Tech Start-up Ecosystem in Dar es Salaam FINDINGS AND RECOMMENDATIONS Content Authors and Acknowledgements 1 Executive Summary 2 Measuring and Analyzing the Tech Start-up Ecosystem in Dar es Salaam 5 Measuring the Tech Start-up Ecosystem 5 Analyzing the Tech Start-up Ecosystem 6 The Tech Start-up Ecosystem in Dar es Salaam 9 Skills 11 Supporting Infrastructure for Entrepreneurship 13 Investment 15 Community 16 Gap Analysis and Policy Recommendations 20 Summary of Gap Analysis and Stage of Ecosystem 20 Policy Recommendations 21 Sector in Focus: Climate Tech 24 References 26 Appendix A. Survey Methodology and Analysis 27 LIST OF TABLES Table 1.1 Networking Assets (Ecosystem’s Support Infrastructure) 7 Table 1.2 Categories of Ecosystems 8 Table 2.1 Top Event Hosts in Dar es Salaam 14 Table 3.1 Development Stage of Ecosystem (Per Area and Overall) 20 Table 3.2 Policy Recommendations 21 LIST OF FIGURES Figure 2.1 Start-up Growth in Dar es Salaam 9 Figure 2.2 Time to Complete Procedural Tasks in Life Cycle of a Start-up Across Regions 10 Figure 2.3 Gender Distribution and Job Functions of Founders 10 Figure 2.4 Average Age of Founders 11 Figure 2.5 Educational Level of Founders in Dar es Salaam 11 Figure 2.6 Educational Experience of Founders in Dar es Salaam 12 Figure 2.7 Previous Function and Role Type of Founders in Dar es Salaam 12 Figure 2.8 Start-ups in Accelerator Programs in Dar es Salaam 13 Figure 2.9 Accelerator’s Quality/Quantity Ratio (Selected cities) 14 Figure 2.10 Median Investment Amount by Year of Existence 15 Figure 2.11 Growth of Founders and Connections in Dar es Salaam 16 Figure 2.12 Connections in Dar es Salaam’s Ecosystem 17 Figure 2.13 Visualization of Ecosystem Connectivity 18 Figure 2.14 Start-up Success Factors in Dar es Salaam 19 List of Boxes Box 1.1 GERN Ecosystems Connection Project 6 Box 2.1 Accelerators and Incubators 13 Box 3.1 What are Coding Bootcamps? 22 Terms Used Start-up A newly establish business venture that is in its first stages of operation. This report focuses on tech start-ups, which are start-ups that have a technological component. These start-ups are typically designed to scale up quickly. Start-up Ecosystem The combination of people, start-ups at various stages and other stakeholders and organizations supporting or connecting to these start-ups, interacting in multiple dimensions to create and scale new start-up ventures. Scale-up (Firm) OECD defines a scale-up as a firm that has an average annualized return of at least 20 percent in the past three years with at least 10 employees at the beginning of the period (OECD 2007). Venture Capital (VC) An institutional investor that provides financing to start-ups and small early stage firms. Usually VCs look for high growth potential firms to exit the investment in the short term. Angel Investor An investor who invest in ventures (primarily at an early stage) in their personal capacity (that is, investing their personal money) and may or may not have an active advisory or guidance role for the founders in the venture. Mentor An experienced person who can provide advice, knowledge, or connections to a start-up founder. Mentors usually have strong business acumen and practical experience through former entrepreneurship experience or industry knowledge. Business Acumen Theoretical or practical knowledge of how to develop and manage a business, including proclivity and speed in understanding and dealing with risks and opportunities in the business environment. Exit Refers to the point at which a founder or early stage investors sell their stakes (start-up exit) in the venture, generally either in a private acquisition or public offering. This report refers more widely to “start-up exit” as the point at which a start-up is sustainable or it has received sufficient funding to grow in the medium-term (that is, over the next five years). Authors and Acknowledgements The authors of this report are Victor Mulas, Kathy Qian, and Scott Henry. Ainsley Lloyd, Matt Lerner, Kwame Robinson, Mireille Raad, Nga Nguyen and Cecilia Paradi-Guilford contributed to this report through data, economic analysis and other inputs. Studio19 conducted the survey in Dar es Salaam. The authors of this report would also like to recognize Edward Anderson, Frederick Mbuya, and the survey partners listed in Annex A for their support with the dissemination of the survey tool that was used to collect the data for this report. The report was edited by Colin Blackman (Camford Associates) and designed by Wenceslao Almazan. The peer reviewers were Steven Dimitriyev, Qursum Qasim, Megha Mukim and Dipta Shah. This report was partly funded by the Launchpad Program that forms part of the Climate Technology Program at the World Bank which aims to accelerate the growth of local green technology sectors that contribute to climate change mitigation and adaptation.  1 Executive Summary Technology is one of the main drivers of analysis of four key components of the tech start- productivity and economic growth (Isaksson, up ecosystem (skills, finance, entrepreneurial Ng, and Robyn 2005). Developing countries have supporting infrastructure, and community). traditionally had difficulties in both developing technology and absorbing foreign technology. The objective of this report is to provide a Seventy to eighty percent of the productivity gap better understanding of the status of Dar Es between developed and developing countries is Salaam’s start-up ecosystem and provide policy estimated to result from the lag in the adoption recommendations for policy makers and other of technologies in these countries (Comin and stakeholders who are interested in supporting the Marti 2013; and Comin and Hobijn 2010). growth and sustainability of the ecosystem. Tech start-ups are an effective a mechanism to both create local technology and absorb Analysis Limitations foreign technology. In recent years, there has been a surge in tech start-ups across the world. Measuring the tech start-up ecosystem is Fueled by global technology-led cost reductions difficult. Relevant databases of start-ups are and increased access to resources, tech not readily available, and the fast-paced and entrepreneurs have emerged in both develop multidimensional dynamics of start-up ecosystems, and developing countries. However, there is little with new ventures constantly being created, understanding of how these tech entrepreneurs failing, being closed, being bought or transformed form ecosystems, their internal dynamics, how (changing names and/or purpose), makes accurate they work, what makes them grow and achieve measurement over time inherently difficult. sustainability, how they connect with the local economy to drive productivity and employment, For this analysis, 221 entrepreneurs were surveyed in and why some ecosystems are more effective Tanzania from July to September 2016 and data was than others. collected for 159 relevant start -ups and 239 start- up founders. The survey was based on the standard This report is part of a broader research initiative questionnaire from the Global Entrepreneurship (See Box 1.1) aiming to provide answers to Research Network (GERN) Ecosystem Connection these questions. It provides new data and project (see Box 1.1). The findings and conclusions analysis of the tech start-up ecosystem in Dar of this analysis are based on this survey and so there es Salaam, Tanzania. The analysis comprises: (i) are some limitations to this analysis. The dataset an attempt to provide an accurate description is not exhaustive and only represents a subset of and measurement of the city’s tech start-up the ecosystem’s start-ups. Moreover, it is subject to ecosystem; and (ii) a comparison and gap survivorship bias and does not include start-ups that 2 TECH START-UP ECOSYSTEM IN DAR ES SALAAM were no longer in business when data was collected. business acumen, which may affect the survival rate Historical data about start-ups was collected through of start-ups since business knowledge is essential for existing founders who were available at the time of sustainability beyond the founding phase. Indeed, the survey. founders that have successfully obtained funding in Dar es Salaam have a higher rate of business Despite these limitations, the subset of start- education. ups surveyed provides unique insights into the ecosystem. Data was collected by snowballing from The supporting infrastructure (for example, public data contained in existing databases, networks accelerators, mentors) and the community from key stakeholders (for example, accelerators, are still nascent. When compared with more events, and so on), as well as recommendations from mature ecosystems (such as in Cairo or Medellin), founders. Although there are start-ups that were not the community in Dar es Salaam only features captured in this survey, nevertheless it provides one a small cluster with low-density connectivity. of the richest samples of data collected to date on The accelerators are the key connectors of the the most influential founders. ecosystem, holding it together and providing the higher connectivity within their networks. However, they are not producing a high number of start-ups Findings that can sustainably and independently obtain further funding. The reasons for this may lay in The tech start-up ecosystem in Dar es Salaam is low-quality services, capacity building, or network a nascent ecosystem in its initial growth phase connections provided by the accelerator. Research (see table below). The ecosystem is rapidly growing, also highlighted the need for increased availability with a 33 percent compound growth rate in start- of mentorship. up creation since 2009. However, it consists of fairly young start-ups, with most founders being in Investment options are limited for start-up their first venture, with a limited number of serial founders in Dar es Salaam. This may be because entrepreneurs and few successful cases of scale-up. of a lack of maturity in the investment infrastructure to support and follow up the growth of start-ups, A key characteristic of the ecosystem is the either because scale-up has not occurred very presence of educated founders (with 80 percent often or because the investors available for start- having a university degree and 15 percent an ups (with the risk appetite for early ventures) are additional graduate degree, masters, or professional limited. In addition, larger constraints highlighted by qualification). However, the education backgrounds entrepreneurs relate to incorporation (when outside of founders (for example, in engineering) suggest of Dar es Salaam), and processes related to obtain a highly technically driven ecosystem with low close loans and funding.  