Impacts of SMEs on Africa

IMPACTS OF SMEs ON AFRICA

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Impacts of SMEs on Africa

1.1 Research Background

            According to the IMF growth outlook of the Africa, the sub-Saharan Africa which for the last decade has maintained robust growth rates seems to be slowing down. The IMF notes that apart from Kenya and Senegal that are experiencing the growth rates above 5%, the majority of other countries in the sub-Saharan Africa are experiencing a depressed growth rate. The group trimmed the initial focus for the economic growth rate in Africa of 2.9% to 2.6% (Aboah, White, and Meuwissen, 2015; pp.7). Some of the major causes of the downward review of the economic performance of the continent are the low-performance rates in the Africa two biggest economies. In 2016 for example, the Nigeria economy which is classified as the largest in the Africa continent in terms of the GDP slipped into recession.

            The situation has not been any different in Africa second major economy, South Africa which is also in recession. Therefore, without the robust economic performance of these major economies, the contributions by others such as Rwanda, Ethiopia (which have experienced incredible growth rates in the past decade) is small. However, what plagues Nigeria and South Africa economy is what essentially inflicts several other African economies. Notably, except for the South Africa, investments in basic infrastructure in Africa is poor. Much of the continent's population lives in the rural areas (Aboah, White, and Meuwissen, 2015; pp.9). However, the basic infrastructure such as roads and electricity in the countryside of Africa is lacking. Much of the investments in infrastructure is made in the major cities. Even then, much of the infrastructure is put in areas that are in rich suburbs.

 Lack of these basic infrastructures particularly when the majority of the population is located has been the Achilles hill in the Africa battle of modernization. Moreover, it makes the trade in Africa expensive. For example, it takes close to a week to transport a container cargo from Kenyan port of Mombasa to the Uganda capital city, Kampala. Therefore, goods will reach the port from China much faster, but it will take days before the products reach the owner in Nairobi or Kampala (Aboah, White, and Meuwissen, 2015; pp.13). The lack of the investments in infrastructure of the rural areas has also dwindled the global multinationals from doing business in Africa. Another factor that has also been slowing down the Africa economic engine is the reliance on raw commodities and the lack of value addition of the exports (Mpofu, Milne. and Watkins-Mathys, L., 2013; pp. 7). It is the case that the fall in mineral prices in South Africa has contributed to the fall in the GDP forecasts. It is the same case with Nigeria economy which is heavily dependent on oil and gas. Therefore, the fall in the global oil prices negatively affected much of the country economic output.  

            In many African countries, unemployment rates are markedly high. It should be noted that in the recent past there have been xenophobic attacks in South Africa as the locals protest against the influx of the migrants in the country. Notably, many African young women and men make a journey across the continent from Ethiopia for example on the road to South Africa, where opportunities are seen as relatively high. It is likely that some of the migrants eventually secure a job, but the majority remains unemployed. The fall into the recession and the few employment opportunities that exist and which lead to the competition between the locals and the migrants were the contributing factors to the fracas that has been experienced in the country in the recent past (Kira, 2013; pp. 9). 

            In the recent past, the number of African youths graduating from local universities has increased drastically. However, the growth in the graduation rates has not matched the economic growth that would generate jobs for them (Etuk, Etuk and Michael, 2014; pp.9). Therefore, many of the recent African graduates remain without a job. The IMF forecast of a drop in the growth rates of Africa only makes the dream of ever getting a job ever more distant. Furthermore, the IMF in the forecasts estimated that the population growth rates of Africa would remain above the economic growth rates (Gbandi and Amissah, 2014; pp.6). In other words, the population will expand at a much faster rate than the opportunities that will be created for the people to earn a living.

            It is apparently a vicious cycle of poverty. Also contributing to the problem as noted by the IMF is the lack of critical policy adjustments by the governments that would otherwise help to stabilize the African economies. The lack of such policy adjustments means that the countries macroeconomic parameters are exposed to the international shocks. Civil wars in Africa is also another factor that has contributed to the impoverishment of the African countries (Akuru and Okoro 2014; 47). After many of the countries gained independence from the European colonizers less than six decades ago, they plunged into civil and ethnic wars. In countries such as Uganda, Rwanda, Mozambique, Angola civil wars have had tremendous negative impacts on the expansion of infrastructure. In countries such as DRC Congo and Chad, the continuing civil wars and battle for resources has affected the growth momentum of the continent economy (Akuru and Okoro 2014; 47).

            In all their reviews, IMF has highlighted the tremendous growth potential that is held in sub-Saharan Africa and the continent at large. Times and again, the IMF has indicated the need for these countries to strengthen their macroeconomic stability through policy adjustments. The organization notes that adjustments will help the countries in the continent better manage instabilities in the market (Jaiteh and Jakobsson, 2014 pp. 13). Furthermore, they will help restart the now silent engines of economic growth thereby creating opportunities for the many unemployed young people across the continent. The IMF also highlights the need for countries in sub-Saharan Africa to address structural weakness which has thus far led to a poor fiscal position and crediting (Akuru and Okoro 2014; 48). Addressing the challenges will allow for the rebalancing of the macroeconomic and make the economy more stable. The IMF has also advised that the poor performance of the economy risks crawling back the efforts the countries have made in combating poverty. Therefore, it has recommended that the countries take measures to protect the vulnerable in the society through the social protection measures.

            Like already mentioned, the industries that would otherwise create employment for the youths in Africa are few and in many places non-existent. In Kenya for example, the informal sector often called the juakali comprising of small artisans employs close to 60% of the youths (Jaiteh and Jakobsson, 2014 pp. 8). Among the jobs that are created annually in the economy, many of them are in the juakali sectors. The other small and medium sized enterprises in Kenya are in agricultural processing especially doing small value addition.

            In Nigeria, it is estimated that there are over 200 SMEs which just like in Kenya are the major drivers of the economy (Akuru and Okoro 2014; 48). The contribution of the SMEs to the overall GDP has also seen a major policy focus shift towards supporting them. It should be noted that majority of the people that run these SMEs lacks skills in management, financial resource reporting and appropriation (Jaiteh and Jakobsson, 2014 pp. 11). The policy focus that is often carried out in conjunction with international donors and organizations such as IMF is designed to help the SMEs run more effectively by equipping the holders with relevant skills that can better help them manage and run the businesses efficiently. Notably, when the SMEs are performing poorly, the economies also struggles (Beck and Cull, 2014; pp. 585). Therefore, supporting SMEs is also helping the economy at large, and this is crucial in tackling the many problems that bedevil Africa especially fighting extreme poverty and diseases.

            Past strategies of focusing the growth of the big business that is heavily dependent on the imports to have proved virtually ineffective. In other words, despite the investments in large industrial plants, little value has been realized especially concerning providing employment opportunities (Jaiteh and Jakobsson, 2014 pp. 14). The policy shift allows the governments to concentrate much of their focus on helping the SMEs start and grow. Importantly, World Bank has underscored the importance of these SMEs in promoting greater local material utilization, creating the jobs, mobilizing resources and supporting the rural development (Akuru and Okoro 2014; 48). Like alluded to earlier, much of the African rural areas remain underdeveloped. Supporting development in these impoverished areas could be crucial in unlocking the growth potential of the Africa (Cant. and Wiid, 2016; pp. 9).

