Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Marcio Cruz Zenaida Hernandez Uriz © 2022 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Rights and Permissions The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to: Attribution Please cite the work as follows: Cruz, Marcio and Hernandez Uriz, Zenaida (2022) Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic. Washington, D.C. The World Bank. World Bank Publications The World Bank Group 1818 H Street NW Washington, DC 20433 USA Fax: 202-522-2625 TABLE OF CONTENTS Overview 6 Chapter 1: MSMEs and the entrepreneurship ecosystem in Kenya: A cross-country context analysis 11 1.1 MSMEs in Kenya 12 1.2 Entrepreneurship output 16 1.3 Entrepreneurship ecosystem pillars 18 1.3.1 Supply factors 19 1.3.2 Demand pillars 22 1.3.3 Barriers to allocation and accumulation 25 1.4 Strengths and challenges for entrepreneurship in Kenya 28 Chapter 2: Sub-national entrepreneurship ecosystems in Kenya 30 2.1 The regional landscape of MSMEs and entrepreneurship in Kenya 31 2.2 Identifying sub-national entrepreneurship ecosystems in Kenya 33 2.3 Entrepreneurship characteristics and performance across sub-national ecosystems 39 2.4 Main barriers faced by local entrepreneurship ecosystems 43 2.4.1 Perceived obstacles faced by entrepreneurs 43 2.4.2 The local availability of resources 46 2.5 The demand for policies 51 2.5.1 Firms receiving support 52 2.5.2 Summary of policy needs 54 Chapter 3: Mapping public instruments and enablers supporting businesses 57 3.1 The structure of the public support for Kenya’s entrepreneurship and MSME 58 3.2 The methodology for mapping the entrepreneurship supporting environment 62 3.3 Mapping of public programs supporting entrepreneurship 63 3.3.1 Services and instruments provided by public programs 64 3.3.2 Management characteristics 65 3.3.3 Beneficiaries of public programs 65 3.3.4 Resources available 68 3.3.5 Monitoring and evaluation process 69 3.3.6 Response to COVID-19 70 3.3.7 Summary of key areas covered by public programs 71 3.4 Mapping of Intermediary Organizations (IOs) in Kenya 73 3.4.1 Services and instruments provided by IOs 74 3.4.2 Management characteristics of IOs 75 3.4.3 Beneficiaries of IOs 76 3.4.4 Resources available and allocation in IOs 77 3.4.5 Monitoring and evaluation process 78 3.4.6 Adjustments through COVID-19 79 3.4.7 Summary of key areas covered by IOs 80 3.5 A summary of the policy mix to support entrepreneurship and MSMEs in Kenya 81 3.5.1 The complementarity between public programs and IOs: Are they addressing the key gaps? 81 3.5.2 The association between programs’ characteristics and services provided 85 Chapter 4: The impact of COVID-19 on businesses and policy recommendations 87 4.1 The impact of COVID-19 on Kenya businesses 87 4.1.1 Impact on sales and jobs 87 4.1.2 Firms’ Responses 88 4.1.3 Expectation and Uncertainty 89 4.1.4 Access to and demand for policy support 90 4.2 Policy recommendations to support MSMEs targeting ecosystems 91 4.2.1 Improve infrastructure 93 4.2.2 Enhance entrepreneurial and firm capabilities 94 4.2.3 Support access to finance 95 4.2.4 Promote access to new markets 96 4.2.5 Improve regulations 97 4.2.6 Improve access to information, coordination, and capabilities of business supporting programs 97 References 99 Appendix 101 A1) Results referred to in chapter 1 101 A2) Results referred to in chapter 2 102 A3) Results referred to in chapter 3 105 A3a: List of mapped public programs 105 A3b: List of mapped intermediary organizations 106 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Acknowledgements This report was prepared by a World Bank team led by Marcio Cruz and Zenaida Hernandez Uriz under the overall supervision and guidance of Martha Licetti, Denis Medvedev, and Niraj Verma. The core team that contributed to the preparation of this report includes Cecilia-Paradi Guilford, Justin Hill, Pinyi Chen, Jeffrey Dickinson, Subika Farazi, Leah Kiwara, Lynette Ndile, Maria de Paz Perez, and Jessica Torres, with contributions from Sarah Hebous, Kyungmin Lee, and Santiago Reyes Ortega. Additional contributions and comments were received from Allen Dennis, Gabi George Afram, Ganesh Rasagam, as well as Irene Wagaki and John Wainaina from Strategic Business Advisors Ltd. Grant support from the Korea-World Bank Partnership Facility (KWPF) Trust Fund contributed to fund this report. The team would like to thank public officials and representatives of intermediary, private sector and partner organizations that generously shared their time. This report was edited and designed by Anthony Wafula. Cover photo by Sambrian Mbaabu. 5 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Overview Entrepreneurship and micro, small, and medium enterprises (MSMEs) play a critical role in creating more and better jobs. MSMEs across the world account for a large share of jobs. Many jobs created by MSMES over time tend to be concentrated among a small share of young firms that grow rapidly in relatively brief spurts, with some of these firms starting small and others already operating at scale. These impactful businesses operate in many sectors of economic activity and tend to be more innovative, more connected to global value chains, and more likely to benefit from economies of agglomeration. Even if high potential enterprises are difficult to identify ex-ante, they are more likely to arise in dynamic ecosystems characterized by: i) high entry rates of (better quality) firms; ii) capacity of those firms to scale up; and iii) continuous innovation and technological upgrading. To transform the country into a globally competitive and prosperous economy with a high quality of life as stated in Vision 2030, Kenya needs enterprises that create more and better jobs. MSMEs in Kenya account for approximately 46 percent of jobs in formal firms and 82 percent of jobs in both locally licensed (some of which could be formally registered) and unlicensed firms (households with entrepreneurial activities).1 Cross- country comparisons suggest that a reasonable number of formal firms enter the Kenya economy annually but these businesses exhibit low dynamism in terms of both scaling up and technological upgrading. The COVID-19 pandemic could potentially aggravate the structural challenges faced by entrepreneurs and MSMEs in Kenya. The COVID-19 pandemic has resulted in an unprecedented shock to the private sector, threatening the global progress in poverty reduction and shared prosperity made in recent years. The impact on firms could have large effects on the growth prospects. Restrictions to mobility and economic activity can limit the allocation of resources within countries and across sectors, worsening misallocation in the economy and lowering aggregate productivity growth.2 Addressing the structural challenges faced by entrepreneurs and MSMEs in Kenya has become urgent in this context. To deliver more effective policies supporting MSMEs and entrepreneurs starting new businesses, it is critical to understand their key barriers from the perspective of their entrepreneurial ecosystems. An entrepreneurial ecosystem is a set of complementary factors such as knowledge and resources available through institutions and individuals within a region to support the development of new and economically impactful businesses.3 Successful entrepreneurs not only require entrepreneurial talent but also access to knowledge, skilled labor, a suitable physical infrastructure, and enablers that facilitate the optimal allocation of resources into firms. Entrepreneurs access physical capital, intermediate goods, human capital and knowledge in input markets; combine these resources applying their talent to the production process; and sell the final good or service in output markets. This production process takes place in an ecosystem—a geographical location (commune, region, or country) characterized by the quality of inputs and entrepreneurship outputs.4 These complementary factors are the resources that entrepreneurs need to start a business and succeed. From the moment an entrepreneur has an idea or sees an opportunity, several resources outside the enterprise are needed. In a functional ecosystem, entrepreneurs with good, potentially profitable projects 1 In both the 2017 Establishment Census and the 2016 MSME Survey businesses with 149 or less workers account for 99 percent of businesses. 2 Apedo-Amah et. al. (2020) 3 Excerpt from World Bank (2020a), page 12 4 Cruz, Torres, and Trang (2020) 6 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic and capacity to execute them are more likely to find the resources that are needed, such as human capital, knowledge, machinery and equipment. In addition, they may also have access to the market for their products and services within the ecosystem or beyond it; interact with other firms’ entrepreneurs and advisors with good capabilities; and face fewer barriers to use such resources. Consequently, designing and implementing policies that aim to support entrepreneurship and innovation becomes complex given the multiplicity of problems to be addressed and the limited financial and human capacity to design and implement policies. In this context, the conceptual framework described in Figure 1 incorporates these complementary factors to guide the elaboration of the diagnostic presented in this report. Figure 1. Entrepreneurial ecosystem framework Final Outcome: Productivity and Jobs Intermediate Output New Firms Firm Growth Innovation (Entry) (Scale Up) (Upgrade) Entrepreneurship Ecosystem Supply Factors (Inputs) Accumulation/Allocation Demand Firms Barriers Physical Capital & Access to Finance Access to Market Infrastructure Human Capital Regulations Firm Capabilities Entrepreneurial Knowledge Capital Social Capital Characteristic Policy Instruments & Intermediate Organizations to Support Entrepreneurship Source: Audrestch, Cruz, and Torres (2022) Policy makers can influence the entrepreneurial ecosystem to maximize job creation and economic growth by addressing market failures. The combination of supply and demand factors within a healthy operating environment results in job creation, increased exports, innovation and productivity growth. Under this conceptual framework, entrepreneurial outputs and outcomes are assessed in terms of firm dynamics (entry, exit, and survivorship) and entrepreneurship impact (jobs, revenues, exports, innovation, and productivity growth). Market failures may arise in any of the initial conditions of the entrepreneurial ecosystem. These failures can be associated with supply or demand pillars or associated with the barriers to accumulation and allocation.5 Policy instruments and intermediary organizations supporting entrepreneurship usually aim to address some 7 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic of these market failures (bottom of Figure 1 in red), but they also face their own challenges, including potential coordination and implementation failures.6 This report presents a diagnostic of MSMEs and entrepreneurship ecosystems in Kenya. The report is organized in four chapters combining a comprehensive set of information assessing the entrepreneurship ecosystems and MSME performance pre- and post-COVID-19. The assessment of the performance of MSMEs and entrepreneurship as well as the diagnostic of the availability of structural factors to support the ecosystem pre-COVID-19 in Kenya are not only critical to inform policy makers on the challenges burdening entrepreneurs and how the COVID-19 shock has led to additional constraints, but also to highlight potential opportunities that may emerge through the crisis. The analysis relies on several data sources. First, it combines several data sources such as the World Bank’s entrepreneurship database and enterprise surveys to conduct an aggregated analysis with cross-country comparison. Second, it uses a nationally representative firm-level survey conducted by the Kenya National Bureau of Statistics (KNBS) to identify the potential of local entrepreneurship ecosystems and assess them at the sub-national level. Third, it presents the original results of primary data collected from public programs and intermediary organizations (IOs) supporting entrepreneurship, collected between July-August 2020. Fourth, it uses information from three rounds of the World Bank’s Business Pulse Survey (BPS), collected during August 2020 – June 2021 with nationally representative data to assess the impact of COVID-19. The findings were also supported by focus groups interviews conducted with entrepreneurs and policy makers. The combined analysis across these chapters provides a comprehensive perspective of structural and short-term challenges to boost businesses in Kenya. Chapter 1 examines the context of entrepreneurship and MSMEs in Kenya before the COVID-19 shock. It is based on a conceptual framework that covers indicators of key outcomes and structural pillars of the entrepreneurship ecosystem. These pillars include the supply and demand factors, and the barriers to the flow of resources that are critical to generate more and better firms. The analysis exploits both firm-level data and cross-country indicators. Firm-level data analysis from the Census of Establishments (CoE) and the MSME Survey shows that in line with other developing economies, Kenya has about 138,000 formal establishments. Most of these are MSMEs and are complemented by a large amount of unlicensed or only locally licensed businesses (at the county level). In this universe of businesses, MSME account for 46 percent of employment in formal businesses (CoE) and 82 percent of employment in licensed and unlicensed firms (MSME survey). Cross-country comparisons use the most recent data available across several publicly available databases. Looking at entrepreneurship outputs, Kenya has a relatively high entry rate of new firms compared to other countries with similar level of per capita income. However, there seems to be significant restriction in terms of scaling up and innovating. The analysis related to key inputs to entrepreneurship identifies areas in which Kenya has significant room for improvement, including investment in physical capital and infrastructure, human capital, access to finance, regulations, and overall firm capabilities. 5 Some of these failures can be government failures or inefficiencies created by interventions of the government itself (e.g., distortive regulations). 6 (Audretsch, Cruz, and Torres, 2020). 8 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Chapter 2 examines the potential and key challenges of entrepreneurship ecosystems at the sub-national level in Kenya. It presents the regional landscape of MSMEs and entrepreneurship in the context of the Kenya’s Economic Blocs, a recent institutional arrangement aiming to facilitate the collaboration across counties through common actions to support economic prosperity. The analysis starts by identifying local entrepreneurship ecosystems based on the correlation of sectoral agglomeration of businesses across counties along two dimensions: quantity of firms in activities of strategic economic sectors and quality of those agglomerations. The method captures the relevance and connection of strategic activities across counties and uses this information as a proxy to assess the potential of these ecosystems across different regions and economic blocs. Furthermore, the chapter analyzes the performance and key obstacles faced by sub-national entrepreneurship ecosystems across economic blocs. Finally, it identifies key policy demands from entrepreneurs, examines their access to the entrepreneurship supporting system such as non-financial programs providing business training and technological advice and concludes with a set of policy recommendations. Chapter 3 analyzes the characteristics of and the resources available through public programs and intermediary organizations (IOs) supporting entrepreneurship and MSMEs in Kenya. The analysis relies on a new dataset collected between July and September 2020 from 26 public programs and 62 IOs in Kenya. The data collection uses a new survey instrument, the World Bank’s Entrepreneurship Ecosystem Enabler survey, aiming to assess and identify key gaps for policy interventions supporting entrepreneurship and MSMEs. The most typical services provided by public programs and IOs are related to financial services followed by managerial training. Overall, the top managers of these programs are well educated and have experience in the sector. Yet, these characteristics do not seem to be associated with better practices in term of monitoring and evaluation systems such as the adoption of impact evaluation, which is implemented by only a third of public programs or IOs.7 The main beneficiaries of this supporting system are individuals (entrepreneurs) and firms. A significant share of funding, even for IOs, comes from public sources and donors. While public programs in Kenya are more likely to target manufacturing firms, IOs tend to be more sector neutral. The main response of these programs to COVID-19 shock has been associated with the expansion of services to support digital solutions, and most of them are expecting a similar or larger budget in the next 6 months. Chapter 4 examines the impact of COVID-19 on businesses and provides policy recommendations based on the findings of this report. The COVID-19 pandemic had severe impacts on the Kenyan economy and society. While chapters 1 and 2 provide an overall perspective of structural challenges faced by entrepreneurs at the national and sub-national levels, the pandemic significantly increased these challenges. At the same time, firms reacted by adopting more digital technologies and innovating. Chapter 3 also shows that the pandemic had important implications across the supporting system, either through public programs or other supporting organizations. Understanding the combination of structural challenges and the current implications of COVID-19 on business performance, as well as their supporting structure, is critical to proposed interventions. This report 7 This lack of association between management characteristics and the implementation of impact evaluation exercises with counterfactual might be explained by the fac that monitoring and evaluation (M&E) framework is not designed by the program manager. 9 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic analyzes the impact of COVID-19 on businesses in Kenya based on three rounds of a nationally representative Business Pulse Survey. This report proposes some areas of intervention to help mitigate the adverse impacts of COVID-19, but more importantly, turn the challenges of this crisis into an opportunity to revamp entrepreneurship and MSMEs in Kenya. Based on the findings of the BPS surveys in Kenya, combined with the results presented in chapters 1, 2, and 3, Table 1 describes a summary of the recommendations. Table 1. Policy actions to support entrepreneurship and MSMEs in Kenya Diagnostic and proposed actions Time horizon Enhance entrepreneurial and firm capabilities with target on scaling up and Medium- technology upgrading term • Management capability programs to improve managerial practices • Digital technology extension programs that facilitate the adoption of digital technologies, particularly those applied towards general business functions, such as business administration, production planning, e-commerce, digital payment, and quality control. • Sector technology extension programs that facilitate the adoption of sector-specific digital and non-digital technologies. Support access to finance Short-term • Promote the development of FinTech solutions including lending on digital channels to reach currently underserved segments while ensuring adequate customer protection • Establish conditions to prevent the insolvency of viable firms due to temporary illiquidity due to COVID-19 (with clear sunset clauses and monitoring to prevent financial instability. • Develop sustainable de-risking instruments to encourage financial institutions to lend to MSMEs (e.g., Credit Guarantee Company) Promote access to new markets Medium- term • Networking programs that facilitate the matching of supply and demand across ecosystems. • Matching grants towards local digital business solutions aiming to stimulate the development of digital platforms that facilitate the exchange of information across local producers. Improve infrastructure Short-term • Identify and address infrastructure gaps outside the main urban centers, especially digital infrastructure • Complete the National Spatial Plan to help connect physical and economic planning. Improve regulations Medium- term • Streamline and harmonize licensing requirements and taxes. • Reduce costs to intra-country trade. Improve access to information and coordination of business supporting programs Short-term • Provide information regarding available support for MSMEs using specific channels to reach different firm segments. 10 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Chapter 1: MSMEs and the entrepreneurship ecosystem in Kenya: A cross-country context analysis Kenya exhibits a small mass of formal firms. The latest Kenya Census of Establishments (CoE) suggests that the Kenya economy has approximately 138,000 formal establishments (KNBS, 2017). Among these, only 28 percent (about 38,000 firms) report more than 10 employees. With a population of about 50 million, the Kenya economy has less than one formal business with more than 10 employees for every thousand people.8 These numbers are relatively small if compared to more advanced economies of a similar population size, such as Colombia or the Republic of Korea. Colombia, with a relatively similar population has about 450,000 formal firms. The Republic of Korea, also with a similar population size has about 4.1 million formal businesses, and has about 338,000 establishments with 10 or more employees.9 However, this comparison does not provide the full picture of number of businesses in Kenya. Based on a comprehensive survey conducted by KNBS in 2016, including informal businesses, Kenya has about 1.6 million MSMEs, but many of them are micro businesses with very low capabilities. To increase the number of better-quality jobs, the Kenyan economy must increase the number of better- quality firms, both through upgrading existing businesses and through more new businesses with the capacity to grow. Entrepreneurship associated with the creation of new firms and the conditions for them to expand is critical to generate more and better jobs. Evidence from advanced and developing economies suggest that a small share of young high-growth firms represent a significant share of new jobs created in the economy (Grover, Medvedev, Olafsen, 2019; Haltiwanger et. al., 2013, 2017). Over time, the accumulation of a larger number of firms capable of making better use of technology, expand, innovate, and compete abroad is critical to increasing productivity, jobs, and salaries. This chapter analyzes the context of MSMEs and entrepreneurship in Kenya pre-COVID-19 in terms of both output and inputs in the ecosystem. Section 1.1 describes the population of MSMEs firms in Kenya. This descriptive analysis relies on the 2017 Kenya Census of Establishments and the 2016 MSME Survey, both conducted and made publicly available by KNBS.10 Section 1.3 examines entrepreneurship outputs related to three dimensions: entry (a measure of performance on the extensive margin), scaling up, and upgrading (these latter two measures of performance on the intensive margin).11 Section 1.4 compares Kenya with other global peers in terms of supply factors, demand factors, and barriers to the accumulation and allocation of resources. Finally, section 1.5 summarizes the main findings emphasizing some of the key structural challenges faced by Kenya to enhance entrepreneurship and improve the quality of its MSMEs. 8 The estimates based on the CoE is 0.75 formal establishment for every thousand working age population. 9 This represents about 30 times more formal businesses than the total of formal establishments in Kenya, and 9 times more than the number of establishments with 10 or more employees in Kenya. 10 The information from the 2017 Kenya Establishment Census is extracted from the CoE basic report published by KNBS. The information from the MSME Survey is based on the microdata downloaded from the KNBS official webpage and the MSME basic report published by KNBS. 11 The extensive margin refers to the decision of whether to enter entrepreneurship, whereas the intensive margin corresponds to measures of performance once the business has entered. 11 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 1.1 MSMEs in Kenya The population of formal businesses in Kenya is small and consists of mainly micro and small businesses. However, most jobs in formal businesses are concentrated among medium and large firms. The latest establishment census in Kenya (2017), which only covers formal firms, suggests that the country has 138,190 formal businesses. These businesses are mainly micro (0-9 employees) and account for about 71 percent of businesses in the CoE (Figure 2).12 Among the 3.5 million workers employed by formal enterprises in Kenya, a significant share (28 percent) is working in businesses with less than 50 employees, most of them in the 10-49 size category (small). Despite being less than 1 percent of the formal establishments, large formal firms employ more than 54 percent of the workers in the formal sector. A large share of formal firms in Kenya are in retail or other services and around half have been in operation for 10 years or less. The sectoral distribution of formal firms in Kenya follows a common pattern observed across developing countries with a large share of businesses in retail (32 percent) and other services (51 percent), which combined account for more than 80 percent of formal establishments in the economy. Two additional sectors, in which Kenya has been providing new opportunities, tourism and digital services, combined represent about 10 percent of formal businesses. Formal businesses in Kenya are also young. Close to 55 percent of formal enterprises are less than 10 years of age. The characteristics of the universe of firms, particularly MSMEs, provide an important context for an entrepreneurship ecosystem (Box 2). Figure 2. Features of establishments in the Kenyan establishment census (a) Share of establishments by employment size (b) Employment distribution 60 50.4 54.1 50 40 40 30 % Percent % Percent 25.7 21.7 20 20.7 20 18.2 10 5.3 2.0 0.9 1.7 0 0 0-3 4-9 10-49 50-149 150-Plus 0-3 4-9 10-49 50-149 150-Plus NOTE: Source is Census of Entrepereneurship (2017) NOTE: Weighted calculations. 2070 unweighted observations. Source of firm size is Census of Entrepereneurship (2017) and of employment is BPS. The categorization of MSMEs follows the Census of Establishments. Micro: 0-9 employees; Small: 10-49 employees; Medium: 50-149 12 employees; Large: 150+ employees. 12 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic c) Sector distribution d) Age distribution 50 51.0 40 33.0 40 30.0 30 31.9 30 % Percent % Percent 21.7 20 20 15.3 10 10 7.1 4.4 3.0 2.6 0 0 Agriculture Manufacturing Retail Tourism Digital Other 0-5 6-10 11-20 20 Plus NOTE: Source is Census of Entrepereneurship (2017). Retail refers to wholesale and retail NOTE: Weighted calculations. 2070 unweighted observations. trade; repair of motor vehicles and motorcyles. Source of firm size is Census of Entrepereneurship (2017) and of employment is BPS. Source: 2017 Census of Establishment (KNBS) and World Bank’s Business Pulse Survey Box 1. MSMEs and entrepreneurship in an ecosystem MSMEs and business creation offer interrelated measures of the quality of an entrepreneurship ecosystem. Entrepreneurship is a dynamic process that contributes to improving the stock of MSMEs. Whereas a stricter definition of entrepreneurship focuses on the entry of new firms (extensive margin), different perspectives in the literature define entrepreneurial behavior as that associated with scaling up (intensive margin) and innovation. These measures then link the entrepreneurship concept with action, and therefore with dynamics over time. That is, these measures of firm entry, growth, or innovation necessarily require information from (at least) two points in time.13 From this perspective, entrepreneurship is then a critical measure of the flow and dynamics of the business environment. However, to better evaluate the impact of entrepreneurship on productivity and economic welfare, it is important to diagnose the stock or the baseline population of enterprises. In developing countries, understanding the baseline MSME context to better evaluate the entrepreneurship ecosystem is critical. First, some of the best indicators of entrepreneurship output comparable across countries in publicly available data sources usually rely on data for formal businesses only. However, the stock (baseline) of formal firms in developing countries is small and the definition of formality varies significantly across countries.14 Combining these metrics with a perspective of the stock (baseline) of the total population of firms helps to understand the context of the ecosystem. Second, information about firm dynamics in developing countries is scarce, and most developing countries do not collect firm-level panel data which would allow for a rich analysis of dynamics. Third, while the stock of MSMEs helps shed light on the challenges faced by policy makers, improving the entrepreneurship landscape is about enhancing entrepreneurial behavior of new and existent business. 13 A new firm is a firm that did not exist in time t0 but exists in time t1. Growth is measured comparing the performance of the firm in two periods in time, while innovation, by definition, corresponds to a product or a process that is new in time t1 and did not exist in t0. 14 For example, the World Bank Enterprise Survey, which focus on formal firms with 5+ employees. 13 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Kenya is characterized by high informality. If considered informal businesses, the number of MSMEs in Kenya becomes much larger. Estimates from the 2016 KNBS MSME survey (which covers households and licensed establishments) suggests about 1.6 million MSMEs establishments licensed by county governments in Kenya (some of which are also formally registered).15 The significant difference between the estimated number of licensed MSMEs in the 2016 MSME survey and the formal 2017 CoE is explained by the fact that the MSME survey also includes household businesses that may have a local license but are not necessarily formalized at the national level. Moreover, the CoE only covers entities operating at a fixed location in dedicated premises. Figure 3. Sources of firm-level data in Kenya LICENSE MSME Survey LICENSED BUSINESSES 1.6M Firms 6.3M Workers MSME Survey UNLICENSED BUSINESSES 5.8M Firms 8.6M Workers Census of Establishments FORMAL BUSINESSES 138,190 Firms 3.5M Workers * Note: Some licensed businesses at the county level could also be formally registered. Box 2. Sources of firm-level data in Kenya The 2017 Census of Establishments covered all the formal establishments operating in Kenya. The census defines an establishment as an enterprise or part of an enterprise that is situated in a single geographic location and in which only a single economic activity is carried out or in which the principal productive activity accounts for most of the value added. Examples of establishments are shops, factories, offices, sales offices, banks, schools, shrines, temples, hospitals, inns, private teaching places. A formal business is an enterprise registered at the Registrar of Companies. 15 The MSME Survey also suggest a number of about 5.8 million unlicensed establishments which are mostly micro-businesses, including self-employed, concentrated in agriculture, manufacturing, accommodation, and specially retails. 14 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Box 2. Sources of firm-level data in Kenya (Continued) The World Bank Group’s Business Pulse Survey complemented data from the 2017 CoE report. The Business Pulse Survey (BPS) was designed to measure the pulse of the private sector during the COVID-19 pandemic. The sample in Kenya was obtained from the 2017 census and the survey design provides sampling weights to obtain nationally representative estimates. The micro-data collected includes information on the number of employees and other relevant characteristics of the firm, which can be exploited to obtain estimates on the formal sector to complement publicly available information from the census. The 2016 MSME survey covers both licensed and unlicensed firms. In Kenya, entities undertaking economic activities are required to obtain operating licenses both at the national and county levels. The county license must be obtained on an annual basis and the county governments maintain databases of all these establishments. The databases from the 47 county governments were the sampling frame for the MSME survey of licensed businesses. Some of these licensed businesses have been covered in the 2017 Census of Establishments. In addition, the survey included unlicensed businesses sampled from a household sampling frame. Figure 4. Features of establishments in the Kenya 2016 MSME survey (a) Share of firms by employment size (b) Employment distribution 30 80 26.7 69.3 60 20 18.6 18.9 18.2 17.7 % Percent % Percent 40 19.9 10 20 9.1 1.5 0.2 0 0 0-3 4-9 10-49 50-149 150-Plus 0-3 4-9 10-49 50-149 150-Plus NOTE: Weighted calculations. 