3 DEVELOPMENT STAGE OF DAR ES SALAAM’S START-UP ECOSYSTEM Stage Ecosystem Area Nascent Advancing Mature Community Skills Supporting Infrastructure Investment Constraints OVERALL Policy Recommendations Based on this analysis, a set of policy recommendations were developed (see table below), which are further expanded in the report. Ecosystem Area Policy Recommendation Objective Create community of ecosystem Coordination mechanism and ecosystem stakeholders and coordinate private support program with stakeholders and public support actions Catalyze community spaces (for example, Expand community of entrepreneurs coworking spaces) and events (for example, and increase both density and volume Community meetups, competitions, and so on) of connectivity Increase business skills among entrepreneurs, Address gap in business skills and expand practical education in universities and business acumen and provide pipeline through rapid skills training programs of talent for start-up growth Skills Capacity building of mentors in accelerators and attraction of international talent (as Address lack of quality mentors and mentors, entrepreneurs, or capacity builders) support services Support Infrastructure to the ecosystem Catalyze privately managed seed-funding Address limited availability of funding options for start-ups Investment Address processes constraints (for example, Reduce constraints for start-ups business registration and access to loans) incorporation and operationalization Constraints 4 TECH START-UP ECOSYSTEM IN DAR ES SALAAM Measuring and Analyzing the Tech Start-up Ecosystem in Dar es Salaam This report provides an analysis of the tech start-up ecosystem (countrywide). Databases are both open and proprietary. in Dar es Salaam. The analysis comprises: (i) an attempt to Access to proprietary databases is limited and in some cases provide an accurate description and measurement of the restricted (not being open to wider research). The most relevant city’s tech start-up ecosystem and (ii) a comparison and gap open databases of start-ups are provided by CrunchBase and analysis of four key components of the tech start-up ecosystem AngelList. Neither of these databases, however, necessarily (skills, finance, entrepreneurial supporting infrastructure, provides accurate or complete information. CrunchBase is and community). a self-reported database that is not curated by an official entity, and as such it can have inaccurate information such For the purposes of this report, tech start-ups are defined as as closed or transformed ventures still being posted with the for-profit business ventures that a) have a financial model original data, founders omitted, and so on. AngelList generally targeting high growth and b) employ an innovative and contains more accurate information since start-ups listed there technology-enabled approach to the product or service that have received or are actively soliciting investment from angel they provide to ensure scalability. These ventures may not be investors or venture capital (VC) firms. Other global start- profitable at their current stage. ups repositories, such as Startup Genome,1 build on these databases with additional self-reporting data from start-ups, In order to capture the whole tech start-up ecosystem, this and therefore are subject to similar limitations. Finally, while report expands the concept of start-ups to include newly LinkedIn can provide more accurate data of start-ups through emerging start-ups as well as small and medium enterprises funders and employers, data access and use restrictions make (SMEs) that were once start-ups and have reached the scaling its use for independent research purposes difficult. phase. This definition allows us to collect data to describe the evolution of the tech start-up ecosystem over time as these These global databases, however, are of little use in building start-ups grow and succeed. an overall picture of the tech start-up ecosystem in many developing countries, as they contain little information on these regions. At the time of conducting this analysis, Measuring the Tech CrunchBase contained only 43 start-ups from Tanzania, and Start-up Ecosystem AngelList only 76. Measuring the tech start-up ecosystem is a difficult task. Relevant databases of start-ups are not readily available, and Regional and local start-up databases can be richer in data the fast-paced and multidimensional dynamics of start-up and more accurate, since they are often the result of an active ecosystems, with new ventures constantly being created, failing effort to track the activity and lifecycle of start-ups. Examples and being closed, and being bought or transformed (changing of these databases are Digital NYC2 in New York or Tech Map3 names and/or purpose), makes accurate measurement over in London. However, these databases are not present in many time inherently difficult. ecosystems, particularly in developing country ecosystems, and given their localized methodologies, their data is difficult Some databases have tried to collect information on start- to utilize for comparative analytics. Other datasets, such as that ups. These databases are global, local (mostly at the level of of the Global Accelerator Learning Initiative,4 only have enough metropolitan areas’ ecosystems) and, in some cases, domestic power to report data at the regional level. Measuring and Analyzing the Tech Start-up Ecosystem in Dar es Salaam 5 While official government or NGO-managed databases in The resulting dataset is not exhaustive and only represents a developing countries can provide richer and more accurate subset of the Dar es Salaam ecosystem’s start-ups. Moreover, data from SMEs and larger companies, they also lack both data it is subject to survivorship bias and does not contain start- breadth and depth when it comes to start-ups. In the case of ups that were no longer in business when data was collected. Tanzania, the census (last conducted in 2012) only contains Historical data about start-ups is collected from existing information on employment by sector and age, but does not founders available at the time of surveying. These limitations include a category for start-ups or SMEs.5 are discussed in detail in the Methodology section of Appendix A. Despite these limitations, the subset of start-ups surveyed is To combat this poor data availability, a survey was designed likely to be a representative sample since the start-up data was and deployed by extending the standard questionnaire collected by snowballing from public data contained in existing from the Global Entrepreneurship Research Network (GERN) databases, networks from key stakeholders (for example, Ecosystem Connection project (see Box 1.1). For a broader accelerators, events, and so on), and recommendations from description and technical details of this survey, please see the founders. Although there are start-ups that were not captured “Survey Questions” section in the Methodology portion of the tin his survey, nevertheless it provides one of the richest Annex. We surveyed 221 entrepreneurs in Tanzania from July to samples of data collected to date on the most influential September 2016 using an online interactive survey distributed founders, start-ups, intermediaries, and other ecosystem through local partners by email, phone, and in person.6 stakeholders in Dar es Salaam. Out of these int erviews, we collected data for 159 relevant start-ups and 239 start-up founders. This sample gives us This report assumes that, owing to the fast-moving nature unique insights into the characteristics of founders, start-ups, of start-up ecosystems, any attempt to accurately measure investors, and supporting infrastructure in Tanzania, as well as the tech start-up ecosystem is inherently flawed – any the relationships between them.7 measurement will be obsolete immediately after collection. The findings and recommendations provided in this analysis should be taken with this limitation in mind. Less emphasis BOX 1.1 GERN ECOSYSTEMS should be placed on exact numbers, which are subject to CONNECTION PROJECT change with the addition of more start-ups and sensitive to minor tweaks in methodology. Rather, the data collected provides insights into general trends and the dynamics of the ecosystem that can inform specific policies. This analysis should The Ecosystem Connections Mapping Project not be considered in isolation, however, and policy makers are goal is to map start-up ecosystems across the encouraged to confirm these findings through other available world by collecting data on start-up founders resources (for example, perspectives from practitioners and (for example, education, work experience, anecdotal evidence). serial entrepreneurship, and so on) and the connections between themselves and other key stakeholders in their ecosystem (for example, Analyzing the Tech mentors, investors, accelerators, universities, and so on) to better understanding and support Start-up Ecosystem entrepreneurs in local start-up ecosystems. This The following analysis of the tech start-up ecosystem in Dar data aims to identify gaps in ecosystems and es Salaam is based on the data collected through the survey provide a base for policy action to address these described above. This report analyzes four key elements: i) skills, gaps and support growth and sustainability of ii) finance, iii) supporting infrastructure for entrepreneurship, start-up ecosystems. and iv) community. The analysis first describes the status of each of these elements based on the data collected, and then The project has mapped over ten start-up compares the results with those reported by both average and ecosystems in cities across the world (including, successful start-up founders. Bogota, Cairo, London, New York and Singapore). The survey conducted for this report is also part For the purposes of this analysis, successful start-ups are of this project, adding Dar es Salaam to the ecosystems mapped. defined as those that have been funded and those that employ people. “Short-term success” is defined as obtaining funding The leading partners of this project are Endeavor once; “long-term success” is defined as continuously hiring Insight and the World Bank. employees (as a proxy for growth). Source: http://gern.co/gern/ecosystem-connections- Comparing average founders and start-ups with successful mapping. ones highlights which characteristics (in terms of education, experience, connections, and so on) are more predominant in successful start-ups in Dar es Salaam and whether they are 6 TECH START-UP ECOSYSTEM IN DAR ES SALAAM consistent with those in other ecosystems or with global trends of the ecosystem as a network of stakeholders that support (where data is available). Where comparable data is available each other (directly or indirectly) for the successful outcome on other ecosystems surveyed under the GERN Ecosystems of start-ups. Connections Project, local results are benchmarked with other ecosystems to understand if there are gaps that could be This analysis is conducted under the premise that start-up addressed. ecosystems are communities of stakeholders and that the success of such ecosystems is linked to the maturity, health, The four elements this report analyzes represent the key and sustainability of the community. Previous World Bank ingredients needed for tech start-up ecosystems to grow and research (Mulas, Minges, and Applebaum 2015) shows be sustainable. Skills aims at understanding the educational that tech start-up ecosystems act as communities and that and work experience that founders have and those that are centrality (that is, the number of ecosystem stakeholders more common for successful founders. Finance looks at the to which a founder is connected to directly or indirectly) is funding obtained by start-ups during their lifecycle and the critical for start-up success. This finding is also consistent general availability of such funds. Supporting infrastructure for with research from Endeavor Insight showing that access to entrepreneurship seeks to understand the quantity and quality mentors increases the probability of start-up success.8 In this of support programs and resources for start-ups to succeed. environment, the supporting infrastructure acts both as a skills Supporting infrastructure encompasses accelerators and and network provider and is critical for ecosystem sustainability. incubators, mentors, events, and other ecosystem and/or skills The ecosystem’s supportive infrastructure is mainly comprised building resources. Finally, community looks at the maturity of networking assets (see Table 1.1). TABLE 1.1 NETWORKING ASSETS (ECOSYSTEM’S SUPPORT INFRASTRUCTURE) Collaboration Community-Building Skills Training Collaboration Spaces/Networks Network of Events Events Spaces of Mentors Mentors Meetups Bootcamps and Collaboration Accelerators Angel investors technology training and community- (network value) (network value) linked to community building spaces (e.g., building coworking spaces, maker spaces, fab labs) Tech community events/ Rapid technical and Incubators Venture capital conferences entrepreneurial skills (network value) (network value) programs Networks of mentors and start-up “alumni” networks (if different from accelerators, incubators, angel investors, and venture capital) Source: Mulas, Minges, and Applebaum 2015. Measuring and Analyzing the Tech Start-up Ecosystem in Dar es Salaam 7 Based on these four elements, the analysis categorizes There are an increasing number of international connections ecosystems into three broad categories: a) nascent ecosystems, and mentors with local start-up experience. The finance b) advancing ecosystems, and c) mature ecosystems (see Table pipeline is starting to form with increasing private sector 1.2): investment in early stage start-ups, but there are still gaps in the path to scale up and exit. a) Nascent Ecosystem. There are a limited number of start- ups, most of which are in the very early or early stages. The community of entrepreneurs is forming, and has low c) Mature Ecosystem. These ecosystems have a large density of connections with few clusters, if any. In these number of start-ups in all stages (for example, growing, ecosystems, founders lack business experience, mentors scaling up, exits) and