Innovative Cluster Organizations in Tanzania

A Minor Field Study evaluating cluster performance and actor

collaborations within the clusters included in ISCP-Tz

IDA STADENBERG

Master of Science Thesis Stockholm, Sweden 2016

     

Innovative Cluster Organizations in Tanzania

A Minor Field Study evaluating cluster performance and actor

collaborations within the clusters included in ISCP-Tz

Ida Stadenberg

Master of Science Thesis INDEK 2016:32 KTH Industrial Engineering and Management

SE-100 44 STOCKHOLM

     

This study has been carried out within the framework of the Minor Field Studies Scholarship Programme, MFS, which is funded by the Swedish International Development Cooperation Agency, Sida. The MFS Scholarship Programme offers Swedish university students an opportunity to carry out two months’ field work, usually the student’s final degree project, in a country in Africa, Asia or Latin America. The results of the work are presented in an MFS report which is also the student’s Bachelor or Master of Science Thesis. Minor Field Studies are primarily conducted within subject areas of importance from a development perspective and in a country where Swedish international cooperation is ongoing. The main purpose of the MFS Programme is to enhance Swedish university students’ knowledge and understanding of these countries and their problems and opportunities. MFS should provide the student with initial experience of conditions in such a country. The overall goals are to widen the Swedish human resources cadre for engagement in international development cooperation as well as to promote scientific exchange between unversities, research institutes and similar authorities as well as NGOs in developing countries and in Sweden. The International Relations Office at KTH the Royal Institute of Technology, Stockholm, Sweden, administers the MFS Programme within engineering and applied natural sciences. Erika Svensson Programme Officer MFS Programme, KTH International Relations Office

KTH, SE-100 44 Stockholm. Phone: +46 8 790 6561. Fax: +46 8 790 8192. E-mail: erika2@kth.se

www.kth.se/student/utlandsstudier/examensarbete/mfs

     

Master of Science Thesis INDEK 2016:32

Innovative Cluster Organizations in Tanzania A Minor Field Study evaluating cluster performance and actor

collaborations within the clusters included in ISCP-Tz

Ida Stadenberg

Approved

2016-07-01 Examiner

Anders Broström Supervisor

Kristina Nyström

ABSTRACT Cluster Organizations, as a means of promoting competition and innovation in industrial clusters, have become increasingly popular over the world. Cluster organizations aim to increase growth and competitiveness of clusters within a region, and have become a central part of economic policy-making across the world. Recently, the concept has been used to a larger extent as a tool for economic development and poverty alleviation. This thesis seeks to examine the cluster organizations that are part of the Sida funded program Innovation Systems and Cluster development in Tanzania (ISCP-Tz), by evaluating performance, goals and development of the program based on cluster facilitators perceptions, and assess linkages and actor collaborations between clustered actors. The data in this thesis is collected through a telephone-administered questionnaire, as well as interviews and visits to cluster sites. The results show a positive impact on cluster firms performance as assessed by cluster facilitators, but show that actor collaborations in many cases are inadequate and need to be improved. Key-words Economics, Development Economics, Economic development, Cluster, Cluster organization, Cluster Initiative, Agglomeration economics, Innovation, Tanzania, ISCP-Tz

     

ACKNOWLEDGEMENTS I would like to thank my supervisor, Associate professor Kristina Nyström, for her valuable guidance and support during the thesis process. Your suggestions, comments and encouragement have been very appreciated. I also want to express my deepest gratitude to COSTECH for giving me the opportunity to write this thesis, for introducing me to key persons and for valuable input during my time in Tanzania. I would like to express a special thank you to Dr. Dugushilu Mafunda, Furaha Kabuje, Dan Nerén and Julieth Kweka, for your precious time spent in helping me during the process. Additionally, I would like to thank the respondents of the survey for taking your time to provide valuable and useful information. I would also like to thank Professor Ramon Wyss for introducing me to COSTECH and the Innovation Systems and Cluster development Program in Tanzania, and for providing me with guidance and valuable feedback. I would also like to thank Göran Lindqvist, Director of Research at Stockholm School of Economics, for excellently providing me with inspiration and knowledge during the early stage of the thesis process. A special thank you to the Swedish International Development Cooperation Agency, Sida, for giving me the opportunity to carry out this study in Tanzania within the Minor Field Studies Scholarship Programme, MFS. Lastly, I want to express my deepest gratitude to my sister Elin for your endless support in life.

     

TABLE OF CONTENTS 1. INTRODUCTION .................................................................................................................... 1

1.1 Background ....................................................................................................................................... 1 1.1.1 Clusters ........................................................................................................................................ 1 1.1.2 Sida’s support of cluster organizations in Tanzania .................................................................... 1 1.1.3 Tanzania National Innovation System ........................................................................................ 2

1.2 Previous research and my contribution .......................................................................................... 3 1.3 Purpose .............................................................................................................................................. 3 1.4 Limitations and sustainability implications ................................................................................... 4 1.5 Outline of the thesis .......................................................................................................................... 4

2. THEORETICAL FRAMEWORK .......................................................................................... 5 2.1 Cluster theory ................................................................................................................................... 5

2.1.1 Benefits of agglomeration ........................................................................................................... 5 2.1.2 The role of competition ............................................................................................................... 6

2.2 The cluster life cycle ......................................................................................................................... 7 2.3 Cluster organizations – the gap model ........................................................................................... 7

3. LITERATURE REVIEW ...................................................................................................... 10 4. CLUSTER ORGANIZATIONS IN ISCP-TZ ...................................................................... 12

5. METHODOLOGY ................................................................................................................. 14 5.1 Research design ............................................................................................................................... 14

5.1.1 Data collection ........................................................................................................................... 14 5.1.2 Questionnaire ............................................................................................................................ 14 5.1.3 Translation of questionnaire ...................................................................................................... 15 5.1.4 Interviews and cluster visits ...................................................................................................... 15

5.2 Limitations and validity of the study ............................................................................................ 16 5.2.1 Validity and reliability .............................................................................................................. 16 5.2.2 Challenges with cluster policy evaluations ............................................................................... 16

6. EMPIRICAL ANALYSIS ...................................................................................................... 18 6.1 Results .............................................................................................................................................. 18 6.2 Analysis ............................................................................................................................................ 26

6.2.1 The gap between firms .............................................................................................................. 26 6.2.2 The government gap .................................................................................................................. 27 6.2.3 The capital gap .......................................................................................................................... 29 6.2.4 The academia gap ...................................................................................................................... 29 6.2.5 The gap between clusters .......................................................................................................... 30 6.2.6 Economic impact ....................................................................................................................... 30

7. CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH ........................... 32 8. REFERENCES ....................................................................................................................... 34

APPENDIX 1. TANZANIA ....................................................................................................... 37 APPENDIX 2. QUESTIONNAIRE ENGLISH ....................................................................... 38

APPENDIX 3. QUESTIONNAIRE SWAHILI ........................................................................ 45

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1. INTRODUCTION

1.1 Background

1.1.1 Clusters Cluster organizations (COs) as a means of promoting competition and innovation in industrial clusters, have become increasingly popular all over the world. Recently, it is also used to a larger extent as a tool for economic development and poverty alleviation (UNIDO, 2010). COs are organized attempts to increase growth and competitiveness of clusters within a region, and have now become a central part of economic policy-making across the world. Although COs are used and adopted in most parts of the world today, they started out in the developed world, and have increased in popularity in developing countries after year 2000. International organizations like the UN and the World Bank are using clusters as an economic development tool, which has resulted in many donor-initiated COs (Ketels, Lindqvist and Sölvell 2006). This thesis uses Porter’s definition of clusters as “a geographically proximate group of interconnected companies and associated institutions in a particular field, linked by commonalities and complementarities” (Porter 2008: 215). A Cluster initiative (organization) is defined as “organized efforts to increase growth and competitiveness of clusters within a region, involving cluster firms, government and/or the research community.” (Ketels, Lindqvist and Sölvell, 2003: 9). The terms cluster initiatives and cluster organizations will be used interchangeably throughout the thesis.

1.1.2 Sida’s support of cluster organizations in Tanzania Tanzania is a low-income country in Sub-Saharan Africa, GDP per capita was $955.11 2014 according to The World Bank (2015a). Despite the low GDP, the overall macroeconomic performance in the country is strong and Tanzania has enjoyed stable economic growth rates recent years. There are many challenges; a large part of the population lives below the poverty line and inequality increases as GDP increases, leaving many people behind (The World Bank, 2015b)2. Sweden and Tanzania have a long history of development cooperation, over 50 years, with the aim to reduce Tanzania’s aid dependency. The support goes through the Swedish International Development Cooperation Agency (Sida) and has covered many areas over the years. According to the recent strategy adopted in 2013, the current focus areas for the cooperation with Tanzania are: jobs and development of energy and agricultural markets, improved education and increased entrepreneurship, strengthened democratic accountability and transparency, increased awareness of human rights (Sida, 2015). Sweden has, through Sida, supported cluster organizations in Tanzania since 2005 through a program called Innovation Systems and Cluster development Program Tanzania (ISCP-Tz). ISCP-Tz is part of Innovation Systems and Cluster development Program East Africa (ISCP- 1 As a comparison, GDP per capita in Sweden 2014 was $58,898.9 2 See Appendix 1 for general data about Tanzania

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EA), which includes Tanzania, Uganda and Mozambique. The idea of the program came up when representatives from the participating countries attended the 6th Global Conference on Innovative Clusters in Gothenburg, Sweden, 2003, a conference organized by VINNOVA (the Swedish Innovation Agency) and TCI (the Competitiveness Institute). The support went through the University of Dar es Salaam (UDSM) in the beginning, but was changed to the Tanzania Commission for Science and Technology (COSTECH)3 in 2011, thereby moving the coordination to a national level. The project started with the support of eight clusters in 2006, and has now grown to 67 clusters in 2015. The innovative cluster organizations aims to contribute to regional and national economic growth, and to strengthen the business environment and make a positive socio-economic impact. The overall aim with the cluster-based initiative is to strengthen the linkages between the cluster actors through collaborative activities, to enhance knowledge transfer and thereby improve innovation, value addition and competitiveness. This in turn will contribute to poverty alleviation, strengthening of local agricultural products and natural resources, preservation of the environment and improved gender equality. The COs combine university-industry-government relationships in a triple helix model4; in that way the clusters will lead to increased productivity, higher quality of products and services and generate employment opportunities (Rath et al, 2012; Rydhagen and Trojer, 2014).

1.1.3 Tanzania National Innovation System Tanzania formulated a long term National Development Vision 2025 in the year 2000. The vision aims at transforming Tanzania to a middle-income country by 2025, with a focus on five attributes: good governance, high quality livelihoods, peace, stability and unity, a well educated and learning society and a competitive economy capable of producing sustainable growth and shared benefits (United Republic of Tanzania, 2000). To be able to reach the goals, Tanzania is working according to five-year development plans (FYDP). The first one came into effect 2011 and focuses on strengthening the country’s infrastructure; roads, port, energy, and information and communication technology. The second one from 2016 highlights the importance of developing the industrial sector, and the third five-year development plan from 2021 will focus on making manufacturing and service sectors more competitive. The three five-year plans build upon each other, and the success of the former is crucial for the implementation of the latter. Overall, the plans aim to increase the productivity of the agricultural sector and transform the structure of the economy from a mainly agrarian to a mixed economy. Before the five-year plans came into force, Tanzania was working according to National Strategy for Growth and Reduction of Poverty, known in Tanzania as MKUKUTA (Mkakati wa Kukuza Uchumi na Kupunguza Umaskini Tanzania), and MKUZA (Mkakati wa Kukuza Uchumi Zanzibar) in 3 COSTECH is a parastatal organization with the responsibility to coordinate and promote research and technology development within Tanzania. The organization is an advisory organ to the Government concerning all matters related to science and technology, for example policy formulation, allocation of resources and to facilitate national, regional and international cooperation in scientific research and technology development.

4  A triple helix model is a model that enhances the importance of collaboration between universities, governments and industries. The model stresses the role of universities in innovation, and emphasizes that innovation and economic development comes from interaction between the three elements.        

