Читать книгу The Political Economy of Economic Performance - Voxi Heinrich Amavilah - Страница 10
ОглавлениеIntensity of Technology Use and Economic Performance
African countries have fared poorly compared to some countries in other regions, but relative to their own performance history some SSACs have done quite well over the recent past. The 1992–2007 decade was especially good, raising the question about how such performance can be made to stick and even expand. This requires better understanding of the source of this good performance, and the current essay, beginning with the assumption that a feeble technology was responsible for slow growth in the past and finds that the impacts of new technologies, measured by the intensities of internet and cell phone use are very strong. The policy implications of the findings recommend increased investment in new technologies for which productivity is high and the adoption and diffusion costs seem low.
African countries suffer from Africa-itis—a stigma that makes it hard for observers to notice good economic performance of African countries, and makes it obsessively easy to point out the bad news. As David Bloom and Jeffrey Sachs (1998, 37) put it, it is not only that “Africa surely suffers from a remarkable inattention of the international scientific community,” it is also that the little attention that African countries receive is inordinately normative and pessimistic. Rarely do even the best of expert writings on African countries, for example, reveal that although over the 1960–1990 decades African countries made up a large percentage of the “growth disasters,” lately some African countries did make it to the list of the “growth miracles” (see Temple 1999, Table 2, 116; Ndulu and O’Connell 1999). During the late 1990s, for example, up to 39 percent of African countries did catch a little of the receding dotcom wave, and many of them grew at rates not lower than two percent per annum, and strong growth continued past 1998. As the dotcom wave subsided, growth slowed to 3.2 percent in 2002, but it climbed back up to 4.3 percent in 2003, 4.6 percent in 2004, and higher still in 2005 (United Nations Economic Commission for Africa—UNECA 2005). In sum: over the past years to-date African countries have grown at annual rates exceeding four percent, so that by 2004 there were only a few trouble areas in Africa (OECD Observer 2005). Yet, the whole of Africa is more likely to be defined by these few areas than Asia is likely to be characterized by Afghanistan, Pakistan, Burma (Mymar), and North Korea put together. 1
The cloud of Africa-itis is misleading to the extent to which it masks diverse performance across African regions, countries, and even sub-regions within countries, see, for example, IMF (2005). For instance, Botswana and Mauritius have been two of the best performers in the world for nearly four decades. Central African countries have grown at an annual average rate of more than 14 percent during the year 2004–2005. These differences in performance should not be surprising as standard economics teaches that the production possibilities of any economy depend on its technical capability. Technical capability is defined by the quantity and quality of available resources and the current level of technology, so that economic growth is the expansion of production possibilities resulting from improved technical capability, subject to the initial and current institutional conditions, and the policies that govern both. Here is the point: because capabilities differ across economies, so too does economic performance (Amavilah 2006), and no two African should be expected a priori to be the same. Yet, too often, analyses of the sources of the economic performance of SSACs focus exclusively either on external factors for which subsequent policy is exogenous, or on some loose generalizations of internal sources of growth for which useful policy is nearly impossible to conduct. For instance, the UNECA study above lists “macro-stability” and “tourism” as the main internal sources of economic performance for Africa in 2004, but then the same report goes on to lament the weak domestic investment, low domestic savings, and the risk of currency appreciation as “some areas of concern.” Research must do better than this if it is to serve a credible policy function!
While the quality of resources, such as human capital, which individual SSACs have is a matter of considerable debate, quantitatively most SSACs are no more resource-poor than their counterparts in other regions of the world. For the most part, African countries had colonial experiences not unlike those of other developing countries, suggesting congruent initial conditions across some world regions. Additionally, many developing countries around the world pursued similar economic and political policies immediately upon their political independences, and making similar policy mistakes in the process (Nyarko 2007). For instance, the movements toward resource nationalization and import substitution policies were not unique to African countries. This all seems to imply that observed economic growth rate differences are not principally due to resources, initial conditions, or policy alone.
A reasonable constraint on the economic growth of African countries has been the fact that technological change has never “etherealised” progressively and adequately. This is not a new thesis. Different schools of thought have argued for the fact that it was technological advancement that was responsible for the economic development of industrialized countries, and without innovations, the United States could not have grown, or grown as rapidly. Indeed, the Industrial Revolution set off modern economic growth. However, the new growth theory has brought additional clarity to the understanding that technological advancements are no “manna from the heaven.” Technology determines and is determined by economic performance, a joint determination that makes common sense while at the same time raising vexing questions about causality.