the ecosystem is highly interconnected. are scarce and inexperienced and there are few generations The majority of entrepreneurs have business acumen and of entrepreneurs (most entrepreneurs are in their first or previous relevant business experience, there are several second venture), there are few or no start-up exits or, if there generations of start-ups with multiple serial entrepreneurs are any, they are outliers. There are few or no international and successful exits. Mentors are abundant, they have connections. The finance pipeline has multiple gaps and solid practical experience, and there is a solid base of angel private early stage finance is rare (if it exists). investors. The ecosystem is an international hub itself and attracts international talent. The finance pipeline has no gaps and early stage funding is provided by sustainable b) Advancing Ecosystem. These ecosystems have an private funds. increased number of start-ups, with most in the early stages but with increasing numbers of scale-ups. The community of entrepreneurs has several clusters and a high density, These categories are broad and serve to provide a sense and there are a handful of successful start-up exits. There of where ecosystems are in their life cycle. As this research is still a lack of business acumen among entrepreneurs but continues and we can access data from a larger sample of there are a growing number of serial entrepreneurs and the ecosystems from the GERN Ecosystems Connection project, we ecosystem has more than three generations of start-ups. will be able to provide more concrete metrics on these stages. TABLE 1.2 CATEGORIES OF ECOSYSTEMS Stage Ecosystem Area Nascent Advancing Mature • Increased number of start-ups, • Limited number of start-ups, • Large number of start-ups most in early stage with increasing most in early stage in all stages number of scale-ups • Low density of connections • Highly dense, • Several clusters • Low number of clusters hyperconnected clusters Community • High density in clusters • Lack of business acumen • Limited business acumen • Business acumen and and experience practical experience • Increasing number of serial • Very few serial entrepreneurs entrepreneurs and more than 3 • Several generations of and limited generations of generations start-ups entrepreneurs • Exits start to appear • Successful exits Skills • No substantial exits • Plenty of mentors with • Mentors are available and they have • Mentors are scarce and sound practical experience local practical experience inexperienced • International hub • Increasing number of international Supporting • No international connections attracting international connections Infrastructure talent • No gaps in finance • Gaps in finance pipeline • Finance pipeline with some gaps pipeline • Very few private sector • Private early stage investment exists • Private early stage finance funding opportunities Investment sustainable 8 TECH START-UP ECOSYSTEM IN DAR ES SALAAM The Tech Start-up Ecosystem in Dar es Salaam The tech start-up ecosystem in Dar es Salaam is a nascent a total of 196 jobs created. The median start-up that provided ecosystem in its initial growth phase. On average, each year, employment data was one year old and hired three people per 11.5 more start-ups are created than in the previous year, year. This suggest that Dar es Salaam is a nascent ecosystem, resulting in a 33 percent compound growth rate in start-up with fairly young start-ups and few successful cases of scale-up. creation since 2009 (see Figure 2.1).9 Start-ups in Dar es Salaam face some bureaucratic hurdles in FIGURE 2.1 START-UP GROWTH IN their life cycle (see Figure 2.2). Ventures can rapidly open a bank account but incorporation, renting an office, and hiring DAR ES SALAAM qualified personnel typically takes about a month. The time for incorporation increases substantially if the start-up is # ACTIVE OF STARTUPS IN ECOSYSTEM BY YEAR located outside Dar es Salaam, as the only accessible registrar for incorporation is in the capital city.10 Obtaining funding is a much lengthier process taking about three months to procure 80 bank credit (for example, a loan) and four months to close 70 funding (for example, individual or institutional investor). 60 Compound Annual Growth Rate: 33% Respondents also expressed sentiments such as, “Taxation 50 and laws governing filing of taxes are not readily available # of Startups and friendly for the start-up scene. Hiring services for filing 40 taxes is expensive and difficult to find one that can help a 30 start-up,” and, “The business registration process is frustrating especially because I am a foreigner. I cannot register a business 20 without a permit and I cannot get a permit without a business 10 registration.” One entrepreneur expressed disappointment with “bureaucracy in the government policies, as it took us more 0 than two years, to just get the Impact Assessment certificates,” and another found that “patenting procedures in Tanzania is not very clear.” Another relayed that, “Only until recently office space has been readily available and affordable for start-ups to rent for less than three month advance payments.” There 2009 2010 2011 2012 2013 2014 2015 is a general sentiment that, “Government processes and YEAR regulations are a challenge, they are very slow, and most of the time, there is no clear process to follow.” Note: Data shows tech start-up ventures as reported by founders of active start-ups during date of survey. Data of start-ups founded in year 2016 was not included in this figure because the survey was carried out in mid-2016, Compared to other early and middle stage ecosystems (such which was not comparable with all previous data from complete years. as Lebanon and Palestine), processes in Tanzania are lengthier for receiving credit and funding, indicating greater difficulty About one-third of the start-ups surveyed reported hiring at (linked to procedures or access to finance requirements) in least one employee, with a median of four jobs per start-up and obtaining funding for start-ups. The Tech Start-up Ecosystem in Dar es Salaam 9 On average, start-ups have 1.5 founders and each founder Founders are predominantly male, and they are as likely to have has launched 1.1 start-ups. This is consistent with nascent business and technical functions, with about a quarter taking stage ecosystems, when serial entrepreneurs are yet on business functions, a quarter taking on technical functions, to feature. and half doing both simultaneously (see Figure 2.3). FIGURE 2.2 TIME TO COMPLETE PROCEDURAL TASKS IN LIFE CYCLE OF A START-UP ACROSS REGIONS MEDIAN # OF DAYS PER TASK Set up Bank Dar es Salaam 4 COUNTRY Account West Bank & Gaza 3 Dar es Salaam West Bank & Gaza Beirut Beirut 5 Become Dar es Salaam 22 Incorporated West Bank & Gaza 30 Beirut 28 Rent an Dar es Salaam 30 O ce West Bank & Gaza 15 Beirut 30 Hire an Dar es Salaam 30 Employee West Bank & Gaza 15 Beirut 30 Obtain Dar es Salaam 90 Credit West Bank & Gaza 15 Beirut 30 Obtain Dar es Salaam 125 Funding West Bank & Gaza 33 Beirut 120 0 10 20 30 40 50 60 70 80 90 100 110 120 130 MEDIAN DAYS FIGURE 2.3 GENDER DISTRIBUTION AND JOB FUNCTIONS OF FOUNDERS Gender Distribution of Founders Job Functions of Founders GENDER JOB FUNCTION Male Both (Business & Technical) Female Business Other 21.54% Technical 14.56% 3.08% 53.46% 85.44% 21.92% 10 TECH START-UP ECOSYSTEM IN DAR ES SALAAM Founders are quite young, with an average age of 23-24 at the time of founding (see Figure 2.4).11 As a comparison, mid-stage Skills ecosystems, such as Bogota, and mature ecosystems, such as Dar es Salaam founders are highly educated for Tanzania, but New York, have an average age of founders in the late 20s and they lack critical business acumen and are fairly inexperienced. early 30s.12 Education is high among founders in the context of Tanzania13 FIGURE 2.4 AVERAGE AGE OF FOUNDERS with 80 percent having a university degree (65 percent bachelor’s and 15 percent an additional graduate degree, Average Age of Founders Across Various Regions masters or professional qualification).14 Funded start-ups were more likely to have higher education, with a third of them REGION having founders graduate education in the form of a masters Dar es Salaam or professional degree (see Figure 2.5) West Bank & Gaza Beirut 34 New York City FIGURE 2.5 EDUCATIONAL LEVEL OF FOUNDERS 33 Bogotá IN DAR ES SALAAM 32 HIGHEST DEGREE 31 Professional 30 Master Bachelor 29 Avg. Age Associate 28 High School 27 26 Highest Degree Earned by Founders at Time of Founding 25 24 Dar es Salaam New York City 23 6.86% West Bank 22 13.73% Bogotá Beirut 21 8.82% 20 5.88% Age Distribution of Founders in Tanzania 64.71% 70 65 AGE (BIN) 60 Below 20 20-24 Highest Degree Earned by Funded Founders at Time of Founding 25-29 # of Fouders 50 30-34 35-39 40 40-44 55-59 14.29% 30 17.86% 7.14% 20 18 14 14.29% 10 8 2 1 1 46.43% 0 Below 20 20-24 25-29 30-34 35-39 40-44 55-59 The Tech Start-up Ecosystem in Dar es Salaam 11 The majority of founders (62 percent) have a degree in science, The average founder in Dar es Salaam has 5.4 years of work technology, engineering or mathematics (STEM), 20 percent experience with no more than two companies (1.6 companies have a degree in business, and 2 percent had both STEM and on average). This work experience is similar to other emerging business degrees.15 Twenty percent of STEM degrees were ecosystems. For instance, in Bogota, the average founder and master’s or higher. This suggest a highly technically driven has worked in an average of 1.7 companies. In a much more ecosystem with low business acumen, which may affect the mature ecosystem, such as New York City, the experience of survival rate of start-ups since business knowledge is essential founders is much higher, with 9.9 years in 3.3 companies. for sustainability beyond the founding phase. By comparison, in a more mature ecosystem, such as New York, almost half The largest category of previous experience is analyst, with 24 of the founders hold a graduate business degree.16 Indeed, percent of founders with such experience. Only about one in founders that have successfully obtained funding in Dar es ten founders have experience in a managerial role, confirming Salaam have higher rate of business education with about a the lack of business acumen in the ecosystem (see Figure 2.7). third of them having degrees in business (see Figure 2.6). Indeed, most founders have technical experience, with only 35 percent having business work experience. There are few serial entrepreneurs, with most founders being in their first venture. FIGURE 2.6 EDUCATIONAL EXPERIENCE OF This job experience of Dar es Salaam founders is consistent FOUNDERS IN DAR ES SALAAM with the preeminence of younger founders and the nascent stage of the ecosystem.17 Educational Experience of Founders at Time of Founding FIGURE 2.7 PREVIOUS FUNCTION AND ROLE 1.94% TYPE OF FOUNDERS IN DAR ES SALAAM Previous Role Type of Founders 20.39% JOB LEVEL 24% 23.75% 22% 20% Precentage of Founding Instances 18% 62.14% 15.53% 16% 14% 12% 11.11% MAJOR 10% Business 8% Other Educational Experience of Funded Founders 6% STEM at Time of Founding 4% 3.45% Both (STEM & Business) 2% 1.53% 0.77% 0.77% 1.15% 6.90% 0% c-level advisor director manager analyst freelancer intern Previous Functions of Founders at Time of Founding 10.81% 27.59% 24.32% 58.62% 6.90% Both Business 64.86% Technical 12 TECH START-UP ECOSYSTEM IN DAR ES SALAAM Supporting Infrastructure for BOX 2.1 ACCELERATORS AND INCUBATORS Entrepreneurship Consistent with the ecosystem maturity, the supporting Although the dataset and analysis does not infrastructure for start-ups in Dar es Salaam’s ecosystem is differentiate between incubators and accelerators, nascent but growing. The two main elements of the supporting there is a difference in their definition: infrastructure analyzed are accelerators and incubators, terms that are used interchangeably in this report (see Box 2.1), and Accelerators support entrepreneurs and start-ups mentors. in the early stages of development and they are often comprised of the following features: (i) a highly competitive and open application process Accelerators for entrepreneurs, (ii) provision of small amounts Accelerators support start-up growth by providing skills and of seed investment, (iii) a focus on small teams networks of connections. Previous research from the World rather than individual founders, (iv) intensive Bank shows that accelerators have a key role in supporting the support for a limited period of time (usually 3-6 community of start-ups that generate the ecosystem, providing months), with active mentorship and networking, the necessary social connectivity among entrepreneurs and and (v) collaborative work among start-ups through cohort or classes of start-ups. other ecosystem stakeholders (Mulas, Minges, and Applebaum 2015). Research from the Aspen Institute also suggests that Incubators are spaces that support start-ups by accelerators have a positive impact in supporting early stage providing an office space and administrative ventures by providing access to a network of mentors and support services. The most typical services are capacity building, particularly regarding business skills and legal, recruiting, IT, accounting, public relations and acumen (I-DEV International 2014; Baird, Bowles, and Lall 2013; pooled buying programs. In addition, incubators and Roberts, Lall, and Baird. 