It is reasonable to assume that the variables influencing the probability of service provision are also able to influence the quality and appropriateness of the services. Additional research would be needed to further explore this statement. The results from the logistic regression show us a few remarkable things. As discussed extensively in the theoretical background, innovation is a product of the relations and exchanges
67 between different actors from different domains. The results in this master thesis seem to confirm this theory: All explanatory variables with significant effects on knowledge services provision are aspects of the AIS of cooperatives (proximity to extension, relations to apex and NGOs, outsider initiative and management training), with exception of age and size. This strongly suggest that not only the amount of connections in the AIS, but also the type of relations with other actors influence how cooperatives fulfill their role as knowledge intermediaries, especially for extension services. The most interesting findings regarding to voicing and advocacy, however, can be extracted from the case studies. They show how cooperatives are ‘information machines’ with internal structures, not only to disseminate knowledge to farmers, but also how information from farmers moves upwards to the management. For example, the manager of MDFA gives feedback to input suppliers about the inputs (seeds, agro-chemicals) that they sell to farmers. The information that farmers provide about on-farm issues (such as feedback on products and techniques, information about soil quality, and about production challenges and successes), is highly relevant for a diversity of stakeholders: For private companies to improve their products and sales due to selling the appropriate products to farmers, governments and public extension can use on-farm data to improve agricultural development in the country, and for NGOs wo aim at improving livelihoods of farmers. In this context, on-farm data should be seen as a product that has a real value, as explained by RASMUSSEN (2016). He explains that farmers should not give their data away for free, from a business perspective as well as from a privacy perspective. In the USA, Monsanto uses farm data to design precision agriculture practices, transforming themselves into a ‘big data’ company (HUFSTUTTER AND GILLAM, 2015). The Grower Information Services Cooperative (GISC) is an example from the USA of farmers who have organized themselves into cooperatives in order to demand payments for their data from Monsanto or other companies. The data of one single farm will not be of much value, but accumulating the data from hundreds of farmers will highly increase the price (SYKUTA, 2016). Data management could at the same time have benefits for the cooperative members as well (BOYERA
AND
ADDISON, 2017;
SCHIMMELPFENNIG, 2016). Both can be economic incentives for farmers to establish cooperatives bottom-up, as opposed to external interference at their start-up, and a way to generate income for management and staff which is necessary to organize large amounts of data. This in turn will then lead to better performance as knowledge intermediaries in the long run as well.
68 Based on this research, practitioners, including leaders and managers of agricultural cooperatives who aim to strengthen the intermediary role of cooperatives, can be advised to aim at building relations between cooperatives with a diverse set of supporting institutes, especially NGOs. This knowledge could be part of training programs for cooperative leaders and managers, such as the Cooperative Leadership Events of the EDC project. Awareness about the opportunities of data management should be brought under their attention. Practioners of support organizations should be cautious with initiating the establishment of cooperatives, but instead focus on giving support to farmers who form cooperatives themselves. This will ensure to select those cooperatives who have an inherent economic motivation. This is in line with scholars who found that autonomously formed cooperatives are more sustainable over time. Governments can use this knowledge to strengthening the AIS by further supporting collaboration between cooperatives and public extension. They should facilitate a better coverage of public extension offices, to enhance knowledge exchange and extension provision by cooperatives. The knowledge intermediation cooperatives serve with unpaid members implies that their functioning can even be more improved when cooperatives can expand their employee base: For example, by hiring a manager and extension staff. Also ‘regular farmers’ who are skilled at knowledge dissemination could be paid for their extension work, to avoid competition with farming activities. A support strategy for Uganda could thus be to enable cooperatives to hire management and staff, and to make sure these are well-educated on how to manage agricultural cooperatives, especially on how to design internal rules and structures in order to ensure collective investment and knowledge intermediation in the long term. The role of apex organizations is widely discussed among development practitioners, but underrepresented in the literature. Research should therefore aim to reveal the underlying reasons for the negative effect of national unions and federations on the service provision of cooperatives. Besides, further research could aim to find out what the effects of relations with other actors in the AIS are, for example the difference between private and public sector, and the type of relationship. Further research could also do more in-depth and qualitative research to the functioning of cooperatives as knowledge intermediaries, and find out what the relation is between (economic) performance of cooperatives and knowledge intermediation. Last, the opportunities for agricultural cooperatives in developing countries in data management, especially their effect on performance, is a timely and relevant topic for further investigation.
