sadegh bafandeh imandoust; ali mofidi
Abstract
The GCI combines 113 indicators. These indicators are grouped into 12 pillars: institutions, infrastructure, macroeconomic environment, health and primary education, higher education and training, good market efficiency, labor market efficiency, financial market development, technological readiness, ...
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The GCI combines 113 indicators. These indicators are grouped into 12 pillars: institutions, infrastructure, macroeconomic environment, health and primary education, higher education and training, good market efficiency, labor market efficiency, financial market development, technological readiness, market size, business sophistication and innovation. These are in turn organized into three sub-indexes, in line with three main stages of development; basic requirements, efficiency enhancers and innovation and sophistication factors. In this paper, the effect of global competitiveness index (GCI) on economic growth has been studied. To this end, panel data of 42 countries collected in the period 2010 to 2014, and the model is estimated. The model estimation results show that the GCI score has positive and significant effects on GDP per capita growth among selected developed and developing countries.
Methodology
Competitiveness is defined as the set of institutions, policies, and factors that determine the level of productivity of a country. Many determinants drive productivity and competitiveness. Understanding the factors behind this process has occupied the minds of economists for hundreds of years, engendering theories ranging from Adam Smith’s focus on specialization and the division of labor to neoclassical economists’ emphasis on investment in physical capital and infrastructure and, more recently, to interest in other mechanisms such as education and training, technological progress, macroeconomic stability, good governance, firm sophistication, and market efficiency among others. While all of these factors are likely to be important for competitiveness and growth. In economic theory of stages of development, the GCI assumes that, in the first stage, the economy is factor-driven and countries compete based on their factor endowments— primarily unskilled labor and natural resources. Companies compete on the basis of price and sell basic products or commodities, with their low productivity reflected in low wages. Maintaining competitiveness at this stage of development hinges primarily on well-functioning public and private institutions (pillar 1), a well-developed infrastructure (pillar 2), a stable macroeconomic environment (pillar 3), and a healthy workforce that has received at least a basic education (pillar 4). As a country becomes more competitive, productivity will increase and wages will rise with advancing development. Countries will then move into the efficiency-driven stage of development, when they must begin to develop more efficient production processes and increase product quality because wages have risen and they cannot increase prices. At this point, competitiveness is increasingly driven by higher education and training (pillar 5), efficient goods markets (pillar 6), well-functioning labor markets (pillar 7), developed financial markets (pillar 8), the ability to harness the benefits of existing technologies (pillar 9), and a large domestic or foreign market (pillar 10). Finally, as countries move into the innovation-driven stage, wages will have risen by so much that they are able to sustain those higher wages and the associated standard of living only if their businesses are able to compete with new and unique products. At this stage, companies must compete by producing new and different goods using the most sophisticated production processes (pillar 11) and by innovating new ones (pillar12). The Global Competitiveness Index (GCI) has been used by the World Economic Forum to assess the level of productivity of an economy. Hall and Jones (1996) have shown that around 89 percent of the variation in GDP per capita is due to variation in the level of productivity. As a result, GDP per capita can be used as a proxy for the level of productivity of a country. The regress of the log level of GDP per capita on the GCI score reveals that about two-thirds of the variation in GDP per capita can be explained by the GCI. However, estimating a bivariate relation between the growth rate and the GCI would be a mistake. The reason for that lies in what economists call the “conditional convergence effect”, which posits that, all other things being equal, there is a natural tendency for poor economies to grow faster—a phenomenon known as conditional convergence. In other words, if all countries had the same investment and population growth rates and the same levels of productivity, then we should observe poor countries growing faster than rich ones. Conversely, if all countries had the same level of income, then those that were more competitive would experience higher rates of long-term economic growth. In reality, however, countries differ both in their levels of income and their levels of productivity, and therefore it is very hard to predict the relationship between the growth rate and the level of productivity with a bivariate correlation analysis that includes the initial level of income. Formally, in a growth convergence equation, the growth rate of GDP per capita of country is a positive function of the GCI score and a negative function of GDP per capita.
Results and Discussion
The model estimation results show that the GCI score has positive and significant effects on GDP per capita growth among selected developed countries and a %10 increase in a country’s GCI score would lead to an increase in the economic growth by 17.32588 percentage points. This amount is 15.49522 for selected developing and emerging countries. Results of this paper show that “net growth rate” against the GCI score, revealing a positive and strong correlation, which is consistent with the view that the GCI is a good proxy for the level of productivity or competitiveness of an economy.
sadegh bafandeh imandoust; ali mofidy
Abstract
Undobtedly, the Information and Communication Technology (ICT) has affected the country’s economic and social interactions. The Networked Readiness Index (NRI) comprising four subindexes that measure the enviroment for ICT’s, the readiness of a society to use ICT’s, the actual usage of all main ...
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Undobtedly, the Information and Communication Technology (ICT) has affected the country’s economic and social interactions. The Networked Readiness Index (NRI) comprising four subindexes that measure the enviroment for ICT’s, the readiness of a society to use ICT’s, the actual usage of all main stakholders (Individual, Business and Government), and finally, the impacts that ICT’s generate in the economy and in the society, the three first subindexes can be regarded as the driver that establish the conditions for the results of the fourth subindexes, ICT impacts. These four subindexes are divided into 10 pillars composed of 54 individual indicators in total. Therefore, in this paper, the effect of Networked Readiness Index (NRI) on economic growth has been studied. To this end, panel data of 77 countries collected in the period 2007 to 2012, and the model is estimated by Generalized Method of Moments(GMM). The model estmation results show that the NRI score has positive and significant effects on GDP per capita growth in developed countries and this amount is equal 0.202848. This effect is positive and non significantin developing countries.