2016). may also provide coaching, mentorship, and help with access to funding on an ad hoc basis. Start- There are five relevant accelerator and mentorship programs ups pay rent (which is usually below the market supporting a total of 34 of 159 start-ups in the ecosystem (see rate) for the office space and normally a time limit Figure 2.8). Three of these programs stand above the rest: 1) is not set for start-ups staying in the incubator Buni Hub,18 2) Small Industries Development Organization (the average length of stay ranges widely from 18 (SIDO),19 and 3) Dar Teknohama Business Incubator (DTBI).20 Two months to five years). Some incubator providers of these (SIDO and DTBI) are government-funded programs may ask for a share of future profit or require provided by the Ministry of Trade and Economy and the minority stake in the start-up as a prerequisite for Tanzanian Commission for Science and Technology (COSTECH), access to the incubator. respectively. Buni Hub is funded by COSTECH and is based in their government offices. A subset of start-ups accelerated by Source: Mulas, Minges, and Applebaum 2015. these programs was included in the survey. FIGURE 2.8 START-UPS IN ACCELERATOR PROGRAMS IN DAR ES SALAAM 16 ACCELERATOR NAME 1 KINU INNOVATION HUB 14 NBC BANK SIDO 12 5 BUNI INNOVATION HUB # of Startups DAR ES SALAAM 10 TEKNOHAMA BUSINESS INCUBATOR DTBI 8 6 1 9 4 1 2 4 2 2 1 0 1 1 1 2009 2010 2011 2012 2013 2014 2015 The Tech Start-up Ecosystem in Dar es Salaam 13 Buni Innovation Hub has the largest portfolio of start-ups, Of start-ups that were funded, accelerated start-ups received and it also hosted 10 percent of the events entrepreneurs an average of 1.16 investments, while unaccelerated start-ups reported attending, suggesting a broader impact of this received an average of 1.15 investments.22 an acceleration accelerator in the ecosystem buildup (see Community section multiplier of investment 1.01.23 If we interpret the number of below for further details) beyond supporting the start-ups in investments a start-up receives as a signal of its quality, this its program (see Table 2.1). About 20 percent of start-ups in suggests that accelerators in Dar es Salaam are not having our sample in Dar es Salaam participated in an acceleration a meaningful impact in improving the quality of start-ups. program and those who received funding from acceleration In fact, of the 38 start-ups that were funded, more (26) were obtained a median of $1,000 in initial investment. unaccelerated than accelerated (12). TABLE 2.1 TOP EVENT HOSTS IN Using these numbers, the effectiveness of accelerators in Dar es Salaam’s ecosystem may be estimated by calculating a DAR ES SALAAM ratio of quality, as represented by the acceleration multiplier of investment (1.01), to quantity, as represented by the acceleration multiplier for funding probability (1.70), which results in a ratio of 0.6. This is consistent with the quality to Number of Events Attended Event Host quantity ratio from other less mature ecosystems such as Cairo, by Entrepreneurs which also has a ratio less than 1, while more mature ecosystems such as New York City will have a ratio of more than 1. FIGURE 2.9 ACCELERATOR’S QUALITY/QUANTITY Buni 7 RATIO OF 0.6. (SELECTED CITIES) 1.40% 1.28 1.26 Costech 5 1.20% 1.04 1.00% 0.95 Government of 4 Tanzania 0.80% 0.60 0.60% Others (World Bank, 0.55 Sahara Parks, Tantrade, 0.47 University of Dar 3 or fewer 0.40% es Salaam, 2 Seeds 0.36 Network) West Bank & Gaza Dar es Salaam New York City 0.20% 35.3 percent of accelerated start-ups were funded, while 20.8 Singapore Santiago Medellin percent of unaccelerated start-ups were funded. This means Beirut Cairo that the acceleration multiplier for funding probability is 0.00% 1.70.21 This is a normal effect for nascent ecosystems, where accelerators are crucial in increasing the quantity of funded start-ups in the ecosystem’s pipeline. Preliminary analysis of These results support the conclusion that simply producing a similar measurements in more mature ecosystems, such as number of start-ups does not necessarily mean that they will New York City or Santiago, shows that this ratio reduces to be sustainable or will receive additional funding. In fact, only become closer to 1 as the funding ecosystem matures and five percent of these accelerated start-ups received additional accelerators are no longer the gatekeepers to investment investment in the years after graduating, indicating that the networks. 14 TECH START-UP ECOSYSTEM IN DAR ES SALAAM vast majority of funding obtained by accelerated start-ups occurred in the same year as acceleration. This suggests that, Investment while accelerators are directly connecting start-ups with The value of investors extends beyond the money they provide. investors, they are not producing a high number of start- Early stage investors are often valued both for their networks ups that can sustainably and independently obtain further and for their experience and subject area expertise, hence the funding. The reasons for this may lay in low-quality services, phrase “smart money.” This report considered all organizations capacity building, or network connections provided by the that invest in high-growth start-ups, venture capital firms, angel accelerator, the lack of available funding in later stages of investors, and other individuals. start-ups (see Investment analysis below) or both. This is a common characteristic of accelerators in nascent ecosystems, Investment options are limited for start-up founders in Dar es that lack the talent and expertise to produce high-quality, Salaam,26 the study identifying only 11 investors. A third of these internationally competitive services with a recognizable brand were venture capital firms, and two-thirds were angel investors. that holds its value after the end of the program. They made a total of 11 investments in nine start-ups, of which two (18 percent) were identified as debt financing, and four (36 Mentors percent) were identified as equity financing.27 Mentorship is a knowledge transfer mechanism for Almost all of the investments occurred in the first year of entrepreneurs to acquire business acumen, understand the operation of the start-up concerned with a long-tail distribution, unspoken rules of start-up challenges, and access networks consistent with the nascent and young ecosystem observed in of talent, knowledge, and resources. Mentors need to Tanzania (see Figure 2.10). One company raised capital four times, be knowledgeable and experienced. A study for the U.K. one three times, and one twice. The remaining companies raised government found that the most important characteristic of capital once. While the majority of fundraising captured in the a mentor is proven business success in the area of work and dataset is in the very small seed range, 84 percent of investments network of contacts (BMG Research and Galli 2013). Mentorship received (37 out of 44) are below $25,000, although there are a relationships were found to often develop informally through few large outliers (one start-up raised $4.5 million in one round). a preexisting relationship. For example, acceleration and incubation programs typically assign start-ups formal mentors This may suggest that there is a lack of maturity in the investment to assist them for a designated period of time. Research from infrastructure to support and follow up the growth of start-ups, Endeavor Insight shows that top performing start-ups have either because it has not occurred very often (for example, much higher support from mentors.24 because there are not many start-ups and few have advanced to the scale-up stage) or because the investors available for start- Thirty-five percent of founders in Dar es Salaam received ups (that is, with the risk appetite for early ventures) is limited. mentorship. On average, mentorship was provided for a Further analysis is needed to understand the availability of duration of one year. The top three mentors are related to funding through the early stage pipeline. Buni Hub or DTBI. Individuals affiliated with these institutions mentor around 30 percent of founders. This is consistent with A respondent also suggested that “funding agencies, should the wider impact of accelerators supporting the ecosystem stop giving people money, as grants, and rather find a better way (see Community section below) and highlights the need to to fund start-ups, potentially loans, because it floods the market FIGURE 2.9 have experienced people in these accelerators (see Box 2.2).25 with cheap cash.” FIGURE 2.10 MEDIAN INVESTMENT AMOUNT BY YEAR OF EXISTENCE #of startups receiving funding 30 25 20 15 10 5 0 $5,000 $6,900 $7,500 $4,000 $6,881 $9,174 $49,546 $45,876 $2,500 $10,000 $25,000 Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 8 Year 10 Year 11 Year 12 Year 13 Startup year of existence when receiving funding Note: Only three start-ups received investment more than once. The Tech Start-up Ecosystem in Dar es Salaam 15 Community Start-up ecosystems operate as communities, where knowledge When comparing the density and clusters of the community spillovers and access to resources flow through a network of with those of more advanced ecosystems (see Figure 2.13), embedded connections (see Figure 2.11). The tighter and Dar es Salaam only features a small cluster with low-density more connected an ecosystem, the more efficient is the flow connectivity. Cairo or Medellin have already evolved into highly of knowledge and access to resources. The less connected it connected clusters driving connectivity in the ecosystem. is, the less effective is the ecosystem in spotting talent and nurturing potential ventures into successful start-ups. Connectivity matters because success of start-ups is impacted by FIGURE 2.11 GROWTH OF FOUNDERS AND their connectivity and access to other ecosystem stakeholders CONNECTIONS IN DAR ES SALAAM and their networks (Mulas, Minges, and Applebaum 2015). Networking assets (see Supporting Infrastructure for Entrepreneurship section above), and accelerators in particular, Year act as key connectors of ecosystem stakeholders, creating events and networks among stakeholders and creating clusters EDGES LABEL that strengthen the ecosystem. Invested in Accelerated/incubated In Dar es Salaam, accelerators are the key connectors of the Mentored ecosystem, holding the ecosystem together and providing 550 Founded the higher connectivity within their networks. They have the highest centrality measures in the ecosystem by all social network measures (see Analysis section of Appendix A). 500 The largest two networks of connectivity in Dar es Salaam’s ecosystem are Buni Innovation Hub and the University of 450 Dar es Salaam (see Figure 2.12). For start-ups to raise funding successfully, they need to be part of the core clusters of connections. In Dar es Salaam, the ecosystem is less densely 400 connected, meaning that founders already need to have a connection with the clusters of accelerators, investors, or funded start-ups or reach out to those clusters as outsiders. 