Human capital contribution to economic growth in Sub-Saharan Africa: Does health status matter more than education?

Economic Analysis and Policy 58 (2018) 131–140

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Human capital contribution to economic growth in Sub-Saharan Africa: Does health status matter more than education? Kolawole Ogundari a, *, Titus Awokuse b a b

Department of Applied Economics and Statistics, University of Delaware, Newark DE, USA Department of Agriculture, Food and Resource Economics, Michigan State University, MI, USA

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Article history: Received 17 November 2016 Received in revised form 23 October 2017 Accepted 5 February 2018 Available online 8 February 2018 Keywords: Human capital Education Health Economic growth Sub-Saharan Africa

a b s t r a c t This paper revisits the debate on the possible impact of human capital on economic growth in Sub-Saharan Africa (SSA) and considers two alternative measures of human capital: health and education. The study employs a dynamic model based on the system generalized method of moments (SGMM) and analysed a balanced panel data covering 35 countries from 1980–2008. The empirical results show that the two measures of human capital have positive effects on economic growth, although the contribution of health is relatively larger than the impact of education. This finding emphasizes the importance of both measures of human capital and aligns with the argument in the literature that neither education nor health is a perfect substitute for the other as a measure of human capital. Published by Elsevier B.V. on behalf of Economic Society of Australia, Queensland.

1. Introduction In the development economics literature, human capital is viewed as an important driver of economic growth (Romer, 1986; Mankiw et al., 1992; Barro and Sala-i-Martin, 2004; Gyimah-Brempong and Wilson, 2004; Hanushek and Woessmann, 2008; Hartwig, 2010; Qadri and Waheed, 2014). The potential positive labour productivity effect of investment in human capital makes it an issue of high relevance for development policy in Africa. Nevertheless, the concept of human capital is complex and multidimensional. Schultz (1961) and Becker (1964) define human capital as the set of knowledge, skills, competencies and abilities that are embodied in individuals and which individuals acquired over time, through training, education, work experience, medical care and migration. Human capital can thus be divided into three key components: health, education and experience/training; and its stock could increase through better education, higher health status and new learning. Possibly because new learning and training cannot be measured easily, health and education statuses have been the more commonly used human capital measures in previous empirical studies on the relationship between human capital and economic growth. Bloom et al. (2004) argue that adequate education and good health spur a more productive labour force that could consequently stimulate national economic growth. Ogundari and Abdulai (2014) show that a better educated and healthier workforce are more likely to create and adopt new technologies. Better health, according to Mayer-Foulkes (2001), increases workforce productivity and wages by reducing incapacity, debility, and the number of days lost to sickness. In contrast, poor health and corresponding loss in work-hours corresponds to a decline in workers’ physical and mental capacities, productivity and overall wages. According to Thomas and Frankenberg (2002), healthier individuals have higher

*

Corresponding author. E-mail address: [email protected] (K. Ogundari).

https://doi.org/10.1016/j.eap.2018.02.001 0313-5926/Published by Elsevier B.V. on behalf of Economic Society of Australia, Queensland.

