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3 The Literature on Growth and Policy

3.1 Growth Models

One of the oldest and most basic growth models is the classical growth model, which perceives increases in profits to induce investment, and thereby raise the stock of capital. The model believes in a capitalist economy in which the main motivator is profit. A growth in the stock of capital allows a steady growth in technological progress and the wage fund. A growth in the wage fund in turn accelerates population growth and expansion in the labour force causes diminishing returns to set in. This increases labour costs, forcing the profit margin to decline. The literature that evolved afterwards identified two major limitations of this model—first, its inability to assign any important role to entrepreneurship despite recognizing it as critical and second, the inability of the model to envisage the changing role of increasing returns to scale that could make profits not to fall against the predictions of the model.

Consequently, Keynes proposed a model of economic growth that suggests growth as being largely self-adjusting, moving through a cycle of peaks and troughs, or “booms and busts”. At the start of the upturn, firms produce increasing amounts of goods and services, increasingly employing more people at higher wages. Consumption expenditures from wages fuel the boom further, until the economy reaches full capacity. To prevent price pressures, investment must increase at this point to expand the economy's production possibilities. But this investment process takes some time so that inflation cannot be completely avoided thereby forcing prices to nearly always start to rise. This causes demand for goods and services to begin to drop. Firms will begin to scale back production and lay off workers—the slowdown has begun. The economy keeps shrinking until economic growth sometimes goes negative before prices begin to drop in response. This reduction in inflation kick-starts another recovery process, causing growth to pick up again. Contrary to the classical view, Keynes' model suggests that the government should intervene to level out the cycle by employing various methods of aggregate demand management—the manipulation of interest rates, taxation and public spending levels. During downturns, the government can reduce interest rates, pump money into the economy by raising public spending, and cut back taxes to boost aggregate demand and hence economic growth. During boom periods, reverse tactics should be employed to reduce aggregate demand. One major shortcoming of this model is that it omits important factors such as the supply side (whether the conditions exist for firms to be able to invest and expand), and external shocks from the global economy such as trade and foreign exchange rate levels. In the face of global economic forces such as the oil price shocks of the 1970s and the Wall Street crash of 1987, there was little national governments could do to cushion the effects (Sally and Khaw 2006).

The neoclassical growth model, also known as the Solow-Swan growth model or exogenous growth model attempts to explain long run economic growth by looking at productivity, capital accumulation, population growth and technology. It extends the Harrod-Domar model by adding labour as a factor of production and by ensuring that capital-labour ratios are not fixed. These extensions allow increasing capital intensity to be distinguished from technological progress. Since capital is produced based on known technology and technology improves with time, new capital will be more productive than old capital. By implication, policy measures like tax cuts or investment subsidies can affect the steady state level of output but not the long-run national curve (Solow 1956). Growth is affected only in the shortrun as the economy converges to the new steady state output level. The rate of growth as the economy converges to the steady state is determined by the rate of capital accumulation. Capital accumulation is in turn determined by the savings rate (the proportion of output used to create more capital rather than being consumed) and the rate of capital depreciation. The long-run rate of growth is exogenously determined. A country with a higher saving rate will experience faster growth. For example, Singapore had a 40 % saving rate in the period 1960–1996 and annual GDP growth of 5–6 %, compared with Kenya in the same time period which had a 15 % saving rate and annual GDP growth of just 1 % (Haines 2006). In the very long-run, capital accumulation appears to be less important than technological innovation in the Solow model.

A key prediction of the neoclassical growth models is that the income levels of poor countries will tend to catch up with or converge towards the income levels of rich countries as long as they have similar characteristics—for instance saving rates. Since the 1950s, the opposite empirical result has been observed on average. Haines (2006) posits that if the average growth rate of countries since, say, 1960 is plotted against initial GDP per capita (i.e. GDP per capita in 1960), one observes a positive relationship. In other words, the developed world appears to have grown at a faster rate than the developing world, the opposite of what is expected according to a prediction of convergence. However, a few formerly poor countries, notably Japan, do appear to have converged with rich countries. In the case of Japan, it actually exceeded other countries' productivity. Some theorize that this is what has caused Japan's poor growth recently—convergent growth rates are still expected, even after convergence has occurred; leading to over-optimistic investment, and actual recession. Haines rather insists that the evidence is stronger for convergence within countries. For instance, the per-capita income levels of the southern states of the United States have tended to converge to the levels in the Northern states. Whether convergence occurs or not, according to him, depends on the characteristics of the country or region in question, such as institutional arrangements, trade policy with other countries and education policy.

