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Journal of Money, Investment and Banking ISSN 1450-288X Issue 26 (2012) © EuroJournals Publishing, Inc. 2012 http://www.journalofmoneyinvestmentandbanking.com

Are Financial Flows and Human Capital Working for Economic Growth in the Sub-Sahara African Countries? Empirical Evidence Tchouassi Gérard Department of Economics, University of Yaoundé II Yaoundé, Cameroon, P.O. Box 1365, Yaoundé E-mail: [email protected] Tel: + 23799920998 Abstract Using panel data over the 1985-2010 periods, we document the contribution of financial flows and human capital to the economic growth in the Sub-Sahara African countries. We find that there is a positive relationship between human capital variables such as, total population, life expectancy, and the gross domestic product per capita. Total population and life expectancy are key drivers working for economic growth in the 28 selected Sub-Sahara African countries. Improvements in public health expenditure may increase life expectancy and gross domestic product not only through labor productivity, but also through the accumulation of capital.

Keywords: Foreign direct investment, Aid, Education, Health, Economic growth JEL classification: C23, F43, O47

1. Introduction Analysis on economic growth has enjoyed a renaissance in the last decades, as indicated by the vast theoretical and empirical literature on the subject (Barro and Sala-i-Martin, 1995; Aghion and Howitt, 1998; and Temple, 1999). The seminal work is invariably Solow’s (1956, 1957) model in which sustained growth in output per capita is only possible as a result of exogenous technical change. However, the resurgence of interest in growth theory has been inspired largely by the Romer (1986)Lucas (1988) paradigm of endogenous growth, in which the key determinants of output growth may be endogenous variables. In this paradigm, output per capita can grow over time because of endogenous forces within the economy, particularly human capital and the knowledge base. Sustained growth depends on levels of human capital whose stocks increase as a result of better education and innovation, new methods of building capacities, new learning and training procedures, higher levels of health and life expectancy. Without a labor force with the minimum levels of education and health, a country would not be capable of maintaining a state of continuous economic growth. The effects of human capital variables imply that the investment rate tends to increase as the levels of education and health rise. Both these variables evolve systematically according to levels of development, and these changes may be linked to increases in the investment rate. A more highly educated, healthier workforce finds it easier to create, use, and adapt innovations and new technologies. Poor Sub-Sahara African (SSA) countries have lower levels of human capital and therefore have greater difficulties in competing with those that are more highly developed. In order to

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foment economic growth in these developing societies their levels of human capital must begin to converge with those of richer nations. Fisher’s definition of capital as any asset that gives rise to an income stream requires the inclusion of human capital, even if it cannot be bought and sold (it is, of course, rented), and even though investments in such capital often involve non-market activities (Fisher, 1930). Human capital is a broad concept which identifies human characteristics which can be acquired and which increase income. It is commonly taken to include peoples’ knowledge and skills, acquired partly through education, but can also include their strength and vitality, which are dependent on their health and nutrition. Human capital is widely accepted as an important determinant of economic growth and the importance of human capital accumulation is unconditionally acknowledged in the existing exogenous and endogenous growth theories (Mankiw, Romer and Weil, 1992; Howitt, 2005). However what is still debatable? What factors should be considered as financial or human capital? Financial determinants of economic growth are based on foreign direct investment, official development assistance flows and financial transfers from migrants. Human capital theory focuses on health and education as inputs to economic production. This is in contrast to the concept of human development which views health and education as intrinsically valuable outcomes to be placed alongside economic production as measures of human welfare. In understanding the role of human capital as an input into development it is necessary to consider the possible links between human capital, other forms of capital, income and growth. Considering the links by which investment may affect the growth of output, financial, physical and human capital directly impact on the productive capacity of an economy. However such direct effects may not be the most important. More human capital (education and health resources) may itself affect the rate of growth of financial and physical capital. If financial, human and physical capitals are complements then increasing human capital raises the rate of return on financial and physical capital. The underlying rate of technical progress in an economy may depend on how much educated labor there is in the economy. Rates of return on investment must consider both the direct and indirect effects of such investment. In assessing the effects of financial flows and human capital on output we have both macro and micro evidence in the developing societies. Financial flows have three components: flows of official development assistance (ODA or aids), foreign direct investment (FDI), and financial transfers. The objective of the flows of official financing administered is the promotion of the economic development and welfare of developing countries (OECD, 2003). Much aid is public, passing from government to government, and donated directly by an individual country (bilateral aid). Public aid is also given multilaterally, through pooled funds from many different donor countries facilitated by International Finance Institutions (IFIs), such as the World Bank, the International Monetary Fund (IMF), and through regional development banks. The United Nations also runs an international development assistance program funded by member nations. Are multilateral channels more effective and efficient in the delivery of public money? This can be affirmative because they reduce the number of bilateral players and agendas, and theoretically direct funds have more potential for impact. Private sources of development assistance funds are significant as well, and are growing. Much private aid is also channeled through donations to philanthropic or non-profit organizations. It is important to note that many development initiatives are funded by a hybrid of all of the above donor sources. All three types of donors – bilateral, multilateral, and private – tend to combine and leverage their funds at some point along the way, whether it is intentionally conceived in a comprehensive public/private initiative, or whether this blurring lies in the reality of the paychecks, procurements, and activities on the ground. Is aid helped in conducting economic growth in recipient countries? Foreign direct investment (FDI) augments domestic resources to enable the country carry out effectively her development programmes and raise the standard of living of her people. Foreign private investment is made up of foreign direct investment, and foreign portfolio investment. Is foreign direct investment often preferred as a means of boosting the economy growth? This is because FDI disseminates advanced technological and managerial practices through the host country and thereby exhibits greater positive externalities compared with foreign portfolio investment which may not

