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Special Issue "Economic Fitness and Complexity"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: closed (31 July 2018).

Special Issue Editors

Guest Editor
Prof. Dr. Luciano Pietronero

Department of Physics, University of Rome La Sapienza, Piazzale A. Moro 2, 00185 Rome, Italy
IFC-World Bank, Washington, DC 20433, USA
Institute of Complex Systems, National Research Council (CNR), 00185 Rome, Italy
Website | E-Mail
Interests: condensed matter theory; statistical physics; self-organized criticality; complex systems; economic fitness and complexity
Guest Editor
Dr. Andrea Tacchella

Institute of Complex Systems, National Research Council (CNR), 00185 Rome, Italy
IFC-World Bank, Washington, DC 20433, USA
E-Mail
Interests: complex-systems; macroeconomic forecasting; machine-learning; network-science; economic fitness and complexity
Guest Editor
Dr. Andrea Zaccaria

Institute of Complex Systems, National Research Council (CNR), 00185 Rome, Italy
IFC-World Bank, Washington, DC 20433, USA
Website | E-Mail
Interests: economic fitness and complexity; agent-based models; financial time series; order book; complex networks

Special Issue Information

Dear Colleagues,

Economic Complexity and the Fitness Method:

Economic Complexity (EC) is a new field of research that consists of a radically new methodology. It describes economics as an evolutionary process of ecosystems made of industrial and financial technologies, as well as infrastructures that are all globally interconnected. The approach is multidisciplinary, addressing emerging phenomena in economics from different points of view: Analysis of complex systems, scientific methods for dynamical systems and the recent developments in big data (in the spirit of Google Page Rank, deep learning, and beyond). This approach offers new opportunities to constructively describe technological ecosystems, analyse their structures, understand their internal dynamics, as well as to introduce new metrics. It provides a new paradigm for a fundamental economic science based on data without any role of ideologies or interpretations.

A key feature of this new approach is to go from 100 parameters to zero parameters and obtain results which can be tested scientifically. This is done by focusing on the data in which the signal to noise ratio is optimal and developing iterative algorithms in the spirit, but other than Google, and optimized to the economic problem in question.

Relation to “Entropy” and Information Theory:

The last two decades have seen an exponential growth of accessible data concerning almost all human activities including economy. This happened in particular about the import-export dynamics of goods by countries and other central aspects of their economic development and of the dynamics of innovations such as patents and scientific production. Correspondingly, in the Economic science the typical response to such growth of data was to increase the number of parameters in order to reach a “more complete” description of the economic system. This approach goes in the opposite direction with respect to the usual methods of statistical physics where in order to study systems with a large number of degrees of freedom a “reduced” description in terms of few statistically motivated macroscopic thermodynamic variables, characterized by a large signal to noise ratio, is adopted. In this way, if from one hand we lose the microscopic details of the system, from the other one we gain in terms of robustness, classification and reproducibility of the description of the studied systems. Economic Fitness and Complexity was originally introduced exactly in the spirit of introducing a thermodynamic description of the import export dynamics of countries in such a way to get a robust classification of the degree of economic development of countries, of the industrial complexity of the productive sectors and to build a reliable prediction scheme for the evolution of this highly non-linear and interactive system.

Dynamics and Forecasting

The dynamics in the new GDP-Fitness space opens up to a completely new way for monitoring and forecasting. The trajectories of countries in this new space show regions of laminar and turbulent behaviour, which lead to a heterogeneous, non-linear approach to forecasting. This is similar to physical dynamical systems and modern weather forecasting. Then, the network of products and their evolutionary dynamics is built using machine learning methods. Finally, the same approach is applied to scientific production, patents and technologies, so to open up the possibility of analysing the core elements of the innovation process. All this provides a disciplined set of indicators that can be used for the industrial planning of countries and regions.

Some of these results have already attracted the attention of the scientific media of highest level like the editorials of Nature and Bloomberg Views.

http://www.nature.com/news/physicists-make-weather-forecasts-for-economies-1.16963

https://www.bloomberg.com/view/articles/2017-10-01/a-better-way-to-make-economic-forecasts

In the past two years, the IFC-World Bank has tested, in great detail, this methodology and found it better that the standard analysis. Then it was applied for the study of more than 50 countries. Our colleagues at the IFC-World Bank agree to participate in this Special Issue with about four papers.

This Special Issue of “Entropy” will provide a state-of-the-art overview of this rapidly-evolving field, which is setting the basis for a scientific foundation of industrial economics. It will stimulate further activity in the field and outline the most promising lines of evolution.

