The Political Economy of Economic Performance. Voxi Heinrich Amavilah
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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
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
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,
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
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