24164 unweighted observations. Source is MSME (2016). NOTE: Weighted calculations. 24164 unweighted observations. Source is MSME (2016). (c) Sector distribution d) Age distribution 60 56.6 42.7 40 40 30 28.4 % Percent % Percent 21.0 20 20.4 20 10.7 10.4 10 7.9 0.1 1.8 0 0 Agriculture Manufacturing Retail Tourism Digital Other 0-5 6-10 11-20 20 Plus NOTE: Weighted calculations. 24164 unweighted observations. Source is MSME (2016). NOTE: Age is constructed as survey year (2016) minus the starting year of the business plus 1. Weighted calculations. 24164 unweighted observations. MSME (2016). Source: 2016 MSME survey, KNBS. 15 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Most firms in the MSME survey are micro businesses with 3 or less employees; are in retail and are young (0-5 years). In the MSME survey, the largest number of businesses are in sectors with relatively low entry barriers (e.g., retail and tourism related activities) while only about 11 percent of businesses are in manufacturing (Figure 4.c). Compared to the overall structure of the 2017 CoE, the micro and small firms in the MSME survey are also more relevant in terms of jobs. In total, the MSMEs covered in the 2016 MSME survey account for about 6.3 million workers working in licensed businesses. This number is much larger if combined with unlicensed businesses that cover about 8.6 million workers, summing up almost 14.9 million workers outside formal businesses. This is a significantly larger number of workers compared to the approximately 3.5 million workers estimated by the formal establishments. The large number of informal MSMEs in Kenya suggests a high potential for entrepreneurial improvement, but also significant challenges faced by entrepreneurs. The next section examines the entrepreneurship outcomes and the ecosystem pillars as relevant determinants of entrepreneurship. 1.2 Entrepreneurship output Entrepreneurship in Kenya exhibits high firm creation relative to what would be expected considering its level of development in cross-country analysis. The registration of new businesses in Kenya amounts to 1.5 per 1,000 working age population. Compared to global peers in similar stages of development, firm creation in Kenya is significantly higher (Figure 5) and even approximates that of peers with higher levels of GDP per capita. Figure 5. Business creation (a) Density of new businesses relative to GDP per capita 20 ZAF New registered business per 1,000 working 10 age population (ages 15-64; log scale) 5 COL MYS RWA KEN VNM 1 KHM LKA SEN .5 PHL .1 BGD 2,500 5,000 15,000 30,000 60,000 GDP per capita (log scale) Source: World Bank Group Entrepreneurship Survey and World Development Indicators. Source: World Bank Group Entrepreneurship Survey and World Development Indicators. Note: The countries used in the analysis as global peers for comparison include: LKA – Sri Lanka; MYS – Malaysia; PHL – Philippines; BGD – Bangladesh; VNM – Vietnam; KHM – Cambodia; SEN – Senegal; COL – Colombia; RWA – Rwanda; and the average for Sub-Saharan countries. Note: The countries used in the analysis as global peers for comparison include: LKA – Sri Lanka; MYS – Malaysia; PHL – Philippines; BGD – Bangladesh; VNM – Vietnam; KHM – Cambodia; SEN – Senegal; COL – Colombia; RWA – Rwanda; ZAF - South Africa and the average for Sub-Saharan Africa (SSA) countries.16 16 The selection of countries included peer and aspirational comparators and considered several factors, including the economic diversity of those economies, both in terms of size of the country and per capita income, as well as qualitative information in consultation local experts. 16 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic There is a small number of registered establishments that effectively survive and prosper. Compared to the total number of formal businesses in the latest establishment census, the entry rate of new formal businesses in Kenya is around 30 percent. The total number of new businesses registered in the 2017 and 2018 were 37,300 and 44,400 respectively, while the total number of businesses reported in the 2017 CoE is 138,200. This gives an estimated entry rate of around 30 percent (27 percent in 2017 and 32 percent in 2018). This rate is high compared to the stock of establishments suggesting that many of these new registered firms do not end up operating at a fixed location in dedicated premises, face high exit rates, or do not manage to scale up. Business scale-up has been limited in Kenya. Existing formal firms in Kenya exhibit limited growth. In 2018 the average number of employees in formal businesses with more than 5 employees increased by 6 percent, while it increased by 7.5 percent in similar firms on average in SSA countries (Figure 6; panel a). This feature is reflected in the relatively small size of formal firms that have been operating for five and twenty years. While the average size of young formal firms in Kenya is 15, the average in the region is 21 (Figure 6; panel b). Similarly, while the average size of 20-year-old firms in the region is 114 workers, in Kenya the average size is 71 workers.17 Figure 6. Business scale-up. (a) Average annual employment growth (percentage) (b) Number of workers After surviving 20+ years Note: Source: Panel a and b– World Bank Enterprise Survey. A relatively large share of firms report that they invest in Research and Development (R&D) and innovate with new products or new services, but firms in Kenya are still far from the technological frontier. 18 About 20 percent of firms reported that they invested in R&D activities in 2018 (Figure 7; panel a). Moreover, Kenyan firms have reported a relatively high level of product or service level innovation (Figure 7; panel b) which may not necessarily mean that these are disruptive innovation at the national or international level, but suggest that they have been upgrading. Compared to country peers, these indicators may suggest a dynamic process of innovation in Kenya. Yet, these numbers may reflect only marginal improvements made by firms given that they do not capture the quality of innovation.19 New evidence from the firm-level adoption of technology survey recently implemented in Kenya suggests that on average, firms in Kenya are still far from the technological 17 A These estimates are based on the World Bank Enterprise Survey, which includes establishments with 5+ employees. 18 Product innovation refers to a good or service that is new or significantly improved. This includes significant improvements in technical specifications, components and materials, software in the product, user friendliness or other functional characteristics; Process innovation refers to a new or significantly improved production or delivery method. This includes significant changes in techniques, equipment and/or software. Marketing innovation: A new marketing method involving significant changes in product design or packaging, product placement, product promotion or pricing. 19 Cirera (2015) analyzes the lack of clear association between reporting innovation activity and productivity in Kenyan firms. 17 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic frontier (Box 3). Even when firms are adopting advanced technologies in functions such as payment methods, most firms are still not using digital payment methods intensively and rely mostly on cash and cheques. Figure 7. Innovation (a) Share of firms investing in R&D (b) Share of firms that introduced a product or service innovation Panel Source:Note: a – World Source: Panel Bank Enterprise a – World Survey. Bank Enterprise Based Survey. on formal Based firms on formal with firms 5+5+ employees. with employees. Box 3 – Kenyan firms can significantly improve the intensive use of technology New evidence from the World Bank’s firm-level adoption of technology survey, suggests that Kenyan firms have significant room to improve the level of technology. About 31 percent of firms in Kenya make use of specialized software and 21 percent of them use Enterprise Resource Planning (ERP), respectively for the business administration processes. However, only 5 percent and 3.1 percent of establishments in Kenya use these methods intensively. The gap between the extensive and intensive margins is more pronounced in the use of technologies related to sales and payment despite Kenya being known for its success on the diffusion of digital and mobile money. a) Technology used for business administration b) Technology used for payment methods 100 98.5 100 96.0 80 77.0 80 67.9 69.8 65.3 60 60 56.6 51.1 % Percent % Percent 40 39.3 40 34.4 31.4 23.6 21.0 18.6 20 20 15.2 10.2 5.0 6.5 1.1 3.1 0.2 0.0 0 0 Handwritten Standard Mobile Apps Specialized ERP Exc. of Goods Cash Check/Wire Debit/Credit Online Bank Online Software Software Card Platform Extensive Intensive Extensive Intensive Source: Cirera, Comin, and Cruz (2022) Note: Result based on the Firm-level Adoption of Technology Survey recently implemented in Kenya 18 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 1.3 Entrepreneurship ecosystem pillars The selection of indicators to assess the quality of the nine pillars of the ecosystem in Kenya exploits cross- country and time variation in measures correlated with entrepreneurship. The literature has suggested a series of critical structural factors that can either limit or boost entrepreneurship. In practice, the availability and quality of data (i.e., lack of indicators for critical factors, diversity of measures for the same factors, and lack of sample coverage) affects the inclusion of these factors in the analysis. Table A1 of the appendix shows the correlation between the rate of new business registration per thousand working age population and several indicators used in the analysis presented in this chapter. These cross-country comparisons of these indicators are discussed in detail in the following sub-sections. 1.3.1 Supply factors 1. Physical capital and infrastructure Kenya’s lagging infrastructure constrains entrepreneurship. The availability of physical capital and infrastructure are important complementary factors that affect the return on innovation to entrepreneurs.20 There is still a large share of population without access to electricity (Figure 8; panel a) which can also constrain entrepreneurs access and use better technologies. Despite having a robust digital infrastructure, only 30 percent of the Kenyan population uses internet compared to Sri Lanka’s 35 percent and Malaysia’s 90 percent respectively (Figure 8; panel b). The low internet usage imposes a challenge on the digitalization process and has become urgent given the COVID-19 pandemic and expected post-pandemic digital reformation. It can also severely impede the accumulation of human capital, adoption and diffusion of innovation.21 Substantial improvement in capital stock and internet access and affordability is therefore fundamental for Kenya to develop a sound entrepreneurial ecosystem and compete globally.22 Figure 8. Physical capital and infrastructure (a) Access to electricity (% of population) (b) Share of the population using Internet 90 100 100 100 100 100 97 96 86 70 70 70 84 Access to electricity (% of population) 80 71 50 70 43 60 35 30 33 30 48 27 47 25 40 20 Note: Source: Panel a – PWT; Panel b –World Development Indicators. 20 Cirera and Maloney, 2017 21 Jiménez, Matus, and Martínez, 2014 22 Capital stock per engaged person is an estimated value of machinery and equipment per engaged person. 19 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 2. Human capital Kenya has a rich talent pool marked by its SSA - average stock of science and engineering graduates with, significant room for improvements.23 The quality of local human capital directly impacts on entrepreneurship through the availability of high-quality entrepreneurs who can manage the many challenges of a growth focused business. According to the World Bank’s Human Capital Index (2020), Kenya scores above SSA region (0.55 compared to 0.40) and is the third highest score in the region.24 Such skill supply is promising for Kenya as it eases the finding of qualified employees, facilitates the operation of enterprises and complements innovation and technology adoption. Additionally, science and engineering graduates, if supported by complementary policies, are especially pivotal for empowering the manufacturing and technology sectors. However, there is still room to catch up with the top performers among peers, such as the Philippines and Vietnam, whose shares of science and engineering graduates in tertiary institutions almost doubles that of Kenya (Figure 9; panel a). Besides, there is a relatively small share of firms identifying educated workforce as a major constraint for Kenya (Figure 9; panel b). Continued improvements with respect to human capital among young workers is particularly relevant because Kenya working age population will continue to grow in the next 3 decades even if at a lower rate (Box 4). Figure 9. Human capital (a) Graduates in science and engineering (b) Share of firms identifying an inadequately educated workforce as a major constraint 40 38 educated workforce as a major constraint Share of firms identifying an inadequately 30 20 18 16 16 11 12 10 10 08 08 09 05 0 Note: Source: Panel a – GII; Panel b – WBES. Box 4: Demographic change, human capital and entrepreneurship in Kenya Kenya is going through a rapid demographic change with improvements in human capital that can lead to better entrepreneurship outcomes. The share of working age population is likely to increase in the coming decades (Figure B4.1a), while the share of workers with secondary and tertiary education will likely be larger by 2050 (Figure B4.1b). Entrepreneurship requires energy, creativity, business skills, and appetite for risk. Having an increasing share of working age population associated with lower child dependency rate can lead to positive economic outcomes associated with demographic dividend including higher economic 23 There are many relevant dimensions to be considered in terms of human capital. In this regard, STI graduates are used, and overall level of advanced education are used as proxies. 24 World Bank. Human Capital Project. https://www.worldbank.org/en/publication/human-capital. 20 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic growth and lower poverty rates (Cruz and Ahmed, 2020). There are several mechanisms in place, among them, the fact that an increased working age population may lead to more entrepreneurship. Liang, Wang, and Lazear (2018) show that a one standard deviation decrease in a country’s median age increases new business formation by 2.5 percentage points, which is about 40 percent of the mean rate. Furthermore, they provide evidence suggesting that older societies have lower rates of entrepreneurship at every age. Figure B4.1. Demographic transition and human capital in Kenya (a) Working age share (projection) 70 65 % Percent 60 55 50 2019 United Nations, DESA, Population Division. Licensed under Creative Commons license CC BY 3.0 IGO. United Nations, DESA, Population Division. World Population Prospects 2019. http://population.un.org/wpp/ (b) Human capital projection 60,000 40,000 20,000 0 2015 2020 2025 2030 2035 2040 2045 2050 No Education Primary Education Secondary Education Tertiary Education Source: (a) UN Population projection and (b) IASA. Kenya can benefit from the demographic dividend to support entrepreneurship though this relationship is not automatic. The United Nations (UN) population projections for Kenya suggest that the country will reach the peak of working age population around 2060. Yet, the median projection suggests a lower growth rate of the working age population in the next 40 decades compared to the last similar period. Estimates for education suggest that the share of tertiary education is expected to be relatively low by 2050 when the country still expects a relatively large share of population with no more than primary education. Overall advances in education combined with a favorable demographic pattern can bring positive effects for entrepreneurship outcomes in Kenya. 21 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 3. Knowledge capital As for knowledge capital, there is a striking gap between Kenya and the leading peers regarding the supply of researchers and top universities’ quality. The number of researchers per million people is only 221 for Kenya, dwarfed mainly by South Africa’s 518, Senegal’s 564, Vietnam’s 708 and Malaysia’s 2397 (Figure 10; panel a). Kenya’s gap is even more notable when it comes to its low score of the top 3 universities’ average ranking (Figure 10; panel b). Although Kenya is performing relatively better than its peers in SSA region, it is still lagging in these indicators when compared to other middle-income countries such as Vietnam. A recent World Bank study on higher education in Kenya found a contrasting picture. On one hand, a handful of Kenyan universities perform well in regional comparison and are acknowledged for their technological innovation. On the other hand, public universities face financial constraints while expansion has not been matched by quality improvements partly due to a growing gap between the number of students and qualified faculty.25 Figure 10. Knowledge capital (a) Number of researchers per million people (b) Average ranking of top 3 universities Note: Source: Panel a – World Development Indicators; Latest information available between 2010-2017.Panel b –GII. 1.3.2 Demand pillars 1. Access to markets Kenyan entrepreneurs face an internal market that is larger than the average in SSA but substantially smaller than other middle-income countries with a relatively large share of formal businesses exporting abroad (Figure 11; panels a and b). Kenyan entrepreneurs face significant constraints in terms of the market size. This is based on the estimates of the market size by gross domestic product (GDP), based on the purchasing-power-parity (PPP) valuation of country GDP in current international dollars (billions). Although there is a clear endogeneity problem when considering the market size as a potential (initial) determinant of entrepreneurship, the point is that it matters as a proxy of initial constraints faced by entrepreneurs from the demand side.26 Kenya not only has a relatively larger internal market than its SSA peers but also has 25 World Bank (2019b) 26 Entrepreneurship itself, across the different dimensions, affects market size over time. 22 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic proportionally more formal firms exporting. Access to external markets for Kenya can benefit from its strategic geographic position towards the Indian Ocean. Kenya’s membership in the East African Community customs union represents access to a market of 146 million consumers.27 Figure 11. Access to markets (a) Domestic market scale (b) Fraction of formal firms exporting abroad Note: Source: Panel a – GII, based on G – PPP from IMF, Panel b – World Bank Enterprise Survey. 2. Firm capabilities The management quality scores for Kenya suggest that relatively larger firms use better management practices than the average in countries with similar per capita income. Evidence across countries suggest that the adoption of good management practices is positively associated with business performance and the lack of management capabilities may explain a large share of differences in productivity across firms.28 The World Management Survey (WMS) shows that firms in Kenya have a relatively better performance than other African countries and countries with relatively similar per capita GDP, but still have significant room to improve poor performance when compared globally 29 (Figure 12, panel a). Addressing this managerial gap is key for innovation since the ability to transform innovation inputs on innovation outputs and firms’ performance largely depends on the managerial ability to implement innovation projects effectively.30 Additionally, almost half of the Kenyan firms (above 5 employees) have a website suggesting a relatively better use of digital technologies than most of its peers (Figure 12, panel b). 27 The African Continental Free Trade Area (AfCFTA) may present an important opportunity for extension. 28 Bloom and Van Reenen 2002 29 The sample only includes businesses in manufacturing with 50+ employees. 30 Bloom and Van Reenen (2007). 23 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 12. Firm capabilities (a) Managerial capabilities relative to GDP per capita (b) Fraction of formal firms with own website Index of managerial capabilities 3 VNM KEN COL 2 2,500 5,000 15,000 30,000 60,000 GDP per capita (log scale) Note: Source: Panel a – World Management Survey; Panel b – World Bank Enterprise Survey. 3. Entrepreneurial skills Perception-based indicators suggest a high appetite for entrepreneurial risk in Kenya although a relatively small number of research talent has been involved in business enterprises. The perception of a positive entrepreneurial behavior in Kenya is supported by the fact that a large share of working age population is self-employed. Considering the total estimates of the 2016 MSME survey, that would lead to approximately 7 million business owners including those under informality and self-employment. The challenge is that many of those are relatively low-skilled workers in the market who are in this position out of necessity rather than opportunity. Figure 13. Entrepreneurial skills (a) Appetite for entrepreneurial risk (b) Research talent in business enterprise Note: Source: Panel (a)– Global Competitiveness Index; Panel (b) -Global Innovation Index, refers to the share of researchers on business enterprises per thousand population. Panel (a) refers to perceived information in a 1-7 scale. 24 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 1.3.3 Barriers to allocation and accumulation 1. Access to finance Improving access to finance for SMEs is still an important challenge in Kenya in comparison to other economies. Figure 14 shows that lending rates in Kenya are much higher, averaging around 13 percent compared to most of its peers (5 percent in Malaysia and Senegal and 10 percent in South Africa). Kenya is also near the tail end of the spectrum when comparing the depth of its financial sector with bank credit to private sector representing around 32 percent of GDP. This ratio is close to 150 percent for South Africa and greater than 120 percent for Vietnam and Malaysia. Kenya has a vibrant financial sector but still MSMEs are credit constrained and there is limited diversification of MSME finance products by commercial banks. Traditional loan products are the main products in the market for MSMEs. Bank lending is mainly characterized by overdrafts with little evidence of loans of longer maturities (that is, longer than two years) while digital loan tenors also targeting MSMEs tend to be 30–day loan.31 Figure 14. Access to finance (a) Average lending interest rate (b) Domestic credit to the private sector (fraction of GDP) Note: Source: Panel a – World Development Indicators; Panel b – GCI. A crucial element to promote entrepreneurship and develop the SME sector is the availability of timely and sufficient capital. Access to finance is cited as a major challenge by almost 18 percent of firms in the country based on the latest World Bank Enterprise Survey.32 This could partly be explained by high cost of loans and lack of depth of Kenyan financial sector. Commercial banks dominate the financial sector representing 65 percent of credit to the economy, while credit providers serving different MSME segments include microfinance institutions, SACCOS, early-stage finance providers and fintechs, with the last two experiencing rapid growth in recent years. However, several factors still constrain the availability of credit to MSMEs (Table 2). 31 See more details of the key challenges associated with access to finance in the World Bank (2021), which provides a detailed finance diagnostic report for MSME. 32 WBES, 2018 25 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Table 2. Factors affecting the availability of finance to SMEs by segment Commercial • Delayed payments to suppliers by the Government of Kenya (including Banks, SACCOs, those related to VAT refunds) have resulted in cashflow problems and debt MFIs service challenges. • Lack of adequate collateral – with weaknesses and shortfalls to the recovery of collateral • Registering liens at the land registry is costly, slow and at times unreliable – particularly ill-suited for securing small loans • Gaps in the credit information infrastructure - Weaknesses include: (i) the technology in use by Kenya’s 3 Credit Reference Bureaus (CRB) precludes real- time sharing of information; (ii) data provided by the CRBs is inconsistent; and (iii) the use of third- party data has been limited to date • Low managerial capacity and low financing literacy • Complex, changing, and high taxes that have resulted in increased costs and uncertainties. They also contribute to informality, which generally precludes bank support Early-Stage • Kenyan enterprises still face constraints in the policy and regulatory Financing environment, i.e., burdensome business registration procedures, high taxation levels, etc • Women-owned businesses receive less investment than would be expected given their share of early-stage enterprises • Gaps at the earliest stages of the financing chain - high failure rates, a lack of requisite skills and high transaction costs deter angel investors and seed capital providers • Gaps at the high end of the financing chain - exit opportunities are limited by the small scale of the domestic pool of investment capital • Mismatch between the supply and demand for early-stage capital - Entrepreneurs cite a shortage of finance available, while fund managers indicate they are actively searching for investment opportunities but are unable to find quality firms that meet their requirements pool Fintechs • High levels of non-performing loans on digital credit • Exorbitant interest rates • An unregulated environment - Uneven playing field with low entry and exit barriers which have resulted in a proliferation of unregulated players • Limitations in the credit information mechanism - In April 2020, the CBK ceased all sharing of credit data from digital lenders with the CRBs, citing insufficient complaint redress mechanisms, predatory lending practices, and unethical handling of delinquent customers • Over-indebtedness • Lack of transparency – on effective pricing and the use of unclear terminology may leave borrowers vulnerable to abusive and predatory practices • Data usage and privacy issues remain unresolved Source: World Bank, The Next Frontier for Kenya’s Financial Sector: Supply Side Assessment – MSME Financing, manuscript. 26 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 2. Regulations The regulatory framework in Kenya needs strengthening to improve the overall business environment for SMEs and new entrepreneurs. Business regulations play an important role in promoting entrepreneurship and SME development at all stages of the business life cycle, including entry, investment and expansion, transfer and exit. Having effective and transparent regulations is therefore key to developing a healthy and dynamic private sector. The regulatory environment has improved in recent years but regulatory barriers in Kenya seem to be fairly high as shown in Figure 15. The number of days to start a business in Kenya is more than 3 weeks while it takes only 4 days in Rwanda and 6 days in Senegal. The regulatory burden for Kenyan firms is also much higher with senior management of businesses in Kenya spending significantly more time in dealing with regulations than in other peer countries – highlighting the complexity and inefficiency of the business environment in Kenya. Despite some progress in areas like business and property registration and construction licensing in recent years, the regulatory environment remains complex and unpredictable. Sectoral regulations present barriers to entry and competition, benefiting incumbent firms including State Owned Enterprises (SOEs).33 Government presence and regulations undermining competitive neutrality in sectors like electricity, air transport, telecommunications and agriculture, crowds out private investment and hinders productivity growth.34 Figure 15. Regulations (a) Number of days required to start a business (b) Fraction of time dealing with requirements of government regulation Note: Source: Panel a – Doing Business Indicators; Panel b - World Bank Enterprise Survey. Note: Source: Panel a – Doing Business Indicators; Panel b – World Bank Enterprise Survey. 3. Social capital and culture In terms of barriers associated with social capital and culture, Kenya seems to be doing better in comparison to peer economies. The fraction of Kenyan firms with women managers is above the regional average for SSA (Figure 16, panel a), although it is still lower than a number of comparator economies in East Asia. In terms of social capital – measured by an index ranging from 0 (low) to 100 (high) which assesses social cohesion and engagement, community and family networks, political participation and institutional trust, Kenya shows the highest level of social capital among all its regional and global peers (Figure 16, panel b). The indicators of culture and social capital plays a key role in promoting entrepreneurship by enabling risk taking and the creation of new firms. Lower barriers along social and cultural norms resonate with Kenya’s high level of firm creation as shown in section 2. 33 IFC (2019) 34 World Bank (2020) 27 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 16. Social capital (a) Fraction of firms where top manager is a woman (b) Index of social capital (scale 0-100) Note: Source: Panel a – World Bank Enterprise Survey; Panel b – GCI. 1.4 Strengths and challenges for entrepreneurship in Kenya Despite having relatively high levels of firm creation, the Kenyan private sector suffers from limited scale up and technological progress of existing firms. Entrepreneurship dynamics which relate to ease of firm entry and exit show a more promising picture with Kenya being at par with its regional peers and even outperforming its global comparators in terms of new registered firms. Kenya also has a growing working age population and given the ease of entry and exit of firms, this presents an opportunity for Kenya to strengthen its entrepreneurship outcomes. However, the stock of formal firms operating at a fixed location in dedicated premises is small and the performance of existing firms is weak, showing low levels of growth (in terms of jobs created) and innovation. The average employment size of formal firms in Kenya is below the SSA average, while the average growth of employment in Kenya is lower than the SSA average, falling behind regional peers. To improve entrepreneurship outcomes, Kenya will need to invest in improving the quality and availability of key inputs that make up the pillars of an entrepreneurship ecosystem. In terms of supply of factors of production, Kenya shows low levels of both physical and knowledge capital. Though supply of human capital is relatively better, this resource may not be used to its full potential due to existing weaknesses in other parts of the ecosystem. Despite showing certain promising areas in the physical infrastructure (e.g., lower percentage of Kenyan firms find electricity as a major constraint for their business compared to SSA average), Kenya’s overall stock of capital and use of internet is markedly low. Likewise, in knowledge capital, Kenya boasts better research collaborations and higher R&D expenditures compared to the SSA average and most of its international peers, but has low numbers of researchers per capita and rankings of its universities. In terms of human capital, Kenya’s standing is better, but there is a mismatch in the demand and supply of human skills. There is some evidence highlighting a general mismatch between the skills required by SMEs and those imparted on university graduates necessitating additional on-the-job training.35 35 Start-up ecosystem report - Nairobi | Enpact labs - https://www.enpact.org/wp-content/uploads/2019/08/print-nairobi.pdf 28 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Demand side factors of the ecosystem show a relatively better picture in comparison to supply factors, but it is crucial to expand the market for domestic firms beyond Kenya’s border. Kenyan formal firms have better access to global markets as shown by the percentage of firms exporting internationally but face significant constraints in terms of the domestic market size. Regulations, taxes and subsidies distort competition to a greater extent in Kenya than in most of its regional and international peers. Kenya shows much higher appetite for entrepreneurial risk but faces a challenge in improving entrepreneurial skills to enhance the acceptance of disruptive ideas. Lastly, the assessment of barriers to accumulation shows access to finance and business regulations to be significant constraints for businesses. The KNBS MSMEs survey suggests that a small share of firms in Kenya face rejection of their loan application. Despite this positive outlook, a significant fraction of firms identify access to finance as a major constraint and do not even apply for a loan. Kenya’s financial/banking sector also lacks depth compared to its international peers. The business environment shows weaknesses in Kenya with firms having to face high number of days to start business and management spending higher fraction of its time dealing with regulatory requirements. Although Kenya does show stronger adaptability to digital business models in its legal framework, the overall business environment and regulations need improvement. Barriers associated with social capital and culture are not as stringent and Kenya is doing better than its peers. However, one of the areas where Kenya needs to do better is around women’s ownership of businesses and their role in management. The key factors necessary to strengthen Kenya’s entrepreneurial ecosystem are structural and require investment, resources, and time to mature. The availability of quality resources combined with effective institutions and regulations can support the process of creating new firms, the expansion of young firms, and the technological catch up/upgrading of firms in Kenya. Some of these resources necessary to strengthen an entrepreneurial ecosystem (e.g., knowledge, human capital, entrepreneur talent, managerial capacity, and effective institutions) are structural in nature and require long-term investments, effective coordination within and between government and private sector and time to mature. In this regard, identifying the local potential and the needs of local ecosystems is a critical prerequisite for designing effective and well- targeted interventions and policies across Kenya. Identifying local high-potential ecosystems can provide policymakers the opportunity to understand why they are successful, and design customized and targeted interventions that can be implemented on a broader scale. The mapping of policy instruments and intermediary organizations can provide new metrics to evaluate the ecosystem by collecting information that is usually not readily available. Chapters 2 and 3 of this report aim to address these challenges and provide additional inputs based on new methodologies and data collected in Kenya.  