350 Indeed, there appears to be an inner circle in the investment Count Cumulative ecosystem, outside of which raising capital is difficult. This hypothesis is further supported by a comment from a survey 300 respondent who stated, “On many occasions, start-ups in Tanzania, unless housed at the few existing hubs, have little to no access to programs inside and outside of Tanzania that can 250 help them get funding or participate in competitions.” The community of Dar es Salaam’s ecosystem is consistent with its nascent stage, with very low density and limited 200 number of clusters.28 Higher density and more clusters allow entrepreneurs to connect to knowledge and resources through other actors in the ecosystem. The less dense the ecosystem, 150 the more difficult it is for a founder to find their way to mentors, investors, or other relevant knowledge or resources required for their venture. Clusters serve as multipliers of 100 density, helping founders leapfrog orders of connection (for example, connections that in other case are a 5th or 7th order connections, that is, the founder is connected through five or 50 seven connections to the target person, become a 2nd or 3rd order connection, where the founder is connected through two or three people to the target connection). 0 2009 2010 2011 2012 2013 2014 2015 2016 16 TECH START-UP ECOSYSTEM IN DAR ES SALAAM FIGURE 2.12 CONNECTIONS IN DAR ES SALAAM’S ECOSYSTEM BUNI UNIVERSITY OF DAR INNOVATION ES SALAAM HUB HUB Note: This graph highlights University of Dar es Salaam (red) and Buni Innovation Hub (orange), two of the most influential actors in Dar es Salaam’s innovation ecosystem. The Tech Start-up Ecosystem in Dar es Salaam 17 FIGURE 2.13 EVOLUTION OF START-UP CLUSTERS AND CONECTIVITY IN ECOSYSTEMS MEDELLIN Advancing CAIRO Note: Network graphs were created by taking all the people in the ecosystem and creating edges to all other people they were directly or DAR ES Nascent indirectly connected to in order to exaggerate SALAAM the effects of clusters for illustration purposes. Because this treatment visualizes the influence of edges seen in Figure 2.12 by counting them more than once, the density of the clusters and the overall graph will appear different from Figure 2.12, where each edge is represented only once, even though the underlying data is the same. 18 TECH START-UP ECOSYSTEM IN DAR ES SALAAM Start-up Success Factors Start-up success is difficult to determine as tech ventures are unable to benefit in terms of access to opportunities and operate in a fast-paced environment under continuous change. resources of the ecosystem. Another entrepreneur laments, To analyze factors that have determined start-up success, this “There are gaps in the ecosystem, no platform for people to analysis identifies two moments in the growth of start-ups. meet, everyone is doing their own thing” and another agrees We consider “short-term success” as when a venture obtains that, “The start-up ecosystem is very young and fragmented. funding from an outside investor and “long-term success” as Current initiatives being made are very scattered.” There is an when a venture hires employees consistently (this assumes additional sense that the ecosystem is not working together, continuous growth as the talent-knowledge assets of the start- and a respondent suggests that “players in the ecosystem, for up grow). example. incubators, hubs, accelerators, and so on, should work together to build a stronger ecosystem rather than competing and dividing opportunities,” perhaps through, as another respondent suggests, “joint forums where people can Factors of Short-term Success collaborate and share ideas.” The most significant factor for raising funding in Dar Es Salaam is to be part of an influential investment cluster and be well connected within the cluster. Given the early stage of the Factors of Long-Term Success ecosystem’s community (see Community section), there are few clusters available for start-ups and few start-ups inside The most significant factors for long-term success (that is, hiring these clusters. Those start-ups who are inside these clusters employees over time and continuing to do so) are: a) obtaining have access to investors and can leverage those connections an investment, b) having founded a previous start-up, c) having to obtain their first funding. Once start-ups have become part a mentor, and d) having an additional founder in the venture. of one of these clusters and obtained funding, they increase The most significant is having an investment. This is consistent substantially their network of investors, reinforcing their with previous findings, confirming that once start-ups enter positioning in relation to future investments. the inner circle, they obtain the full benefits of the ecosystem, being able to access both resources and knowledge. These results suggest that there appears to be an inner circle in the investment ecosystem, outside of which raising capital Apart from investment, all other factors are related to increased is difficult. This hypothesis is supported by a comment from a knowledge and experience, either direct or indirect (for survey respondent who stated, “On many occasions, start-ups example, mentors) practical founding experience, or additional in Tanzania, unless housed at the few existing hubs, have little knowledge and experience from another founding member.29 to no access to programs inside and outside of Tanzania that Overall, these factors suggest that the low success rates of can help them get funding or participate in competitions.” start-ups are directly related to the general lack of practical This results in a divide between “privileged’ (that is, part of the experience and knowledge, particularly business acumen, in inner group) start-ups and “non-privileged” start-ups, which the ecosystem. FIGURE 2.14 START-UP SUCCESS FACTORS IN DAR ES SALAAM SHORT TERM LONG TERM SUCCESS SUCCESS BE A CORE MEMBER OF AN INFLUENTIAL OBTAINING AN INVESTMENT INVESTMENT CLUSTER Having founded a previous start-up Having a mentor Having an additional founder in the venture RAISING FUNDING HIRING EMPLOYEES OVER TIME The Tech Start-up Ecosystem in Dar es Salaam 19 Gap Analysis and Policy Recommendations Summary of Gap Analysis and Stage of Ecosystem The evidence from our analysis points to the conclusion that the start-up ecosystem in Dar es Salaam is at a nascent stage that is just beginning to develop (see Table 3.1). TABLE 3.1 DEVELOPMENT STAGE OF ECOSYSTEM (PER AREA AND OVERALL) Stage Ecosystem Area Nascent Advancing Mature Community Skills Supporting Infrastructure Investment Constraints OVERALL There are multiple gaps that can be addressed through policy but they do yet not seem to be providing services of sufficient action and support. The ecosystem is not functioning as a solid quality to support sustainable ventures. Investment options community yet and start-ups cannot take advantage of social are limited and scarce beyond seed funding. There is a lack connectivity to reach out to talent, skill capacity, mentors, of experienced talent and relevant business acumen in and investment. Events and community building relies on founders, which together with the limited availability of mainly isolated actors, although connectivity is growing with quality mentorship, reduces the availability of young talent to increasing activity by some actors, for example, such as Buni) complete the cycle of a successful venture. Other constraints and donor and government action still drives a large number (for example, processes for establishing a business, hiring, of activities in the ecosystem. There is an incipient number of obtaining space and funding) reduce the ability of start-ups support programs for entrepreneurs (for example, accelerators) to grow. 20 TECH START-UP ECOSYSTEM IN DAR ES SALAAM Policy Recommendations Table 3.2 summarizes the policy recommendations for the Dar es Salaam ecosystem based on the key gaps and constraints identified. TABLE 3.2 POLICY RECOMMENDATIONS Ecosystem Area Policy Recommendation Objective Create community of ecosystem Coordination mechanism and ecosystem stakeholders and coordinate private and support program with stakeholders public support actions Catalyze community spaces (e.g., Expand community of entrepreneurs Community coworking spaces) and events (e.g., and increase both density and volume of meetups, competitions, etc.) connectivity Increase business skills among Address gap in business skills and entrepreneurs, expand practical business acumen and provide pipeline of education in universities and through talent for start-up growth rapid skills training programs Skills Capacity building of mentors in accelerators and attraction of Address lack of quality mentors and international talent (as mentors, support services entrepreneurs, or capacity builders) to Support Infrastructure the ecosystem Catalyze privately managed seed- Address limited availability of funding for funding options start-ups Investment Address processes constraints (e.g., Reduce constraints for start-ups business registration and access to loans) incorporation and operationalization Constraints Gap Analysis and Policy Recommendations 21 These policy recommendations only address short-term BOX 3.1 WHAT ARE CODING BOOTCAMPS? actions to support the Dar es Salaam ecosystem. Policy makers should constantly monitor the ecosystem (which can be done through the coordination mechanism once in place) and iterate the policy approaches as needed and address new upcoming gaps or growth hurdles as they arise. As the ecosystem grows Coding bootcamps are intensive short-term and evolves into more maturity, new needs will emerge and programs, typically lasting three to six months, other specific policies would be more applicable. designed to train participants in programming skills to make them immediately employable 1. Community in entry-level tech positions.30 In essence, they combine characteristics of traditional Establish a coordination mechanism and codevelop vocational training programs with the intensity support program with ecosystem stakeholders. At the of military bootcamps for new recruits, early stage of an ecosystem, stakeholders are uncoordinated, intermingling soft and tech skills learning in and information about actions and activities does not flow an intense manner, in what could be referred transparently through the community. Successful ecosystems, to as “skills accelerators.” Coding bootcamps such as in Buenos Aires, Tel Aviv, or New York, create different follow a structured process with three main types of coordination mechanisms among stakeholders led by characteristic features: 1) intense rapid skills public policy actors (for example, municipal or government training, 2) an experiential learning approach, innovation agencies). This varies from stakeholder roundtables and 3) curricula based on, and continuously to continuous consultation processes to online platforms to adapting to, industry’s demand. share events and intermediaries’ activities (ITIF 2017). Although the bootcamp methodology has primarily focused on coding skills, it has also A practical way to catalyze this coordination is through been adapted for business and entrepreneurial cocreation of the support program, where ecosystem gaps skills as well as other technical skills. Usually, are validated with the stakeholders and support activities bootcamps programs embed “life skills” in are allocated and coordinated among public support their curriculum, enabling their graduates to policies and stakeholder actions, avoiding duplication be competitive irrespective of the industry in and catalyzing sustainability. A follow-up mechanism to which they choose to work, for example, the coordinate implementation of the program would serve to ability to master new knowledge quickly and institutionalize coordination. An example of such an approach efficiently, effectively work in a team, meet was implemented in Chile to catalyze start-up ecosystems in tight deadlines, and so on. Evidently, these “life secondary cities (Mulas and Barroca 2015). skills” belong to the subset of future-proof soft skills (World Bank, 2017). Expand community spaces and events. Public policies can support the number and expansion of community spaces (for example, coworking spaces, accelerators, and so on) and events. In many early stage ecosystems, government (for example, municipalities, or regional or national government) have sponsored or implemented periodic competitions to address challenges and foster start-up creation. Chile, Barcelona, and Amsterdam are examples of such an approach. In addition, ecosystems in cities such as Buenos Aires, Tel Aviv, and New York, catalyzed community spaces by providing space and operational funding for a limited period (one to three years) to coworking spaces, accelerators, and incubators to set up in In New York, these community spaces were required to create the city. These public-private partnerships (PPP) resulted in a and provide a network of mentors, provide capacity training, growing number of community spaces that create their own and develop a local community with entrepreneurs and clusters, increasing the density and breadth of connectivity. neighborhood actors (Mulas and Gastelu-Iturri 2016). 