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life expectancy – which stimulates growth by accelerating demographic transition (Weil, 2007) – and thus have greater incentives to invest in training and in the acquisition of improved skills. Of the two commonly used measures of human capital, however, education has often been viewed as a more important source of human capital accumulation. This is because in a knowledge economy, education plays the crucial role of providing the highly skilled human capital needed for job creation, economic growth, and prosperity of the individual and the society (Pegkas and Tsamadias, 2014). As such, it has often been used in empirical research as a factor of production besides labour and capital (Lucas, 1988). Thus, less emphasis has been devoted to the contribution of health status on economic growth. Most of the existing empirical studies on the human capital–economic growth nexus focused only on the one-to-one relationship between education and economic growth on the one hand, and health and economic growth on the other (Aka and Dumont, 2008). Bloom et al. (2004) observed that most cross-country studies identify human capital mostly with education. They argue that ignoring health as a crucial aspect of human capital makes policy discussion for economic growth less comprehensive. As such, a meta-analysis of the literature by Benos and Zotou (2014), reviewed 57 macro-level studies that examined only the effect of education on economic growth for a cross-section of countries. Few of the studies in the literature have examined the joint growth effects of both health and education measures of human capital. Thus, many previous studies on this issue may suffer from omitted variable bias, as neither education nor health is a perfect substitute for the other as a measure of human capital. The joint inclusion of education and health measures of human capital would allow for more accurate estimates and inference on assessing the contribution of human capital to economic growth and would help address omitted variable issues in previous studies (Glewwe et al., 2014; Aka and Dumont, 2008). For instance, Li and Liang (2010) in their study found a positive impact of schooling on economic growth and the effect reduced with the addition of variable representing health. A careful review of the human capital–economic growth literature that focuses on the sub-Saharan Africa (SSA) region shows that almost all the previous cross-country analyses only investigated the impact of education on economic growth. To our knowledge, only Gyimah-Brempong and Wilson (2004) and Gyimah-Brempong et al. (2006) have attempted to investigate the contribution of both health and education measures of human capital to economic growth for a panel of SSA countries. Glewwe et al. (2014) reviewed macro-level studies that estimate the impact of education on SSA economic growth and found that the impact of education on economic growth in SSA is lower than in other countries, likely due to lower quality of schooling. A few studies investigated the effects of health on economic growth at the individual country level (Ndiyo, 2007; Onisanwa, 2014; Babatunde, 2014). However, the estimated results from cross-country analysis are more useful sources of empirical evidence for making crucial policy decisions at the regional level (Ogundari et al., 2016). Unfortunately, only a limited number of cross-country studies have been conducted for SSA, hence limiting evidenced-based policy discussions and decisions on the role of human capital in the context of SSA (Glewwe et al., 2014). The present study revisits the relationship between human capital and economic growth in the SSA region and contributes to the existing literature in several ways. First, the study adds new cross-country evidence to the few existing studies that have so far investigated the link between human capital and economic growth in SSA countries. Second, this current analysis addresses the omitted variable bias in previous studies by simultaneously estimating the impact of both education and health measures of human capital. Third, this study contributes to the literature in terms of the broadness of the time period covered. As mentioned earlier, the two previous studies that focused exclusively on a sample of 21 countries from the SSA region (Gyimah-Brempong et al., 2006; Gyimah-Brempong and Wilson, 2004) used data covering 1960–2000 while this current study utilizes a larger sample of data covering 33 SSA countries over 1980–2008. The main objective of the study is twofold. First, we reexamine the nature of the relationship between human capital and economic growth for a comprehensive panel of countries in SSA.1 Second, we account for some limitations in previous studies jointly estimating the parameters for two common measures of human capital. Specifically, we investigate which of the two forms of human capital (i.e. health and education) contributes most to economic growth in the SSA region. The preliminary results from our empirical analysis indicate that education and health measures of human capital have positive and statistically significant effects on economic growth. We also found that the contribution of health to economic growth is relatively larger than the impact of education. This finding emphasizes the importance of both measures of human capital and aligns with the argument in the literature that neither education nor health is a perfect substitute for the other as a measure of human capital. Thus, joint inclusion of both variables in growth regression models should be preferred in order to address omitted variable issues. The rest of the paper is organized as follows: Section 2 contains a brief overview of human capital and economic development in the SSA region, while Section 3 presents an overview of the theoretical framework and the empirical model. Section 4 describes the variable definition and data sources and Section 5 contains a discussion of the empirical results. Lastly, Section 6 presents concluding remarks. 2. Human capital and economic development in SSA Economic theory suggest that sustained growth in the Gross Domestic Product (GDP) as a key component of economic development. Although varying greatly among SSA countries, GDP per capita growth has been on the increase in the region 1 This is necessary to ascertain whether the general concession in most of the literature that human capital contributes positively to economic growth exists in the region based on current extended data.