Prof. Dr. Luciano Pietronero
Dr. Andrea Tacchella
Dr. Andrea Zaccaria
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • economic fitness
  • complex systems
  • dynamical systems
  • industrial policy
  • economic forecasting and planning
  • innovation
  • product progression network
  • development economics
  • statistical physics

Published Papers (25 papers)

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Research

Open AccessArticle
Influence of Technological Innovations on Industrial Production: A Motif Analysis on the Multilayer Network
Entropy 2019, 21(2), 126; https://doi.org/10.3390/e21020126
Received: 3 August 2018 / Revised: 14 January 2019 / Accepted: 28 January 2019 / Published: 30 January 2019
Cited by 2 | PDF Full-text (299 KB) | HTML Full-text | XML Full-text
Abstract
In this work we aim at identifying combinations of technological advancements that reveal the presence of local capabilities for a given industrial production. To this end, we generated a multilayer network using country-level patent and trade data, and performed motif-based analysis on this [...] Read more.
In this work we aim at identifying combinations of technological advancements that reveal the presence of local capabilities for a given industrial production. To this end, we generated a multilayer network using country-level patent and trade data, and performed motif-based analysis on this network using a statistical-validation approach derived from maximum-entropy arguments. We show that in many cases the signal far exceeds the noise, providing robust evidence of synergies between different technologies that can lead to a competitive advantage in specific markets. Our results can be highly useful for policymakers to inform industrial and innovation policies. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
Industry Upgrading: Recommendations of New Products Based on World Trade Network
Entropy 2019, 21(1), 39; https://doi.org/10.3390/e21010039
Received: 31 July 2018 / Revised: 20 November 2018 / Accepted: 4 January 2019 / Published: 9 January 2019
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Abstract
GDP is a classic indicator of the extent of national economic development. Research based on the World Trade Network has found that a country’s GDP depends largely on the products it exports. In order to increase the competitiveness of a country and further [...] Read more.
GDP is a classic indicator of the extent of national economic development. Research based on the World Trade Network has found that a country’s GDP depends largely on the products it exports. In order to increase the competitiveness of a country and further increase its GDP, a crucial issue is finding the right direction to upgrade the industry so that the country can enhance its competitiveness. The proximity indicator measures the similarity between products and can be used to predict the probability that a country will develop a new industry. On the other hand, the Fitness–Complexity algorithm can help to find the important products and developing countries. In this paper, we find that the maximum of the proximity between a certain product and a country’s existing products is highly correlated with the probability that the country exports this new product in the next year. In addition, we find that the more products that are related to a certain product, the higher probability of the emergence of the new product. Finally, we combine the proximity indicator and the Fitness–Complexity algorithm and then attempt to provide a recommendation list of new products that can help developing countries to upgrade their industry. A few examples are given in the end. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessFeature PaperArticle
Unfolding the Complexity of the Global Value Chain: Strength and Entropy in the Single-Layer, Multiplex, and Multi-Layer International Trade Networks
Entropy 2018, 20(12), 909; https://doi.org/10.3390/e20120909
Received: 19 September 2018 / Revised: 22 November 2018 / Accepted: 23 November 2018 / Published: 28 November 2018
Cited by 2 | PDF Full-text (2837 KB) | HTML Full-text | XML Full-text
Abstract
The worldwide trade network has been widely studied through different data sets and network representations with a view to better understanding interactions among countries and products. Here we investigate international trade through the lenses of the single-layer, multiplex, and multi-layer networks. We discuss [...] Read more.
The worldwide trade network has been widely studied through different data sets and network representations with a view to better understanding interactions among countries and products. Here we investigate international trade through the lenses of the single-layer, multiplex, and multi-layer networks. We discuss differences among the three network frameworks in terms of their relative advantages in capturing salient topological features of trade. We draw on the World Input-Output Database to build the three networks. We then uncover sources of heterogeneity in the way strength is allocated among countries and transactions by computing the strength distribution and entropy in each network. Additionally, we trace how entropy evolved, and show how the observed peaks can be associated with the onset of the global economic downturn. Findings suggest how more complex representations of trade, such as the multi-layer network, enable us to disambiguate the distinct roles of intra- and cross-industry transactions in driving the evolution of entropy at a more aggregate level. We discuss our results and the implications of our comparative analysis of networks for research on international trade and other empirical domains across the natural and social sciences. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
A Technology-Based Classification of Firms: Can We Learn Something Looking Beyond Industry Classifications?
Entropy 2018, 20(11), 887; https://doi.org/10.3390/e20110887
Received: 2 August 2018 / Revised: 30 October 2018 / Accepted: 9 November 2018 / Published: 18 November 2018
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Abstract
In this work we use clustering techniques to identify groups of firms competing in similar technological markets. Our clustering properly highlights technological similarities grouping together firms normally classified in different industrial sectors. Technological development leads to a continuous changing structure of industries and [...] Read more.