1.2 Research Rationale

             Like vastly discussed in the research introduction, the African population is increasingly expanding. However, the economic growth rates have not been matched by the population growth. Therefore, many of the people in Africa continent remain poor, and employment opportunities are few. The government cannot give employment to all the citizens, more so in Africa where corruption is endemic, and hiring is based on nepotism. Therefore, the private sector has been seen as the solution to helping support growth in Africa, create jobs and bring about development in the rural areas. The large companies that are synonymous in the developed countries and which creates the majority of the jobs in Africa. In many cases, the SMEs are poorly funded but continue to thrive and create employment opportunities for many youths. Therefore, the significance of the SMEs in helping create jobs and bring about development in Africa cannot be underestimated. Therefore, it is necessary to carry out research on the role that the SMEs plays in the growing the economies of Africa and identify ways through which they can be supported to expand and help in fighting extreme poverty in the African continent (Beck and Cull, 2014; pp. 586). The research is qualitative and will rely on descriptive data for presentation and analysis.  

1.3 Research Aim

            Africa is plagued with many challenges of underdevelopment, low human resource index among and extreme poverty. For example, in Nigeria close to 60% of the population is said to live below the poverty line (Beck and Cull, 2014; pp. 586). The story is the same in several other African countries. Notably, the employment opportunities for the youths remains low in Africa due to the lack of quality jobs created by the manufacturing. In turn, many youths are starting up small businesses to support themselves financially and create employment opportunities for the others. In Kenya for example, juakali which is essentially SMEs employs close to three-quarters of the workforce. However, despite the role that the SMEs plays in creating jobs and ending poverty, they are poorly supported by funding and government policies and regulations (Beck and Cull, 2014; pp. 587). The aim of this research paper is to look at the subject of the SMEs more deeply, thoroughly explain the contribution of the SMEs to the economies of Africa, state the challenges that they face and how they can be supported to become huge businesses. The observation is that if the SMEs expand, they will create more opportunities for the youths, bring about development in the rural areas and end extreme poverty.

1.4 Research Objectives

            The objectives of this study is to evaluate the impacts of the SMEs are on the GDP of the African economies. In other words, what contributions do the SMEs make in the GDP of the African economies? Another objective that the research seeks to achieve is to establish the impacts of the SMEs on the FDI inflows in a country. Essentially, the FDI inflows support the economic growth and performance of a given country. The important factor, in this case, is whether the FDI is designed to support the growth of the SMEs for example through collaboration such as mergers and acquisitions.  The last objective of the research is to establish the effects that SMEs have on the overall unemployment rates in Africa.

1.5 Research Questions

            The key questions that the research seeks to find answers to are, the impacts of the SMEs on the Gross Domestic Product. The other question is what impacts the SMEs have on influencing the FDI levels in the country. The last question which is central to this research is the role that is played by the SMEs in fighting unemployment in Africa.

1.6 Structure of Thesis

            The problem of the unemployment in Africa is of monumental magnitude and poses a challenge to many other countries. In other words, unemployment in Africa if not addressed may become a global challenge. For instance, the majority of the people making journeys across the Mediterranean from Africa to Europe are often young unemployed youths who are looking for greener pastures in Europe. Consequently, there is resistance in the European countries as the economies there remain depressed and the jobs become scarce. From another perspective, it is likely that youths who do not have a job may end up joining terror groups that operate in Africa thereby increasing the global insecurity.

            However, the Africa has been regarded as the next frontier of the growth after Asia and Latin America. Therefore, if investments are made into the African continent, more people will get opportunities there reducing the social impacts that come with immigration and further strain on the developed economies. The question that remains is if investments are needed in Africa in what areas can they have the greatest of the impacts? The paper seeks to answer this question by analyzing the role played by the SMEs in the African economies to work as a guideline for the policy makers, business leaders and charity organization to use in making useful investments that will have positive impacts on the African youths and population at large (Matamanda and Chidoko, 2017; pp.14). Usually, if a sector contributes significantly to the GDP, then it is considered crucial for the growth of the country and a focus on the investments. The research seeks to establish this connection to act as a guiding principle to the investors interested in doing business in Africa. Another crucial consideration is whether the SMEs attracts the FDI in the countries of Africa that would spur economic growth and help lift people out of poverty.

Chapter 2-Key Concepts

2.1 SMEs

             Globally, there is no one accepted a definition of what an SME is, because the abbreviations imply the ‘'small'' and ‘'medium'' which have various meanings in different countries. For instance, what may be considered small in the United States may be regarded as big in Africa? Among the considerations that are made in the United States in defining SMEs are the size of the workforce and the financial parameters (Busse, Erdogan, and Mühlen, 2016; pp. 238). In the perspective of the first consideration, an SME would be any business that has employee less than or equal to 500 (Busse, Erdogan, and Mühlen, 2016; pp. 238). The EU uses the criteria of the headcount and balance sheet total to define an SME. A medium enterprise using the parameters is any firm that has less than 250 staff a turnover that is equal or less than € 50 million (Busse, Erdogan, and Mühlen, 2016; pp. 238). A small business based on EU standards has staff less than 50 and turnover that is equal or less than € 10 million. The Canadian business classification standards define a medium business as having less than 500 employees. On the other hand, a small business has less than 50 employees (Busse, Erdogan, and Mühlen, 2016; pp. 239). The Canada, therefore, defines a SMEs as any business that has 0-499 employees and a gross revenue of $ 50 million (Busse, Erdogan, and Mühlen, 2016; pp. 239).

             The consideration for the revenues differs depending on the sector. For example, within the service sub-sector, an SME would be considered to have less than $7 million annual revenues (Beck and Cull, 2014; pp.588). Note that other service sectors such as the computers may have a relatively small workforce and greater annual revenue and hence the need to use the two parameters in the classification but also to group the businesses according to the sectors. In Malaysia, a medium business has employees from 75- to 200 (Beck and Cull, 2014; pp.588). A small business has employees from 5-75. In Kenya, SMEs are defined depending on the employees and the annual revenues. Therefore, any business that employs 50 or less than staff is considered an SME. In addition, the revenues should not exceed $ 1 million (Beck and Cull, 2014; pp.588). For this paper, the 50 employees and less than $1 million dollars in revenues will be used as the definition of an SMEs (Beck and Cull, 2014; pp.589).