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since the late 1990s. King and Ramlogan-Dobson (2015) observed that a significant number of African economies have recorded strong GDP growth since According to the World Bank (2015), SSA economies experienced an average GDP growth of about 4.5% in 2013 and 4.2% in 2014. Sala and Trivin (2014) observe that the region’s cumulative growth of real GDP per capita has significantly risen, from about 3.5% in the 1980s to the phenomenal level of 29% in the 2000s. To provide some context, we compare SSA’s GDP per capita growth with those of other regions worldwide. Over the period 1980–2000, the average growth rate for East Asia was 4.9%, Latin America 0.5%, Middle East 1.2%, and South Asia 3.6% (Glewwe et al., 2014; Sala and Trivin, 2014). As some of the SSA countries only recently emerged from civil wars that set back their development, the implementation of structural reforms as well as greater priority for public spending on health, education and other social services stimulated the GDP per capita growth in recent decades (Calamitsis et al., 1999). UNESCO (2010) reported that the primary school enrolment ratio in SSA (an important index of human capital) increased dramatically from an average of 56% to 73% in less than a decade, between the early 2000s and 2010. Despite the upturn in economic growth rates, economic and social conditions in SSA countries remain poor and fragile. Based on endogenous growth theory, investment in human capital should stimulate innovation and encourage the adoption of modern, output-increasing technologies that should consequently engender economic growth. Relative to the rest of the world, investment in education is low in the SSA region. For example, countries in Asia and Latin America made much larger investment than SSA countries in primary school education (Gyimah-Brempong et al., 2006). Over the period covering 1980– 2000, the primary gross enrolment rate in SSA declined from 80% to 77%, reflecting lower investment in education (Glewwe et al., 2014). For East Asia, Latin America, Middle East and South Asia, however, this statistic increased or held steady at about 111%, 127%, 87%, and 98%, respectively (Glewwe and Kremen, 2006). SSA countries’ expenditures on their health sectors have, however, been higher than those spent on their education sectors. As revealed in Fig. 1, which shows the SSA education and health expenditure shares of GDP for the period 1999–2010, expenditure on health is about 60% higher than expenditure on education. 3. Theoretical framework and empirical model 3.1. Theoretical framework In the Solow (1956) neoclassical growth model, per capita gross domestic product (GDP) is specified as a function of human capital, technology, labour and physical capital. The Solow model is widely known and has undergone a series of modifications, so the present study follows an augmented version as shown below (see: Dulleck and Foster, 2008; Tiwari and Mutascu, 2011)2 :

∆yit = f (hit , kit , yit −1 , zit )

(1)

where ∆yit denotes growth in real GDP per capita, hit is a vector of education and health human capital defined as hit ≈ Eduit , Healthit ; kit denotes physical capital; yit −1 represents lagged real GDP per capita (i.e. initial real GDP per capita); zit is a vector of other control macroeconomic variables contributing to ∆yit (e.g. population growth, trade openness, and democracy index). 3.2. Empirical model specification The study follows a dynamic model specification that explicitly describes the endogenous growth regression model of Eq. (1). Similar to previous studies, we employ a dynamic specification to estimate the parameters of Eq. (1) because causal relationship between economic growth and policy variables, such as education and health human capital, follows a dynamic causal structure (Belke and Wernet, 2015). To this end, the dynamic model for the study is specified as:

∆yit = φi ∆yit −1 + τi hit −1 + βi kit −1 + ϕi yit −1 + δi zit + γi + ηit

(2)

where ∆yit represents economic growth defined as growth in real GDP per capita; lagged human capital, denoted by hit −1 , is represented by education and health; kit −1 is lagged physical capital and is defined as investment share of purchasing power parity (PPP) adjusted GDP per capita; yt −1 is lagged per capita GDP; γi denotes country specific effects; and ηit is the error term of the regression. We also control for structural differences across countries by including relevant macroeconomic determinants of ∆yit represented by zit .These macroeconomic variables include population growth, trade openness, and democracy index (a measure of civil liberties and political institutions). The choice of these control variables was guided by previous studies from the region and other developing countries with common economic and human development patterns similar to the SSA (see: Li and Liang, 2010; Siddiqui and Rehman, 2016; Sala and Trivin, 2014; Bloom et al., 2004; GyimahBrempong et al., 2006; Gyimah-Brempong and Wilson, 2004). Also, consistent with previous studies using a dynamic model specification (Glewwe et al., 2014), we included a lag of the dependent variable. 2 Detail description of the model can be found in Sala-i-Martin (1996), Barro and Salai-i-Martin (1992), Bassanini et al. (2001).

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Fig. 1. Education and health expenditure share of GDP in SSA. Source: World Bank (2015).