In this work we use clustering techniques to identify groups of firms competing in similar technological markets. Our clustering properly highlights technological similarities grouping together firms normally classified in different industrial sectors. Technological development leads to a continuous changing structure of industries and firms. For this reason, we propose a data driven approach to classify firms together allowing for fast adaptation of the classification to the changing technological landscape. In this respect we differentiate from previous taxonomic exercises of industries and innovation which are based on more general common features. In our empirical application, we use patent data as a proxy for the firms’ capabilities of developing new solutions in different technological fields. On this basis, we extract what we define a Technologically Driven Classification (TDC). In order to validate the result of our exercise we use information theory to look at the amount of information explained by our clustering and the amount of information shared with an industrial classification. All-in-all, our approach provides a good grouping of firms on the basis of their technological capabilities and represents an attractive option to compare firms in the technological space and better characterise competition in technological markets. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
The Role of Complex Analysis in Modelling Economic Growth
Entropy 2018, 20(11), 883; https://doi.org/10.3390/e20110883
Received: 31 July 2018 / Revised: 2 November 2018 / Accepted: 5 November 2018 / Published: 16 November 2018
Cited by 1 | PDF Full-text (4065 KB) | HTML Full-text | XML Full-text
Abstract
Development and growth are complex and tumultuous processes. Modern economic growth theories identify some key determinants of economic growth. However, the relative importance of the determinants remains unknown, and additional variables may help clarify the directions and dimensions of the interactions. The novel [...] Read more.
Development and growth are complex and tumultuous processes. Modern economic growth theories identify some key determinants of economic growth. However, the relative importance of the determinants remains unknown, and additional variables may help clarify the directions and dimensions of the interactions. The novel stream of literature on economic complexity goes beyond aggregate measures of productive inputs and considers instead a more granular and structural view of the productive possibilities of countries, i.e., their capabilities. Different endowments of capabilities are crucial ingredients in explaining differences in economic performances. In this paper we employ economic fitness, a measure of productive capabilities obtained through complex network techniques. Focusing on the combined roles of fitness and some more traditional drivers of growth—GDP per capita, capital intensity, employment ratio, life expectancy, human capital and total factor productivity—we build a bridge between economic growth theories and the economic complexity literature. Our findings show that fitness plays a crucial role in fostering economic growth and, when it is included in the analysis, can be either complementary to traditional drivers of growth or can completely overshadow them. Notably, for the most complex countries, which have the most diversified export baskets and the largest endowments of capabilities, fitness is complementary to the chosen growth determinants in enhancing economic growth. The empirical findings are in agreement with neoclassical and endogenous growth theories. By contrast, for countries with intermediate and low capability levels, fitness emerges as the key growth driver. This suggests that economic models should account for capabilities; in fact, describing the technological possibilities of countries solely in terms of their production functions may lead to a misinterpretation of the roles of factors. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
Estimation of Economic Indicator Announced by Government From Social Big Data
Entropy 2018, 20(11), 852; https://doi.org/10.3390/e20110852
Received: 7 August 2018 / Revised: 19 October 2018 / Accepted: 31 October 2018 / Published: 6 November 2018
Cited by 1 | PDF Full-text (1031 KB) | HTML Full-text | XML Full-text
Abstract
We introduce a systematic method to estimate an economic indicator from the Japanese government by analyzing big Japanese blog data. Explanatory variables are monthly word frequencies. We adopt 1352 words in the section of economics and industry of the Nikkei thesaurus for each [...] Read more.
We introduce a systematic method to estimate an economic indicator from the Japanese government by analyzing big Japanese blog data. Explanatory variables are monthly word frequencies. We adopt 1352 words in the section of economics and industry of the Nikkei thesaurus for each candidate word to illustrate the economic index. From this large volume of words, our method automatically selects the words which have strong correlation with the economic indicator and resolves some difficulties in statistics such as the spurious correlation and overfitting. As a result, our model reasonably illustrates the real economy index. The announcement of an economic index from government usually has a time lag, while our proposed method can be real time. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
Subnational Analysis of Economic Fitness and Income Dynamic: The Case of Mexican States
Entropy 2018, 20(11), 841; https://doi.org/10.3390/e20110841
Received: 13 July 2018 / Revised: 18 September 2018 / Accepted: 29 October 2018 / Published: 2 November 2018
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Abstract
In recent years, analytical tools of network theory have provided strong empirical support to the well-known hypothesis that regions develop through the local learning of capabilities (tacit productive knowledge). In this paper, we compare two indexes of competitiveness (or accumulated capabilities) for a [...] Read more.
