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Economic complexity measures have been constructed on the basis of bipartite country-product network data, but without paying attention to the technological dimension or manufacturing capabilities. In this study, we submit a Ternary Complexity Index (TCI), which explicitly incorporates technological knowledge as a third dimension, measured in terms of patents. Different from a complexity indicator based on the Triple Helix model (THCI) or a measure based on patents and countries (PatCI), TCI — products, countries, and patents — can be modeled in terms of Lotka–Volterra equations and thus the further evolution of an innovation eco-system can be specified. We test the model using empirical data. The results of a regression analysis show that TCI improves on Hidalgo and Hausmann's [The building blocks of economic complexity, Proc. Natl. Acad. Sci.106 (26) (2009) 10570–10575] and Tacchella et al.'s [A new metrics for countries fitness and products complexity, Sci. Rep.2 (2012)] complexity measures with respect to both the ranking of countries in terms of their complexity and in terms of the correlation with GDP per capita. [ABSTRACT FROM AUTHOR]

The Economic Complexity Index (ECI; Hidalgo and Hausmann, 2009) measures the complexity of national economies in terms of product groups. Analogously to ECI, the 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 world's 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. [ABSTRACT FROM AUTHOR]

Abstract: Using information theory and data for all (0.5million) Norwegian firms, the national and regional innovation systems are decomposed into three subdynamics: (i) economic wealth generation, (ii) technological novelty production, and (iii) government interventions and administrative control. The mutual information in three dimensions can then be used as an indicator of potential synergy, that is, reduction of uncertainty. We aggregate the data at the NUTS3 level for 19 counties, the NUTS2 level for seven regions, and the single NUTS1 level for the nation. Measured as in-between group reduction of uncertainty, 11.7% of the synergy was found at the regional level, whereas only another 2.7% was added by aggregation at the national level. Using this Triple-Helix indicator, the counties along the west coast are indicated as more knowledge-based than the metropolitan area of Oslo or the geographical environment of the Technical University in Trondheim. Foreign direct investment seems to have larger knowledge spill-overs in Norway (oil, gas, offshore, chemistry, and marine) than the institutional knowledge infrastructure in established universities. The northern part of the country, which receives large government subsidies, shows a deviant pattern. [Copyright &y& Elsevier]