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involve positive transfers, just being a change in ownership. In addition, available data suggest that FDI flows tend to be more stable compared to foreign portfolio investment (Lipsey, 1999). This is because of the liquidity of foreign portfolio investment and the short time horizon associated with such investments. Also, FDI inflows can be less affected by change in national exchange rates as compared to foreign portfolio investment. However, a balanced combination of the two, taking into consideration the unique characteristics of the recipient economy will bring about the required effects on the economy. The benefits of foreign private investment include transfer of technology, higher productivity, higher incomes, more revenue for government through taxes, enhancement of balance of payments ability, employment generation, diversification of the industrial base and expansion, modernization and development of related industries. Financial transfer considered in this paper as remittances, is a transfer of money by a migrants or a foreigner worker to his or her home country. Money sent home by migrants constitutes the second largest financial inflows to many developing countries, exceeding international aid. Are remittances contributed to economic growth and to the livelihoods of people in SSA countries? Moreover, remittance transfers can also promote access to financial services for the sender and recipient, thereby increasing financial and social inclusion. Remittances also foster, in the receiving countries, a further economic dependence on the global economy instead of building sustainable, local economies. Remittances to Sub-Sahara African countries play an important role to national economies, but little data exists as many rely on informal channels to send money home (Carrasco and Ro, 2007). Are financial flows (FDI, aids, and financial transfers) helped to channelize economic growth? Is human capital (education and health) driving economic growth? This paper attempts to address the following questions: are foreign direct investment, aids, financial transfers and human capital major engines for attending economic growth in SSA countries? Is there a relationship between foreign direct investment, aids, financial transfers, human capital and economic growth in SSA countries? This paper therefore aims at examining the contribution of financial flows and human capital to the economic growth in the Sub-Sahara African countries. This is done by means of cross-country regressions for the period 1985-2010. Thus, the contents of the paper are as follows. Section 2 presents a review of the literature. Section 3 presents the methodology, the econometric model, and the data. The empirical results are presented in section 4, and the last section concludes.

2. Review of the Literature The works of Solow (1956) and Swan (1956) gave rise to a wide range of literature on economic growth and this in turn fueled the debate on the capital production relationship and the properties that determine market equilibrium. Taking as a starting point the inter-temporal optimization approach of Ramsey (1928), the contributions of Cass (1965) and Koopmans (1965) effectively transformed the classical growth model into a tool capable of analyzing optimum consumer behavior. However, the limitations of the neoclassical model in explaining long term growth and the persistent interest of economists in short term phenomena led to a dearth of ideas during the 1970’s, the nature of which were to change only in the second half of the 1980’s. In the neoclassical model technological progress is exogenous and long term economic growth is directly related to diminishing returns to capital and this has limited the analytical capacity of the model and its empirical verification. The constant increase in the differences between rich and poor economies stimulated economists to look at new theories that they felt might explain this dynamic. As a direct result, a new family of theories emerged that were better capable of explaining long-term growth. Central to these models is the idea that technology is endogenous to the growth process. Since endogenous technology determines economic growth in an endogenous way they are known as “endogenous growth models”. The first of these models was published by Romer (1986). The concept of technology here depends on economic factors such the capital-labor relationship. An increase in this ratio explains not only an increase in income but also the ability to maintain high levels of growth in the long term. From the early 1990’s various studies have attempted to identify the determinants of