For example, a quick glance at the sources of economic growth of the United States over the years would show a clear shift from reliance for growth on resources (Romer’s objects) in the early years to healthy interactions and intra-actions of ideas and objects in the middle years, and increasingly to ideas and technology in more recent times (Denison 1967; Gordon 2002; Aghion 2006). Since nothing of the sort has been documented for African countries, it seems reasonable to suspect that a major obstacle to the economic performance of SSACs has been a feeble technological foundation. In dramatic words, just as the craftiest of construction engineers cannot erect a skyscraper on Jell-O pudding, so too strong and sustained growth needs a strong technological foundation.
The objective of this chapter is to quantify some of the technological foundations of economic performance in African countries, using as an example the forty-six African countries listed below. 2 The objective is important because technology improves the productivity of other resources. It is especially crucial where the relative productivity of other factors of production is a matter of considerable concern (Dasgupta and Stoneman 1987). As Aaron Segal (1985) points out “of all gaps that separate Africa from the rest of the world science and technology is probably the most critical, and the most profound” (110, italics added). The first section below outlines the theory behind the analysis; the second section turns to practical issues including data, estimations, and results; and the final section makes a concluding remark.
Theory
This section first sketches the relevant literature and then states a simple and practical model.
Literature
Paul Collier and Jan Willem Gunning (1999) review a very large set of literature on African performance in an attempt to uncover commonalities of the sources of economic performance there. They relate sources of the economic decline of Africa to the lack of social capital, trade openness, public services, financial depth, a hostile geography, and over-dependence on foreign aid. This list is not new; what stands out from this literature review at the aggregate level, however, is the negative effect on performance of the so-called Africa dummy. Consistent with Collier and Gunning, many other researchers report a significant negative African dummy ranging from −0.010 to −0.54 over the decades 1960–1989 (Benhabib and Spiegel 1994; Alcala and Ciccone 2001; Barro 1991; Easterly and Levine 1997). Also during the years 1960–2003, Africa’s total factor productivity (TFP) has remained low at between −0.05 and −1.34 according to some estimates (Ndulu and O’Connell 1999; Soderbom and Teal 2003; Jorgenson and Vu 2005). 3 The negative effects of Africa’s TFP and dummy are discernible despite the fact that other sources of growth, such as physical capital per worker, or human capital per worker, are not that different from those of other regions. 4
Temple (1999) looks at the new growth evidence from the perspectives of the old (exogenous) neoclassical and new (endogenous) growth theories. The evidence concludes that differences in economic growth are mainly due to differences in capital investment in equipment, people, and R & D; income inequality and the implication of that for (in-)stability; economic freedom and security of property rights; government and its effects with respect to taxation, spending, regulation, and the financing of infrastructure; and openness to trade. However, Temple’s “new evidence” is not really new either; W. Arthur Lewis (1965) argues that “the proximate causes of economic growth are the effort to economize, the accumulation of knowledge, and the accumulation of capital” (164). These causes have strong basis in the quantity and quality of the human population and other natural resources, and in government and government policy. It is not surprising that Temple and Johnson (1998) in associating economic growth with social capability are following Lewis’s lead, and here is one insightful sign Lewis posted along the way:
Economic growth depends both upon technological knowledge about things and living creatures, and upon social knowledge about man and his relations with his fellowmen. The former is often emphasized in this context, but the latter is just as important since growth depends as much upon such matters as learning how to administer large scale organizations, or creating institutions which favour economizing effort, as it does upon breeding new seeds or learning how to build bigger dams (164; italics added).
The interesting part of this thoughtful line of work is how seemingly non-economic factors affect the relationship between economic growth and technology (cf. Hoselitz 1952, Fafchamps 2000). This is made clear in a recent paper by Los and Verspagen (2001) which “distinguish[es] four ways in which technology and innovation have their impact on growth” (p. 2). The first channel treats technology either as a pure public good and in that case its rate of change is exogenous, or as a quasi-public good, the rate of which is endogenous. In both cases, technology drives the steady state rate of growth. In turn, technology is a function of factor ratios so that in the exogenous version as the capital-labor ratio increases, the rate of technical change first rises and then falls as diminishing returns to scale set in (Solow 1956, 1957; Swan 1956 [2002]).
In the endogenous version of the model, factor ratios such as the human capital-labor ratio are dynamic with the potential for postponing diminishing marginal products and sustaining convergent/divergent (steady) states (Romer 1989, 1990; Lucas 1988, 1993; cf. Ocampo, Jomo K.S., and Vos 2007). Bennett McCallum (1996) and Mark Rogers (2003) provide excellent reviews of neoclassical exogenous and endogenous growth theories, while Nazrul Islam (2004) assesses the normative (policy) value of endogenous growth theories to developing countries (cf. Stoneman 1987, 154–68).