2.2 Economic Growth:

            Assessment of how an economy grows is done after a given time interval say for instance, a year. Usually, it involves comparing how much the economy has generated the present year compared with the previous. Therefore, if the economy produced less the current year compared to the previous, then it is considered to have grown negatively. Typically, a negative growth is defined as a recession, or contraction of the economy (Gbandi and Amissah, 2014; pp.7). On the other hand, if the economy produces more than the year before, it is said to have experienced a positive growth. The rate of the growth is determined by finding the difference between the current and the previous. Important also to note, if the economy is growing, the companies also produce more and earns greater profits. Therefore, economic growth rates are the key factor that investors consider when making investment decisions. The rise in the profits and stock price due to the economic growth also allows companies to expand thus producing more goods and services and thereby further spurring growth (Gbandi and Amissah, 2014; pp. 9). The economic growth is not only enjoyed by the companies but the society too. An expansion of the economy implies more money to spend and better standards of living. Therefore, people living an economy that is expanding can afford housing, vacation, and luxuries. Due to the benefits of the economic growth highlighted above, many countries across the globe always strife to maintain the economy on an upward trend by developing policies that support the growth of the industries (Ibrahim, Keat and Abdul-Rani 2016; pp. 12).

            However, the growth of the economy has many factors at the play some of which are outside the limits of a country’s leaders. Therefore, sometimes like mentioned in the case of Nigeria and South Africa, countries sometimes experience a negative growth or a recession (Gbandi and Amissah, 2014; pp. 9). The implication is that the companies in such countries struggle to make a profit and hardly make any hiring. On the contrary, during the recession, many businesses lay off employees, which create social problems such as poverty and diseases such as depression. Moreover, instead of the firms expanding their production, they scale them down since the demand is low. In addition, the government earns lesser revenues and thus struggle to provide basic services to the citizens. In some cases, economic growth has been the start of the civil war and the breakdown of the economies. For instance, the SMEs contributed close to 30% of the total exports and are a crucial cog in pushing the economic wheel of the country. It is, therefore, no doubt that a lot of questions and discussions have been held in public with regard to developing tax regulations that would support the growth of SMEs otherwise called broad-based strategy.

2.3 Review of Theories and Models

            Usually, economic models are designed to break down the complex nitty-gritty of an economic concept into something that can easily be comprehended by the people. Importantly, the model is supposed to yield postulations that can help explain economic behaviors that can be tested. However, one outstanding feature of the economic model is that it is often subjective because it lacks the necessary objective measures that can be tested. The two main classes of the economic models are theoretical and empirical. This section of the paper will seek to discuss the theoretical and empirical models that can be applied in explaining the economic growth and the SMEs. Notably, the theoretical models only use defined constraints in the model to verify the economic behaviors. The purpose is to explain specific questions by giving qualitative economic answers. On the other hand, the empirical models seek to transform qualitative predictions into numerical values.

            Purchasing power parity is a crucial economic theory that argues that after the adjustments in the exchange rates between two given countries, the price levels should be the same. The argument is based on another theory called the law of the price which postulates that an identical item should cost the same across the countries (Fatoki, 2014; 922). In other words, if a car costs $ 1200 in the United States, once the adjustments are made on the exchange rates say with naira, the Nigeria currency, the car should cost the same in Nigeria as the United States. Evidently, such a theory assumes many things, for instance, it fails to account for the cost of transporting the car to the Nigeria, the levies that may be imposed by the government and the profit margins that the importer would seek to get from the sale. What is most crucial for this theory is that if the price levels are different for the same product after the exchange rate adjustments, then people will seek to buy the product from the country with the lowest price (Angelo, 2017; pp.8). For instance, if an Africa country, for instance, manufactures a car, but the price of the same car with the similar specification is lower in Japan, then people will start buying from Japan than South Africa.

            The model best explains how the Africa SMEs struggle to remain competitive in a globalized market. For instance, if there is a small computer assembly plant in South Africa and another one in China, the technologies in China helps them assemble more products per unit time and thus enjoy the economies of the scale (Ardjouman, 2014; pp.179). Consequently, people will purchase from China than South Africa, the SME in South Africa can therefore only buy what is assembled in China, rebrand and resell it in Africa. In the process, the jobs that would have been created through the assembly are lost.

            Interests Rate Parity is also another key theory to consider, and it compares the values of the assets. If the exchange rate adjustments are made between two countries, then the value of the assets should be the same (Angelo, 2017; pp.9). The theory is of importance concerning the FDI flow into Africa. The companies buying assets in those countries wish for them to gain in value. Usually, the value of the assets does not remain the same and changes with the economic performance and outlook.

The Solow-Swan Economic Growth Model

            The building blocks for the Solow-Swan model is the evaluation of the population growth, the rate of the economic and technological progress a country makes over a given time. In other words, the economy of a given country over a certain period expand as the population increase and the technology of the production becomes more advanced (Greiner, Semmler, and Gong, 2016; pp. 8). For instance, if the initial technology of farming land is an ox and plow the economy will be less developed or grow slower than when the technology is tractors. Another consideration is that what is being produced must have people that want to consume it. Therefore, if the population increases, then the demand for the goods improves, and the technology used in the production also is enhanced. The assumptions of this model are that the economy is operating in a perfectly competitive market. Other assumptions include work L and capital K which are production factor.

            In a Solow-Swan the assumption is that labor and capital are moving freely. In other words, the model also suggests that the production factors are moving freely. Moreover, the resources in the model are assumed to have been used completely. Another consideration made by the model is that the economy is closed. In other words, production is homogeneous, and the output is either channelled towards the investments or consumption. In this model of economic growth, any new capital units are equal to the investments. Therefore, an increase in the capital also causes a rise in the output. Importantly, if the capital inflow reduces the output from the economy will also decrease. Notably, as the economy grows and the capital axe is undertaken, the output margin reduces as compared with the previous period. The implication is that all economies experience a law of diminishing returns. Using this model, the FDI inflows to Africa will increase the working capital (Francis and Willard,  2016; pp.17).

Harrod-Domar Growth Model

            The basic premise of this model is that the growth of the economy depends on the capital output and the level at which the citizens make the savings. Therefore, an increment of savings should contribute to greater productivity and enhanced economic growth rate. The model is applied in analyzing the business cycle thus helping support economic growth (Busse, Erdogan, and Mühlen, 2016; pp. 285). The key concept of this model is that savings and investment are central in supporting the economic growth of the country. Therefore, if the savings increases and invest the savings, the economy will grow. Taken in the light of African economy context, low savings levels lowers the amount invested and thus poor economic growth performance. Therefore, if more is saved and invested, the production or output will increase, and the country will grow. In this model, the capital-output ratio suggests the level of the capital that needs to be invested in the economy to produce a dollar worth of output. Therefore, if the capital-output ratio is low, the economic growth increases and so do the output that is generated.

            Some of the challenges that are associated with the model are that the growth of the economy is the same as the development. In other words, if an economy A grows at 10% the development also increases at the same level (Beltratti, 2013; pp. 8). In reality, the statistics that illustrate an increase in the economic performance cannot be the same as improvement of the literacy levels, health, and education among other social needs. The model also is reliant on the growth of the foreign aid. Another shortcoming of this model is that physical capital as a means of evaluating an investment is inefficient in countries of Africa because of corruption and wastages (Jones and Muller, 2016; pp. 32). In addition, the savings ratio in developing countries cannot be reliable in evaluating economic growth. Usually, the marginal propensities to save in developing countries is low as more people spend their earnings to support the families. The financial system is also another factor that makes this model inefficient because, in countries that have an undeveloped financial system, the level of the savings cannot indicate the amount of investment.