In recent years, analytical tools of network theory have provided strong empirical support to the well-known hypothesis that regions develop through the local learning of capabilities (tacit productive knowledge). In this paper, we compare two indexes of competitiveness (or accumulated capabilities) for a subnational database of 32 Mexican states in the period 2004–2014. We find that Endogenous Fitness (i.e., region fitness and product complexity are derived jointly using only a Mexican exports database) has a better performance than Exogenous Fitness (i.e., product complexity comes from a world exports database and fitness is the sum of the complexity scores for the region’s competitive products). The performance criterion is established with the indicator’s capacity to meet a requirement of growth predictability: the existence of at least one laminar (ordered) regime in the fitness–income plane. In the Mexican data, Endogenous Fitness is a reliable predictor of per capita GDP in two distinct areas of the plane: one of continuous progress and opportunities, and another of stagnation and deteriorating fitness. The predictive capacity of this indicator becomes clear only when the metrics’ calculations are filtered by removing raw petroleum or oil-dependent states, while such capacity is robust to the inclusion of tourism—another important industry of the Mexican economy. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
A Context Similarity-Based Analysis of Countries’ Technological Performance
Entropy 2018, 20(11), 833; https://doi.org/10.3390/e20110833
Received: 26 July 2018 / Revised: 10 October 2018 / Accepted: 17 October 2018 / Published: 31 October 2018
PDF Full-text (418 KB) | HTML Full-text | XML Full-text
Abstract
This work contributes to the literature in the field of innovation by proposing a quantitative approach for the prediction of the timing and location of patenting activity. In a recent work, it was shown that focusing on couples of technological codes allows for [...] Read more.
This work contributes to the literature in the field of innovation by proposing a quantitative approach for the prediction of the timing and location of patenting activity. In a recent work, it was shown that focusing on couples of technological codes allows for the formation of testable predictions of innovation events, defined as the first time two codes appear together in a patent. In particular, the construction of the vector space of codes and the introduction of the context similarity metric allows for a quantitative analysis of technological progress. Here, we move from that result and we show that, through context similarity, it is possible to assign to countries a score which measures the probability of being the first to patent a potential innovation. In other words, we show that we can not only estimate the likelihood that a potential innovation will be patented in the imminent future, but also forecast where it will be patented. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
Complexity of Products: The Effect of Data Regularisation
Entropy 2018, 20(11), 814; https://doi.org/10.3390/e20110814
Received: 12 August 2018 / Revised: 12 October 2018 / Accepted: 15 October 2018 / Published: 23 October 2018
Cited by 1 | PDF Full-text (1814 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Among several developments, the field of Economic Complexity (EC) has notably seen the introduction of two new techniques. One is the Bootstrapped Selective Predictability Scheme (SPSb), which can provide quantitative forecasts of the Gross Domestic Product of countries. The other, Hidden Markov Model [...] Read more.
Among several developments, the field of Economic Complexity (EC) has notably seen the introduction of two new techniques. One is the Bootstrapped Selective Predictability Scheme (SPSb), which can provide quantitative forecasts of the Gross Domestic Product of countries. The other, Hidden Markov Model (HMM) regularisation, denoises the datasets typically employed in the literature. We contribute to EC along three different directions. First, we prove the convergence of the SPSb algorithm to a well-known statistical learning technique known as Nadaraya-Watson Kernel regression. The latter has significantly lower time complexity, produces deterministic results, and it is interchangeable with SPSb for the purpose of making predictions. Second, we study the effects of HMM regularization on the Product Complexity and logPRODY metrics, for which a model of time evolution has been recently proposed. We find confirmation for the original interpretation of the logPRODY model as describing the change in the global market structure of products with new insights allowing a new interpretation of the Complexity measure, for which we propose a modification. Third, we explore new effects of regularisation on the data. We find that it reduces noise, and observe for the first time that it increases nestedness in the export network adjacency matrix. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessFeature PaperArticle
The Role of Data in Model Building and Prediction: A Survey Through Examples
Entropy 2018, 20(10), 807; https://doi.org/10.3390/e20100807
Received: 27 July 2018 / Revised: 18 October 2018 / Accepted: 19 October 2018 / Published: 22 October 2018
Cited by 2 | PDF Full-text (1450 KB) | HTML Full-text | XML Full-text
Abstract
The goal of Science is to understand phenomena and systems in order to predict their development and gain control over them. In the scientific process of knowledge elaboration, a crucial role is played by models which, in the language of quantitative sciences, mean [...] Read more.
The goal of Science is to understand phenomena and systems in order to predict their development and gain control over them. In the scientific process of knowledge elaboration, a crucial role is played by models which, in the language of quantitative sciences, mean abstract mathematical or algorithmical representations. This short review discusses a few key examples from Physics, taken from dynamical systems theory, biophysics, and statistical mechanics, representing three paradigmatic procedures to build models and predictions from available data. In the case of dynamical systems we show how predictions can be obtained in a virtually model-free framework using the methods of analogues, and we briefly discuss other approaches based on machine learning methods. In cases where the complexity of systems is challenging, like in biophysics, we stress the necessity to include part of the empirical knowledge in the models to gain the minimal amount of realism. Finally, we consider many body systems where many (temporal or spatial) scales are at play—and show how to derive from data a dimensional reduction in terms of a Langevin dynamics for their slow components. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
