This dissertation has had a focus on the relationship and links between social infrastructure and economic growth in the context of Sub-Saharan countries. Predictability in development and growth is oftentimes linked to various endogenous conditions that can ultimately enhance or detract from the overall potential of a modern nation. The object of this research was represented by social and economic indicators in 23 Sub-Saharan countries. The aim was to find out whether the social infrastructure has a statistically significant impact on economic growth in this region. In order to reach this aim, the method of regression analysis has been implemented. The study has covered a wide range of social and economic variables observing them for a period from 1980 to 2008. The results of the study revealed that only population growth, life expectancy and savings rates are statistically significant determinants of economic growth in Sub-Saharan countries. This finding has supported the assumption that social infrastructure is an important factor that effect economic growth and development. However, the research has been limited by the lack of information on all 33 countries in Sub-Saharan region. Therefore, the sample was reduced to 23 countries. Furthermore, some social indicators such as Gini coefficient and mortality rates were not available for some of the older years in the sample. The study ends with recommendations to policy makers and discussion of implications.
This dissertation is dedicated to my parents who I love unconditionally. I would like to express my gratitude to the University staff for the knowledge they shared with me and inspiring me to think critically.
I declare that the research project has been independently prepared by myself and represents an original work with no plagiarism. All external ideas and quotations have been properly referenced. The full list of references is contained at the end of the dissertation.
As economic theory has evolved over the past several decades, a variety of variables have begun to infiltrate the standard models of growth and development. Roseta-Palma et al. (2010), for example, recognise that human capital has become an increasingly important variable in growth modelling, suggesting that the force behind such capital can radically alter the shape and potential of industry and markets. However, there is an inherent expectation of support, one which is based on the conceptualisation of the social infrastructure that leads the vocal masses to expect national investment in their wellbeing. To perform within a developing nation, society must be supported. The support must include effective health care and improved educational standards. The perpetuation of economic performance within diverse marketplaces ultimately relies upon the sustainability of such practices, leveraging human capital and contributing to market development.
The theoretical background of this dissertation is represented by the elements of the economic theory that explains the growth and expansion as well as the role and influence of social factors. Econometric models put forward by the UN will be of particular importance. The research project will also review the arguments of famous contemporary economists such as Stiglitz (2009) and Jones and Klenow (2010). This will serve a useful theoretical background to the wider analysis, which is required for answering the research questions. The literature review will also cover the mainstream development theories such as dependency theory and social justice theory. From a conceptual perspective, researchers such as Newman and Tomson (1989) provide a precedence of focusing on social factors in economic development. They argue that social infrastructure is an essential element in sustainable long term growth of the economy. This theory may only be accepted as valid if it is statistically supported using the case studies of the economies. The testing has previously conducted by Jones and Klenow (2010). The researchers indeed supported the theory by finding that economic growth was boosted by increasing life expectancy in many countries. However, these researchers have not found such support of the theory for African countries in Sub-Saharan region. Therefore, this dissertation will attempt to study this region in more details and find statistical dependency between the social infrastructure and economic growth. Using the method of multiple regressions, a sample of 23 countries in Sub-Saharan region will be explored. The study will cover a period from 1980 to 2009.
In African countries, economic growth is a function of a wide range of variables such as foreign aid, foreign direct investments (FDI), policy reformation and liberalisation and others. This investigation seeks to examine a link, which is frequently overlooked in this dynamic and evolving economic system: social infrastructure. There is an innate reciprocity between social infrastructure and economic growth, one which requires further definition within the context of African evolution in order to determine the true order of events. In order to limit the scope and breadth of this study, the following aims and objectives were established:
• To determine whether social infrastructure is a fundamental determinant of economic growth.
• To explore statistical significance of social infrastructure as a determinant of the economic growth in Sub-Saharan countries.
• To recommend strategic policy implications for the transition economies in Sub-Saharan region that would help them to grow and expand.
Based on the aforementioned aims and objectives, particular research questions were defined. They focus on investigation of the relations between the African social infrastructure and economic growth experienced by various nations within this region. It may be argued that the socio-economic aspects, which contributed to successes for many nations, remain inconsistent and non-definable today. Based on this supposition, the following research questions were defined prior to engaging in the investigative process:
• What are the primary threats/pitfalls associated with economic growth in Sub-Saharan Africa today?
• Does social infrastructure have a statistically significant impact on economic growth in Sub-Saharan countries?
In order to standardise the research project, it was important to create a clear structure and presentation format. The study is structured in a way that would allow for progressing from more general information regarding the Sub-Saharan countries to more specific information regarding the variables that have a direct impact on the growth of the transition economies. The rest of the research project has the following structure.
