  In this paper we compare Skill-Relatedness Networks (SRNs) for selected
countries, that is to say statistically significant inter-industrial
interactions representing latent skills exchanges derived from observed labor
flows, a kind of industry spaces. Using data from Argentina (ARG), Germany
(DEU) and Sweden (SWE), we compare their SRNs utilizing an
information-theoretic method that permits to compare networks of "non-aligned"
nodes, which is the case of interest. For each SRN we extract its portrait, a
fingerprint of structural measures of the distributions of their shortest
paths, and calculate their pairwise divergences. This allows us also to
contrast differences in structural (binary) connectivity with differences in
the information provided by the (weighted) skill relatedness indicator (SR). We
find that, in the case of ARG, structural connectivity is very different from
their counterpart in DEU and SWE, but through the glass of SR the distances
analyzed are all substantially smaller and more alike. These results qualify
the role of the SR indicator as revealing some hidden dimension different from
connectivity alone, providing empirical support to the suggestion that industry
spaces may differ across countries.