29 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Chapter 2: Sub-national entrepreneurship ecosystems in Kenya The concept of local entrepreneurial ecosystems is grounded in the understanding that entrepreneurs and firms are inextricably linked with the local surrounding environment. The local environment plays an important role in providing critical resources to entrepreneurs to enhance productivity and competitiveness, but may need structural changes requiring investment and time to develop. The availability of resources combined with effective institutions can support the process of creating new firms, the expansion of young firms and the technological catch up/upgrading of firms. However, many of these resources face mobility costs across sectors and regions. Resources such as human capital face mobility costs both geographically and across sectors. Their availability and factors that facilitate access to them such as regulations and finance vary significantly across sectors and regions. Some of the key resources necessary to strengthen an entrepreneurial ecosystem such as knowledge, human capital, entrepreneur talent, managerial capacity and effective institutions are structural in nature requiring long-term investments and time to mature. Assessing the potential of entrepreneurial ecosystems at the local level and identifying the availability of resources needed by entrepreneurs are important prerequisites for designing effective and well-targeted interventions and policies. Entrepreneurship plays a critical role in economic development by promoting innovation, job creation, productivity, technology transfer and knowledge spillovers. Policy makers worldwide are keen to identify the set of policies, regulations and institutions that can help put in place a system that promotes entrepreneurship. However, without a consistent methodology that offers measures of entrepreneurship performance and indicators enabling a better understanding of its drivers and barriers, it is difficult to identify the main gaps and assess the performance of interventions. Identifying high-performing local ecosystems in given economies can provide policymakers the opportunity to have an in-depth look at successful ecosystems in their regions/countries and experiment with targeted interventions that can be implemented on a broader scale. This chapter identifies potential entrepreneurship ecosystems at the sub-national level in Kenya and assesses the key challenges to enhance their performance. It applies a new methodology for identifying and assessing local entrepreneurship ecosystems that combines data on density, diversity and quality of firms.36 It also examines the interactions between different pillars of the entrepreneurship ecosystem based on the supply and demand for knowledge and resources. The methodology adopted in this note assesses the availability of knowledge and resources (inputs), the demand and access for them, and the interaction among the key actors (entrepreneurs, institutions, and policy makers). The above-mentioned factors influence the performance of an ecosystem in generating entrepreneurial outcomes and therefore an assessment focusing on these different pillars of an ecosystem can help identify key gaps that can be addressed through appropriate policy instruments, regulations or intermediary organizations. 36 The methodology is described by Cruz, Torres, and Tran (2020). 30 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 2.1 The regional landscape of MSMEs and entrepreneurship in Kenya The 2010 constitutional framework for governance in Kenya introduced a two-tier structure with a national government and 47 county governments.37 Article 6.2 of the constitution defines that “the governments at the national and county levels are distinct and inter-dependent and shall conduct their mutual relations on the basis of consultation and cooperation.” With the aim to promote economic integration and optimize the comparative advantage of counties, six economic blocs in distinct regions across Kenya have emerged. These are the Lake Region Economic Bloc (LREB), the North Rift Economic Bloc (NOREB), the Mt. Kenya & Aberdare Economic Bloc (CKEB), Jumuiya ya Kaunti za Pwani (JKP), the South Eastern Kenya Economic Bloc (SEKEB) and the Frontier Counties Development Council (FCDC). A national policy framework for the regional economic blocs has been developed led by the Kenya Law Reform Commission (KLRC) and the Ministry of Devolution and Arid and Semi-Arid Lands ASAL.38 Figure 17. A snapshot of Kenya’s economic blocs FCDC NOREB LREB MT. KENYA & ABERDARE SOUTH EASTERN KENYA ECONOMIC BLOCK JUMUIYA YA KAUNTI ZA PWANI Source: Authors, based on Ministry of Devolution 39 37 Kenya Law Reform Commission . 38 Frontier Counties Development Council (FCDC) comprising of seven (7) counties namely; Garissa, Wajir, Mandera,Isiolo, Marsabit, Tana River and Lamu; North Rift Economic Bloc (NOREB) comprising of seven (8) counties namely Uasin Gishu, Trans-Nzoia, Nandi, Elgeyo Marakwet, West Pokot, Baringo, Samburu and Turkana; Lake Region Economic Bloc (LREB) comprising of thirteen (14) counties namely Migori, Nyamira, Siaya, Vihiga, Bomet, Bungoma, Busia, Homa Bay, Kakamega, Kisii, Kisumu, Nandi, Trans Nzoia and Kericho; Jumuia ya Kaunti za Pwani comprising of six (6) counties namely, Tana River, Taita Taveta, Lamu, Kilifi, Kwale and Mombasa; South Eastern Kenya Economic Bloc comprising of three (3) counties namely Kitui, Machakos and Makueni; Mt. Kenya and Aberdares Region Economic Bloc Comprising of ten (10) counties namely Nyeri, Nyandarua, Meru, Tharaka Nithi, Embu, Kirinyaga, Murang’a, Laikipia, Nakuru and Kiambu. The counties of Nairobi, Narok and Kajiado are not part of any bloc, while the counties Tana River and Trans-Nzoia are included in two different blocs. 39 Ministry of Devolution: https://www.devolution.go.ke/regional-economic-blocs/ . 31 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic This new geo-political context brings opportunities and challenges to identify common economic ground that can benefit from mutual efforts across county members within economic blocs. One key question faced by policy makers leading this process refers to what sort of interventions would promote businesses by facilitating entrepreneurship performance. From this perspective, the public policy debate can be informed by assessing the economic relationship across geographic regions through local entrepreneurship ecosystems. To perform this assessment, this note relies on the Kenya MSME survey data.40 Table 3 provides the distribution of firms across regions, including some characteristics.41 Table 3. MSMEs characteristics across economic regions Share Blocs Firms Young Firms Registered College owners 5+ employees CKEB 28.6% 23.4% 18.3% 20.0% 16.6% FCDC 2.9% 18.7% 14.2% 11.3% 15.5% JKP 7.6% 25.5% 27.4% 16.6% 20.4% LREB 23.1% 20.5% 24.4% 22.6% 21.2% NOREB 8.1% 17.9% 20.3% 24.7% 19.8% SEKEB 6.6% 17.2% 13.5% 12.5% 13.3% Nairobi 19.0% 26.5% 57.6% 38.8% 44.0% Narok 1.3% 34.3% 57.1% 15.0% 21.7% Kajiado 3.0% 30.7% 25.2% 29.5% 24.2% Source: MSME Survey, KNBS (2016). Note: Young firms are defined as those with less than 5 years of age. The Kenya MSME survey provides comprehensive indicators for MSMEs on their characteristics, operations, dynamics in terms of revenue, employment, sectors, constraints and access to amenities and support services. The sample consists of more than 24,000 observations representing a population of 1.8 million establishments. Around 89 percent of enterprises in the sample are micro firms (1-9 employees), 9 percent are small firms (10-49 employees), 1.4 percent are medium firms (50-99 employees), while large firms with more than 100 employees represent only 0.45 percent of firms in the sample. Nairobi County has the highest number of observations representing 6 percent of the sample. The other counties with the highest frequency of observations include Makueni, Uasin Gishu, Nyeri and Samburu with former two counties covering 3.8 percent and latter two counties 2.7 percent of the sample respectively. Around 22 percent of firms are young (less than 3 years), 77 percent have less than 6 employees while 26 percent are formal (registered by registrar of companies). 40 The MSME Survey was published by KNBS in 2016. The data is representative at the national and at county levels. 41 Because the microdata from the CoE is not publicly available, this chapter complement the analysis with two recent World Bank surveys that use the CoE as a sampling frame, and therefore provide overall estimates for the population of formal firms: The 2018 World Bank Enterprise Survey (WBES) and the 2020 World Bank Covid-19 Business Pulse Survey (BPS). 32 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 2.2 Identifying sub-national entrepreneurship ecosystems in Kenya To identify local entrepreneurship ecosystems, this section relies on a methodology that evaluates the diversity and the quality of geographical agglomerations of firms within a broad sector. The methodology identifies and analyzes the potential of local entrepreneurship ecosystems associated with six relevant sectors or value chains in Kenya i.e., agribusiness, light manufacturing, tourism, retail, financial services, and digital businesses, based on the Kenya MSME survey data. Agribusiness and light manufacturing are strategic sectors for the Kenyan economy as part of Kenya’s vision 2030, while digital, finance, and tourism sectors are important service activities for Kenya. Wholesale and trade represents more than fifty percent of the sample. Table 4. Descriptive Statistics Share Sector Firms Young Registered College owners 5+ employees Agribusiness 2.6% 15% 17% 16% 18% Light Manufacturing 6.4% 14% 16% 7% 17% Wholesale and Retail 56.6% 24% 23% 20% 15% Digital 1.8% 10% 36% 40% 25% Finance 4.1% 25% 46% 35% 22% Tourism 10.4% 29% 31% 27% 42% Other 17.6% 21% 43% 38% 43% Source: MSME Survey, KNBS (2016) There is significant heterogeneity in terms of average firm characteristics across these sectors. Among different sectors reported in table 4, the finance sector has the highest percentage of formal firms (57 percent registered with registrar of companies), the highest percentage of firms keeping records (82 percent), and the highest percentage of firms with college educated owners (51 percent). In contrast and perhaps unsurprisingly, agribusiness has the lowest presence of registered firms (17 percent), lowest level of ICT usage (50 percent), lowest number of firms keeping records (35 percent) and after light manufacturing sector, has the lowest number of college educated business owners (16 percent). The methodology identifies agglomerations of firms within these sectors across different sub-sectors and a variety of quality indicators that are correlated across regions. To measure diversity, the methodology first looks for statistically significant agglomerations of counties with a high density of establishments within each 2-digit sub-sector in the economic sector, and then counts the number of sub-sectors for which a county is part of an agglomeration The indicator of diversity is then sorted into 3 broader measures of diversity: no agglomerations, agglomerations in one sub-sector and agglomerations in more than one sub-sector. The left panel of Figure 18 shows the diversity across different counties for light manufacturing sector with counties highlighted in darker shades representing diversity across multiple sub-sectors within light manufacturing sector. 33 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 18. Identifying diversity and quality in agglomerations (Light Manufacturing Sector) Diversity Quality Uasin Gishu Meru Meru Kisumu Nyeri Nakuru Nakuru Homa Bay Kirinyaga Kiambu Kiambu Nairobi Machakos Nairobi Machakos Similarly, to measure potential or quality, the methodology first looks for agglomerations of counties in measures of business dynamism and measures of potential for additional growth within an economic sector. These include young firms, large firms, firms that are registered, those that use ICT equipment, those that keep business records and have college educated owners.42 The indicator of quality then counts the number of quality indicators for which a county is part of an agglomeration and is sorted into 3 broader measures of quality: no quality agglomerations, agglomerations in one quality indicator, and agglomerations in more than one quality indicator. The right panel of Figure 18 highlights the counties with agglomeration in quality indicators with darker shades representing quality across multiple quality indicators within light manufacturing sector. Potential for each ecosystem is then defined by the combination of both diversity and quality. The broad indicators of diversity and quality are combined into a typology with 9 categories to identify regions in Kenya with agglomerations in a high diversity of industries and with high-quality firms within a value chain (see Figure 19 for examples in Agribusiness and light manufacturing). High potential ecosystems exhibit agglomerations in more than one quality indicator and agglomerations in at least one sub-sector within the value chain; maturing ecosystems exhibit agglomerations in one quality indicator and at least one-subsector; incipient ecosystems exhibit agglomerations in more than one-subsector but no quality agglomerations. This exercise provides a summary of the key relevant agglomerations of MSMEs representing potential local ecosystems. Figure 19 shows the results for key strategic sectors, including agribusinesses, light manufacturing, tourism, retail, digital business, and finance. Overall, these sectors are relevant in terms of employment and play an important role in the Big Four Agenda Plan for Kenya. 42 The firm-level literature has emphasized that these factors (large, formal, using ICT, and keeping records) as positively associated with firm- performance. Thus, they are used as proxies for quality of firms. In addition, the number of young firms captures some dynamic characteristics in terms of entrepreneurship. 34 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 19. Identifying potential local ecosystems in Kenya a) Agribusiness b) Light Manufacturing Kakamega Laikipia Meru Kisumu Kisumu Nyeri Homa Nakuru Homa Nakuru Kirinyaga Bay Nyamira Bay Kisii Kiambu Kiambu Nairobi Nairobi Machakos Tana River Multi-sector - Multi-quality Mono-sector - Multi-quality Multi-sector - Mono-quality Mono-sector - Mono-quality Multi-sector - No-quality Mono-sector - No-quality c) Tourism d) Retail Trans-Nzoia Uasin Gishu Kakamega Meru Meru Kiambu Kiambu Narok Nairobi Machakos Nairobi Machakos Kajiado Multi-sector - Multi-quality Mono-sector - Multi-quality Multi-sector - Mono-quality Mono-sector - Mono-quality Multi-sector - No-quality Mono-sector - No-quality 35 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic e) Digital f) Financial Services Kericho Homa Nakuru Nyeri Nakuru Homa Bay Bay Kiambu Migori Kiambu Nairobi Nairobi Multi-sector - Multi-quality Mono-sector - Multi-quality Multi-sector - Mono-quality Mono-sector - Mono-quality Multi-sector - No-quality Mono-sector - No-quality The average sales per worker in MSMEs located in high-potential ecosystems (multi-quality combined with mono or multi-sector agglomerations) are higher than in other locations. Figure 20 shows that across different sectors, businesses located in multi-quality ecosystems tend to perform better in terms of average sales per worker. This evidence is also supported by other performance indicators (e.g., income per worker or business growth). In the case of Kenya, the result is driven by the fact that MSMEs ecosystems with multi-quality agglomeration are strongly located and connected with Nairobi.43 In addition to Nairobi, the three economic blocs showing agglomerations with multi-quality in the sectors analyzed in this chapter are CKEB, strongly driven by Kiambu and its connection with Nairobi, SEKEB and LREB. The analysis also identifies potential local ecosystems in other economic blocs, including FCDC, JKP, LREB, and NOREB. 44 43 Nairobi is widely recognized to have a vibrant start up ecosystem and is also attracted the regional headquarters of global technology firms. Firms from other areas gravitate towards Nairobi attracted by the opportunity and access to networks. World Bank (2019). 44 Because the agglomeration analysis is based on the MSME survey (not the establishment census) at a very aggregated level, both in terms of sector and regions, this should be used only as a proxy for potential ecosystems. 36 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 20. Average sales across ecosystems Average sales per worker Agrobusiness Light Manufacturing Wholesale & Retail Trade 60000 Predicted average sale per worker 40000 20000 0 Tourism Digital Services Finance 60000 40000 20000 0 Other Multiquality Other Multiquality Other Multiquality Source: Results based on the MSME Survey, KNBS (2016) Note: Predicted estimations based on average sales per workers controlling for multi-quality ecosystem location, formal status, sector, age of the businesses, and initial size in terms of workers. There is a significant concentration of local high-potential ecosystems – based on broad sectors -- around Nairobi and Kiambu. Table 5 summarizes some of the key ecosystems following the typology previously described along the county and economic block they belong to. While the digital ecosystem is concentrated around Nairobi, other sector of activities (e.g., agribusinesses, light manufacturing, tourism, and financial services) tend to have relevant agglomeration of firms – both in terms of diversity by economic activity and relative quality of firms – across different regions, particularly in CKEB and LREB economics blocs. The distribution of potential local entrepreneurship ecosystems across counties also provides a description of economic activities that may be more common and connected across borders to support the economic identity of economic blocs. The next section analyzes the pillars of entrepreneurship ecosystem at the sub-national level using the economic blocs as a unit of analysis. Table 5. Number of potential ecosystems based on quantity-quality MSME’s businesses agglomeration County Economic Agribusiness Light Retail Tourism Digital Finance Bloc (Add) Manufacturing Nairobi - Mono-sector Multi-sector - Multi-sector Multi-sector Multi-sector - Multi-sector - Multi-quality Multi-quality -Multi-quality - Multiquality Multi-quality -Multi-quality Kiambu CKEB Mono-sector Multi-sector - Multi-sector Multi-sector- Multi-sector Multi-sector - Multi-quality Multi-quality -Multi-quality Multi-quality -Multi-quality -Multi-quality Kajiado - No sector - Mono-sector Mono-sector Mono-sector Multi-quality -Multi-quality -Multi-quality -Multi-quality Machakos CKEB* Mono-sector Mono-sector Mono-sector -Multi-quality - No quality -Multi-quality 37 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Table 5. Number of potential ecosystems based on quantity-quality MSME’s businesses agglomeration (Continued) County Economic Agribusiness Light Retail Tourism Digital Finance Bloc (Add) Manufacturing Meru CKEB* Multi-sector - Mono-sector Mono-sector Multi-quality - No quality - Multi-quality Nakuru CKEB* Mono-sector - Multi-sector - Mono- Mono-sector Mono-quality Multi-quality sector Kakamega LREB* Mono-sector Mono-sector - Multi-quality Homa Bay LREB Mono-sector Mono-sector Mono- Mono-sector- - No quality sector Mono-quality Kisii LREB Mono-sector - Mono-quality Kisumu LREB Mono-sector Mono-sector Mono-sector - No quality Nyeri CKEB Mono-sector Mono- sector Uasin NOREB Mono-sector Gishu Bungoma LREB Mono-sector Kericho NOREB Mono- sector Kirinyaga CKEB Mono-sector Laikipia CKEB Mono-sector Migori LREB Mono-sector- Mono-quality Mombasa* JKP Mono-sector Narok LREB Mono-sector Nyamira LREB Multi-sector Tana River FCDC Mono-sector Trans- LREB Mono-sector Nzoia Note: Kiambu is part of the CKEB economic bloc while Mombasa is part of JKP economic bloc. Multi- quality ecosystems are also identified as high-potential by combining significant economic agglomerations in at least one economic activity within a given strategic sector and multi-quality indicators. 38 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 2.3 Entrepreneurship characteristics and performance across sub-national ecosystems Following the conceptual framework presented in chapter 1, this section takes a deep dive into entrepreneurial ecosystems pillars at the sub-national level. The analysis compares businesses performance across regional blocs and presents information at the county-level for Mombasa, Kiambu, and Nairobi.45 The analysis for JKP and CKEB refers to indicators related to other counties excluding Mombasa (JKP) and Kiambu (CKEB). The analysis also provides sector-level indicators focusing on agribusiness, light manufacturing, retail, tourism, digital, and finance to give some perspective of sector-specific heterogeneity. Despite variation across regions, relatively few businesses seem driven by opportunities associated with skills or demand. Businesses that reported the main reason for starting a business as “skilled in the activity” or “high-demand,” are more likely to be formal and more likely to be associated with entrepreneurs with higher levels of education. Taking both options as a reference for “business as an opportunity,” figure 21 shows that the share of businesses that identify one of these two options as a main reason to start vary between 20 percent and 40 percent, except in the FCDC bloc. The share of businesses associated with “no alternative,” “preference for self-employment,” or “better income,” suggests that these drivers tend to dominate the decision of entrepreneurship in most regional ecosystems (beyond 40 percent combined). Indeed, the most common reasons reported by entrepreneurs are related to the prospect of better income and the preference for self-employment. These options are followed by being skilled in the activity and identifying high demand for the businesses which tend to be options more related with opportunities. These results do not show much difference across Kenya. Licensed businesses across different sectors and digital services or light manufacturing businesses (either formal or informal) are more likely to start their businesses due to high demand or the skills. This significant difference observed across sectors in terms of the motivation of the entrepreneurship activity is a relevant feature to characterize high-potential ecosystems (Figure 22). These sectors do not show significant differences in the likelihood of starting because of skill or high demand between registered (formal) and unregistered (informal) businesses either. The CKEB region, including Kiambu, and Nairobi, has been identified as having higher concentration of businesses in light manufacturing and digital services. 45 Nairobi, Mombasa, and Kiambu are the counties with the largest amount of formal registered establishments based on the 2017 census of establishments from the KNBS. Moreover, Nairobi and Kiambu were found to have multi-quality agglomerations across the different sectors analyzed. Thus, given the economic relevance of these counties in terms of local entrepreneurial ecosystems, Nairobi, Mombasa, and Kiambu are kept separated as a benchmark to the other regions. 39 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 21. Main reason for starting the business 1.CKEB 2.FCDC 3.JKP 60 40 20 0 1.Skill or High-demand 2.Family experience 3.Advise or capital 4.Better income 1.Skill or High-demand 2.Family experience 3.Advise or capital 4.Better income 1.Skill or High-demand 2.Family experience 3.Advise or capital 4.Better income 5.Other 5.Other 5.Other Main reason to choose the sector 4.LREB 5.NOREB 6.SEKEB 60 40 20 0 1.Skill or High-demand 2.Family experience 3.Advise or capital 4.Better income 1.Skill or High-demand 2.Family experience 3.Advise or capital 4.Better income 1.Skill or High-demand 2.Family experience 3.Advise or capital 4.Better income 5.Other 5.Other 5.Other 7.MOMBASA 8.KIAMBU 9.NAIROBI 60 40 20 0 1.Skill or High-demand 2.Family experience 3.Advise or capital 4.Better income 1.Skill or High-demand 2.Family experience 3.Advise or capital 4.Better income 1.Skill or High-demand 2.Family experience 3.Advise or capital 4.Better income 5.Other 5.Other 5.Other 1.Skill 1.High-demand 2.Family experience 3.Advised 3.Advertisement 3.Capital available 4.Better income 5.Preference self-emplyment 6.No alternative Source: Results based on the MSME Survey, KNBS (2016) Figure 22. Propensity to start the business because of skill or high demand (by sectors) Main reason to start a business: Skill or Demand (likelihood) Agrobusiness Light Manufacturing Wholesale & Retail Trade .6 .4 .2 Predicted Margins 0 Informal Formal Informal Formal Informal Formal Tourism Digital Services Finance .6 .4 .2 0 Informal Formal Informal Formal Informal Formal Source: Results based on the MSME Survey, KNBS (2016) Note: Estimations of the average sales per workers controlling for multi-quality ecosystem location, formal status, sector, age of the businesses, and initial size in terms of workers. 40 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Across strategic sectors, formal and informal firms in digital services which are predominantly concentrated around the Nairobi ecosystem tend to innovate more. Overall, formal businesses are significantly more likely to innovate than informal businesses and compared across both groups, digital businesses tend to be more innovative. An important feature regarding digital entrepreneurship ecosystems in Kenya is that digital entrepreneurs are more likely to start a business because of skills than opportunities related to demand. Figure 23. Propensity to innovate on products or services (by formal status) Share of firms innovating on product or service Agrobusiness Light Manufacturing Wholesale & Retail Trade .5 Predicted Margins 0 Tourism Digital Services Finance .5 0 Informal Formal Informal Formal Informal Formal Source: Results based on the MSME Survey, KNBS (2016) Note: Estimations based on average sales per workers controlling for multi-quality ecosystem location, formal status, sector, age of the businesses, and initial size in terms of workers. There is significant variation across sub-national ecosystems regarding three key entrepreneurship outputs: new entry, scale up, and innovation. First, when looking at the number of new businesses per capita across sub-national entities, there are different patterns for registered or unregistered businesses. While Nairobi takes the lead on number of registered businesses, counties in the FCDC and NOREB blocs tend to lead on number of new unlicensed businesses. In terms of the average size of firms under 5 years old that might be used as proxy for potential to scale up, MSMEs in Nairobi are clearly in advantage vis-à-vis other regions. This is partially explained by the largest share of formal businesses and entrepreneurship quality. In terms of innovation either related to new products, services or processes, there seems to be much more heterogeneity across regions. 41 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 24. Entry, Scale up, and Innovation a) Number of new firms per 1,000 people b) Average size of firms under 5 years old 5 Number of new firms per 1K habitants Average size of firms under 5 years old 4 3 2 1 0 LREB Nairobi LREB SEKEB CKEB JKP NOREB FCDC FCDC JKP SEKEB CKEB NOREB Other Kiambu Mombasa Other Kiambu Mombasa Nairobi Not Registered Registered c) Average growth across counties d) Estimated average growth by formal status and ecosystem potential Average growth of businesses Informal Formal 3 3 2 2 Predicted Margins 1 1 0 0 Others High-potential Others High-potential NOREB SEKEB LREB Mombasa CKEB JKP Kiambu FCDC Other Nairobi Source: Results based on the MSME Survey, KNBS (2016) Note: Estimations based on average sales per workers controlling for multi-quality ecosystem location, formal status, sector, age of the businesses, and initial size in terms of workers. 42 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic e) Share of firms innovating on product or services f) Share of firms innovating in processes .08 Share of firms innovating on product or service Share of firms innovating on product or service .15 .06 .1 .04 .05 .02 0 0 NOREB CKEB JKP SEKEB LREB Kiambu FCDC Mombasa Other Nairobi Mombasa FCDC Other LREB NOREB JKP CKEB Nairobi SEKEB Kiambu Source: Results based on the MSME Survey, KNBS (2016) Note: Estimations based on average sales per workers controlling for multi-quality ecosystem location, formal status, sector, age of the businesses, and initial size in terms of workers. Firms in Nairobi or other high-potential local ecosystems also achieved higher levels of growth in terms of employment and are more likely to innovate in terms of new products, services, or processes. Comparing the total number of workers at the time of inception of the enterprise, controlling for the age, firms in Nairobi tend to reach the highest average growth compared to other regions. A regression analysis controlling for age, sector, and initial size of the firm shows that firms in high-potential ecosystems (either formal or informal) are significantly more likely to have higher average growth. Firms in Nairobi, CKEB, and SEKEB regions are more likely to innovate in terms of products, services or processes.46 Expectedly, the regions with higher levels of innovation in addition to Nairobi also have larger numbers of high-potential ecosystems. 2.4 Main barriers faced by local entrepreneurship ecosystems This section assesses the strengths and weaknesses of entrepreneurship pillars across sub-national ecosystems. The analysis combines perceived obstacles (what firms report as being their main obstacle) with factual information reported by the firms, which are associated with the entrepreneurship pillars. The combination of these sources of information is important to provide clarity on some of the key issues but also take into consideration the perception of entrepreneurs to understand their decision-making process. These pillars are divided into three: 2.4.1 Perceived obstacles faced by entrepreneurs Three key factors come out as most relevant across all regions: poor quality of infrastructure (physical capital), lack of markets, and regulations. Lack of infrastructure is usually part of a general problem that does not only affect entrepreneurship and business performance but also has important complementary effects. Firms in the largest urban agglomerations (Nairobi and Mombasa) report less poor infrastructure as an issue and tend to disproportionally emphasize issues related to market, which includes lack of markets and competition. 46 Innovation indicator is self-reported by firms based on what is a new product, service, or process based on their previous condition. 