The three alternative measures of education used in the study were school enrolment ratio (i.e. enrolment for primary, secondary and tertiary levels), average years of schooling of the adult population, and government expenditure on education. For the indicator of health, we follow previous studies (Bloom et al., 2004) by using life expectancy. Higher life expectancy is generally associated with better health status and lower morbidity (Murray and Lopez, 1997). The inclusion of the trade openness variable is important because of the well-documented positive effect of trade expansion on economic growth. Import and export growth can stimulate GDP growth via its beneficial impact in encouraging efficient resource allocation, greater capacity utilization, exploitation of economies of scale and promotion of technological improvement due to foreign market competition (Helpman and Krugman, 1985; Awokuse, 2003, 2008). Following Bloom et al. (2004), we also include a democracy index to capture the effect of the quality of political institutions on economic growth via the provision of social stability and public services and the enforcement of private contracts. The population growth variable captures the contribution of the labour force and the effect of capital diffusion as the contribution of capital available to each worker shrinks as a country’s population increases (Weil, 2013). Lastly, lag of GDP per capita (yt −1 ) was included to test the convergence hypothesis that over time countries with lower per capita income tend to have more rapid growth in GDP per capita relative to richer countries (Hanushek, 2013). Eq. (2) is estimated using the system-generalized method of moments (SGMM) estimator for dynamic panel data model proposed by Blundell and Bond (1998). Hoeffler (2002) and Glewwe et al. (2014) noted that cross-country growth regression is likely to suffer from endogeneity problems besides the dynamic specification. Fortunately, the system-dynamic GMM is an appropriate estimation approach that explicitly accounts for endogeneity issues and collinearity of regressors and produces consistent estimates in the presence of endogenous regressors (Farhadi et al., 2015). Also, Hauk and Wacziarg (2009) noted that the use of the SGMM estimator can account for reverse causality by producing valid instruments under the assumption that current period shocks in the error term do not affect past values of the regressors and that the past values of the regressors do not directly affect current values of the dependent variables. Jaunky (2013) argued that the SGMM makes an exogeneity assumption where any correlation between endogenous variables and unobserved fixed effects are constant over time, allowing the inclusion of level equations in the system and the use of lagged differences as instruments for the levels. The system-dynamic GMM is also able to overcome the econometric problems of cross-sectional dependence of countries and multi-correlation that are prevalent in macro panel models (Arellano and Bond, 1991). 4. Data and sources We employ a balanced panel data over 28 years (1980–2008) for 35 countries in SSA.3 Data were obtained from the Penn World Table (PWT) database (PWT, 2013)4 for real GDP per capita (adjusted by PPP), investment share of real GDP per capita, trade openness, and population size (for the construction of population growth). Data on health measures such as life expectancy at birth were taken from the World Development Indicators (World Bank, 2012). Enrolment ratios for primary, secondary, and tertiary levels, average years of schooling, government expenditure on education, and democracy index were obtained from the CANA database (Castellacci and Natera, 2011). Table 1 presents the summary statistics of the variables used in the study. Since there is no consensus on what constitutes education human capital, the study uses different measures of education to provide opportunities for cross-comparison and robustness check as noted by Siddiqui and Rehman (2016). To this end, we employ education enrolment ratio, average of education, and government expenditure on education as proxies for education 3 The countries are Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Chad, Côte d’Ivoire, Ethiopia, Gabon, Gambia, Ghana, Guinea, GuineaBissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, South Africa, Swaziland, Tanzania, Togo, Uganda, Zambia and Zimbabwe. 4 The PPP adjustment was based on 2005-dollar prices.

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Table 1 Descriptive statistics of the variables in empirical model. Variables

Description

Mean

Std. Dev.