69
6 CONCLUSIONS Agricultural cooperatives are considered a key factor in innovation uptake by smallholder farmers in developing countries, fulfilling the role of knowledge intermediaries in bridging the demand of farmers to suppliers of innovation. However, the performance and hence service provision of cooperatives in developing countries is highly variable, which can partly be attributed to the organizational design and incentives during their establishment phase. The objectives of this master thesis are to (1) develop a quantitative framework to measure the performance of agricultural cooperatives in providing knowledge services; (2) gain more insight in the provision of knowledge services by agricultural cooperatives in developing countries; and (3) identify which factors influence the performance of Ugandan agricultural cooperatives as knowledge intermediaries. Quantitative survey data of 99 agricultural cooperatives in Uganda are complemented by qualitative data from case studies. Knowledge intermediation is measured as the provision of extension, training, and advocacy services combined with collective investments in paid staff. Almost half of the cooperatives don’t provide any knowledge services at all, likely to be too small to bring up the costs for paid staff. Almost the other half of the cooperatives provide both extension and training, while thirty percent advocate for their members’ interests. Three logistic regression models are developed, using a backward selection method to find the best fit and predictability for every model. The model performance of the full and reduced models is tested with the likelihood ratio test and post-estimation tests - marginal and discrete changes in predicted probability - are applied for interpretation of the results. The results show the following significant effects:
More relations with NGOs and closer proximity to public extension have positive effects on the probability that Ugandan cooperatives provide extension and have paid staff. Initiative by an outsider and membership of an apex organization both have negative effects on the probability that Ugandan cooperatives provide extension and have paid staff. Extension services are also positively correlated with the size and age of cooperatives.
More relations with NGOs have a positive effect on the probability that Ugandan cooperatives provide training and demonstration and have paid staff. Training and demonstration is also positively correlated with the size and age of cooperatives.
70
More relations with NGOs has a positive effect on the probability that Ugandan cooperatives advocate for their members and have paid staff. Advocacy is also positively correlated with a larger size in membership and training on management skills of the board. The research of this master thesis has a few important limitations for the interpretation and
extrapolation of the results. The first is the lack of randomized sampling, which gives reasons to be cautious with generalization. The second is the lack of linearity between distance to extension and relations with NGOs and the outcome variables. The predicted changes shouldn’t be interpreted literally, but merely indicate the direction and strength of the effects of these variables. Third, the three outcome variables don’t accurately reflect the complex concepts of knowledge intermediation and cooperative performance, especially qualitative information about the quality and adequacy of knowledge services provided by cooperatives would complement this research. Despite these limitations, some important conclusions can be made. The results are clear indicators that the AIS of cooperatives at time of establishment is influencing the knowledge services that they provide later in their life cycle. Moreover, the provision of knowledge services to members is a first important step for cooperatives towards knowledge intermediation. This master thesis can be concluded by sketching an ideal picture of the AIS of cooperatives: Ideally, they are located close to public extension offices to enhance knowledge exchange and collaboration in extension services. Relationships with NGOs are beneficial for all knowledge services, but initiative for establishment should come from farmers. The negative effect of national union membership shows that cooperatives should choose their connections with care. For development practitioners, cooperative leaders and governments, it is recommended to support and improve relations between cooperatives and public extension and NGOs. Support should specifically focus on cooperatives after establishment. Extension services should have a broader coverage in remote areas in Uganda to support collaboration and knowledge exchange in order to improve cooperatives’ role in knowledge intermediation. Research should further explore the influence that different types of relations have on the performance of cooperatives and critically investigate the ambiguous role of apex organizations. Finally, Ugandan cooperatives and their internal knowledge transfer structures show how they fulfill the important role as knowledge intermediaries. Especially in the context of big data, the potential of cooperatives in developing countries for farm data management is a salient topic worth further investigating.
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DECLARATION OF ORIGINALITY I hereby declare that the present thesis has not been submitted as a part of any other examination procedure and has been independently written. All passages, including those from the internet, which were used directly or in modified form, especially those sources using text, graphs, charts or pictures, are indicated as such. I realize that an infringement of these principles which would amount to either an attempt of deception or deceit will lead to the institution of proceedings against myself.
March 1, 2017
Marianne Helena Poot
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APPENDICES Appendix A. Survey questions Table A.1 Overview of selected question from the CLE Uganda 2016 survey. Question
Answer options
ID2
When was your coop established? (specify the date in which the coop started to operate)
a. Month b. Year
01
How far is the coop from Kampala? (Put zero if the coop is located in Kampala)
Km
07
How far is the coop from the nearest extension office of the Ministry of Agriculture?
Km
038
Was the coop originally established as a member of a regional or national farmer union?
0. No 1. Yes
040
How many NGOs did the coop deal with at the start?
N.
A4
Who introduced the idea to establish the coop?
0. Somebody who was, or still is, a member of the coop 1. An outsider (from government, extension services, farmer union, NGO, private company, etc.)
A14 Which services was the coop established to provide? (Multiple answers are possible)
14. Collective marketing
A17 Which services is the coop providing today?
4. Advocating agricultural policies/programs on behalf of farmers 5. Extension/advisory/ information services 6. Training/demonstrations
(Multiple answers are possible)
A20 Did your coop receive any grant to establish itself?
0. No 1. Yes
B4
Ugandan shillings per member
What annual membership fee did your coop charge to its members at the start? [Put zero if the coop charges no annual membership fee]
82 B6
How often did the general assembly meet during the first year after establishment? [Answer this question only if the coop is at least two years old. Only one answer is possible, the answer does not have to be exact, just pick the closest one to reality]
0. 1. 2. 3. 4. 5.