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Zanzibar. MKUKUTA aimed at reaching sustained high economic growth and alleviate poverty, but after realizing that poverty remained high despite the country’s high rates of economic growth, the FYDPs were implemented (Economic and Social Research Foundation, 2015). A review of the National Innovation System (NSI) by the Ministry of Communication, Science and Technology (2012) concludes that one of the weaknesses of the NSI in Tanzania is the lack of partnership and collaborations between academia, industry and the government. Improved collaborations between R&D institutions, the industry and the government are important for establishing IPR systems, enhancing innovations and commercialization of indigenous technologies. The same applies to R&D activities, which are stated to be supply- rather than demand driven. This means that the connection between research institutions and the private sector needs to be improved so that immediate needs are satisfied and urgent innovation opportunities utilized. The research results need to be converted to services, products and processes to be able to enhance the business environment and contribute to socio-economic development. Further, the report recommends a system for protecting and commercializing local resources and knowledge, combined with a national IPR framework (Ministry of Communication, Science and Technology, 2012).

1.2 Previous research and my contribution Most of the research and literature concerning clusters and cluster organizations are made in a developed country context, and are therefore not always applicable in a developing country. Most COs in developed countries are initiated through industries or governments, whereas in developing countries many COs are donor-initiated. Donor-initiated COs usually take place where government support for clusters and competitiveness is low, and where the level of trust is lower; hence they usually operate in a different environment. Donor-initiated COs tend to focus more on “basic” industries that are not yet well developed, and company- or government initiated COs in developed countries are more focused on enhancing innovative capacity in already developed high-tech clusters. Additionally, previous research show that COs in developing countries are less likely to have quantified targets and goals (Ketels, Lindqvist and Sölvell, 2006). This thesis contributes to the research of cluster initiatives in developing regions by focusing on the clusters included in the Innovation Systems and Cluster development Program in Tanzania. The thesis shows how the cluster initiatives work and collaborate, and provides an assessment of the general performance of clusters in Tanzania.

1.3 Purpose The main purpose with the thesis is to assess the economic impact of the cluster organizations included in the ISCP-Tz and evaluate bridge building between the clustered actors, thereby contribute to the research of clusters in developing regions. Further, my research questions are:

• What are cluster organizations perceptions of cluster performance, goals and development?

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Theoretical framework Literature review CO's in ISCP-Tz Methodology Empirical

analysis Conclusions

• How has the linkages and actor collaborations within the clusters evolved since the implementation of the ISCP-Tz?

1.4 Limitations and sustainability implications Difficulties with data collection of economic indicators of clustered firms limit the thesis in terms of the programs’ economic impact on firms. A majority of the firms within the clusters are informal and do not keep track on their financial performance. Therefore, data on performance is based on cluster facilitators’ assessments. Further, the cluster facilitators’ assessments serve as an indication of the economic performance, but it needs to be stressed that they are based on their subjective view. The thesis aims to examine collaborations and linkages among the clusters in general, on an aggregate level, and do not provide deep insights into each cluster. Since the focus is on Tanzania, the results cannot be generalized to a larger population than the clusters included in the sample. Sustainability aspects in this thesis concern economic- and social sustainable development. The aims with the Innovation Systems and Cluster development Program in Tanzania is to make a positive socio-economic impact that contributes to poverty alleviation, through strengthening the business environment. Hence the economic and social development goes hand in hand in this matter. The thesis will contribute to economic and social sustainable development through evaluating the impact of the cluster program.

1.5 Outline of the thesis Part two provides a theoretical framework with cluster theory as a foundation, followed by a theory of cluster life cycles and the role of cluster organizations. Part three focuses on previous studies conducted on clusters and cluster initiatives, part four describes the cluster program in Tanzania, and part five describes the methods and research design used for data collection. Part six presents the results from data collection followed by an analysis, and finally part seven provides a conclusion and suggestions for future research.

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2. THEORETICAL FRAMEWORK

2.1 Cluster theory

2.1.1 Benefits of agglomeration “A cluster is a geographically proximate group of interconnected companies and associated institutions in a particular field, linked by commonalities and complementarities” (Porter 2008, 215). Cluster theory states that firms that are located in clusters benefit from agglomeration economies, such as; scale economies, external benefits of production, spillover effects and access to specialized labor. The reason for this is their proximity to each other, both geographically and by activities. Benefits also stem from collaborations between the business community, government, and supporting institutions like research organizations and financial supporting institutions, which creates value through joint interests (The World Bank, 2009). Cluster theory stems from theories of agglomeration and localization economics. One of the first contributors to agglomeration economics was Alfred Marshall (1890), who analyzed the reason why firms locate in close proximity to each other. Marshall stated that the main reason for firms in related industries to cluster within the same area is the physical conditions available there. This means that it did not have as much to with agglomeration externalities caused by firms, but rather exogenous factors like natural resources that draw firms to the same place (McCann and Folta, 2008). Further, Marshall highlighted three benefits of agglomeration, related to reduced transport costs of firms when it comes to people, ideas and goods. The first one, about people, relates to labor market pooling. Clustered firms can benefit from a large pool of specialized labor, facilitate matching between labor and firms and maximize productivity. The second, about ideas, concerns knowledge transfer between clustered firms, such as workers exchanging tacit knowledge. The third, goods, is related to the proximity to supporting industries, like complementary products or downstream suppliers, which reduces transaction costs (Ellison, Glaeser and Kerr, 2010). Even though the benefits of agglomeration dates back to Marshall in the 1890s, the larger strand of the literature about agglomeration economies and evaluations of clusters received increased attention with Michael Porter in the 1990s. According to Porter (2008), extensive literature about clusters and location economics were written in the first fifty years of the 1900s, then moving out of the mainstream economics for some time, to return again in the 1990s. One of the reasons for this might be that past localization theories, and arguments promoting agglomeration were developed within a different industrial landscape, and that globalization changed the pillars that these theories were based upon. Cluster theory today is adjusted to globalization and the dynamic economic landscape in which the firms operate (Porter, 2008). Clusters appear in all kinds of industries and economies, both within basic and high-tech industries. They also vary in size; both the number of firms and the size of existing firms. The essence of cluster benefits lies in the linkages between its members, both within the vertical and horizontal chain and when it comes to supporting functions. Supporting firms can include infrastructure providers, training institutions, firms that produce complementary products, education, research, technical support etc. (Porter, 2008). According to Porter (2008), the

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location of firms affects their competitive advantages, which in turn affects productivity and productivity growth. The description above stating that the essence of a cluster lies in its linkages between its members fits well when it comes to competition as “the productivity of a location rests not on the industries in which its firms compete, but on how they compete” (Porter, 2008: 225). A high technological firm in a location that lacks high technological infrastructure, transportation, well-educated workers etc. will hence not generate the productivity and innovations it otherwise could. Being located in a cluster can increase access to specialized inputs and reduce costs. When a firm outsource to another firm located in the same cluster, local outsourcing, costs such as transportation costs, transaction costs etc. can be reduced. It also reduces the risk of moral hazard and other opportunistic behavior, since the contracting firms are members of the same cluster. Additionally, local outsourcing facilitates support services like repairs, installations and trainings. Just like increased access to inputs is valuable by firms within the cluster, so is increased access to complementarities by customers and workers. Potential customers visiting a cluster are able to visit the intended firm, but also firms offering complementarities located nearby. Firms can also take advantage of joint marketing. A large cluster could also influence government’s investments in public goods such as infrastructure, education or fairs (Porter, 2008).

2.1.2 The role of competition In Porter’s work The Competitive Advantage of Nations (1998) he uses four interrelated forces to demonstrate the effect of location on competition, a model that is commonly referred to as Porter’s diamond. All four parts are important when describing the context in which a firm operate, but the part focusing on clusters is mainly the part of the model named related supplier or support industries, and even more importantly through the linkages between all four parts.

FIGURE 1. PORTERS DIAMOND Source: Porter, M. (2008) On Competition. Harvard Business Press, p. 227.

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Factor (input) conditions have an indirect effect on the productivity of firms. Factor conditions include infrastructure, human resources, information, the legal system, universities and research institutes, and their degree of efficiency matter for the firms within the cluster. Firm strategy, structure and rivalry refer to the context in which the clustered firms operate. Low rivalry generates low productivity, according to Porter (2008), since competition forces firms to innovate and be productive. The local rivalry is constituted of the rules and norms within the cluster, but the macroeconomic conditions, such as political stability, and the microeconomic policies, like the tax system, intellectual property rules and labor market policies, within a country also influences the level of competition within a cluster. Demand conditions have direct impact on the productivity of firms, and the firms’ ability to compete by differentiating. Related and supporting industries, and the linkages between them, are important features of a cluster and its benefits.

2.2 The cluster life cycle The benefits of agglomeration seem to change with time as clusters change and decline, a process referred to as the life cycle of a cluster (Ketels and Memedovic 2008; Menzel and Fornahl 2009). The model by Menzel and Fornahl (2009) suggests that the development of a cluster through a cluster life cycle depends on the level of heterogeneity among the actors of a cluster, and thereby also by its size. Large clusters that consist of many firms has a larger potential to diversify in terms of technology and size, while small clusters need to be specialized for the actors to take advantage of each other. The size and heterogeneity of a cluster will evolve as it moves through the four different stages of the cluster life cycle. In the emerging stage, the cluster consists of a few firms with growing potential, the level of innovation is increasing and so does heterogeneity as more firms enter. In the growth stage, the cluster becomes more specialized and focused. According to the model, the level of heterogeneity reaches the highest peak between the emergence and growth stages. The next step, sustainment, shows a matured stage where the level of heterogeneity is decreasing and the cluster enters a sustainable path. For the cluster to be sustainable, it needs to maintain the level of renewal and heterogeneity to keep the innovation level and avoid decline. The renewal can occur by adapting new technology to the cluster or using existing knowledge within the cluster to adapt to new environments. The benefits of maintaining heterogeneity within a cluster are connected to the linkages between the actors within it. The knowledge and experiences by the different actors have to accessible, which is made by strengthening actor collaborations (Menzel and Fornahl, 2009).

2.3 Cluster organizations – the gap model Although the mechanisms behind cluster theory and improved relations between its members are clearly beneficial to firms, network failures seem to hinder clustered members to take full advantage of the external economies of scale. This is where cluster organizations come in. COs are used to strengthen the clusters and increase the competitiveness of the firms within them.

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Figure 2 shows a cluster and the main actors that form a cluster; the government, research institutions, capital providers, education institutions and the most central actor – firms (Ketels, Lindqvist and Sölvell, 2013). FIGURE 2. ACTORS WITHIN A CLUSTER Source: Ketels C., Lindqvist G., Sölvell Ö. (2013) The Cluster Initiative Greenbook 2.0, Ivory Towers Publishers Stockholm, p. 37 If actors within the cluster would collaborate perfectly, they would be able to enjoy benefits of agglomeration and contribute to innovation and economic growth. However, there are barriers between the actors that prevent collaboration and thereby innovation, barriers that Ketels, Lindqvist and Sölvell (2013) refer to as gaps. These are illustrated in the gap model in figure 3. According to Ketels, Lindqvist and Sölvell (2003) COs are “ (…) collaborative actions by groups of companies, research and educational institutions, government agencies and others, to improve the competitiveness of a specific cluster” (Ketels, Lindqvist and Sölvell, 2003).

FIGURE 3. THE GAP MODEL Source: Ketels C., Lindqvist G., Sölvell Ö. (2013) The Cluster Initiative Greenbook 2.0, Ivory Towers Publishers Stockholm, p. 38 Ketels and Memedovic (2008) emphasize three approaches of cluster organizations. The first one focuses on creating a platform for interaction between the actors, so as to enable collaborations and knowledge transfer. The second highlights the importance of collaboration between the private and public sector, since it is important that the government is aware of what kind of investments and policies that affect the firms’ success. The third highlights the importance of

Research institutions

Education institutions Capital providers

Government

Firms

The gap between firms

The research gap

The education gap

The government gap

The capital gap

The global market gap

The gap between clusters

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collaborations and joint actions between the clustered firms, and the activities they perform together to reach their goals (Ketels and Memedovic, 2008). Joint actions can take place vertically or horizontally; where vertical cooperation refers to joint actions among firms at different stages along the supply chain, and horizontal cooperation means joint actions among competitors (McCormick, 1998).