GDP

Real Gross Domestic Product (GDP) per capita adjusted by PPP at 2005 constant price Per capita annual GDP growth, 1980–2008, as a percentage Ratio of total enrolment, regardless of age, to the population in the age group that officially corresponds to the primary level Ratio of total enrolment, regardless of age, to the population in the age group that officially corresponds to the secondary level Ratio of total enrolment, regardless of age, to the population in the age group that officially corresponds to the tertiary level Investment in physical capital as share of PPP-adjusted GDP per capita in Year 2000 USD Average years of school completed in population over 14 Current and capital public expenditure on education Ratio of the sum of total imports and exports divided by PPP-adjusted GDP in year 2000 USD Average years a person may live An index with political participation tends towards +10 and autocratic tends towards -10 Population growth per annum, 1980–2008, as a percentage

1843.12

2346.84

3.3857 83.67

7.2269 28.84

25.72

18.81

4.48

4.30

19.52

11.53

4.39 3.84 64.28

1.95 1.89 33.69

51.17 −1.27

6.93 6.03

2.6737

1.1223

GDP growth Primary level enrolment Secondary level enrolment Tertiary level enrolment Investment Average years of schooling Expenditure on education Trade openness Life expectancy at birth Democracy index Population growth

human capital in the paper. As for health human capital, the study employs life expectancy as proxy variable. A possible limitation noted by van Leeuwen and Foldvari (2008) is that none of these measures of education capture the differences in the quality of schooling but rather quantity measures. Instead of using measures of educational attainment or government expenditure on education, it would have been preferable to use measures of student-to-teacher ratio or cognitive skills of the population such as students achievement such as mean test scores (see; Hanushek, 2013; Hanushek and Kimko, 2000; Li and Liang, 2010). However, the lack of data on cognitive skills or student-to-teacher ratio from SSA countries precludes their inclusion in the present study. Similar to Bloom et al. (2004), we use life expectancy as a proxy for health status. Life expectancy is generally associated with better health status and lower morbidity (Murray and Lopez, 1997). While many of the previous empirical studies use life expectancy as a proxy for health status (see; Glewwe et al., 2014; Bloom et al., 2004), the literature also revealed that a few number of studies employed mortality rate, calorie intake per capita, adult survival rate, or healthcare expenditure as potential proxies for health in growth regression model (Li and Liang, 2010; Hartwig, 2010; Acemoglu and Johnson, 2007; Bloom et al., 2013). However, we used life expectancy as the only measure of health status in the present study due to the absence of available data for other potential proxies of health status for SSA countries. Similar data limitations exist for other alternative measures of education that could have been considered for this study. 5. Results and discussion 5.1. Correlation matrix of the explanatory variables Table 2 contains the estimated parameters for the model specification in Eq. (2) with three alternative variations in the choice of the human capital variables. First, we examine the pattern of the relationships between the growth regression model’s explanatory variables. The results of the correlation matrix of the explanatory variables employed are presented in Tables A.1–A.3 of the Appendix, respectively. Correlation matrices provide intuitive information on the strength of the bivariate relationships between variables (Self and Grabowski, 2004). The results show that most of the correlation coefficients among the explanatory variables are less than 0.50. These weak bivariate correlations suggest that multicollinearity should not be a serious problem for the estimated model. 5.2. Diagnostic test results The consistency of the estimated parameters of the growth regression model is based on the diagnostic test results from the estimated dynamic GMM model indicated by the presence of first-order autocorrelation (AR [1]) and the absence of second-order autocorrelation (AR [2]) in the residuals of the model. The GMM estimator is consistent only if there is no second-order serial correlation (i.e. AR [2]) in the idiosyncratic error term of the system of equations as observed in the present study (Yu et al., 2011). Also, we employ the Sargan–Hansen test to ascertain the validity of the instruments, to ensure that the model is not misspecified. The lower panel of Table 2 contains the reported AR [1], AR [2], and Hansen test from the estimated dynamic-system GMM model. Both the first-AR [1] and second-AR [2] order autocorrelation suggest that no serial correlation exist in the disturbances, while the Sargan–Hansen test shows that the instruments used in the GMM specification are valid.