The Domar Model

            Among the key considerations of this model are the impacts of the investments on the economic growth. The model argues that investment generate income in the economy and expand the capacity for productivity. In this model, joblessness in the economy is seen as a disadvantage (Chambers, 2014; pp. 33). The inadequate use of the capital dwindles investments and drives joblessness. In Africa due to inefficiency and wastage, the resources or capital may not be well utilized. Therefore, the investments are low, and the income reduces. Therefore, unemployment of the capital affects the income levels of the country. The reduction of the income also affects the demands for goods and services which further slows down the economic growth of the country. The conditions that define a steady growth rate in this model is G, G_w, and G_n or the changes in the capital inflows and the employment of the resources.

Lewis Structural Change

            The model suggests that the growth of the modern industrial sectors would attract the rural workers. Importantly, the approach would allow industrial firms to offer better income to the rural workers thus improving industrial output. However, since the people from the rural areas are less skilled, their impacts on the overall industrial productivity is insignificant. As the rural workers improve their skills, the industrial productivity increases thus helping support economic growth through enhanced output levels (Chambers, 2014; pp. 34). Importantly, the people that migrate to the towns to support industry earn higher incomes than if they were undertaking the traditional agricultural activities. The increase in the incomes also enhance savings that increase the capital that can be invested. As the savings are put in the increased productivity of the industry, the output increase and the economy grows faster.

Rostow’s Model

            This model of the economic growth of the development looks at the stages that a country or economy undergoes before it matures. Therefore, in the beginning, the economy is reliant on simple traditional means of production. Therefore, people rely on subsistence agriculture and partly in the barter trade (Lewis, 2013; pp.162). Many of the African economies actually lies at this stage of the economic development. The next step in the growth and development is a transition in which the economy start becoming more specialized and see an expansion of the infrastructure which helps increase surplus in the output of the country (Cingano, 2014; pp. 8). In stage three of the economic growth and development, the economy simply takes off, the growth is seen in the industry, more investment is recorded, and the growth is regional, and there is a change in the political climate. For instance, people get more political freedoms and voice and the standards of living increases greatly (Lewis, 2013; pp.163). In stage four of the development, the economy simply matures up, at this point, it becomes more diversified, relies more on the innovation and depends on less on the imports and investments. In the last stage of the growth and development, the economy becomes more consumer oriented. Moreover, the economy produces more durable goods and the service sector rather than the manufacturing dominates the economy.

Neoclassical Model

            The model looks at the economic growth by evaluating from the aggregate output by considering factors such as capital, labor, and the technological progress. Simply the model is simplified as:

Yt = At f [Kt, Lt]

The t in the model represents the period, Y_t is the output value of an economy. Importantly, the greater the Y value, the greater the economic growth for a given period t. The K_t represents the capital output, and L_t denotes the level of labor utilization (input). The A_t is for the technological progress. Usually, advancement in technology would increase the output for the period under review. In other words, the greater the A_t, the larger the Y_t. However, the main drivers of the technology are the entrepreneurs and innovation (Lewis, 2013; pp.161). Therefore a complete representation of the neoclassical model of the economic growth is: 

Yt = β0 + β1Kt + β2Lt + β3SMEt + β4EXRt + β5INFt + µt

The equation above incorporates the impacts that the inflation would have on the overall growth of the SMEs. The α and µ consider the parameters and errors in the evaluation. 

2.4 Empirical studies of SMEs on Economic Growth

            A study done by Matilla (2017), found that the Chinese inflows in the Sub-Saharan Africa have supported the growth of the GDP and increased the employment opportunities. Further, in his study Matillla (2017), correlated the impacts that the FDI inflows in China have had in supporting the economic growth. Matilla (2017) established that just like in the case with China, FDI inflows in Sub-Saharan Africa have supported the growth of the GDP and employment creation.

            Senkuku and Gharleghi (2015; p.54) sought to investigate how the government policies attracts the FDI. The study focused on Tanzania. The findings from the study was that change or reforms in government policies positively attracted the FDI inflows. Importantly, it established that FDI was central in supporting GDP growth of Tanzania and fighting unemployment.

            Ayentimi, et al., (2016, p.10) found that a correlation exist between the financial integration in  a particular county and FDI inflows into a country or region. Therefore, the greater the financial integration, the larger the FDI that a country may attract. Ayentimi et al., (2016, p.11), specifically found out that increased financial integration in West Africa was supporting the inflow of FDI in the region.

            Adams and Ansah, (2015; p112), found that the financial reforms in Ghana had attracted FDI inflows in that country. The researchers underscored the importance of the FDI in supporting the economic growth of a country. Importantly Adams and Ansah (2015; p.113) established that financial reforms especially in the banking sector contributed positively to the economic growth.

            The empirical study assumes an autoregressive distributed lag method to evaluate the contributions that have been made by the SMEs, in helping extreme fighting poverty in Africa, promoting economic growth and development. Recent research shows that in many countries, the SMEs have grown incredibly. The increase in the number of the SMEs has also be merged with a rise in the output and productivity of the firms (Rjoub et al., 2017; pp. 7). Evidently, the SMEs have the potential to be the catalysts for the socio-economic development of the African societies, improving the standards of living.  Equally, the SMEs has been primed as the solution to helping the African economies attain macroeconomic objectives, which include creating jobs for the youths, improving the entrepreneurial capabilities of a country and poverty reduction (Rjoub et al., 2017; pp. 7). The three critical empirical data that will be evaluated in this study is the impacts that SMEs have made in the GDP of a country, encouraging FDI inflows and fighting the problem of the unemployment in Africa.

Conceptual Framework:

GDP

 

           

FDI

SMEs

 

           

Unemployment

 

2.5 Research Variables

            The three research variables that will be used to investigate the study questions are described as follows. First is the SMEs and the GDP, for example, evaluate the number of SMEs in a country and whether the growth in the number of the SMEs has led to an increase in the GDP of a given country? Therefore, a rise with SMEs and a resultant rise in the GDP then it will imply that there is a direct correlation between the number of SMEs and the GDP growth of a country (Jerven, 2013; pp. 9).

            Another pair of the variables that is will be investigated by the study is the impact of the SMEs on the FDI. The idea is to investigate the role played by the SMEs in attracting the FDI into a country. The assumption is that as SMEs grow, investors from other countries become interested depending on the returns potential of a given SME and the sector it operates. The assumption is that the expansion of the SMEs illustrates the confidence mood of the economy and thus attracts the interests of the foreign investors thus promoting FDI to the country (Jerven, 2013; pp. 9). Therefore, there should be a correlation between the growth of the SMEs and the FDI inflows.