The Middle-Income Trap and the Coping Strategies From Network-Based Perspectives
Entropy 2018, 20(10), 803; https://doi.org/10.3390/e20100803
Received: 30 July 2018 / Revised: 15 October 2018 / Accepted: 15 October 2018 / Published: 18 October 2018
PDF Full-text (2308 KB) | HTML Full-text | XML Full-text
Abstract
When a developing country reaches a relatively average income level, it often stops growing further and its income does not improve. This is known as the middle-income trap. How to overcome this trap is a longstanding problem for developing countries, and has been [...] Read more.
When a developing country reaches a relatively average income level, it often stops growing further and its income does not improve. This is known as the middle-income trap. How to overcome this trap is a longstanding problem for developing countries, and has been studied in various research fields. In this work, we use the Fitness-Complexity method (FCM) to analyze the common characteristics of the countries that successfully get through the middle-income trap, and show the origin of the middle-income trap based on the international trade network. In the analysis, a novel method is proposed to characterize the interdependency between products. The results show that some middle-complexity products depend much on each other, which indicates that developing countries should focus on them simultaneously, implying high difficulty to escape the middle-income trap. To tackle the middle-income trap, developing countries should learn experiences from developed countries that share similar development history. we then design an effective method to evaluate the similarity between countries and recommend developed countries to a certain developing country. The effectiveness of our method is validated in the international trade network. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
Identifying Systemically Important Companies by Using the Credit Network of an Entire Nation
Entropy 2018, 20(10), 792; https://doi.org/10.3390/e20100792
Received: 27 July 2018 / Revised: 8 October 2018 / Accepted: 8 October 2018 / Published: 16 October 2018
Cited by 1 | PDF Full-text (5511 KB) | HTML Full-text | XML Full-text
Abstract
The notions of systemic importance and systemic risk of financial institutions are closely related to the topology of financial liability networks. In this work, we reconstruct and analyze the financial liability network of an entire economy using data of 50,159 firms and banks. [...] Read more.
The notions of systemic importance and systemic risk of financial institutions are closely related to the topology of financial liability networks. In this work, we reconstruct and analyze the financial liability network of an entire economy using data of 50,159 firms and banks. Our analysis contains 80.2% of the total liabilities of firms towards banks and all interbank liabilities in the Austrian banking system. The combination of firm-bank networks and interbank networks allows us to extend the concept of systemic risk to the real economy. In particular, the systemic importance of individual companies can be assessed, and for the first time, the financial ties between the financial and the real economy become explicitly visible. We find that firms contribute to systemic risk in similar ways as banks do. We identify a set of mid-sized companies that carry substantial systemic risk. Their default would affect up to 40% of the Austrian financial market. We find that all firms together create more systemic risk than the entire financial sector. In 2008, the total systemic risk of the Austrian interbank network amounted to only 29% of the total systemic risk of the entire financial network consisting of firms and banks. The work demonstrates that the notions of systemically important financial institutions (SIFIs) can be directly extended to firms. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
Colombian Export Capabilities: Building the Firms-Products Network
Entropy 2018, 20(10), 785; https://doi.org/10.3390/e20100785
Received: 31 July 2018 / Revised: 1 October 2018 / Accepted: 2 October 2018 / Published: 12 October 2018
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Abstract
In this paper, we analyse the bipartite Colombian firms-products network, throughout a period of five years, from 2010 to 2014. Our analysis depicts a strongly modular system, with several groups of firms specializing in the export of specific categories of products. These clusters [...] Read more.
In this paper, we analyse the bipartite Colombian firms-products network, throughout a period of five years, from 2010 to 2014. Our analysis depicts a strongly modular system, with several groups of firms specializing in the export of specific categories of products. These clusters have been detected by running the bipartite variant of the traditional modularity maximization, revealing a bi-modular structure. Interestingly, this finding is refined by applying a recently proposed algorithm for projecting bipartite networks on the layer of interest and, then, running the Louvain algorithm on the resulting monopartite representations. Important structural differences emerge upon comparing the Colombian firms-products network with the World Trade Web, in particular, the bipartite representation of the latter is not characterized by a similar block-structure, as the modularity maximization fails in revealing (bipartite) nodes clusters. This points out that economic systems behave differently at different scales: while countries tend to diversify their production—potentially exporting a large number of different products—firms specialize in exporting (substantially very limited) baskets of basically homogeneous products. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
A New and Stable Estimation Method of Country Economic Fitness and Product Complexity
Entropy 2018, 20(10), 783; https://doi.org/10.3390/e20100783
Received: 26 July 2018 / Revised: 3 October 2018 / Accepted: 4 October 2018 / Published: 12 October 2018
Cited by 1 | PDF Full-text (915 KB) | HTML Full-text | XML Full-text
Abstract