22 TECH START-UP ECOSYSTEM IN DAR ES SALAAM 2. Skills Increase business skills and practical education. Public international talent.33 This has led to a broader connection policies can catalyze and support practical educational of the ecosystem’s intermediaries (for example, community programs to address the skills gaps in tech start-up spaces, accelerators, and so on) with international talent ecosystems. Coding bootcamp methodologies (a rapid and partners, increasing the ecosystem’s connectivity and skills training methodology) have proven successful in capacity building overall. quickly assessing market gaps and demands in tech start-up ecosystems. For instance, New York City’s initiative to support Santiago, Chile, followed a more structured approach, rapid skills training programs resulted in General Assembly, creating a program (Startup Chile)34 to attract international one of the largest providers of bootcamps worldwide, which talent to the ecosystem. The program is in essence an serves to address skills gaps ranging from entrepreneurship acceleration-funded program for high-skilled international and business skills to specific coding and technical skills talent to conduct their ventures’ initial stage in Chile. The through rapid skills training programs (see Box 3.1) (Mulas program introduced specific activities to ensure knowledge and Gastelu-Iturri 2016). Public policy programs can also help spillovers between international talent and domestic community spaces, and accelerators can also be supported to entrepreneurs. For instance, international entrepreneurs enhance their capacity to provide higher quality mentorship were collocated in a coworking space with domestic and training to start-up ventures with potential through their entrepreneurs with a requirement that they provide capacity programs (in the case of accelerators) of related activities. building and workshops on entrepreneurship and technical skills for domestic entrepreneurs. This mechanism has been Additionally, public policies can catalyze the introduction emulated by other ecosystems, including the K-Startup of practical education programs for university students and Grand Challenge in the Republic of Korea.35 address part of the gap in business acumen. Initiatives, such as Demola in Tampere,31 Finland and Jacobs Technion-Cornell Institute32 in New York were created to address this specific 4. Investment gap in their ecosystems. In both cases, a practical project- Catalyze privately managed seed-funding options. based education is added for students to learn-by-doing Access to finance for early stage ventures and their scale-up with businesses. is key for sustainability of the ecosystem. This, along with the lack of mentorship and skills, are the main constraints for new ecosystems in their nascent stage. Successful ecosystems, 3. Support Infrastructure such as in New York or Tel Aviv, applied policies to catalyze Increase capability of mentors and attraction of the attraction and development of local seed investment international talent. Support can also be provided to options for their ecosystems. In both instances, policy increase the capability of mentors (for example, through support was focused on catalyzing private-sector led early training and capacity-building programs). However, local stage financing, using public funds to derisk the entrance of capacity programs have limited impact in nascent stages of players, without interfering in investment decisions (Mulas ecosystems. This is because there is a general lack of practical and Gastelu-Iturri 2016). experience and knowledge among mentors and trainers since there have not been many cases of successful ventures 5. Constraints that have been proven internationally. Address processes constraints. The larger constraints A way to address this lack of experience and knowledge is to highlighted by entrepreneurs relate to incorporation (when attract international talent with such practical acumen. There outside of Dar es Salaam), and processes related to obtain are several support programs that can achieve this goal, close loans and funding. Policy reforms can streamline ranging from events that gather international talent and these processes, by establishing specific conditions for connect it to the ecosystem, to more structural programs. start-up processes (for example, by reducing requirements) For instance, in Lebanon, the Central Bank took an informal or, to facilitate access to finance (for example, by providing approach, organizing an annual conference and inviting top guarantees for loans). Gap Analysis and Policy Recommendations 23 Sector in Focus: Climate Tech One particular challenge in ecosystem-building is identifying asked regarding the nature of respondents’ businesses, as competitive advantages inherent to each region and building well as the attractiveness of the sector and what might upon existing strengths in order to create unique local hubs. improve that attractiveness. The results of this sector in As a respondent summarizes, “there should be more locally focus survey are as follows: relevant innovation and not western copied innovation.” The start-up ecosystem in Dar es Salaam has a significant As a sector in focus, the climate tech sector potential was proportion of ventures employing climate technologies. analyzed within the start-up ecosystem in Dar es Salaam. About one-third of start-ups (32 percent) incorporate Climate tech was defined to include those ventures that climate technology in their projects. Moreover, the incorporate climate adaptation (for example, agricultural ecosystem has a much broader portion of ventures resilience) and/or mitigation (for example, renewable energy) conscious of climate-related effects, presenting a larger activities in addition to their use of technology. opportunity for expanding the climate tech sector among start-ups. Over 70 percent of entrepreneurs To understand the size of this sector and the opportunity surveyed indicated that their ventures had a positive and challenges for its expansion, specific questions were environmental impact. FIGURE B.1 START-UP INTEREST IN CLIMATE TECH IN DAR ES SALAAM Do Your Ventures Have a Positive Have You Ever Considered Launching a Does Your Current Venture Involve Environmental Impact? Climate Tech Venture? Climate Technology? 29% 58% 32% 71% 42% 68% YES NO 24 TECH START-UP ECOSYSTEM IN DAR ES SALAAM For those that answered “no” to whether they had ever FIGURE B.2 OPPORTUNITIES AND SUPPORT considered launching a climate venture, the three most NEEDED FOR CLIMATE TECH IN DAR ES SALAAM common reasons given were that 1) they had never thought about it, 2) they lack interest, or 3) they lack skills/ideas. Fewer indicated that there was not enough funding or that Opportunities in the Climate Tech Sector opportunities in the sector were not apparent. Those that answered “yes” commonly listed both environmental and economic motivations for entering the climate tech market. 9% Regarding opportunities in the climate tech sector, the most common types of opportunities cited were in renewable energy (18 mentioned), agriculture (18 mentioned), recycling (10 mentioned) and various IT (6 mentioned). Fifteen respondents referred to opportunities to generate employment in the 27% 27% sector. When asked what support would make the climate tech sector attractive, respondents listed the following most commonly: 22% finance - 80 respondents, incubation - 47 respondents, mentorship - 32 respondents, education/skills training - 19 15% respondents, marketing/sensitization - 13 respondents. Agriculture Note that the former three were all listed as options in the Generate Employmen questionnaire, whereas the latter two were proposed by Recycling respondents. Renewable Energy Various IT This mirrors the general challenges that the ecosystem identified in the broader analysis, with the great need for access to finance, shrewd and relevant business/sector mentorship, and support programs (for example, incubators, accelerators), and options to improve skills. To address the expansion of this specific sector, policies Support Needed to Make the Climate Tech Sector Attractive could support: (i) the establishment of specific tracks in accelerator programs, (ii) attraction of technical and astute business mentors, (iii) provision of specific training (rapid and experiential) for both technical and business climate tech subjects, as well as (iv) catalyzing specific seed financing 10% 17% facilities for climate tech ventures. However, the definition of climate tech was quite broad and open to interpretation from the entrepreneurs, whose 7% comments indicated involvement ranging from using “devices that are environmental friendly” to helping to “prevent the use of paper through our ventures” to businesses that “use recyclable materials” to “soil and weather analysis” to general 42% climate advocacy using technology. Most respondents indicating a climate tech venture could in reality be more 25% accurately categorized as “tech for climate” versus “climate tech” in that they express a clear interest in working with climate and Education/Skills Training environmental issues but lack the technological sophistication Finance to be true “climate tech.” As such, further analysis would require Incubation more in-depth research on the operations and products of Marketing/Sensitization each business to gauge whether or not the business falls into a Mentorship stricter definition of climate technology. Sector in Focus: Climate Tech 25 References Baird, Ross, Lily Bowles, and Saurabh Lall. 2013. Bridging ITIF (Information Technology and Innovation the “Pioneer Gap”: The Role of Accelerators in Launching Foundation).2017. Learning from Leading Startup Ecosystems. High-Impact Enterprises. Aspen Network of Development Washington, DC: ITIF. https://itif.org/events/2017/05/02/ Entrepreneurs and Village Capital. https://assets. learning-worlds-leading-startup-ecosystems. aspeninstitute.org/content/uploads/files/content/docs/ ande/Bridging%20the%20Pioneer%20Gap%20The%20 Mulas, Victor, and Jean Barroca. 2015. Introducing Role%20of%20Accelerators%20in%20Launching%20 Sustainable Open Innovation in Government: Applied High%20Impact%20Enterprises%20.pdf Methodology for Cities. Washington, DC: World Bank. http://documents.worldbank.org/curated/ BMG Research and Leandro Galli. 2013. Demand en/666831468179646130/pdf/102761-WP-OUO-9- for Mentoring Among SMEs. BIS Research Paper Box391846B-SCGC-Report-new-upd.pdf. Number 158. London: Department for Business Skills and Innovation. https://www.gov.uk/government/uploads/ Mulas, Victor, and Mikel Gastelu-Iturri. 2016. New York City: system/uploads/attachment_data/file/263226/demand_ Transforming a City into a Tech Innovation Leader. Washington, for_mentoring_among_SMEs.pdf. DC: World Bank. https://openknowledge.worldbank.org/ handle/10986/25753. Comin, Diego, and Bart Hobijn. 2010. “An Exploration of Technology Diffusion.” American Economic Review Mulas, Victor, Michael, Minges, and Hallie Rocklin 100 (December): 2031–2059. https://www.aeaweb.org/ Applebaum. 2015. Boosting Tech Innovation Ecosystems articles?id=10.1257/aer.100.5.2031. in Cities: A Framework for Growth and Sustainability of Urban Tech Innovation Ecosystems. Washington, DC: World Comin, Diego, and Mestieri Marti. 2013. “Technology Bank Group. https://openknowledge.worldbank.org/ Diffusion: Measurement, Causes and Consequences.” In handle/10986/23029. Handbook of Economic Growth, Volume 2, edited by Philippe Aghion and Steven Durlauf, 565-622. Amsterdam: North OECD (Organization for Economic Co-operation and Development). Holland. 2007. Eurostat-OECD Manual on Business Demography Statistics. Paris: OECD. http://www.oecd.org/std/39974460.pdf. I-DEV International. 2014. Measuring Value Created by Impact Incubators and Accelerators. I-DEV International, Roberts, Peter, Lall Saurabh, and Ross Baird. 