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3. LITERATURE REVIEW

Ketels, Lindqvist and Sölvell (2003, 2006, 2013) have made contributions to the cluster literature through their evaluations of cluster organizations across the world. The Cluster Initiative Greenbook (Ketels, Lindqvist and Sölvell, 2003) and the Cluster Initiative Greenbook 2.0 (Ketels, Lindqvist and Sölvell, 2013), conduct a Global Cluster Initiative Survey (GCIS) to perform a comprehensive evaluation of drivers and success factors of clusters across industries and countries. They evaluate objectives, performance, how the clusters operate and cluster policies. Their research includes survey responses from 233 and 356 cluster managers in 2003 and 2013, respectively. The largest part of their sample constitutes OECD countries. For example, only two African countries are included in sample of the GCIS performed 2003 and 2013, South Africa in the first and Tanzania in the latter. A larger diversity of countries are included in the report Cluster Initiatives in Developing and Transition Economies (Ketels, Lindqvist and Sölvell, 2006), where the GCIS is performed on 450 cluster organizations across the world. The report compares cluster organizations in developed, transition and developing country contexts, so as to contribute to a better understanding of their differences and similarities. They find that the objectives, activities and performances of the clusters differ according to the context. For example, they find that COs in developing countries to a larger extent are donor-initiated and focused on agriculture and basic industries, while the COs in advanced economies typically are government-initiated and characterized by high technology. The survey conducted in their report denotes the importance of contextualization (Ketels, Lindqvist and Sölvell, 2006). Klofsten (2009; Klofsten et al, 2015) derives general success factors of clusters, based on the Business Platform Model (Klofsten, 1992), and case studies of five Swedish clusters. His findings are based on interviews with the facilitator of each cluster, which result in five general factors that contribute to success of a cluster: Idea, Activities, Critical mass, Commitment and driving forces and Organization. The study does not evaluate the clusters but gives examples of ways to operationalize the success factors into interview questions for data collection (Klofsten, 2009). Previous literature brings up a number of obstacles that can hinder the clustered actors to benefit from agglomeration economies, obstacles more common in developing regions (UNIDO, 2010; McCormick, 1998). One reason is that small-scale firms are more prone to prioritize short term interests, meaning that they might reject long term benefits because of high short term costs or investments (UNIDO, 2010). In addition, transaction costs are usually high, especially in regions with low levels of trust. Low levels of trust hinder collaborations and innovation since it makes firms reluctant of sharing information. Developing regions generally have weak institutions, which implies that it is difficult to enforce sanctions in case of opportunistic behavior (UNIDO, 2010; McCormick, 1998). The weak institutions combined with low levels of trust hinder the development of partnerships in the long run. However, if trust levels would be higher, there is a possibility for it to balance the negative effects of weak institutions, by establishing a reputation mechanism that work as a legal contract (UNIDO, 2010). A common socio-cultural identity within the cluster can provide a basis for trust and hinder opportunistic behavior (Schmitz,

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1995). Further, the paper by UNIDO (2010) states that relationships between local governments and the private sector are weak and that financial institutions often are designed for large-scale firms and hence is unable to deliver services to small-scale enterprises (UNIDO, 2010). Sonobe, Akoten and Otsuka (2011) study the relationship between years of schooling on enterprise size and growth, in an informal metalworking cluster in Nairobi. Their findings show that highly educated entrepreneurs are more likely to innovate to increase profitability of the firm. An interesting aspect about their data is that they use the variable number of workers as enterprise size and growth, and do not take revenue, value-added or similar measures into account. The paper states that there seem to be difficult gathering data on such measures in enterprise surveys in Sub-Saharan Africa in general, especially in the informal sector. Porter’s definition5 of clusters has been criticized for being broad and unclear, and one of the reasons is that he uses the term ‘geographical proximity’ without defining a clusters boundaries. This has made previous literature question the importance of geographical proximity, since the definition does not include any limits on how large a cluster can be. The difficulties in delimiting the boundaries of a cluster taken together with the fact that outcomes are hard to measure, makes evaluation of clusters difficult (Swords, 2013). This is discussed in an article by Martin and Sunley (2003), which states that the reason for the popularity of clusters lies in the incompleteness of its definition. The authors mean that the fact that the cluster concept is rather vague has saved it from being tested and evaluated, like models and theories usually are. The concept of clusters seems to be universally accepted even though there is a lack of empirical evidence for its benefits. Further, they argue that in cases where clustered firms have shown economic growth, one should be careful to interpret it as causality, as there are many possible influences that contribute to a firms’ success than its location relative to other firms (Martin and Sunley, 2003). Aziz and Norhashim (2008) argue that cluster analysis lacks a holistic framework that includes all actors within the cluster. The frameworks used in analysis usually concentrate on the development of firms, while ignoring other clustered actors and their progress (Aziz and Norhashim, 2008). The involvement of many different actors in a cluster aggravate efforts to set objectives, since expectations and goals probably differ between firms, local politicians, coordinators etc. Fromhold-Eisebith and Eisebith (2008) suggest that cluster evaluation should divide its focus in objective and subjective goals, where the former evaluates indicators commonly associated with clusters that allow for comparison, while the latter evaluates subjective outcomes for the different actors. However, they too, emphasize the problems of comparisons of cluster developments, and highlight regional influences as the main obstacle. 5 “A cluster is a geographically proximate group of interconnected companies and associated institutions in a particular field, linked by commonalities and complementarities” (Porter 2008, 215)

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4. CLUSTER ORGANIZATIONS IN ISCP-TZ The cluster organizations included in ISCP-Tz operate in diverse industries, mainly focused on basic industries like agriculture, food and basic manufacturing, while a few of them are capital-intensive. This is in line with previous literature (Ketels, Lindqvist and Sölvell, 2006) that emphasizes that developing countries, where donor-initiated cluster organizations dominate, to a larger extent than advanced and transition economies are focused on basic industries. In contrast, advanced economies typically focus on high tech industries. The clusters are geographically distributed over the whole country, but a majority of them are situated in the regions; Dar es Salaam, Morogoro or Zanzibar. The cluster organizations in Tanzania are presented according to industry group in Table 1. TABLE 1. INDUSTRY GROUPS Industry group Cluster initiative Region Agriculture, food, basic manufacturing

Vegetable Seed CI Oil Seed CI

Arusha Babati

Beekeeping CI Bukombe Nutraceuticals CI Dar es Salaam Eastern Regions Mushroom CI Dar es Salaam Handloom CI Dar es Salaam Textile CI Dar es Salaam Furniture CI Dar es Salaam Tomato CI Iringa Cassava Processing CI Kibaha Soap CI Kigoma Cassava CI Kigoma Sisal CI Kishapu Small Scale Sisal farming CI Korogwe Rice Processing CI Magugu Bee Keeping CI Manyoni Food Processors CI Morogoro Rice Processors CI Morogoro Furniture CI Morogoro Meat CI Morogoro Poultry Keeping CI Morogoro Textile CI Morogoro Sunflower CI Mpwapwa Fish Farming CI Mwanga Soap CI Mwanza Livestock Keepers CI Nkasi Mushroom CI Ruvuma Sunflower CI Singida Fish Farming CI Ugweno Sea Weed CI Zanzibar Fruit, Vegetables and Spice Processing Zanzibar Unguja Poultry CI Zanzibar Unguja Soap CI Zanzibar Unguja Fruit, Vegetables and Spice Processing Zanzibar Pemba Fish Farming and Processing CI Zanzibar Pemba Honey CI Zanzibar Pemba Capital intensive manufacturing Metal works CI

Small scale mining CI Dar es Salaam Kilindi

Engineering and Metal works CI Morogoro Milling CI Morogoro Oil Processing CI Morogoro Engineering CI Shinyanga "High tech", advanced services Bio Fuels CI

ICT CI Dar es Salaam Dar es Salaam

Tourism Cultural Heritage Tourism CI Bagamoyo Mwenge Wood Carving CI

Tourism CI Cultural Heritage Tourism CI Cultural Heritage CI

Dar es Salaam Morogoro Tanga Zanzibar Unguja

Other Educational Services CI Dar es Salaam

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The clusters included in ISCP-Tz are natural agglomerations, so called organic clusters that existed prior to the program. The program has then built up and strengthened cluster organizations so as to enhance innovation and competitiveness of the cluster. ISCP-Tz has supported the cluster organizations through capacity building to cluster facilitators, business support, technology development, support with linking the clusters to markets and financial institutions, and also through providing entrepreneurship- and business skills. A cluster facilitator is the person who is responsible for the development of the cluster organization. The facilitators are appointed by COSTECH, and earlier by UDSM, and are persons with various backgrounds and functions. So as to prepare them for the role as a facilitator, they have been provided trainings, like leadership training, entrepreneurship and management training. The cluster facilitators work voluntarily and are hence not paid. The business support has focused on formalizing firms through product certifications, registration of business name and adapting to certain standards. This has been done through connecting the cluster organizations to Tanzania Bureau of Standards (TBS) and Tanzania Food and Drugs Authority (TFDA) etc. The cluster organizations have been exposed to technology development centers and research institutions to facilitate technology development. When it comes to finance, the cluster organizations have been provided with seed funds, to support the establishment of the cluster and activities to get started. Additionally, the program has established links with banks to facilitate access to finance for clustered firms (COSTECH, 2016). TABLE 2. EXAMPLES OF CLUSTER INITIATIVES Zanzibar Seaweed The Seaweed cluster consists of Seaweed farmers, exporters, buyers, academic actors represented by the University of Dar es Salaam (UDSM) and Institute of Marine Sciences and government actors like the Department of Fisheries and the Department of Agriculture. The cluster exports dry seaweed to international markets, and has also engaged in value-creation by the introduction of new products such as seaweed soaps, seaweed oil, seaweed juice, jam and cookies. These are not exported but sold on the local market.  Morogoro Rice Processing Cluster The Rice Processing cluster consists of rice farmers, rice processors and rice traders, as well as representatives from government through Morogoro Municipal Agricultural office and the District Trade office, and academia through Cholima research center. The cluster focus on increasing rice-farming productivity and improve the quality standards as well as market linkages, and has also expressed a need to improve financial linkages so as to increase access to capital. Eastern Regions Mushroom Cluster The Eastern Regions Mushroom cluster consists of mushroom farmers, mushroom processors and spawn makers, and has been collaborating with the College of Engineering and the Faculty of Science at the University of Dar es Salaam (UDSM), as well as with the local governments. The efforts have been directed towards sharing of experiences among farmers and trainings of mushroom cultivation and increasing the quality of mushroom spawns. Source: COSTECH, Knowledge products from sti clusters

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5. METHODOLOGY

5.1 Research design The method in this thesis is a combination of quantitative and qualitative elements, mainly focusing on the quantitative aspect. A number of previous studies evaluating cluster organizations emphasize the importance of combining quantitative and qualitative measures (Klofsten, Bienkowska, Laur and Sölvell, 2015; Klofsten, 2009; Diez, 2001). The quantitative part of the thesis collects data on clustered firms performance and actor collaborations within the cluster organizations, through a telephone administered questionnaire. The qualitative aspect aims to contribute to a deeper understanding of the cluster organizations; used both before the construction of the questionnaire as a means of adjusting the questions to fit the Tanzanian context, and during the data collection as a complement to the questionnaire in form of interviews.

5.1.1 Data collection The data in this thesis is collected using an interviewer-administered questionnaire. When using a telephone questionnaire, the response rate is usually higher than for an internet-mediated questionnaire (Saunders, Lewis and Thornhill 2009), and this is particularly true for Tanzania where Internet access is low and not all respondents have access to email. The method of using an interviewer-administered questionnaire was chosen because it makes it possible to reach a large sample of respondents, despite the large distance between the clusters. The respondents to the questionnaire are the cluster facilitators of the cluster organizations included in the ISCP-Tz. A cluster facilitator is the person who has the overall responsibility for the cluster organization, and the one that has received training through the program. Hence the facilitator is the person who has knowledge about the development of the cluster, and the collaborations that take place between the actors within it. The names and contact details to cluster facilitators were provided by COSTECH. According to COSTECH (2016) there are 67 cluster organizations included in the program. However, after going through the number of clusters with COSTECH, the remaining number is 51. The reason for the reduced number is that some of the clusters included in the 67 clusters were identified but were not included in the program, and hence did not receive support. Another reason is that previously sponsored clusters left the program. Additionally, the cluster organizations initiated after year 2013 were excluded, since the cluster organizations need some time for the collaborations and connections to grow, and hence time before evaluation. Out of the 51, COSTECH provided names and contact details to 45 cluster facilitators. This means that the final number of respondents that were contacted and asked to participate in the study by responding to the questionnaire was 45.