Researchers have also found that while the standard approach of Solow model requires the growth rate of the labour force to be taken as exogenously determined, the structuralist growth model takes investment growth to be determined exogenously in the long run. Also, for the structuralist model to reliably converge to steady growth, considerable attention must be given to how agents make investment decisions. The standard model relies less on agency than does the structuralist. The structuralist growth model (SGM) has its roots in the General Theory of Keynes to extend the Keynesian principle of effective demand to the long run. The central concept of growth models in this tradition is the dual role played by investment, both as a component of aggregate demand and as a flow that augments the stock of capital. The basic structuralist model has been extended to cover a wide variety of topics, including foreign exchange constraints, human capital (Gibson 2005), the informal sector and macroeconomic policy analysis (Lima and Setterfield 2008). The exogenously given rate of growth of a key variable in the case of the standard model is the labor force and for the structuralists, it is the growth of effective demand. So part of the structure is the investment climate. The capital stock will only achieve steady growth when investment and the capital stock are growing at the same rate, and this is true for both the standard and the structuralist models. Steady growth of the capital stock, at whatever rate, therefore necessarily implies steady growth of investment. One of the major hurdles of the structuralist framework is getting the effect of capacity utilization on the growth path of investment to dampen out as the model reaches full capacity utilization. Here the shortage of capacity is at its greatest and one would expect that investment would surge. In fact, other forces must always come into play to keep investment in check. The irony of the structuralist model is that these forces are themselves variables that cannot be determined structurally but requires that agencies must intervene. Also in the neo-classical growth models, the long-run rate of growth is exogenously determined by either the savings rate (the Harrod-Domer model) or the rate of technical progress (Solow model), yet the savings rate and rate of technological progress remain unexplained.

Finally, the endogenous growth models, otherwise known as knowledge-based growth models developed because in the mid-1980s, a group of growth theorists became increasingly dissatisfied with common accounts of exogenous factors determining long-run growth. They favored a model that replaced the exogenous growth variable (unexplained technical progress) with a model in which the key determinants of growth were explicit in the model. Paul Romer (1986), Lucas (1988) and Rebelo (1991) omitted technological change. Instead, growth in these models was due to investment in human capital which had spillover effect on the economy and reduces the diminishing return to capital accumulation. A general feature of these models is rather the presence of constant or increasing returns in the factors that can be accumulated. The model holds that investment in human capital, innovation and knowledge are significant contributors to economic growth. It also focuses on positive externalities and spillover effects of a knowledge-based economy leading to growth. The long run growth rate of an economy, according to this model, depends on policy measures. For example, subsidies for research and development or education increase the growth rate by increasing the incentive for innovation. The A-K model which is the simplest endogenous model assumes that the production function does not exhibit diminishing returns to scale. It attributes this to the positive spillovers from capital investment to the economy as a whole and improvements in technology leading to further improvements (i.e. learning-bydoing). Spillovers are positive externalities, benefits that are attributed to costs from other firms. The model also incorporates imperfect markets and R&D to the growth analysis. The endogenous growth model implication is that policies which embrace openness, competition, change and innovation will promote economic growth. Conversely, policies which have the effect of restricting or slowing change by protecting or favouring particular existing industries or firms are likely over time to slow growth to the disadvantage of the community. Sustained economic growth is a process of continual transformation. To this effect, Howitt (2006) insists that the sort of economic progress that has been enjoyed by the richest nations since the Industrial Revolution would not have been possible if people had not undergone wrenching changes. Economies that cease to transform themselves are destined to fall off the path of economic growth. The richest countries, according to Howitt, need to engage in the never-ending process of economic development if they are to enjoy continued prosperity. Researchers, however, argue that this new growth theory has proven no more successful than the exogenous growth theory in explaining the income divergence between the developing and the developed countries despite usually being more complex.

3.2 Empirical Evidence

The debate on the fundamentals of economic growth in Africa has divided social scientists. In this section, we refer to a few works that have been done to outline major drivers of growth in Africa. First, evidences suggest that there is a strong correlation between economic growth and export performance. The consensus is that increased export growth leads to overall economic growth, thus supporting the experiences of the Asian Tigers and more recently of Brazil, China, India and South Africa. This correlation stems from the fact that increasing exports is associated with access to larger markets which in turn enables exploitation of economies of scale, efficiency gains from technological spillovers and better resource allocation, employment generation and foreign exchange earnings. African exports rose from 22 % of GDP in 1983 to an average of 32 % during the last two decades. Likewise, real GDP growth rose from an average of negative 3 % in 1983 to an average of over 4 % during the past two decades (Mutenyo 2013). But this rise in export revenues has not impacted significantly on its overall export performance as a share of the world total since that share has persistently been declining during the same period. This is in sharp contrast to export performance in China. For instance, Mutenyo insists that Africa's share of total world exports declined from 4.1 % in 1981 to 1.7 % in 1998, only rising slightly to 2.4 % in 2009. Over this entire period, growth has on average only accounted for about 2 % of total global exports, of which 30 % is attributed to South Africa. But for China, export revenue was 28 % of that of Africa in 1980 but by 2009, that same ratio had risen to 408 %. More importantly, China's exports accounted for 1.1 % of total global exports in 1981, but by 2009 its share had risen to 9.8 %.