A second channel is technological diffusion which enables lagging economies to catch up with frontrunners. However, the rate at which economies close the technological gap between them is a function of “social capability” and “technological congruence” (Los and Verspagen 2001). Social capability is the basis for technological “absorptive capability” (Kneller and Stevens 2006), and it has two interactive dimensions: the infrastructural base of which fixed capital such roads, railways, and so on are a major part, and the super-structural base including institutions, social capital, and the like. Technological congruence implies applicability to one country of technology developed elsewhere. This neither dismisses lightly the importance of early arguments about the appropriateness of technology nor overlook a nuanced hypothesis advanced by Yaw Nyarko (2007) of New York University that economic performance is a country-specific problem-solving activity. It does point out, however, the constraining role of rules, and why Paul Romer (2010) is correct in claiming that economic growth in the twenty-first century will depend on trading in rules as opposed to trading in things (Lyons 1987, 169–205).
As third and fourth ways in which technology and innovations enter economic growth, Los and Verspagen point to learning-by-doing (Arrow 1963) and roundabout production (Young 1928). Combined, these two channels permit a demand-driven cumulative impact of technology on economic performance, variously called the Verdoorn-Young-Arrow learning effect, or the Myrdal-Kaldor secular and causal effect (Young 1928; Solow 1997; Arrow 1962, 1969; Kaldor 1966; Thirlwall 1978; Stiglitz 1987, 125–53; Jones and Romer 2010).
Measures of technological capability differ and rank countries differently as Daniele Archibugi and Alberto Coco (2005, 2003) describe (cf. Jeffrey Jones 2006). What is disturbing, however, is that African countries invariably rank low on all key indices of technology. The Technology Achievement Index (TAI), inspired by the UN Development Programme (UNDP) and outlined in Desai, Fukuda-Parr, Johnson, and Sagasti (2002) focuses on seventy-two countries, dividing them into groups: “leaders,” “dynamic adopters,” “marginalized,” and “others.” This index has four dimensions (technology creation, diffusion of new technologies, diffusion of old technologies, and technology relevant human skills). A few African countries on the list score low. African countries are also not doing well on the Technology Index (Technologyi in this essay) assembled by the World Economic Forum. The current essay seeks to understand the effects of technology on per capita real GDP across forty-six African countries in 2004/2005. Such an understanding will help focus the search for the location of the negativity of both the Africa dummy and TFP.
Model
As a starting point, assume a homogeneous Cobb-Douglas technology for the ith African country to be
(2.1)
where Y i is gross domestic product (GDP), N i is the population, K i is the capital stock given by the perpetual inventory formula as net new investment plus old capital stock less depreciation, Z i is a vector of other output determinants, Ai is the level of technology, and α and β are constant parameters to be estimated. Dividing through by N i gives (2.1) in logarithmic per capita terms as
(2.2)
where y i = ln (Y i /N i ), k i = ln (K i /N i ), z i = Z i /N i , and a i = ln A i . Subsequent estimations focus on six different versions of (2.2). Next take a look at some practical issues.
Practice
This section describes measurement issues, estimations, and results.
Measurement Issues
The dependent variable y i is real per capita GDP in US dollars (US$). Chief among independent variables is the capital per capita (k i )—assuming the rate of labor growth equals that of population. For the lack of data on capital stock, a reasonable measure of k i is the share of GDP that went to capital formation averaged over the period 2000–2004. The vector matrix z i includes independent variables such as per capita trade Openness measured as the ratio of per capita exports plus imports to per capita GDP, inflation rate averaged over the years 2000–2004 (ν), and regional dummies for Eastern Africa, Western Africa, Northern Africa, and Southern Africa. Finally, is a Hicks-neutral productivity shock—a measure of the technological basis of economic performance.
For most countries in this sample, the main data source for y i are www.earthtends.wri.org and www.finfacts.com. Missing and incomplete data are supplemented by similar data from the International Monetary Fund’s (IMF) International Financial Statistics—IFS (2005). Inflation rate (ν) also comes from IFS (2005). The data for Openness, mobile phone (Cellphone), and Internet come from www.Joinafrica.com.
