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How did the hierarchy of the world-system adapt to the impact of the 2008–09 global economic crisis? How did a country's position in the world-system influence their upward mobility during the crisis? This paper investigates the core/periphery hierarchy of the global trade network before and after the 2008–09 crisis. The central argument posits that the global trade network follows a core/periphery hierarchy in relation to the new international division of labor (NIDL) in the twenty-first century, and a country's placement within that hierarchy had a varying effect on their upward mobility following the 2008–09 crisis. Utilizing social network analysis of 191 countries engaged in global trade, I discover that the core/periphery structure remained unchanged after the 2008–09 global financial crisis, although many countries in intermediate positions experienced upward shifts. However, not all countries were able to achieve upward mobility, indicating that only a few semi-peripheral and peripheral countries were better positioned to improve their status compared to most non-core countries.

AbstractMany important problems in Operations Research and Statistics require the computation of nondominated (or Pareto or efficient) sets. This task may be currently undertaken efficiently for discrete sets of alternatives or for continuous sets under special and fairly tight structural conditions. Under more general continuous settings, parametric characterisations of the nondominated set, for example through convex combinations of the objective functions or ε‐constrained problems, or discretizations‐based approaches, pose several problems. In this paper, the lack of a general approach to approximate the nondominated set in continuous multiobjective problems is addressed. Our simulation‐based procedure only requires to sample from the set of alternatives and check whether an alternative dominates another. Stopping rules, efficient sampling schemes, and procedures to check for dominance are proposed. A continuous approximation to the nondominated set is obtained by fitting a surface through the points of a discrete approximation, using a local (robust) regression method. Other actions like clustering and projecting points onto the frontier are required in nonconvex feasible regions and nonconnected Pareto sets. In a sense, our method may be seen as an evolutionary algorithm with a variable population size. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.