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Register a new userMeasuring economic complexity of countries and products: which metric to use?
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Added by
Manuel Sebastian Mariani
Publication date
2015
fields
Financial
and research's language is
English
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Evaluating the economies of countries and their relations with products in the global market is a central problem in economics, with far-reaching implications to our theoretical understanding of the international trade as well as to practical applications, such as policy making and financial investment planning. The recent Economic Complexity approach aims to quantify the competitiveness of countries and the quality of the exported products based on the empirical observation that the most competitive countries have diversified exports, whereas developing countries only export few low quality products -- typically those exported by many other countries. Two different metrics, Fitness-Complexity and the Method of Reflections, have been proposed to measure country and product score in the Economic Complexity framework. We use international trade data and a recent ranking evaluation measure to quantitatively compare the ability of the two metrics to rank countries and products according to their importance in the network. The results show that the Fitness-Complexity metric outperforms the Method of Reflections in both the ranking of products and the ranking of countries. We also investigate a Generalization of the Fitness-Complexity metric and show that it can produce improved rankings provided that the input data are reliable.
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Two network measures known as the Economic Complexity Index (ECI) and Product Complexity Index (PCI) have provided important insights into patterns of economic development. We show that the ECI and PCI are equivalent to a spectral clustering algorithm that partitions a similarity graph into two parts. The measures are also related to various dimensionality reduction methods and can be interpreted as vectors that determine distances between nodes based on their similarity. Our results shed a new light on the ECIs empirical success in explaining cross-country differences in GDP/capita and economic growth, which is often linked to the diversity of country export baskets. In fact, countries with high (low) ECI tend to specialize in high (low) PCI products. We also find that the ECI and PCI uncover economically informative specialization patterns across US states and UK regions.
The Economic Complexity Index (ECI; Hidalgo & Hausmann, 2009) measures the complexity of national economies in terms of product groups. Analogously to ECI, a Patent Complexity Index (PatCI) can be developed on the basis of a matrix of nations versus patent classes. Using linear algebra, the three dimensions: countries, product groups, and patent classes can be combined into a measure of Triple Helix complexity (THCI) including the trilateral interaction terms between knowledge production, wealth generation, and (national) control. THCI can be expected to capture the extent of systems integration between the global dynamics of markets (ECI) and technologies (PatCI) in each national system of innovation. We measure ECI, PatCI, and THCI during the period 2000-2014 for the 34 OECD member states, the BRICS countries, and a group of emerging and affiliated economies (Argentina, Hong Kong, Indonesia, Malaysia, Romania, and Singapore). The three complexity indicators are correlated between themselves; but the correlations with GDP per capita are virtually absent. Of the worlds major economies, Japan scores highest on all three indicators, while China has been increasingly successful in combining economic and technological complexity. We could not reproduce the correlation between ECI and average income that has been central to the argument about the fruitfulness of the economic complexity approach.
In the current era of worldwide stock market interdependencies, the global financial village has become increasingly vulnerable to systemic collapse. The recent global financial crisis has highlighted the necessity of understanding and quantifying interdependencies among the worlds economies, developing new effective approaches to risk evaluation, and providing mitigating solutions. We present a methodological framework for quantifying interdependencies in the global market and for evaluating risk levels in the world-wide financial network. The resulting information will enable policy and decision makers to better measure, understand, and maintain financial stability. We use the methodology to rank the economic importance of each industry and country according to the global damage that would result from their failure. Our quantitative results shed new light on Chinas increasing economic dominance over other economies, including that of the USA, to the global economy.
Inside the EU, the commercial integration of the CEE countries has gained remarkable momentum before the crisis appearance, but it has slightly slowed down afterwards. Consequently, the interest in identifying the factors supporting the commercial integration process is high. Recent findings in the new trade theory suggest that FDI influence the trade intensity but the studies approaching this relationship for the CEE countries present mixed evidence, and investigate the commercial integration of CEE countries with the old EU members. Against this background, the purpose of this paper is to assess the CEE countries intra-integration, focusing on the Czech Republic, Hungary, Poland and the Slovak Republic. For each country we employ a panel gravitational model for the bilateral trade and FDI, considering its interactions with the other three countries in the sample on the one hand, and with the three EU main commercial partners on the other hand. We investigate different facets of the trade -- FDI nexus, resorting to a fixed effects model, a random effects model, as well as to an instrumental variable estimator, over the period 2000-2013. Our results suggest that outward FDI sustains the CEE countries commercial integration, while inward FDI has no significant effect. In all the cases a complementarity effect between trade and FDI is documented, which is stronger for the CEE countries historical trade partners. Consequently, these findings show that CEE countries policymakers are interested in encouraging the outward FDI toward their neighbour countries in order to increase the commercial integration.
Industrial development is the process by which economies learn how to produce new products and services. But how do economies learn? And who do they learn from? The literature on economic geography and economic development has emphasized two learning channels: inter-industry learning, which involves learning from related industries; and inter-regional learning, which involves learning from neighboring regions. Here we use 25 years of data describing the evolution of Chinas economy between 1990 and 2015--a period when China multiplied its GDP per capita by a factor of ten--to explore how Chinese provinces diversified their economies. First, we show that the probability that a province will develop a new industry increases with the number of related industries that are already present in that province, a fact that is suggestive of inter-industry learning. Also, we show that the probability that a province will develop an industry increases with the number of neighboring provinces that are developed in that industry, a fact suggestive of inter-regional learning. Moreover, we find that the combination of these two channels exhibit diminishing returns, meaning that the contribution of either of these learning channels is redundant when the other one is present. Finally, we address endogeneity concerns by using the introduction of high-speed rail as an instrument to isolate the effects of inter-regional learning. Our differences-in-differences (DID) analysis reveals that the introduction of high speed-rail increased the industrial similarity of pairs of provinces connected by high-speed rail. Also, industries in provinces that were connected by rail increased their productivity when they were connected by rail to other provinces where that industry was already present. These findings suggest that inter-regional and inter-industry learning played a role in Chinas great economic expansion.
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