            The third variable in the study is what the impacts of the SMEs would be the total unemployment levels of a country. The assumption again is that as the SMEs increase, they create more work opportunities for the unemployed. Therefore, increase in the number of the SMEs in a country should be matched with a decrease in the unemployment levels of a given country. Therefore, just like the FDI and the GDP, the growth of the SMEs should have a direct correlation to the reduction of the unemployment.

Chapter 3

3.1 Research Design

            The design used is a descriptive survey. It is appropriate for this research because it allows for the use of the elements of both quantitative and qualitative research elements thereby allowing for a more wholesome look at the impacts of the SMEs on the economy and the social well-being of them to the country under consideration (Lambert and Lambert, 2012; pp. 255). In other words, the model does not seek to infer what the impacts of the parameter are but rather examine its contribution to the factors under the considerations. The study will importantly use the secondary data in evaluating the impacts of the SMEs on the overall vibrancy of the African economies. In addition, the study design allows for a correlation analysis of the factor under consideration and the impacts that it has on the overall outcome. For example, like mentioned in the variables, examining the relationship that exists between the SMEs expansion and the reduction in the unemployment rate.

3.2 Research Methodology

            The subject of the study is the countries in the African continent which have the desired statistics on the subject matter. Therefore, the study will seek to examine the overall FDI inflows into the African continent. Such data will be analyzed alongside the number of the SMEs that were recorded in a given year (De Beer, 2016; pp. 33). The approach first offers a chance to look directly at the descriptive data such as tables and infer the impacts of the inflows of the SMEs. For example, does the data show that a rise in the SME for a given year, the FDI inflows increased. At the same time, what was the economic performance of the continent for that given year? The approach, therefore, allows to analyze the impacts of the FDI flows on the SMEs and at the same time examine whether there was any improvement in the economic performance. Equally, the SMEs impacts on the job creation and the correlation to the economic progression. The data collection for this research is to utilize available secondary economic data on African economy at large (Hopkins, 2014; pp. 27). The data evaluated would be for the past ten years. The period of five years allows for a better look at the economic growth of African countries and correlate it to the number of SMEs that were added in the economy (Rjoub et al., 2017; pp. 8).  Furthermore, the period allows accommodating for the inefficient economic data reporting system. Therefore, for a given country say X the last economic data update may have occurred five or a year ago.  

3.3 Data Collection Methods

            The methods that would be used in the analyzing the data is a linear regression. The analytical approach is appropriate for the project because it allows establishing the relative importance of the independent and the dependent variable (Issawi, 2013; pp. 9). Therefore, the method enables to evaluate the significance made by the SMEs say for example on the FDI inflows, unemployment and GDP.

3.4 Population and Sample Size

            The importance of sampling in research cannot be understated. It allows for using a small sample of the population to predict the overall impact of the parameter on the total population. In this research, a number of African countries will be used to estimate the impacts of the SMEs on the GDP, FDI, and unemployment. Therefore, the sampling will help in making inferences about a statistical parameter of the whole population.

	African Countries	Number of African Countries	Number of selected African Countries
	Eastern Africa	18	4
	Central Africa	9	3
	Northern Africa	8	2
	Southern Africa	5	2
	Western Africa	17	4
	Total	57	15

            The selection criteria used for samples were the availability of the statistical data and the significance of the country in the region. For example, in the eastern African region, Kenya, Rwanda, and Ethiopia would be considered. The observation is that for close to a decade now, these countries have recorded significant expansion of the GDP and attracted interests of the multinational companies (McMillan, Rodrik, and Verduzco-Gallo, 2014; pp. 15). For instance, Kenya is already home to regional or continental headquarters of several multinationals such as IBM among others. Moreover, the country has a thriving private sector which is obviously critical in measuring the contribution of the SMEs in the job creation, FDI inflows and GDP expansion (De Beer, 2016; pp. 34). Moreover, the country has been at the centre of innovation and technology in Africa with innovations such as M-Pesa money transfer services which have been credited worldwide as revolutionary in promoting financial inclusion of the developing world (De Beer, 2016; pp. 34). It also has to be said that the country has some geopolitical significance due to its location among other factors. Therefore, it is a justified pick for this study. Other countries such as Rwanda and Ethiopia have sustained an economic growth rate of over 6% in close to a decade, and therefore they make a good case for the inclusion in this study (Acheampong and Osei, 2014; pp. 242). It is important also to note that the eastern Africa region has maintained a high rate in Africa than any other region. Moreover, there has been the discovery of oil and gas in the region thus attracting investors, and it would be crucial to examine what these recent discoveries have done in promoting the growth of the SMEs and job creation.

            The recent civil strives in the Northern Africa has dented the growth momentum witnessed in the area in the years past. However, Morocco has sustained an impressive growth rate and continue to attract FDI and the increase in the SMEs (Ayandibu and Houghton, J., 2017; pp. 14). Therefore, an evaluation of the SMEs in this area would again help project the overall impacts of them on the overall economic performance of the region. South Africa has been a model of success in Africa, great infrastructure, stable governments and good standards of living (Acheampong and Osei, 2014; pp. 242). Importantly, it has quite a substantial number of industries and SMEs. It is for this reason that the county is selected for this study. Another country from the south that is also selected is Botswana, in the recent past, the country has undergone tremendous developments in energy, infrastructure, and construction (Liedholm and Mead, 2013; pp. 5). It would appear that these developments would encourage the growth of the SMEs and creation of the jobs. Therefore, it is a relevant addition to the sample. Other countries such as Mozambique and Zambia, Namibia have experienced good economic growth rates. In the Central Africa, two countries were selected for the study, Angola, and Gabon. Angola after years of the civil wars has been able to recover and restart growth (Acheampong and Osei, 2014; pp. 242). The presence of gas and oil in the country has also attracted FDI inflows into the country, thereby giving a chance to query the impacts the FDI have had on the SMEs and job creation. Another country in this region that is also of interest is Gabon, which has among the highest per capita income in Africa. Just like Angola, the country is also has high amounts of oils and gas and this may be the factor attracting FDI inflows (World Bank. 2017; para 2).

            The Western Africa has been plagued by a number of challenges especially the insecurity posed by terror groups such as Boko Haram. Moreover, the terror cells operating in the North Africa may infiltrate the region posing a big challenge to the government and the economic growth of these regions. The region is home to Africa largest economy (Nigeria) and thus is of major significance in this study (Acheampong and Osei, 2014; pp. 242). Much of the economic studies conducted in the past always include Nigeria in the analysis, because it has a vibrant economy with a thriving private sector. Therefore, it is justified to include it as part of the study for the reasons given above. Moreover, the country has been close to the last two years. Therefore, it can help in analyzing the impacts of this negative growth on the GDP, SMEs, and FDI and unemployment levels. The last country from this region is Ghana, which has enjoyed robust economic expansion in the last decade (Acheampong and Osei, 2014; pp. 242). The country also has a vibrant private sector and is greatly peaceful.

