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Skill Shapes for the Future of Cities

One of the most fascinating aspects of Amazon’s overly hyped HQ2 search—beyond large and growing regions scrambling like mad to bundle together opulent packages to define their competitive edge—was that it seemed nearly impossible for smaller, especially rural, towns and cities to compete with established hubs to which more and more talent is obviously migrating. It became clear that as much as states, cities, or regions would have loved to position themselves as the next Silicon Valley or Research Triangle, most regions simply do not have a strong grasp of their unique advantages or regional DNA to develop thriving economies and talent pipelines. 

Mark Muro and Rob Atkinson, researchers from Brookings Institution, view the disparity as so dire, in fact, that they believe that federal interventions are required to focus on 10 inland cities with large enough populations to build new economies. The idea is that sites like St. Louis and Indianapolis could be the beneficiaries of massive public investment to offset the flood of resources, talent, and companies on the coasts. Muro asserts, “It is wishful thinking we will turn this around without some directed federal support.”

But a new analysis by Strada Institute for the Future of Work and Emsi offers a new model called “skill shapes” for spurring economic growth so that all cities and regions—not just 10 or 15 of them—can compete. 

Each role in any industry has a specific skill shape, depending on where geographically that role is situated. So, when an employer is looking for a digital marketing specialist in Denver, that skill shape looks unique when compared to the same role in Boise or Atlanta. 

Every skill shape is defined by its regional context, and multiple factors drive the unique skill demands of a given region. Some of the variation can be attributed to differences in the kind of job openings offered by the unique set of employers in each region, as well as migration patterns, supply chains, and the regional supply of talent. 

People, too, have distinct skill shapes; your skill shape looks different from mine. And with the ability to access social profile and resume data, we can assess the skill shapes of people and talent in a certain area. Together, these data can expose regional skills gaps and surpluses. 

Take a cybersecurity specialist as an example. 

In Washington, DC, knowledge of federal information security systems and protocols are the dominant skills, including a combination of fraud identification, hacking, and digital forensics, which commonly involves analyzing digital network vulnerabilities related to counterintelligence activities or law enforcement. In St. Louis, cybersecurity looks like a subset of data science, with advanced statistics, data modeling, and data visualization as more prominent skills. In Columbus, cybersecurity skills have a different emphasis.

Columbus is becoming a hub for financial tech, particularly cybersecurity. But the talent in the area is not keeping up with the demand for ethical hacking, intrusion detection and prevention, and network forensics. Many of the roles associated with these skills are hybrid, requiring skills both in financial services and machine learning simultaneously. There is a shortfall of talent in cloud computing, especially in Apache suite and Cassandra.

These supply-demand gap analyses can be done for any region in any industry domain. If policymakers, workforce and economic developers, learning providers and employers understand these gaps, it becomes much more obvious how to support the design and development of well-calibrated and more precise learning pathways to close those gaps.

The new model of skill shapes can provide governors, mayors, and impact investors actionable data upon which they can grow their local economies. We don’t have to pick and choose the cities and states we invest in. Each region has the potential to grow their local economies by aligning training dollars to more precise learning pathways that match local talent to the skill shape of the region.

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