Author: Sanjeev Dasgupta
My research project focuses on two linked questions. The first part of my project attempts to understand how well different countries utilize their stocks of human talent. The second part of my project focuses on two case studies – Ethiopia and Uzbekistan – to understand the ways in which certain countries identify and nurture talent for high levels achievement.
Key Social Mobility Findings
- There is significant variation with respect to how different countries utilize their stocks of human talent. While there are some outliers, the global distribution of talent utilization largely reflects a North-South divide, with countries in the Global North being able to utilize their stocks of human talent better than those in the Global South.
- For outlier countries – those that perform poorly on average on the talent utilization index but perform very well in one indicator – there are specific national pathways through which individuals are identified and nurtured for high achievement.
Research on how well one can move up the economic or social ladder in society – broadly termed social mobility – has rapidly expanded over the last decades. Many have looked at the various factors that influence social mobility. But the majority of the work is focused on developed countries, largely due to reasons of data availability. But considering that the majority of the world’s population currently resides in developing countries, with more expected growth over the next few decades, there is a big gap in our understanding of social mobility around the world.
Limitations with using traditional measures of social mobility based on income and occupational status – particularly in the developing country context, although not limited to it – have spurred academics to look at some alternative measures of studying social mobility. Work on such alternative measures, however, is so far largely in the nascent stage. One such measure is how well countries utilize their stocks of human talent. Assuming that talent is randomly distributed at birth, the logic goes, some countries make better use of their talent pools, and this better utilization is reflected in diverse indices of collective achievement.
The first part of my project deals with how well different countries utilize their stocks of human talent. I used indices of five different types of talent: sports (Olympic medals per million persons), innovation (patents registered per million persons), job creation (new businesses registered per million residents), knowledge production (citable publications released per million residents), and art and culture (nationally produced films released per million persons). I then ranked countries in each index according to how well they utilize talent, and gave each country a quartile rank for each index. Finally, I created a global distribution of countries by comparing how well they did across the five indices. Countries were classified into five types (A through E). Types A and E reflected countries that were in the top 2 quartiles and bottom 2 quartiles respectively for each index. Types B and C referred to countries that were the top 2 quartiles or bottom 2 quartiles respectively for all but one index. Type C reflected countries that had a mixed range of quartile positions.
Figure 1: The five indicators used to measure the level of talent utilization
Based on this analysis, I focused the second part of my project on Type D countries – countries that only performed well on one indicator. I specifically chose to concentrate on Type D countries that performed well in Olympic Games, and focused on two qualitative case studies – Ethiopia and Uzbekistan – to try and understand how these countries identify and nurture talents. I identified each athlete who has won a medal over the past six Olympic Games. I then briefly traced the background of each athlete – although I was limited by the amount of information available – to identify common elements of that back story to indicate how they became high achievers. I supplemented this data with information on aspects such as availability of sports facilities in schools, the frequency of nationally organized sporting events, national sports policies, and funding and other support given by the government to sporting institutions.
Figure 2: This map shows the global distribution of how well countries utilize their stocks of talent. Countries are classified into five types – ranging from Type A to Type E – based on their performance along each indicator. Type A and Type E countries represent the two ends of the spectrum, with Type A countries representing those that are best at utilizing talent.
Findings from the first part of my project indicated a general split along the lines of the Global North and Global South; developed countries in general tend to better utilize talent than developing countries. There are, however, a few anomalies to this pattern, especially indicated by certain Type D countries. Type D countries are particularly interesting because they perform poorly across all metric except one. This suggests that each country successfully nurtures only one type of talent, thus indicating interesting outliers. Studying these outliers can give interesting insights into the kinds of policies and pipelines that are able to facilitate talent identification and facilitation of high achievement.
I focus the second part of my project on two case studies – Ethiopia and Uzbekistan – two type D countries that perform consistently poorly across all indicators except one, Olympic Medals.
Figure 3: Ethiopia is one of the Type D countries identified by the project. It performs poorly on two of the three indicators – citable publications and new businesses – that have information available but performs relatively well in Olympic medals.
Figure 4: Uzbekistan is another Type D identified by the project. It performs poorly in four out of the five indicators – citable publications, new businesses, national films, and patents – but performs anomalously well in Olympic medals.
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