5.1.2 Questionnaire The questionnaire aims to collect data on the existence of actor collaborations so as to be able to analyze cluster gaps and bridge building in accordance with the Gap Model. It also includes a

      15

part where the cluster facilitators are asked to provide financial performance data of the clustered firms (See the questionnaire in Appendix 1). The questions designed for the questionnaire are a mix of questions developed for this thesis, and questions used in previous literature. The questionnaire consists of nine closed-ended questions and one open-ended question in the end where the respondents are given the opportunity to share their view. In four of the nine closed-ended questions, there is a possibility for the respondents to add a choice that fits their situation better than the ones outlined in the questions. Questions 5, 6, 8 and 9 in the questionnaire are adopted from Ketels, Lindqvist and Sölvell (2006), and slightly adapted. The adoption of questions from Ketels, Lindqvist and Sölvell (2006) will enable a comparison of the results to a large sample of developing regions included in their research. The questions constructed for this thesis are developed with inspiration from the cluster literature, question 2, 3 and 4 are related to the Gap Model described in section 2. The questionnaire consists of rating questions in matrices, one ranking question and one open question. The rating questions are designed with a likert scale, and are a way to collect opinion data (Saunders, Lewis and Thornhill 2009). In this case, assessment on the cluster development, and ratings on the frequency of different activities. The ranking question collects data on the relative importance of the different alternatives, in this case the goals of the cluster organization (Saunders, Lewis and Thornhill 2009). Finally, the open question allows for additional input that the respondents want to share. The questions were pre-coded prior to data collection to facilitate analysis, with numbers representing each alternative.

5.1.3 Translation of questionnaire The first version of the questionnaire was constructed in English, and then translated to Swahili. The translation was made by a translator provided by COSTECH, and proofread and corrected by a cluster expert. When translating a questionnaire, it is important to bear in mind the different meanings of words; the lexical meaning, idiomatic meaning and experiential meaning. Translating the questions due to their lexical meaning, which is the direct translation of words, might give a result that is far from what the question was intended to ask. To make sure the questionnaire did not include direct translations or words unfamiliar to the context, a cluster expert at COSTECH proofread, and corrected, the translated questionnaire.

5.1.4 Interviews and cluster visits Since most of the cluster theories are developed in a developed country context, interviews with cluster facilitators and visits to cluster sites were included, so as to have their input on the theories and material. The interviews were semi-structured, so as to enable new thoughts and ideas, but at the same time stay within the subject of clusters. Interviews with four cluster facilitators were conducted, and they were chosen according to proximity and industry group. Due to time- and financial constraints, visits and interviews had to be conducted in close proximity to the COSTECH office in Dar es Salaam. The interviewees were spread out according to industry group, with two clusters representing the group Agriculture, food, basic manufacturing, one in Dar es Salaam and one on Zanzibar, and one cluster within Capital intensive manufacturing, and lastly one within Tourism, both in Dar es Salaam. Visits were also

      16

made to the Seaweed cluster initiative and Soap cluster initiative at Zanzibar, Mwenge Wood Carving cluster initiative, Textile cluster initiative, Handloom cluster initiative and Eastern Mushroom cluster initiative in Dar es Salaam.

5.2 Limitations and validity of the study

5.2.1 Validity and reliability The validity of a questionnaire refers to the ability of the questionnaire to measure what is intended. Saunders, Lewis and Thornhill (2009) explain different kinds of validity of a questionnaire, two of them will be discussed here; content validity and criterion-related validity. The first one refers to the extent to which the questions in the questionnaire cover appropriate information. To ensure a high content validity and hence include questions adjusted to the cluster facilitators, I have studied previous literature concerning cluster organizations and discussed the proposed questions in the questionnaire with key persons at COSTECH. It was of importance to discuss the questionnaire with experts at COSTECH since most of the theories concerning cluster organizations are made in the contexts of developed countries. By receiving their input, the questionnaire was adjusted to make sure the questions are relevant for the cluster facilitators in Tanzania. Additionally, a pilot test including two respondents was undertaken, in order to make sure the questions are suitable and understandable. After the pilot test, two changes were made in the questionnaire. The first one was made in question 2A and 2B where the respondent noted there was no option for selecting every month, hence that was added. The second change was made regarding the translation of the English word cluster, also in question 2A and 2B, where the first version used the Swahili translation nguzo, which was changed to kongano. Criterion-related validity of the questionnaire refers to the extent to which the questions measure what is intended; in this case performance and actor collaborations within the cluster organizations included in ISCP-Tz. Reliability refers to whether the questionnaire will result in consistent findings when used with different samples and in different time periods. The response rate in this thesis is not 100 %, which means a different sample could have generated different results. Different time periods could also generate different results, since economic performance and collaborations evolve and develop over time (Saunders, Lewis and Thornhill, 2009).

5.2.2 Challenges with cluster policy evaluations According to Schmiedeberg (2010), there are four main challenges with cluster policy evaluations; the organization of evaluation, how to define performance, the time lag between policy intervention and impact, and data availability. The first one refers to a principal-agent problem since many evaluators are assigned by the policy makers and hence are likely to satisfy the policy maker instead of conducting an independent evaluation. Even though this thesis is written independently of any organization, the collaboration with COSTECH, for example in terms of being introduced to cluster facilitators, could influence the answers of the respondents. The second challenge about how to define performance lies in defining which outcome the

      17

evaluation should focus on. An evaluation can focus on the cluster itself, the collaborations between the actors and the growth of the cluster, or it could focus on firm performance or macroeconomic factors evaluating regional performance. Hence it is important to define the boundaries of the cluster and which indicators to measure. It is also important to bear in mind that firm- and regional performance is influenced by external factors as well, and there is also a time lag between the intervention and the impacts, which makes it difficult to evaluate possible causalities. The fourth and last challenge brought up by Schmiedeberg (2010) concerns data availability. Data availability is a challenge that has been prominent in this thesis, since a majority of the firms do not keep track on their financial performance.

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6. EMPIRICAL ANALYSIS

6.1 Results This section provides the results from the questionnaire. Most of the cluster initiatives included in the survey were initiated during 2012-2013, 18 of 32. Six of them were initiated 2006-2008 and four 2009-2011, and initiation year is missing for the four cluster organizations left. 22 % of the respondents are women, and 78 % are men. Figure 4 shows that being part of a cluster organization has led to an increase in sales, wages, innovation (as measured by new products and improved products), turnover and number of employed, based on an assessment by the cluster facilitators. 90 % of the respondents indicate that being part of a cluster organization has led to an increase in improved products and turnover, and the results are almost as high for sales, 87 %. Some of the respondents indicate that there is no difference by being part of a cluster organization, and a few report a decrease. It is worth noting that 10 %, which is approximately 3 cluster organizations, have experienced a decrease in sales since they became part of a cluster organization.

Figure 5 shows the contact frequency between the clustered firms and the actors within the cluster. 60 % of the firms within the cluster are in contact every quarter or less, which means a very low contact frequency. Only 40 % of the firms are in contact with other firms within the same cluster monthly or weekly, and when it comes to government institutions the results show 27 %. However, a majority of the firms are never, or less frequently than every year, in contact with academia, financial institutions, other cluster organization and international markets6.

6 A low contact frequency with international markets is explained by the fact that most firms do not operate on the international market

10% 3% 7%

0% 3% 3% 3%

23%

37%

10% 6%

26%

87% 74%

57%

90% 90%

71%

0%

25%

50%

75%

100%

Sales Wages New products

Improved products

Turnover Number of employees

Perc

enta

ge o

f res

pond

ents

FIGURE 4. CHANGE IN INDICATORS Question 1: In your view, has being part of a cluster organization led to a change in the following indicators for the firms within the cluster?

Decrease

No difference

Increase

      19

83%

59%

58%

53%

47%

29%

17%

28%

32%

31%

47%

35%

0%

13%

10%

16%

6%

35%

0% 25% 50% 75% 100%

International markets

Financial Institutions

Academia

Other cluster organizations

Regional markets

Governmental institutions

FIGURE 6: CONTACT FREQUENCY CO's Question 2b: How often is the cluster organization in contact with the following actors

Never or less frequently than every year

Every year or every quarter

Every month or every week

70%

65%

58%

55%

37%

30%

23%

27%

23%

29%

28%

57%

43%

37%

3%

13%

13%

17%

7%

27%

40%

0% 25% 50% 75% 100%

International markets

Other cluster organizations

Financial Institutions

Academia

Regional markets

Government institutions

Firms in the same CO

FIGURE 5: CONTACT FREQUENCY FIRMS Question 2a: How often are the firms within the cluster organization in contact with the following actors?

Never or less frequently than every year

Every year or every quarter

Every month or every week

The results concerning contact frequency in figure 6 between the cluster organizations, managed by the facilitator, and the actors, show a similar pattern. The actor with the highest contact frequency is governmental institutions, where 35 % of them are in contact monthly or weekly. Figure 7 shows the priorities given to the different collaborations. It shows that 52 % of the respondents give high priority to firm-to-firm collaboration within the cluster organization, 35 % give high priority to collaboration between firms and government, 29 % give high priority to collaboration between clusters, 27 % give high priority to collaboration with other markets, 23 % give high priority to collaborations between firms and financial institutions, and lastly 17 % give high priority to firm-academia collaborations. 20 % have not engaged in any firm-academia collaboration, and the corresponding number for firms and financial institutions is 16 %.

      20

0%

10%

20%

30%

40%

50%

60%

Firm to firm collaboration

within the cluster organization

Firms and governmental

institutions

Collaboration with other

clusters in similar or other sectors

Collaboration with other

markets (local, regional or

international)

Firms and financial

institutions

Firms and academia

FIGURE 7: PRIORITIZED COLLABORATIONS Question 3: Please rank the following collaborations according to priority:

Have not engaged in any collaboration

Low priority

Medium priority

High priority

0%

10%

20%

30%

40%

Shar

e of

res

pond

ents

FIGURE 8. CONTACT FREQUENCY WITH PRIORITIZED ACTORS

High priority and are in contact quarterly, monthly or weekly

High priority and are in contact yearly, less frequently than once a year or never

Figure 8 combines the respondents who assigned high priority to the different actors, with the level of contact frequency for the same actors. The green bars show the share of respondents who assigned both high priority to the collaboration, and chose quarterly, monthly or weekly contact frequency with the same actor. This is to see if the respondents who assigned high priority to the different collaborations have a higher contact frequency with those actors. The results show that prioritized collaborations have a higher contact frequency for firm-to-firm and firm-to- government, and to a small degree when it comes to firm-to-academia. For the other actors, contact frequency is low although the respondents assigned them high priority. Figure 9 shows the average reply of five cluster organizations in the regions Morogoro, Zanzibar (including both Unguja and Pemba) and Dar es Salaam. The comparison between these three particular regions was made because they have the highest concentration of clusters. The figure shows that contact frequency with the clustered actors are on average higher in the clusters located in Morogoro than in the other regions, with all actors except regional and international markets, where Zanzibar dominates the previous, and Dar es Salaam the latter.

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0%

25%

50%

75%

100%

Firm to firm collaboration

within the cluster

organization

Firms and academia

Firms and governmental organizations

Collaboration with other clusters in similar or

other sectors

Collaboration with other markets (local,

regional or international)

Firms and financial

institutions

Perc

enta

ge o

f res

pond

ents

FIGURE 10. COLLABORATIONS Question 4: How has the following collaborations evolved during the past five years?

