Title: Mapping job fitness and skill coherence into wages: an economic complexity analysis

Authors: Sabrina Aufiero (Università “Sapienza”- Roma, University College-London), Giordano De Marzo (Università “Sapienza”- Roma, Centro Ricerche Enrico Fermi, Complexity Science Hub – Vienna), Angelica Sbardella (Centro Ricerche Enrico Fermi), Andrea Zaccaria (Centro Ricerche Enrico Fermi, Istituto dei Sistemi Complessi (ISC) – CNR)

Published on: Scientific Reports volume 14, Article number: 11752 (2024)

Link: https://rdcu.be/dJ5uS

Overview

Characterizing jobs in terms of human capital and understanding the relationship between skill requirements and wages are pivotal issues in the economic discourse, particularly for knowledge-based industries. This is increasingly crucial in the heated debate about the job characteristics complementary to automation or the occupational and sectoral shifts brought about by the sustainable transition. Several contributions emphasise the importance of skill diversity and recombinant processes in knowledge generation; treating human capital as uniform and skills as interchangeable falls short of acknowledging their multi-dimensional and diverse nature. A network-oriented and data-driven perspective may illuminate the disaggregated and interactive aspects of skill and knowledge recombination in each job, enhancing the information on the human capital content of occupations while mitigating the inherent bias often associated with broad skill aggregation approaches.

To this end, the present paper investigates the network structure and relatedness within the US occupational labor market. By relying on the discrete skill and knowledge worker requirements—which we will often refer to as skills for brevity—documented by O*NET for each SOC occupation, our empirical approach leverages various network-based tools from the Economic Complexity framework to characterize occupations based on their multi-dimensional human capital content. This approach provides insights into the interplay between wages and the complexity or relatedness of the skill sets within each occupation, complementing conventional human capital frameworks.