How To Interpret Talent Cluster Charts

The following sample data and figures originate from our recent report: The U.S. STEM Talent Cluster Report | Big Regions 2019. LUFT is a research firm listed with the U.S. Cluster Mapping Project initiated by cluster research pioneer Micheal Porter at Harvard University.

What are talent clusters?

Talent or industry clustering is an economic process that involves the co-location of specialized professionals and entities e.g. businesses, universities, incubators, VCs and other local affiliations that together create an environment conducive to accelerated innovation and value creation. Classic examples include Silicon Valley’s tech ecosystem or Detroit’s automotive sector, where a high concentration of firms and capital attract further investments and a certain culture of productivity.

Clustering is also a method of regional industry & workforce analysis that segments local employment and occupational data into related skill types. These data sets offer employers, investors, and policymakers a way to compare talent supply and general competitiveness of regions. Resulting analysis can be used to guide investment decisions and policy design in site selection, economic development, and visitor attraction initiatives.

For this analysis, we have segmented out six distinct talent clusters from the 100 occupations identified as STEM by the U.S. Bureau of Labor Statistics:

STEM Cluster Definitions

- Computing & Digital Sciences
- Data & Business Sciences
- Biological & Chemical Sciences
- Electrical, Materials, Mechanical Engineering
- Facilities & Infrastructure Engineering
- Post-Secondary STEM Education

The following figures map the relative concentration of these clusters within the 20 largest regional labor markets in the United States: Atlanta, Baltimore, Boston, Chicago, Dallas, Denver, Detroit, Houston, Los Angeles, Miami, Minneapolis, New York City, Philadelphia, Phoenix, Riverside, San Diego, San Francisco, Seattle, St. Louis, Washington D.C.

Note: The data only tells part of the story and is a relative indicator useful for discovery and comparison. Concentration levels (vertical axis) are important because this suggests a certain level of regional specialization. There is further nuance to consider within each cluster category. For the below example, certain regions might have higher concentrations of coders and data scientists compared to other regions mapped out on our matrix. The upper left quadrant of our matrices is an interesting one. Rarely do regions have large concentrations of specialized talent at competitive relative wages. Those within this quadrant may warrant further analysis of talent potential.

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