Better or much better

No difference

Worse or much worse

4,2 3,4 3,4 2,2 1,6

3,4 3,6 2,2 2,8 2,8

1,2 1,8 2,4 2 2,4 2 1,8 1,8

0 2 4 6

Government institutions

Academia Other cluster organizations

Regional markets International markets

Financial institutions

FIGURE 9: CONTACT FREQUENCY COs IN THREE REGIONS Reply scale; 1 - never; 6 - every week Average reply of five cluster organizations in each region

Morogoro Zanzibar Dar es Salaam

In question 4, figure 10, the respondents were asked how the collaborations with different actors evolved during the past five years. In cases where the cluster organization was initiated less than five years ago, they were asked to assess how they have evolved since the start. The majority thinks the collaborations are better or much better now than five years ago, some chose no difference and a few worse or much worse. Worse or much worse is particularly true for financial institutions, which in turn is the actor with least better or much better among the alternatives. In question 5, figure 11A, the respondents were asked to select three goals of the cluster organization that they considered most important and rank them according to priority. The three most important goals were Increase efficiency, Support innovation and Improve business environment. The results can be compared to the findings by Ketels, Lindqvist and Sölvell (2006) where the respondents from developing countries selected Increase value added7, Increase exports and Support innovation as their three most important goals. The only difference was

7 When constructing the questionnaire, the term Increase value-added was excluded because it was expected to confuse respondents. The choice Increase efficiency was included instead.

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58%

32% 48%

16% 16%

48%

10% 13% 19% 26%

0%

25%

50%

75%

100%

Perc

enta

ge o

f res

pond

ents

FIGURE 11 A. GOALS Question 5: Share of respondents who indicated this as one of three most important goals of the cluster organization (%)

Increase efficiency 33%

Increase exports 20%

Support innovation 17%

Increase employment 6%

Improve business environment

10%

Attract new firms to the

cluster 7%

Commercialize academic research

7%

FIGURE 11 B. Share of respondents who ranked this as the most important goal

increased exports, which can be explained by the fact that a majority of the firms in this sample do not export their products, although 32 % actually seek to increase exports or start to export. Given the difficulties with financial institutions, one would expect the goal of attracting investments to be higher than 13 %, since it is crucial for firm growth. It might have something to do with the wording of the question, where attract finance might had generated a different result. The top priority goals are to Increase efficiency, Increase exports and Support innovation, as shown in figure 11B. Question 6, figure 12, aimed to examine what kind of activities the clusters engage in. Activities performed together strengthen collaborations among the actors in the cluster and hence contribute to its success. The respondents were asked to what extent they performed the outlined activities, which was indicated at a 5-point likert scale where 1 corresponded to not done and 5 to main activity.

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43% 48%

23% 42%

22% 22%

42% 19%

30% 19%

9% 19% 19%

13% 26%

13% 13%

37% 26%

48% 29%

47% 47%

26% 45%

33% 44%

53% 42% 41%

44% 29%

41% 33%

20% 26%

29% 29%

31% 31% 32%

35% 37% 38%

38% 39% 41%

44% 45% 47%

53%

Promote joint logistics Promote joint innovation of new products/services

Lobby government for changes in regulations Analyze and inform about technical trends

Conduct joint branding of products/services Efforts to make firms aware of each other

Facilitate joint promotion in other markets Establish standards for industry

Conduct joint branding of the cluster organization Collect market intelligence

Promote production process improvement Promote supply chain development

Promote joint purchasing Promote joint or bundled production

Provide management training Lobby government for infrastructure investments

Provide technical training

FIGURE 12. ACTIVITIES Question 6: Please indicate to what degree the cluster organization has engaged in the following activities:

1 (Not done) 2,3 4,5 (main activity)

Top three of the activities performed as a main activity by the large share of the clusters are to provide technical training, lobby government for infrastructure investments and provide management training. Technical- and management training have been part of the ISCP-Tz and is hence not a surprising result. It is interesting to see that a large amount of the clusters use their collective voice to lobby the government for infrastructure investments. This question is the same as used in the report by Ketels, Lindqvist and Sölvell (2006), which opens up for comparison. Their report finds that the most popular activities for the developing countries in their sample are supply chain development and joint logistics. Supply chain development seem to be a popular activity among the clusters in Tanzania as it is performed to some extent by 81 %. Promote joint logistics however, is one of the least performed activities by the clusters in Tanzania according to the results, with 43 % responding that they have not done it at all. The reason for the low level of joint logistics could depend on a reluctance to share information by the firms, since insights in a firm’s logistics could provide competitors with valuable information. Figure 5 showed that the contact frequency among firms is low, which may have implications when it comes to trust and sharing of information. It could be that the clustered firms in the sample of Ketels, Lindqvist and Sölvell (2006) have a higher degree of trust and are in contact more frequently. Other activities that are associated with information sharing to a large extent are to analyze and inform about technical trends and promote joint innovation of new products/services, and those are not performed to a large extent either. According to figure 13, the firms within the cluster organizations seem to measure most of the economic indicators at least once a year. However, the results are most probably biased since the indicators differ a lot. An example is that 90 % measure costs while only 57 % measure wages and 31 % number of employees, while wages are a large part of firms’ costs. The low number that measure number of employees could be explained by the fact that most firms are individual-

      24

69%

39%

68%

43%

79%

30% 26% 10% 16%

31%

61%

32%

57%

21%

70% 74% 90% 84%

0%

25%

50%

75%

100%

Perc

enta

ge o

f res

pond

ents

FIGURE 13. INDICATORS Question 7: Do the firms within the cluster organization measure the following indicators?

No, or less frequently than once a year Yes, at least once a year

or micro firms, and therefore do not have anything to measure. The same reason can be used to explain the low number for wages within individual firms, however that is not applicable to all firms. One would expect the number of firms that measure costs also would measure production, prices and sales, since those indicators are connected. Question 7 was twofold, where 7A asked if the firms within the cluster organization measure the indicators, and 7B asked about the specific numbers. Although a majority of the respondents report that they measure production, innovation, prices, costs and sales, the response rate for question 7B was remarkably lower. This further enhances the conclusion that the results in 7A are biased. Table 2 shows the results for the clusters that reported their results for both sales and number of employees for the years 20108 and 2015. The large increase in number of employees could be explained by the small size of the firms, where increasing employment from one to two persons result in a 100 % increase in number of employees. The same explanation could be used for the large increases in sales. However, even though the results seem exaggerated, they still show an indication that the economic indicators are moving in a positive direction. The results are in line with figure 13 that shows the assessed impact of being part of a cluster organization. TABLE 3. CHANGE IN SALES AND NUMBER OF EMPLOYEES Cluster organization Δ sales 2010 – 2015 Δ number of employees 2010-2015 Kigoma Soap9 60 % 60 % Nkasi Livestock Keepers10 51 % 45 % Kishapu Sisal 89 % 80 % Mwanza Soap11 36 % - 30 % Morogoro Milling12 19 % 13 % Handloom Textile 13 37 % 60 % Morogoro Furniture14 69 % 83 % Magugu rice processing15 39 % 67 %

8 The cluster initiatives initiated after 2010 provided the number for their starting year instead. 9 Initiated 2013, hence numbers from 2013 are provided 10 Initiated 2012, hence numbers from 2012 are provided 11 Initiated 2013, hence numbers from 2013 are provided 12 Initiated 2013, hence numbers from 2013 are provided  13 Initiated 2011, hence numbers from 2011 are provided 14 Initiated 2013, hence numbers from 2013 are provided

      25

0% 3% 0% 6% 9%

3% 9%

3% 9% 6% 6%

19% 25%

16%

34%

16%

88% 88% 91%

72% 63%

78%

53%

75%

0%

25%

50%

75%

100%

Cooperation Economic performance

Growth Market reach Number of firms

Innovative capacity

Use of local suppliers

Competition

FIGURE 14. IMPACT Question 8: Please assess the impact of being involved in a cluster organization concerning the following economic indicators

Negative impact

No impact

Positive impact

0%

25%

50%

75%

100%

Firms' trust in firms Firms' trust in government

Firms' trust in academia

Governments' trust in firms

FIGURE 15. TRUST Question 9: How would you grade the level of trust between the following actors within the cluster organization?

High

Not applicable

Low

In spite of the general positive impact of being involved in a cluster organization, it is worth noting that some cluster facilitators have experienced a negative impact, while a fairly large part experienced no impact, see figure 14. In the open question, some of the facilitators explained that they received a lot of support and a high level of engagement in the beginning of the program period, but that they during the past two years have felt forgotten, something that has affected the cluster in a negative way. The level of trust among the actors within the cluster organizations is generally high. Trust between firms, and between firms and government has the highest values. It is also worth noting that governments’ trust in firms and firms’ trust in government has been depicted low by 19 % and 16 %, respectively. This contrasts previous findings that trust is generally low in developing countries and within donor-initiated cluster initiatives.

15 Initiated 2011, hence numbers from 2011 are provided  

      26

6.2 Analysis The results show that the cluster organizations within ISCP-Tz are diverse when it comes to how they operate, collaborate, and the kind of activities they are engaged in. The analysis will be provided according to the gap model (Ketels, Lindqvist and Sölvell, 2013) described in section 2. The gaps refer to the degree of market failures between actors, where a large gap means there are barriers between the clustered actors and hence the collaborations need to be improved, while a small or no gap means the clustered actors collaborate perfectly and are able to enjoy the benefits of agglomeration. The global market gap is excluded in the analysis since most of the cluster organizations do not export, and are therefore not part of the global market.

6.2.1 The gap between firms The gap between firms refers to the lack of collaboration between the firms in a cluster. Although improved relations between the member firms are clearly beneficial according to theory, barriers hinder the firms to benefit from agglomeration economics and economics of scale. The firms are the main actors of the clusters and the ones that mainly contribute to cluster growth. All possible collaborations between clustered actors share the goal to increase competitiveness of the firms, so as to create value for the firms and the region in which they operate. Marshalls (1890) three benefits of agglomeration; reduced transport costs when it comes to people, ideas and goods, all refer to the firms, and the gap model (Ketels, Lindqvist and Sölvell, 2013) also highlights the firms as the most central actors. The gap between firms differ between the evaluated cluster organizations, with some experiencing a large gap that need to be addressed, while some have managed to reduce or even overcome it. The disparity is expected since the clusters operate in diverse industries influenced by different factors, and the way the clusters operate, the contact frequency and level and type of activities performed, differ. The results show that 60 % of the firms within a cluster organization are in contact with other firms within the same cluster as little as once a year, or even less than every year or never. The results are low, considering that the essence of a cluster lies in its linkages between the actors, and that frequent contacts and meetings are fundamental for increased collaborations. 23 % of the clusters say that the firms within the cluster never have contact with each other, or less frequently than every year. 40 % report weekly contact. When analyzing the results for prioritized collaborations and contact frequency between firms, it shows that 16 % of the clusters that have assigned high priority to collaborations between firms, only are in contact with each other yearly, less than yearly or never. Judging from these figures,

      27

it seems like something hinders the firms to collaborate even though collaborations are prioritized. One of the obstacles expressed by many of the cluster facilitators is lack of finance, when it comes to have meetings, but also when it comes to activities. Sometimes the firms are situated far from each other, which makes it expensive to visit them, both by the facilitator and by participating firms. One of the cluster organizations has solved this by sponsorship from an NGO, which enables them to conduct meetings quarterly. Receiving sponsorship from the NGO is a short-term solution that can boost collaboration, but support from NGOs do not last forever and therefore long-term solutions are needed to make sustainable relationships. There can also be obstacles with such collaborations since the NGOs have their own agenda that might conflict with the participating firms. On the other hand, in some cases there might be conflicting interests between the cluster facilitator and the firms within the cluster, where the priorities of the facilitator might not be in line with that of the firms or other actors. Many of the respondents expressed in the open question that they would like to send more members of the clusters to participate in trainings, since that would contribute to a deeper understanding of the cluster concept, and higher willingness to work together for common goals. It is also important to note that 40 % of the firms are in contact with the firms within the same cluster every month or every week, of which 20 % are in contact every week. This means that these clusters have worked to overcome the barriers and hence do not experience a gap between firms anymore. Additionally, the results from the question about activities, figure 11, show that many of the cluster organizations do not perform the listed activities. When having a closer look at the result, it shows that some of the clusters perform many of the listed activities, while others do not perform any or very few of them. In fact, 34 % of the cluster organizations have answered 4 or 5 (on a 1 to 5 scale) at only two or less activities out of 17 activities. Furthermore, the respondents that performed two or less activities also left the space for additional activities open. 25 % of the respondents have answered 4 or 5 on at least 9, or more, of the 17 activities, which means that some of the clusters are very active when it comes to collaborating and bridging the gaps. However, combining the answers in figure 5 and 11 for the 25 % that perform many of the activities, it shows that half of them have answered that the firms within the cluster are in contact with each other once a year or less, which is rather contradictive and questions the frequency of the activities.

