Frequently Asked Questions

Economic clusters reveal regional economic competitiveness. These data and metrics can provide insights for regional economic and workforce development strategies, particularly the Comprehensive Economic Development Strategies (CEDS) for economic development districts. The three specific types of data categories included are industry, skills, and knowledge clusters.

Cluster data can help your organization in several ways:

• Get a broad overview of competitive clusters, especially jobs and LQ in the current period and average earnings.
• For industry clusters, identify if a region is more specialized in a subcluster within the selected industry cluster. Look into jobs, LQ and average annual earnings of the subclusters. Star and Mature industry and subindustry clusters are drivers of the regional economy.
• For skills and knowledge clusters, assess if there are Star and Mature occupation clusters which reveal strengths and competitiveness in skills and knowledge areas of the regional workforce.
• EDD staff and planners need to work with their CEDS policy and steering committees to identify and select areas of opportunities and the industry, skills and knowledge clusters for CEDS plan.

Please use the email regionalclusters@purdue.edu and mention the subject title such as GIS maps, data dashboards, definitions, etc., and describe your questions in as much detail as possible. The email will be checked by several personnel of the project team.

Economic cluster data can be used to assess competitiveness in industries and skills and knowledge available within the regional labor force and occupations. A competitiveness in an industry cluster means the region is specialized and has the capacity for exports outside of the region. A competitiveness in a skill cluster means the regional work force is specialized in that skill. Similarly, a competitiveness in a knowledge cluster means the regional work force is specialized in that area of knowledge.

The competitiveness in industry, skills, and knowledge can be used to develop strategies for regional economic and workforce development. As temporal data are added into the dashboards in the coming years, they can be used to identify trends in economic competitiveness, track changes in an industry, skill or knowledge cluster, especially if it has moved its quadrant (Star, Mature, Transforming, Emerging) in the cluster bubble chart.

A cluster in a Star quadrant means it is specialized in the current year and has increased its specialization in the data period. A Mature cluster is specialized but decreased its specialization. A Transforming cluster is not specialized and has lost specialization. An Emerging cluster is not specialized but has increased its specialization.

Star clusters are drivers of the regional economy. Mature clusters need policy and infrastructure support. Emerging clusters should be watched because with support they can move into the Star quadrant. Transforming clusters are declining and may need significant support.

The Economic Clusters Project was funded by the U.S. Economic Development Administration (EDA). Purdue Center for Regional Development (PCRD) completed the project. We also acknowledge partnership of Lightcast, LLC to use the Lightcast Data to derive the economic metrics for the clusters. Refer to the team web page to see the personnel of the research and implementation team.

Industry clusters include select sets of industry sectors that have interdependencies such as buying and selling from each other, using similar technologies, sharing supply chains, using similar supporting services and specialized infrastructure, sharing labor pools with common occupational and skill requirements, etc.

There are 21 industry clusters which include 68 industry subclusters. The industry clusters are defined based on North American Industry Classification System (NAICS) 6-digit industry sectors, and their definitions overlap. Hence, industry clusters are not mutually exclusive.

The industry clusters are defined based on quantitative analysis, expert inputs, and discussions within the project team. The quantitative analyses include input-output value and supply chains analysis by using the principal components and agglomerative hierarchical cluster analysis methods. Network analysis is used to identify modularity or clusters by using geographical co-location data and occupational patterns of industry sectors.

Data sources include input-output tables from Bureau of Economic Analysis (BEA), geographical colocation data from the County Business Patterns (CBP), and occupations patterns from Lightcast, LLC and Bureau of Labor Statistics (BLS).

Skills-based occupation clusters explore the skills domain of occupation clusters. The analysis can provide a meaningful tool for workforce development, especially opportunities for labor force training and upskilling programs.

They help regional planners identify what kind of specialization and concentration of skills and workers are present in the region.

Knowledge clusters or knowledge-based occupation clusters include groups of occupations that share similar knowledge and educational preparedness based on O*NET typology.