We present a new metric estimating fitness of countries and complexity of products by exploiting a non-linear non-homogeneous map applied to the publicly available information on the goods exported by a country. The non homogeneous terms guarantee both convergence and stability. After a [...] Read more.
We present a new metric estimating fitness of countries and complexity of products by exploiting a non-linear non-homogeneous map applied to the publicly available information on the goods exported by a country. The non homogeneous terms guarantee both convergence and stability. After a suitable rescaling of the relevant quantities, the non homogeneous terms are eventually set to zero so that this new metric is parameter free. This new map almost reproduces the results of the original homogeneous metrics already defined in literature and allows for an approximate analytic solution in case of actual binarized matrices based on the Revealed Comparative Advantage (RCA) indicator. This solution is connected with a new quantity describing the neighborhood of nodes in bipartite graphs, representing in this work the relations between countries and exported products. Moreover, we define the new indicator of country net-efficiency quantifying how a country efficiently invests in capabilities able to generate innovative complex high quality products. Eventually, we demonstrate analytically the local convergence of the algorithm involved. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
Link Prediction in Bipartite Nested Networks
Entropy 2018, 20(10), 777; https://doi.org/10.3390/e20100777
Received: 31 July 2018 / Revised: 8 October 2018 / Accepted: 8 October 2018 / Published: 10 October 2018
Cited by 1 | PDF Full-text (397 KB) | HTML Full-text | XML Full-text
Abstract
Real networks typically studied in various research fields—ecology and economic complexity, for example—often exhibit a nested topology, which means that the neighborhoods of high-degree nodes tend to include the neighborhoods of low-degree nodes. Focusing on nested networks, we study the problem of link [...] Read more.
Real networks typically studied in various research fields—ecology and economic complexity, for example—often exhibit a nested topology, which means that the neighborhoods of high-degree nodes tend to include the neighborhoods of low-degree nodes. Focusing on nested networks, we study the problem of link prediction in complex networks, which aims at identifying likely candidates for missing links. We find that a new method that takes network nestedness into account outperforms well-established link-prediction methods not only when the input networks are sufficiently nested, but also for networks where the nested structure is imperfect. Our study paves the way to search for optimal methods for link prediction in nested networks, which might be beneficial for World Trade and ecological network analysis. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
Green Technology Fitness
Entropy 2018, 20(10), 776; https://doi.org/10.3390/e20100776
Received: 31 July 2018 / Revised: 27 September 2018 / Accepted: 8 October 2018 / Published: 10 October 2018
Cited by 1 | PDF Full-text (2001 KB) | HTML Full-text | XML Full-text
Abstract
The present study provides an analysis of empirical regularities in the development of green technology. We use patent data to examine inventions that can be traced to the environment-related catalogue (ENV-Tech) covering technologies in environmental management, water-related adaptation and climate change [...] Read more.
The present study provides an analysis of empirical regularities in the development of green technology. We use patent data to examine inventions that can be traced to the environment-related catalogue (ENV-Tech) covering technologies in environmental management, water-related adaptation and climate change mitigation. Furthermore, we employ the Economic Fitness-Complexity (EFC) approach to assess their development and geographical distribution across countries between 1970 and 2010. This allows us to identify three typologies of countries: leaders, laggards and catch-up. While, as expected, there is a direct relationship between GDP per capita and invention capacity, we also document the remarkable growth of East Asia countries that started from the periphery and rapidly established themselves as key actors. This geographical pattern coincides with higher integration across domains so that, while the relative development of individual areas may have peaked, there is now demand for greater interoperability across green technologies. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
Nestedness Maximization in Complex Networks through the Fitness-Complexity Algorithm
Entropy 2018, 20(10), 768; https://doi.org/10.3390/e20100768
Received: 31 July 2018 / Revised: 25 September 2018 / Accepted: 25 September 2018 / Published: 8 October 2018
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Abstract
Nestedness refers to the structural property of complex networks that the neighborhood of a given node is a subset of the neighborhoods of better-connected nodes. Following the seminal work by Patterson and Atmar (1986), ecologists have been long interested in revealing the configuration [...] Read more.
Nestedness refers to the structural property of complex networks that the neighborhood of a given node is a subset of the neighborhoods of better-connected nodes. Following the seminal work by Patterson and Atmar (1986), ecologists have been long interested in revealing the configuration of maximal nestedness of spatial and interaction matrices of ecological communities. In ecology, the BINMATNEST genetic algorithm can be considered as the state-of-the-art approach for this task. On the other hand, the fitness-complexity ranking algorithm has been recently introduced in the economic complexity literature with the original goal to rank countries and products in World Trade export networks. Here, by bringing together quantitative methods from ecology and economic complexity, we show that the fitness-complexity algorithm is highly effective in the nestedness maximization task. More specifically, it generates matrices that are more nested than the optimal ones by BINMATNEST for 61.27% of the analyzed mutualistic networks. Our findings on ecological and World Trade data suggest that beyond its applications in economic complexity, the fitness-complexity algorithm has the potential to become a standard tool in nestedness analysis. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessFeature PaperArticle
