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Read the article and answer questions 17-23. The Rise of Knowledge Economics. By César A. Hidalgo, Scientific American Nearly 30 years ago, Paul Romer published a paper exploring the economic value of knowledge. Extending Romer's ideas to the world of big data we can create empirical measures of knowledge. A few years later we published a metric measuring the total knowledge in a country, region, or city. This measure focused on the intensity of knowledge - the fact that knowledge cannot be simply added, since it has overlaps and comes in discrete chunks. The basic idea was that the knowledge of a place was expressed in the activities present in it, and the knowledge of an activity was expressed in the places where that activity was present. This allowed us to define knowledge in a completely circular manner using either recursions or a mathematical technique related to principal component analysis. The good news was that this made no assumptions about which places or activities were most knowledge intense. We called this metric the Economic Complexity Index. But did economic complexity vindicate Romer's vision? The answer was a resounding yes. Countries that were more knowledge-intense were richer and less unequal, and when they had an excess of knowledge per unit of GDP per capita, they grew faster. The magic metric predicted the rise of East Asia, the crisis of Greece and the stagnation of Latin America. Yet, these findings still told us little about how knowledge got into new places. This is where research is going next. When I present my work to scholars, entrepreneurs, ministers and public servants, they want to know the list of the activities that are most related to their location - a list of where to focus their industrial development efforts. In the recent paper we explored millions of lists, instead of focusing on just one. The math showed that following a list in decreasing order of relatedness was actually suboptimal. This was because the list contained products that were highly related, but were also dead-ends (that is, products that were not connected to other products). Dead-ends can rank high on “the list,” but sometimes, it is better to focus on products that are harder to develop but that open new paths. Moreover, the math showed that there was a narrow window of opportunity when it was optimal for countries to deviate from the most related activities. Being too ambitious too early led to failed development projects. Being too conservative during the optimal window wasted an opportunity. Task 3 For sentences 17-23, choose the correct ending. Mark one letter (A, B or C) on your answer sheet. 17. Knowledge can be measured as A. the total of expertise in a particular activity. B. knowledge concentrated in a location associated with activities performed in this location. C. a series of mathematical recursions. 18. The Economic Complexity Index A. allows predicting which area or activity has the greatest knowledge potential. B. allows assessing knowledge intensity of an area or activity. C. rates economic advancement of an area. 19. The concept of economic complexity A. clearly supports Romer's views. B. contests Romer's views. C. expands on Romer's ideas. 20. Knowledge intensity in a country is a precondition for A. its industrial development. B. knowledge overspill. C. decrease in income disparity. 21. Using an ECI based list of activities related to a definite area A. is a good way to determine the areas for potential economic growth – the top areas are the best. B. is not as efficient as common logic implies. C. is practically useless. 22. The author argues that A. maths can be used to avoid dead-ends. B. it is better to develop the areas where there are many related products already. C. it is better to develop less developed areas. 23. Decision making in choosing the right area for development A. should consider the development areas with the highest knowledge intensity scores. B. should account for country's geographic location. C. should be timely.