2016. in conjunction with the Aspen Network of Development What’s Working in Startup Acceleration: Insights from Entrepreneurs (ANDE) and Agora Partnerships. https://assets. Fifteen Village Capital Programs. Aspen Network of aspeninstitute.org/content/uploads/files/content/docs/ Development Entrepreneurs. http://ande.site-ym.com/ resources/ANDE%20I-DEV%20INCUBATOR%20REPORT%20 blogpost/737893/242298/What-s-Working-in-Startup- 11-21-14%20FINAL%20FOR%20DISTRIBUTION.pdf. Acceleration-Insights-from-Fifteen-Village-Capital- Programs. Isaksson, Anders, Thiam Hee Ng, and Ghislain Robyn. 2005. Productivity in Developing Countries: Trends and Policies. World Bank. 2017. Coding Bootcamps: Building Future- Vienna: United Nations Industrial Development Organization. Proof Skills through Rapid Skills Training. Washington, DC: http://www.unido.org/fileadmin/user_media/Publications/ World Bank. http://documents.worldbank.org/curated/ Pub_free/Productivity_in_developing_countries_trends_ en/795011502799283894/Coding-bootcamps-building- and_policies.pdf. future-proof-skills-through-rapid-skills-training 26 TECH START-UP ECOSYSTEM IN DAR ES SALAAM Appendix A. Survey Methodology and Analysis Methodology Additional free response questions were also included: 1. What are the three biggest challenges you are currently Survey Questions facing? The survey of start-up founders used the standard questionnaire 2. How important are the following criteria when developed under the Global Entrepreneurship Research evaluating whether to pursue a venture?: Network (GERN) with some additional questions to understand •  Size of market/opportunity specific constraints faced by start-ups. The GERN-developed •  Profitability standard questionnaire includes the following sections: •  Social impact •  Ease of validation (product/market fit) 1. Educational history (including vocational, bootcamps, •  Existing relationships/Access to potential customers and certificate programs) •  Personal passion/skillset 2. Employment history •  Access to financing 3. Founding history (serial entrepreneurship) •  Regulatory/legal environment 4. Support programs (for example, acceleration, incubation, and so on) history Specific questions on climate technology were also included 5. Connections with mentors and mentees in the survey: 6. Connections with investors (angel and institutional) 1. Do your ventures have a positive environmental In addition to this questionnaire, the survey captured the impact? geographic location of start-ups and intermediaries (for the 2. Have you ever considered launching a climate tech geographic analysis) and included the following standard venture? questions: 3. Why or why not? 4. Does your current venture involve climate technology? 1. Incorporation: On average, how many days did it, or 5. Please explain further. would it, take you to incorporate a new start-up? 6. What opportunities do you see within the climate tech 2. Funding: On average, how many days did it, or would it, sector? take you to set up a bank account for your start-up? 7. What support mechanisms would make it an attractive 3. Credit: On average, how many days did it, or would it, sector? (for example, business incubation, financial, take you to get a line of credit for your start-up? incubation, mentorship). 4. Funding: On average, how many days did it, or would it, take you to raise a round of equity funding? Outreach Strategy 5. Hiring: On average, how many days did it, or would it, take you to hire an employee, from job posting to Entrepreneurs filled in an online survey available at employee start? http://survey.techecosystems.org/. Studio 19 Limited was 6. Office space: On average, how many days did it, or would responsible for preparing a team that directed start-ups to it, take you to obtain office space for your start-up? the online survey. Telephone calls and face-to-face meetings 7. Exit: On average, how many days did it, or would it, take were the primary methods for collecting data. you to exit your start-up? Sector in Focus: Climate Tech 27 The following strategy was implemented: Bank, included the potential for their company to feature in an official World Bank case study, and the chance to win T Sh 1. First, the team collected e-mail addresses and telephone 500,000 (about $225; one prize per 33 respondents). numbers for start-ups. Then they e-mailed start-ups with the above link and explained the importance of the Sampling and Engagement survey. Two rounds of emails were to be sent. 2. Second, the team followed up the e-mail with a The survey included key players in the innovation ecosystem in telephone call to encourage start-up founders to Tanzania, including innovation hubs, incubators, accelerators, complete the survey. funds, government entities, and competitions. Partners in the 3. Third, where needed, physical visits and interviews with survey were identified by the study team and extended through start-ups and SMEs were scheduled and performed. Studio 19’s experience in the ecosystem and the working 4. As a way of increasing the response rate, group meetups relationships they had built over time. were planned to supplement the individual efforts to collect data. Partners played a crucial role, not only identifying start-ups, but also in connecting and introducing the study team and the survey Incentives given to start-ups to participate in the survey, apart to relevant start-ups. Table A.1 lists the partner organizations from contributing to an important research study by the World that collaborated with Studio19 in reaching start-ups TABLE A.1 SURVEY PARTNERS Organization Type Location Anza 360 Accelerator Moshi Tigo Reach for Change Competition Dar es Salaam Teknohama (DBTi) Incubator Dar es Salaam SIDO Business and Technology Incubator Programme Incubator Dar es salaam Twende - AISE Accelerator Arusha Zanzibar Living Lab (TAYI) Coworking space Zanzibar Rlabs Iringa Innovation space Iringa Zanzibar Technology and Business Incubator Incubator Zanzibar University of Dar es salaam Incubator (UDCTII) Incubator Dar es salaam University of Iringa | Tanzakatemia University Iringa Sokoine University Agribusiness Incubator (SUGECO) Incubator Morogoro Niwezeshe Lab Innovation space Dar es salaam Mbeya University of Science and Technology (MUST) University, Innovation space Mbeya State University of Zanzibar University Zanzibar Sengerema Living Lab Innovation space Mwanza Iringa Living Lab Incubator Iringa Buni Hub Innovation space, Coworking space Dar es salaam STIC Lab Innovation space Dar es salaam Iringa Innovation Space Innovation space Iringa 28 TECH START-UP ECOSYSTEM IN DAR ES SALAAM Mbeya Living Lab Innovation space Mbeya Zanzibar Innovation Space Innovation space Zanzibar Seedstars World Competition Dar es Salaam, Switzerland Apps & Girls Innovation program Dar es salaam TANZICT Government, Fund Dar es salaam Commission for Science and Technology (COSTECH) Government Dar es salaam Human Development Innovation Fund (HDIF) Fund Dar es salaam Dar es salaam had by far the largest number of partners, and they Data Pipeline greatly helped with sharing their networks and introducing Studio 19 to start-ups. Other regions such as Arusha, Iringa, and Moshi, Survey data used in this report originated with this also had helpful partners. Partners were engaged at different levels, custom survey and was initially retrieved in raw JSON depending on their willingness and access to start-ups as shown in (JavaScript Object Notation) format. Data was converted the table below: to CSV (commas separated values), combined with raw data from additional sources, then cleaned of outliers and testing data. Nodes without location data, and locations TABLE A.2 ENGAGEMENT BY PARTNER without geocodes, were passed through the Google Maps application programming interface (API) in order Engagement to obtain standardized location data wherever possible. Partners Strategy This new dataset was deduplicated using a process that marked similarities between names, e-mail addresses, URLs, and dates. Entities that were determined to be likely duplicates were then merged, maintaining all existing data and privileging more recent data in the event of Mbeya University of Science and conflict. Duplicate edges resulting from this process were E-mail only Technology (MUST), SUGECO, Seedstars removed. Finally, college majors and company industries World, TANZICT were categorized using a machine learning approach that used a set of manually categorized responses to predict category based on word similarity. From this cleaned and augmented dataset, panel and graph data datasets for analysis were then generated. Sengerema Living Lab, State University Limitations E-mails and of Zanzibar, Zanzibar Innovation Centre, It is difficult to determine whether the sample is telephone calls Tanzania Youth Icon, Apps & Girls, representative. The collection of the data was heavily Niwezeshe Labs, COSTECH, HDIF dependent upon the participation of individuals in existing networks – for example, the social coordinator at one of the incubators in the Tanzanian ecosystem sent the survey out to all of the individuals working with that incubator, resulting in a sample that likely has a disproportionate representation from this incubator’s network. To a certain extent, this bias is not problematic, as it means that our data and analysis document those Mbeya Living Lab, Stic Lab, UDCTI, Tigo individuals and nodes that are actively and currently E-mails, telephone Reach for Change, DTBi, Iringa University, engaged in developing the ecosystem. This suggests calls and physical Iringa Living Lab, Buni Hub, Anza, Twende, that they are also more likely to participate in and visits SIDO, respond to policies designed to develop the ecosystem. However, since there are few other databases with which to compare the data, it is difficult to examine exactly whether the sample is truly representative. Sector in Focus: Climate Tech 29 The sample is small and influenced by outliers. To address creating a year-by-year catalogue of start-ups. Although start- this, where possible medians rather than means were used ups may close, socially they still function as nodes within the as descriptive statistics. Some of the analysis (for example, urban innovation ecosystem that may introduce other nodes that of “successful” start-ups, rely on very small portions of to second-order connections. the dataset, and thus give relatively weak information on unobserved, population-level characteristics. FIGURE A.1 CUMULATIVE SUMMARY OF NODES Data cleanliness is difficult to evaluate. Missing data could YEAR OF YEAR represent a true lack of connection is the ecosystem, or potentially could be the result of some respondents failing 1,200 to complete all survey questions or a lack of interest from 1,100 potential respondents. Sometimes conflicting data or meta 1,000 ACCELERATOR NAME information was recorded from multiple sources, in which Event 900 case the source closest to the entity (that is, the founder) Organization Count Cumulative was privileged. In addition, respondents were expected to 800 Person accurately select names of entities already in the database 700 through an autofill mechanism in order to properly attribute 600 new information to existing entries. Although a machine 500 learning driven data deduplication process was employed before analysis, this may not have resolved every duplicated 400 entry, and such fuzziness may affect exact numbers in the 300 social network analysis. 200 The sample is highly influenced by survivorship bias. 100 Entrepreneurs that are so successful that they leave the 0 ecosystem were not captured. For example, the lack of serial 2009 2010 2011 2012 2013 2014 2015 2016 entrepreneurs in Tanzania may suggest that, once they have had one successful venture, they pursue subsequent start- ups in other more developed ecosystems, rather than that they do not repeatedly start businesses. Currently there is no FIGURE A.2 CUMULATIVE SUMMARY OF EDGES simple way to capture data on those individuals that leave, or a way of identifying them in the dataset. In addition, the YEAR survey only captures entrepreneurs who were active during the survey period, and as such it does not capture failed 1300 EDGES LABEL entrepreneurs who have dropped out of the ecosystem. 