The Heterogeneous Dynamics of Economic Complexity

Abstract

What will be the growth of the Gross Domestic Product (GDP) or the competitiveness of China, United States, and Vietnam in the next 3, 5 or 10 years? Despite this kind of questions has a large societal impact and an extreme value for economic policy making, providing a scientific basis for economic predictability is still a very challenging problem. Recent results of a new branch—Economic Complexity—have set the basis for a framework to approach such a challenge and to provide new perspectives to cast economic prediction into the conceptual scheme of forecasting the evolution of a dynamical system as in the case of weather dynamics. We argue that a recently introduced non-monetary metrics for country competitiveness (fitness) allows for quantifying the hidden growth potential of countries by the means of the comparison of this measure for intangible assets with monetary figures, such as GDP per capita. This comparison defines the fitness-income plane where we observe that country dynamics presents strongly heterogeneous patterns of evolution. The flow in some zones is found to be laminar while in others a chaotic behavior is instead observed. These two regimes correspond to very different predictability features for the evolution of countries: in the former regime, we find strong predictable pattern while the latter scenario exhibits a very low predictability. In such a framework, regressions, the usual tool used in economics, are no more the appropriate strategy to deal with such a heterogeneous scenario and new concepts, borrowed from dynamical systems theory, are mandatory. We therefore propose a data-driven method—the selective predictability scheme—in which we adopt a strategy similar to the methods of analogues, firstly introduced by Lorenz, to assess future evolution of countries.

Related variety and economic development: a literature review

“…They show there is a negative relationship between these two dimensions, that is, diversified countries tend to export less ubiquitous products. For further refinements, see Tacchella, Cristelli, Caldarelli, Gabrielli, and Pietronero (2012) and Cristelli, Tacchella, and Pietronero (2015).…”

A Comparative Analysis of the Predictive Abilities of Economic Complexity Metrics Using International Trade Network

“…The ones in the region with high GDP per capita compared to their degree (such as Norway and Iceland) tend to have a more static trajectory along the GDP per capita axis. This remark was made in [16] for fitness metric. It is hard to distinguish the different features between degree and Fitness on this graph; the same three countries (Czech Republic, China, and India) stand out.…”

“…The shadowed areas represent the standard deviation over 256 different choices of axis limits. has already proven to be a good predictor for countries in specific location [19].…”

“…Similarly to [16], we average the trajectories from 1998 to 2014 in Figure 3 for the three different metrics coupled with GDP per capita. In the degree plot, we see that every country which has a degree superior to about 100 has a significant GDP growth.…”

“…In [16], a method based on the method of analogues [17,18] was applied to the prediction of the countries' GDP. This method is named the selective predictability scheme.…”