Human-capital-centred Regionalism in Economic Development: A Case of Analytics Outpacing Institutions?

Abstract

Drawing on the case of the Delaware Valley Innovation Network, a regional consortium funded under the US Department of Labor, the paper argues that sophisticated analytical tools developed to facilitate workforce-and occupation-led economic development are running ahead of the institution-building required to put new approaches into practice. There are two main reasons for this. First, tensions persist around the role of the public-sector workforce system in regional development initiatives. Secondly, regional stakeholders disagree about whether 'knowledge economy' investments should include the training of manufacturing, transport and logistics workers. The documentation of regional occupational specialisations, 'talent gap' analyses and the clarification of career pathways are crucial components of human-capital-centred regionalism in economic development. However, best analytical practices are of little use without the institutional capacity to translate analysis into coherent, effective policy.Parallelling a reassertion of regionalist thinking in economic development policy in the 2000s has been an increased emphasis on human capital as a source of innovation and competitive advantage. As a result, human capital has become a focus of economic development analysis and policy. Scholars, many of them funded by the US Economic Development Administration, have developed empirical techniques for investigating regions' occupational profiles and have proposed ways in which knowledge about human capital can be used to devise smarter growth strategies.While scholars and practitioners advocate regionally implemented policy efforts that rest on developing human capital, actual instances of such efforts remain underinvestigated. In the context of human-capital-centred regional economic Laura Wolf-Powers is in the

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“…However, the implementation can be challenging, as clusters are defined based on a subset of occupations and industries, and definitions can overlap for non-mutually exclusive clusters. In addition, researchers identified the analytics as too complex ( 21 ). This study proposes a simpler OCIC-LQ construct, where the denominator ( E jR and E jN ) includes total jobs in all types of occupations within the industry cluster.…”

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