James Foreman-Peck in The Experience of Economic Growth and Human Capital, attempts to engage the debate as to whether poorer countries grow faster? In this respect, he gives two common ways of assessing a country’s economic growth. The first is via the use of Beta (β) convergence and the second is through the use of sigma (σ) convergence.
Beta (β)-convergence utilizes regression analysis and it’s a means to show how converged the economic status is. The following parameters apply.
Growth (of any country x) is equivalent to the sum of a constant and the beta of the initial Gross Domestic Product per worker in that economy.
If β is less than zero, then it indicates that poorer countries on average grow faster.
On the other hand, σ-convergence measures the dispersion through the variance of the Gross Domestic product per Worker in an economy in a given year. It this dispersion falls over time it can be hypothesized that the country is ‘converging’.
On a survey that comprised of 110 countries only, the following revelations were made. The value of beta was zero on all the countries and thus led to the conclusion that there was no beta convergence. However, this study was limited to the fact that there are more than 110 countries and James found that the poorest countries do not have data hence his findings cannot be generalized.
Another researcher Pritchett in his study Divergence, Big Time, in 1997, found out that between the years 1870 and 1990, rich countries got much richer in a ratio of 9/1 in the 1870s. The ratio shifted to 45/1 in the 90’s. This observation depicts the period 1960-1980 as the good decades for poorer countries since it was a time of normal divergence.
Conditional divergence is assessed through regression analysis where the analysis is used for controlling other factors such as investments in an economy. Through this form of divergence, poorer countries do seem to grow faster. However this hypothesis seems to lack a well defined and listed number of what this other factors other than investments might be.
In the course of utilizing sigma convergence, it is evident that the variance of the 110 countries GDP per worker sought increases over time; this shows that there was divergence since the 1960s. However, the above sentiments were considered on the fact that the individual countries need to be weighted according to population and when this was done it showed that there was convergence. The weighting index changed entirely due to China as espoused by Milanovic. It is important to note that if you do not weight the countries you give populous countries such as China same ‘importance’ or ‘weight’ with least populous countries.
The debate has generated much interest amongst researchers and now some are working on a ‘true’ world inequality data. This is combining within and across countries data. The initial results show that the ‘world’ inequality has been increasing since the late 1980s.
‘Convergence’ Driving Mechanisms
Beyond the basic data lies the real issues; it is important to understand the real data but beyond is where you find the mechanisms. This is done through the consideration of theories such as the open economy growth model and the models of technological catch up. It is however important to note that this ‘convergence’ is not the same as the ‘neoclassical convergence to steady state’. This can be done through a consideration of countries in the steady state model with an assumption that technology is common to all countries.
All of the above can be summarized by concluding that if unweighted measures are used, cross-country convergence results would otherwise show or be interpreted as ‘divergence’. However, divergence is seen in the recent studies of ‘world inequality’ measures based on both within and across country data.
Contribution of Human Capital to Economic Growth
New ideas are generated after careful thought and this in turn gives rise to formulae, designs and software; all of which are productive. The machines and infrastructure are critical components of an economy and they are generated via human capital. Many useful applications include medicine, high speed trains, computers, aircrafts etc.
Creative Destruction and Firm-Level Activity
It has been found out that many endogenous growth models assume profit-seeking firms invest in research and development with an aim of gaining new ideas and knowledge. However, profits from these firms are gained through incentives such as those expected from a monopoly i.e. gains from profits on new products or processes. This depends on probability of inventing and if successful and expected length of monopoly this is gained from the strength of their intellectual property rights such as patents. The costs incurred are the expected labor costs. These costs depend on productivity which in turn also depends on the extent of spillovers.
The endogenous growth models are based on a ‘monopolistic competitive’ ideology. This is through free entry into research and development which implies that zero profits comes from fixed costs of research and development being equal to the monopoly profits. ‘Creative destruction’ is present since new inventions destroy markets of some if not all the existing products. Without ‘knowledge spillovers’ such firms run into diminishing returns. Such models have three potential market failures which make policy implications unclear. Some implication of augmenting neo-classical models with human capital have been cited to be that higher saving rates in physical capital not only increases but also human capital. This also applies for higher investment in human capital. The share of each factor is dependent on the relative contribution of physical and human capital in output.
International Technology Diffusion and Economic Growth
The disembodied human capital of Mankiw, Romer and Weil implies that a unitary change in the work force has a grater positive effect on output than a unitary rise in human capital per worker. For many developing countries, with high population and workforce growth, this disembodied model if more optimistic than an endogenous growth Cobb-Douglas production function.
It is evident that in a low income open economy, technology transfer is likely to be a major source of growth. The scope for transfer will depend on the technological progress of the leaders in the world economy which are assumed to advance at a rate given by the technological frontier economy total factor productivity index (F). It is however a fact that, technology can only be transferred if an economy has the absorptive capacity. Benhabib and Speigel in 1994 concluded that international technology spillover rates depend on levels of education in the follower countries.
It is also true to say that a plausible formulation for a poorer economy allows greater technical progress the higher is the human capital that promotes this capacity. The gap between a follower economy’s technology and the leader’s depend upon the follower’s average human capital and the level of the leader’s technology. Technical progress is exogenous or neoclassical to the domestic economy but the impact of technology itself is endogenous. With endogenous growth function and the labor output elasticity being the same, an economy with high workforce growth and weak human capital investment will increasingly miss out as the world technology frontier advances.
Barro in his Empirical Neoclassical Growth Model, showed that diminishing returns on technological progress means that, as output rises, growth rate falls back to the value determined by technical progress. He also showed that conditional convergence arises due to higher starting values which are associated with lower growth rate. Poor countries do not grow faster if they have low steady outputs. He did also show that there was never absolute convergence on growth of economies and that dispersion increased from 1960-90 across 114 countries.