Andrea Tacchella

A New Metrics for Countries' Fitness and Products' Complexity

Classical economic theories prescribe specialization of countries industrial production. Inspection of the country databases of exported products shows that this is not the case: successful countries are extremely diversified, in analogy with biosystems evolving in a competitive dynamical environment. The challenge is assessing quantitatively the non-monetary competitive advantage of diversification which represents the hidden potential for development and growth. Here we develop a new statistical approach based on coupled non-linear maps, whose fixed point defines a new metrics for the country Fitness and product Complexity. We show that a non-linear iteration is necessary to bound the complexity of products by the fitness of the less competitive countries exporting them. We show that, given the paradigm of economic complexity, the correct and simplest approach to measure the competitiveness of countries is the one presented in this work. Furthermore our metrics appears to be economically well-grounded.

Measuring the Intangibles: A Metrics for the Economic Complexity of Countries and Products

We investigate a recent methodology we have proposed to extract valuable information on the competitiveness of countries and complexity of products from trade data. Standard economic theories predict a high level of specialization of countries in specific industrial sectors. However, a direct analysis of the official databases of exported products by all countries shows that the actual situation is very different. Countries commonly considered as developed ones are extremely diversified, exporting a large variety of products from very simple to very complex. At the same time countries generally considered as less developed export only the products also exported by the majority of countries. This situation calls for the introduction of a non-monetary and non-income-based measure for country economy complexity which uncovers the hidden potential for development and growth. The statistical approach we present here consists of coupled non-linear maps relating the competitiveness/fitness of countries to the complexity of their products. The fixed point of this transformation defines a metrics for the fitness of countries and the complexity of products. We argue that the key point to properly extract the economic information is the non-linearity of the map which is necessary to bound the complexity of products by the fitness of the less competitive countries exporting them. We present a detailed comparison of the results of this approach directly with those of the Method of Reflections by Hidalgo and Hausmann, showing the better performance of our method and a more solid economic, scientific and consistent foundation.

A Network Analysis of Countries’ Export Flows: Firm Grounds for the Building Blocks of the Economy

In this paper we analyze the bipartite network of countries and products from UN data on country production. We define the country-country and product-product projected networks and introduce a novel method of filtering information based on elements’ similarity. As a result we find that country clustering reveals unexpected socio-geographic links among the most competing countries. On the same footings the products clustering can be efficiently used for a bottom-up classification of produced goods. Furthermore we mathematically reformulate the “reflections method” introduced by Hidalgo and Hausmann as a fixpoint problem; such formulation highlights some conceptual weaknesses of the approach. To overcome such an issue, we introduce an alternative methodology (based on biased Markov chains) that allows to rank countries in a conceptually consistent way. Our analysis uncovers a strong non-linear interaction between the diversification of a country and the ubiquity of its products, thus suggesting the possible need of moving towards more efficient and direct non-linear fixpoint algorithms to rank countries and products in the global market.

The Heterogeneous Dynamics of Economic Complexity

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.

Diversification versus Specialization in Complex Ecosystems

By analyzing the distribution of revenues across the production sectors of quoted firms we suggest a novel dimension that drives the firms diversification process at country level. Data show a non trivial macro regional clustering of the diversification process, which underlines the relevance of geopolitical environments in determining the microscopic dynamics of economic entities. These findings demonstrate the possibility of singling out in complex ecosystems those micro-features that emerge at macro-levels, which could be of particular relevance for decision-makers in selecting the appropriate parameters to be acted upon in order to achieve desirable results. The understanding of this micro-macro information exchange is further deepened through the introduction of a simplified dynamic model.

An Overview of the New Frontiers of Economic Complexity

The fundamental idea developed throughout this short overview on Economic Complexity is that a revolution of the revolution of Economics is needed to turn this field into a mature discipline. The first revolution of Economic Complexity (Bouchaud in Nature 455:1181, 2008) led to a conceptual paradigm shift and agent-based models have shown, from a qualitative point of view, the crucial role played by concepts like agent heterogeneity and herding behavior to understand the non-trivial features of financial time series. The second revolution must lead the paradigm shift from a conceptual and qualitative level to a quantitative and effective description of economic systems. This can be achieved through the introduction of new metrics and quantitative methods in Social Sciences (Economics, Finance, opinion dynamics, etc.). In fact, the concept of metrics is usually neglected by mainstream theories of Economy and Finance. Only in that way Economic Complexity can concretely affect the thinking of Economic mainstream and, in this sense, become a mature discipline. The large availability of datasets (the so-called Big Data Science) has recently revealed new promising path towards such perspectives and, as an example, we briefly discuss how archival data about export flows can be turned into a concrete tool to assess the competitiveness of countries and the complexity of products.

The Virtuous Interplay of Infrastructure Development and the Complexity of Nations

Does the infrastructure stock catalyze the development of new capabilities and ultimately of new products or vice-versa? Here we want to quantify the interplay between these two dimensions from a temporal dynamics perspective and, namely, to address whether the interaction occurs predominantly in a specific direction. We therefore need to measure the complexity of an economy (i.e., its capability stock) and the infrastructure stock of a country. For the former, we leverage a previously proposed metrics, the Economic Fitness (Tacchella, A.; et al. Sci. Rep. 2012, 2, 723). For the latter, we propose a new purely statistical indicator which is the principal component performed on the 47 infrastructure indicators published by the World Bank. The proposed indicator still belongs to the class of linear combination of relevant indicators but, differently from standard economic indicators of the same type as the Connectivity Index, the HDI, etc, the weights of the linear combination are not subjectively chosen or re-calibrated on a regular basis but they are those which capture the highest fraction of the information encoded in the initial dataset. The two metrics allow the study of the dynamics in the Economic Fitness-Infrastructure plane and reveal the existence of two regimes: one for low Fitness where the infrastructure and the complexity of an economy are unrelated and a second regime where the two dimensions are tightly related. To quantify the interplay of the two dimensions in this latter regime, we assume a parsimonious linear dynamic model and the emerging picture is that: (i) the feedback occurs in both directions; (ii) on the short-term (<3 years) the predominant direction of interaction is from infrastructure to capability stock; (iii) while for longer time scale (>3 years) the interaction is reversed, new capabilities lead to increasing infrastructure stock.

TIME EVOLUTION OF FINANCIAL CROSS-CORRELATION COEFFICIENTS ACROSS MARKET CRISIS

We investigate the time evolution of financial cross-correlation coefficients during financial crises and compare them to what is observed in periods of stability. We choose three main events, the Dot.Com Bubble, the market crisis which followed the attacks at the Twin Towers in 2001 and the recent subprime crisis. Each of them has a different nature and a different impact on the market, which we analyze by studying separately different economic sectors. As a general trend, we observe an increase of correlation during these high volatility periods and a broadening of the distributions of correlation coefficients. We then compare the spectra of the cross-correlation matrices, calculated in different periods of three years, with the distribution of eigenvalues predicted by the Random Matrix Theory. We find that these spectra are markedly perturbated during crisis periods. Finally we show how a simple stochastic model can produce similar results.