• Chapter 2: Literature Review. This chapter focuses on a broad range of theoretical and empirical data that has been retrieved from a variety of academic sources. This literature review explores the determinants of the social infrastructure for developing nations, focusing on Sub-Saharan Africa.
• Chapter 3: Methodology. This chapter highlights the research methodology chosen during the collection and analysis of empirical data. Based on the precedence established by the past researchers, econometric modelling is used as the main method of the research. This chapter also discussed the strategies and approached that were used with their justification.
• Chapter 4: Data Presentation. This chapter reveals the main findings and results of the research. The historical statistical data is presented and analysed. Correlation and regression analysis is applied to the data. The main results are summarised in tables and figures.
• Chapter 5: Discussion and Analysis. In this section, a synthesis of academic and empirical data is presented. The discussion is focused on the original research questions and objectives. They are compared to the findings achieved by previous researchers. Similarities and differences are analysed and explained.
• Chapter 6: Conclusions and Recommendations. The final chapter provides the final insight into the relations between social infrastructure and economic growth in Sub-Saharan countries. Recommendations for future research are offered since the research project has encountered particular limitations that have to be addressed in the future. Furthermore, policy implications are recommended in this chapter.
While it is widely recognised that the measurement of economic growth provides an accurate picture of development and achievement in transition nations, the inherent value of such metrics has been questioned during the last decade because of several pitfalls. Hoogvvelt (2001:8) argues that in early development models, all emphasis was placed on strategic enhancement of the transition economies with impoverished nations. A traditional indicator of economic growth is represented by GDP. While conceptually indicative of growth and economic expansion, this indicator has been recently challenged as an effective measurement of sustainable national development. In fact, researchers such as Stiglitz (2002, 2007) and Collier (2007) have offered the arguments on the fact that GDP fails to represent an accurate picture of national economic welfare. It is argued to limit the identification of economic inequality and circumvent such influential social indicators as mortality rates, GNI per capita, education levels, etc. Other researchers such as Thakur (2006) have suggested that the United Nations Human Development Index (HDI) should be used as an alternative measure of economic growth besides the GDP.
The economic theory provides different growth models that explain the factors of economic growth and help to determine what cause an economy to expand. Among the well-established theories of growth are the neo-classical models suggested by Solow (1956: 65) and Ramsey (1928: 543). However, there are also alternative models that have recently been proposed. The most notable example is the endogenous growth model.
The exogenous growth model has been originally presented by Solow (1956: 65). It is an extension of the previously formulated Harrod-Domar growth model. The latter suggests that the rate of economic growth is a function of the productivity of the country’s capital and the savings rates. Solow (1956: 65) has improved the Harrod-Domar growth model by differentiating between the new capital that emerged from the use of new technology and old capital. Diminishing returns started playing an important role in the exogenous growth model.
Solow (1956:65) has also added labour to the determinants of the economic growth. The researcher argues that more than one factor of production should be included in the growth model. These factors are capital and labour. The researcher also emphasises the role of the technological progress in the economic growth. However, the model may be criticised for failing to provide the explanation of how and why the technology develops. In addition, the exogenous growth model may be criticised for neglecting the factor of entrepreneurship, which is argued to have a strong impact on economic growth (Braunerhjelm, 2008: 51; Audretsch et al, 2006: 119).
Mathematically, the exogenous growth model may be presented as follows:
Where Y is the output of the country; K is total capital (both new and old); L stands for labour; A represents technological development.
The exogenous growth model is heavily reliant on the indicators estimated per capita. Hence, it places a significant emphasis on the role of the population growth in the economic growth. The capital per worker is argued to be growing only if the savings rates exceed the rate of population’s growth and the level of depreciation of the capital. The exogenous growth model also suggests that the savings rate would be steady in the long run and have a positive correlation with the economic growth, i.e. the countries with higher savings rates will be expected to have higher economic growth. However, this notion was criticised by Ramsey (1928: 543) who proposed an alternative neo-classical model of growth.
In his model the savings rates are assumed to be varying and not constant. The Ramsey model has changed the way the capital is modelled. Mathematically, it is represented as follows:
Where k is capital; c is consumption; δ is the rate of depreciation of capital; f (k) is the value of total production. Since the savings rates are not viewed as constant, the level of consumption is also considered as a varying process since it is tightly connected to savings. Since neither Ramsey nor Solow model of growth included the factor of entrepreneurship and explained technological progress as an endogenous process, an alternative model has been developed. It is called endogenous growth model (Barro and i-Martin, 2004: 205).