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economic growth. Many variables have been tested, but only a few have been accepted as being economically and statistically significant in explaining economic growth. Theorists generally accept human capital to be one of these variables. The role of human capital is almost universally regarded as being indispensable to the engine of economic growth. Right up until the second half of the 1990s the role of human capital was, in the main, linked to education, although a few authors recognized the importance of other factors such as health and nutrition. Mankiw, Romer and Weil (1992), in a groundbreaking analysis, cite the importance of including health and nutrition together with education in a broader concept of human capital. There was however a delay of several years before the link between economic growth and health became widely accepted as a licit field of economic debate. Fogel (1994), Barro and Sala-i-Martín (1995), and Barro (1996) were among the first in examining the relationship between economic growth and health, and their research has subsequently given rise to a substantial store of work focusing on the link between wealth and health. According to Anyanwu (2012), foreign direct investment, as a key element of the globalization and of the world economy, is a driver of employment, technological progress, productivity improvements, and ultimately economic growth. It plays the critical roles of filling the development, foreign exchange, investment, and tax revenue gaps in developing countries (Quazi, 2007). FDI can play an important role in the process of the development of SSA countries including: supplementing domestic savings, employment generation and growth, integration into the global economy, transfer of modern technologies, enhancement of efficiency, and raising skills of local manpower (Dupasquier and Osakwe, 2003; Anyanwu, 2003, 2012). A popular theoretical framework for FDI determinants is the “eclectic paradigm” attributed to Dunning (1977, 1993). It provides a conceptualization that groups micro- and macro-level determinants in order to analyze why and where multinational companies invest abroad. The framework posits that firms invest abroad to look for three types of advantages: Ownership (O), Location (L), and Internalization (I) advantages. Hence it is called the OLI framework. The ownershipspecific advantages allow a firm to compete with others in the markets it serves regardless of the disadvantages of being foreign. The location advantages are those that make the chosen foreign country a more attractive site for FDI than the others hence the reason for the FDI is to supply the domestic market of the recipient country through an affiliate (horizontal FDI). Internalization advantages arise from exploiting imperfections in external markets, including reduction of uncertainty and transaction costs in order to generate knowledge more efficiently as well as the reduction of state-generated imperfections such as tariffs, foreign exchange controls, and subsidies. In this case, the delocalization of all or a portion of the production process leads to low costs benefits -vertical FDI (Baniak et al, 2005; Sekkat and Veganzones-Varoudakis, 2007; Pantelidis and Nikolopoulos, 2008; Kinda, 2010; and Anyanwu, 2012). Following on these, Dunning (1993) identified four categories of motives for FDI: resource seeking, market seeking, efficiency seeking, and strategic-asset seeking (Cleeve, 2008). The pull factors or domestic factors include economic, socio-political and structural conditions, including uncertainty, while the push factors relate to cyclical and structural conditions, irreversibility and herding (Fernandez-Arias, 1996; Fernandez-Arias and Montiel, 1996; Gottschalk, 2001). Analyzing the relationship between international capital flows and development or between foreign capital and economic growth, Reinhardt, Ricci and Tressel (2010) and Prasad, Rajan, and Subramanian (2007) revisit the Lucas (1990) paradox on “why doesn’t (bank) capital flow from rich to poor countries” and explore the role of capital account restrictions in shaping capital flows at various stages of economic development. They find that, when accounting for the degree of capital account openness, the prediction of the neoclassical theory is confirmed: less developed countries tend to experience net capital inflows and more developed countries tend to experience net capital outflows, conditional of various countries’ characteristics. A main argument for aid, at least on economic grounds, is that it contributes to economic growth in host countries. Two researches have breathed new life into the empirical question of aid