43 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic In terms of regulations, the NOREB and CKEB blocs and Kiambu have more entrepreneurs reporting taxes as an issue compared to other regions. The large share of businesses reporting lack of market or local competition as main constraints is consistent with the fact that most MSMEs are acting in markets with relatively low entry barriers (e.g., small retail) relying mostly on local consumers. Figure 25. The largest obstacles to business identified by firms 1.CKEB 2.FCDC 3.JKP 60 40 20 0 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 6.Social Capital 6.Social Capital 6.Social Capital Percent of firms identifying each the most serious obstacle 4.LREB 5.NOREB 6.SEKEB 60 40 20 0 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 6.Social Capital 6.Social Capital 6.Social Capital 7.MOMBASA 8.KIAMBU 9.NAIROBI 60 40 20 0 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 6.Social Capital 6.Social Capital 6.Social Capital 1.Poor roads 1.Raw material shortage 1.Power interruption 1.Electricity inaccessibility 1.Poor water access 1.Lack of space 2.Lack of skill 3.Lack of markets 3.Local competition 3.Foreign Competition 4.Credit collateral shortages 5.Authority interference 5.Licenses 5.Taxes 5.Other govt regulations 5.Poor security 6.Impact of HIV-AIDS Source: Results based on the MSME Survey, KNBS (2016) The perceived obstacles related to electricity inaccessibility, lack of skills, lack of markets, foreign competition and taxes are negatively and significantly associated with business growth in terms of jobs. Figure 26 shows that these results are statistically significant, controlling for sector, formal status, county, age groups, and number of workers at the starting year. The results suggest that for an important measure of entrepreneurship outcome (scale up), the perceived barriers by entrepreneurs are indeed associated with performance. Importantly, there is no significant difference across sectors regarding the likelihood of reporting lack of markets, local or foreign competition (market-related issues) as the main obstacle, except to light manufacturing, which shows a slightly higher probability. 44 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Despite a relatively small share of firms reporting credit collateral shortages, other sources of information suggest that lack of access to credit is still an important constraint for businesses in Kenya. In the 2018 World Bank Enterprise Survey, over 18 percent of firms mentioned access to finance as the main obstacle. Access to finance is the second most mentioned obstacle after informality. In addition, the International Finance Corporation (IFC) estimates an SME finance gap of US$19.38 billion for Kenya representing 30 percent of GDP. Figure 26. Association between largest perceived obstacles and firm growth Average Marginal Effects with 95% Confidence Intervals 2 1 Predicted Margins 0 -1 -2 -3 2.Barrier 3.Barrier 4.Barrier 5.Barrier 6.Barrier 7.Barrier 8.Barrier 9.Barrier 10.Barrier 11.Barrier 12.Barrier 13.Barrier 14.Barrier 15.Barrier 16.Barrier 17.Barrier 18.Barrier Source: Results based on the MSME Survey, KNBS (2016) Note: Margin effects based on an OLS estimations controlling for sector, formal status, county, age groups, and number of workers at the starting year. Barriers refer to 2. Raw material shortage; 3. Power interruption; 4.Electricity inaccessibility**; 5.Poor water access; 6.Lack of space*; 7.Lack of skill**; 8.Lack of markets***; 9. Local competition. 10. Foreign Competition***; 11 Credit collateral shortages; 12.Authority interference; 135. Licenses; 14.Taxes**; 15. Other government regulations; 16. Poor security; 18. None. Results based on the full sample. These results are consistent with the sub-sample of sectors: agribusiness, light manufacturing, retail, tourism, digital services and finance, expect for lack of skills. The next section examines the availability of resources across sub-national regions based on factual data. Perceived obstacles are important references for assessing key challenges across sub-national ecosystems. Even if they do not necessarily reflect the main obstacles imposing barriers to entrepreneurs (e.g., the perception of lack of market may be a result of lack of capabilities to produce a more competitive product), they are important for the policy dialogue. The next section examines the availability of resources across sub-national regions based on factual information to further understand how perceived obstacles are associated with key challenges faced by entrepreneurs at the sub-national level. The resources are grouped in five categories: a) Access to physical capital, infrastructure, and digital technology; b) Access to human capital; c) Access to markets; d) Regulatory environment; e) Access to finance. 45 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 2.4.2 The local availability of resources A – Access to infrastructure and digital technology There is a large gap in terms of access to digital technologies by MSMEs across different economic blocs. Consistent with the fact that firms in Nairobi and Mombasa report fewer constraints in terms of physical capital and infrastructure in their locality, these counties, in addition to Kiambu, have a larger share of MSMEs with access to electricity, computer and Internet. These regions including the NOREB bloc, Kajiado and Narok are more likely to have MSME businesses with a website. Yet, the results suggest a much larger gap between Nairobi and the rest of Kenya with respect to access to ICT equipment (computers) and digitalization (measured through access to Internet for business purpose or use of website) than the gap with respect to electricity. Given the increasing importance of access to digital technologies, particularly in a context of COVID-19, this factor highlights an important inequality across regions and is consistent with the fact that businesses in Nairobi and Mombasa are less likely to report constraints related to infrastructure. While electricity access in Kenya reached 71 percent of the population in 2020 well above the SSA average of 48 percent,47 there are still gaps in regional access. In fact, a 2018/19 survey covering 23 counties found that only 35 percent of SMEs in the North rated electricity access as high compared with 77 in Central Kenya and 64 percent in the West.48 Figure 27. Physical capital, infrastructure, and ICT a) Share of firms which have access to electricity b) Share of firms which have a computer Share of firms which have access to electricity Share of firms which have access to electricity 1 .4 0.8 0.3 0.6 0.2 0.4 0.1 0.2 0 0 FCDC SEKEB JKP LREB NOREB CKEB SEKEB Other Kiambu Nairobi Mombasa FCDC JKP LREB NOREB CKEB Other Kiambu Mombasa Nairobi 47 World Bank. World Development Indicators (2019) 48 International Trade Center (2019). 46 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic c) Share of firms with access the internet d) Share of firms with a website Share of firms which used a computer to acccess the internet 0.25 .4 Share of firms which have a website 0.2 0.3 0.15 0.2 0.1 0.1 .05 0 0 FCDC JKP SEKEB CKEB LREB NOREB Other Mombasa Kiamabu JKP FCDC CKEB SEKEB LREB Mombasa NOREB Kiamabu Other Nairobi Nairobi Source: Results based on the MSME Survey, KNBS (2016) B –Access to human capital Regarding human capital, both the number of managers and workers with college degrees tend to be concentrated around Nairobi. Some regions such as the FCDC and JKP are significantly behind regarding the number of managers and workers with college degrees, as a measure of human capital. There is a significant difference in the availability of skilled workers across sub-national regions of Kenya. As in the case of physical infrastructure, firms in the JKP (excluding Mombasa), FDCC and SEKEB regions have a relatively low number of businesses with managers holding college degree. Similarly, these regions also face significant gaps in terms of number of workers with college degree in MSMEs. Figure 28. Share of businesses with managers with college degree and share of workers with college degree a) Share of business with managers with college degree b) Share of workers with college degree Share of firms with Posr-Graduate Degree or Training Share of firms whose Owner Attended Collage 0.4 0.15 0.3 0.1 0.2 0.05 0.1 0 0 JKP FCDC SEKEB CKEB LREB NOREB FCDC NOREB JKP Other Mombasa CKEB LREB SEKEB Kiambu Mombasa Other Nairobi Kiambu Nairobi Source: Results based on the MSME Survey, KNBS (2016) 47 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic C – Access to markets The main market reported by most businesses across all regions is “individuals,” but ecosystems more connected to Nairobi tend to benefit more from government purchases. Overall, most MSMEs’ sub-national ecosystems strongly rely on individuals as main consumers. Despite the relatively low share of main buyers being non-MSMEs, foreign markets or government, firms that reach these markets tend to have a relatively better performance. Nairobi has the largest share of firms diversifying their markets (beyond local individuals). One issue limiting access to markets across counties and firm types is the availability of market information. Lack of data limits the firms’ ability to viability of expanding to a different county. This mostly affects startups, SMEs and women-led businesses. The government established an Open Data Initiative in 2011 but participation by government entities remains low.49 Most of the sub-national MSMEs ecosystems are not well-connected with external markets. Indeed, the share of MSMEs reporting direct exports as their main market is less than 0.5 percent, with relatively higher shares, but still below 1 percent for Nairobi.50 Although this is expected relatively to the size of those firms, the results suggest that sub-national ecosystems in Kenya are not connected to the external markets. These results contrast with the comparison at aggregated level in chapter 1 based on formal establishments with 5 or more employees, which showed Kenyan firms were more likely to export than their SSA peers. There is diversification across regions in terms of buyer’s contracts. Despite being geographically connected to Nairobi and being an integral part of several local sectorial ecosystems based in Nairobi, Kiambu presents a relatively low number of businesses with access to government contracts. It is also the region with the lowest level of non-MSMEs businesses as their main markets. Figure 29. Main market of firms across subnational ecosystems (non-individual markets) a) Main market of firms (non-individuals) .05 .04 .03 .02 .01 0 Kiambu LREB CKEB JKP Mombasa SEKEB NOREB FCDC Nairobi Non-MSMEs Exports Government Source: Results based on the MSME Survey, KNBS (2016) 49 World Bank (2020). 50 The share of businesses that export might be larger. This indicator captures only firms that has “export” as the main market. 48 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic .2 .15 .1 .05 0 b) Sources of contract with buyers (non-individuals) Kiambu JKP FCDC LREB SEKEB Mombasa CKEB Nairobi NOREB Non-MSMEs Middleman Government Source: Results based on the MSME Survey, KNBS (2016) D – The regulatory environment The cost of licenses and taxes relative to the average costs related to rent, electricity, and salaries are much higher in Nairobi compared to other regions. Outside Nairobi, Mombasa and the CKEB region, the average cost of license is under 50 percent of the costs related to rent, electricity and salaries. Yet, there are some significant differences in terms of tax rates, relative to costs.51 Besides, double and inter-county taxation have become a source of concern for the private sector and are an important challenge to MSMEs.52 Overall, Kenya’s devolution process has strong popular support and is critical to improve equity and quality of service delivery. At the same time, improvements are needed to clarify responsibilities between the national and county governments and to strengthen county government capacity and accountability.53 To boost their revenue collection, county governments have imposed cess (infrastructure development levies); distribution licenses; and motor vehicle branding on enterprises domiciled outside their jurisdiction driving up the cost of doing business within the country, and raising potential challenges to market access and competition. This is attributed to insufficiently harmonized legislative frameworks and guidelines to govern the imposition of these taxes and licenses. 51 There are important heterogeneities across sectors with respect to cost of licenses. The relative cost for license in Nairobi compared to other counties is larger for retail, but lower for manufacturing. Thus, part of the differences are also explained by sector composition. 52 KNCCI Policy Brief – Eliminating business unfriendly policies on multiple inter-county taxation affecting MSMEs - https://www.kenyachamber. or.ke/wp-content/uploads/2019/09/kncci_policy_brief_on_multiple_intercounty_licenses_designed_copy.pdf 53 World Bank (2020b). 49 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 30. Cost of license and taxes a) Ratio of cost of license with respect to total cost of b) Ratio of cost of taxes with respect to total cost of rent, electricity, and salaries rent, electricity, and salaries 0.5 2.5 0.4 2.0 0.3 1.5 1.0 0.2 0.5 0.1 0 0 JKP SEKEB FCDC LREB NOREB CKEB FCDC NOREB JKP Other Mombasa CKEB LREB SEKEB Other Kiambu Mombasa Nairobi Kiambu Nairobi Source: Results based on the MSME Survey, KNBS (2016) E – Access to finance A small share of firms has been applying for credit with little variation on the successful rate of applications across regions. Yet, the large majority of entrepreneurs still rely on their own resources to start a business. The relatively high success rate of applications is consistent with the fact that a relatively low number of businesses apply for credit. Most businesses (about 65 percent) report that they do not need credit as the main reason why they do not apply for it. This result does not vary much across regions. Excluding this option (no need), it is observed that in regions with the lowest rates of credit application (FCDC and JKP), a large share of entrepreneurs reports that they “do not like to be in debt,” which suggests a potential cultural challenge for local entrepreneurs. Except for FCDC, most regions, including multi-quality ecosystems like Nairobi-Kiambu, report that credit is too expensive, followed by inadequate collateral or costly procedures. These might help explain the low share of firms that apply for credit. Figure 31. Application for credit a) Share of firms which applied for credit b) Share of firms that have got credit, conditional on having applied for it Share of firms which applied for credit in the last 12 months .20 1 0.8 .15 0.6 .10 0.4 .05 0.2 0 0 FCDC JKP Other Nairobi SEKEB NOREB Kiambu LREB CKEB FCDC JKP Other Nairobi LREB CKEB SEKEB Mombasa NOREB Mombasa Kiambu Source: Results based on the MSME Survey, KNBS (2016) 50 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 32. Main reasons to not apply for credit (excluding “no need”) 1.CKEB 2.FCDC 3.JKP 60 40 20 0 4.LREB 5.NOREB 6.SEKEB Main reason did not apply 60 40 20 0 7.MOMBASA 8.KIAMBU 9.NAIROBI 60 Believed would be refused Too expensive Too much trouble for what it is 40 Inadequate collateral Do not like to be in debt 20 Do not know any lender size of loan and maturity insuff 0 Source: Results based on the MSME Survey, KNBS (2016) 2.5 The demand for policies In line with their main perceived obstacles, most policy suggestions coming from MSMEs are concentrated around physical capital, market, and regulations. Most suggestions from businesses in Mombasa, Kiambu, and Nairobi are associated with improving access to markets and regulations. In terms of physical capital and infrastructure, the demand is focused on improving access to water, electricity, and better business sites. The highest demand for infrastructure and physical capital-related interventions comes from FCDC and SEKEB. Overall, these demands are consistent with the position of businesses from these counties with respect to access to infrastructure. The challenges related to infrastructure and physical capital, despite being critical for business performance, go beyond entrepreneurship policies. Still, counties from these blocs may target projects related to infrastructure as a key common area of interest to support businesses. Regarding access to markets, the demand is driven by improving access to public procurement and equitable competition. Despite MSMEs in Nairobi being more likely to have the government as the main buyer, only a small share of them is in this condition. Thus, the demand to facilitate access to procurement processes seems consistent with the fact that most MSMEs do not have many options to diversify their markets and governments might be an important source of demand. It is important to understand practical challenges associated with regulations that make the bidding processes more accessible, transparent, and fairer, and 51 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic implementing efficient processes regarding payment and monitoring systems. During the focus groups interviews with entrepreneurs, one of the issues raised was related to payment delays from the public sector that led them to avoid further businesses with the government as a buyer. With respect to regulations, the demand is driven mostly by relaxing licensing. A large share of businesses in Kiambu, NOREB, CKEB, and LREB reported “relax licensing” as an important area of intervention. Although the cost of license in these regions is relatively lower than in Nairobi and Mombasa, it is higher than in other regions and almost equivalent to the costs related to rent, electricity, and salaries for a month. In many cases, this can impose a significant constraint to new entrepreneurs. There is little demand towards access to better technologies. These proposed solutions are associated with perceived constraints by entrepreneurs. Many of them are unable to identify issues related to lack of managerial practices or overall firm capabilities as their main obstacles due to an information failure. This can also be an important challenge towards policies aiming to promote technological upgrade. Figure 33. Main policy demands from MSMEs 1.CKEB 2.FCDC 3.JKP 1.Improve Water 60 1.Improve Electricity 40 1.Better locations/sites 20 2.Access to Technology 0 2.Awareness campaign 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 6.Social Capital 6.Social Capital 6.Social Capital 3.Equitable competition Percent of firms suggesting this primary solution 3.Public procurement 5.Relax licensing 5.Land tenancy reforms 4.LREB 5.NOREB 6.SEKEB 60 6.Improve security 6.Improve political stability 40 20 0 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 6.Social Capital 6.Social Capital 6.Social Capital 7.MOMBASA 8.KIAMBU 9.NAIROBI 60 40 20 0 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 1.Physical Capital 2.Human Capital 3.Market 4.Finance Access 5.Regulation 6.Social Capital 6.Social Capital 6.Social Capital Source: Results based on the MSME Survey, KNBS (2016) 2.5.1 Firms receiving support In terms of entrepreneurship supporting system regarding non-financial support and technology advice, firms around the Nairobi-Kiambu ecosystem and Mombasa tend to benefit most. Consistent with other indicators showing relevant gaps in complementary factors for entrepreneurship, the FCDC and SEKEB regions have the lowest share of firms with access to non-financial support or, more specifically, technology advice. 52 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 34. Share of businesses that received (non-financial) business support and technology advice a) Share of firms receiving support b) Technology advice Share of firms which applied for credit in the last 12 months .20 1 0.8 .15 0.6 .10 0.4 .05 0.2 0 0 FCDC JKP Other Nairobi SEKEB NOREB Kiambu LREB CKEB FCDC LREB CKEB SEKEB NOREB Mombasa JKP Other Nairobi Mombasa Kiambu Source: Results based on the MSME Survey, KNBS (2016) The most common type of non-financial support is business advice associated with marketing information, business, planning, and training while the main source of technological advice varies across regions. A relatively small share of businesses report receiving technological advice from government institutions or research institutions. Figure 35. Main type of non-financial support a) Overall business advice 1.CKEB 2.FCDC 3.JKP Marketing information 50 Accounting 40 Legal 30 Training 20 Business Planning 10 Stock Layout 0 Type of Support Received 4.LREB 5.NOREB 6.SEKEB 50 40 30 20 10 0 7.MOMBASA 8.KIAMBU 9.NAIROBI 50 40 30 20 10 0 Source: Results based on the MSME Survey, KNBS (2016) 53 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic b) Technology advice Government Institutions 1.CKEB 2.FCDC 3.JKP Research Institutions 60 NGO 40 MSME NON-MSME 20 Salesmen Main Source of Technological Advice Publications 0 4.LREB 5.NOREB 6.SEKEB 60 40 20 0 7.MOMBASA 8.KIAMBU 9.NAIROBI 60 40 20 0 Source: Results based on the MSME Survey, KNBS (2016) 2.5.2 Summary of policy needs The analysis of local entrepreneurship ecosystems points at potential areas of attention for policymakers seeking to support MSME development at the sub-national level in Kenya. This note addresses a knowledge gap by identifying ecosystems with potential and challenges to MSMEs using the regional blocs as units of analysis. It identifies possible policy areas of focus as these entities, still largely in formative stages, are developed. As mentioned in the introduction, the analysis here is based primarily on the 2016 MSME survey. It should be complemented with other quantitative and qualitative sources to further inform the development of policies and programs to support MSMEs across regional economic blocs and their member counties. The analysis identified issues that will require coordinated interventions at the national and sub-national levels of government. First, different policies should be pursued depending on the potential of the ecosystem. The analysis showed that firms in high potential ecosystems tend to grow more. Therefore, policies that support their development may have more impact in terms of business growth, job creation, and potential spillovers across different regions. Second, in areas where infrastructure is a constraint, governments could facilitate the development of serviced industrial and commercial land. The location and characteristics of these sites should respond to demand and based on economic and financial feasibility assessments to ensure their sustainability. Public- private partnerships can be considered. Third, there is room for collaboration within and across regional blocs to reduce the cost of doing business, and in particular, licenses. The high cost of licenses represents a constraint for MSMEs at the local level. Local entrepreneurs participating in focus group discussions highlighted the challenges of complying with multiple requirements and the lack of information about applicable regulations and procedures at the country 54 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic level. Regional blocs could aim at reducing entry barriers and red tape, facilitating access to information, and harmonizing regulatory requirements. County governments could assess the cost of compliance and hindrance to formalization that licensing fees and other requirements impose on businesses and possible alternative ways to collect revenues (e.g., property tax). Fourth, county governments could promote the development and reach of business support services in collaboration with the national government and private sector providers. Local governments may focus their efforts in areas and sectors where some agglomerations already exist to allow for economies of scale. In areas with lower density, other alternatives such as digital delivery of business advice or mobile services could be considered. To complement the quantitative analysis on local entrepreneurship ecosystems, a qualitative information gathering exercise consisting of virtual focus groups discussions (FGDs) was conducted in 5 counties (Kiambu, Mombasa, Nakuru, Kisumu, and Bungoma). The counties were selected based on their agglomeration of MSMEs in relevant sectors for Kenya’s economy in regions outside Nairobi: agribusiness (Kisumu and Bungoma), manufacturing (Mombasa) and light manufacturing (Nakuru) and digital services (Kiambu). Box 5 summarizes the main constraints identified by country and sector based on this exercise. Box 5: Main insights from focus groups on local entrepreneurship ecosystems Ten focus group discussions (FGDs) took place in December 2020-February 2021 involving 69 entrepreneurs and representatives of local government and intermediary organizations. The FGDs followed a semi- structured format along a questionnaire developed by the World Bank encompassing the key pillars of the ecosystem: supply factors, demand factors, and barriers (see Figure 1, in chapter 1). A. Supply factors Physical capital: Overall, urban counties have access to hard infrastructure - electricity, roads, water and internet – rural-based enterprises are constrained by these structures. For Kiambu digital entrepreneurs, the availability of digital infrastructure across Kenya is a constraint to expansion. Limited local availability of suitable machinery represents a challenge with entrepreneurs typically having to import it. Human capital: Most entrepreneurs mentioned challenges in finding skilled workers and having to invest in training new staff despite the risk of them leaving once trained. The shortage of qualified personnel for machinery maintenance was mentioned as a specific constraint for manufacturing MSMEs in Nakuru and Mombasa. Knowledge: Kenya’s policy and intermediary initiatives have expanded their outreach to the counties. However, the awareness among entrepreneurs about existing support and perceptions on its relevance and accessibility varies across countries and sectors. For example, manufacturing MSMEs in Mombasa lamented limited access to managerial and specialized technical training. Yet, those in digital sectors in Kiambu have better access to support services but are more constrained by lack of seed funding. Participants across sectors emphasized peer networks as an important source of knowledge and mentioned involvement in business associations and peer-learning platforms that enable them to mentor each other. These networks are stronger in the digital sector in Kiambu, whereas they seem less developed among agribusinesses in Kisumu and Bungoma. 55 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Box 5: Main insights from focus groups on local entrepreneurship ecosystems (Continued) Other sources of knowledge mentioned include incubators, for example the Chandaria Incubation Center (CIC) in Nairobi and hubs like Startup Grind, Nakuru Box and the Kenya National Chamber of Commerce and Industry (KNCCI) in Nakuru. Agribusiness MSMEs in Bungoma mentioned having access to extension services and incubation from the Kenya Climate Innovation Centre (KCIC), Institute of Tropical Agriculture in Nairobi, Agricultural Sector Development Support Program (ASDSP) and Kenya Agricultural and Livestock Research Organization (KALRO). Participants showed awareness about local presence of government initiatives such as the Women Enterprise Fund and the Youth Enterprise Development Fund. More support was requested from local governments to make information available about support, facilitate partnerships to access equipment and office space, mobilize funding, and support marketing/networking opportunities. B. Demand factors Access to markets: Agribusiness SME participants in Bungoma and Kisumu target local markets and nearby counties where good road connectivity allows it. Manufacturing SMEs in Mombasa and digital SMEs in Kiambu target primarily the Nairobi market and those in Nakuru focus on the Rift Valley and Western Kenya. Limited value chain linkages hinder logistics and access to larger markets. Selling to the government represents a challenge due to late payments. Limited capacity to meet standards due to high costs and order volumes constraints access to export markets. Adoption of digital marketing tools such as social media is at the core of the marketing strategy especially connecting to Nairobi markets and for exports. Conversely, lower costs of operation or inputs were mentioned as a location advantage in Nakuru and to some extent in Mombasa. Firm capabilities: Entrepreneurs face challenges in managing business risks and accumulating the required financial investments to set-up. Other constraints include gaps in financial reporting and management, compliance with tax and business regulations, sector specific technical knowledge and business models (such as contract-farming in agribusiness). Entrepreneurial characteristics: A common motivation is to be self-employed and follow a passion. However, many MSMEs underestimate the investment, discipline and consistency that come with establishing a successful venture. Entrepreneurship is generally well regarded but participants highlighted the preference of many youth for wage employment. C. Barriers Access to finance: Availability of finance, especially early-stage financing, came up repeatedly. Agribusiness entrepreneurs in Bungoma mentioned support from SACCOs. Generally, banks were not perceived as supportive of MSMEs. Digital-savvy entrepreneurs, for example in Kiambu, have turned to digital loans, but those carry high interest rates of up to 40 percent. There is limited understanding of the pros and cons of various forms of financing and mixed feelings about equity financing. Some grants offered by local hubs helped in initial stages but these are insufficient to meet capital needs for growth. Regulatory environment: Across counties, entrepreneurs face burdensome and costly government requirements in particular for county licenses and KEBS certification. Industrial activities often require multiple licenses and entrepreneurs lamented having to pay licenses and other administrative fees in each location where they operate. 56 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Chapter 3: Mapping public instruments and enablers supporting businesses A key challenge faced by policy makers aiming to promote more effective instruments to support businesses in developing countries is the lack of clarity on the relative composition, strength, and performance of their entrepreneurship ecosystems. This lack of information is also a challenge in more advanced economies. However, the magnitude of the problem tends to be bigger in countries with larger constraints in monitoring and evaluation systems of public policy. Public or non-public interventions aiming to support private sector development can fail through different dimensions related to the design, implementation or governance.54 Yet, without knowledge on the support initiatives and intermediary organizations supporting businesses in an ecosystem, it is difficult to provide guidance on policies or making decisions related to them. In the case of entrepreneurship and SMEs support, this exercise of “mapping” the resources currently available to support the ecosystem might be challenging than other areas given the relevance of intermediary actors that are independent from the public sector (e.g., private institutions such as incubators, accelerators, and private sector in general). This chapter analyzes the resources and instruments available by mapping the relevant public programs and intermediary organizations supporting entrepreneurship and MSMEs in Kenya. The policy mapping gathered and analyzed data such as how much is expended by the government in programs oriented to foster entrepreneurship, by which agencies, and to achieve what objectives. The intermediary mapping aimed at obtaining detailed information about the interventions, the budget, and the number of beneficiaries. Both mapping exercises aim to provide new metrics to assess the entrepreneurship and MSME ecosystem by providing insights that are generally not available in other frameworks. The objective of this assessment is to identify whether the entrepreneurship and MSME policy instruments and the support provided by intermediary organizations are consistent with the country’s development goals. This exercise complements the analysis at the national and sub-national levels regarding the performance of entrepreneurship and the resources available to identify key policy gaps and provide policy recommendations on how to address them. The mapping helps to understand whether interventions are connected and whether there is support at different stages of entrepreneurship, or whether it is concentrated around particular phases. This life cycle approach is important because the economic impact of entrepreneurship occurs when ventures scale, but there is often disproportional focus on the start-up process only. Figure 36 illustrates the life cycle linked to possible policy instruments. 54 The WBG has developed in implemented Public Expenditure Reviews evaluating policies to support STI in several countries. 