The Economic Development Administration’s (EDA) Regional Technology and Innovation Hubs (Tech Hubs) program is an historic place-based investment that aims to strengthen US economic and national security. Regions across the country have the opportunity to accelerate their tech industry and ecosystem through substantial federal funding. This includes rural communities building tech economies.
If you’re building your case for a Tech Hub Designation and/or a Strategy Development grant, you need quantitative workforce data, relevant to your tech focus area, to create your narrative.
For workforce, the narrative needs to establish the current tech talent, explain how it will grow and evolve to maintain global competitiveness, and describe how the tech industry will develop opportunities for workers without a four-year degree and underserved populations.
Articulate your workforce in emerging and fast-changing technologies
The Key Technology Focus Areas (KTFAs) outlined in the Tech Hubs program are emerging and fast-changing sectors. Traditional data sources that use SOC and NAICS codes are lagging and won’t be able to accurately capture sector dynamics in your region.
For instance, there isn’t a SOC code for many of the roles in the “Artificial intelligence, machine learning, autonomy, and related advances” KTFA. There isn’t even a SOC for Machine Learning Engineers. But Lightcast can leverage our proprietary skills and occupation taxonomies to capture the workforce of these emerging industries and disruptive technologies in a region.
EDA’s goal is to create new centers of innovation across America. Thus, we removed the top 20 MSAs with the largest absolute number of innovation jobs, as determined by The Brooking Institution (using Lightcast, then Emsi, data) in The Case for Growth Centers. Once removed, the MSAs with the highest existing concentration of AI, machine learning, and autonomous talent include Detroit, MI; Baltimore, MD; Pittsburgh, PA; Raleigh, NC; and Charlotte, NC.
How did we determine AI, machine learning, and autonomous talent when there aren’t such SOCs? The Lightcast skill taxonomy allows us to search and filter by thousands of skills. And since more are added daily, it captures emerging and changing industries such as those targeted by the Tech Hubs program. We created a search filter of profiles and postings that include any of the 32 skills or qualifications relevant to this KTFA. Examples include machine learning algorithms, artificial intelligence systems, and Oracle Autonomous Database Course Certification.
In Charlotte, the most frequent skills amongst their talent in this KTFA are machine learning, AI, data analysis, Python, and SQL.
Conversely, in Detroit, autonomous vehicles, marketing, customer support, new product development, and project management are the top skills.
Professional profiles provide the talent supply data of a region, job postings reveal the demand—and the need for investment to build additional capacity and pipeline. Over the last five years, the Salem, OR; Barre, VT; and Topeka, KS MSAs have seen the largest increase in job postings (of MSAs with at least 1,000 job postings) in the AI/Machine Learning KTFA.
Additional data points such as the number of employers competing, median advertised salary, and others can also be pulled in.
EDA Tech Hubs will make investments in geographies that have an existing foundation of assets, resources, and capacity. These are just a few of many ways Lightcast skill and occupational taxonomies can articulate your regional workforce in emerging and shifting technology areas to demonstrate this foundation.
Quantify the pipeline of local talent
Not only is an existing tech workforce needed, but one that can grow to meet the tech and industry needs of the future. This growth has a market goal (globally competitive) and a timeframe (approximately 10 years). To quantify the pipeline that can meet the market and timeline goals, a number of data points can be used.
Many of these data points are readily available via Developer. You can compare profiles and job postings to determine tech skill demand for the AI and Machine Learning KTFA.
Or using the Gazelle platform, map and list views can be generated of companies by the type and date of their venture capital (VC) funding. Those companies can also be sorted by revenue, number of employees, exporter status, and likeliness to expand. A map view of VC-funded firms in Salt Lake City can quickly be produced and detailed info of those firms generated.
Programs of local institutions producing talent for roles in new and emerging technologies can be garnered from various education reports. For the Mechatronics Engineer occupation in Detroit, the Program Snapshot report quickly provides a host of information, including program completions relevant to the occupation and from which institutions.
This is a sampling of the various reports to show how your region’s talent pipeline is aligned with a chosen EDA Tech Hub KTFA. If time or staff doesn’t allow for such research, or you are seeking even greater detail, Lightcast can quickly analyze your tech workforce using these data points and others to help you build the case/narrative for Tech Hub designation or Strategy Development grant.
Skills-based pathways and skill transferability
Meeting the globally competitive vision of the Tech Hubs program will require the engagement of as many workers as possible. As our recent semiconductor research shows, the first need for tech industry scaling is skilled and qualified workers. To double American semiconductor production, over 230,000 new workers will need to be employed in the industry.
The Tech Hubs Notice of Funding Opportunity (NOFO) makes clear that “To become globally competitive, a region must…have the workforce and workforce development systems necessary to scale.” Semiconductors (a sector included in a KTFA) is a prime example of an industry that needs a workforce to scale.
How is this done, especially with such nascent skills as machine learning and artificial intelligence? By identifying pockets of the workforce that can be reskilled or upskilled to meet the new technology demand. This will draw on workers with similar skills or in skill-adjacent jobs and build on current qualifications by adding the specific skills needed for whichever KTFA(s) are being targeted.
Skills-based career pathways are a great way to see and understand workers who can transition into tech jobs in high demand. The growth of AI is driving demand for data roles (Data Scientist, Data Specialist, Data Analyst, etc.) and data skills. If trying to fill Data Specialist roles in Pittsburgh, career pathways reveal feeder roles likely not typically thought of for supporting the AI industry: Human Resource Analyst, Technical Sales Specialist, and Clinical Data Coordinator all have skill relevance to Data Specialist.
This approach can be used with a focus on building pathways into tech for those in lower-paying jobs/sectors or transitioning workers from legacy industries based on skills. Other strategies include talent attraction focused on in-demand tech jobs where a region has a competitive advantage on wages, quality of life, etc. ChamberRVA in Richmond used profile data to understand where alumni from local education institutions are migrating to. This informed their strategy for how to better retain talent or attract it back.
All hands on deck
Tech Hub designations and Strategy Development grants will only be awarded to consortia which must include a higher ed institution, subnational or tribal government, relevant technology industry firms, economic development organizations (EDO), and labor or workforce organizations. The consortium must also demonstrate a regional nexus, showing in their chosen geography tightly connected assets, capital, R&D, workforce, and infrastructure relevant to their key tech focus area.
Getting all of these players on the same page across a large (or even small) geography is difficult, but vital to a successful Tech Hub application. One of the best ways to get on the same page is to be speaking the same language, reading from the same book. Lightcast skills data and occupation taxonomy provides that common language. A university, EDO, or local employer can all discuss the current and future needs of tech skills, as opposed to degrees, SOCs, and HR job titles. What’s more, Lightcast data is available at the ZIP code, census tract, county, MSA, and state level. So no matter the geography chosen for the Tech Hub, the data can be surfaced for that region.