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effectiveness. First, Burnside and Dollar (2000) find that when other determinants of growth are controlled for, especially an indicator of economic policy, aid has no effect. Aid only makes a positive contribution to economic growth in those countries with high values for the policy indicator. If policy is poor, aid is ineffective. This result is explained by the tendency of recipients, especially if they have poor policies, to divert aid to government consumption spending rather than using it to finance growthpromoting investment. Secondly, Hansen and Tarp (2001) beg to differ. Using essentially the same data for the same sample, but with different specifications and estimators, they find that aid does have a positive effect on economic growth and this result is not conditional on policy. There are a few researches which examine the relation between foreign aid and FDI by using cross-country panel data, most notably Kimura and Todo (2010), Harms and Lutz (2006), Yasin (2005), and Karakaplan et al (2005). Kimura and Todo (2010) investigate whether and how foreign aid facilitates FDI flows into less developed countries and find that foreign aid in general does not have any significant effect on FDI but when they allow for differences in the size of aid effects across donor countries, they find robust evidence that foreign aid from Japan in particular has a vanguard effect. That is, Japanese aid promotes FDI from Japan but does not attract FDI from other countries. Their finding is consistent with Blaise (2005) who uses province-level data for China and finds that Japanese aid in China has a positive and significant impact on the locational choice of Japanese private investors in China. On the other hand, Harms and Lutz (2006) find that the effect of aid on FDI is generally insignificant but significantly positive for countries in which private agents face heavy regulatory burdens. Yasin (2005) empirically investigates the relationship between official development assistances and foreign direct investment flows using panel data from 11 Sub-Sahara African countries for the period 1990-2003. The results show that bilateral official development assistance has a significant and positive influence on foreign direct investment flows while multilateral development assistance does not have a statistically significant effect on foreign direct investment flows. Karakaplan et al (2005) also find an insignificant effect of aid on FDI, but that good governance and developed financial markets lead to a positive effect of aid. Anyanwu (2012) results from cross-country regressions for the period 1996-2008 indicate that: there is a positive relationship between market size and FDI inflows and openness to trade has a positive impact on FDI flows. Also higher financial development has negative effect on FDI inflows and the prevalence of the rule of law increases FDI inflows. Then higher FDI goes where foreign aid also goes and agglomeration has a strong positive impact on FDI inflows. Last, natural resource endowment and exploitation (such as oil) attracts huge FDI and East and Southern African sub-regions appear positively disposed to obtain higher levels of inward FDI. Sub-Sahara Africa is especially susceptible to climatic and agricultural risk and especially vulnerable to terms of trade shocks, famines, political conflict, droughts and, more recently, floods. Guillaumont et al (1999) find that SSA has higher levels of primary instabilities (political, climatic and terms of trade) than other developing country regions. Such vulnerability is a source of ‘economic uncertainty’ that may reduce growth rates and help to explain aid ineffectiveness. Lensink and Morrissey (2000) use aid instability, deviations of aid from a trend incorporating adaptive expectations, as a measure of uncertainty. There is related evidence for the importance of instability or uncertainty in SSA. Gyimah-Brempong and Traynor (1999) find that political instability has a direct negative effect on growth and also an indirect effect via discouraging investment. Guillaumont et al (1999) find that primary instabilities in SSA reduce growth by distorting economic policy; the rate of investment is volatile, hence the growth rate is lowered. According to Gani (2011) remittances can influence economic growth in two main ways. First, financial capital that is transferred to home country can contribute to domestic private investment required for economic development. Second, the growth effect can be looked at from the monetary side. The theoretical argument is that remittance increases the supply of money and an expanded supply of money in circulation increases the availability of loanable funds, which lowers the interest rates. If the transfers are deposited in the destination country currency accounts, the banking system is likely to experience an increase in liquidity and domestic credit will probably expand. This can aid