Economic Complexity: Correlations between Gross Domestic Product and Fitness
Entropy 2018, 20(10), 766; https://doi.org/10.3390/e20100766
Received: 27 July 2018 / Revised: 21 September 2018 / Accepted: 25 September 2018 / Published: 7 October 2018
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Abstract
In this paper we study the causal relation between country Economic Fitness Fc and its Gross Domestic Product per capita (GDP). Using the Takens’ theorem, as first suggested in (Sugihara, G. et al. 2012), we show that there [...] Read more.
In this paper we study the causal relation between country Economic Fitness F c and its Gross Domestic Product per capita ( G D P ). Using the Takens’ theorem, as first suggested in (Sugihara, G. et al. 2012), we show that there exists a reasonable evidence of causal correlation between G D P and F c for relatively rich countries. This is not the case for relatively poor countries where F c and G D P do not show any significant causal relation. We also present some preliminary results to understand whether G D P or F c are driving factor for economic growth. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
How News May Affect Markets’ Complex Structure: The Case of Cambridge Analytica
Entropy 2018, 20(10), 765; https://doi.org/10.3390/e20100765
Received: 31 July 2018 / Revised: 2 October 2018 / Accepted: 3 October 2018 / Published: 6 October 2018
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Abstract
The claim of Cambridge Analytica, a political consulting firm, that it was possible to influence voting behavior by using data mined from the social platform Facebook created a sudden fear in its users of being manipulated; consequently, even the market price of the [...] Read more.
The claim of Cambridge Analytica, a political consulting firm, that it was possible to influence voting behavior by using data mined from the social platform Facebook created a sudden fear in its users of being manipulated; consequently, even the market price of the social platform was shocked.We propose a case study analyzing the effect of this data scandal not only on Facebook stock price, but also on the whole stock market. To such a scope, we consider 15-minutes prices and returns of the set of the NASDAQ-100 components before and after the Cambridge Analytica case. We analyze correlations and Mutual Information among components finding that assets become more correlated and their Mutual Information grows higher. We also observe that correlation and Mutual Information are mutually increasing and seem to follow a master curve. Hence, the market appears more fragile after the Cambridge Analytica event. In fact, as it is well-known in finance, an increase in the average value of correlations augments the systemic risk (i.e., all the market can collapse as a whole) and decreases the possibility of allocating a safe investment portfolio. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessFeature PaperArticle
The Virtuous Interplay of Infrastructure Development and the Complexity of Nations
Entropy 2018, 20(10), 761; https://doi.org/10.3390/e20100761
Received: 17 August 2018 / Revised: 26 September 2018 / Accepted: 27 September 2018 / Published: 3 October 2018
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Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
Country Case Studies in Economic Fitness: Mexico and Brazil
Entropy 2018, 20(10), 753; https://doi.org/10.3390/e20100753
Received: 24 August 2018 / Revised: 14 September 2018 / Accepted: 27 September 2018 / Published: 1 October 2018
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Abstract
We leverage a new complexity framework called Economic Fitness, which characterizes an economy’s level of diversification and its capabilities to produce more complex products. It can be used to predict economic growth and competitiveness. This paper describes an application of Economic Fitness called [...] Read more.
We leverage a new complexity framework called Economic Fitness, which characterizes an economy’s level of diversification and its capabilities to produce more complex products. It can be used to predict economic growth and competitiveness. This paper describes an application of Economic Fitness called the Country Opportunity Spotlight (COS) that assesses a country’s current level of capabilities and demonstrates which industries have upgrade and diversification potential given those capabilities. It helps unlock the explanatory and predictive power of Economic Fitness for policymakers. COS results serve as a starting point for policymakers to shape and validate priorities, compare countries, asses the capabilities needed in specific industries and begin identifying constraints to growth. We showcase the use of this framework for Mexico and Brazil. These countries provide an interesting case study, as they have similar growth outlooks yet demonstrate different productive capabilities. Examining Mexico and Brazil side by side illustrates the value this analysis can have on deciphering structural change and decision making and at the same time reinforces the need for a nuanced consideration of each country’s unique context. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
Zipf’s, Heaps’ and Taylor’s Laws are Determined by the Expansion into the Adjacent Possible
Entropy 2018, 20(10), 752; https://doi.org/10.3390/e20100752
Received: 31 July 2018 / Revised: 17 September 2018 / Accepted: 25 September 2018 / Published: 30 September 2018
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Abstract
Zipf’s, Heaps’ and Taylor’s laws are ubiquitous in many different systems where innovation processes are at play. Together, they represent a compelling set of stylized facts regarding the overall statistics, the innovation rate and the scaling of fluctuations for systems as diverse as [...] Read more.