Researchers have observed that one of the problems facing exports, and consequently growth, in Africa is that African exports are not diversified, with 80 % of its exports concentrated in oil, minerals and primary agricultural commodities. Fuel and minerals alone account for over 50 % of Africa's total exports. Stressing this point further, Mutenyo (2013) posits that “in Angola, 94 percent of exports are in crude oil; in Burundi, 72 percent of exports are in coffee; in Equatorial Guinea, 99 percent of exports are oil and gas; in Malawi, 55 percent of exports are in tobacco; in Nigeria, 82 percent of exports are in crude petroleum; in Sierra Leone, 90 percent of exports are in diamonds; in Zambia, 70 percent of exports are in copper”. Africa's lack of export diversity and dependence on commodities are further compounded by its share of industry to total GDP, which declined from 37 % in 1981 to 33 % in 2010. But primary commodities are vulnerable to changes in world prices, leading to deterioration in the terms of trade. Experience from the recent global financial crisis, according to Mutenyo, shows that less diversified African countries-particularly those that are natural resource rich and depend on oil and minerals such as Angola, Botswana, Equatorial Guinea-were affected most during the crisis. Those African countries with greater diversification tended to be more resilient during the global financial meltdown (Ghana, Senegal, Tanzania and Uganda) or recovered faster (Kenya, Mauritius and South Africa). Another problem confronting export growth in Africa is that the destinations for its exports are also less diversified. About 70 % of Africa's exports go to the United States and the European Union, while less than 10 % is traded within Africa. Researchers have shown that countries that are more dependent on the U.S. and EU markets for exports were more negatively affected by the global financial melt down than those countries that depended on intra-regional trade within Africa because of the spillover effects. Thus, in addition to expanding exports, there is need for African countries to exploit intra-Africa trade for sustainable economic growth. Other problems constraining exports in Africa include high levels of corruption, high costs of doing business due to poor institutions and infrastructure and trade protectionism in the form of non-tariff barriers on Africa's exports.

Many Researchers (Ndulu and O'Connell 1999, 2009; Hoeffler 2002; Tahari et al. 2004) often argue that low total factor productivity is the main impediment to African growth. Others (Berthelemy and S€oderling 2001; Aka et al. 2004) argue that physical and human capital accumulation on the other hand have been identified to facilitate growth in Africa. But Badunenko et al. (2012) employed bootstrap techniques (see Simar and Wilson 1999) in a production frontier framework to provide statistical inference for each component in the decomposition of labor productivity in 35 African countries over the 1970–2007 period. They differed from Henderson and Russell (2005) and Kumar and Russell (2002) who had taken cross-country labor productivity growth over two time periods and decomposed it into different sources by insisting in their study that African countries have access to their own production frontier, and not necessarily to the world production frontier, thus benchmarking African economies against one another. Furthermore, other studies that had used nonparametric production frontier measurement have largely ignored the issue of statistical inference when identifying the sources of labor productivity growth.

Badunenko et al. (2012) is a bit detailed enquiry into the application of the endogenous growth model in Africa. The study uses the HR methodology to decompose labor productivity growth into components attributable to (i) efficiency changes (ii) technological change (iii) capital deepening and (iv) human capital accumulation. They specified the technology that contains four macroeconomic variables: aggregate output and three inputs—labor, physical capital, and human capital. In their model, the number of workers is obtained as the product of per capita GDP computed via the chain method and the population taken as a ratio of real GDP per worker. The measure of output is calculated output per worker multiplied by the number of workers. Real aggregate investment is computed as the product of 2005 indexed real output, population and the investment share of GDP. The study follows Caselli and Feyrer (2007) and applied the perpetual inventory method to the real investment series to construct the physical capital stock. They also followed the method of HR and adopted the Hall and Jones (1999) construction of human capital using an updated education database (Barro and Lee 2010) to find the average (African) returns for each level of education. Their results suggest that capital deepening is the primary driver of labor productivity growth in Africa followed by human capital accumulation. Technological change is essentially nonexistent and of the four components, only efficiency changes and human capital accumulation are significant on average. They attributed the insignificance of physical capital contribution to the fact that the value of capital stock in developing countries does not necessarily reflect its public investment cumulated at cost. The researchers also maintain that if government investment spending has created little useful capital, its contribution to productivity growth will likely be insignificant. Their results indicate that human capital accumulation plays a larger role than physical capital.

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