The previously discussed exogenous growth models suggested that a country’s GDP is a function of the savings rate and technological advances. Nonetheless, these exogenous growth models failed to show how savings are determined and how the technological changes are driven.
These limitations are effectively solved by the endogenous growth model. It suggests that savings rates are simply a function of the utility maximising actions of the economic agents. Given the financial constraints, companies would aim to maximise their net income while consumers will tend to maximise their utility (Romer, 1986: 89).
The endogenous growth model also explains technological progress as a result of the favourable policies from the government that do not restrict innovations and changes in the industries. It is valid to argue that in developing countries the governments may attempt to put certain restrictions on changes and innovations in order to protect the key sectors of the economy. The endogenous growth theory suggests that such actions would lead to a slowdown in the economic growth in the longer term. The theory also views company investments in the research and development as the way to technological progress and faster economic growth. Hence, the theory explains the growth of the economy with microeconomic elements (Aghion and Howitt, 1992: 323).
However, the model has also been criticised in the economic literature. For example, Parente (2001: 51) argues that the endogenous growth model, even though being more complex, still fails to explain why there is a divergence in the national income per capita in emerging economies and developed countries.
A widespread academic research on social equality demonstrates that impoverished nations have traditionally failed to achieve healthy social infrastructure, which can sustain development amongst all groups of the population. Sebitosi and Pillay (2005:2045), for example, argue that poverty “is largely due to failure by society to productively deploy human resources” (Sebitosi and Pillay, 2005: 2045). The researchers argue that the governments of the countries with transition economies and policymakers cannot actively engage every individual in economic activities. In many cases, funding welfare programmes that were fiscally unsustainable has had minimal impact on the social welfare of the national inhabitants (Sebitosi and Pillay, 2005:2045). This is also illustrated by the efforts made by the African National Congress (ANC) in the late 1990’s and early 2000’s.
Ultimately, it is the strategic utilisation of national resources that will allow for perpetuated social stability, gradual reduction of poverty over and improvement of social infrastructure. Sebitosi and Pillay (2005:2048) argue that availability of resources and the specifics of the culture determine the social infrastructure in a country. This, in turn, plays a role in the economic growth and development.
Equality is a term used for describing the gap between the rich part of the population and the poor. This term is also expanded to describe the difference in rights between males and females, young and old, native and foreign ethnic groups, etc. Researchers such as Morvaridi (2008) and Houtzager (2005) argue that the merits of equality should be used as indicators of long term sustainability and economic growth of a nation. Anderson and Cavnagh (2009) have presented empirical evidence on the existence of income inequality and gender inequality that negatively impact the economic growth and development. Other academics (e.g. Sen, 2001) suggest that innate human rights must play a fundamental role in the development discourse, emphasising deficiencies within the national infrastructure that interrupt widespread equality.
Accessibility and availability of resources and the level of social equality in developing nations are frequently identified as primary indicators of social development. Researchers such as Moradi and Baten (2005) have modelled social inequality according to anthropometric data. The model is focused on the level of development of social groups over the past decade.
Their evidence highlights two different phenomena that have implications for policymakers in the future. First, the authors argue that evolution of the food supply has a direct and measurable impact on the physical characteristics of the population. Second, the marked increase in the social inequality has a direct impact on the resource accessibility and, subsequently, on the growth pattern of the surveyed nations (Moradi and Baten, 2005:1254). The implications of such evidence transcend the limitations of the model itself. The researchers recommend the governments to provide favourable external conditions for redistribution of wealth and resources in order to achieve higher rates of economic growth and development.
In economic analysis of national development, indicators of sustainable growth are oftentimes linked to the advancement of technology, resources, and industrial activity. From a social standpoint, it is the access to resources and provision of more advanced amenities that allow researchers to effectively measure progress. Buys (2009:1496), for example, explored a widespread diffusion of cellular phones throughout Sub-Saharan Africa, modelling competitive networks according to the population concentration and government policy measures. Their time-scale representation of progress in cell-phone usage throughout this continent suggests that strategic policy reform has provided the most significant opportunity for widespread distribution of such technologies (Buys, 2009:1497).
Improved competition amongst providers led to the spread of a sustainable low cost technology across the countries. This evidence suggests that opportunities play an important role in social and economic development. These opportunities, however, should be provided by the government and policy makers.
