Conclusion and Discussions:

This chapter concludes by, comparing the previous results derived from general macro environment analysis with total factor productivity growth (TFP) analysis to highlight the impacts and contributions of the three supporting industries on the overall potential attractive markets. These results, derived through DEA Malmquist productivity index and AHP, compared and contrasted in figure 3.2 below. Reading from left to right presented first, the weight priorities from AHP and the TFPs from the supporting industries namely agriculture, financial and energy. The letter “P” indicates, the supporting industry had positive impacts or contribution towards the decomposition of the general macro environment, letter “N” indicates, neutral effects while the letter “L”

indicates the supporting industry had negative effects and a liability towards potential attractive market. The causes of neutral effects could be the industry maturity or products or services lifecycle.

  Figure 3.2 Sectors (TFP) Potential Attractive Markets contributions.

As the figure indicates, the contributions of the supporting industries in overall potential attractive markets in the top two countries (Mauritius and South Africa) with weights over 5000 in general macro environment is enormous. In Mauritius, apart from sound macro policies, the agricultural sector and financial contributed positively towards

overall potential attractive market. While there were no changes in energy sector assumptions made, the sector matured long before the year 2000. The contributions of all three sectors in potential attractive market in South Africa are positive. However, this is not surprising bearing the mind the size of the South African economy. Nigeria, third in general macro environment analysis has agriculture and financial influencing positively the overall potential market. However, the energy sector is a liability. Gabon with all three supporting industries as liabilities towards overall potential attractive markets is the only country that warrants further analysis and worthy mentioning, given the importance of these supporting industries it is difficult to comprehend how it, ranked among the top ten countries in general macro analysis. As the result indicates, there are a number of crucial policy implications arising from the results of this study.

First and foremost the poor overall productivity performance in agriculture is a cause for concern, as agriculture is important for the overall economic growth especially other studies have argued that it’s the main supporting sector for the rest of the industries in terms of raw materials, overall economic growth and job creation. With its contributions towards overall potential attractive markets a liability in almost all the countries. This is an indication of dire challenges in boosting total factor productivity growth in the sector.

Given SSAs projected increase in food requirements and the limits to extensive agricultural growth, progress in agricultural sector is urgently required. As Kato, 2013, observed, innovations alone are not enough to solve the problems in SSAs agricultural sector, a large number of complementary institutional and policy reforms are necessary.

However, the good news is that unlike the agriculture in Asia, Latin America, African agriculture has not gone through the transition process to modern agriculture, and adoption of agricultural technology through the Green Revolution, and agricultural land productivity has been stagnant.

In the financial sector, the TFP growth for all countries is 7.3 % an indication of the sector has influenced positively the regions overall potential market attractiveness. In 12 countries the sector have identical or similar contributions over the period understudy, the sector influenced no changes in five countries in status quo. In three countries, the

sector is a liability or contributed negatively in potential attractive market. The industry performance is far much better than that of the agriculture sector. This attributed to foreign companies in the region, in countries such as Angola, Malawi, Nigeria and Ghana. The effects of TFP on potential attractive markets, the range of variation between the countries, which the TFP has positive influence the range is very narrow;

Malawi 1.47, Angola 1.27, Nigeria 1.72, Ghana 1.16, Senegal 1.13, Botswana 1.12, Zambia 1.11, Namibia 1.09, Uganda 1.04, South Africa 1.03, Mauritius 1.01 and Tanzania 1.02. The sector influenced no changes in Kenya, Lesotho, Seychelles and Togo.

In energy, in TFP for all countries is 8.5 %, which indicates the energy sector positive influence in the regions potential attractive markets. However, as a general observation the technical efficiency had the greatest impact on the decomposition of the TFP across the countries. Seychelles 1.62 followed by South Africa 1.61, Zambia 1.39, Guinea 1.19, Angola 1.12, Malawi 1.09, Ghana 1.03, and Kenya 1.005, and Tanzania 1.002 and Botswana 1.001. As the results indicate, the agriculture sector had the least effect in contributions towards the overall potential attractive markets. Remarkably, the countries ranked top in general macro environment analysis (Mauritius, South Africa, and Nigeria) also has better performance in term of TFP in all supporting industries. South Africa has total factor productivity in agriculture, energy and financial, while both Mauritius and Nigeria has total factor productivity in agriculture and financials respectively. Therefore, the importance of these three industries in overall general environment on market attractiveness is apparent. Those countries weighted lowly may learn from Mauritius, South Africa or Nigeria how to develop and implement crucial agriculture, energy and financial policies.

The regression analysis reveals that, all the three models are weak especially in the financial intermediaries with an R-square (0.1541) and Adjusted R-square of only (0.0546). In agriculture sector no single variable is higher enough to correlate with the TFP however, the model suggests that Consumption on Fixed Capital, Net Mixed Income, and Gross may explains 36.5 % of the variance of TFP. With Electricity, Gas

and Water, with an adjusted R square of 0.4603 indicates that, Net Operating and Gross may explain the 46 % of the variance of TFP. This result confirms that Gross alone are influencing TFP as observed; this attributed to the fact that the Gross variable composition contains the components of export and imports variables, which were not included in the original formation of the TFP growth. Overall, the findings of the industries are poor; managers and policy makers might want to consider adding more independent variables to explain the remaining variability in the TFP. Ideally, if data is readily available we should work on the firm level instead of the industry in each country to get better measurement of technical efficiency and technical change across countries. We hope to do the same in future for better and meaningful results. However, overlooking the limitations, this study contributes to the understanding of the impact of these crucial supporting industries under study on potential attractive markets or in development in general. The finding my also serve as a base for further analysis aimed at understanding how investment in these supporting industries may influence the development of other underperforming countries.