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: Canonical correlation complexity of European regions
Abstract: In an earlier paper (Nomaler & Verspagen, 2022) we introduced a
	 'supervised learning' based alternative to the competing unsupervised
	 learning algorithms (e.g., Hidalgo and Hausmann, 2009 vs. Tacchella et
	 al, 2012) proposed in the so-called 'economic complexity' literature.
	 Similar to the existing ones, our alternative, which we refer to as the
	 "Canonical Correlation Complexity Method (CCCM)", also aims at reducing
	 the high dimensionality in data on the empirical patterns of co-location
	 (be it nations or regions) of specializations in products or
	 technologies, while the ultimate objective is to understand the
	 relationship between specialization, diversification, and economic
	 development. In our alternative method which combines the toolkit of the
	 Canonical Correlation Analysis with that of Principal Component
	 Analysis, the data on trade or technology specializations and multiple
	 dimensions of economic development are processed together from the very
	 beginning in order to identify the patterns of mutual association. 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
	 (un)employment. In this follow up paper, we use the CCCM to analyse the
	 development patterns of European regions in relation to their respective
	 technology specializations. Our findings provide insights for EU's
	 industrial policies, especially those considered under the 'smart
	 specialization' framework.
Classification-JEL: F14, F63, O11, O33, R11
Keywords: Economic complexity, economic development, supervised learning, Canonical
	 Correlation Analysis, Principal Component Analysis, technological
	 specialization, revealed technological advantage, European regional
	 development, smart specialization
Series: UNU-MERIT Working Papers
Creation-Date: 20220422
Number: 2022-016
File-URL: https://unu-merit.nl/publications/wppdf/2022/wp2022-016.pdf
File-Format: application/pdf
File-Size: 275 Kb
Handle: RePEc:unm:unumer:2022016