Canonical correlation complexity of European regions
Önder Nomaler & Bart Verspagen
#2022-016
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.
Keywords: Economic complexity, economic development, supervised
learning, Canonical Correlation Analysis, Principal Component Analysis,
technological specialization, revealed technological advantage, European
regional development, smart specialization
JEL Classification: F14, F63, O11, R11