Social factors in the sustainability of economic growth can oftentimes be overlooked in academia. Researchers focus instead on more tangible variables, attempting to model economic growth using purely economic variables and neglecting social factors. Chou (2006:910) demonstrates how social capital, as a strategic resource, can have a measurable and long term impact on the growth of a nation and its economic development. Essentially, as policymakers provide the resources for social capital to develop and expand, the infrastructure will simultaneously expand, allowing individuals to use the skills they have developed in a more effective and productive way. Over the long term, Chou (2006) suggests that technology and favourable policies of the government will lead countries to sustainable economic growth and stronger social infrastructure.
Other models of social infrastructure have focused on the more practical composition of this expanding network. They emphasised such factors as the progress in transportation and population movement patterns. Porter (2002:286), for example, suggests that sustained improvements in both rural and urban transportation signal development progress in African nations. In particular, the author argues that economic recession of the 1980’s and 1990’s in African countries was reinforced by the poor condition of roads, transport and weak infrastructure. The deterioration of the transport infrastructure would reduce transport efficiency for the exchange of goods and services, resulting in a downward spiral in commercial activities (Porter, 2002:287).
Porter (2002:296) argues that one of the methods to provide sustainable economic development is to stimulate the ‘scaling up’ of the national economy through the installation and evolution of social institutions.
So, focusing on inequity in social development and the limitations imposed on infrastructure development and sustainability, the reviewed academics demonstrate how the consequence of restrictive social development is ultimately the deterioration of economic growth. The following empirical investigation will attempt to model such occurrences in modern Sub-Saharan Africa, highlighting those key variables that affect economic development.
Researchers such as Moradi and Baten (2005:1234) argue that anthropometric models are fundamentally beneficial in the studies of national development, providing valuable insight into particular social factors that are indicative of long term development. In their analysis of Sub-Saharan African development, the authors used such models to analyse the data on accessibility of resources (i.e. nutritional and health inputs), providing a bounding metric by which they were able to evaluate inequality in the region (Moradi and Baten, 2005:1236).
In a research model that was focused on a similar issue regarding the social determinants of economic growth, Newman and Tomson (1989:464) used World Bank databases to identify particular social indicators and statistically connect them to economic development. The methods and models of this dissertation are based on the research methodology of Newman and Tomson (1989: 464) and Jones and Klenow (2010). The econometric models will be represented by several equations that start from simpler ones and progress to the more complicated, which include additional variables and dummies. The list of equations that will be used is provided below.
gdpij = α + βpopij + γpop65ij + λlifij + δsavij + εij (1)
gdpij = α + βpopij + γpop65ij + λlifij + δsavij + σmortfij + νmortmij + εij (2)
gdpij = α + βpopij + γpop65ij + λlifij + δsavij + σmortfij + νmortmij + ωhexpij + εij (3)
gdpij = α + βpopij + γpop65ij + λlifij + δsavij + σmortfij + νmortmij + ωhexpij + ρdummyrij + εij (4)
gdpij = α + βpopij + γpop65ij + λlifij + δsavij + σmortfij + νmortmij + ωhexpij + ρdummytij + εij (5)
gdpij = α + βpopij + γpop65ij + λlifij + δsavij + σmortfij + νmortmij + ωhexpij + ρdummyrij + τdummytij + εij (6)
Among these models, the best one will be selected with the Akaike information criterion. Random and fixed effects will be used in the panel regression models to investigate, for example, the impact of the geographical location on the economic growth and other factors.
A general form of the panel regression model with fixed effects will be as follows:
yij = α + β’Xij + uij,
where the error term u is assumed to be a sum of the fixed effect and another error term:
uij = μi + νij.
The random effect model will be different from this one in how it explains μi and νij . These terms are assumed to be completely independent. Furthermore, they random variable effect implies that these terms are normally distributed, i.e.
The choice of the methodology is consistent with the theoretical concepts of the growth models reviewed in the literature and supported by such economists as Solow (1956), Romer (1986) and Barro and i-Martin (2004). The theory of economic growth expressed by these economists mainly suggests that a country’s GDP is a function of both economic variables and social. In particular, it has been seen in the literature review that exogenous growth model connects GDP with the savings rates and technical progress. The theory of Solow (1956) and the growth theories in Barro and i-Martin (2004) also suggest that GDP is related to the population (social variable) because the latter determines the amount of capital and labour as factors of production. Hence, the core of the econometric model has been built on the exogenous growth theory proposed by Solow (1956) and explained in Barro and i-Martin (2004). However, it was found in literature review that this theory was also criticised. The models has been enhanced by inclusion of additional variables to make it more complicated and create a representation of social infrastructure, which is a key focus of the research.