            Algeria is another North Africa country that is added in this research. The county is rich in natural resources especially oil and gas and the evaluation is meant to see how SMEs in the country have performed. Tanzania is also included in this research. The country has had recent discoveries in gas and is rich minerals. It also has a vibrant private sector. The Democratic Republic of Congo (DRC) is also rich in natural resources, but several civil wars. It is an interest thus to find the performance of the SMEs in the economy. In the West Africa, Senegal which has a stable democracy and vibrant private sector, and thus seeks to evaluate the performance of the SMEs (Page and Söderbom, 2015; pp. 13). The last country that is included in the study as a sample is Ivory Coast. Another country with a thriving private sector (Dewan, Banerjee and Randolph, 2014; pp. 9). The purpose is to evaluate the contribution of SMEs in the FDI inflows, unemployment and GDP growth. 

3.5 Reliability

            The reliability of the data collected is a concern to provide the consistency in the research. In other words, if someone else was to repeat the research afresh, would they get the same set of results? Furthermore, the reliability also helps to illustrate that the scores obtained from the study have not occurred due to chance but because of the variables under consideration. One way of enhancing the reliability of the data is ensuring that statistical data is obtained using the same manner to ensure consistency. In this paper, the economic data for the countries selected for research will be obtained from international organizations such as World Bank, IMF among others. Such organizations have a standardized system for collecting and reporting data which enhances reliability. Furthermore, such systems allow for a better regression analysis of the data.

3.6 Accessibility

            The ease with which the research data or material can be accessed is also conscious in this research. In many countries of Africa, data collection, storage and transmission is often a challenge. Other problems affecting the access to data is the seemingly political system that seeks to control access to some information that may be regarded as private. Therefore, national data is often regarded as inaccurate or exaggerated to meet a certain political objective. Therefore, inflation index may be reported at a lower rate than the actual figure. However, organizations such as World Bank, conduct independent research annually and report their findings in their websites. The information is available to the members of the public for viewing and other considerations. The research will access data from websites of these organizations. Moreover, it will seek to compare the data reported by a country against that given by the international organizations such as World Bank. (Gebremichael, 2014; pp.9).   Information can also be obtained from the private sector alliances and groups that actively monitor the FDI inflows or outflow in Africa.

3.7 Ethical Consideration

            Research must be objective in the collection and analysis of the data and not simply designed to achieve a pre-determined outcome from the analysis. The hypothesis and not the bias should be the basis of concluding the outcome of a research question. Like already mentioned, the research question that the study seeks to answer is the correlation that exists between the SMEs and the FDI, GDP growth and reduction in the unemployment (Quartey et al., 2017; pp. 19).

            Another ethical consideration to make in this research is ensure that the data and the source are credited to avoid plagiarism or intellectual property theft. The point is particularly important considering that the research relies heavily on the analysis of the secondary data and giving credence to the source is thereby important. The study also maintains professional standards by ensuring that all the stages of the study are subjected to a rigorous scientific procedure to minimize bias and upload the objectiveness of the research. In all the studies, it is crucial to observe and maintain privacy and confidentiality of the participants and privileged information that may be availed in the course of the study (Fakieh, Bloun and Busch., 2016; pp. 14). Furthermore, such information should only be obtained through the consent of the owner.

            The study will also develop and use protocols that guide in obtaining the information that is necessary for the study.  In the analysis of the data and its collection, considerations would also make on the sensitivity to cultural and social differences that exists among the African communities. The sensitivity allows for the cultural values of the African countries to be respected and make sure that negative stories are not spurned out just to tarnish the progress or lack thereof on the continent (Mary, Ngozi and Simon, 2015; pp.18). Another consideration in this research is to allow for the access to the data that is presented in the research for the validity of the information thereof. Such an open access help people that may doubt the findings or suspect flaws in the process to repeat the study and see whether same results would be reproduced (Janet et al., 2015; pp. 7). The study also has appropriate quality measures to ensure that the data used in the research meets the quality standards expected for the inclusion in the final analysis.  Other ethical considerations are also made of the research designs, model and analysis to minimize biases.

3.8 Data Analysis Method

            The first method of the data analysis applied in this research is descriptive data analysis that is meant to highlight the salient features of a given data set. The two particular areas of focus on the description of the data are the measures of the central tendency and variability. Some of the consideration to make in regard to the measures of the central tendency is the mean. The average can help describe the overall trend in a study like this, for example, what is the average impacts of the FDI inflows in the African growth of the SMEs (Han, Xiang and Yang,  2017; pp.12). In addition, by comparing the average number of the SMEs and the mean rate of unemployment, then the contribution of the SMEs in fighting joblessness on the African continent could be demonstrated.    The measures of the variability are also crucial in carrying out the analysis of the distribution of the data. For example, the standard deviation can help determine how the values obtained for a given country differ from the mean. The measures of the variability also help in determining the distribution of the data set from the normal standard curve of distribution. In other words, it may help in evaluating whether the data is evenly distributed or skewed.

Correlation Analysis

            The idea of the correlation study is the comparison of the sets of the data. It is important to keep in mind that the study looks at the three data sets. The SMEs and FDI, SMEs and the GDP growth and lastly the SMEs and unemployment. The correlation analysis allows for the evaluation of these variables and how they fluctuate together. Notably, if the correlation is positive, then it illustrates how the factors under consideration reduce or increase in parallel (Amoateng, Cobbinah and Ofori-Kumah, 2014; pp.332). For example, a positive correlation would be obtained if the increase in SMEs leads to a rise in job creation. A negative correlation will occur if the SMEs growth is not seen as supporting the creation of the jobs. One value that is relied upon in correlation analysis is the correlation coefficient, which helps to estimate how the fluctuations in one variable affect another.

Regression Analysis

            An integral part of this analysis is the regression. The approach is crucial in establishing the relationships that exist among the variables that are being considered in the study. Therefore, it helps to create a relationship between the FDI, GDP, SMEs and Unemployment. Notably, all these factors are central to this study because they seek to establish the relationship that is there between these variables (Oyelana and Smith 2015; pp. 8). The relationship that is sought in regression analysis exists between the independent and independent variables. For this study, the relationship is between the SMEs and the FDI, GDP and unemployment. Establishing such a relationship in the context of the African economy is crucial because it can be used as the basis by the policy makers in pushing for investments decisions that would allow for the expansion of the African economies to create more jobs for the youths.

3.9 E-views

            The tool allows for the prediction of the economic scenarios that would result for given condition, thus giving insights into the impacts of each variable into the overall performance of the economy. For example, e-view would help estimate what would happen if the SMEs increased significantly and the FDI also record huge increments. For example, what would happen to the GDP under such conditions?