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Table 2 Human capital contribution to economic growth with joint inclusion of education and health. Variables

GDP per capita growth, lagged Primary enrolment, lagged Secondary enrolment, lagged Tertiary enrolment, lagged Total years of schooling, lagged Govt. Expenditure on education, lagged Life expectancy at birth, lagged Investment, lagged GDP per capita, lagged Trade openness, lagged Population growth Democracy index Constant # Observation # Instrumentsa Wald Test p-valueb AR (1) p-value AR (2) p-value Hansen test p-value

Model 1

Model 2

Model 3

Coefficient [SE]

Coefficient [SE]

Coefficient [SE]

0.0534 0.0821** 0.0460* 0.0007

[0.0523] [0.0420] [0.0251] [0.0050]

***

0.4887 −0.0072 −0.2038*** 0.1406*** −0.5314** −0.0001 −1.2249***

[0.1525] [0.0151] [0.0589] [0.0339] [0.2437] [0.0008] [0.3187] 1015 86 0.000 0.025 0.407 0.983

0.1557**

[0.0795]

0.0167*

[0.0098]

**

0.2151 −0.0149* −0.0723* 0.0727*** −0.2231 0.0020*** −0.5490*

[0.1064] [0.0080] [0.0427] [0.0293] [0.2474] [0.0007] [0.3274] 1015 79 0.000 0.011 0.150 0.928

0.0443

[0.0362]

0.0098 0.2632*** −0.0119* −0.1119** 0.1238*** −0.5063* 0.0003 −0.7028**

[0.0127] [0.1068] [0.0068] [0.0538] [0.0334] [0.2852] [0.0007] [0.2184] 1015 79 0.000 0.001 0.318 0.971

Notes: Values in parentheses are standard errors of the estimates. Model 1: Based on school enrolment ratio; Model 2: Based on total years of schooling; Model 3: Based on government expenditure on education. The following variables were defined in logarithm for the analysis: primary, secondary and tertiary enrolment, average years of schooling and government expenditure. * Denote 10% significance level. ** ***

Denote 5% significance level. Denote 1% significance level.

a

It worth noting that we follow the advice of Roodman (2009) to overcome the difficulty of instrument proliferation by using lags of endogenous variables (i.e., enrolment ratio, years of education, expenditure on education, life expectancy, GDP per capita, trade openness, and population growth) as instrument. Exogenous variable is the year dummies, while GDP per capita growth and democracy index are taken as the predetermined variables. b Joint test significance of the variables.

5.3. Effect of education on economic growth Table 2 contains the estimated results on the effect of human capital on economic growth in SSA (education and health proxies of human capital are jointly included). As mentioned earlier, three different alternative measures of education were considered in the study: enrolment ratio was included only in model 1, average years of schooling of the adult population was included only in model 2, and government expenditure was included only in model 3. In each model, the table includes life expectancy as a proxy for health status. The effects of enrolment ratio at the primary, secondary, and tertiary levels on economic growth, as captured by model 1, show that the education elasticity of growth is positive in the SSA region. Specifically, the results from model 1 suggest that a 10% increase in primary, secondary, and tertiary school enrolment would result in about a 0.82%, 0.46%, and 0.01% increase in per capita GDP growth, respectively. However, only the elasticity of growth with respect to primary and secondary school enrolment is statistically significant. The results lend support to the work of Sala-i-Martin et al. (2004) and Artadi and Sala-i-Martin (2003), who found primary school enrolment to be the most robust variable with a significant positive effect on economic growth. Also, the significance of primary enrolment may reflect the productivity-enhancing effect of the recent increase (since 2000) in the primary enrolment ratio in the SSA region (World Bank, 2012). The results appear to support Petrakis and Stamatakis’s (2002) argument that the growth effects of education depend on a country’s level of development and that low-income developing countries benefit more from primary and secondary education, while high-income developed countries benefit more from tertiary education. In model 2, the effects of average years of schooling of the adult population on economic growth are positive and statistically significantly. The results in model 2 imply that a 10% increase in a population’s average years of schooling would result in about a 0.17% increase in per capita GDP growth in SSA. For model 3, the effects of government education expenditure on economic growth are also positive, but are statistically insignificant. We note that the positive and significant effect of average years of schooling on economic growth in the present study conforms to the findings of Gyimah-Brempong and Wilson (2004) from a dynamic GMM model using 1975–1994 data on 21 SSA countries. 5.4. Health effect on economic growth As shown in Table 2, the contribution of health (measured as life expectancy) to economic growth shows that health’s elasticity of growth is positive and significantly different from zero across models 1, 2 and 3. Specifically, the results show

