Regression Results for low-income and middle-income SSA countries

An empirical analysis has been conducted by using SURE and Three-Stages Least Square Estimations (using instrumental variables) for both the low-income and middle-income SSA countries separately in comparison with East Asia. Accordingly, the regression results displayed on table 6.9 reveal no evidence for the effect of vertical export diversification on growth in income per capita either in middle-income SSA and low-income SSA. This is quite different from the result for East Asia in the same table 6.9, that vertical export diversification was the key determinant for growth in income per capita, which is consistent with the assumptions made in the research hypothesis. On the contrary, the attempts made by the majority of SSA countries (both middle and low income) to vertically diversify their production and exports towards vibrant and value-added products are still at an early stage and have not yet contributed to economic growth.

162 Likewise, the results from table 6.9 reveal that it is only middle income SSA countries that have promoted horizontal export diversification, at least to some extent, which might have contributed to economic growth. This is again in line with the reality on the ground, that most of the SSA countries especially those from with low-incomes are still dependent upon the production and exports of a few primary commodities to keep their economies running. This again suggests the need for African policy makers to exert utmost efforts for export diversification both vertically and horizontally. Interestingly, the analysis from table 6.9 confirms that domestic investment is positively and statistically significant and it can play a significant role for growth in income per capita both in middle-income and low income SSA countries. The SURE as well as the instrumental variable analysis confirm a significant relationship between domestic investment and growth in the case of middle-income SSA, while only the SURE estimation confirms a significant relationship in the case of low-income SSA. In the case of East Asia, the SURE estimation suggests that domestic capital has been statistically significant at the 1% level, which implies the importance of domestic capital formation in East Asia in the development process.

By the same token, the FDI variable which was found to be one of the key factors for success in East Asia was also found to be statistically significant for low-income SSA countries but not middle-income SSA countries. This was because either the sample size of middle income SSA countries in this study is small or because FDI in Africa in general has been focusing mainly on natural resource rich countries such as Nigeria, Congo Democratic Republic, Sudan, etc. and yet most of these natural resources rich countries are grouped in the low income SSA category.

Initial GDP per capita, which indicates the rate of convergence for poor countries to catch up with the rich ones, has been found to be statistically highly significant for middle-income SSA countries;

but only at the 10% level of significance for low-income SSA countries. This implies that there is more evidence of middle-income SSA economies converging and catching up with the rest of the world than the low-income SSA countries, where there is little evidence of convergence. For instance, the coefficient of initial GDP per capita for middle-income SSA countries using instrumental variable analysis shows a convergence of 8.5 points every year compared to 3.3 points of convergence for low-income SSA economies in catching up with rich countries. This same variable has been found to be highly significant in the case of East Asia, using SURE as well as instrumental variable estimators, implying strong evidence of convergence in East Asian countries in catching up with the rest of the developed world.

The study reveals that population growth negatively affects income per capita growth in the case of middle income SSA countries, but is a positive factor in low income SSA countries. Likewise, no evidence has been found for the population factor affecting growth in income per capita for East Asian countries. This reminds us that the impact of population growth on the economy is still debatable. The main explanation for the positive and statistically significant population factor for growth in income per capita of low income SSA countries is that most of them are mainly dependent on the agriculture and

163 mineral production sectors that require tremendous amount of labor compared to other sectors such as manufacturing and services.

Table 6.9: SURE and 3-Stage Least Square Estimation with Instrumental Variables for Middle and Low Income SSA, and East Asia Sub-Samples

GDP/C growth Low-income SSA Middle-income SSA East Asia

SURE INST SURE INST SURE INST

Vertical Export Diversification

.025 (.018)

-.004 (.026)

-.015 (.034)

-.001 (.072)

.065***

(.017)

.090***

(.023) Horizontal

Diversification

-.001 (.007)

.011 (.009)

.026**

(.012)

.023 (.037)

.006 (.009)

.008 (.014) Domestic

Investment

.141***

(.044)

-.076 (.091)

.338***

(.073)