57 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 36. Mapping of policy instruments across business cycle Intellectual property system Competition & trade regimes Human capital Public sector R&D Enterpreneurial universities with linkages to industry Dynamic innovation & entrepreneurship ecosystems FRAMEWORK CONDITIONS Vouchers for innovation and collaboration Business advisory services FIRM CAPABILITY Technology extension services Accelerators Networks Grants and matching grants for innovation and/or R&D projects Loans for innovation FINANCE Early-stage equity financing Tax incentives for R&D Start-up grants/loans SME lending Public procurement Corporate open innovation DEMAND SIDE GVC/supply chain development Export support Business parks INFRASTRUCTURE Incubators Technology transfer offices Technology centers Qaulity infrastructure Pre-Seed/Seed Start-up Growth Developed/Established Large Firms Source: Cirera et. al. (2020) 3.1 The structure of the public support for Kenya’s entrepreneurship and MSME The evolution of Kenya’s policy framework reflects the importance of entrepreneurship and MSMEs in achieving inclusive and sustainable growth. Kenya’s development blueprint “Vision 2030” aims to transform it into a globally competitive, newly industrialized, high middle-income country. It recognizes the role MSMEs can play in achieving these goals and also highlights the importance of strengthening these enterprises through productivity improvements and innovation to spur key future industries. Similarly, SMEs are considered the ‘bedrock’ for manufacturing and have been identified as central enablers towards realizing the ‘Big Four’ transformational agenda (see Box 6 for more details). 58 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Box 6: Vision 2030 and the Big 4 Agenda The Vision 2030 and the Big 4 Agenda stand out as important initiatives that can influence the entrepreneurship and MSME policy framework in Kenya. Vision 2030 is Kenya’s long-term economic development blueprint that will steer the country to high middle-income and industrial country status. It is organized into economic, social, political and foundations pillars. All government entities at national and county levels align their development planning priorities and strategies to the Medium-Term Plans (MTPs) of Vision 2030. It recognizes the need for capacity building, developing appropriate financial services for micro and small enterprises (MSEs) and proposals for the establishment of Small and Medium Enterprise (SME) industrial parks. Vision 2030 guides priorities of the national government in raising public revenues, resource allocation, enhancing productivity, job creation and increasing firm owners’ incomes, therefore representing a key influence on medium-to-longer-term development of MSMEs in the country. It proposes a number of interventions to steer industrial growth such as development of SME parks, industrial and technology parks, industrial manufacturing clusters and upgrading of products from SMEs. The Big 4 Agenda is the current government’s economic development blueprint which prioritizes four sectors for greater resource allocation: agriculture, health, manufacturing and housing. The government plans to enhance food security, achieve universal health coverage, increase job creation through manufacturing, and improve access to affordable housing, all of which is meant to contribute to the broader Vision 2030. This is a key strategy guiding policy and programs by development partners involved in private sector development and non-governmental (NGO) sectors aimed at MSME development in Kenya. The enactment of the Micro and Small Enterprises Act (2012) was the cornerstone of the evolution of MSME policy framework in Kenya. Figure 37 shows the evolution of the MSME policy framework in Kenya and the various relevant statues and regulations which have shaped the present-day MSME & entrepreneurship ecosystems in the country. The Micro and Small Enterprises Act (2012), developed through a stakeholders’ consultation process, represents the first comprehensive law addressing MSEs in Kenya. The main objective of this Act is to provide a legal and institutional framework that can support growth and regulation of micro and small firms. It aims to strengthen the enabling business environment (including infrastructure), facilitate access to business development services for capacity building of micro and small firms, support formalization and upgrading of informal firms, support entrepreneurship and representative associations of micro and small firms. The Act provides direction on several areas including the legal and regulatory environment, development of markets and provisioning of marketing services, promoting technology transfer and utilization of knowledge, skills and resources, enhancing business linkages and improving access to credit and other financial services by micro and small firms.55 55 For more details, refer to the Act https://www.msea.go.ke/images/downloads/the-micro-and-small-enterprise-act.pdf 59 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Sessional paper no. 2 on Small Figure 37. Evolution Enterprises &of Kenya’s Jua MSME policy framework Kali Development (1992) is published. An MSE division is created within the Micro & Small Microfinance Act Enterprises Act National Banking Min. of Labour & (2006) enacted. (2012) is Payments Act Amendment Act Human enacted. (2011) (2016) Resources. 1992 2006 2012 2011 2016 1986 2005 2008 2013 Sessional paper Sessional paper SACCO Societies Credit Reference No.1 on Economic No. 2 on Act (2008) Bureau Management for Development of enacted. Regulations Renewed Growth Micro & Small (2013) (1986) is Enterprises for published and a Wealth & task force is set Employment up to review all Creation for MSE laws and Poverty regulations. Reduction (2005) is published. Source: Authors’ elaboration based on a summary of Kenya’s legislations. Another important contribution of the Micro and Small Enterprises Act (2012) was the establishment of the Micro and Small Enterprises Authority (MSEA). MSEA is a state-owned corporation housed in the Ministry of Industrialization, Trade and Enterprise Development (MoITED). As part of its mandate, MSEA is responsible for formulating, reviewing, and evaluating policies and programs; coordinating the integration of various public and private sector activities, including programs and development plans relating to MSEs; promoting and facilitating research, product development and patenting in the micro and small enterprises sector, including facilitating technology development, adoption, and innovation; mobilizing resources for the development of micro and small enterprise sector and promoting their access to markets; formulating capacity building programs for micro and small enterprises; promoting the mainstreaming of youth, gender, and persons with disabilities in all micro and small enterprises activities and programs aiming to promote the collaboration with key stakeholders to support MSMEs. The Micro and Small Enterprise Act (2012) established the Micro and Small Enterprises Development Fund (MSEDF). The fund is primarily responsible for promoting financing of MSE; capacity building; research, development, innovation and technology transfer; and providing affordable and accessible credit to MSEs. The fund provides financial resources to MSEs and associations, community-based organizations, umbrella organizations and non-governmental organizations involved in MSE development. Several other regulations complement the MSME policy and regulatory framework, mainly focusing on financing of micro and small enterprises. These include the Microfinance Act (2006), developed to regulate and license microfinance institutions and the SACCO Societies Act (Act 14 of 2008) created to govern the regulation, licensing, and supervision of Savings and Credit Cooperatives (SACCOs). Microfinance institutions (MFIs) and SACCOs are the main providers of financial services to the MSE sector. The following legislative instruments have enhanced the governance quality within this financial sub-sector. The Credit Reference Bureau Regulations (2013), which was introduced to create a framework that would enable risk-based lending and in 60 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic so doing, reduce the collateral requirements that dominated SME lending,56 while the National Payments Act (2013) enabled agency banking in Kenya which has deepened touchpoints for MSMEs’ access to financial services and extended banking hours to match trade hours. Over the last decade other regulatory efforts were complementary to the 2012 MSE Act to improve business regulations. These efforts include the Legislation of the Companies Act (2015), the Insolvency Act (2015), the Business Registration Act (2015), the Movable Property Act (2017) and automation of various administrative processes. More recently, the Credit Guarantee Scheme Regulations (2020) were passed to operationalize the credit guarantee scheme established in 2019. The regulations aim to promote access to quality and affordable credit to MSMEs by providing the legal framework for the government to issue partial guarantees as an incentive to lenders to extend credit to MSME. The Kenyan government allocated KES 3 billion as seed capital to operationalize the scheme as part of the government’s interventions to cushion MSMEs from the effects of the Covid-19 pandemic.57 This initiative represents a new mechanism to promote access to affordable finance by MSMEs. In addition, the government announced the National Information Communications and Technology Policy Guidelines (2020) which among other objectives aim to promote entrepreneurship. The guidelines aim to modernize the sector in line with Kenya’s Vision 2030. They provide for the creation of a rotating venture capital fund which will provide first loss capital to start-ups to enable them seek funding from private sector. The guidelines also provide a framework for the establishment of crowd-funding and mentoring networks and supports development of innovation hubs and funding platforms through legislative incentives. They require that foreign firms interested in providing ICT services cede 30 percent of ownership to local investors so as to enhance technology and skills transfer. The previous guidelines (2006) had a similar threshold which was later lowered to 20 percent.58 This set of regulations have established new public agencies as well as facilitated the expansion of intermediary organizations supporting MSMEs and entrepreneurship. In addition to these regulatory reforms, it is also key to understand the availability of resources of public and non-public programs supporting businesses in Kenya. This chapter summarizes the results of a mapping exercise describing the characteristics of key business supporting programs in Kenya. This information is critical to identify key gaps in the supporting system and propose policy recommendations based on the diagnostics at aggregated and sub-national levels. 56 The enactment of these regulations led to the creation of credit reference bureaus that provide information on individuals’ and companies’ credit history that informs credit providers’ loan appraisal processes. 57 Additional information can be obtained from The National Treasury website (https://www.treasury.go.ke/) 58 For more information refer to The Kenya Gazette Notice No. 5472, 7 August 2020. 61 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 3.2 The methodology for mapping the entrepreneurship supporting environment The mapping of public programs and intermediary organizations supporting MSMEs and entrepreneurship was administered through two complementary surveys.59 The questionnaires related to the two surveys include similar questions but they differ in their scope. The first questionnaire (Q1) targeted public programs, while the second questionnaire (Q2) targeted institutions and organizations that support entrepreneurship beyond public agencies such as incubators, accelerators and industry associations which are referred as “intermediary organizations.” The combination of these public policy instruments and intermediary organizations provides a general picture of the support being provided for the development and sustainability of entrepreneurship and MSME ecosystems. A list of relevant Kenyan ministries and agencies with substantial programs was identified along with a list of intermediary organizations. The following ministries were identified as the most relevant based on their programs supporting MSMEs (but the list of ministries targeted was not limited to the one provided): Ministry of Industry, Trade and Enterprise Development (MoITED), Ministry of ICT, Ministry of Public, Youth and Gender Affairs and Ministry of Agriculture.60 The MoITED is the main ministry supporting business/entrepreneurship in Kenya. It coordinates programs co-implemented by other ministries and agencies at different levels, which were covered under Q1 respondents. MoITED also regulates many of the intermediary organizations targeted in the Q2 survey and therefore, was a critical source of information for the mapping exercise. The list of intermediary organizations included institutions such as incubators, accelerators, investors or financial institutions, research institutions and industry associations (see appendix A 3a and A 3b for a complete list of ministries/programs and intermediary organizations). Several WB projects/initiatives were consulted to compile the list for the questionnaires and feedback from local staff and consultants was also incorporated. Programs that clearly identify supporting entrepreneurship and/or MSMEs were included in the sample.61 Overall, 33 public programs and 124 intermediary organizations supporting entrepreneurship were identified as the universe of interest for this analysis. The response rate for survey of policy instruments was 82 percent while that of intermediary organizations was 50 percent. Out of 33 public programs targeted, 27 answered the questionnaire, while out of a total of 124 intermediary organizations approached, 62 participated (Table 6). Table 6. Sample and response rates Indicator Public programs (Q1) Intermediary Organizations (Q2) Number of mapped public programs/IOs 33 124 Total responses 27 62 Response rate 82% 50% 59 See the full list of public programs and intermediary organizations that participated in the survey in the appendix A 3a and A 3b. 60 Other relevant ministries and agencies targeted for the survey include: Innovation and Youth Affairs (MIYA); the Ministry of Sports, Culture and Heritage; the Ministry of Devolution. Government agencies such as the Micro and Small Enterprises Authority (MSEA), devolved funds (Women Enterprise Fund (WEF) and Youth Enterprise Development Fund (YEDF), National Productivity and Competitiveness Centre, National Industrial Training Authority (NITA), Kenya Industrial Property Institute (KIPI), Kenya National Innovation Agency, Numerical Machining Complex, Nairobi Securities Exchange (NSE) and Konza Technopolis Development Authority. 61 More specifically, programs whose objectives are outrightly stated as contributing to either creation of new firms, the expansion or scale up of existing firms, and the adoption of technology were targeted for the survey. Programs that were inactive for no more than two years were included in the list to ensure that the survey did not miss capturing any important programs offering interesting lessons, despite being inactive. However, the decision to include data pertaining to inactive programs in the final sample/analysis was made on a case-by-case basis 62 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Most of these programs are relatively new. About 68 percent of the public programs and 64 percent of IOs started to be implemented after 2011 (Table 7).62 This is more noticeable among public programs that normally follow political cycles as well as among intermediary organizations. Although these numbers do not imply any causality, they are consistent with the fact that a new regulatory environment post-2012 may have facilitated the expansion of the ecosystem supporting entrepreneurship and MSMEs (e.g., institutions and programs providing financial services). Even if that was not the case, it is important to highlight that most of these institutions and programs were implemented in a relatively new regulatory environment. Table 7. Distribution of the public programs and IOs by year of implementation Year of program implementation Public programs IOs Pre-2005 16% 16% 2005-2010 16% 21% 2011-2015 8% 38% 2016-2020 60% 26% Source: Kenya’s WB Entrepreneurship Ecosystem Mapping (2020) The survey was conducted online and by phone between July and September 2020.63 A focal point in each government agency and intermediary organization was identified and was approached by project team members over the phone to introduce the survey. An official email containing a link to the survey was shared with the focal point. The respondents were given 2 weeks to complete the questionnaire during which project team members also reached out to them by phone to ensure timely completion of the surveys. In situations where questionnaires were not completed sufficiently in the first attempt, an additional attempt was undertaken to gather information from an alternative respondent. The study methodology was adapted to the Kenyan context while implementation had to be compliant with COVID-19 government protocols. This led to the use of Computer Assisted Web Interviewing (CAWI) and Computer Assisted Telephone Interviewing (CATI) to replace face to face interviews. 3.3 Mapping of public programs supporting entrepreneurship This section examines the characteristics and initiatives of public programs aiming to support MSMEs and entrepreneurship in Kenya. The mapping exercise identified a total of 33 programs out of which 27 participated in the WB Entrepreneurship Ecosystem Enabler’s survey, which is used as main reference for the analysis. Public programs focusing on entrepreneurship and MSMEs play an important role in providing support through complementary factors that are critical to enhance an entrepreneurship ecosystem. These programs vary in nature and may support different elements of an entrepreneurship ecosystem ranging from infrastructure, financing, regulations and human capital. In addition, these programs are usually carried out by wide-ranging institutions that often have different objectives and compete for public resources and beneficiaries. Thus, it is critical to have precise and efficient allocation of public resources to ensure that every primary obstacle to entrepreneurship and MSME development is fairly and adequately addressed.64 62 Distribution based on the sample of respondents. See more details in the next section. 63 The survey was conducted from July to September 2020, but the pilot was done in June. 64 Cirera et al, 2020 63 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 3.3.1 Services and instruments provided by public programs The two most common objectives of public programs supporting entrepreneurship and MSMEs in Kenya are related to firms’ scale up and job creation (Figure 38, panel a). These are two complementary and consistent objectives associated with an area of significant challenges for businesses in Kenya. Despite having a relatively high rate of firm entry, young firms in Kenya seem to struggle to grow and face significant obstacles to scale up. Existence of programs that specifically target challenges that firms face to scale up is a welcome sign. Nevertheless, there are several areas that are not as adequately covered by public programs. Despite high levels of informality and a growing young-working-age population, less than 5 percent of public programs seem to focus on issues related to formalization or youth/gender. Other area that seems to be less of a priority for these programs includes firm survival, access to markets, and technology adoption. Access to finance, human capital development, management training, and support to infrastructure are the main services provided by public programs in Kenya (Figure 38, panel b). More than 30 percent of public programs provide at least one service or support related to these areas. Much less attention is being given to issues related to entrepreneurial mindset, knowledge and R&D, or regulatory environment.65 The most common financial support instruments are grants and equity finance, whereas business education and collaborative networks are the most common type of non-financial support (Figure 38, panels c and d). Several areas not as adequately covered by public programs, include among others, technology adoption and access to markets. Both these areas are important for promoting firm growth. Gender is another area that public programs do not have large coverage. The main services provided by public programs are in line with their main objectives and aim to address some of the critical constraints existing in Kenya’s entrepreneurship ecosystem. The development of human capital and provision of management training serve to enhance skills development and equip firms with the required capabilities to promote innovation, entrepreneurship, and growth. Providing firms with access to finance and infrastructure facilities can address important challenges and constraints prevailing in Kenya’s entrepreneurship ecosystem (see chapter 1 for details). An area where more can be done is promoting technology adoption. Even though Kenya underperforms in technology adoption compared to its Asian peers (see chapter 1), only 15 percent of programs treat adoption of technology as their main objective and only 12 percent provide services in R&D. 65 A reason why few programs are providing services focused on entrepreneurial mindset might be associated with the fact that these tend to be more relevant for pre-entrepreneurship and not with programs supporting ventures that are already established or being established. 64 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 38. Main objectives and services provided by public programs supporting entrepreneurship & MSMEs a) Main objective of public programs b) Main services provided by organizations 0.8 0.70 0.6 0.54 0.50 0.6 0.59 0.4 0.35 0.35 Share Share 0.4 0.33 0.33 0.22 0.2 0.19 0.2 0.15 0.12 0.12 0.12 0.12 0.11 0.07 0.04 0.04 0 0 Note: 27 Observations Note: 26 observation; each institutuion up to 3 services Firm scale-up Job creation Firm creation Skill development Finance Human capital Managerial training Infrastructure Build ecosytem Tech adoption Market access Firm survival Market access Entrepreneurial mindset Collaboration Youth/women entry Formalization R&D Regulations c) Financial support provided d) Other types of support provided 0.5 0.6 0.59 0.59 0.41 0.4 0.4 0.3 0.33 Share Share 0.26 0.2 0.22 0.15 0.2 0.1 0.11 0.04 0.04 0.04 0 0 Note: 27 Observations Note: 27 Observations Grants/vouchers Equity finance Credit guarantees Tax incentives Business education Collaborative network Early-stage infra Tech extension Loans/credit Public procurement Tech hub Source: World Bank Entrepreneurship Enablers Survey (2020) 3.3.2 Management characteristics Most top managers of public programs have had experience with starting businesses, 15 or more years of experience in the sector of activity, and post-graduation degree. About 77 percent of the top managers either helped start or owned other businesses (Figure 39, panel a). Managers have significant experience with the sector of activity and more than 3 years of experience with the program they were interviewed about. Moreover, about 81 percent of the managers have post-graduate degree while 48 percent have studied abroad. In terms of gender, managers consist of 58 percent males and 42 percent females, exhibiting comparable educational backgrounds and work experiences, with female managers being more likely to study abroad. On average, female managers are 43 years old, with 3 years of work experience in the respective programs and 15 years in the respective sectors, whereas male managers are only slightly older and more experienced (Figure 39, panel b). Both male and female managers exhibit similar educational levels and are 65 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic highly educated with about 80 percent having received post-graduate education (Figure 39, panel c). However, a larger share of female managers (70 percent) than male managers (33 percent) has studied abroad for at least a month (Figure 39, panel d). Figure 39. Management characteristics a) Top manager experience on starting business b) Average years of work experience and age of top managers 50 50 43 40 33% 33% Average years 30 20 17 15 25% 8% 10 5 3 0 Female Male Note: 22 observations. Helped Start Both Owned Owned Age Sector experience Program experience c) The highest level of education of top manager d) Did the top manager study abroad for more than a month? Collage Post-grad Yes No Female Male Total Female Male Total 100 100 30 80 80 52 67 82 80 81 Percent Percent 60 60 40 40 70 48 20 20 33 18 20 19 0 0 Note: 26 Observations Note: 25 Observations Source: World Bank Entrepreneurship Enablers Survey (2020) 3.3.3 Beneficiaries of public programs Most public programs target individual entrepreneurs (Figure 40, panel a). Among various types of beneficiaries that public programs are targeting, the 77 percent are targeting individuals and 69 percent are targeting firms.66 In terms of sectors, 41 percent of public programs report that their main target are beneficiaries from manufacturing sector whereas only 15 percent indicate agriculture as the primary sectoral focus of their beneficiaries (Figure 40, panel b), and 30 percent of programs are sector agnostic. The larger prevalence of programs targeting manufacturing is consistent with the “Big 4 Agenda” policy objective. Yet, despite being the sectors with the largest share of employment and also among the “Big 4 Agenda” priority, agriculture and services 66 The questionnaire allows that firms may report that they target more than one type of beneficiary (e.g., both individuals and firms). 66 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic are relatively less prevalent among programs that target specific sectors. The case of services is particularly relevant given the importance of this sector among MSMEs and young firms. Within firms, public programs are mainly focusing on young businesses (Figure 40, panel c). About 88 percent of public programs in the sample are associated with young businesses that are one to three years old, while 64 percent cater to businesses that are a year old at most. This prevalence of young firms is, to some extent, in line with the reality that a large fraction of firms in Kenya are young.67 Figure 40, panel d presents the total number of beneficiaries of public programs in 2019 and shows that about 180,000 beneficiaries were individuals, followed by almost 80,000 firms, and other groups with less than 70,000. About 20 percent of the public programs utilize online advertising for recruiting beneficiaries suggesting that this is still not a very common strategy for businesses.68 Figure 40. Target beneficiaries a) Main target beneficiaries b) Main targeted sector .8 0.77 0.69 15% .6 30% Share .4 0.31 41% 0.27 15% 0.19 0.19 0.19 .2 0.04 0 Note: 26 observations. The y-axis measures share of programs targeting each beneficiary type. Note: 27 observations. Individuals Firms Cooperatives Associations Financial inst. No sector Agriculture Manufacturing Other services Research inst. Other gov't agencies SOEs c) Phase of business d) Number of beneficiaries by target group (2019) 0.9 0.88 200K 180,923 Sum of beneficiaries in 2019 0.64 150K 0.6 0.52 Share 0.44 100K 79,490 65,489 65,082 64,803 0.3 50K 385 89 0 0 0 Note: 25 observations. Select all that apply. Note: 20 observations. 1-3 years <= 1 Year 3-8 years >= 8 Years Individuals Firms Cooperatives Associations Financial inst. Research inst. Other gov't agencies SOEs Source: World Bank Entrepreneurship Enablers Survey (2020) 67 MSME Survey, 2016 68 36% of the public programs use other strategies not listed in the questionnaire. 67 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 3.3.4 Resources available The two main sources of funding for public programs are the central government and donors. The central government (41 percent) and provincial governments (19 percent) account for about 60 percent of the financial resources available to public programs, followed by donors that represent 26 percent of total funding. About 7 percent of the funding is from the private sector (Figure 41, panel a). Because these are public programs, this distribution of sources of funding is expected. The allocation of funds by public programs towards beneficiaries is relatively even across financial (36 percent) and non-financial services (38 percent) (Figure 41, panel b). Figure 41. Source of funding and spending categories a) Source of financing b) Categories of budget spent 7% 7% 26% 41% 36% 26% 38% 19% Note: 20 observations.This figure presents the percent of programs Note: 10 observations. receiving funding from each source Central gov Provincial gov Donors Private sector Other Financial Non-financial Other costs Source: World Bank Entrepreneurship Enablers Survey (2020) In terms of regional distribution, a large share of funds by public programs is allocated in Nairobi although most programs have national coverage. About 36 percent of resources are allocated to Nairobi while the remaining 64 percent are shared by the rest of Kenya (Figure 42, panel a).69 The large share of funding received by Nairobi is explained by the location of the targeted beneficiaries of these programs, 80 percent of which cover Nairobi or other regions (Figure 42, panel b). Only a small share of funding is excluding Nairobi or targeting specific regions in terms of target population. This reiterates the gap between the Nairobi ecosystem and other regions. 69 This number needs to be interpreted with cautions, given the lower response rate for this question. 68 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 42. Regional distribution a) Budget b) Geographical coverage of programs 8% 12% 36% 64% 80% Note: 14 observations. Note: 21 observations. Nairobi The rest Nairobi Nairobi & others Others (excl. Nairobi) Source: World Bank Entrepreneurship Enablers Survey (2020) 3.3.5 Monitoring and evaluation process All public programs in the sample have identified key performance indicators that they aim to achieve. The three most common key performance indicators (KPIs) are the number of training courses attended (training), job creation, and firm creation adopted by 64 percent, 55 percent, and 50 percent of programs respectively. In addition, 32 percent of the programs target to facilitate firm scale-up or technology adoption and 27 percent aim to improve survival rate (Figure 43, panel a). Around half of the programs conduct quarterly reviews of their KPIs while 26 percent do an annual review and 22 percent do monthly review. Only 4 percent of programs carry out a weekly review of their KPIs. Though most programs collect beneficiaries’ feedback, only a few of them conduct impact evaluation with a counterfactual exercise. For 95 percent of the programs, feedback is collected from their beneficiaries regularly. However, only 81 percent of the programs used the feedback to modify their implementation. This can be improved to enhance the design of programs and tailor them to beneficiaries’ needs. Additionally, the coverage of impact evaluation is lower (38 percent), and should expand to all the programs to track their progress and improve their implementation (Figure 43, panel b). The relatively small share of public programs conducting rigorous impact evaluation (with a counterfactual exercise) provides a significant opportunity to improve MSMEs and entrepreneurship policy in Kenya. Even without assessing the quality of the impact evaluations conducted by those programs, this number suggest that very little is known in terms of the impact of current public programs implemented in Kenya. 