Chapter 4

4.1 Descriptive Statistics

FDI inflows in Africa table and Graph

Country		FDI ($billions)			2009	2010	2011	2012	2013	2014	2015	2016
Ethiopia					0.221	0.288	0.627	0.279	0.953	2.132	2.168
Rwanda					0.119	0.423	0.106	0.16	0.258	0.292	0.323
Nigeria					8.555	6.026	8.841	7.07	5.563	4.656	3.129
Morocco					1.97	1.241	2.521	2.842	3.361	3.526	3.253	2.318
Angola					2.205	-3.227	3.0324	-6.898	-7.12	1.922	9.282
Gabon					0.573	0.499	0.696	0.832	0.771	1.011	0.624
South Africa					7.624	3.693	4.139	4.626	8.233	5.792	1.521	2.25
Botswana					0.209	0.218	1.371	0.543	0.398	0.515	0.393
Ghana					2.373	2.527	3.248	3.295	3.227	3.363	3.192
Kenya					0.116	0.178	0.14	0.163	0.372	0.944	1.437
Algeria					2.747	2.3	2.571	1.5	1.692	1.503	-4.03
Cote d'Ivoire					0.396	0.358	0.302	0.33	0.407	0.439	0.43
DRC					-2.47	2.742	1.687	3.312	2.098	1.843	1.674
Tanzania					0.953	1.813	1.229	1.8	2.087	2.045	1.961
Senegal					0.33	0.272	0.338	0.276	0.311	0.403	0.345

Unemployment Rate in the Selected Countries

Country		Unemployment Rate (%)			2009	2010	2011	2012	2013	2014	2015	2016
Ethiopia					7.283	7.317	7.386	8.302	7.064	6.995	7.353	8.065
Rwanda					2.794	3.393	3.712	4.321	4.317	4.386	3.624	3
Nigeria					11.232	11.311	11.394	11.812	11.039	7.454	6.661	7.835
Morocco					18.083	17.747	17.926	18.615	18.964	19.92	20.359	20.584
Angola					11.513	11.526	11.532	11.544	11.561	11.524	11.306	11.159
Gabon					37.478	35.723	35.754	35.715	35.734	39.646	39.67	39.664
South Africa					48.336	51.212	50.282	51.698	51.426	51.291	50.13	52.288
Botswana					29.504	33.779	33.713	33.666	33.766	31.904	32.727	33.29
Ghana					15.587	8.614	8.492	8.526	9.846	10.007	10.958	11.52
Kenya					23.99	24.074	24.049	24.036	24.063	23.857	22.762	22.168
Algeria					21.508	21.97	22.56	27.617	25.062	25.688	26.273	26.554
Cote d'Ivoire					13.991	13.976	13.909	14.071	14.063	13.997	13.904	13.864
DRC					6.596	6.614	6.615	6.618	6.626	6.613	6.55	6.52
Tanzania					5.074	5.883	7.092	6.526	5.903	4.211	4.855	5.236
Senegal					14.689	13.769	12.522	12.546	12.533	13.499	13.242	13.097

Country	No. of SMEs			2012	2013	2014	2015	2016
Ethiopia				8.648		10.257	10.392	7.562
Rwanda				8.84		7.62	8.873	5.932
Nigeria				4.279		6.31	2.653	-1.541
Morroco				3.01		2.551	4.508	1.1
Angola				5.155		4.804	3.007	0
Gabon				5.251		4.315	3.879	2.262
South Africa				2.213		1.7	1.299	0
Botswana				4.456		4.149	-1.7	2.9
Ghana				9.293		3.986	3.916	3.577
Kenya				4.563		5.352	5.713	5.932

GDP growth Rate for Selected Countries

Country	GDP Growth Rate (%)				2009	2010	2011	2012	2013	2014	2015	2016
Ethiopia					8.803	12.551	11.178	8.648	10.582	10.257	10.392	7.562
Rwanda					6.288	7.291	7.787	8.84	4.7	7.62	8.873	5.932
Nigeria					6.934	7.84	4.887	4.279	5.394	6.31	2.653	-1.541
Morocco					4.244	3.816	5.246	3.01	4.535	2.551	4.508	1.1
Angola					2.413	3.408	3.919	5.515	6.814	4.804	3.007	0
Gabon					0.13	7.09	7.092	5.251	5.638	4.315	3.879	2.262
South Africa					-1.538	3.04	3.284	2.213	2.489	1.7	1.299	0.279
Botswana					-7.652	8.564	6.048	4.456	11.343	4.149	-1.7	2.9
Ghana					4.846	7.9	14.046	9.293	7.313	3.986	3.916	3.577
Kenya					3.307	8.402	6.112	4.563	5.88	5.352	5.713	5.849
Algeria					1.632	3.634	2.892	3.375	2.768	3.789	3.763	3.7
Cote d'Ivoire					3.251	2.018	-4.387	10.707	8.889	8.794	9.163	8.755
DRC					2.855	7.079	6.865	7.158	8.504	9.51	6.883	2.205
Tanzania					5.382	6.359	7.905	5.141	7.263	6.965	6.959	6.958
Senegal					2.423	4.179	1.761	4.411	3.485	4.311	6.485	6.65

Country	No. of SMEs			2012	2013	2014	2015	2016
Ethiopia				8.648		10.257	10.392	7.562
Rwanda				8.84		7.62	8.873	5.932
Nigeria				4.279		6.31	2.653	-1.541
Morroco				3.01		2.551	4.508	1.1
Angola				5.155		4.804	3.007	0
Gabon				5.251		4.315	3.879	2.262
South Africa				2.213		1.7	1.299	0
Botswana				4.456		4.149	-1.7	2.9
Ghana				9.293		3.986	3.916	3.577
Kenya				4.563		5.352	5.713	5.932

SMEs

Country		SMEs	2009	2010	2011	2012	2013	2014	2015	2016
Ethiopia			1327
Rwanda			3028	3129	4627	6655
Nigeria			65089	65074	72396	81144
Morocco						34658
Angola			850	6571	1327
Gabon			3490
South Africa			253217	199754	59731	217624
Botswana			10852	11639	12217	15447
Ghana			15324	13760	15649	13154
Kenya								45366
Algeria			10661	9564	12256	13938
Cote d'Ivoire			2600
DRC					411
Tanzania				1163
Senegal								2375

Correlation Statistics

SMEs and GDP growth Rate

Correlation between SMEs and Average is:
-0.52855978(0.0428)

Correlation between SMEs and FDI

Correlation between SMEs and Average is FDI:
0.70089163

SMEs and Unemployment Rate

Correlation between SMEs and Average is:
0.63852987(0.0104)

Regression Analysis

SMEs and Average Unemployment

Simple linear regression results:
Dependent Variable: Average
Independent Variable: SMEs 
Average = 13.346299 + 0.00018041584 SMEs
Sample size: 15
R (correlation coefficient) = 0.63852987
R-sq = 0.4077204
Estimate of error standard deviation: 10.78871

Parameter estimates:

Parameter	Estimate	Std. Err.	DF	95% L. Limit	95% U. Limit
Intercept	13.346299	3.1981971	13	6.4370143	20.255584
Slope	0.00018041584	0.000060309419	13	0.000050125257	0.00031070641

Analysis of variance table for regression model:

Source	DF	SS	MS	F-stat	P-value
Model	1	1041.641	1041.641	8.9490929	0.0104
Error	13	1513.1514	116.39626
Total	14	2554.7923