.529**

(.256)

.136***

(.046)

.055 (.095)

FDI .241***

(.090)

.528**

(.248)

-.151 (.204)

-.218 (.482)

.271***

(.086)

.587***

(.155) Initial GDP -2.375*

(1.363)

-3.129*

(1.625)

-9.126***

(2.408)

-8.535**

(3.696)

-5.17***

(1.241)

-8.125***

(1.855) Population Growth .588**

(.231)

.568**

(.255)

-2.828*

(1.540)

-3.982*

2.459)

.256 (.472)

.633 (.632) Education- initial -.186

(.094)

-.273 (.112)

-.039 (.313)

-.128 (.375)

-.212 (.093)

-.298 (.120) Education-square .006**

(.003)

.007**

(.003)

.002 (.004)

.003 (.004)

.003**

(.001)

.004**

(.002) Life Expectancy .063

(.049)

.067 (.055)

.078 (.107)

-.051 (.136)

-.014 (.128)

.065 (.161) Exchange Rate .415***

(.108)

.496***

(.126)

1.784 (1.134)

1.157 (2.226)

.068 (.425)

.813 (.583) Degree of

Openness

-.079**

.036)

.013 (.087)

.267 (.243)

4.117 (16.06)

.087 (.095)

.297*

(.161) Political Stability 2.043***

(.543)

1.659**

(.650)

-5.929 (4.540)

-4.094 (7.296)

-1.518 (1.071)

-1.889 (1.328) Rule of Law .464**

(.215)

.581**

(.244)

.849**

(.413)

.909*

(.527)

.479*

(.249)

.133 (.353)

Constant 1.239

(4.339)

7.211 (5.404)

25.894 (10.034)

31.844 (12.366)

15.656 (6.262)

17.780 (7.470)

No.of obs. 156 156 36 36 54 54

R Square 0.3558 0.2349 0.5645 0.4287 0.5551 0.4149

*P ≤ 0.10; **P ≤ 0.05, ***P ≤ 0.01 refer statistically significance level at 10%, 5% and 1%, respectively.

Ironically, the education variable is statistically significant for low-income SSA countries rather than middle-income SSA countries, while its contribution to East Asian economic growth is also obvious as shown in table 6.9. Similarly, the study reveals that the degree of openness, a depreciating and stable

164 exchange rate policy, and political stability are much more important factors in low-income SSA countries. It is particularly worth noting that political instability has been found to be a negative factor for growth in income per capita in the case of low-income SSA, compared with either middle-income SSA or East Asia, implying that most low-income SSA countries are characterized by political turmoil, civil war and frequent coups that have made sustainable economic development impossible. This, therefore, calls for a tremendous effort in restoring peace and law in the region in order to encourage investments and savings.

In relation to this, the “rule of law” variable which is a proxy for “good governance” in this study has been found to be important and a significant factor for growth in income per capita for both low-income SSA and middle-low-income SSA countries. This implies that “rule of law” in a given country creates conditions that may encourage FDI and presumably private domestic investment as well, by protecting privately held assets from arbitrary direct or indirect appropriation.

In summary, the empirical analysis has revealed that unless countries invest in education and physical infrastructure through domestic investment and FDI, together with adapting a fair and open trading system along with macroeconomic and political stability, neither vertical nor horizontal export achieved. Countries from SSA need to learn from the East Asian development paradigm with regard to capital accumulation, export diversification and economic transformation.

In line with this, SSA countries ought to shift from their existing high export concentration towards a diversified and value-added production system. Hence, without neglecting the effective utilization of natural resource, policy makers in Africa should emphasize more on vertical export diversification through expanding agro-industrial and light manufacturing plants that may create backward and forward production linkages with primary sectors such as agriculture and mineral resources. It is only through this strategy that countries in Sub-Saharan Africa can rapidly catch-up with the rest of the world. Because, vertical diversification induces an increased TFP growth, enhances competitiveness, enlarges production scales, accelerated technology transfer, and enhances forward and backward linkages among various production sectors.

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