Modular structure in labour networks reveals skill basins

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

Leveraging the discrete skill and knowledge worker requirements of each occupation provided by O*NET, our empirical approach employs network-based tools from the Economic Complexity framework to characterize the US occupational network. 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. Our empirical strategy is threefold. First, we construct the Job and Skill Progression Networks, where nodes represent jobs (skills) and a link between two jobs (skills) indicates statistically significant co-occurrence of skills required to carry out those two jobs, that can be useful tools to identify job-switching paths and skill complementarities Second, by harnessing the Fitness and Complexity algorithm, we define a data-driven skill-based complexity measure of jobs that positively maps, but with interesting deviations, into wages and in the bottom-up and broad abstract/manual and routine/non-routine job characterisations, however providing a continuous and endogenous metric to assess the degree of complexity of each occupational skill-set. Third, building on relatedness and corporate coherence metrics, we introduce a measure of each job's skill coherence, that negatively maps into wages. Our findings may inform policymakers and employers on designing more effective labour market policies and training schemes, that, rather than fostering hyper-specialization, should favor the acquisition of complex and "uncoherent" skill sets, enabling workers to more easily move throughout the job and skill progression networks and make informed career choices decisions while unlocking higher wage opportunities.

The role of relatedness and strategic linkages between domestic and MNE sectors in regional branching and resilience

Despite the key role of multinational enterprises (MNEs) in both international markets and domestic economies, there is no consensus on their impact on their host economy. In particular, do MNEs stimulate new domestic firms through knowledge spillovers? Here, we look at the impact of MNEs on the entry and exit of domestic industries in Irish regions before, during, and after the 2008 Financial Crisis. Specifically, we are interested in whether the presence of MNEs in a region results in knowledge spillovers and the creation of new domestic industries in related sectors. To quantify how related an industry is to a region’s industry basket we propose two cohesion measures, weighted closeness and strategic closeness which capture direct linkages and dense inter-industry links between local industries respectively. We use a dataset of government-supported firms in Ireland (covering 90% of manufacturing and exporting) between 2006 and 2019. We find that domestic industries are both more likely to enter and less likely to leave a region if they are related to so-called ‘overlapping’ industries containing both domestic and MNE firms. In contrast, we find a negative impact on domestic entry and survival from cohesion to ‘exclusive MNE’ industries, suggesting that domestic firms are unable to ‘leap’ and thrive in MNE-proximate industries likely due to a technology or know-how gap. Finally, the type of cohesion matters. During the economic recovery (2015–2019), it is strategic rather than weighted closeness to overlapping industries that is associated with both domestic industry entry and survival.