Never Once Twice 3-5 times Every other month Monthly
B11 Total numbers of board members at the start:
N.
B13 Number of female board members at the start:
N.
B23 Number of board members with tertiary diploma or university degree at the start
N.
B32 Have any of the board members been trained or received professional advice on book-keeping, accounting, finance, marketing and/or business planning over the past 12 months?
0. No 1. Yes
B53 How many internal committees did this coop have at the start?
N.
B84 What is the total number of equity shares sold by your coop to its members? [Put zero if members hold no shares.]
N.
B85 What is the current value of one share?
Ugandan Shillings
C1
a. At establishment b. Now
How many members in your coop?
C18 Total value of salaries paid to employees during last month (in Ugandan Shillings):
a. At establishment b. Now
83
Appendix B. Case study interviews Bafiirawara, Maurice (6/6/2016): Interview Maurice Bafiirawara, environmental officer KDLG. Kalangala, Kalangala District, Uganda. Waveform Audio File Format, Word file. Balironda, David (5/31/2016): Interview David Balironda, Head of the Agricultural Production Office, Kalangala District Local Government. Kalangala, Uganda. Waveform Audio File Format, Word file. Basaalidde, Nelson (5/30/2016): Interview Nelson Basaalidde, General Manager KOPGT. Kalangala, Kalangala District, Uganda. Waveform Audio File Format, Word file. Boaz, Jonathan (6/1/2016): Interview Jonathan Boaz, field officer of KOPGT and secretary of Block Bbeta East, and Enid Tkyongyeirwe, field officer KOPGT. Kalangala, Kalangala District, Uganda. Waveform Audio File Format, Word file. Emmanuel (6/23/2016): Interview Emmanuel, farmer Mukono District Farmers Association. Kiwebwa, Mukono District, Uganda. Word file. Joseph (6/2/2016): Interview with Joseph, oil palm farmer at Kizira unit, Kayunga Block. Kayunga Block, Bugala Island, Kalangala District, Uganda. Waveform Audio File Format, Word file. Lubega, Ben (6/28/1986): Interview Ben Lubega, Chairperson Mukono District Farmers Association. Mukono, Uganda. Word file. Lugambwe, Martin (6/6/2016): Interview Martin Lugambwa, chairperson KOPGA. Kalangala, Kalangala District, Uganda. Waveform Audio File Format, Word file. Lugambwe, Martin; Namulindwa, Josephine (6/1/2016): Interview with Martin Lugambwa, chairperson KOPGA, and Josephine Namulindwa, secretary KOPGA. Kalangala, Kalangala District, Uganda. Waveform Audio File Format, Word file. Lumagi, Charles (6/2/2016): Interview Charles Lumagi, farmer and Unit leader, and Saveri Sevame farmer Bbeta East Block. Bbeta East, Bugala island, Kalangala District, Uganda. Waveform Audio File Format, Word file. Musaka, Fred (7/7/2016): Interview Fred Mukasa, Field Officer Mukono District Farmers Association. Mukono, Uganda. Word file.
84 Nalugo, Rinet (7/7/2016): Rinet Nalugo, Extension-linked-farmer, Mukono District Farmers Association. Mukono, Uganda. Word file. Namvira, Therese (6/2/2016): Interview with Therese (Teddy) Namvira, Unit secretary of publicity and farmer Bbeta East Block. Bbeta East, Bugala island, Kalangala District, Uganda. Waveform Audio File Format, Word file. Nganda, Silver (6/23/2016): Interview Silver Nganda, Coordinator Mukono District Farmers Association. Mukono, Uganda. Word file. Saradin, Samanik (5/30/2016): Interview Damanik Saradin, General Manager OPUL. Kalangala, Kalangala District, Uganda. Waveform Audio File Format, Word file. Ssemugenyi, Edward Maalo (6/1/2016): Interview Edward Maalo Ssemugenyi, coordinator Kadingo. Kalangala, Kalangala District, Uganda. Waveform Audio File Format, Word file. Tebukkoza, Nelson (6/1/2016): Interview Nelson Tebukkoza, Credit Manager KOPGT. Kalangala, Kalangala District, Uganda. Waveform Audio File Format, Word File.
85
Appendix C. Additional tables and figures Table C.1 Chi square test for extension and training services. Extension (no)
Extension (yes)
Total
Training (no)
47
4
51
Training (yes)
8
40
48
Total
55
44
99
Pearson chi2 (1) = 57.07
P = 0.000
Table C.2 Chi square test for collective marketing at the start and in 2016. Marketing 2016 (no)
Marketing 2016 (yes)
Total
Marketing at the start (no) 18
10
51
Marketing at the start (yes) 16
55
48
Total
55
44
99
Pearson chi2 (1) = 15.52
P = 0.000
Figure C.1 Histogram of the size of Ugandan agricultural cooperatives.
86
Figure C.2 Histogram of the size of smaller cooperatives with up to 150 members.
Figure C.3 Histogram of the age in years of Ugandan agricultural cooperatives.