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economic growth as more liquidity in the banking sector encourages borrowing, which then gets invested. Gani (2011) examines the effect of remittances on economic growth of small states using the general aggregate production function and finds a positive but statistically insignificant coefficient of remittances. Many other researches agree that remittances are primarily used for household expenditures, such as the construction of homes and consumption (Black, King and Tiemoko, 2003; Martinez and Villa, 2005). These financial transfers have consequences at both the household level and at the level of the economy as a whole, affecting macroeconomic management, labor force participation, education and health outcomes, income distribution and patterns of household expenditure. Several studies have been undertaken to test the impact of remittances on GDP growth. Their results have been mixed. Faini (2002, 2003) finds a positive relationship between growth and remittances using cross-country data. Bougha-Hagbe (2004) finds that increased construction activity is correlated with remittances. Chami et al (2005), using a panel of data for 113 developing countries find that, remittances have a negative effect on economic growth. They argue that receiving remittances might lower recipient households’ labor force participation or savings rates and limit their job search efforts. Stark, Helmenstein and Prskawetz (1998), Vidal (1998), and Beine (2001) find that migration prospects could foster human capital formation and economic growth in sending countries. Education and health are both components of human capital and contributors to human welfare. As Sub-Sahara Africa (SSA) has found since 1980, slow economic growth severely limits the ability of governments and households to fund further investments in health and education. Low investments in human capital may impinge on already low growth rates of income. Such interrelations might be thought to imply a vicious circle of development, but this should not be overstated. Poor SSA countries have considerable discretion over how much to invest in health and education. According to Adelakun (2011), as the global economy shifts towards more knowledge based sectors (the manufacture of ICT based services, R&D) skills and human capital development becomes a central issue for policy makers and practitioners engaged in economic development, both at the national and regional levels. Yet, the impact of education and vocational training activities exert upon changing national and regional economies remains less than thoroughly explained and analyzed. Since the introduction of human capital theory in the 1960s, a number of studies have attempted to address this and other related issues. Human capital theory views schooling and training as investment in skills and competences (Schultz, 1961). It is argued that based on national expectation of return on investment, individuals make decisions on the education and training they receive as a way of augmenting their productivity. Contemporary discussions on human capital development and economic growth have been dominated by three theories: human capital theory, modernization theory and dependence theory. The human capital theory shows how education leads to increase in productivity and efficiency of workers by increasing the level of their cognitive skills. Mincer (1958), Schultz (1961), and Becker (1964) introduced the notion that people invest in education or as to increase their stock of human capabilities which can be formed by combining innate abilities with investment in human beings. The modernization theory focuses on how education transforms an individual’s value, belief and behavior. Exposure to modernization institutions such as schools, factories, and mass media inculcate modern values and attitudes. The attitude include openness to new idea, independences from traditional authorities, willingness to plan and calculate further exigencies and growing sense of personal and social efficacy. According to the modernization theorists, these normative and attitudinal changes continue throughout the life cycle, permanently altering the individual’s relationship with the social structure. The greater the number of people exposed to modernization institutions, the greater the level of individual modernity attained by the society. Educational expansion through its effects on individual values and benefits sets in motion the necessary building blocks for a more productive workforce and a more sustained economic growth. The dependence theory arose from conceptualizations based on the dynamic world system that structures conditions for economic transformation in both the core and periphery of the world economy. Certain features of the world

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polity such as state fiscal strength, degrees and regime centralization and external political integration may contribute to economic growth in the developing world. How does health affect growth? Scheffler (2004) identified three mechanisms by which education and health become important in what is called the demographic transformation -the link of health and economic growth. First, as health improves, the infant mortality rates drop. He tried to link this with the attitudes of people in developing countries where they have large families because of high infant mortality. With better educated population, they tend to become healthier and more resources are expended on the fewer family rather than on multitude. This is very important to the economic growth model. Another fact that is well known is that ill health is a major cause of poverty, and not the reverse. When one is sick, there is high tendency for the person to become poor and become poorer if the sickness persists. Hence, the common parlance - health is wealth. Finally, the quantum of education a woman receives reduces the cost of making the population healthy. An educated female in a population increase the health of the family. This increases the output of the family and subsequently increases the national output. This is an indication that educated female are critical to the process of economic growth. Therefore, health as human capital affects growth directly through, for example, its impact on labor productivity and the economic burden of illness. Health is so important as both a source of human welfare and a determinant of overall economic growth (Ogundipe and Lawal, 2011). Analyzing the effect of health on economic growth, Bloom, Canning and Sevilla (2004), find that good health has a positive, sizable, and statistically significant effect on aggregate output. Therefore, they have suggested that a one year improvement in a population life expectancy contribute to a 4% increase in output. So that health is one of the direct source of human welfare and also an instrument for raising income levels and economic growth.

3. Methodology 3.1. Empirical Model Based on the theoretical framework presented above and the structure of Sub-Sahara African economies as well as the characteristics of financial and human capital inflow to Africa, the empirical analysis is carried by estimating variables of the following model: (1) Where the subscript i from 1 to 28 denote countries and t from 1985 to 2010 denotes times. The variable gdpcit, represents gross domestic product per capita, is used as dependant variable to capture the economic growth of the SSA countries under study. The explanatory variables, Popit, Urbpopit, infit, Hcapit, Ecapit, Lexpectit, Aidit and fdiit, represent respectively: total population, urban population, inflation, health public expenditure, education public expenditure, life expectancy, public aid and foreign direct investment of the selected SSA countries. The coefficients coefficients to estimate.

and

are the associate

represent the individual specific effect and the rest of the error term.

