In the current era of worldwide stock market interdependencies, the global financial village has become increasingly vulnerable to systemic collapse. The recent global financial crisis has highlighted the necessity of understanding and quantifying in
terdependencies among the worlds economies, developing new effective approaches to risk evaluation, and providing mitigating solutions. We present a methodological framework for quantifying interdependencies in the global market and for evaluating risk levels in the world-wide financial network. The resulting information will enable policy and decision makers to better measure, understand, and maintain financial stability. We use the methodology to rank the economic importance of each industry and country according to the global damage that would result from their failure. Our quantitative results shed new light on Chinas increasing economic dominance over other economies, including that of the USA, to the global economy.
Politicians world-wide frequently promise a better life for their citizens. We find that the probability that a country will increase its {it per capita} GDP ({it gdp}) rank within a decade follows an exponential distribution with decay constant $lam
bda = 0.12$. We use the Corruption Perceptions Index (CPI) and the Global Competitiveness Index (GCI) and find that the distribution of change in CPI (GCI) rank follows exponential functions with approximately the same exponent as $lambda$, suggesting that the dynamics of {it gdp}, CPI, and GCI may share the same origin. Using the GCI, we develop a new measure, which we call relative competitiveness, to evaluate an economys competitiveness relative to its {it gdp}. For all European and EU countries during the 2008-2011 economic downturn we find that the drop in {it gdp} in more competitive countries relative to {it gdp} was substantially smaller than in relatively less competitive countries, which is valuable information for policymakers.