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that a 10% increase in the population’s life expectancy at birth would bring about an increase of 4.9%, 2.2% and 2.6% in per capita GDP growth for models 1, 2 and 3 respectively. Current results are consistent with Bloom et al. (2004), who also found evidence of a positive and statistically significant effect of life expectancy on economic growth. However, the estimated coefficients of life expectancy from their review of 12 different studies are substantially lower than those in the current study. 5.5. Comparing the effects of education and health on economic growth It is useful to compare the relative strength of the contributions of education and health measures of human capital. First, a choice should be made on which of the three measures of education to use in the comparisons. Gyimah-Brempong et al. (2006) note that although having the advantage of being comparable across countries, neither enrolment ratio nor government expenditure on education is a particularly appealing measure of education endowment. Despite possible crosscountry data constraints, average years of schooling is a more appropriate measure. Reverse causation when using either enrolment ratio or education expenditure to proxy education is another source of concern (Gyimah-Brempong et al., 2006). Hence, given these issues that are associated with the use of enrolment ratio and expenditure on education as proxies for education, the subsequent discussion focuses on model 2, which is based on average years of schooling of the adult population in the region. To this end, model 2 in Table 2 shows that the effect of average years of schooling of the adult population taken as a proxy for education on economic growth is very small (0.0167).5 Consistent with the work of Li and Liang (2010), but contrary to the findings by Gyimah-Brempong and Wilson (2004), our study found that the health elasticity of growth estimate is substantially larger than the education estimate, all things being equal. It should be noted that even in models 1 and 3, the contribution of health to economic growth is also substantially larger than that of education. First, similar to the observation in Glewwe et al. (2014), the relatively small size of the elasticity of growth with respect to the three education proxies in the study could be capturing the low quality of education, coupled with the fact that the health care expenditure share of GDP is relatively much larger than that of education in many SSA countries. Second, the current results for the SSA region also support Weil’s (2007) argument that the positive effect of health on GDP growth is usually strongest among poorer countries. The large and statistically significant estimates for health in this analysis are even more striking when compared with existing studies, which have mostly mixed and ambiguous empirical evidence as to whether or not health stimulates GDP growth in developed countries (Weil, 2007). 5.6. Contribution of the macroeconomic variables to economic growth Although our study’s focus is on the link between human capital and economic growth, we also examined the effects of various macroeconomic variables on economic growth. In contrast to findings in Gyimah-Brempong and Wilson (2004) and Gyimah-Brempong et al. (2006), we find that the investment share of GDP per capita contributes negatively to economic growth in SSA. This result is surprising. Nevertheless, we believe the outcome could be an indication that investment in physical capital formation may not be large enough to translate into increased growth over the period covered by the study. Also, the coefficient of initial GDP per capita is consistently negative and significant across the models in Table 2, suggesting that the convergence hypothesis is supported at the cross-country level in the study.6 With the exception of model 1, the effect of population growth is negative and statistically significant, supporting the idea of capital dilution that occurs when population growth reduces the available capital per worker (Weil, 2013). The effect of trade openness is positive and statistically significant across the three model specifications, supporting the notion that market liberalization policies that encourage international trade tend to boost national economic growth. A search of the literature shows that similar finding from the region was obtained by Hossain and Mitra (2013) and Adams and Opoku (2015). The importance and positive contribution of trade openness in this study further confirms similar findings in previous studies (Awokuse, 2008). The effect of the democracy index on economic growth is positive and statistically significant in model 2 only. 6. Concluding remarks In this study, we revisited the question of whether human capital stimulates economic growth in sub-Saharan African (SSA) countries by jointly modelling the contributions of education and health measures of human capital in a growth regression model. First, we investigated whether the general consensus that health and education contribute positively to economic growth exists in SSA based on current data from the region. Second, we compared the relative contributions of two alternative forms of human capital (i.e. health and education) to economic growth in the region. In a system-dynamic GMM model based on a balanced panel data series covering 1980–2008 for 35 SSA countries, we employed three alternative measures of education (i.e. enrolment ratio for primary, secondary, and tertiary levels; average years of schooling of the adult population; and government expenditure on education) and life expectancy at birth as a proxy for health. 5 This observation is robust to the other two measures of education considered in the analyses. 6 The convergence hypothesis states that countries with lower per capita income (GDP) tend to increase more rapidly relative to richer countries over time.

