The Political Economy of Economic Performance. Voxi Heinrich Amavilah

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СКАЧАТЬ impact of the technology variable means that technology is a major constraint of the performance of African countries. This conclusion is consistent with previous observations of a negative total factor productivity (TFP) and or “Africa dummy.” Since TFP is a catch-all “measure of our ignorance” (Abramovits 1956), subsequent estimations assess the effects on per capita GDP of the intensity of use of two newer technologies: Internet and Cellphone. Along with the macroeconomic environment, these two variables explain real GDP per capita across countries well. However, there are considerable regional variations.

      A number of implications for research and policy emerge from the concluding remark. For instance, the results suggest a need for improved technology. Increasing the distribution and use of internet and cellphone technologies is one way of doing just that. These new technologies have a good chance of rapid diffusion because “social capability” and “technological congruence” already exist in these countries and the cost of diffusing newer technologies is lower than the cost of adopting and sustaining older technologies. As for further research a key implication of the results is a need to investigate the impacts of old technologies, increasing the sample size, and using alternative modeling and estimations techniques, and better data.

      NOTES

      1. Kenyan journalist B. Wainaina in “How to Write about Africa I and II” (2005; 2006/2007) argue that there is a standard way people expect about Africa, and unless anyone conforms to that standard, he or she will never be listened to.

      2. 1. South Africa, 2. Kenya, 3. Tanzania, 4. Zimbabwe, 5. Mauritius, 6. Cote d’Ivoire, 7. Morocco, 8. Egypt, 9. Mozambique, 10. Namibia, 11. Uganda, 12. Botswana, 13. Zambia, 14. Rwanda, 15. Swaziland, 16. Nigeria, 17. Benin, 18. Democratic Republic of Congo, 19. Madagascar, 20. Senegal, 21. Cameroon, 22. Burkina Faso, 23. Guinea, 24. Tunisia, 25. Sierra Leone, 26. Mali, 27. Togo, 28. Libya, 29. Congo, Republic, 30. Ghana, 31. Malawi, 32. Angola, 33. Ethiopia, 34. Sudan, 35. Burundi, 36. Chad, 37. Somalia, 38. Central African Republic, 39. Lesotho, 40. Gabon, 41. Reunion, 42. Seychelles, 43. Mauritania, 44. Gambia, 45. Cape Verde, 46. Niger.

      3. The Africa dummy and Africa TFP are not directly comparable because of different models and estimators. However, the negative signs of the coefficients have been revealingly consistent.

      4. Among few exception Kwabena Gyimah-Brempong and Mark Wilson (2005) dispute the Africa differentness.

      5. Land-based telephone, railway, and highway intensities were also considered, but dropped because they were multicorrelated with each other and with the capital-labor ratio.

      6. Preliminary estimations included land phones, railways, and highways, but there dropped for statistical reason as mentioned above.

Chapter 3

       Technology-as-Knowledge and Economic Performance

      This chapter examines annual pooled observations on Botswana, Namibia, and South Africa over the 1976–2004 period to estimate the marginal impacts of technology-as-knowledge on economic performance. It finds that conventional factors of production like capital (k), trade openness (τ), and government expenditure (G) among others, influence economic performance. However, the economic performance of countries like Botswana, Namibia, and South Africa depends largely on technology. For instance, measured as a homogeneous “manna from the heaven,” technology is the strongest determinant of real per capita income of the three nations. The strength of such technology as a determinant of performance depends on the knowledge underpinnings of technology (technology-as-knowledge) here measured as the total number of academic publications (Q) and discipline-specific number of the same (q). Both Q and q correlate with the countries’ performance, which seems to suggest that the “social capability” and “technological congruence” of these countries are improving, and that dev eloping countries like Botswana, Namibia, and South Africa gain from increased investment in knowledge-building activities including publishing.

      The objective of this essay is to estimate the impact of what may be called technology-as-knowledge on the economic performance of SSACs, using limited annual publications data for Botswana, Namibia, and South Africa over the years 1976–2004. The estimation is important for policy and research reasons. For policy, the results informs predictions of long-run economic performance of SSACs like Botswana, Namibia, and South Africa, and other developing countries. Regarding research, it contributes, albeit modestly, to the understanding of the factors and forces that determine economic performance. Such a contribution goes a long way towards opening up opportunities for further examinations of the processes of technical progress and development.

      Over the years, growth experts have identified many factors that ostensibly explain the performance of nations, see, for example, Collier and Gunning (1999), Temple (1999), Temple and Johnson (1998), Fafchamps (2004), Barro (1991, 1999), Easterly and Levine (1997), Benhabib and Spiegel (1994), Romer (1993), Artadi and Sala-i-Martin (2003), and Sachs and Warner (1997). ¹ In the case of African countries, however, factors undermining growth receive more emphasis than factors promoting growth. W. Easterly and R. Levine (1997), for instance, state that “Africa’s growth tragedy” is a consequence of “low schooling, political instability, underdeveloped financial systems, distorted foreign exchange markets, high government deficits, and insufficient infrastructure” (see both the abstract and conclusion of that paper). Their statement may be termed a regression of the negatives, in that both the left-hand side and right-hand side variables are negative and the results appear predictable a priori. The opportunity cost of these negative over-emphases is the crowding-out of clarity about the factors that do promote Africa’s good performance, which leaves policy-makers scared, but less prepared about what to do. Thus, Kenneth Arrow, in a recent interview with Juan Dubra (2005), is correct that economists still neither understand well the causes of growth, nor do they know why growth rates differ across economies. This situation is not helpful to either policy or further research.

      Botswana, Namibia, and South Africa together offer an excellent study example, not because they are located in the same geographical region of the world, but because they represent developing countries in their differences and similarities. In terms of the differences, South Africa is the most technologically advanced of the trio in some respects. However, its nascent democratic institutions and a long Apartheid history have disadvantaged the country in many other important areas. Consequently, South Africa is only an upper-middle-income country. Botswana, on the other hand, has been one of the fastest growing economies in the world for nearly five decades (Acemoglu, Johnson, and Robinson 2001a, 2001b, 2002; Robinson 2009). Rapid performance puts Botswana in the same income category as South Africa. Although Namibia is a lower-middle income country, its modern sectors compares favorably with those of Botswana and South Africa. ² In some cases Namibia’s physical infrastructure is the best in Africa.

      With respect to similarities, all three economies are resource-based export economies, with mining (diamond for Botswana, diamond and uranium for Namibia, and precious metals like gold for South Africa) piggybacking each economy for many years. While South Africa’s industrial base is the largest of the three, a significant number of people in all these countries depend on domestic subsistence farming for a livelihood. This observation suggests that resource endowment are necessary, but inadequate, conditions for strong industrial growth, an observation not peculiar to developing countries. Relatively resource-poor countries like Finland, or Japan, have done far better than resource-rich countries like the Democratic Republic of Congo. Barbier (2007) made a reasonable case using the United States as an example that natural resource endowment there enables initial growth, but the nexus was not a direct one. Instead, resource endowment generated resource rents (static income), which was then invested in knowledge and technology building. It was the latter two which increased progress. The pathway does not always guarantee success, because resource dependence has led to resource curses and Dutch СКАЧАТЬ