Regression Analysis

SMEs and FDI

Simple linear regression results:
Dependent Variable: FDI
Independent Variable: SMEs 
FDI = 0.94449519 + 0.000026679284 SMEs
Sample size: 15
R (correlation coefficient) = 0.70089163
R-sq = 0.49124908
Estimate of error standard deviation: 1.3470651

Parameter estimates:

Parameter	Estimate	Std. Err.	Alternative	DF	T-Stat	P-value
Intercept	0.94449519	0.39932298	≠ 0	13	2.3652413	0.0342
Slope	0.000026679284	0.0000075301603	≠ 0	13	3.5429902	0.0036

Analysis of variance table for regression model:

Source	DF	SS	MS	F-stat	P-value
Model	1	22.778078	22.778078	12.55278	0.0036
Error	13	23.589598	1.8145844
Total	14	46.367676

Predicted values stored in new column: Predicted Values

SMEs and GDP Growth

Simple linear regression results:
Dependent Variable: GDP
Independent Variable: SMEs 
GDP = 5.7631069 - 0.000022901735 SMEs
Sample size: 15
R (correlation coefficient) = -0.52855978
R-sq = 0.27937544
Estimate of error standard deviation: 1.8249102

Parameter estimates:

Parameter	Estimate	Std. Err.	Alternative	DF	T-Stat	P-value
Intercept	5.7631069	0.54097502	≠ 0	13	10.653185	<0.0001
Slope	-0.000022901735	0.000010201338	≠ 0	13	-2.2449737	0.0428

Analysis of variance table for regression model:

Source	DF	SS	MS	F-stat	P-value
Model	1	16.784389	16.784389	5.039907	0.0428
Error	13	43.293865	3.3302973
Total	14	60.078253

Predicted values stored in new column: Predicted Values

4. 4. Impact of SMEs ON GDP

            The implication is that the GDP growth in Africa from the sample may not necessarily be what drives the economic growth. Instead, big projects such as infrastructure could be what spurs the economic growth in the continent. Therefore, the more investments are made in the infrastructure such as roads, airports, the greater the economy expands. It is possible that the contribution of the SMEs in the African economy is understated due to the poor reporting systems (Inyang, 2013; pp.7). In several African countries, data about SMEs is ad hoc such that it is impossible to state the number of the businesses that operate in a country and thus difficult to predict their contribution to the economic growth of such countries. Usually, a positive correlation should exist between the SMEs numbers and the GDP growth. However, the unique challenges posed in accurately recording data could explain the anomalies noted in this study. The regression analysis also gives a small R-squared value which could indicate small variability in the distribution of the data (Gbandi and Amissah, 2014; pp. 34).

4.5 Impact of SMEs on FDI

            The correlation shown between the SMEs and FDI is positive. Usually, as the FDI increases the performance or the number of SMEs is also expected to rise. In other words, as more money flows into an economy it should stimulate or support the growth of the SMEs. Therefore, the FDI and SMEs performance are expected to be in parallel, meaning when one reduces the other is also expected to drop (Bouazza, 2015; pp. 5). However, the correlation, therefore, agrees with the hypothesis that as the SMEs increases the FDI

4.6 Impact of SMEs on Unemployment

            What the correlation establish in this case is positive. The implication is that the reduction in the performance of the SMEs leads to a drop in the employment opportunities that are created by the economy. On the other hand, a rise in the SMEs activities creates more employment opportunities for the people.

4. 7 Result Summary

            The results of the regression and correlation analyses suggest that negative association exists between the SMEs and the FDI and GDP growth. Usually, it would be expected that an increase in the FDI flow into a country would encourage the SMEs to flourish. But it is also possible that the investments could be government to government and not necessarily money used to support the growth of the private sector. Therefore, it is likely that large FDI inflows may not lead to a rise in the performance of the SMEs. However, if much of what is coming into a given country is in the form of private equity, then it may support the growth of the SMEs. The analysis also showed that a positive correlation exists between the SMEs performance and the unemployment rate. Like already mentioned, the SMEs are expected to create jobs for the unemployed, therefore, their increase in number should cause a reduction in the unemployment rate.

Chapter 5: Conclusion and Recommendations

5.1  Conclusion

            Africa is home to some of the world fastest growing economy like the case with Ethiopia. Some economies such as South Africa are struggling. The unemployment rates in countries such as Gabon, South Africa and Kenya remains remarkably high which implies that young people in such countries are struggling to make a living. The data analysis showed the positive correlation that exists between the SMEs and the unemployment rate. Therefore, it is fair to say that the SMEs are central in addressing the problem of unemployment in Africa. The Africa infrastructure remains largely underdeveloped, and this impedes growth and development. Some of the FDI coming into the Africa could be directly channelled towards supporting the development of the African infrastructure which will enhance intra-Africa trade.

            The civil wars had plagued the continent over decades. However, these are being replaced by stable governments in places such as Rwanda which is essential in helping SMEs to thrive. In the research objective one of this study, SMEs and GDP do not have a positive correlation which is rather an unusual scenario but not impossible. The study also showed that in the African context and the countries selected for this study, the SMEs do not necessarily attract FDI into Africa. It could be explained regarding the interests of the investors that want to put their money in Africa. Perhaps the industries that the SMEs operate in are not so attractive as to attract the FDI inflows. The objective three of the study gave a positive correlation, thus confirming the hypothesis that a rise in the SMEs also leads to a jump in the jobs that are created in an economy. Equally, if the SMEs are struggling the jobs that are generated in an economy are fewer.

5.2  Recommendations

The first recommendation is for the need for the private sector in Africa to be helped in keeping data of the businesses that are started in a year. Furthermore, the data from different countries could be put into one database for ease of access and comparisons. 

1.      The second recommendation is the need to use a larger sample when researching this kind to minimize chances of errors and biases in the selection of the countries to be included in the study.

2.      The SMEs in Africa as evidenced by analysis support the growth of jobs and should thus be helped to grow by giving relevant funding assistance.

3.      The governments in Africa should seek to initiatives that would support the growth of SMEs to expand economies and create employment.

5.3  Limitations

1.      Limited access to the SMEs data in the population sample

2.      Lack of data on FDI, GDP and unemployment in some countries selected for the study

3.      Inconsistencies in the data provided by the national government of the sample countries

4.      The failure to integrate unique challenges faced by African countries in data collection

5.4 Implications

1.      The research implies that if the SMEs are supported, they could help fight the problem of unemployment in Africa.

2.      Another implication is that in countries such as Gabon, there is an outflow of the FDI

3.      In a country such as Angola, the inflow of the FDI does not seem to translate into the growth of the SMEs.

5.4  Future Research Directions

1.      The future research on this area of SMEs should seek to find out the areas of the economy that the SMEs make most of the impacts

2.      Another direction is to find out what impedes the growth of the SMEs in Africa

3.      Future research should also seek to establish the challenges that the SMEs faces in Africa and how governments can assist them to overcome the challenges.

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