69 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 43. M&E systems a) Main KPIs b) Impact Evaluation has been conducted 0.8 Share of institutions choosing each KPI 0.64 0.6 0.55 0.50 38% 0.4 62% 0.32 0.32 0.27 0.23 0.23 0.2 0.18 0.18 0.09 0 Note: 22 Observations Note: 21 observations. Training Job creation Firm creation Firm scale-up Tech adoption Survival rate Other Foreign markets Yes No Follow-up funding New products Acquisition Source: World Bank Entrepreneurship Enablers Survey (2020) 3.3.6 Response to COVID-19 Digitalization has become the most common service expansion in response to social distancing during the pandemic. The need to connect people and businesses through digitalization has become urgent. Nevertheless, 48 percent of the public programs provide training and technical support related to digital solutions to their beneficiaries. In terms of changes observed in the services offered by the programs, the share of programs helping beneficiaries meet regulatory requirements climbed from 12 percent pre-COVID-19 to 30 percent during the pandemic. In contrast, the share for financial services declined from 54 percent to 22 percent (Figure 44, panel a). The outbreak has imposed challenges on the programs’ implementation across various dimensions. Reduction in mobility resulting from lockdowns is the most common challenge identified by 56 percent of the public programs (Figure 44, panel b). Besides, the virus spread worsened the programs’ ability to finance their beneficiaries, which may be detrimental to them since access to credit is a significant constraint faced by businesses in Kenya. About 32 percent of the programs identify a declining budget as a new challenge (Figure 44, Panel b), and 41 percent expect the budget to decline during the next 6 months (Figure 44; Panel d). However, some programs took measures that may help ease beneficiaries’ financial burden: 25 percent chose to reconstruct repayment conditions and 15 percent changed collateral conditions (Figure 44; Panel c). The pandemic has also affected the services offered by some of the programs. Around 20 percent of the programs claim the pandemic has led to decreases in existing services (Figure 44; Panel b), 10 percent decided to serve less customers, and 5 percent expected to remove services (Figure 44; Panel c). Despite the challenges, COVID-19 shock may also provide new opportunities. About 44 percent and 32 percent of public programs indicate that the pandemic has bred prospects of increasing new and existing services, respectively (Figure 44; Panel b), 35 percent planned to add new services (Figure 44; Panel c), and 30 percent decided to serve more customers (Figure 44; Panel c). The pandemic also propelled other changes 70 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic in the public programs’ implementation. More than a third of programs planned to reduce required paperwork, a quarter of them are to change evaluation methods, 15 percent plan to serve informal firms, and 10 percent are to change strategies of how they target/recruit beneficiaries (Figure 44; Panel c). Figure 44. Services needed to support business responding to COVID-19 a) Services to support business b) New opportunities or challenges .6 0.56 .5 0.48 0.44 .4 0.35 .4 0.30 .3 0.32 0.32 Share Share 0.22 .2 0.17 0.20 0.20 .2 0.13 .1 0.09 0.09 0.04 0 0 Note: 23 observations. Note: 25 observations. + means increase in demand; - means decrease in demand. Digital solutions Other Regulations Finance Managerial Training Mobility capacity reduced New services + Donations/budget - Infrasture New grants Market access Existing services + Others Existing services - None c) Changes in services planned d) Expected changes in budget in the next 6 months .4 0.35 0.35 0.5 0.41 0.30 .3 0.4 0.25 0.25 0.32 0.3 Share Share 0.27 .2 0.15 0.15 0.2 0.18 0.10 0.10 .1 0.1 0.05 0.05 0.05 0 0 Note: 23 observations. Note: 22 observations. Add new services Reduce required paperwork Serve more customers Decrease The same Increase Don’t know Restructure repayment terms Change evaluation method Change collateral conditions Serve informal firms Serve less customers Change recruiting strategy Other Remove services No change Source: World Bank Entrepreneurship Enablers Survey (2020) 3.3.7. Summary of key areas covered by public programs This section summarizes the supply of services provided by public programs across the entrepreneurship ecosystem pillars described by the conceptual framework presented in figure 1. Relying on the use of heat maps, it addresses three questions: i) How have financial resources (budget) of public programs been allocated across the entrepreneurship ecosystem pillars in terms of supply factors, demand factors and barriers for allocation by targeted sector? ii) How have beneficiaries been distributed across target sectors? iii) What is the 71 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic association between the main objective of the programs and the main service provided (grouped by ecosystem pillar)? To address the first two questions, an assumption is made regarding the distribution of resources and beneficiaries – such that budget is evenly allocated to the services (grouped by ecosystem pillars) provided by the program. For example, if a program provides support related to access to finance and managerial training, the budget of these programs is distributed evenly across these pillars. More precise information in terms of budget could provide a more detailed picture of this distribution. Access to finance, human capital, and physical capital (e.g., infrastructure, software, and machines) are the most widely funded pillars of entrepreneurship ecosystem. Access to finance receives the largest share of the budget across the pillars (Table 8). This is consistent with the fact that 54 percent of programs provide services related to finance (Figure 38; Panel a), the most common type of support. Access to infrastructure and physical capital, as well as human capital, two critical obstacles for Kenyan enterprises identified in chapter 1, are the two other heavily funded pillars, followed by programs supporting access to market. Although many programs provide managerial training, they do not appear among the most funded initiatives. Table 8. Heat map: allocation of budget by sector and service provided (the darker the color the more budget allocated) Ecosystem No sector Agriculture Manufacturing Other services pillars/ Sector Supply pillars Physical Human Knowledge Barriers Access to finance Regulations Culture/Network Barriers Market access Managerial training Change of mindset Note: The budgets allocated to the services of providing access to infrastructure and access to equipment are summed up in the first column. Assume each program’s budget is evenly allocated to the services provided by it. One program with a 2300 million budget (LCU) does not offer any service so that its budget is not considered in this table. Most of the resources go to programs targeting the manufacturing sector, followed by agriculture and programs that are sector agnostic. These results are consistent with the fact that 41 percent of the public programs indicate that their beneficiaries’ main target sector is manufacturing (Figure 40; Panel b). This is also consistent with long term strategic policies (e.g. The Big 4 Agenda) that have been emphasizing manufacturing activities. Yet, despite being a sector that absorbs a very large share of the workers, “services” receive the least amount of the budget compared to other sectors. 72 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Most of the beneficiaries are individuals (entrepreneurs) or firms. Under the assumption that programs targeting specific sectors or not allocate proportionally the number of beneficiaries across these sectors, the data suggest that those programs targeting agriculture and other services have a larger number of beneficiaries than those focusing on manufacturing or other services. These programs are also more plural in terms of beneficiary types (Table 9). Although programs mostly targeting manufacturing tend to be larger in terms of budget, they seem to be selective with fewer beneficiaries receiving larger average support and more likely to be allocated towards firms. Having a large number of beneficiaries in programs focusing on agriculture and services is consistent with the distribution of firms and employment in Kenya. An important question not addressed by this analysis is the effectiveness and potential heterogeneity regarding the return of these interventions across sector. Table 9. Heat map of number of beneficiaries by sector and type of target beneficiaries (Q1) No sector Agriculture Manufacturing Services Individuals Firms Cooperatives Associations Financial institutions Research inst. SOEs Other gov’t agencies Note: Each program’s beneficiaries are evenly distributed by type of beneficiaries targeted by it. 3.4 Mapping of Intermediary Organizations (IOs) in Kenya In addition to public programs, there are several intermediary organizations such as financial institutions, incubators, and accelerators supporting MSMEs entrepreneurship ecosystem in Kenya. The services offered by these organizations in many cases are meant to complement those offered by public programs requiring large coordination challenges.70 Most typical intermediary organizations in the sample are financial institutions, incubators, and accelerators. About 30 percent of the sample is composed of financial institutions, 18 percent of incubators, and 17 percent consists of accelerators (Figure 45; Panel a). This group is followed by non-government organizations, industry associations, and research institutions supporting MSMEs and entrepreneurship in Kenya. Forty-four percent of these intermediary organizations are private institutions, including for profit institutions, while the second most common type of institutions are NGOs, representing slightly more than a quarter of the sample (Figure 45; Panel b) 70 This section is based on the analysis of the responses to the WB Entrepreneurship Ecosystem Enabler survey from 62 intermediary organizations in Kenya. 73 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 45. General characteristics of intermediary organizations a) Type of organization b) Type of organization 0.30 0.3 Share of institutions choosing each KPI 20% 0.2 0.18 44% 0.17 0.15 0.15 0.13 0.1 26% 10% 0.02 0 Note: 60 Observations Note: 61 observations Financial inst. Incubator Accelerator Donors/international org Private Public NGO Other Industry association Research inst Gov’t agency Source: World Bank Entrepreneurship Enablers Survey (2020) 3.4.1 Services and instruments provided by IOs The main objectives reported by these organizations are related to firm scale up and job creation, followed by the goal of building an ecosystem (Figure 46; Panel a). These objectives are aligned with those reported by public programs. Supporting technology adoption, formalization, and creation of new firms are among the least reported objectives even though these areas represent weaknesses in Kenya’s entrepreneurship ecosystem. The main types of services provided by these institutions are related to finance (67 percent), managerial training (52 percent), and collaboration and network (40 percent) (Figure 46; Panel b). These indicators are consistent with the fact that most of the organizations in the sample are financial institutions, incubators, and accelerators. The most common instruments used by IOs to provide financial support include grants and equity finance, while business education and collaborative networks are the most common types of non-financial support (Figure 46, Panels c and d). Figure 46. Main services, type of support, and expected outcomes a) Main expected outcomes b) Main services 0.8 0.8 0.67 0.67 0.6 0.6 0.55 0.52 Share Share 0.40 0.40 0.4 0.4 0.33 0.29 0.28 0.2 0.19 0.19 0.2 0.16 0.14 0.10 0.12 0.10 0.09 0.09 0.09 0 0 Note: 27 Observations Note: 26 observation; each institutuion up to 3 services. Firm scale-up Job creation Build ecosytem Youth/women entry Finance Human capital Managerial training Infrastructure Market access Firm survival Workplace skills Tech adoption Market access Entrepreneurial mindset Collaboration Formalization Firm creation R&D Regulations 74 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic c) Financial support d) Other types of support 0.8 0.5 0.41 0.65 0.6 0.4 0.57 0.25 Share Share 0.4 0.38 0.2 0.25 0.22 0.2 0.1 0.07 0.03 0.02 0.02 0 0 Note: 60 Observations Note: 60 observation. Business education Collaborative networks Loans/credit Tech extension Early-stage infra Public procurement Tech hub Grants/vouchers Equity finance Credit guarantees Tax incentives Source: World Bank Entrepreneurship Enablers Survey (2020) 3.4.2 Management characteristics of IOs Most of the top managers of IOs are not among the founders of these organizations, but they have helped to start and/or owned other businesses (Figure 47, panel a). These managers tend to have a college education or higher level of education with more than 70 percent of them holding a postgraduate degree. Only 4 percent of the managers have a high school education. Most top managers have spent at least a month abroad pursuing academic objectives. Among IOs, female top managers tend to be younger, have more years of experience, higher levels of education, and are more likely to have studied abroad. Similar to public programs, 43 percent of top managers in IOs are women. They are slightly younger than male managers (39 versus 43) and have more years of experience working in the sector than men (Figure 47, panels b). Female managers also tend to be more educated and are more likely to have studied abroad (Figure 47, panels c and d), which is consistent with female managers leading public programs. Figure 47. Management characteristics a) Has the top manager helped to start or owned other b) Average years of working experience and age of businesses? top managers 30% 45 43 39 33% Average years 30 16 15 11 37% 0 Female Male Note: 57 observation; Both indicates having helped start and owning the busness. Note: 50 observations. Helped start Both Neither Age Sector experience 75 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic c) What is the highest level of education of the top d) Did the top manager study abroad for more than a month? manager? Female Male Total Female Male Total 100 100 21 80 80 28 33 82 80 81 Percent Percent 60 60 40 40 78 67 72 20 20 18 20 19 0 0 Note: 58 Observations Note: 57 Observations High-school College Post-grad Yes No Source: World Bank Entrepreneurship Enablers Survey (2020) 3.4.3 Beneficiaries of IOs As in the case of public programs, IOs in Kenya are targeting mostly young businesses, tend to be more sector agnostic, and also target mostly individuals and firms. About 86 percent of the programs are serving businesses between 1 and 3 years of age. 49 percent serve businesses less than a year old, and 42 percent service businesses older than 8 years of age (Figure 48; Panel a). 64 percent of the IOs in the sample do not report targeting a specific sector. Among those that are targeting specific sectors, agriculture is the most common, catered by 15 percent for the IOs. Only 2 percent of IOs focused on beneficiaries in the manufacturing sector and another 2 percent on businesses providing IT services (Figure 48 panel b). These results are very different from those for public programs where a large share of the programs (40 percent) is targeting manufacturing. Most programs are targeting individuals or firms as beneficiaries, and these are also the most representative groups among beneficiaries.71 The most used method for recruiting enterprises to the programs was through word of mouth and social networks, followed by online advertising and offline advertising. Contests and public presentations are less popular methods of reaching out to beneficiaries. Figure 48. Target beneficiaries a) Phase of business b) Main targeted sector 0.9 0.86 17% 2% 0.63 2% 0.6 64% Share 0.49 0.42 15% 0.3 0 Note: 57 observations. No sector Manufacturing Other services 1-3 years 3-8 years 1 year to less More than 8 years Agriculture IT services 71 The IOs in the sample reported that 4.8 million individuals, 1.7 million firms, and almost 1 million cooperatives have benefited from their services in 2019. 76 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic c) Main target beneficiaries .8 Individuals 0.73 Firms Cooperatives 0.66 Associations .6 Financial inst. Research inst. Other gov't agencies SOEs Share .4 0.29 0.25 .2 0.17 0.17 0.14 0.05 0 Note: 59 observations. The y-axis measures share of programs targeting each beneficiary type. Source: World Bank Entrepreneurship Enablers Survey (2020) 3.4.4 Resources available and allocation in IOs A large share of IO’s funds is coming from public sector or donors and is spent on non-financial support but there is significant heterogeneity across type of organization. With respect to the intermediaries which are privately run, most of their funding comes from private sources, with the remainder split between donor funds and fees charged for services. Looking at public intermediaries, most of their funding comes from public sources with the second-largest source being private funding. Nearly all NGOs are funded by international donors (Figure 49, panel a). Almost half of budgetary expenditure of IOs is in the form of providing financial services in privately-run and public intermediaries, and NGOs, with the second largest share of expenditure being the extension of non-financial services to enterprises and entrepreneurs (Figure 49, panel b). Figure 49. Source of funding and spending categories a) Source of financing b) Categories of budget spent 100 100 23 18 29 29 29 80 80 48 14 65 60 38 86 60 24 Percent 30 Percent 34 29 40 40 45 41 47 44 20 35 20 34 37 12 2 7 0 0 Private Public NGO Other Private Public NGO Other Note: 12 obs of public, 25 of private, 30 of fees, and 28 of donor. Note: 32 obs of financial, 37 of non-financial, and 36 of other. Public Private Services fees Donor Financial Non-financial Other costs Source: World Bank Entrepreneurship Enablers Survey (2020) In terms of geographic coverage most programs cover Nairobi and other regions. Seventy-two percent of IOs target Nairobi and other regions, 23 percent target exclusively Nairobi and only 5 percent of programs target exclusively regions outside Nairobi (Figure 50; panel a). Program budgets were more focused on Nairobi, with 70 percent of private programming funds allocated for expenses in the capital, and the remainder allocated to other regions. For public sector programs and NGOs the fraction of the budget spent in Nairobi is smaller at 41 percent (Figure 50; panel b). 77 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 50. Regional distribution a) Targeted Beneficiaries b) Budget spent between capital and rest of the country by firm type 5% 100 23% 30 80 42 59 59 60 Percent 40 72% 70 57 20 41 41 0 Private Public NGO Other Note: 57 observations. Note: 38 observations. Nairobi Nairobi & others Others (excl. Nairobi) Nairobi Rest of the country Source: World Bank Entrepreneurship Enablers Survey (2020) 3.4.5 Monitoring and evaluation process Almost all IOs reported following key performance indicators, but as in the case of public programs, only a small share performs impact evaluation. The majority of the intermediaries supporting entrepreneurial activities performed quarterly performance reviews (63 percent), with only 29 percent conducting annual reviews. The vast majority of KPIs are reviewed at a higher frequency. Of the indicators, firm creation, firm scale-up, and job creation are the top performance indicators with well over half of programs indicating these as their principal KPIs. Other popular indicators include number of training courses provided, follow-up funding, firm survival rate, technological adoption, and aiding firms in accessing foreign markets (Figure 51; panel a). Nearly all programs collect feedback (98 percent) on a regular basis, and programs use this feedback to adapt their performance as well. However, only about 35 percent of programs completed any kind of impact evaluation to evaluate their success (Figure 51; panel b). The share of programs conducting impact evaluation among IOs is relatively similar to public programs and reinforces the opportunity of learning further from current interventions aiming to support businesses in Kenya. Figure 51. M&E systems a) Main KPIs b) Impact Evaluation has been conducted 0.6 0.58 0.58 0.53 Share of institutions choosing each KPI 0.4 0.40 0.36 35% 0.32 65% 0.25 0.2 0.15 0.13 0.11 0.09 0 Note: 22 Observations Note: 54 observations. Firm creation Firm scale-up Job creation Training Follow-up funding Yes No Other Survival rate Tech adoption Foreign markets New products Acquisition Source: World Bank Entrepreneurship Enablers Survey (2020) 78 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 3.4.6 Adjustments through COVID-19 In response to the COVID-19 pandemic, intermediary organizations planned to add new services, specially related to digital solutions. Programs adapted to the changing circumstances, with one principal challenge being the reduction in mobility, reported by 60 percent of IOs (Figure 52; panel a). With respect to changes in opportunity, a majority of program managers described the pandemic as an opportunity to add new services (68 percent) and to amplify existing services (37 percent) while others reported that there could be a reduction in demand for existing services (28 percent). The COVID-19 outbreak seems to have not constrained the budget of IOs. In addition to the plan of expanding their services, many program managers expected their budgets to increase (35 percent) or to remain the same (31 percent) over the next 6 months. About 22 percent of the program managers expected a decline in their budgets. The top 3 service changes planned by IOs to their beneficiaries are about digital solutions (50 percent), financing (35 percent), and managerial training (26 percent) (Figure 52; panel c). When asked what type of digital solutions they were planning to support most, 74 percent of the IOs added marketing and sales aspects to their programming mix. A small number of organizations report adding teleworking or service delivery (36 percent), incorporating online and electronic payments (35 percent), and adding supply chain management capacity (30 percent) (Figure 52; panel d). Figure 52. Services needed to support business responding to COVID-19 a) New opportunities or challenges b) Expected changes in budget in the next 6 months .8 .4 0.68 0.35 0.60 0.31 .6 .3 0.22 Share Share .4 0.37 .2 0.32 0.28 0.13 .2 .1 0.02 0.00 0 0 Note: 57 observations. + means increase in demand; - means decrease in demand. Note: 54 observations. New services + Mobility capacity reduced Donations/budget - Increase The same Decrease Don't know Existing services - Others None 79 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic c) Changes in services planned d) Digital solutions responding to COVID-19 0.50 .5 .8 0.74 .4 0.35 .6 .3 Share 0.26 Share 0.39 0.22 0.22 0.22 .4 0.35 .2 0.30 0.11 .2 .1 0.04 0 0 Note: 54 observations. Note: 23 observations. Digital solutions Finance Managerial Training New grants Regulations Decrease The same Increase Don’t know Market access Other Infrasture Source: World Bank Entrepreneurship Enablers Survey (2020) 3.4.7 Summary of key areas covered by IOs In the case of IOs, the allocation of resources is mostly associated with financial services (Table 10).72 Excluding financial services, which clearly represents most of the resources available by IOs, other ecosystems pillars with better provision of funding are those related to culture and networking, managerial training, and entrepreneurial mindset. Differently from the public programs, most support provided by IOs is sector neutral and very little is allocated towards manufacturing. As in the case of public programs, very few resources have been allocated to support knowledge and/or activities related with technology adoption. Individuals and firms with no clear sector orientation are the principal beneficiaries of the IOs, followed by services. There are far more individuals benefiting from programming than firms. SOEs and other government agencies are the least representative among all the beneficiaries. Table 10. Heat map: allocation of budget by sector and service provided by IOs Sector No sector Agriculture Manufacturing Services Supply pillars Physical capital Human capital Knowledge Barriers Access to finance Regulations Culture/Network Demand pillars Market access Managerial training Change of mindset Note: Each program’s beneficiaries are evenly distributed by type of beneficiaries targeted by it. The darker the color of a cell, the more programs covering the service-sector pair the cell represents As in the case of section 2.7 for public programs, this section summarizes the supply of services provided by IOs across the entrepreneurship 72 ecosystem pillars described by the conceptual framework presented figure 1. Relying on the use of heat maps, it addresses similar questions with similar assumptions, as described in section 2.7, with the difference that it focuses on IOs. 80 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Table 11. Heat map of number of beneficiaries by sector and type of target beneficiaries (Q2) No sector Agriculture Manufacturing IT services Other services Individuals Firms Cooperatives Associations Financial institutions Research inst. SOEs Other gov’t agencies Note: Each program’s beneficiaries are evenly distributed by type of beneficiaries targeted by it. The darker the color of a cell, the more programs covering the service-sector pair the cell represents. 3.5. A summary of the policy mix to support entrepreneurship and MSMEs in Kenya Public programs and IOs play a complementary role in supporting businesses as part of an entrepreneurship ecosystem. The previous sections provide a general perspective of key features and characteristics of public programs and IOs, as well as their main objectives and services. This section summarizes the results of the analysis comparing the coverage and the association across key characteristics of these programs. First, it analyzes the complementarities of services and support provided by public programs and IOs and analyzes how the current policy mix is suitable to address key gaps of the local ecosystem. Second, it identifies the association across key characteristics of those interventions aiming to understand what key observable characteristics are associated with the provision of key services, the adoption of good managerial practices, and the responses towards the COVID-19 shock. 3.5.1 The complementarity between public programs and IOs: Are they addressing the key gaps? Finance and managerial training are the most typical support towards entrepreneurship and MSMEs provided by public programs and IOs in Kenya (Figure 53). While the IOs represent a larger number of institutions, if compared to public programs, they are less present in supporting activities associated with physical capital, human capital, and knowledge (e.g. R&D and technology extension programs). Business supporting programs related to infrastructure and human capital usually demand larger amount of resources and, in many cases, public goods (e.g. investment in human capital) that can be more mobile across firms. On the financial side, the most common instruments are grants, loans, and credit. On the non-financial side, the most common support is business education and activities to promote collaboration and networking. 81 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 53. The main services provided by public programs and IOs 60 40 Number of programs 20 0 Infrastructure Human capital R&D Finance Regulations Collaboration Market access Managerial Entrepreneurial training mindset Public sector programs Programs by IOs Source: World Bank Entrepreneurship Enablers Survey (2020) Knowledge, particularly related to technology and R&D, is among the areas with minimal support. The number of programs or IOs focusing on knowledge capital (including R&D and technology extension services) is relatively small. The lack of support on knowledge, particularly those related to technology adoption, is also critical to provide necessary conditions to improve productivity and competitiveness. Regarding the main objectives, both public programs and IOs place emphasis on accelerating business development and expanding the number of jobs (Figure 54). The emphasis towards these objectives is consistent with key challenges observed in Kenya related to scaling up businesses and generating jobs, particularly good quality jobs. Developing the entrepreneurial ecosystem and promoting the entry of women and youth entrepreneurs are two other common objectives. But they are mostly adopted by the IOs with a limited number of public programs targeting these two groups. Other areas of focus for both public and IO programs are the development of skills, creation of new firms, as well as facilitating access to national markets. Figure 54. The main outcomes targeted by public programs and IOs grouped by program type Intermediary Orgs Public Sector 60 40 Number of programs 20 0 Creation of Accelerate Job Survival Promote Facilitate Facilitate the Promote the Promote the Develop the Other New Firms Business Creation of Firms Skills Access to adaption of New Entry of Women Formalization Entrepren. Dev. Dev. National Markets Technologies & Youth of Business Ecosytem Source: World Bank Entrepreneurship Enablers Survey (2020) 82 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Public programs and IOs significantly differ in terms of budget allocation. For example, the portion is relatively even across financial and non-financial activities for the public programs but covers more financial categories than non-financial ones for the IOs. According to the budget allocation’s heat maps, each service pillar receives a roughly comparable amount of budget from the public programs. In terms of regional distribution of beneficiaries and budget, IOs and public programs seem to be disproportionally concentrated in Nairobi which accounts for nearly 20 percent of all programs. Regarding the regional distribution of beneficiaries, 22.8 percent of public programs solely target Nairobi, which is about two times of the respective number for IOs (12 percent). This regional bias in public funding and beneficiaries could be corrected to allow support in enhancing the ecosystem for entrepreneurs in other urban centers of Kenya as well as rural entrepreneurs outside of the capital. Table 12. Comparison of regional distribution of beneficiaries Support Region Percent Public Sector Programs Only Nairobi 22.8% Nairobi and other regions 71.9% Other regions, excluding Nairobi 5.3% Intermediary Organizations Only Nairobi 12% Nairobi and other regions 80% Other regions, excluding Nairobi 8% Impact evaluation is another area that both the public programs and IOs need to improve to track and refine their implementation. While they have mostly set KPIs to follow and collected feedback from their beneficiaries, less than 40 percent of both public programs and those run by intermediary organizations have evaluated their projects using a control group for benchmarking and comparison (Table 13). Impact evaluations using control groups are critical for assessing project outcomes and best practice dictates applying these techniques wherever it is feasible. Table 13. Comparison of adoption of impact evaluation Support Impact Evaluation? Percent Public Sector Programs Yes 38% No 62% Intermediary Organizations Yes 35% No 65% The COVID-19 shock has provided challenges for businesses in Kenya, and it is also reshaping the services and resources to support them by public programs and IOs.73 One major concern is the worsening financial situation. More than 40 percent of the public programs expected their budget to decline, and more than 30 percent identified declining funding as a significant challenge imposed by the pandemic. However, the IOs seem to be much less concerned about financing as 35 percent of them expected their budgets to increase. A common area that the public programs and IOs emphasized is digitalization in the background of social distancing and reduced mobility capacity. About 50 percent of the programs provided digital solutions 73 Cruz et. al. (2020) show that businesses in Kenya faced a drop of about 50% on sales, on average, comparing to the same period in the last year, in the post-COVID19. 83 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic services, which are critical in Kenya, where 30 percent of the population uses the internet. While some programs planned to discontinue some services, many programs found opportunities to expand businesses and update implementation. While the programs handled the shocks incurred by the virus, they also had the chance to lay practical foundations for building resilient implementation. Although there is enough room for the public programs and IOs to improve, it is encouraging that they are both equipped with well-educated and experienced managers. A majority of them have post-graduate education, more than ten years of sectoral experience, or have studied abroad for more than a month. The most common barrier reported by program managers is access to finance, an area where most public programs and IOs are engaged.74 To identify the key barriers for entrepreneurship, from the perspective of managers of public programs and IOs supporting entrepreneurship, both surveys have asked them to identify key barriers usually faced by their beneficiaries. Access to markets is a primary obstacle reported by both public programs and IOs. While knowledge related issues rank in the second commonly encountered challenge by the public program’s managers (36 percent), it has barely reported by IOs as a key barrier (5 percent). Human capital is an area that has largely held back entrepreneurship and MSMEs according to IOs’ managers (44 percent) but is seen as much less constrained by managers of public programs (16 percent). Figure 55. Barriers to entrepreneurship in Kenya as reported by public policy instruments and intermediaries a) Public policy instruments (Q1) b) Intermediaries (Q2) 0.60 0.6 0.8 0.75 0.6 0.4 Share Share 0.36 0.36 0.47 0.36 0.44 0.4 0.20 0.20 0.20 0.28 0.2 0.25 0.16 0.16 0.21 0.19 0.2 0.09 0.04 0.07 0.05 0 0 Note: 25 Observations Note: 57 observation Finance R&D Access to markets Entrepreneurial mindset Finance Market access Human capital Managerial training Collaboration Infrastructure Managerial training Human capital Regulations Entrepreneurial mindset Collaboration Infrastructure Regulations Crime/corruption Crime/corruption R&D Source: World Bank Entrepreneurship Enablers Survey (2020) While there seems to be convergence in identifying access to finance as a key barrier and providing financial services as the most typical support, other areas seem to face relatively low supply or demand. Figure 56 shows the relationship between services supplied by public programs (panel a) and IOs (panel b), in Kenya (vertical axis) and the main barriers identified by top managers of public programs and IOs – which 74 Although program managers report access to finance as a substantial problem and indeed, they do target those areas programmatically, relatively few firms name access to credit as being a principal constraint, and 30 percent of firms report applying for credit in the last three years, while only 13 percent of firms reported applying for financing or credit in the past 12 months (2016 MSME Survey). 