Based on the research model presented by Moradi and Baten (2005) and Newman and Tomson (1989), this investigation is focused on the changes in economic growth as a result of a number of social and economic variables that have been described. Researchers Thomas (2003) and Creswell (2009) provide models of empirical research, emphasising a unique link between both quantitative (statistical, data-driven) and qualitative (phenomenological, experience-driven) data streams. Their mixed method research approach places one of these two methods in a primary position over the other, allowing the subsequent research to serve as a validation mechanism.
The data used in the dissertation is entirely based on economic development statistics within the Sub-Saharan African counties. However, the various phenomena, which contribute to such development, are of primary concern for the relevance and validity of this investigation. Therefore, a mixed method research approach was chosen for the study. Using this method, statistical findings will be achieved and later compared with various economic and social phenomena across the surveyed nations.
Because there are 33 different nations currently associated with Sub-Saharan Africa, this research has chosen a sample of the top 23 countries in terms of population, attempting to retrieve data that is directly relevant to the conceptualisation of the long term sustainable growth and the impact of the social infrastructure on this process. Non-probability sampling technique has been implemented in choosing the countries. This decision may be justified by the fact that total population represented by the 33 countries in Sub-Saharan region is quite small and could be used without picking a sample. However, sampling was needed since a limited amount of data was available for the countries. Historical statistics have been gathered from Penn World Table, International Monetary Fund (IMF, 2010) and World Bank (2010) database. These sources provided information for only 23 counties in the Sub-Saharan region.
While all of Africa could have provided very general information relative to the development of these nations as a conglomerate, it was important to evaluate the social infrastructure of these nations to narrow the scope of the research. The case study research strategy has been employed in order to investigate the social and economic situation in all the companies within the chosen sample. The case study strategy, which was popularised by Yin (2009), allows the researcher to extract particular data from complex problems and identify those variables, which are most significant. Furthermore, this strategy allows for effective exploration of both the statistics and context of the problem (Saunders et al, 2007, p.119).
Yin (2009) presents a model of the investigative case study, suggesting that the breadth and focus of research questions will ultimately define the methods employed during the study. His validation of the case study strategy as a valuable tool within academic research is based on the depth and scope of the data generated from such investigation (Yin, 2009:14). Following such case study guidance and the mixed method approach previously discussed, this research was conducted in an effort to determine whether or not the social infrastructure has a direct and measurable impact on overall economic performance of the countries in Sub-Saharan region.
The data sources were retrieved from two globally respected sources: The World Bank and the International Monetary Fund (IMF). Economic indicators were also gathered from Penn World Table. These databases have compiled specific economic and social data on the majority of the nations in the world, providing a resource for academics and policymakers. While the World Bank (2010) remained the primary source of the data, the IMF (2010) database was used for comparative purposes and in order to identify several variables not found within the World Bank annals. All analysis was conducted using Microsoft Excel and Eviews 6 statistical package.
As previously mentioned, the scope of the research in this empirical case study was limited to the top 23 countries in Sub-Saharan region. This limitation arose from the lack of economic and social data for the rest ten countries in the region. World Bank (2010) provided most but not all information that was needed.
Another important limitation of the research, which is worth noting, is the lack of observations for several social indicators. It was noted previously that the sample of data covers 23 countries with the time range from 1980 to 2009. While many of the economic variables such as GDP were available for this period, some social indicators such as mortality rate were available only for a period of up to 5 years. Therefore, the overall sample will have to be shrunk to run the regression with these variables that have fewer observations. This is expected to have a negative impact on the accuracy of the study and estimated statistics.
World Bank (2010) has provided economic and social data for twenty three countries in Sub-Saharan region. However, most of the data contained missing points. In order to avoid the problem of missing points, sixteen Sub-Saharan countries have been selected to be analysed for which more complete data was available.
The data ranges from 1980 to 2008. However, some of the social indicators such as health expenditure and mortality rate were available only for a limited time period. The health expenditure indicator was available only for a period from 2003 to 2007. Mortality rate indicator was available only for a period from 1998 to 2008. Due to the differences in the time period of data several panel regressions will be run and the best model will be chosen by means of the Hausman test.
Panel regression analysis has provided a number of advantages to the research project. First of all, it has allowed for gathering a large number of observations that totalled 4,250. If only time-series analysis was used, there would have been fewer observations. Similarly, in a cross sectional analysis the number of observations would solely depend on the number of countries included. Panel regression analysis has allowed for combining both time and cross sectional dimensions making the analysis more advanced. Secondly, another advantage of using the panel data analysis was higher degrees of freedom. This is a result of the more observations that the method has provided. Degr
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