1200 Inspired Invested in Analysis 1100 accelerated/incubated knows 1000 hosted Stakeholders in the Tanzanian start-up ecosystem can be member of represented using a social network comprised of nodes 900 worked at Count Cumulative of Edges (people, organizations, groups, and events), and edges, which mentored attended are the relationships between them. For the social network 800 studied at analysis, an edge was considered as a part of the ecosystem founded 700 if either of its endpoints are in the region. From these edges, relevant nodes for the network were extracted. Technically, 600 the network is multipartite. However, given that individual investors can function similarly to institutional investors, 500 founders of small start-ups are practically synonymous with their companies, and events and groups are often start-ups 400 themselves, the network is treated as if it only contains one 300 type of entity in order to simplify exploration. 200 Nodes are never removed from the dataset, even if the represented entity no longer exists. In other words, once a 100 start-up has appeared in the dataset, it remains there for all 0 subsequent years (see Figure A.1). There were two reasons 2009 2010 2011 2012 2013 2014 2015 2016 for this decision. First, accurate end-dates for start-ups were difficult to gather. Second, the primary interest was in Edges can be further grouped into categories in order to create mapping the social dimensions of the start-up network, not different types of networks and narrow the focus of analysis. 30 TECH START-UP ECOSYSTEM IN DAR ES SALAAM TABLE A.3 TYPES OF NETWORKS Primary Edges: Founded, Invested in, Accelerated/incubated, Acquired, Partnered with, or Mentored Investment Edges Invested in, Accelerated/incubated Job Edges Founded, Worked at Social Edges Hosted, Attended, Member of Education Edges Studied at, Accelerated/incubated BOX A.1 WHAT IS CENTRALITY? Degree centrality By calculating centrality measures of stakeholders in the dataset, key players in the community can be identified quantitatively. The diagrams below, while not specific to our dataset, help illustrate the definition and interpretation of each type of centrality. Red indicates higher centrality values. Blue indicates lower centrality values. Degree centrality measures the number of other nodes within the Closeness centrality ecosystem each node is directly connected to. It does not take into account any second-order connections. Closeness centrality measures a node’s social distance to other nodes. It is expressed as the inverse of the average distance from each node to every other node in the network. A low closeness centrality indicates that the firm is on the edge of the network. Eigenvector centrality Eigenvector centrality augments degree centrality by taking into account the connectivity of the nodes a node is connected to. Highly connected nodes within highly interconnected clusters have high eigenvector centrality. Betweenness centrality Betweenness centrality measures how many times a node acts as a gateway in the network. The higher the betweenness centrality of a firm, the more paths run through that firm to connect two other firms. High betweenness centrality means that a node is a key bridge or facilitator between different clusters. Source: Diagrams are from https://en.wikipedia.org/wiki/Centrality. Calculating centrality measures on different subnetworks Salaam, the analysis reveals that accelerators and incubators in the data builds understanding of which players in the are the most connected of all key players. ecosystem are the most important. In the case of Dar es Sector in Focus: Climate Tech 31 TABLE A.4 CENTRALITY OF KEY PLAYERS IN NETWORK WITH ALL EDGES Degree Betweenness Eigenvector Closeness Accelerator 7.71429 101.381 0.145957 7.2721E-07 Founder 4.15966 8.55122 0.035004 7.26092E-07 Investor 4.22222 3.44444 0.0336266 7.26982E-07 School 2.04902 1.27451 0.0138073 7.22488E-07 Start-up 1.98101 0.117993 0.0242286 7.22515E-07 TABLE A.5 CENTRALITY OF KEY PLAYERS IN NETWORK WITH PRIMARY EDGES Degree Betweenness Eigenvector Closeness Accelerator 4.85714 6.28571 0.143656 2.56725E-06 Founder 2.04641 1.32068 0.0225922 2.55495E-06 Investor 2.11111 0.222222 0.0463585 2.56109E-06 School 3 Negligible Negligible 2.54776E-06 Start-up 1.93671 0.0253165 0.0317955 2.54786E-06 TABLE A.6 CENTRALITY OF KEY PLAYERS IN NETWORK WITH SOCIAL EDGES Degree Betweenness Eigenvector Closeness Accelerator 5.5 13 0.147682 7.44538E-06 Founder 2.25 Negligible 0.0416781 7.41881E-06 Investor 2 Negligible 0.00243833 7.40437E-06 School 1 Negligible 3.76317E-19 7.37421E-06 Start-up 1.25 Negligible 0.130622 7.38423E-06 32 TECH START-UP ECOSYSTEM IN DAR ES SALAAM Short-Term Success survey respondent who stated, “On many occasions, start-ups in Tanzania, unless housed at the few existing hubs, have little A fixed effects logit model was used where the dependent to no access to programs inside and outside of Tanzania that variable is the probability of the start-up raising funding in can help them get funding or participate in competitions.” a given year of existence36 and the explanatory variables are lagged in order to gauge the effect of centrality in the investment network on a start-up’s short-term success. Only the Long-Term Success degree centrality is a direct measure of the number of investors a start-up has. Eigenvector and closeness centrality capture the In the long-term, we are interested in a start-up’s ability to effects of second-order and beyond connections to investors. create jobs for the ecosystem. As such, we use a logit model where the dependent variable is hiring occurrence weighted The greater a start-up’s eigenvector centrality, the more likely it by years of existence, such that we capture the percentage of is to raise funding in the next year. In other words, if a start-up years that the firm hires employees.37 For simplicity, we can is well connected within a cluster of accelerators, investors, and also interpret this number as the average probability a firm funded start-ups, it is more likely to receive investment. This will hire in a given year. effect is offset by the finding that the more direct investment connections the start-up has, the less likely it is to receive The likelihood that a given firm in the ecosystem will hire in funding in the next year. The latter effect is likely due to the fact a given year is 7 percent.38 One additional mentor increases that once a start-up has received funding once, it is less likely to hiring probability by 60 percent. One additional member on need it in future periods. a founding team increases the odds that a firm will hire in a given year by 45 percent. One additional previous founding These results suggest that there appears to be an inner circle experience or investor increases the likelihood of job creation in the investment ecosystem, outside of which raising capital by more than 100 percent. There is no significant impact of is difficult. This hypothesis is supported by a comment from a educational factors on job creation. TABLE A.7 EFFECT OF CENTRALITY IN INVESTMENT NETWORK ON SHORT-TERM FUNDING SUCCESS Estimate Std. Error z value Pr(>|z|) startup_prev_degree_all_investment -2.209e+00 1.030e+00 -2.145e+00 0.0345 * startup_prev_eigenvector_all_investment 2.279e+16 6.304e+07 3.615e+08 <2e-16 *** startup_prev_closeness_all_investment 1.228e+03 8.190e+02 1.499e+00 0.1371 year_existence 3.232e-02 1.108e-01 2.920e-01 0.7712 TABLE A.8 EFFECT OF START-UP CHARACTERISTICS ON LONG-TERM FUNDING SUCCESS Estimate Std. Error z value Pr(>|z|) sum_acceleration_occurrence -4.187e-01 3.642e-01 -1.150 0.25026 sum_funding_amount -2.496e-06 4.014e-06 -0.622 0.53400 num_distinct_investors 1.775e+00 7.646e-01 2.321 0.02028 * num_distinct_investor_regions 9.030e-01 9.118e-01 0.990 0.32198 num_distinct_mentors 4.688e-01 1.512e-01 3.101 0.00193 ** num_distinct_founders 3.688e-01 1.687e-01 2.186 0.02881 * sum_founder_previous_startups_founded 7.386e-01 3.451e-01 2.140 0.03235 * sum_founder_previous_jobs -1.595e-01 1.830e-01 -0.872 0.38336 sum_bachelor_master_professional_doctorate_degrees -1.832e-01 3.035e-01 -0.604 0.54602 sum_associate_bootcamp_certificate_degrees -4.558e-01 5.135e-01 -0.888 0.37478 (Intercept) -2.638e+00 3.829e-01 -6.889 5.6e-12 *** Sector in Focus: Climate Tech 33 NOTES 1. https://startupgenome.com/. 22. 38 distinct start-ups received funding out of a total of 159 unique start-ups. 2. http://www.digital.nyc/. 23. Ratio of the number of investments received by funded start-ups that 3. http://www.techmap.london/. were accelerated divided by the number of investments received by funded start-ups that were not accelerated. 4. https://www.galidata.org/. 24. https://techcrunch.com/2015/03/22/mentors-are-the-secret- 5. http://www.devinfo.org/CensusInfoTanzania/libraries/aspx/Home.aspx. weapons-of-successful-startups/. 6. See “Outreach Partners” in Appendix A for full list of partners 25. Entrepreneurs also provided comments on mentorship, with one respondent suggesting that “to have a larger impact, business 7. See “Limitations” section in Methodology section of Appendix A for incubated in places like DTBI should be given the responsibility more information on the limitations to our approach. mentor other organizations.” 8. https://techcrunch.com/2015/03/22/mentors-are-the-secret- 26. This is confirmed by survey respondents, who ranked finance as one weapons-of-successful-startups/. of their key constraints. 9. The data collected for this analysis suffers from an inherent 27. Debt financing generally refers to interest-bearing loans. Equity survivorship bias, the precise impact of which is difficult to quantify financing gives a certain percentage of ownership in a start-up in (see “Limitations” section in Appendix A for more details). exchange for funding. 10. Reported by Studio 19 in their post-survey assessment. 28. This is confirmed by respondents to the survey. One participant stated that, “Players in the ecosystem, for example, incubators, hubs, 11. Of 261 instances of founding, only 41 percent (109) of those instances accelerators, and so on, should work together to build a stronger have the age of the founders available. ecosystem rather than competing and dividing opportunities.” 12. http://nyctechmap.com/. 29. This last factor is the least significant when compared to all others, suggesting that its influence depends on multiple factors (for 13. Gross enrollment in tertiary education in Tanzania for both sexes was example, how much complementary experience the additional 3.5 percent in 2015 (http://data.uis.unesco.org). founder brings, how well the team work together, and so on). 14. No founders indicated having a doctorate. Professional degrees are 30. http://www.skilledup.com/articles/the-ultimate-guide-to-coding- defined as postgraduate qualifications in law, business, or medicine. bootcamps-the-exhaustive-list. 15. Of 261 instances of founding, only 39 percent (103) of those instances 31. https://tampere.demola.net/. have major available. 32. https://tech.cornell.edu/jacobs-technion-cornell-institute/overview. 16. http://nyctechmap.com/. 33. http://bdlaccelerate.com/2016/. 17. Of 429 instances of work history at the time of founding, only 25 percent (111) have job functions listed. 34. http://www.startupchile.org/. 18. http://www.buni.or.tz. 35. http://www.k-startupgc.org/. 19. http://www.sido.go.tz. 36. The amount of funding raised is not accounted for, since this is heavily influenced by the type of business and prone to outliers. 20. http://www.teknohama.or.tz. 37. The number of employees hired is not accounted for, since this is 21. Ratio of accelerated firms that received investment over heavily influenced by the type of business and prone to outliers. nonaccelerated firms that received investment. A ratio of 1 means equal opportunities. A ratio below zero means nonaccelerated 38. 7 percent is e^-2.638. firms are more likely to receive investment. A ratio of more than one means accelerated firms are more likely to receive investment. 34 TECH START-UP ECOSYSTEM IN DAR ES SALAAM This work is available under the Creative Commons Attribution Non-Commercial 3.0 IGO license (CC BY NC 3.0 IGO). Under the Creative Commons Attribution Non- Commercial license, you are free to copy, distribute, transmit, and adapt this work for non-commercial purposes, under the following conditions: Attribution— Please cite the work as follows: Mulas, Victor; Qian, Kanty; and Henry, Scott. 2017. Tech Start-up Ecosystem in Dar es Salaam. Findings and Recommendations. License—Creative Commons Attribution Non-Commercial CC 3.0 IGO Translations—If you create a translation of this work, please add the following disclaimer along with the attribution: This translation was not created by The World Bank and should not be considered an official World Bank translation. The World Bank shall not be liable for any content or error in this translation. 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