The Great Convergence
Description
To work out the distribution of knowledge in terms of technology creation and diffusion across the globe. An update on ARCO index from 2000-2015, for Technology Achievement using Worldbank WDI Data and application of factor analysis, cluster analysis and convergence analysis. It was found that the world is much more heterogeneous and diverse in terms of patters of creation and diffusion of technology, particularly in a time of rapid diffusion of ICT innovations the grouping of nations or technology clubs change rapidly, they expand and contract as nations move up the technology ladder. More new players have entered the club of traditional leaders in technology creation or innovation. A huge global technology middle class has emerged having a strong absorptive capacity they have mastered both older innovation and learnt to augment it with recent innovations. These nations have shown rapid growth and progress in the twenty first century. but almost One fourth of humanity is still living in technologically marginalized countries. They are still as far behind the rest in terms of education and adoption of older innovations as they were in the last century. Although diffusion of recent innovations has been very rapid among this group as well but the technology gaps are still vast. Besides that these nations are unable to fully utilize the recent innovations as they have not grasped even the mature technologies. However with the advent of smart phones and therefore internet to the remotest and least developed parts of these nations there is hope for emergence of a new type of educational revolution, having a direct impact on reduction in poverty and improvement in livelihood of the marginalized people.
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Steps to reproduce
calculate arco index by computing five year moving average of each variable for the the years 2000. 2005. 2020 and 2015. Select the whole period upper and lower thresholds or goalposts for index calculation. Calculate index using geometric means. apply factor analysis to the variables, save the latent variables. Use the latent variables as inputs in hierarchical cluster analysis to find the most suitable cluster solution. Assess Beta and Sigma Convergence for the whole data-set and for individual clusters. Apply multinomial logistic regression to test the accuracy of the cluster analysis results as well as to predict the chances of cluster shift for each group of nations.