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Table A.1 Correlation matrix for explanatory variables in GMM model. GDP_Growtht −1 Primaryt −1 Secondaryt −1 Tertiaryt −1 Life Investmentt −1 GDPt −1 expt.t −1 GDP_Growtht −1 Primaryt −1 Secondaryt −1 Tertiaryt −1 Life expt.t −1 Investmentt −1 GDPt −1 Opennesst −1 Popu_Growth Democracy

1.000 0.066 0.047 −0.039 0.101 0.058 0.114 0.044 0.228 0.165

1.000 0.689 0.234 0.430 −0.052 0.447 0.244 −0.015 0.261

1.000 0.454 0.548 0.062 0.623 0.415 −0.056 0.362

1.000 0.169 0.055 0.325 0.143 −0.090 0.131

1.000 0.010 0.558 0.249 0.060 0.311

1.000 0.244 0.249 0.015 −0.0002

Opennesst −1 Popu_Growth Democracy

1.000 0.205 1.000 −0.002 −0.104 0.221 0.192

1.000 0.032

1.000

Table A.2 Cross-tabulation of explanatory variables in model 2 of Table 2.

GDP_Growtht −1 Year_Edut −1 Life expect.t −1 Investmentt −1 GDPt −1 Opennesst −1 Popu_Growth Democracy

GDP_Growtht −1

Year_Edut −1

Life expect.t −1

Investmentt −1

GDPt −1

1.000 0.071 0.103 0.062 0.117 0.049 0.228 0.166

1.000 0.456 −0.033 0.473 0.204 −0.039 0.153

1.000 0.015 0.560 0.249 0.073 0.315

1.000 0.249 0.212 0.021 0.003

1.000 0.460 0.010 0.227

Opennesst −1

Popu_Growth

Democracy

1.000 0.043

1.000

1.000

−0.091 0.201

Table A.3 Cross-tabulation of explanatory variables in model 3 of Table 2.

GDP_Growtht −1 Edu_expt −1 Life expect.t −1 Investmentt −1 GDPt −1 Opennesst −1 Popu_Growth Democracy

GDP_Growtht −1

Edu_expt −1

Life expect.t −1

Investmentt −1

GDPt −1

1.000 0.014 0.103 0.062 0.117 0.049 0.228 0.166

1.000 0.345 −0.015 0.305 0.343 0.013 0.279

1.000 0.015 0.560 0.249 0.073 0.315

1.000 0.249 0.212 0.021 0.227

1.000 0.460 0.010 0.201

Opennesst −1

Popu_Growth

Democracy

1.000 0.043

1.000

1.000

−0.9010 0.2005

The results show that the estimated coefficients for primary and secondary school enrolment and average years of schooling used as measures of education have a positive and statistically significant effect on economic growth in SSA. In contrast, the estimates for both tertiary school enrolment and government expenditure on education are not statistically significant. In addition, the estimated parameters for health have a positive and statistically significant effect on economic growth in SSA. In comparing the estimated human capital elasticity of growth for the two forms of human capital, the health measure of human capital appears to make a larger contribution to economic growth in SSA than the education measure of human capital. From the policy standpoint, a possible explanation for the small elasticity of growth with respect to education relative to that of health could be linked to the quality of education in SSA. This observation therefore calls for improvement in the quality of education in the region. The findings also show that joint inclusion of both measures of human capital in growth regression models should be preferred in order to minimize the omitted variable bias. This result aligns with the argument in the literature that neither education nor health is a perfect substitute for the other as a measure of human capital. A possible limitation of this study is the inability to extend the data set beyond 2008 due to the lack of access to more recent data on the measures of education across a large panel of SSA countries. Future studies should address this issue as data become available and also consider other proxies for health and education when possible. Acknowledgements The authors thank the participants at the annual African Economic Conference (AEC), Kinshasa Democratic Republic of Congo (DRC), 2–4 November 2015 for their valuable comments on the earlier version of this paper. In addition, the authors would like to thank the anonymous reviewers of this paper for the valuable comments. The views in this paper are authors’ own and do not necessary represent those of the affiliated institutions. Appendix See Tables A.1–A.3.

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