Zipf’s, Heaps’ and Taylor’s laws are ubiquitous in many different systems where innovation processes are at play. Together, they represent a compelling set of stylized facts regarding the overall statistics, the innovation rate and the scaling of fluctuations for systems as diverse as written texts and cities, ecological systems and stock markets. Many modeling schemes have been proposed in literature to explain those laws, but only recently a modeling framework has been introduced that accounts for the emergence of those laws without deducing the emergence of one of the laws from the others or without ad hoc assumptions. This modeling framework is based on the concept of adjacent possible space and its key feature of being dynamically restructured while its boundaries get explored, i.e., conditional to the occurrence of novel events. Here, we illustrate this approach and show how this simple modeling framework, instantiated through a modified Pólya’s urn model, is able to reproduce Zipf’s, Heaps’ and Taylor’s laws within a unique self-consistent scheme. In addition, the same modeling scheme embraces other less common evolutionary laws (Hoppe’s model and Dirichlet processes) as particular cases. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
Maximum-Entropy Tools for Economic Fitness and Complexity
Entropy 2018, 20(10), 743; https://doi.org/10.3390/e20100743
Received: 31 July 2018 / Revised: 10 September 2018 / Accepted: 19 September 2018 / Published: 28 September 2018
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Abstract
The concepts of economic fitness and complexity, based on iterative and interdependent definitions of the quality of exporting countries and exported products, have led to novel insights into the dynamics of production and trade. A key step in the calculation of these quantities [...] Read more.
The concepts of economic fitness and complexity, based on iterative and interdependent definitions of the quality of exporting countries and exported products, have led to novel insights into the dynamics of production and trade. A key step in the calculation of these quantities is the preliminary identification of statistically relevant country-product pairs.In this paper, we propose a method that could improve the current practice of filtering based on the revealed comparative advantage, by employing the maximum-entropy principle to construct an unbiased link weight probability distribution that, unlike the traditional thresholding method, allows for the statistical assessment of empirical trade volumes. The result is an adjusted geometric distribution for trade links that refines the revealed comparative advantage approach. This allows us to define the statistical significance of each trade link weight, leading to statistically supported trade link filtering decisions. Using this statistically justified filtering method, we have obtained results that are similar in nature to those that were found without this method, even though there are significant deviations in the details. In addition, the statistical information thus obtained on each trade link allows us to perform a spectral analysis of the export portfolio of individual economies. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
Data Driven Approach to the Dynamics of Import and Export of G7 Countries
Entropy 2018, 20(10), 735; https://doi.org/10.3390/e20100735
Received: 31 July 2018 / Revised: 17 September 2018 / Accepted: 19 September 2018 / Published: 25 September 2018
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Abstract
The dynamics of imports plus exports of 226 product classes by the G7 countries between 1962 and 2000 is described in terms of stochastic differential equations. The model allows interesting comparisons among the different economies related to the compositions of the national baskets. [...] Read more.
The dynamics of imports plus exports of 226 product classes by the G7 countries between 1962 and 2000 is described in terms of stochastic differential equations. The model allows interesting comparisons among the different economies related to the compositions of the national baskets. Synthetic solutions can also be used to estimate hidden and unexploited growth potentials. These prerogatives are strictly connected with the fact that a network structure is at the basis of the model. Such a network expresses the mutual influences of different products through resource transfers, and is a key ingredient producing cooperative growth effects which can be quantified and distinguished from those generated by deterministic drifts and representing direct resource inputs. An analysis of this network, which differs substantially from those previously considered within the economic complexity approach, allows to estimate the centrality of different products in each national basket, highlighting the most essential commodities for each economy. Solutions of the model give the possibility of performing counterfactual analyses aimed at estimating how much the growth of each country could have profited from a general strengthening, or weakening, of the links in the same products network. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessArticle
Economic Complexity Based Recommendation Enhance the Efficiency of the Belt and Road Initiative
Entropy 2018, 20(9), 718; https://doi.org/10.3390/e20090718
Received: 31 July 2018 / Revised: 14 September 2018 / Accepted: 17 September 2018 / Published: 19 September 2018
PDF Full-text (996 KB) | HTML Full-text | XML Full-text
Abstract
The Belt and Road initiative (BRI) was announced in 2013 by the Chinese government. Its goal is to promote the cooperation between European and Asian countries, as well as enhancing the trust between members and unifying the market. Since its creation, more and [...] Read more.
The Belt and Road initiative (BRI) was announced in 2013 by the Chinese government. Its goal is to promote the cooperation between European and Asian countries, as well as enhancing the trust between members and unifying the market. Since its creation, more and more developing countries are joining the initiative. Based on the geographical location characteristics of the countries in this initiative, we propose an improvement of a popular recommendation algorithm that includes geographic location information. This recommendation algorithm is able to make suitable recommendations of products for countries in the BRI. Then, Fitness and Complexity metrics are used to evaluate the impact of the recommendation results and measure the country’s competitiveness. The aim of this work is to provide countries’ insights on the ideal development direction. By following the recommendations, the countries can quickly increase their international competitiveness. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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