6.2.2 The government gap The government gap refers to the lack of collaborations between the firms and the government, due to market failures. Increased collaborations and engagements in activities can reduce the government gap.

      28

The government gap differs among the clusters, both because of various attempts by the cluster organizations to address it, but also because government involvement differs across regions. As figure 6 shows, 35 % of the clusters are in contact with government institutions weekly or monthly, 35 % quarterly or yearly and 29 % less than yearly or never. When comparing the clusters in the regions Dar es Salaam, Zanzibar (including both Unguja and Pemba) and Morogoro concerning contact frequency between cluster organization and government institutions, Morogoro show a higher level of contact than the two other regions. Many of the respondents have expressed the benefits of being able to address common challenges of the cluster directly to the local government. This is also shown in figure 12 about activities, where lobbying government for infrastructure investments are the second main activity for almost half of the respondents. Since infrastructures like roads, electricity, are important parts of the factor conditions crucial for competition in a location (Porter, 2008), the possibility to lobby for changes increases incentives to be part of a cluster. Differences in infrastructure, as well as local differences in the tax system, make competition unfair, and hence it is important to make firms and clusters aware of differences. An example of how increased collaboration between a cluster and the local government in which it operates, comes from Zanzibar, where the Seaweed cluster successfully managed to lobby the government for a clause. The clause says that fishermen destroying seaweed farms will be fined, something that used to be a problem for the farmers and affect their harvest. Another example comes from a woodcarving cluster that has established a relation with the National Arts Council, a public institution that can assist with research and development, advices and technical assistance etc. Some cluster facilitators report that they have had difficulties with their local governments, in the way that they have delayed firm registrations and do not respond when contacted. In the open question, 25 % of the respondents have expressed that the relations with the local government in their region need to be improved. The facilitators explain that they think the relations can be improved if COSTECH would introduce the cluster concept to the local governments and make them collaborate. It should be in the local governments interest to increase collaborations, for example to facilitate firm registrations and thereby move firms from the informal – to the formal sector, which would increase their tax base. Ketels, Lindqvist and Sölvell (2006) problematize the fact that economic policy, like cluster organizations, are coordinated at a national level, and emphasize that clusters would benefit from government involvement at the same geographical level. A higher involvement of local governments would make them more familiar to the cluster concept, and could increase their willingness to facilitate business environment and remove barriers to competitiveness for the specific industry. Among the clusters that have a functioning relationship with their local government, it is common to have a contact person especially assigned to assist the cluster organization, which facilitates contacts and long-term relations. This might be one contributing reason for the large difference in contact frequency. Many of the clusters also express that the support by COSTECH was visible in the beginning of the program period, but that decreasing contact frequency has made them loose interest in the cluster concept since activities or workshops by COSTECH are no longer performed.

      29

6.2.3 The capital gap The capital gap is generally the most prominent, mentioned by a majority of the respondents in the open questions and during interviews and cluster visits. Lack of finance is a hinder for development and growth for the firms as well as for cluster organizations, and alternatives to commercial banks are few according to the facilitators. The respondents express difficulties with receiving loans due to high interest rates and lack of collateral, and many say this hinder investment in new technology and innovation. Contact with financial institutions has been assigned medium or high priority by 68 % of the respondents, while 59 % have answered that they are in contact with them less than once a year or never. The results show that there is a need of access to finance and increased collaborations with financial actors. The low contact frequency might depend on general difficulties of accessing finance in Tanzania for small- and individual firms. Access to finance is related to one of the parts in Porters’ diamond called factor conditions, since difficulties accessing finance has an indirect effect on firms through the context in which they operate, and does not have to do with inefficiency of the cluster itself. Even though there is a willingness to increase collaborations with financial institutions, demonstrated by the fact that such contacts have been assigned medium or high priority by 68 %, there seem to be difficulties in doing so.

6.2.4 The academia gap The academia gap refers to the research gap and education gap taken together, since both research and education belong to academia. The contact frequency between firms-academia and cluster organization-academia follow the same pattern, with an overwhelming majority that never, or less frequently than every year, are in contact with academia. When asked about priorities, 17 % of the respondents answered that they give high priorities to firm-academia collaborations, and 20 % answered that they never engaged in any collaboration. The reason could be the wide variety of industries in which the clusters operate, where some are more likely to see the direct benefits of research or commercialization of new ideas and innovations. An example of a successful collaboration

      30

between a cluster organization and academia comes from the Seaweed cluster. The farmers normally leave seaweed outside in the sun to dry, to be able to export dry seaweed to international markets. This procedure is not possible during the rainy seasons, and the farmers’ income is therefore reduced during theses seasons. To address this problem, the farmers have come together with a research institute and a technical college, and they have together come up with a model that uses solar dryers to dry the seaweed.

6.2.5 The gap between clusters The gap between clusters refers to the lack of collaborations between cluster organizations. A majority of the cluster organizations, 53 %, have never or less frequently than every year had contact with other cluster organizations, although increased collaborations with other clusters is something that many of the facilitators have expressed as desirable. Increased collaboration between the clusters can help actors to come up with solutions to different challenges, when it comes to access to finance for example. Sharing of ideas of how a particular cluster solved problems, and ideas of how to organize and plan activities, is information that many clusters could benefit from. One of the facilitators expressed a need for a common platform where facilitators and clustered firms could meet and discuss common challenges and solutions. Such platform could for example be a website with information and ways to contact the cluster organizations.

6.2.6 Economic impact The results included an assessment by the facilitators on the economic impact of being a part of a cluster organization, since numbers on financial performance were not available. The fact that the firms generally do not measure financial performance makes evaluation difficult. The facilitators also shared that some of the firms are reluctant to share financial information because of taxes. The taxes can sometimes be paid in a lump sum that is decided by the government, and not based on their production, so that sharing financial information could result in increased taxes. However, a system like that might result in some firms paying too high taxes, so keeping track of their financial performance could result in a decrease in taxes, especially within industries dependent on seasons. Something that further makes evaluation difficult is the absence of a baseline of the clusters as they entered the program. Additionally, it is important to emphasize that it might be in the respondents interest, the cluster facilitators interest, to provide positive answers about the development of the clusters, since it is part of their responsibility. This may cause skewed results that show a more positive picture than the reality.

      31

Crucial for economic growth is to collect market intelligence, something that 19 % of the firms do not do, 44 % do sometimes, while only 38 % perform it as their main activity. It is important for the firms to know their market, to be able to take advantages of opportunities of economics of scale or scope. Without sufficient market knowledge, investments become risky and the firms face difficulties accessing finance. An example comes from a farmer during the cluster visits, who were reluctant to invest in high tech facilities because she lacked market knowledge. If she would know the approximate returns on investments in the facilities, her opportunities of receiving a loan would increase and hence her possibility to scale the business. Market intelligence is hence an area where cluster organizations could assist their member firms, since many of them are operating within the same industry they would all benefit. Increased market research could also increase diversification and let firms benefit from economics of scope, and hence make firms less vulnerable to industry shocks or seasons. Alternatively facilitate investments in solutions to overcome seasonal variations, like the Seaweed clusters coming investments in facilities to dry seaweed during the rain season. Despite difficulties with data collection of financial indicators, the overall assessment of the economic impact on firms participating in ISCP-Tz is overwhelmingly positive. 88 % of the cluster facilitators reported a positive impact on economic performance and 91 % a positive impact on growth.

      32

7. CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH The aim of the cluster organizations within ISCP-Tz is, in line with cluster theory, to strengthen the business environment, make a positive socio-economic impact, strengthen the linkages between clustered actors and enhance knowledge transfer and innovation. Tanzania’s ongoing five year development plan focus on increasing the productivity of the agricultural sector, which corresponds to the recent strategy guiding the collaboration between Sweden and Tanzania, where development of agricultural markets and increased entrepreneurship are two of the main objectives. Strengthen the linkages between clustered actors is also stressed by the Ministry of Communication, Science and Technology that points out the lack of partnership and collaboration between academia, industry and the government as one of the weaknesses in Tanzania’s national innovation system (NSI). The objectives of Tanzania’s development plan, the partnership between Tanzania and Sweden, and the Innovation Systems and development Program, seem to go hand in hand. The results from the cluster facilitators’ assessments of the economic impact of the program indicate that a majority reports a positive impact on a number of indicators. However, an assessment of the economic impact of the program requires data on firms’ financial performance, a baseline at the implementation stage and continued data collection during the program. This is something that needs to be improved in order to assess the outcomes of the program. By judging from the cluster facilitators’ perceptions, it seems like the program has contributed to strengthen the business environment and made a positive socio-economic impact for some of the clusters, and that some of them do not notice any improvements. This also became evident during data collection, when the names of cluster facilitators and their contact details of some of the clusters were easily available because of their frequent contact with COSTECH, while others took time to find, and some of them were not found. Even though the cluster facilitators’ perceptions of cluster impact point in a positive direction, the costs and benefits of the ISCP-Tz, as well as alternative ways to enhance the business environment, have to be estimated in order to decide about future support. I would suggest further support within the cluster program to focus on a smaller number of clusters, and possibly within similar industries. This would increase the possibility to formulate specific objectives for the clusters and facilitate proper evaluation. A focus on a smaller number of clusters could also increase collaboration between COSTECH and the clusters, and would give COSTECH the possibility to adjust the support to individual needs instead of distributing general support to a large number. The clusters are found within a large variety of industries and general support might not correspond to the specific needs of the cluster organizations. The linkages and collaborations between the clustered actors differ between the cluster organizations, but there is a general need for improvements and efforts to overcome the gaps to enhance knowledge transfer and innovation. Future research could focus on the qualitative aspects of clusters to dig deep into the needs of the clusters and what kind of support they would benefit from. Interviews with clustered firms would provide an insight to how they view being part of a cluster and their benefits of collaborations.

      33

The qualitative aspect could also be complemented by a quantitative element where financial performance is collected, but this is dependent on that financial data is accessible. It would be interesting to include cluster organizations in various countries, for example in EAC (East African Community), to open up for comparisons on how they have solved issues related to the government gap or the finance gap. Lastly, I would like to make a distinction between economic growth and economic development, where the previous refer to a quantitative change in the economy, and the latter refer to a qualitative change in the well being of a population. The two are often correlated, but it needs to be emphasized that the qualitative change can occur even though the quantitative aspect is difficult to measure. This means that even though financial improvements are hard to measure, it does not mean they are absent. An example comes from a person in an individual firm during the cluster visits, who saw the positive impact of being part of a cluster in that he is now able to pay the school fees for his kids. The same man also emphasized that many firm owners have a low level of education and hence are not familiar to book-keeping, something that makes it hard to measure success, but does not outrule that a positive impact has taken place. A number of firm owners also expressed the benefits of being part of cluster in that they feel a connection to an organization and that their voices have been stronger now that they are working together.