Estimations
I use the OLS estimator of (2) in five fundamental, and four auxiliary versions. In real per capita terms, all nine versions can be generalized to
(2.3)
For example, y i depends on ki, Openness, and an index of macroeconomic environment (Macro) in Version1, where the Macro data comes from the World Economic Forum. When Marco data is missing, I use the Africa average Macro (Amacro) variable, calculated as the average sum of available Macro for n countries, that is,
Version 2 adds to Version 1 Technologyi, drawn from Global Competitiveness Reports. Again, where data is missing a proxy was calculated as
where n are countries for which Technologyi data is available, and 1-n are countries for which Technologyi data is not available, but a partial data series, called Networked Readiness Index (NRIi), is available. For comparison purposes the highest possible score for both Macro and Technologyi is 5.0.
To control for additional variations Version 3 adds regional dummies (Eastern, Western, Northern, and Southern Africa). Version 4 assumes that Technologyi can be decomposed into two components, viz., Cellfone and Internet. The “Internet” variable is the ratio of internet hosts to internet users, and “Cellfone” is per capita cell phones. Both measure the intensity of use of new technologies. 5 Version 5 adds regional dummies to Version 4. Versions 1–5 constitute the fundamental versions of the basic model in (2) or (3). Auxiliary Versions 6–9 are not essential estimations; rather, they are indirect checks on the robustness of the fundamental estimations. For example, Version 6 drops Macro, Version 7 drops Openness, Version 8 drops Internet, and Version 9 excludes Cellfone. The next subsection presents and discusses the results.
Results
Tables 2.1 and 2.2 report estimation results of fundamental and auxiliary versions, respectively. Excluding the constant term the second column of the first table shows, for example, that macroeconomic policy has the largest effect on per capita real GDP across these African countries. Per capita capital and openness to trade are also positively related to per capita real GDP. More than one third of a percentage point increase in y results from a one percentage increase in k (capital) and τ (trade).
Table 2.1 Fundamental Determinants of Per Capita GDP across African Countries, 2004–2005
Table 2.2 Auxiliary Determinants of Per Capita GDP across African Countries, 2004–2005
A large constant term suggests that some other determinants of real per capita GDP are missing from Version 1. The regression results in the next column of the table add a measure of technology using a technology index calculated by the World Economic Forum (2005). In this case while the coefficients of k i and Openness remain largely unchanged, the estimate of Macro more than doubles and the constant term falls. A major finding is a huge, negative, and statistically significant impact of technology on the per capita real GDP of these African countries. This implies that the low level of technology harms real income determination in these countries. In fact, the magnitude of this negative coefficient increases significantly when the regression includes regional dummies, suggesting that the sign is not spurious. Southern Africa has the largest regional dummy and Eastern Africa the smallest. With regional dummies included macroeconomic policy becomes even more important than in previous versions. Openness to trade remains positive for GDP, but it is no longer statistically significant.
Columns 4 and 5 of Table 2.1 consider explicit components of technology: the intensity of internet use (Internet) and mobile phone use per capita (Cellphone). 6 Parameters for both variables are large and statistically strong. Such impacts are unaltered by the inclusion of regional dummies, although in this case trade openness becomes insignificant. Even so, the impact of per capita capital at about 0.37 is robust across all five fundamental versions. The explanatory power ranges from 21 percent to 54 percent, not unreasonable for cross-section regressions and a relatively small sample.
The coefficients of Internet (3.3161) and Cellfone (5.0941) are large. This raises a question about whether or not these variables are overestimated. The results in Table 2.2 indirectly address that concern. For example, Version 6 to Version 9 retain k i and regional dummy variables as the key independent variables and drop one of the remaining variables from each regression. Version 6 drops Macro; Version 7 excludes Openness, Version 8 leaves out Internet and Version 9 goes without Cellfone. Consequently, there is a remarkable improvement in summary statistics in Table 2.2 compared to Table 2.1; both explanatory and predictive power of the regressions, for instance, increase. However, there is no major gain in the technical efficiency of individual parameters. In addition, the values of the log-likelihood functions decline. This seems to indicate that the fundamental versions in Table 2.1 are more informative than the auxiliary versions in Table 2.2.
Concluding Remarks
The objective of this essay is to quantify the impact of technology on per capita real GDP of 46 African countries in 2004/2005. The results are encouraging both for policy and further research. The first estimation begins with per capita capital, trade openness, and macroeconomic policy index as the main independent variables, assuming homogeneous technology across countries. These results show that 12 percent of variations in per capita real GDP are explained by those independent variables. A one percentage rise in capital and trade openness contributes more than a third of one percentage increase in GDP, and for macro-policy the effect is three-fourths of a percentage change.
However, the large constant term motivates the inclusion of a country-specific measure of technology. The negative 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.
