A rigorous, multivariate approach to identifying which industries hold genuine competitive advantage in a regional economy — and mapping the entire economic ecosystem those industries support. Based on the Hill & Brennan (2000) methodology, applied by DMP-DA across statewide and regional studies in multiple Great Lakes states.
"Every regional economy has industries that export output, bring new money in from outside, and form the foundation on which all other growth depends. Driver and cluster analysis finds them — and shows exactly how to build on them."
A driver-cluster analysis is not a description of which industries are large or growing. It is a rigorous, multivariate statistical analysis that identifies which industries hold genuine competitive advantage over equivalent industries in other regions — and maps the entire economic ecosystem those industries support.
Driver industries are those in which a region holds its greatest competitive advantage — industries that produce far more output than local demand requires, export the surplus, and thereby import new money into the regional economy. They are identified through objective, multivariate statistical analysis of competitive position, productivity, specialization, earnings, and market share — not simply by employment size or recent growth.
Driver industries are classified as economic drivers (healthy, exporting), emerging drivers (growing in significance), or declining drivers (losing ground in output or employment).
Source: Hill, E. & Brennan, J. (2000). A Methodology for Identifying the Drivers of Industrial Clusters: The Foundation of Regional Competitive Advantage. Economic Development Quarterly, 14, 65–96.
A cluster is a geographic concentration of competitive firms in the same or related industries that derive collective advantage from proximity to a driver. Cluster industries are those that:
Cluster analysis uses regionalized IMPLAN input-output tables to identify backward (supply-chain) and forward (customer-chain) linkages — measured as a percent of the driver's total outlays and total output.
Figure: Structure of a Competitive Industry Cluster (adapted from Hill & Brennan, 2000)
Driver identification applies hierarchical cluster analysis and discriminant analysis across twelve variables that together capture an industry's competitive position, productivity, growth trajectory, and market orientation. No single metric tells the full story.
A large industry with declining productivity, falling market share, and shrinking relative earnings is not a driver — it is a liability. The 12-variable framework captures past and present performance simultaneously, enabling economic interpretation rather than simple description.
DMP-DA operationalizes the Hill & Brennan (2000) methodology — the most analytically comprehensive driver-cluster framework in the field — combining hierarchical mathematical cluster analysis, discriminant analysis, and IMPLAN-based input-output modeling.
Driver identification proceeds in two sequential quantitative analyses applied to all 4-digit NAICS industries with meaningful regional employment, producing a statistically grounded ranking of each industry's competitive position.
Once drivers are confirmed, cluster mapping uses regionalized IMPLAN input-output tables to trace the supply and customer chains of each driver — identifying which industries are economically tied to its success.
Establish regional economic context — employment, output, productivity, earnings — relative to state and national benchmarks.
Apply hierarchical cluster plus discriminant analysis to all qualifying 4-digit NAICS industries using the 12 performance variables.
Validate and refine quantitative driver candidates with regional industry experts, focus groups, and executive interviews.
Trace forward and backward supply chain linkages using IMPLAN I-O tables; identify technology and labor ties.
Develop actionable economic development strategies, gap analyses, and investment priorities for each driver cluster.
DMP Development Analytics brings firsthand experience applying this methodology at scale — from statewide manufacturing studies to multi-county regional analyses, across multiple Great Lakes states, in partnership with leading national economic research organizations.
Led quantitative driver and cluster analysis for a comprehensive statewide manufacturing competitiveness study covering Pennsylvania's seven Industrial Resource Center (IRC) regions. Applied the Hill & Brennan output-based methodology to identify economic, emerging, and declining drivers at both the state and sub-state regional level. Analysis supported strategic recommendations for IRC programs, state policy, and industry retention and attraction efforts.
Lead quantitative researcher for Ohio's statewide driver and cluster study — one of the most comprehensive analyses of Ohio's manufacturing and non-manufacturing economic base conducted at that time. Identified key driver industries and clusters at both the state and regional level; benchmarked Ohio's competitive position against comparator states; and developed gap analysis findings used to inform the state's economic development strategy and program investments.
Lead quantitative researcher for the Wisconsin Manufacturing Extension Partnership (WMEP) statewide manufacturing study. Applied the Hill & Brennan 12-variable methodology across Wisconsin's seven county-based economic regions. Results identified regional driver industries and their competitive clusters for each region, documented export-oriented manufacturing strengths, and informed WMEP's service delivery strategy and state manufacturing policy.
Applied the Hill & Brennan methodology at the sub-regional level for the Tri-County Business Partnership. Identified driver industries and mapped competitive industry clusters using IMPLAN-based I-O analysis for supply chain linkages. Delivered findings and strategic recommendations tailored to the region's specific industrial base and economic development capacity.
Driver analysis provides the objective, statistically defensible foundation for identifying which industries a region should actively recruit — replacing wish lists with evidence-based targeting.
Understanding which drivers are growing, declining, or emerging allows communities to direct incentives, workforce programs, and infrastructure investments where they will have the greatest economic return.
Cluster mapping — using national vs. regional IMPLAN I-O tables — reveals which supplier industries are absent from the local economy, creating import leakage and opportunities for business attraction.
Labor pool linkages within clusters identify which workforce skills and training programs are most critical to the competitive health of a region's core industries.
Cluster structure reveals the logistics and freight flows linking driver industries to their suppliers and customers — informing infrastructure investment priorities and freight corridor planning.
Driver-cluster findings provide a baseline for evaluating whether economic development programs — MEP services, tax incentives, innovation programs — are serving the industries that matter most to regional competitive advantage.
Most cluster and target industry analyses rely on one or two simple metrics — location quotients, shift-share, or employment size — and produce results that reflect data manipulation rather than genuine competitive analysis. The Hill & Brennan methodology differs in four important ways.
Twelve variables simultaneously capture an industry's competitive position, not just its size or recent growth. An industry that is large but declining, or growing but uncompetitive, is treated differently — and correctly.
The methodology centers on output and market share — what an industry produces and sells — rather than employment counts, which can be misleading when productivity varies widely across industries.
Driver identification uses mathematical cluster analysis and discriminant analysis — not stakeholder preference or policy priority. Results can be examined, replicated, and defended in front of policymakers and grant agencies.
Rather than assuming which industries belong together, cluster mapping traces actual economic relationships through IMPLAN input-output analysis — revealing supply chains and labor ties that qualitative approaches miss entirely.
DMP Development Analytics is developing proposals to apply this methodology for communities and regions across the Great Lakes. We welcome early conversations about scope, fit, and approach.