      34

8. REFERENCES Aziz, K. A., Norhashim, M. (2008) Cluster-based policy making: assessing performance and sustaining competitiveness, Multimedia university COSTECH, Knowledge products from sti clusters, Retrieved 20160430 from: http://www.costech.or.tz/?portfolio=knowledge-products-from-sti-clusters COSTECH (2016) Innovation Systems and Cluster Development Programme, Pan-African Competitiveness Forum, Tanzanian Chapter

Diez, M. (2001) The Evaluation of Regional Innovation and Cluster Policies: Towards a Participatory Approach, European Planning Studies, p. 907-923 Economic and Social Research Foundation (2015) Tanzania Human Development Report 2014 – Economic Transformation for Human Development, UNDP and United Republic of Tanzania Ellison G., Glaeser E. L., Kerr W. R. (2010) What causes industry agglomeration? Evidence from coagglomeration patterns, American Economic Review 100, 1195-1213 Fromhold-Eisebith, M., Eisebith, G. (2008) Looking behind facades: Evaluating effects of (automotive) cluster promotion, Regional studies, 42:10 Ketels, C., Lindqvist, G., Sölvell, Ö. (2003) The Cluster Initiative Greenbook, Ivory Towers Publishers Stockholm Ketels, C. Lindqvist, G. Sölvell, Ö. (2006) Cluster initiatives in developing and transition economies, Center for Strategy and Competitiveness, Stockholm Ketels, C. Memedovic, O. (2008) From clusters to cluster-based economic development, Technological Learning, Innovation and Development, Vol. 1, No. 3

Ketels C., Lindqvist G., Sölvell Ö. (2013) The Cluster Initiative Greenbook 2.0, Ivory Towers Publishers Stockholm

Klofsten, M. (1992) Tidiga utvecklingsprocesser i teknikbaserade företag. Doctoral dissertation, Linköping University

Klofsten, M (2009). Generella Framgångsfaktorer i Kluster: En Studie av Entreprenörskap och Innovation, Helix Working paper, ISSN: 1654:8213

Klofsten, M., Bienkowska, D., Laur, I., Sölvell, I. (2015) Success factors in cluster initiative management: Mapping out the ‘big five’, Industry and higher education 29, p. 65-77 Martin, R., Sunley, P. (2003) Deconstructing clusters: chaotic concept or policy panacea? Journal of Economic Geography 3

      35

Marshall, A. (1890) Principles of Economics, Macmillan, London

McCann B. T., Folta T. B. (2008) Location Matters: Where we have been and where we might go in agglomeration research, Journal of Management, Vol. 34 No. 3, 532-565.

McCormick, D. (1998) Enterprise clusters in Africa: On the way to industrialization? Discussion paper 366, Institute of Development studies, University of Nairobi

Menzel, M-P., Fornahl, D. (2009) Cluster life cycles – dimensions and rationales of cluster evolution, Industrial and Corporate Change, Volume 10, Number 1, p. 205-238

Ministry of Communication, Science and Technology (2012) A review of the National Innovation System (NSI), Tanzania

Porter, M. (1998) The Competitive Advantage of Nations, Free Press, New York

Porter, M. (2008) On Competition, Harvard Business School Publishing, Boston

Rath, A. Diyamett, B. D. Borja, M. F. B. Mendoza, F. P. Sagasti, F. (2012) Evaluation of SIDA’s Support to Innovation Systems and Clusters, a Research Cooperation Initiative – Individual cases, Sida.

Rydhagen, B. Trojer, L. (2014) The Role of Universities in Inclusive Innovation – Cluster development in East Africa, Nelson Mandela African Institute for Science and Technology, Arusha, Tanzania.

Saunders, M. Lewis, P. Thornhill, A. (2009) Research methods for business students, Pearson, England Schmiedeberg, C. (2010) Evaluation of cluster policy: A Methodological Overview, Sagepub, University of Hamburg, Germany Schmitz, H. (1995) Collective efficiency: Growth path for small-scale industry, The Journal of Development Studies, 31:4, 529-566 Sida (2015) Tanzania – Our work in Tanzania, Department for Africa, Retrieved 20151104 at http://www.sida.se/English/where-we-work/Africa/Tanzania/Our-work-in-Tanzania/ Sonobe, T. Akoten, J. E. Otsuka, K. (2011) The growth process of informal enterprises in Sub-Saharan Africa: a case study of a metalworking cluster in Nairobi, Small business economics, 36:323-335 Swords, J. (2013) Michael Porter’s cluster theory as a local and regional development tool: The rise and fall of cluster policy in the UK, Northumbria University, Local Economy, UK

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The United Republic of Tanzania (2000) The Tanzania Development Vision 2025, Planning Commission The World Bank (2009) Clusters for competitiveness, International Trade Department The World Bank (2015a) Data GDP per capita (current US$), Retrieved 20151104 at http://data.worldbank.org/indicator/NY.GDP.PCAP.CD The World Bank (2015b) Tanzania Overview, Retrieved 20151104 at http://www.worldbank.org/en/country/tanzania/overview The World Bank (2016) Data – Indicators, Retrieved 2016-06-23 from http://data.worldbank.org/indicator The World Bank Group (2016) Doing Business – Economy Rankings, Retrieved 2016-06-23 from http://www.doingbusiness.org/rankings Trading Economics (2016) Tanzania – Economic indicators, Retrieved 2016-06-23 from http://www.tradingeconomics.com/tanzania/indicators

UNIDO (2010) Cluster development for pro-poor growth: The UNIDO approach, United Nations Industrial Development Organization, Vienna

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APPENDIX 1. TANZANIA TABLE 4. TANZANIA DATA Number Indicator Value 1. Population (in million) 47.42 2. Labor Force Participation

Rate (%) 86.7

3. Unemployment rate (%) 10.3 4. Inflation rate (%) 5.2 5. Corruption index (on a scale

where 0 is highly corrupted and 100 is not corrupted)

30

6. Corruption Rank (out of 175 countries where 1 is the least corrupted nation)

117

7. Gini coefficient (where 0 represents perfect equality and 100 perfect inequality)

37.8

8. Ease of doing business (ranked from 1 to 189)

139 (Ranked 15 of 47 in Sub Saharan Africa)

9. Net official development assistance received (current US$)

2,647,980,000

10. Lending interest rate (%) 16.3 Sources: Indicator 1-7: Trading Economics (2016) Tanzania – Economic indicators, Retrieved 2016-06-23 from http://www.tradingeconomics.com/tanzania/indicators, Indicator 8: The World Bank Group (2016) Doing Business – Economy Rankings, Retrieved 2016-06-23 from http://www.doingbusiness.org/rankings, Indicator 9-10: The World Bank (2016) Data – Indicators, Retrieved 2016-06-23 from http://data.worldbank.org/indicator

      38

APPENDIX 2. QUESTIONNAIRE ENGLISH Innovative Cluster Initiatives – Questionnaire to Cluster Facilitators

I, Ida Stadenberg, am a Master’s student from KTH – Royal Institute of Technology, Stockholm, Sweden. In collaboration with the Commission for Science and Technology, COSTECH, I am working on my master thesis concerning the cluster organizations included in the Innovation Systems and Cluster development Programme, ISCP-Tz. This questionnaire is intended to collect data on clustered firms performance and actor collaborations within the cluster organizations. Answers to this questionnaire shall be provided by cluster facilitators of the cluster organizations included in ISCP-Tz. I thank you in advance for your time and cooperation in this matter. Date:…………………………………...…… Time:..…………………………. Cluster organization:…………………………………………………………………… Initiation year of the cluster organization:……………………………………………... Name of cluster facilitator:…………………………………………………………….. Number of actively participating firms in the cluster organization:……………………

1. In your view, has being part of a cluster organization led to a change in the following indicators for the firms within the cluster?

Yes

, to

a la

rge

decr

ease

Yes

, to

a sm

all

decr

ease

No

diff

eren

ce

Yes

, to

a sm

all

incr

ease

Yes

, to

a la

rge

incr

ease

Sales 1 2 3 4 5

Wages 1 2 3 4 5

New products 1 2 3 4 5

Improved products 1 2 3 4 5

Turnover 1 2 3 4 5

Number of employees 1 2 3 4 5

      39

2. A) How often are the firms within the cluster organization in contact with the following actors?

Nev

er

Less

fr

eque

ntly

then

ev

ery

year

Ev

ery

year

Ever

y qu

arte

r

Ever

y m

onth

Ever

y w

eek

Firms within the same cluster organization

1 2 3 4 5 6

Governmental institution 1 2 3 4 5 6

Academia 1 2 3 4 5 6

Other cluster organizations 1 2 3 4 5 6

Regional markets 1 2 3 4 5 6

International markets 1 2 3 4 5 6

Financial institutions 1 2 3 4 5 6

2. B) How often is the cluster organization in contact with the following actors?

Nev

er

Less

fr

eque

ntly

then

ev

ery

year

Ev

ery

year

Ever

y qu

arte

r

Ever

y m

onth

Ever

y w

eek

Governmental institutions 1 2 3 4 5 6

Academia 1 2 3 4 5 6

Other cluster organization 1 2 3 4 5 6

Regional markets 1 2 3 4 5 6

International markets 1 2 3 4 5 6

Financial institutions 1 2 3 4 5 6

      40

3. Please rank the following collaborations according to priority:

Hav

e no

t en

gage

d in

any

co

llabo

ratio

n

Low

prio

rity

Med

ium

pr

iorit

y H

igh

prio

rity

Firm to firm collaboration within the cluster organization

1 2 3 4

Firms and academia 1 2 3 4

Firms and governmental institutions 1 2 3 4

Collaboration with other clusters in similar or other sectors

1 2 3 4

Collaboration with other markets (local, regional or international)

1 2 3 4

Firms and financial institutions 1 2 3 4

4. How has the following collaborations evolved during the past five years?

Muc

h w

orse

Wor

se

No

Diff

eren

ce

Bet

ter

Muc

h be

tter

Firm to firm collaboration within the cluster organization

1 2 3 4 5

Firms and academia 1 2 3 4 5

Firms and governmental organizations 1 2 3 4 5

Collaboration with other clusters in similar or other sectors

1 2 3 4 5

Collaboration with other markets (local, regional or international)

1 2 3 4 5

Firms and financial institutions 1 2 3 4 5

      41

5. Please select the three (3) goals of the cluster organization that you consider most important, and rank them according to priority by indicating 1, 2 and 3 in the boxes below.

Increase efficiency

Increase exports

Support innovation

Supply chain development

Increase employment

Improve business environment (make it easier to do business)

Attract new firms to the cluster

Attract investments

Reduce production costs

Commercialize academic research

Other, please specify:.…………………………………………………………..

6. Please indicate to what degree the cluster organization has engaged in the following activities?

1. Not

done 2. 3. 4. 5. Main

activity

Joint production Promote joint purchasing 1 2 3 4 5

Promote joint logistics 1 2 3 4 5

Promote joint or bundled production 1 2 3 4 5

Promote supply chain development 1 2 3 4 5

Joint sales Conduct joint branding of products/services

1 2 3 4 5

Conduct joint branding of the cluster organization

1 2 3 4 5

Facilitate joint promotion in other markets

1 2 3 4 5

Human resource upgrading Provide technical training 1 2 3 4 5

      42

Provide management training 1 2 3 4 5

Promote production process improvement 1 2 3 4 5

Establish standards for industry 1 2 3 4 5

Intelligence Collect market intelligence 1 2 3 4 5

Analyze and inform about technical trends

1 2 3 4 5

Efforts to make firms aware of each other 1 2 3 4 5

Business environment Lobby government for changes in government regulations/policies

1 2 3 4 5

Lobby government for infrastructure investments

1 2 3 4 5

Joint R&D Promote joint innovation of new products/services 1 2 3 4 5

Other Please specify:…………………………... …………………………………………...

1 2 3 4 5

7. A) Do the firms within the cluster organization measure the following indicators?

No,

do

not m

easu

re

this

indi

cato

r Le

ss fr

eque

ntly

then

on

ce a

yea

r

Yes

, onc

e a

year

Yes

, mor

e th

an

once

a y

ear

Number of employed 1 2 3 4

Production 1 2 3 4

Exports 1 2 3 4

Wages 1 2 3 4

Imports 1 2 3 4

      43

Innovation (number of new and/or improved products or services)

1 2 3 4

Prices 1 2 3 4

Costs 1 2 3 4

Sales 1 2 3 4

Other, please specify:………………... ……………………………………….. 1 2 3 4

7. B) If yes, please specify the amount in the following table: Indicator Value 2010 Value 2015 Sales (tsh)

Exports (tsh)

Imports (tsh)

Wages (tsh)

Innovation (number of new and/or improved products or services)

Number of employed

Other, please specify:………………... ………………………………………..