84 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic is used as a proxy of demand for those services (horizontal axis).75 While there seems to be more programs supplying managerial training than identifying them as a key barrier, access to market seems to be relatively neglected if compared to the share of managers identifying them as relevant. Overall, the demand and supply for knowledge activities (R&D), which also includes technology extension services is on average lower than the other pillars. This is an important finding because it suggests that not only is this an area that receives little attention in terms of resources, but also not identified as a key constraint by top managers supporting businesses in Kenya. The convergence of high-demand and high-supply for financial services is consistent in both groups, but there are clearly differences on both: their perspectives on what is needed most as well as the services they provide. Figure 56. Supply of services versus main barriers identified by program managers. a) Public Programs b) IOs 1.00 1.00 0.75 0.75 Share of Programs Supplying Service Share of Programs Supplying Service Access to Finance Access to Finance Managerial Training Human Capital 0.50 0.50 Access to Physical Capital Collaboration Managerial Training Change of Mindset Market Access 0.25 Market Access 0.25 Human Capital Change of Mindset R&D Regulations Regulations Collaboration R&D Access to Physical Capital 0.00 0.00 0.25 0.50 0.75 1.00 0.25 0.50 0.75 1.00 Share of Programs Demanding Service Share of Programs Demanding Service Source: World Bank Entrepreneurship Enablers Survey (2020) 3.5.2 The association between programs’ characteristics and services provided This section analyzes the association across key characteristics of public programs and IOs supporting entrepreneurship and MSMEs in Kenya. While the previous sections are analyzing key features of the programs and IOs, they do not describe the association across these characteristics. This section provides this link by focusing on three key questions. The analysis to address these questions relies on simple correlations – there is no inference in terms of causality. Results are available in the appendix A3, more specifically between tables A3.1 and A3.5 A) Is the type of support associated with the size of programs, by type of beneficiaries or budget? While the provision of grants is negatively associated with size, the provision of loans and credit is positively associated with size measured by number of beneficiaries or budget. Disregarding how the size of the program is measured – either by number of beneficiaries (individual or firms) or budget – the type of financial instruments is significantly associated with them. Equity financing also tend to be negatively correlated 75 While it’s clear that the question on “main barriers” is based on perception and does not necessarily capture the (real) main constraint faced by entrepreneurs in Kenya (which they may not be aware either), this variable captures an important information, which is the perception of top managers of programs and IOs that eventually can influence the allocation of these resources. Moreover, as previously described by the data, these managers are on average well educated, with 15+ years of experience in the sector of activity, and 3+ years of experience in the program of IO they are managing. Given their wide exposure to beneficiaries that usually reach these agencies searching for support, their perspective in terms of main barriers faced by entrepreneurs may be a reasonable proxy for the demand for support, even if they do not capture the actual factor that is limiting entrepreneurship development. 85 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic with size – measured by number of individuals. The results also show that business education tend to be more common across larger programs targeting individuals, while public procurement advice, early-stage infrastructure and advisory, and collaborative networks tend to be more common among smaller programs in terms of budget.76 B) Are managers’ characteristics associated with better M&E practices? The results do not suggest a significant association between management characteristics and variation in M&E practices. Based on the management literature with focus on firm performance, the hypothesis is that programs with more prepared managers (e.g., better educated managers) would be associated with the adoption of better managerial practices. Surprisingly, the results do not show a clear (statistically significant) correlation among those variables. For example, the adoption of impact evaluation practice (with counterfactual) does not seem to be associated with any managerial characteristics, nor the adoption or frequency of KPIs. Managers’ characteristics are not associated with the age of implementation of the program either – suggesting that older programs or IOs do not seem very different in terms of managers’ characteristics. The likelihood of adopting impact evaluation with a counterfactual exercise has no clear association with the gender, age, or level of education of the manager. Given the importance of implementing impact evaluations as a mechanism to learn and improve programs, tables A3.1-A3.5 of the appendix show the conditional correlations among these characteristics and find no significant association. C) Are programs’ characteristics associated with different responses to the COVID-19 shock? Larger programs in number of beneficiaries were more likely to respond to COVID-19 through the expansion of training or support for digital solutions related to payment methods. As previously described, the main response to COVID-19 by public programs and IOs supporting entrepreneurship and MSMEs in Kenya was through the expansion of support to adopt digital solutions. When these responses are split across different types of support, the provision of digital solutions associated with payment methods was more likely among larger programs. 76 Further details on the correlations are available in the appendix. 86 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Chapter 4: The impact of COVID-19 on businesses and policy recommendations 4.1. The impact of COVID-19 on Kenya businesses The Kenyan economy seems to be rebounding from the pandemic slump. The COVID-19 pandemic had severe impacts on the Kenyan economy. The economic and social disruptions created multiple challenges for the private sector through several channels. First, firms faced lower demand due to lower consumption and demand for inputs. Second, supply chains were disrupted, limiting the access to intermediate goods and labor. Third, access to cash and credit deteriorated. Finally, uncertainty dampened investment and innovation prospects. Firms in Kenya were not exceptions to such challenges. However, firms’ sales, as well as the expectation on performance in the new future have gradually improved. Moreover, a large share of firms reported that they started or increased the use of digital technologies in response to COVID-19, opening the prospects for technology upgrade, which can become an important driver of recovery in the aftermath of the pandemic. 4.1.1 Impact on sales and jobs Almost 3 out of 4 firms presented lower levels of sales than in 2019, but there are some signs of recovery. About 18 percent of firms reported having in the last month sales equal or greater than those in 2019. This is an 11 percentage point increase relative to the 7 percent reported on wave 1. The average decline in sales was approximately 28 percent, an improvement of 8 percentage points from wave 2, and 35 percentage points from mid-2020. This pattern of recovery was consistent across all size groups and sectors. The service sector, which was the most affected at the start of the pandemic (-69 percent) was in similar levels as other sectors except for agriculture. Firms outside Nairobi region were still lagging in terms of sales recovery (-34 percent vs -25 percent). Positive news about the latest wave of data is that firms with a high concentration of female workers77 recovered faster and therefore the high-risk for women labor participation, observed at the peak of the pandemic, significantly declined from a decrease in sales of 63 percent to a decrease of 24 percent. Figure 57. Changes in sales a) Change in sales b) Average change in sales Wave 1 Wave 2 Wave 3 Wave 1 Wave 2 Wave 3 Wave 1 Wave 2 Wave 3 Wave 1 Wave 3 Wave 1 Wave 3 Wave 1 Wave 3 Wave 1 Wave 3 Increase Remain the same Decrease Source: Kenya COVID-19 BPS (2021). Note: The figure displays weighted unconditional averages across waves. 77 We define women labor intensive firms to those with share of female participation higher than 30 percent 87 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Although lay-offs have reduced, new job opportunities are still scarce and the rates of reductions in wages/hours remain constant. About 6 percent of firms reported that they were hiring new employees in the last 30 days compared to just 3 percent of firms in wave 1. The percentage of firms that laid-off workers fell from 23 percent to 15 percent. Correspondingly with the sales behavior, firms in Nairobi and firms with high-female labor participation were the ones with the highest reduction of lay-offs. Figure 58. Margin of adjustment in employment 26 25 23 15 6 3 Source: Kenya COVID-19 BPS (2021). Note: Share of businesses reporting at least one employee in each category; excludes businesses that are permanently closed. 4.1.2 Firms’ responses In response to the COVID-19 outbreak, 70 percent of firms started to use or increased the use of digital platforms. Almost two-thirds of small firms, four-fifths of medium firms, and almost all large firms increased the use of digital technologies. The manufacturing sector is where the increase of digital technologies was most prevalent (75 percent). The increase in the use of digital technology was present in traditional sectors like agriculture, where 69 percent of firms reported an increase of digital technologies. Sales in firms which increased their use of digital technologies fell by 6 percentage points less compared to firms which did not. The other common response to COVID-19 was to innovate to fulfill the market needs of the new economy: 51 percent of firms repackaged or created new products in response to COVID-19; 62 percent of firms in the agriculture sector innovated; 48 percent in the manufacturing and retail sector; and 53 percent in the services sector. 88 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Figure 59. Business that increased use of digital platforms in response to COVID-19 Source: Kenya COVID-19 BPS (2020). Note: Question was not asked to micro-sized firms. 4.1.3 Expectation and uncertainty Firms’ expectation that sales will rebound have improved amid uncertainty. On average, Kenyan firms expect sales to increase in 10 percentage points in the next 6 months compared to 2019. This is a reversal from wave 2 where firms expected their sales to be 10 percent lower than 2019 sales. Almost seven out of ten firms expect to increase their sales relative to 2019 and only 25 percent expect their sales to remain below 2019 levels, a decrease from 63 percent during the second interview with them. High expectations are driven by firms of all sizes and all sectors; although agriculture firms’ top expectations with an expected 16 percent increase from 2019 levels. Nevertheless, uncertainty remains relatively high and is constant across firms’ size and sector. Figure 60. Expectations and uncertainty about sales and employment a) Expectations about sales growth for the next 6 b) Uncertainty about employment growth for the months next 6 months Source: Kenya COVID-19 BPS (2020). 89 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 4.1.4 Access to and demand for policy support Six in ten firms in Kenya received public support during the COVID-19 pandemic. Most of the firms that accessed policy support received fiscal and tax exemptions while a smaller percentage received wage subsidies and deferral of credit payments. Firms based in Nairobi have more often received assistance than firms in other regions. Compared to larger firms, smaller firms less often report having received government assistance. Firms in agriculture most often received assistance (67 percent), and retail and manufacturing firms received the least (56 and 54 percent respectively). Figure 61. Share of firms that received any assistance and type of assistance received a) Public policy instruments (Q1) b) Intermediaries (Q2) Source: Kenya COVID-19 BPS (2020). The most needed policy response according to Kenyan firms is wage subsidies followed by skills trainings. Forty-five percent of firms in Kenya call for wage subsidies from the government and 38 percent call for skills trainings. This is a departure from the wave 1 results where monetary transfers were the most popular policy choice. As observed at an earlier stage of the crisis, the type of most-needed assistance however varies with firm characteristics. For instance, exporters disproportionately demand tax deferrals. This could either reflect higher tax and custom duties or the fact that exporting firms do not face liquidity constraints or lack of credit, and therefore do not call for other policy measures.78 Figure 62. Self-reported most needed public policies to support businesses 50 45 40 38 30 20 14 13 12 10 10 4 3 3 Wage Skills Grants Fiscal Support for Support for Access Support for Deferral of subsidies training exemptions health tech to new innovation payments protocal adoption credit adoption Source: Kenya COVID-19 BPS (2020). 78 See Kenya COVID-19 BPS (2020) for more details. The Government of Kenya (GoK) lowered VAT from 16% to 14% in April 2020 because of the COVID-19 pandemic but raised it back to 16% effective Jan 1, 2021. 90 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 4.2 Policy recommendations to support MSMEs targeting ecosystems The COVID-19 pandemic presents additional challenges for MSMEs and practitioners developing policies to support them. As the crisis continues to evolve, policies must shift from short-term interventions to help businesses “keep the lights on” by preserving employment and productive capacity in viable firms, to sustainable recovery plans that facilitate the selection of the most productive firms. In the recovery phase, policies should be geared towards supporting growth-oriented enterprises, promoting the reallocation of resources to more efficient companies, and avoiding measures that risk propping up “zombie” firms (i.e., inefficient firms that can survive only thanks to the artificial support provided to them). 79 To develop more effective policies supporting MSMEs and entrepreneurs, the key barriers from the perspective of their entrepreneurial ecosystems need to be understood and addressed. The analysis in this report shows high levels of firm creation in Kenya, but low dynamism that prevents firms to scale up and upgrade, with women-led businesses suffering further constraints. To overcome this challenge, the following policy areas deserve special attention: firm capabilities, access to new markets, and access to finance. These areas are also relevant to mitigate the adverse impacts of COVID-19 on firms, in particular ensuring sufficient liquidity and helping firms adopt digital technologies. Moreover, ease of access to information about support to MSMEs to mitigate the impact of COVID-19 and support the recovery is critical. Investments in structural aspects of the ecosystem (human capital, knowledge, infrastructure, regulations, and institutions) take time and require coordination, but are necessary to strengthen the existing high-potential ecosystems and develop others and will require coordinated action across levels of government. This report suggests two main dimensions to be considered as a target strategy to support entrepreneurship: i) The characteristics of the entrepreneurship ecosystem; ii) The characteristics of firms and entrepreneurs. One of the main challenges in policies supporting MSMEs, including relief measures for COVID-19 is identifying firms to be targeted. The cautions applicable before the pandemic regarding targeting criteria for interventions to support businesses are still valid – the large heterogeneity between firms belonging to same sector and of similar size must be considered. Policy interventions supporting firms need to be well- justified, cost-effective, and target firms that will yield the greatest benefits to society in terms of quality and number of jobs, inclusion, and spillover effects. COVID-19 exacerbates the importance of adequate targeting given the ubiquity of the shock across the whole economy, and the pressure on scarce government resources.80 Selecting firms or groups of firms to target presents an important challenge but existing evidence suggests potential options. 79 This section is based on the analysis of this chapter, COV-BPS results, and the overall policy guidance described in World Bank (2020). Assessing the impact and policy responses in support of private-sector firms in the context of the COVID-19 pandemic. A previous version of recommendations were presented in the “Socioeconomic Impacts of COVID-19 in Kenya: Firms,” and the “Kenya Economic Update: Navigating the Pandemic” based on preliminary results from this study and the first wave of the BPS. 80 The COVID-19 BPS shows that MSMEs, and particularly those led by females, seem to be the most vulnerable in general. They are disproportionally more impacted in terms of reductions in sales and likelihood of closure – with the associated loss of jobs and productive capacity, partially because they tend to have less access to credit. 91 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Table 14. Type of firms and type of local ecosystems Type of Ecosystems Type Regions Strengths Challenges Advanced Nairobi- Many significant agglomerations The cost for new firms is relatively Kiambu in strategic sectors, high coverage high (e.g., tax, land). Despite its of entrepreneurship enablers. relevance relative to the country, these Kajiado-Narok Comparative advantage in local ecosystems still face significant Mombasa knowledge intensive activities gaps compared to peer countries. Emerging CKEB-LREB Some relevant agglomerations in There is a gap to access knowledge; SEKEB strategic sectors; some coverage of lower presence of support entrepreneurship enablers organizations and thinner networks. Incipient FDDC, JKP, Few or no significant agglomerations There is a very large gap with NOREB in strategic sectors; low coverage of respect to other regions in terms of entrepreneurship enablers infrastructure; access to knowledge. Type of firms Type Age Size Sector Characteristics Micro Any <5 employees Any Usually, subsistence/necessity potential businesses entrepreneurs with potentially low growth prospect. Mostly informal. New SMEs New <=5 >=5 employees Any This is a heterogeneous group that includes new years formal and informal firms in traditional sectors. They usually respond by a large share of jobs’ growth. Established Old > 5 >=5 employees Any This is a heterogeneous group that includes SMEs years established formal and informal firms. Innovative New <=5 Any, but likely Any, but New businesses looking to scale quickly, using Start-ups* years less than 10 likely digital technology and new business models with employees explicit high-growth intent. They represent a small share of businesses, but if successful, can be potential impactful. Note: The proposed references for size and age groups are discrete decisions that can change according to the context. The definition of “innovative start-ups” is the most complex, given that it refers to factors that are not easily observed by policy makers. Yet, this is a disproportionally common characteristics among digital business solution activities, which can be used a proxy in terms of targeting. The ecosystem analysis in this report can offer a pathway to help prioritize policy interventions by taking into consideration these two layers -local ecosystems and firms -targeting. A potential objective is focusing on firms with high potential to be productive in the future. To reduce the cost of implementation and increase the chances of spillovers, interventions could be piloted in high-potential ecosystems that can benefit from economies of agglomeration to optimize potential spillover effects. By targeting specific local ecosystems and rolling out the program gradually (e.g., digital ecosystems in Nairobi and Kiambu, agribusiness ecosystems in CKEB or LREB) policy makers can learn about the impact of interventions using impact evaluations with counterfactual exercises and improve the implementation. Moreover, these activities could experiment with the use of digital tools and consulting based on groups of firms with similar characteristics and operating in similar environments. These are some options to improve the targeting of firms. (i) A “funnel approach” to assistance can help ensure that the firms with the greatest potential for improvement get the most support. This approach might be particularly relevant for interventions that aim to provide business training and financing support through grants but are looking to identify businesses 92 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic with a high level of commitment and need. The program can provide very basic assistance services for many firms (e.g., some online courses, simple benchmarking information, or a short one-hour firm visit). Firms that demonstrate interest and undertake some improvement actions following this first engagement can then be filtered into receiving a second intermediate level of business training support or a specific grant. This approach has the political advantage of offering some assistance to a large number of firms while restricting the most costly and time-consuming parts of the program to firms that demonstrate engagement and immediate improvement.81 (ii) Mobile phones can be used to reach women-owned businesses or other particular groups of entrepreneurs in specific local ecosystems. MSMEs run by women can be disproportionally affected by the COVID-19 shock, and specific targeted interventions already conducted in Kenya suggest that they can lead to effective results.82 Policies could include: (i) outreach and communication strategies targeting women-led and women-owned firms to share information on GoK programs and policies, and (ii) using customer data on mobile phone and mobile banking transactions to identify female-led businesses more likely to be vulnerable during this crisis to more effectively target relief payments. This report proposes a set of policy recommendations that can be used as general guidance for preparing regional entrepreneurship policy strategies considering the heterogeneity of local ecosystems and firms (within these ecosystems). For each of these factors, specific policy strategies could be designed along the potential to focus on barriers that are common across entrepreneurs in particular ecosystems, as well as those that apply for common group of firms within or across ecosystems. These dimensions can be used as a guidance to inform common projects across economic regions. 4.2.1 Improve infrastructure Reducing infrastructure gaps across regions could enhance opportunities to entrepreneurial ecosystems outside Nairobi. Despite upgrades in the road and railways network, and airport expansion, Kenya still faces important infrastructure challenges. Forty-four percent of the road networks are in poor condition and there are regional disparities in access to electricity and broadband internet. For example, access to grid electricity in the 14 underserved counties in Kenya is only 20.9 percent, compared to the nation-wide figure of 53.5 percent, while 21.5 percent have basic off-grid access.83 (i) Establishing infrastructure priorities at the regional level. The assessment of the entrepreneurial ecosystems at sub-national level highlights the significant gap in terms of infrastructure including in terms of digital access. This is not an intervention specifically linked with business support, but the availability of quality infrastructure is a critical input to the ecosystem. Determining the specific infrastructure needs requires assessment beyond the scope of this report as well as spatial planning. The World Bank Systematic Country Diagnostic report recommended completing the National Spatial Plan (2015–2045) which provides a coordinating framework for sectoral planning, particularly infrastructure for basic services, industrialization, and economic prosperity. Medium Term Plans (MTPs) and the lower-level plans, including See more details on McKenzie, D. (2020) Small Business Training to Improve Management Practices in Developing Countries. Policy 81 Research Working Paper, 9408. World Bank. McKenzie, David and Susana Puerto (2017) “Growing Markets through Business Training for Female Entrepreneurs: A Market-Level 82 Randomized Experiment in Kenya”, American Economic Journal: Applied Economics. 83 World Bank (2020) Systematic Country Diagnostic: Kenya. 93 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic at county-level follow the National Spatial Plan. Physical Infrastructure presents common factors across counties within economic blocs that may impose relevant constraints for business development. Thus, articulating the priorities regarding infrastructure with high impact for businesses (e.g., energy, digital infrastructure and roads) can be a common area of interest and articulation across economic blocs. 4.2.2 Enhance entrepreneurial and firm capabilities MSMEs in Kenya lag in technological capabilities,84 while the existing mapping of public programs and IOs suggest that knowledge, particularly related to technology and R&D, is among the areas that received little support. At the same time, the pressures to react to the crisis may offer an opportunity to improve overall managerial and technological capabilities throughout firms in Kenya. (ii) Facilitating access to digital technologies can help increase firm resilience to shocks.85 Evidence across countries suggests that a large proportion of firms are starting to use or increase the use of digital technologies for business purposes. This is also the case in Kenya where the adoption of digital technologies has increased and about 70 percent of formal firms have adopted digital technologies in response to the COVID-19 shock. Facilitating further adoption of digital technologies in general business functions such as business planning, marketing, payment and sales will be critical to helping firms cope with the ongoing COVID-19 crisis and for improving their capabilities going forward. Among the functions with a higher potential for easy adoption are technologies related to business administration, supply chain management, and sales. Kenya has presented large diffusion of digital technologies associated with digital payment. However, this report shows a large regional gap between firms in Nairobi and other parts of Kenya, especially rural areas. Efforts to make digital technologies available in those areas deserve attention. (iii) Providing business development services such as general business training, specific technical training and management advice could lead to increased profitability. International metrics suggest that Kenyan firms have significant room to improve the adoption of good managerial practices, which has been shown to be strongly associated with firm performance measures such as productivity and exports.86 Evidence on business training focusing on improving business practices for SMEs across countries suggests an average impact of 10 percent on profits.87 Indeed, previous experiments with micro and small firms in Kenya suggest that interventions aiming to improve business practices through mentorship can lead to a 20 percent increase in profits on average when properly tailored to firm needs and well implemented.88 While firms are facing significant challenges associated with a reduction in demand and a shortage of cash, the need for innovative business solutions inherent to such a crisis can be used as an opportunity to better prepare firms for the recovery process. Many of these interventions can be facilitated This reference is based on variables used as proxies (e.g., innovation process) available in the 2016 MSME Survey and the WBES. A more 84 detailed diagnostic is being conducted by the World Bank through the implementation of the Firm-level Adoption of Technology survey in Kenya. 85 Recent evidence combining data from the BPS and the Firm-level Adoption of Technology surveys suggest that firms with higher level of technology sophistication pre-COVID 19 were significantly more likely to increase the use of digital technology in response to the crisis and had better performance at early stages of the pandemic (Cirera et. al. 2021). 86 See evidence across countries, including Kenya, from the World Management Survey. https://worldmanagementsurvey.org/. 87 McKenzie, D. (2020) Small Business Training to Improve Management Practices in Developing Countries. Policy Research Working Paper, 9408. World Bank. 88 Brooks, Wyatt, Kevin Donovan, and Terence R. Johnson. 2018. “Mentors or Teachers? Microenterprise Training in Kenya.” American Economic Journal: Applied Economics; Beaman, Lori, Jeremy Magruder and Jonathan Robinson (2014) “Minding small change among small firms in Kenya”, Journal of Development Economics 108: 69-86 94 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic by further coordination across several business supporting programs already in Kenya including those supported by donors and NGOs. (iv) Develop sector technology extension programs that facilitate the adoption of sector-specific digital and non-digital technologies. The mapping of public programs and IOs identified only a small number focusing on knowledge capital (including R&D and technology extension services). The lack of support on knowledge capital, particularly those related to technology adoption is also critical to provision of necessary conditions to improve productivity and competitiveness. This is particularly the case outside Nairobi and requires targeted solutions addressing the technology needs of specific value chains. Some examples include government programs such as the Kenya County Connectivity Project that aims at providing infrastructure connectivity across counties and the Constituency Hubs and Pasha Centers that create digital innovation centers across Kenya.89 A number of tech hubs have emerged in cities outside Nairobi such as Mombasa (SwahiliBox), Kisumu (LakeHub), Eldoret (Dlab Hub), Voi (Sote Hub), Machakos (Ubunifu), Nyeri (Mt. Kenya Hub and DeHub), amongst others – these could be supported by the government.90 (v) Reduce the gap on the access to knowledge across local ecosystems. There is a large gap on the availability and access to business advice between firms in the most developed regional ecosystems (e.g., Nairobi-Mombasa) and other regions. Reducing the cost to provide business training and advice could increase the opportunity to reach firms in less developed regional ecosystems. Recent data collected by the World Bank on the adoption of technology by firms could be used to provide benchmark for firms and to identify business functions for which firms could benefit from technology upgrade. 4.2.3 Support access to finance Providing financial support for technological upgrade is an important step towards enhancing competitiveness of Kenyan firms in the long-term. Policies that spur financial sector innovation to develop products tailored to MSMEs can help bridge the existing gap in access to finance. (vi) FinTech solutions should be further promoted building on the surge of mobile transactions since the COVID-19 onset. Kenya is known for its innovative solutions regarding digital financial services. As mentioned before, digital technology offers an unprecedented opportunity to mitigate the impact of the COVID-19 crisis on MSME financing and supporting access to finance in the recovery phase. Simplified loan application processes and the use of alternative data for credit decision making could be leveraged by banks to reduce turnaround times for MSME loans. At the same time, the use of fintech requires strengthening the regulatory framework to ensure consumer protection on digital channels. In recent years, reports of consumer protection issues and irresponsible conduct in digital lending have emerged in Kenya, including non-disclosure of effective interest rates, fees, and unfair collection methods. (vii) Conditions must be established to prevent the insolvency of healthy firms due to temporary illiquidity. For micro and small businesses, this could mean increasing the debt threshold required for a creditor to initiate bankruptcy proceedings against a debtor or limiting access in modern personal 89 World Bank (2019). 