September 12, 2019 by Joshua Wright
Imagine you’ve landed a meeting with the CEO of an emerging video game design firm. This company is considering opening a second office, and your region is on the short list of finalists.
The 150-employee business wants to expand with room for 75 additional designers, programmers, and marketers, but it needs to tap into a deeper talent pool to realize this growth.
As the executive director of the regional economic development agency, it’s your job to convince the CEO you can supply the talent her company needs to become a major player in the gaming industry.
How are you going to make the case? What data points are you going to use to convince the CEO that your region can supply the mix of design, mobile development, and marketing skills that are musts for this business?
Traditional labor market data—how many workers in computer occupations are working in your four-county area, or the number of software development graduates your higher ed institutions are producing—won’t suffice with this data-savvy company. The CEO wants to see hard numbers by skill.
It’s for these types of situations that Emsi has spent the last two years mapping local labor markets by skill cluster. Instead of starting with structured and hard to keep up-to-date industry or occupation codes, we’ve taken an organic approach to show how groups of related skills mined from employers’ job postings and workers’ online professional profiles coalesce and cut across industries and career areas.
The goal of this work is to help communities more effectively build and market their regional talent ecosystem and partner with higher education to upskill and train workers.
Regional skills clusters are a powerful way to better match workers to in-demand career opportunities and companies to communities where the skills exist (or are being trained for) that they need.
Yes, it’s true, winning over a company takes more than data and more than proving you have a talent pipeline. Still, the right workforce intel and regional talent strategies can vault your community to the top of the list.
Plus, a data-informed strategy can ensure you’re targeting the best companies for your region way before meetings like this one occur.
Your city and every other community in America are in an all-out arms race to develop, attract, and keep talent.
Talent development is the No. 1 focus for communities because it’s the No. 1 issue for businesses. Just as talent is the most important asset for any company, a prepared labor force is the most important asset for any community.
The cold, hard truth for communities is they won’t grow if they don’t have workers with skillsets that match what local companies need.
The operative word here is skillset. Often the skills gap, whether you believe it’s real or made up, has been discussed as though it’s an occupation or industry gap—a shortage of production workers, for example, or a shortfall of mid-level manufacturing managers.
In an age where we use technology to geo-locate the best Thai restaurant in a new city and companies use data and algorithms to know our spending patterns better than we do, communities should be asking more precise questions. Which skills can companies not find? What supply and demands gaps by specific skill or skill area does your region need to address?
To do this, it’s necessary to have workforce supply and demand data that’s both more precise and more aggregated. Which is why Emsi has mapped the shape of the labor market by skill cluster.
The shape of labor markets is different in every region because the companies (and collections of companies that comprise industries) are different in every region. Even if an employer is the same across cities, the skills they need in, say, Dallas vs. Minneapolis very well could be different.
This is important to understand, as Emsi and Strada will discuss in detail in a forthcoming report on the new geography of skills, when you’re trying to do a better job matching the supply of talent to the demand of opportunity in a specific labor market.
It’s also critical because roles at businesses and indeed entire industries are changing so quickly. Technology and automation have ushered in emerging jobs like humans to oversee robots. Advanced manufacturing now blends traditional production work with businesses processes and engineering.
Emerging industries take on different characteristics depending on the region. Skills clustering illuminates these differences like no other by showing a network of statistically related skills as they emerge and shift in a market. Through this, we can organically see the clusters of interrelated skills that form the shape of a local labor market—a blend of finance and big data in New York City or public policy and data skills in Washington, D.C.
To illustrate this, let’s look at a case study in life sciences in a few distinct metro areas.
Boston, Chicago, and San Francisco are known for their biotech or life sciences industry clusters. Indianapolis, meanwhile, has a higher concentration of biopharma industry jobs than any large metro outside of Durham, NC.
Through Emsi’s regional skills clusters, we can see biotech is much more than traditional clinical research. It now blends data science and computational biology with clinical work.
The demand for traditional life scientists is similar in Indianapolis to two powerhouse metro areas, New York and Chicago. But the supply of these scientists, as measured by Emsi’s online professional profile database, is severely lacking.
This is where economic developers in the Indy area may want to up their talent attraction game to complement the impressive biotech businesses it has attracted and developed.
Indianapolis, meanwhile, has a much stronger presence of clinical data scientists with IT and math/analytics skills (see the green bars below). But both Indy and St. Louis trail behind Boston in life scientists with bioinformatics, genomics, DNA sequencing, and other similar life science skills. The presence of these skills in Indy would put it on par, or ahead, of Boston for clinical data science talent.
The beauty of this cluster analysis is it can pinpoint broad and specific skill gaps by comparing employer demand (from job ads) and workforce supply (from online profiles). It also quickly isolated that biotech is emerging to include data science and IT/math, something you couldn’t use NAICS, SOC, or O*NET codes to find.
We discussed clusters and skills data in more depth in an August webinar hosted by IEDC. You can watch the webinar below and download the slides here.
As we saw in our example, regional skills clusters expose the skill gaps in a local market in a much more targeted way that traditional data analysis can. This has major implications on the reskilling of existing workers in a company or community. More targeted research leads to more targeted workforce training, specifically in building microcredentials to get jobseekers specific groups of skills.
A few manifestations of this:
For communities and businesses trying to evaluate local markets, the pain involved in finding workers is acute. So too is the pain they feel using traditional workforce analysis to take stock of the quality and quantity of a community’s labor pool.
The economy is rapidly changing and transforming. An economic downturn may be coming. Automation is more of a reality every day. All this necessitates more refined, targeted data analytics to understand a region’s talent ecosystem.
This is why you’ll see much more from Emsi in the coming months on how we’re helping regions market and match their talent to companies using skills clusters.
Keep your eye out for “The New Geography of Skills” research that Emsi and the Strada Education Network will be releasing to identify skills that are in high regional demand. For more information on your regional skills or to learn more about regional skill clustering, contact Joshua Wright at email@example.com.