3.2. Data Sources The analysis is conducted for a sample of 28 Sub-Sahara African countries with data from 1985-2010. These data are from UNCTAD, IMF, World Bank. The SSA countries in the sample are: Angola, Benin, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo (Republic), Côte d’Ivoire, Democratic republic of Congo (DRC), Djibouti, Equatorial Guinea, Gabon, Ghana, Kenya, Lesotho, Madagascar, Mali, Mauritania, Niger, Nigeria, Rwanda, Senegal, Sudan, Tanzania, Togo, Uganda, and Zambia.

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4. Empirical Results Equation (1) is estimated using panel analysis with data over the period 1985 to 2010 for 28 selected SSA countries. The empirical results are reported in the following Tables. Table 1:

Fixed effects estimation of the effect financial flows and human capital on growth

Fixed-effects (within) regression Group variable: id R-sq: within = 0.1180, between = 0.0328 overall = 0.0070 corr(u_i, Xb) = -0.6103 gdpc Coef. fdi -16.59409 lexpect 31.90574 hcap -229.0928 pop 45.60792 urbpop -.0000666 inf -.0292065 Ecap 2.957845 aid -5.91e-08 Cons -818.3779 sigma_u= sigma_e= 1195.2326 564.20842 Source: author’s calculation

Std. Err. 2.758132 6.996802 32.78276 16.92826 .0000337 .0485228 9.113325 2.13e-07 351.3047 rho =.81777496

Number of obs = 727 Number of groups = 28 Obs per group: min = 25, avg = 26.0 max = 26 F(8,691) = 11.55 Prob > F = 0.0000 t P>t -6.02 0.000 4.56 0.000 -6.99 0.000 2.69 0.007 -1.98 0.048 -0.60 0.547 0.32 0.746 -0.28 0.782 -2.33 0.020 F test that all F(27, 691) = u_i=0: 56.63

[95% Conf. Interval] -22.00942 -11.17877 18.1682 45.64328 -293.4585 -164.727 12.37092 78.84492 -.0001328 -4.68e-07 -.1244762 .0660633 -14.93528 20.85097 -4.78e-07 3.59e-07 -1508.131 -128.6252 Prob > F = 0.0000

Results from Table 1 show that the variables, total population, urban population, health public expenditure, life expectancy and foreign direct investment are significant. Their p-values are less than 5%. The explanatory variables, total population, public education expenditure and life expectancy affect positively the dependent variable, economic growth. Their associate coefficients β1 = 45.60792, β5 = 2.957845, and β6 = 31.90574 are positive. The independent variables, health public expenditure, urban population, inflation, and foreign direct investment are negatively correlated with economic growth (β4 = -229.0928, β2 = -.0000666, β3 = -.0292065, and β8 = -16.59409). This result shows that foreign direct investment negatively affects gross domestic product per capita (economic growth). This is not corroborated with theory, but can be explained by the fact that foreign direct investment at a given time t does not impact gross domestic product at the same period but at the periods after t+1 or t+2. Urban population has practically the same effect. While urban population influences gross domestic product, this variable has approximately a null effect on the gross domestic product per head. This can be explained by the fact that the dependent variable gross domestic product per capita is measured by divided gross domestic product over total population. Total population, public education expenditure and life expectancy impact positively the dependent variable, economic growth. Indeed high life expectancy means that the total active population is stable, healthy and can be well educated. An increase in one unit of foreign direct investment and in one unit of public health capital show certeris paribus respectively a decrease of gross domestic product per capita of 16 and 229 units. But an increase in one unit of total population and in one unit of life expectancy respectively show an increase of 45 and 32 units of gross domestic product per capita. With fixed effects model, the key human capital variables, total population and life expectancy, work for economic growth in the 28 selected SSA countries.