90 Ibid. 95 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic bankruptcy systems to a debtor’s petitions alone. Enacting these measures for a fixed time period may prevent the system from becoming one of debt collection during a pandemic, as well as help control the number of cases entering the overburdened court system. The Central Bank suspended for six months the listing of negative credit information for borrowers whose loans became non-performing after April 1st, 2020. In light of the recovery, the Central Bank has undone this and other emergency financial regulatory measures.91 As the COVID-19 pandemic evolves, there is constant need to assess and adjust the measures that can support firm recovery while keeping in mind the risks they present for financial sector sustainability. (viii) De-risking financial institutions will be important for increasing access to finance for healthy firms in the short and medium-term. Risk aversion is an important factor limiting the willingness of financial intermediaries to increase lending, particularly to MSMEs. The National Treasury has set up a credit guarantee scheme to issue partial credit guarantee scheme for commercial bank loans to MSMEs. (ix) Providing liquidity channeled through micro-finance institutions, SACCOS and digital platforms can help address the liquidity constraints faced by these institutions and their ability to extend credit to micro and small firms. (x) Government arrears on payments to MSMEs must be addressed as they aggravate the impact of the COVID-19 crisis.92 This can be accomplished by setting-up a receivables financing platform that would allow financial institutions to refinance these receivables through an invoice and receivables discounting scheme. To give comfort to financial institutions, the scheme can be supported by the guarantee product. (xi) Early-stage companies should not be left out of safety net provisions. Public policies to help vulnerable but viable firms stay in business and maintain employment during shocks should also include startups. The provision of a cash grants for firms to stay afloat can help overcome the immediate challenges brought on by shocks like the COVID-19 pandemic. Keeping this sum reasonably small can make it feasible from a fiscal perspective while ensuring that it is still relevant for startups. If employment retention is crucial to keep the business alive, then an immediate cash injection either through a grant or loan or guarantee can be explored. Support for publicly funded venture capital companies and funds to inject equity could be considered when market failures are clearly identified.93 Loan or equity injections into venture funds can help them survive through the period when they cannot realize any returns and ease the pressure on them to liquidate companies in which they have invested in the short term. 4.2.4 Promote access to new markets Lack of access to market has been reported as a key challenge for MSMEs’ and new businesses in Kenya. Targeted mechanisms to enhance market access can support the economic recovery. 91 IMF policy tracker. https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19. 92 The Government pending bills as of June 2021 amounted KES 395.5 billion (~$3.3 billion), 90 percent of which correspond to SOEs (National Treasury, Quarterly Economic and Budget Review, August 2021). The GoK allocated KES 13.8 billion to clear arrears and KES 10 billion for VAT refunds as part of its policy responses to the pandemic (World Bank Kenya Economic Update April 2020). 93 For example, France and Germany have a long tradition of using these instruments through state development banks to provide risk capital to MSMEs. 96 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic (xii) Facilitating access to information can support firms in prospecting new markets. Providing information to local producers regarding opportunities in international markets, particularly in exporting sectors such as agricultural commodities (e.g., coffee, tea, fruits), processed food, and apparel, could help boost export potential during a period of global crisis. Such activities could be conducted by the Kenya Export Promotion Agency. The variation in the development of the COVID-19 crisis in different countries can generate significant variation in how global value chains are disrupted which can in turn create business opportunities.94 (xiii) The expansion of domestic markets could benefit from opportunities to strengthen business networks across ecosystems. For example, networking programs that facilitate the matching of supply and demand across ecosystems in the same sector could be developed especially outside Nairobi where networks are less developed.95 Another potential intervention could consist of matching grants towards local digital business solutions aiming to stimulate the development of digital platforms that facilitate the exchange of information across local producers. (xiv) Facilitate access to standards and certification to enable access to export markets by MSMEs. Entrepreneurs participating in focus group discussions pointed at the high cost of obtaining certification by the Kenya Bureau of Standards. Kenyan SMEs have more difficulty to obtain quality, safety, and sustainability certification than larger firms, probably due to their limited capacity to absorb the costs of certification.96 4.2.5 Improve regulations Continuous improvement of the regulatory environment and removal of barriers to entry and competition at the national and sub-national levels is critical to strengthening Kenya’s entrepreneurship ecosystem. In recent years, Kenya has improved business regulations but as this report observes, there is room to strengthen the quality and transparency of regulations; reduce sector-specific barriers to entry and operation that tilt the level playing field towards incumbent firms; and reduce internal costs to internal commerce and investment. The regulatory environment needs to be upgraded to respond to the needs of the digital economy including the regulatory environment for data protection and cybersecurity. (xv) Streamline and harmonize licensing requirements and taxes. Stronger policy and legal frameworks and regulatory management practices can be developed to avoid the multiplication of approvals and fees that add to the cost of doing business for MSMEs and create impediments to formalization. Regional blocs can play a role in promoting dialogue to reduce red tape.97 (xvi) Reduce costs to intra-country trade. The intra-country transport charges (cess) not only increases logistics times and costs, adding to the cost of food products, but are also vulnerable to rent seeking. Counties and the regional blocs could explore avenues to harmonize cess, reduce them were possible, and find less disruptive ways to collect them such as electronic payments. 94 Wall Street Journal. 2020. https://www.wsj.com/articles/high-food-prices-drive-consumers-to-hunt-for-value-11591700401 95 This is an example of initiative going to the direction is https://biasharanow.com/. 96 International Trade Center (2019) 97 World Bank (2019) 97 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 4.2.6 Improve access to information, coordination, and capabilities of business supporting programs (xvii) Improving transparency and access to information about support available for businesses can increase the likelihood of reaching the firms most in need and could help improve expectations overall. This is especially important in the context of increased uncertainty resulting from COVID-19. (xviii) Stronger communication is needed about policy interventions already available to support businesses. A common challenge across many developing countries, including Kenya, is that a very large share of businesses is not aware of the public programs available to support them. This is particularly important for firms outside Nairobi where the information gap is more pressing and the availability of support limited. In the early stages of the COVID-19 crisis, about 80 percent of businesses reported not being aware of the government relief measures available when they most needed them.98 Evidence from a similar survey across countries suggests that firms that are more likely to receive assistance also have better expectations regarding the future of their business. (xix) Targeted channels should be used to reach different types of firms with information regarding government programs. The Government of Kenya could consolidate information about all public programs to support businesses, including the expansion of activities specifically related to COVID-19, and facilitate access to this information for businesses. This could be converted into a sustained practice to optimize public resources. (xx) Improve the M&E system for programs supporting businesses. Very few programs supporting entrepreneurship in Kenya – either public program or through intermediary organizations – are conducting impact evaluations with counterfactual exercises. (xxi) Improve access to firm-level data for facilitating planning and assessment of policy interventions. Keep clear updated mapping of public policies supporting entrepreneurship and businesses in general to facilitate allocation and optimization of resources. 98 Based on the results of the first Covid-19 Business Pulse survey, undertaken between June and August 2020. 98 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic References Apedo Amah, M. C., Avdiu, B., Cirera, X., Cruz, M. Davies, E., Grover, A., Iacovone, L., Kilinc, U., Medvedev, D., and Maduko, F, 2020. “Unmasking the Impact of COVID-19 on Businesses: Firm Level Evidence from Across the World,” Policy Research Working Paper Series 9434, The World Bank. Audretsch, Cruz, and Torres, (2020). Entrepreneurship Ecosystems in Developing Countries. Manuscript. Cirera, Xavier; Comin, Diego; Cruz, Marcio. 2022. Bridging the Technological Divide : Technology Adoption by Firms in Developing Countries. The World Bank Productivity Project;. Washington, DC: World Bank. 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Washington DC. 100 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Appendix A1) Results referred to in chapter 1 Table A1.1 Cross country correlation matrix of output and entrepreneurship ecosystem pillars Outcomes New business New business New business density per 1,000 density per 1,000 density per 1,000 Pillar working-age working-age people working-age people (pooling) (cross-section) people (panel FE) Stock of capital per engaged 0.635*** 0.033*** 1.472*** person Fraction of the population 0.651*** -0.021 0.013*** using the internet Share of graduates in science 0.05 0.077*** 0.010** and engineering in total tertiary graduates Supply Share of working-age 0.191*** 0.228 -0.002 population with advanced education Score of the top 3 universities’ 0.209*** 0.463*** 0.067 average ranking Number of researchers per 0.519*** -0.056 0.443*** million people Domestic market scale -0.039 0.159 0.407*** Percent of firms exporting 0.397*** 0.010 0.019 directly or indirectly (at least 1 percent of sales) Research talent in business 0.133* 0.069 -0.002 enterprise Demand Percent of firms with 0.349*** 2.684** 0.005 internationally recognized quality certification Managerial capabilities 0.451** 0.015*** 0.000 Appetite for entrepreneurial 0.250*** -0.070*** 0.547 risk (perceived; scale 1-7) Domestic credit to the private 0.476*** 0.131 0.004 sector (fraction of GDP) Average lending interest rate -0.271*** -0.608*** -0.026*** Fraction of senior 0.113 4.155*** 0.032* management time dealing with requirements Barrier Time required to start a -0.350*** -0.083 -0.275*** business (days) Index of social capital (scale 0.353*** 0.033*** 1.407*** 0-100) Fraction of firms where top 0.05 -0.021 0.002 manager is a woman *** p<0.01, ** p<0.05, * p<0.1 Variables that are not shares or growth rates are in log. Results are based on a panel data (2006-2020) 101 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic A2) Results referred to in chapter 2 • Agribusiness and Light Manufacturing Ecosystems Agriculture is key sector for Kenya’s economy. As of 2017, the Agriculture sector accounted for 31.5 percent of the country’s GDP, 75 percent of the labor force and over 50 percent of total revenue from exports, thus making it a key driver of economic growth. Moreover, about 65 percent of export earnings are accounted for by the sector and contributes to better nutrition by ensuring there is production of more diverse, safe and nutrient dense foods. The sector also provides markets and inputs for non-agricultural industries such as the education, tourism, transportation and building and construction sectors. Within in the agriculture sector, over 95 percent of the establishments are engaged in crop and animal production, hunting and related service activities. This is also the dominant employment sector accounting for 98.3 percent of all the employees in the agricultural sector.99 In agribusiness, Kiambu, Nairobi and Kakamega counties concentrate the agglomerations that combine sectoral and quality diversification representing high-potential ecosystems. The three counties are high potential ecosystem, showing agglomerations of firms in multiple quality indicators and in at least one sub-sector. Nakuru and Kisii counties represent incipient ecosystems within agribusiness value chain with agglomerations in one sub-sector and one quality indicator. Nyamira, which represents an incipient ecosystem, is a significantly diverse agribusiness region with high densities of businesses in several sub-sectors. The region however does not show any significant spatial agglomerations of high-quality firms. Other counties such as Bungoma, Laikipia and Tana River also present some incipient potential of local agribusiness ecosystems. According to the CoE, most of the establishments in the agriculture sector are in Nakuru, Uasin Gishu, Nairobi and Kiambu counties. The analysis suggests that most potential ecosystem on MSMEs agribusinesses concentrates around Nairobi and the economic blocs of LREB and CKEB. Light manufacturing shows agglomerations across quality and sector diversity in Nairobi, Kiambu, Meru and Nakuru. All these counties exhibit high potential ecosystems and have high density of businesses in several sub-sectors within light-manufacturing and thus represent relatively high potential within Kenya (Figure 19, panel b). Machakos county also represents a high potential ecosystem and shows agglomerations of high-quality firms but lacks in diversity across sub-sectors. The results suggest a significant correlation across Nairobi and CKEB counties which also include Nyeri and Kirinyaga with relevant agglomerations in one of the light manufacturing sub-sectors. A similar feature regarding relevant agglomeration in light manufacturing is observed in Mombasa (JKP), Kisumu and Homa Bay (both from LREB economic blocs). Through the Big Four Agenda Plan, the Government hopes to raise the manufacturing sector’s share of GDP to 15 percent by 2022. Historically, the manufacturing sector’s contribution to the economy has stagnated at around 10 percent of the GDP. In terms of sectoral distribution, based on the Census of Establishments (2017), food processing has the highest share of establishments within manufacturing, followed by apparel. Very few establishments are associated with manufacturing of computer, electronic 99 This information is sourced from the Census of Establishments and includes data from 1,097 establishments in the agricultural sector during the period 2015 and 2016. Yet, the MSMEs survey mainly involve non-primary product activities or businesses. Thus, they exclude many of the primary activities such as agricultural production, animal husbandry, fishing, hunting, gathering, forestry. According to KNBS (2016), the conditions to include primary activities related with agribusinesses were the following: i) They were carried out mainly for profit or for the market; That are carried out with a business focus but not just as a way of life, ii) For subsistence or to just provide basic necessities for the household; iii) Where part of the returns are ploughed back as re-investment and used for diversification and/or expansion of the business. 102 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic and optical products division. Identifying the potential of local ecosystems can also open the opportunity for expansion towards these activities. • Tourism and retail Tourism accounts for approximately 10 per cent of MSMEs in Kenya. In 2019, 2,048,334 international visitors arrived in Kenya. The total revenue from travel and tourism sector in 2019 was USD 1.6 billion 100 before collapsing in March 2020 due to the COVID-19 pandemic. Domestic tourism was up by 10 percent in 2019 following massive promotion campaigns such as “TembeaKenyaNaMimi” and other rebranding efforts by the Kenya Tourism Board to promote domestic tourism in “Magical Kenya”. Domestic tourism has helped cushion the collapse of international arrivals due to the global pandemic. According to the 2016 MSME Survey data, Kajiado, Kiambu, Machakos and Meru counties show high performing ecosystems in the tourism sector (Figure 20.e). Multi-quality agglomerations of retail tend to be strongly concentrated around Nairobi. This is a particular important activity because it represents a very large share (almost half) of establishments in Kenya. Retail tends to be associated with large urban areas that tend to rely mostly on local consumers. This is reinforced by the central role played by Nairobi for this sector. • Digital businesses and finance Digital business ecosystems are significantly concentrated across Nairobi and Kiambu counties and show agglomeration in terms of both quality and diversity. These two counties therefore represent high potential ecosystems. Other sector agglomerations on digital activities have been concentrated around Nakuru, Nyeri, Homa Bay, and Kericho covering the economic blocs of CKEB, LREB, and NOREB. Yet, these counties do not show the same density in terms of the qualitative factors. Digital entrepreneurship in Kenya has been playing an important role in the country based on the dynamism and diversity of its ecosystem. Despite being a regional leader in the digital economy, there is considerable room to expand the reach and benefits of digital technologies to many traditional industries and individuals.101 Through Vision 2030, the Government aims to create a vibrant and globally competitive financial sector. Kenya’s financial sector is increasingly sophisticated and diversified and is ranked the third largest in sub-Saharan Africa. Its contribution to job creation and economic growth is significant. According to the Census of Establishments (2017), the financial sector (which comprises businesses associated with financial intermediation, insurance and pension funding) accounted for 4 percent of total services sector businesses. Among those, the highest numbers of businesses are engaged in financial intermediation services and the lowest in insurance activities. Around 53 percent of firms in the financial sector employ 4-9 people and 41.5 percent of firms have an annual turnover of less than KES 2.5 million. Similar to digital businesses, Nairobi and Kiambu show the highest concentration of financial sector ecosystems, with agglomerations in quantity and quality. Other agglomerations are found in Kajiado, Homa Bay, Migori and Kisumu in the LREB, and Nakuru in the CKEB. 100 KNBS (2019) Kenya Tourism Sector Performance Report for 2019. 101 World Bank (2019) Digital Economy Assessment: Summary Report. Washington DC. 103 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic • Data limitations The lack of granularity of the data, combined with the small size of the sample at the county-sector level, and the fact that the survey does not cover large firms may lead to relevant pieces of information missing, such as some relevant clusters of large firms not being identified. For example, the 2017 establishment census data suggest a relatively high concentration of businesses in Mombasa, but the 2016 MSMEs survey has a relatively small sample covering Mombasa county. The sample is still representative to describe key challenges facing firms across different sectors in each county, including in Mombasa. Figure 63. Main source of credit to start a business 1.CKEB 2.FCDC 3.JKP 100 1.Family own 80 1.Friends loan 60 40 2.Money lender 20 3.Bank 0 3.Credit Insitutions 2.Money lender 3.Bank/Credit Inst. 4.Government 5.NGOs/Coop. 1.Family/Friends 2.Money lender 3.Bank/Credit Inst. 4.Government 5.NGOs/Coop. 1.Family/Friends 2.Money lender 3.Bank/Credit Inst. 4.Government 5.NGOs/Coop. 1.Family/Friends 6.Others 6.Others 6.Others 3.Credit Associations 4.Government 5.NGOs Main sources of initial capital (finance) 5.Cooperatives 4.LREB 5.NOREB 6.SEKEB 100 6.Trade credit 80 6.In-kind 60 40 6.Postal savings 20 5.Chamas 0 2.Money lender 3.Bank/Credit Inst. 4.Government 5.NGOs/Coop. 1.Family/Friends 2.Money lender 3.Bank/Credit Inst. 4.Government 5.NGOs/Coop. 1.Family/Friends 2.Money lender 3.Bank/Credit Inst. 4.Government 5.NGOs/Coop. 1.Family/Friends 6.Others 6.Others 6.Others 7.Mombasa 8.Kiambu 9.Nairobi 100 80 60 40 20 0 2.Money lender 3.Bank/Credit Inst. 4.Government 5.NGOs/Coop. 1.Family/Friends 2.Money lender 3.Bank/Credit Inst. 4.Government 5.NGOs/Coop. 1.Family/Friends 2.Money lender 3.Bank/Credit Inst. 4.Government 5.NGOs/Coop. 1.Family/Friends 6.Others 6.Others 6.Others 104 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic A3) Results referred to in chapter 3 A3a: List of mapped public programs Responded Name of Government Program Name (if applicable) No. to the Ministry/Agency survey? Ministry of Industrialization, Kenya Industry Entrepreneurship Project (KIEP) 1. Yes Trade and Enterprise Generate, Start and Improve Your 2. Yes Development Business (SIYB) Program Business Sector Program Support / Private 3. Yes Sector Development (PSDS) program Stawisha SME Mashinani Program 4. Yes (Industrial Development Bank) UK- Manufacturing Africa COVID response 5. No Technical Cooperation – GIZ Make-IT in Africa 6. Yes Japan International Cooperation Agency (JICA) 7. Yes Kenya Competitiveness Enhancement Program Industrial and Commercial 8. Yes Development Corporation (ICDC) Kenya Industrial Property Institute (KIPI) 9. Yes Numerical Machining Complex (NMC) 10. Yes Kenya Industrial Training Institute (KITI) 11. Yes Kenya Bureau of Standards (KEBS) 12. No Ministry of ICT, Innovation Kenya Youth and Employment 13. Yes and Youth Affairs Opportunities Project KYEOP-1 Micro and Small Enterprise Kenya Youth and Employment 14. Yes Authority (MSEA) Opportunities Project KYEOP 2 County Industrial Development Centre 15. Yes Ministry of Labour and Productivity Improvement Program 16. Yes Social Protection Kenya Institute for Public KIPPRA Mentorship Programme for 17. Yes Policy Research Agency Universities Students (KMPUs) Kenya Industrial Estates Incubation and accelerator Programmes; 18. Yes Provision of credit to MSMEs Nairobi Securities Exchange NSE Ibuka programme 19. Yes Ministry of Agriculture Small-Scale Irrigation and Value 20. No Livestock and Fisheries Addition Project (SIVAP) Capital Markets Authority Regulatory Sandbox 21. Yes Konza Technopolis Agency mandate 22. Yes Development Authority Ministry of Information, Agency mandate 23. Yes Communications and Technology Ministry of Education State Department for Post Training 24. Yes and Skills Development 105 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Responded Name of Government Program Name (if applicable) No. to the ministry/agency survey? Kenya Bureau of N/A 25. No Statistics (KNBS) Ministry of East African Business Registration services- E-Citizen 26. No Community and Regional Development Ministry of Energy & Green Mini Grids (GMG) programme 27. Yes Petroleum – MoEP Ministry of Public Youth Enterprise Development Fund 28. Yes Service and Gender Women Enterprise Fund (WEF) 29. Yes Ministry of Devolution and the Instrument for Devolution Advice and 30. Yes Arid and Semi-Arid Lands Support (IDEAS) Programme National Industrial Kenya Youth and Employment 31. Yes Training Authority Opportunities Project KYEOP 1 Kenya National Innovation N/A 32. Yes Agency (KENIA) A3b: List of mapped intermediary organizations Name of Institution Institution type Responded to the study? 1. Acumen Fund Financial institution Yes 2. AECF Investment Unit/ CONNECT (Embassy of Sweden) Implementer No 3. AFD Group Financial institution No 4. African Agriculture Development Corporation (AgDevCo) Financial institution No 5. AGRA Implementer No 6. AgriProfocus Kenya Network Industry association Yes 7. Akirachix Accelerator No 8. Alternative Circle Digital lender Yes 9. Amani Institute Research and Yes education institution 10. Andela Accelerator No 11. ASM Africa Sustainability Matters Financial institution No 12. Aspen Network of Development Entrepreneurs (ANDE) Industry association Yes 13. Association of countrywide Hubs Industry association Yes 14. Association of Startup & SMEs Enablers of Kenya (ASSEK) Industry association Yes 15. B-Labs Accelerator Yes 16. Biashara SACCO Commercial Bank No 17. BitHub Kenya Accelerator No 18. BotLab Accelerator No 19. Branch International Digital lender No 20. Brave Venture Labs Accelerator Yes 21. Business Partners International (BPI) Financial institution Yes 22. C4D Lab Accelerator No 23. Cap Youth Empowerment Institute Research and Yes education institution 24. Catalyst Principal Partners Financial institution No 106 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Name of Institution Institution type Responded to the study? 25. Centum Foundation Financial institution No 26. Century Microfinance Bank Commercial Bank No 27. Chandaria Business Innovation and Incubation Center Research and Yes education institution 28. Cooperative Bank Commercial bank No 29. Diamond Trust Bank Commercial Bank No 30. DoB Equity Financial institution Yes 31. East Africa Venture Capital Association (EAVCA) Industry association Yes 32. Eastlands College of Technology (Strathmore) Research and Yes Educational Institutions 33. Energy4Impact Accelerator Yes 34. Equity Bank Commercial Bank Yes 35. European Fund for Sustainable Development (EFSD) Financial institution Yes Guarantee/ European Investment Bank 36. European Investment Bank Financial institution Yes 37. Family Bank Commercial Bank Yes 38. Fanisi Capital Financial institution No 39. Faulu Microfinance Bank Commercial Bank No 40. Federation of Women Entrepreneur Association (FEWA) Industry association No 41. FinnFund Financial institution No 42. FSD Africa Implementer Yes 43. Fuse Catalysts Financial institution No 44. Fuzu Accelerator Yes 45. G-TECH Lab Kenya (Centre for Entrepreneurship) Accelerator No 46. Gearbox Accelerator No 47. Global Alliance for Improved Nutrition (GAIN) Implementer Yes 48. GMG Implemented by IED - Innovation Energie Financial institution Yes Development 49. Grofin Financial institution No 50. Growth Africa Financial institution No 51. GSMA Innovation Fund Accelerator Yes 52. Hand in Hand East Africa Implementer Yes 53. Heva fund Financial institution Yes 54. I-DEV International Implementer Yes 55. iBiz Accelerator No 56. ICT for Development Kenya Accelerator No 57. iHub Accelerator No 58. Intellecap Implementer Yes 59. Invest Africa Financial institution Yes 60. Iungo Capital Financial institution Yes 61. Jamii Bora Bank Commercial Bank Yes 62. KCB Bank (KCB Foundation) Commercial Bank Yes 63. Kenya Agribusiness and Agro-Industry Alliance Industry association No 107 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Name of Institution Institution type Responded to the study? 64. Kenya Association of Manufacturers (KAM) Industry association Yes 65. Kenya Association of Women Business Owners (KAWBO) Industry association No 66. Kenya Climate Innovation Centre Implementer Yes 67. Kenya Investment Mechanism (KIM) / Palladium Group Implementer Yes 68. Kenya National Alliance of Street Vendors and Informal Industry association No Traders (KENASVIT) 69. Kenya National Chamber of Commerce and Industry Industry association Yes (KNCCI) 70. Kenya Private Sector Alliance (KEPSA) Industry association Yes 71. Kenya Women Microfinance Bank Commercial bank No 72. KfW (Financial Cooperation) Financial institution Yes 73. Kopokopo Financial institution No 74. Kuwazo Financial institution No 75. Lateral Capital Financial institution No 76. Mennonite Economic Development Aassociates (Equitable Implementer Yes Prosperity Through Private Sector Development) 77. Merck Financial institution No 78. MEST Financial institution Yes 79. Metta Accelerator Yes 80. Microenterprises Support Programme Trust (Green Growth Implementer Yes and Employment Programme) 81. Msingi Financial institution No 82. Mt Kenya Hub Accelerator No 83. Musoni Microfinance Institution Commercial Bank Yes 84. Nailab Accelerator No 85. Nairobi Garage Accelerator Yes 86. NCBA Bank Commercial Bank No 87. NEST Financial institution No 88. Netherlands Enterprise Agency (RVO) Implementer No 89. Nexus Coworking Space Accelerator No 90. Novastar Ventures Financial institution No 91. Ongoza Advisory Financial institution No 92. Open Capital Advisors (Arcadia) Kenya Financial institution Yes 93. Pangeaa Accelerator Accelerator No 94. Pearl Capital Partners Financial institution No 95. Pezesha Digital lender Yes 96. Private Infrastructure Development Group (PIDG) Financial institution No 97. Proparco Financial institution No 98. Rafiki Microfinance Bank Commercial Bank Yes 99. Root Capital Financial institution Yes 100. RTI - Kenya Youth Employment & Skills- K-YES Implementer Yes 101. RTI (KCDMS Programme) Implementer Yes 102. Savannah Fund Financial institution No 108 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Name of Institution Institution type Responded to the study? 103. Self-Help Africa (implementing Agri-Fi Programme) Implementer Yes 104. Sinapis Implementer No 105. Social Enterprise Society of Kenya (SESOK) Industry association No 106. Spark Fund (Safaricom) Financial institution Yes 107. Tala digital lender Yes 108. The Entrepreneurs Hub Accelerator No 109. The Runway Fund Financial institution No 110. Trademark East Africa (Making Trade Work for Women in Implementer Yes EA) 111. UK - Kenya Tech. Hub Accelerator Yes 112. Unaitas SACCO Commercial Bank No 113. Unreasonable/ Shona programme Implementer Yes 114. USAID (Kenya Agricultural Value Chain Enterprises Implementer No (KAVES) Project) 115. Viktoria Ventures Industry association Yes 116. Village Capital Financial institution No 117. Villgro Kenya Accelerator Yes 118. yGap (formerly Spark International) Accelerator Yes 119. Young Enterprise Scale-Up Accelerator Yes 120. Zephyr Acorn Financial institution Yes Table A3.1 Unconditional correlation between cupport type, beneficiary size (by type), and budget Type of support Individuals Firms Budget Grants -0.273* -0.394* -0.348* Equity financing -0.355* -0.322* -0.01 Credit guarantees -0.15 -0.12 0.19 Loans and credit 0.315* 0.510* 0.415* Public procurement 0.11 -0.04 -0.284* Technology extension -0.04 -0.01 -0.1 services Early-stage infrastructure -0.2 -0.23 -0.501* and advisory Business education 0.408* 0.15 0.14 Collaborative networks -0.04 -0.08 -0.373* Other 0.08 -0.13 0.02 Note: Based on number of beneficiaries (individuals or firms) and budget for 2019. Results are consistent with other years (2017 and 2018) in terms of number of beneficiaries or budget. (*) Correlation is significant at 95%. 109 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic Table A3.2 Unconditional correlations between managers’ characteristics and M&E practices Managers’ characteristics Has Previously Sectoral Female Post- Studied Age Private Owned Experience graduation abroad Sector Business Experience Impact -0.12 -0.09 0.04 0.04 -0.17 -0.06 0.09 Evaluation Key 0.03 0.06 -0.15 -0.12 -0.14 -0.15 -0.21 Performance Indicators (KPIs) Frequency of -0.02 -0.03 0.02 0.17 -0.08 0.09 0.16 KPIs Age of the 0.10 0.05 0.17 -0.22 0.05 0.05 -0.01 program *** p<0.01, ** p<0.05, * p<0.1 Table A3.4 – Likelihood of having an impact evaluation and manager’s characteristics (marginal effect) (1) (2) (3) (4) Program Manager is a Woman 0.0416 0.0729 (0.119) (0.127) Age of Manager 0.227 0.312 (0.305) (0.314) Has Post-Graduate Education -0.163 -0.201 (0.121) (0.136) Observations 72 65 73 64 Robust standard errors in parentheses. Controlling for public program or IOs. Coefficient is not significant. *** p<0.01, ** p<0.05, * p<0.1. The results are based on using a simple linear probability model, where the dependent variable is an indicator variable that takes the value 1 if a program did an impact evaluation and 0 otherwise Table A3.5 – Correlation between size of the program in terms of beneficiaries and response to COVID-19 Individuals Firms Type of response to COVID-19 Other 2019 2019 Training or Digital Solutions for Supply Chain Mgmt. 0.14 0.31 -0.27 Training or Digital Solutions for Marketing and Sales 0.03 0.23 0.35 Training or Digital Solutions for Online and Electronic Payments 0.489* 0.522* 0.2 Training or Digital Solutions for Teleworking or Service Delivery -0.15 -0.08 -0.25 110 Entrepreneurship Ecosystems and MSMEs in Kenya: Strengthening Businesses in the Aftermath of the Pandemic 111