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A geometrical imaging of the real gap between economies of China and the United States

Ali Hosseiny

Physica A: Statistical Mechanics and its Applications, 2017, vol. 479, issue C, 151-161

Abstract: GDP of China is about 10 trillion dollars and GDP of the United States is about 18 trillion dollars. Suppose that we know for the coming years, economy of the US will experience a real growth rate equal to %3 and economy of China will experience a real growth as of %6. Now, the question is how long does it take for economy of China to catch the economy of the United States. The early impression is that the desired time is the answer of the equation 10×1.06X=18×1.03X. The correct answer however is quite different. GDP is not a simple number and the gap between two countries cannot be addressed simply through their sizes. It is rather a geometrical object. Countries pass different paths in the space of production. The gaps between GDP of different countries depend on the path that each country passes through and local metric. To address distance between economies of China and of the US we need to know their utility preferences and the path that China passes to reach the US size. The true gap then can be found if we calculate local metric along this path. It resembles impressions about measurements in the General Theory of Relativity. Path dependency of measurements is an old known fact in economy. It is widely discussed in the Index Number Theory. Our aim is to stick to the geometrical view presented in the General Relativity to provide a fast impression about the matter for physicists. We show that different elements in the general relativity have their own counterparts in economics. We claim that national agencies who provide aggregate data resemble falling observers into a curved space time. It is while the World Bank or international organizations are outside observers.

Keywords: Index number theory; General relativity; Aggregate indexes; GDP; Path dependency; Growth rate (search for similar items in EconPapers)
Date: 2017
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