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139 Table 2:

Random effects estimation of the effect financial flows and human capital on growth

Random-effects GLS regression Group variable: id R-sq: within = 0.1095 between = 0.0017 overall = 0.0154 Random effects u_i ~ Gaussian corr(u_i, X) = 0 (assumed) gdpc Coef. Std. fdi -15.11764 lexpect 34.14081 hcap -198.0712 pop 4.60919 urbpop -4.29 e-07 inf -.0372308 Ecap -.5461387 aid -6.19 e-08 Cons -674.0017 sigma_u= sigma_e = 819.96901 564.20842 Source: author’s calculation

Err. 2.769577 6.864872 31.62893 13.32083 .000029 .0488784 8.88454 2.15e07 379.3252

Number of obs Number of groups Obs per group: min avg max Wald chi2(8) Prob > chi2 z -5.46 4.97 -6.26 0.35 -0.01 -0.76 -0.06 -0.29 -1.78

= 727 = 28 = 25 = 26.0 = 26 = 81.02 = 0.0000 P>z [95% Conf. Interval] 0.000 -20.54592 -9.689372 0.000 20.68591 47.59571 0.000 -260.0628 -136.0797 0.729 -21.49915 30.71753 0.988 -.0000573 .0000564 0.446 -.1330307 .0585692 0.951 -17.95952 16.86724 0.773 -4.83e-07 3.60e-07 0.076 -1417.465 69.46193

Rho = .6786742

Empirical results from Table 2 show that the independent variables, health public expenditure, life expectancy and foreign direct investment are significant. Their p-values are less than 5%. The explanatory variables, total population and life expectancy have a positive relationship with the dependent variable, economic growth. Their estimated associate coefficients are positive (β2 = 4.60919, β6 = 34.14081). The variables, urban population, inflation, health public expenditure, public education expenditure, and foreign direct investment, are negatively in relationship with the dependent variable, economic growth (β2 = -4.29 e-07, β3 = -.0372308, β4 = -198.0712, β5 = -.5461387, and β8 = 15.11764). With random effects model an increase in one unit of foreign direct investment and in one unit of public health capital show certeris paribus respectively a decrease of gross domestic product per capita of 15 and 198 units. An increase in one unit of total population and in one unit of life expectancy respectively show an increase of 4.6 and 34 units of gross domestic product per capita. The key human capital variables, total population and life expectancy, work for economic growth in SSA countries. We conduct the following tests to analyze the significance of the variable and the estimators, the variance, the variability and to choose the model. First, the least square with dummy variable (LSDV) regression is an ordinary least square (OLS) with dummy variable (Table 3). In the model (1) above, we could rewrite the αi terms as coefficients on a set of dummy variables indicating membership in cross-sectional unit i and estimate the model simply by including the appropriate dummy variables. A dummy variable is a binary variable that is coded either 1 or 0. It is commonly used to examine group and time effects in regression. Conducting the OLS dummy variable estimation in this paper, all the explanatory variables except public education expenditure are significant. For example, public aid is significant at 10%. Table 3:

gdpc fdi Lexpect Hcap

Ordinary least square (OLS) with dummy variable Linear regression, absorbing F( 8, 691) = 5.59 R-squared = 0.7423 Coef. Std. Err. -16.59409 6.008171 31.90574 7.611817 -229.0928 70.02499

indicators Prob > F = 0.0000 Adj R-squared = 0.7293 t -2.76 4.19 -3.27

Number of obs = 727 Robust Root MSE = 564.21 P>t 0.006 0.000 0.001

Journal of Money, Investment and Banking - Issue 26 (2012) Table 3:

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Ordinary least square (OLS) with dummy variable - continued

pop 45.60792 Urbpop -.0000666 inf -.0292065 Ecap 2.957845 aid -5.91e-08 cons -818.3779 id absorbed Source: author’s calculation

13.41156 .0000233 .0127117 4.094608 3.37e-08 333.7536

3.40 -2.85 -2.30 0.72 -1.76 -2.45

0.001 0.004 0.022 0.470 0.080 0.014 (28 categories)

Second, the Hausman specification test (Table 4) is a statistical hypothesis test in econometrics which evaluates the significance of an estimator versus an alternative estimator. This test helps us here to evaluate if a statistical model corresponds to the data. Since the p-value is less than 5%, the Hausman test accepts the fixed effects estimation. Table 4:

Hausman test Coefficients (b) (B) eq1 eq2

fdinv lexpect hcap pop1 urbpop inf Ecap aid

-16.59409 31.90574 -229.0928 45.60792 -.0000666 -.0292065 2.957845 -5.91e-08

-15.11764 34.14081 -198.0712 4.60919 -4.29e-07 -.0372308 -.5461387 -6.19e-08

(b-B) Difference -1.476449 -2.235068 -31.02153 40.99873 -.0000662 .0080243 3.503983 2.83e-09

sqrt(diag(V_b-V_B)) S.E. . 1.352324 8.620931 10.44613 .0000172 . 2.029196 .

b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test:

Ho:

difference in coefficients not systematic chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 13.67 Prob>chi2 = 0.0335 (V_b-V_B is not positive definite)

Source: author’s calculation

Thirst, the analysis of the variance (ANOVA) permits us to capture the between and within variability (Table 5). Between group variability and within group variability are both components of the total variability in the combined distributions. What we are doing when we compute between and within variability is to partition the total variability into the between and within components. So that: between variability + within variability = total variability. Results in the Table 5 show that, the between variability is greater than the within variability. Then the random effects estimation also gives consistent estimations.