7. C) Please specify the number of firms within the Cluster Organization in the following table:

Indicator Value 2010 Value 2015 Number of firms

8. Please assess the impact of being involved in a cluster organization concerning the following economic indicators:

Stro

ng

nega

tive

impa

ct

Neg

ativ

e im

pact

No

impa

ct

Posi

tive

impa

ct

Stro

ng

posi

tive

impa

ct

Cooperation 1 2 3 4 5

      44

Economic performance

1 2 3 4 5

Growth

1 2 3 4 5

Market reach

1 2 3 4 5

Number of firms

1 2 3 4 5

Innovative capacity

1 2 3 4 5

Use of local suppliers

1 2 3 4 5

Competition

1 2 3 4 5

9. How would you grade the level of trust between the following actors within the cluster organization?

Low

Som

ehow

lo

w

Not

ap

plic

able

Som

ehow

hi

gh

Hig

h

Firms’ trust in firms 1 2 3 4 5

Firms’ trust in government 1 2 3 4 5

Firms’ trust in academia 1 2 3 4 5

Governments’ trust in firms 1 2 3 4 5

10. Is there something else that you would like to share? ………………………………………………………………………………………...... ………………………………………………………………………………………...... ………………………………………………………………………………………...... ………………………………………………………………………………………...... ………………………………………………………………………………………...... ………………………………………………………………………………………......

Thank you for your participation!

      45

APPENDIX 3. QUESTIONNAIRE SWAHILI Mpango Bunifu wa Nguzo – Dodoso juu ya juhudi za uendelezaji kongano

Naitwa Ida Stadenberg, mwanafunzi wa shahada ya Uzamiri kutoka Chuo kikuu cha Tecknologia,Stockholm (KTH), Sweden. Kwa kushirikiana na Tume ya Taifa ya Sayansi na Technolojiia ya Tanzania(COSTECH), nafanyia utafiti juu ya uendelezaji wa kongano, hususan kwa kushirikisha nguzo tatu kuu (Serikali mahalia, Taasisi za Utafiti na Kampuni), ikiwa ni sehemu ya mpango wa kuendeleza ubunifu chini ya mradi wa Sida ( ISCP-Tz). Dodoso hii inalenga kukusanya taarifa za utendaji kazi za kongano, ushirikiano na nguzo zingine ikiwa pamoja na utendaji wa kampuni husika. Matokeo/majibu ya madodoso haya yatatolewa kwa waendeshaji wa nguzo shirika zinazopatikana katika ISCP-Tz ambapo yatasaidia katika kuboresha shughuli za kongano. Natanguliza shukrani zangu kwa ushiriki wako na muda wako katika shughuli hii. Tarehe: ……………………… Muda: ..…………………………………… Jina la Kongano:……………………………………………………………………… Mwaka ambao kongano limeanzishwa :…………………………………………….... Jina la Kiongozi wa kongano:…………………………………………………………. Idadi ya kampuni hai zilizomo ndani ya kongano (wanakongano):……………………

1. Kwa maoni yako, je, kuwa miongoni mwa wanaounda kongano hili kumepelekea kuleta mabadiliko yoyote ndani ya kongano (hususan kampuni wanachama wa kongano)?

Ndi

o kw

a up

ungu

fu

mku

bwa

Ndi

o kw

a up

ungu

fu m

dogo

Hak

una

tofa

uti

Ndi

o, o

ngez

eko

kwa

kiw

ango

ki

dogo

Ndi

o, o

ngez

eko

kwa

kiw

ango

ki

kubw

a.

Mauzo 1 2 3 4 5

Ujira 1 2 3 4 5

Bidhaa mpya 1 2 3 4 5

Bidhaa zilizoboreshwa 1 2 3 4 5

      46

Mapato 1 2 3 4 5

Idadi ya waajiriwa 1 2 3 4 5

2. A) Ni kwa mara ngapi kampuni ndani ya kongano huwasiliana na wadau wafuatao?

Hai

jaw

ahi

Mar

a ch

ache

ha

lafu

kila

m

wak

a K

ila m

wak

a

Kila

robo

m

wak

a

Kila

mw

ezi

Kila

wik

i

Mashirika mwenza ndani ya nguzo 1 2 3 4 5 6

Taasisi za Serikali 1 2 3 4 5 6

Taasisi za Elimu/Taaluma 1 2 3 4 5 6

Kongano nyingine 1 2 3 4 5 6

Masoko yakikanda 1 2 3 4 5 6

Masoko yakimataifa 1 2 3 4 5 6

Taasisi za Fedha 1 2 3 4 5 6

2. B) Je, ni mara ngapi kongano huwasiliana na wadau wafuatao?

Hai

jaw

ahi

Mar

a ch

ache

ha

lafu

kila

m

wak

a K

ila m

wak

a

Kila

robo

m

wak

a

Kila

mw

ezi

Kila

wik

i.

Taasisi za Serikali 1 2 3 4 5 6

Taasisi za Elimu/Taaluma 1 2 3 4 5 6

Kongano nyingine 1 2 3 4 5 6

Masoko yakikanda 1 2 3 4 5 6

Masoko yakimataifa 1 2 3 4 5 6

Taasisi za Fedha 1 2 3 4 5 6

      47

3. Tafadhali ainisha ushirika kulingana na kipaumbele:

Hai

jaw

ahi

kush

iriki

ana

Kip

aum

bele

cha

ch

ini

Kip

aum

bele

cha

w

asta

ni

Kip

aum

bele

ki

kubw

a

Ushirikiano wa Shirika kwa shirika na kongano 1 2 3 4

Mashirika na taasisi za Elimu 1 2 3 4

Mashirika na Taasisi za kiserekali 1 2 3 4

Ushirikiano na kongano nyingine ndani au nje ya secta husika.

1 2 3 4

Ushirikiano na masoko mengine (ya ndani, kikanda au kimataifa)

1 2 3 4

Mashirika na Taasisi za fedha 1 2 3 4

4. Je, kumekuwa na mabadiliko gani katika kipindi cha miaka mitano iliyopita kwenye mahusiano yafuatayo?

Mab

aya

zaid

i

Mab

aya

Hak

una

tofa

uti

Maz

uri

Mau

ri za

idi

Ushirikiano wa shirika kwa shirka na kongano 1 2 3 4 5

Mashirika na Taasisi za Elimu 1 2 3 4 5

Mashirika na Taasisi za Serikali 1 2 3 4 5

Ushirikiano na kongano nyingine ndani au nje ya sekta husika

1 2 3 4 5

Ushirikiano na masoko mengine (Masoko ya ndani, Ya kikanda au Kimataifa)

1 2 3 4 5

Mashirika na Taasisi za fedha 1 2 3 4 5

      48

5. Tafadhali chagua malengo matatu (3) ya kongano linaona ni muhimu zaidi kwa kampuni, na yaainishe malengo hayo kulingana na kipaumbele chake ukitumia nambari 1,2,na 3.

Kuongeza Ufanisi

Kuongeza uuzaji wa bidhaa nje.

Kuchochea Ubunifu

Mwendelezo wa mnyororo wa Ugavi

Kuongeza ajira

Kuboresha mazingira ya biashara (kuyafanya rahisi)

Kuvutia mashirika mapya kwenye nguzo shirika

Kuvuita uwekezaji

Kupunguza gharama za uzalishaji

Kufanya tafiti za kitaaluma kuwa za kibiashara

Mengineo,(ainisha):.…………………………………………………………..

6. Tafadhali ainisha kwa kiwango gani kongano limejihusisha kwenye mambo haya yafuatayo;

1.

Haijafanyika 2. 3. 4. 5.

Shughuli kuu

Uzalishaji wa pamoja Kuchochea manunuzi ya pamoja 1 2 3 4 5

Kuchochea ugavi wa pamoja 1 2 3 4 5

Kuchochea uzalishaji wa pamoja au wamakundi

1 2 3 4 5

Kuchochea Mwendelezo/ukuaji wa mnyororo wa Ugavi

1 2 3 4 5

Mauzo ya pamoja kufanya chapa za pamoja za bidhaa na huduma

1 2 3 4 5

Kufanya chapa za pamoja za nguzo shirika

1 2 3 4 5

Kuwezesha ukuzaji wa pamoja kwenye masoko mengine

1 2 3 4 5

      49

Uboreshaji wa rasilimali watu Kuwapatia mafunzo ya kiufundi 1 2 3 4 5

Kuwapatia mafunzo ya usimamizi(umeneja)

1 2 3 4 5

Kuchoche uboreshwaji wa michakato ya uzalishaji

1 2 3 4 5

Kutengeneza viwango(maadili) vya viwanda

1 2 3 4 5

Taarifa Kukusanya taarifa za masoko 1 2 3 4 5

Uchambuzi na upashaji habari kuhusu muelekeo wa Kiufundi.

1 2 3 4 5

Juhudi zakufanya mashirika yatambuane 1 2 3 4 5

Mazingira ya Biashara Kushawishi serikali kufanya mabadiliko ya miongozo na sera.

1 2 3 4 5

Kushawishi serikali kuwekeza katika miundombinu

1 2 3 4 5

Utafiti pamoja na taasisi za utafiti/vyuo vikuu Kuchochea ubunifu wa pamoja wa bidhaa na huduma 1 2 3 4 5

Mengineo. Mengineo(Ainisha):………….................. …………………………………………...

1 2 3 4 5

7. A) Je, kampuni ndani ya kongano hupima viashiria vifuatavyo?

Hap

ana,

haw

apim

i ki

ashi

ria h

iki

Mar

a ch

ache

hal

afu

mar

a m

oja

kwa

mw

aka

Ndi

o,m

ara

moj

a kw

a m

wak

a

Ndi

o, z

aidi

ya

mar

a m

oja

kwa

mw

aka

Idadi ya waajiriwa 1 2 3 4

Uzalishaji 1 2 3 4

Uuzaji bidhaa nje 1 2 3 4

      50

Ujira 1 2 3 4

Uagizaji/uingizaji bidhaa nje 1 2 3 4

Ubunifu(idadi ya bidhaa au huduma mpya na/au zilizoboreshwa)

1 2 3 4

Bei 1 2 3 4

Gharama 1 2 3 4

Mauzo 1 2 3 4

Mengineo,(Ainisha):……………... …………………………… 1 2 3 4

7. B) Kama jibu ni Ndiyo ,Tafadhali ainisha kiwango kwenye jedwali lifuatalo; Kiashiria Thamani (kiwango)

2010 Thamani (kiwango) 2015

Mauzo (tsh)

Uuzaji bidhaa nje(tsh)

Uingizaji/uagizaji bidhaa kutoka nje (tsh)

Ujira(tsh)

Ububifu (idadi ya bidhaa au huduma mpya na/au zilizoboreshwa)

Idadi ya waajiriwa

Mengineo(Ainisha):………………... ………………………………………..

7. C) Tafadhali oredhesha idadi ya kampuni ndani ya konganokatika jedwali hapo chini:

Kiashiria Thamani 2010 Thamani 2015 Idadi ya kampuni

      51

8. Tafadhali changanua manufaa ya kujihusisha na kongano kwa kuangalia vishiriria

vifuatavyo vya uchumi.

Ath

ari

hasi

sana

Ath

ari

hasi

Hak

una

atha

ri

Mat

okeo

m

azur

i

Mat

okeo

m

azur

i sa

na.

Ushirikiano

1 2 3 4 5

Mwenendo wa kiuchumi

1 2 3 4 5

Ukuaji

1 2 3 4 5

Ufikiaji wa masoko

1 2 3 4 5

Idadi ya mashirika

1 2 3 4 5

Uwezo wa ubunifu

1 2 3 4 5

Matumizi ya wasambazaji wa ndani

1 2 3 4 5

Ushindani

1 2 3 4 5

9. Unazungumziaje kiwango cha imani (kuaminiana) baina ya wadau wafuatato ndani ya kongano?

Ipo

chin

i

Ipo

chin

i K

wa

kias

i

Hai

husi

ani Ip

o ju

u kw

a ki

asi

Ipo

juu.

Imani baina ya mashirika kwa mashirika

1 2 3 4 5

Imani ya mashirika kwa Serikali 1 2 3 4 5

Imani ya mashirika kwa taasisi za Elimu

1 2 3 4 5

Imani ya serikali kwa mashirika 1 2 3 4 5

      52

10. Je, kuna jambo lolote ambalo ungependa kutushirikisha? ………………………………………………………………………………………...... ………………………………………………………………………………………...... ………………………………………………………………………………………...... ………………………………………………………………………………………...... ………………………………………………………………………………………...... ………………………………………………………………………………………......

Asante kwa ushiriki wako!