Template-type: ReDIF-Paper 1.0
Author-Name: Nomaler, Önder
Author-Email: nomaler@merit.unu.edu
Author-Workplace-Name: UNU-MERIT, Maastricht University
Author-Name: Verspagen, Bart
Author-Email: verspagen@merit.unu.edu
Author-Workplace-Name: UNU-MERIT, Maastricht University
Title: The canonical correlation complexity method
Abstract: A relatively recent, yet rapidly proliferating strand of literature in
	 the so-called econophysics domain, known as 'economic complexity' ,
	 introduces a toolkit to analyse the relationship between specialization,
	 diversification, and economic development. Different methods that aim at
	 reducing the high dimensionality in data on the empirical patterns of
	 co-location (be it nations or regions) of specializations have been
	 proposed. In terms of the concepts of machine learning, the existing
	 algorithms follow the framework of 'unsupervised learning'. The
	 competing alternatives (e.g., Hidalgo and Hausmann, 2009 vs. Tacchella
	 et al, 2012) have been based on very different assessments of which
	 products depend on more complex capabilities, and accordingly yield
	 highly different estimations of complexity at the product level. The
	 approach that we developed avoids this algorithmic 'confusion' by
	 drawing on a toolkit of more transparent and long-established methods
	 that follow the 'supervised learning' principle where the data on
	 trade/specialization and development are processed together from the
	 very beginning in order to identify the patterns of mutual association.
	 The first pillar of the toolkit, Principal Component Analysis (PCA),
	 serves dimensionality reduction in co-location information. The second
	 pillar, Canonical Correlation Analysis (CCA), identifies the
	 mutual-association between the various patterns of (co-)specialization
	 and more-than-one dimension of economic development. This way, we are
	 able to identify the products or technologies that can be associated
	 with the level or the growth rate of per capita GDP and CO2 emissions.
Classification-JEL: F14, F63, O11
Keywords: Economic complexity, economic development, supervised learning, canonical
	 correlation analysis, principal component analysis
Series: UNU-MERIT Working Papers
Creation-Date: 20220422
Number: 2022-015
File-URL: https://unu-merit.nl/publications/wppdf/2022/wp2022-015.pdf
File-Format: application/pdf
File-Size: 275 Kb
Handle: RePEc:unm:unumer:2022015