Self-Reinforcing Mechanisms in Economics: with Two Case-Studies from Shipping Industry

Abstract

“Returns to scale”, as a methodology, goes back to Adam Smith (1776). Economists after Marshall (1842-1924), with certain exceptions , confined their interests exclusively to “Constant returns to scale ”. As a return they secured for their analysis a single equilibrium point with convenient convex curves. Early economists were sure that “resource-based” economic sectors like “Agriculture”, “Bulk goods production” and “Mining” exhibited “diminishing returns to scale”, a belief valid till this day. However, at least 9 important “knowledge-based” sectors exhibited increasing returns. More important is that increasing returns provides also no obstacle to growth of a firm, and of an industry…till it becomes a monopoly... We presented arguments against and in favor of this last theory drawn from Shipping and Marketing. Moreover, we examined the existence of increasing returns in relation to shipping industry and especially with 2 case-studies dealing with: 1) the exceptional economies of scale concerning “Vale S.A.” and its giant ships “Valemax” of 400,000 dwt, double in size of existing ships ( i.e. the Capes of about 200,000 dwt) and 2) the case of shipowners locked-in within the extremely high chartering markets during 2003-2008. The first was also examined in relation to the impact of “chance or small historical events”. Traditional economists indeed did not have the mathematical tools to deal with “increasing returns” and “path dependence” till 1983, when “Arthur et al. ” discovered the “Urn schemes of the Polya kind”. This method succeeded in combining probabilities or random facts with deterministic or chaos motions! This had a great impact on methodology, which, prior to that, dealt with either random or deterministic models in a water-proof separate manner. The aim of this paper was to mark out the importance of increasing returns, which has important implications in economic development policies, and comes from Complexity Theory and Chaos theory. With the first case study we showed, in addition, that market does not know best and small chance (historical) events, or mistakes, determine prevalence of a good or of a technical innovation. Moreover, we showed that in case of a lock-in, firms cannot do the best provided they have also the ability to foresee future. This analysis deals with a slice of Complexity theory.