Journal of Money, Investment and Banking - Issue 26 (2012)

141 Table 5:

Between and within variability analysis Min

Max

gdpc

Variable overall between within

640.5251

Mean

Std. Dev. 1083.814 928.024 585.6957

82.67167 138.2401 -1977.908

8845.414 4367.856 6336.79

Observations

fdinv

overall between within

3.452014

8.707035 4.143039 7.695137

-8.589392 .072624 -21.78792

145.2019 20.93094 127.723

lexpect

overall between within

51.08933

5.632203 4.698075 3.226268

26.81871 43.09548 34.81256

66.46707 60.57347 63.05097

N = n = T =

728 28 26

hcap

overall between within

2.235013

1.118276 .8746821 .7153886

.1463853 .9609289 .3310922

8.451191 4.389147 6.657147

N = n = T =

728 28 26

pop1

overall between within

16.16688

22.6116 22.31672 5.511352

.32 .6150769 -18.7609

156.051 113.3628 58.8551

N = n = T =

728 28 26

urbpop

overall between within

5210649

9582865 9200769 3176196

93602.62 184605.3 -1.67e+07

7.76e+07 4.92e+07 3.37e+07

N = n = T =

728 28 26

inf

overall between within

60.85675

477.5028 194.5733 437.5524

-16.715 1.470808 -915.1751

9796.9 980.4749 8877.282

N = n = T =

728 28 26

Ecap

overall between within

4.819834

6.411501 4.762182 4.382787

.58022 .9784262 -11.73165

73.43777 26.20521 52.0524

N = n = T =

728 28 26

aid

overall between within

3.57e+07

1.15e+08 5.06e+07 1.03e+08

-3.71e+07 72692.31 -1.02e+08

2.03e+09 1.92e+08 1.92e+09

N = n = T =

728 28 26

N = n = T =

728 28 26

N = 727 n = 28 T-bar = 25.9643

Source: author’s calculation

And last, the Breusch-Pagan test (Table 6) is now used to test for heteroscedasticity in a linear regression model. It tests whether the estimated variance of the residuals from a regression are dependent on the values of the independent variables. Table 6:

Breusch Pagan Test

Breusch and Pagan Lagrangian multiplier test for random effects gdpc[id,t] = Xb + u[id] + e[id,t] Estimated results: Var gdpc e u Test:

1175856 318331.1 672349.2

sd = sqrt(Var) 1084.369 564.2084 819.969

Var(u) = 0 chi2(1) = Prob > chi2 =

3494.93 0.0000

Source: author’s calculation

The Breusch Pagan test shows us that the random effect is significant (p-value less than 5%). So that the random effect estimation is appropriate in this research. Fixed effects estimation, random effects estimation and pool estimation have been used in this paper for these following reasons: the Hausman test accepts the fixed effects estimation. It is for this reason that we used the estimated coefficients to conduct the analysis. However the between variability of many variables are greater than the within variability and the Breusch Pagan test accepts the presence of the specific random effect estimation.

Journal of Money, Investment and Banking - Issue 26 (2012)

142

5. Conclusion This paper used panel data from a sample of Sub-Sahara African countries for the 1985-2010 periods to investigate the empirical effect of capital flows and human capital on the economic growth. We find that there is a positive relationship between total population, life expectancy and the gross domestic product per capita. Total population and life expectancy are key engines helping for economic growth in the 28 selected Sub-Sahara African countries. Our main result is that good health has a positive and statistically significant effect on economic growth. High life expectancy is related to good health and means that the total active population can be stable, healthy and can be well educated. It suggests that a one-year improvement in a population’s life expectancy contributes to an increase of 34 units of gross domestic product per capita. This is a relatively large effect corroborated to Bloom Canning and Sevilla (2004) findings, indicating that increased expenditures on improving health might be justified purely on the grounds of their impact on economic growth. Thus improvements in health may increase life expectancy and gross domestic product not only through labor productivity, but also through the accumulation of capital.

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