Economic and Technological Complexity: A Model Study of Indicators of Knowledge-based Innovation Systems

📝 Abstract

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 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.

💡 Analysis

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 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.

📄 Content

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Economic and Technological Complexity:
A Model Study of Indicators of Knowledge-based Innovation Systems

Inga Ivanova,* a Øivind Strand,b Duncan Kushnir,c and Loet Leydesdorff d

Abstract 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 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.

Keywords: national innovation system, complexity, patent, technology, triple helix, indicator

a * corresponding author; Institute for Statistical Studies and Economics of Knowledge, National Research University Higher School of Economics (NRU HSE), 20 Myasnitskaya St., Moscow, 101000, the Russian Federation; inga.iva@mail.ru
b Norges teknisk-naturvitenskapelige universitet (NTNU), Department of International Business, Larsgårdsvegen 2, 6009 ÅLESUND, Norway; oivind.strand@ntnu.no
c Chalmers University of Technology, Göteborg 412 58, Sweden; kushnir@chalmers.se d Amsterdam School of Communication Research (ASCoR), University of Amsterdam, PO Box 15793, 1001 NG Amsterdam, the Netherlands; loet@leydesdorff.net
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1. Introduction Hidalgo & Hausmann (2009) proposed the Economic Complexity Index (ECI) using the portfolios of countries in terms of product groups which they export to quantify a country’s economic complexity. A country’s economic growth and income can be expected to depend on the diversity of the products in its portfolio (Cadot et al., 2013). Given the two axes of the matrix of countries versus product groups, Hausmann et al. (2011, p. 24) also introduced the product complexity index (PCI) which measures the spread of the production of each product group over nations. The complexity of a country’s economy, in turn, refers to the set of capabilities, accumulated by that country. According to Hidalgo & Hausmann (2009; henceforth HH) ECI is correlated with a country’s income as measured by GDP per capita (Hidalgo & Hausmann, 2009: Fig. 3 at p. 10573). HH submit that the deviation of ECI from a country’s income can be used to predict long-term future growth because a country’s income can be expected to approach a competitive level associated with its economic complexity (Ourens, 2013, p. 24).5 Hence, ECI could be considered as a predictive measure of a country’s competitive advantage in the future. Since based on the product portfolios, ECI values can be expected to reflect the manufacturing capabilities of countries (Hausmann et al, 2011, p. 7). However, HH did not provide an explicit definition of the manufacturing capabilities and their respective knowledge bases. In our opinion, manufacturing complexity is inevitably related to the knowledge intensity and sophistication of exports of products with comparative advantages (e.g., Foray, 2004; Foray & Lundvall, 1996; OECD, 1996; ECR, 2013). One needs an advanced indicator of

5 Kemp-Benedict (2014) noted that the correlation between income and ECI can also be considered as a consequence of the well-known relation between export and income growth. 3

competitiveness which indicates whether manufacturing industries in a country have a relatively high degree of complexity.
New industries are more likely to be generated in regions where they can be technologically related to existing industries (Boschma et al., 2013; Frenken et al., 2007; Neffke et al., 2011). Although regional diversification is often studied in terms of industrial dynamics, specification of the technological (knowledge) dynamics would enable us to make a direct link between urban diversification and technology portfolios.

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