There are 21 knowledge clusters covering around 64% of 800+ occupations. Only occupations requiring medium, considerable, or extensive preparations are included — generally those requiring an associate, bachelor's, master's, doctoral or higher professional degree. Knowledge clusters are mutually exclusive.

The dashboard allows users to select and search occupation by cluster as well as cluster by occupation:

1. Search Occupation by Cluster: list of occupations in each selected cluster (N=21 clusters).
2. Search Cluster by Occupation: list of all occupations (N=557 out of 873).

The Industry clusters' dashboards allow users to view competitive and emerging industry clusters and subclusters, as well as identify industry sectors within each cluster. Users can:

1. Search Industries by Cluster: which industry sectors are in a cluster.
2. Search Industries by Subcluster: which industry sectors are in a subcluster.
3. Search Cluster by Industry Sector: which cluster an industry sector is in.

Skills clusters or skills-based occupation clusters include groups of occupations that share similar skill characteristics based on the latest skills typology from the Occupational Information Network (O*NET).

There are 38 skills clusters that cover the entire set of 800+ occupations in the labor market. The skills clusters are defined based on O*NET's most detailed occupations and Standard Occupational Classification (SOC) 6-digit codes. Skills clusters are mutually exclusive — an occupation appears in only one skill cluster.

The dashboard allows users to select and search occupation by cluster as well as cluster by occupation:

1. Search Occupation by Cluster: list of occupations in each selected cluster (N=38 clusters).
2. Search Cluster by Occupation: list of all occupations (N=873).

Knowledge-based clusters explore the knowledge domain of occupation clusters. The analysis can provide a meaningful tool for human capital development as well as workforce development for regional economic development.

They can help identify specialization and concentration of knowledge workers in the region and can be used for coordinating regional talent pipelines and workforce development for higher education institutions and state and local agencies.

Refer to the industry cluster dashboard help document. Each cluster database has two types of dashboards: definitions and economic characteristics. Industry clusters are defined based on NAICS 6-digit codes. Occupation clusters (both skills and knowledge) are defined based on SOC 6-digit codes.

Industry clusters are overlapping and not mutually exclusive. In comparison, both types of occupation clusters have mutually exclusive occupations.

The location quotient (LQ) measures the ratio of the cluster's regional employment share to its national employment share. The LQ measure for clusters is based on employment and shows specialization of the region.

The theoretical threshold for specialization is 1 (regional percent share equals national percent share). It is recommended to look for Star and Mature industry clusters that have LQ at 1.2 or higher in the current period.

Refer to the help documents for industry cluster and occupation clusters (skills and knowledge). These guides provide step-by-step instructions for interacting with the Tableau dashboards.

If a region wants to compare or benchmark itself with other competing, aspiring, or peer regions, refer to the GIS maps of LQ, employment, and wages. The maps are interactive — click on a geography and a text box will reveal the numeric value. Location quotients (LQs) can be used to compare the specializations of two different regions.

A subcluster is a smaller category that exists within a larger industry cluster. The subcluster jobs add to the industry cluster's total jobs. The subcluster definitions of industry sectors are mutually exclusive, but industry clusters have overlapped definitions.

Refer to the cluster definitions dashboard to identify the industry sectors included in industry clusters and their subclusters. Note that subclusters do not exist for skills clusters and knowledge clusters.

The graph scale is 1 through 5 with 5 being the strongest content area of the knowledge or skill domain for that occupation. Viewers can compare two clusters at a time to see which areas are best needed for that cluster. Note that both charts show the mean levels of the knowledge or skill domain.

Refer to the help documents for industry cluster and occupation clusters (skills and knowledge). The Tableau dashboards include built-in download functionality for exporting data and visualizations.

In the cluster bubble chart dashboard, identify the industry clusters, skills-based occupation clusters, or knowledge-based occupation clusters in the Transforming quadrant. These are declining clusters. Use the definitions dashboards to identify industry sectors or occupations that are under threat or weak in the region.

Alternatively, check out the clusters in the Emerging quadrant. These clusters are on the cusp of becoming specialized, and any help through policy and programs can grow these clusters and benefit the region.