The Location Quotient (LQ) is a technique used in regional economic analysis. This metric measures the concentration of a particular industry within a specific geographic area, such as a city or a state. By quantifying how localized an industry is, the LQ helps economists and policymakers understand a region’s unique economic structure. It identifies a region’s economic specialization and potential competitive advantages.
Defining the Location Quotient
The Location Quotient is a measure of regional specialization. It is defined as a ratio comparing the local concentration of a chosen industry to the concentration of that same industry at a larger, reference level, typically the nation. This comparison reveals whether a region has a disproportionately large or small share of a specific industry relative to the national average. The LQ measures relative presence, not the absolute size or total output of an industry. This ratio indicates the degree to which a local economy focuses its resources on particular sectors.
How the Location Quotient is Calculated
The calculation of the Location Quotient involves a two-part ratio. Analysts first determine the local share of a specific industry’s employment relative to the total employment across all industries in that local area. This establishes the industry’s footprint within the regional economy being studied. For example, one might calculate the percentage of all jobs in a county that belong to the software development sector.
The second part requires determining the national share of the same industry’s employment relative to the total national employment. This national figure acts as the benchmark against which the local area is measured. This ensures the comparison is normalized and accounts for size differences between the local and national economies.
The final LQ value is derived by dividing the local industry’s employment share by the national industry’s employment share. The formula is: LQ = (Local Industry Employment / Total Local Employment) / (National Industry Employment / Total National Employment). Inputs typically rely on consistent employment figures for specific industries, often categorized using systems like the North American Industry Classification System (NAICS).
Interpreting the Location Quotient Results
The numerical result of an LQ calculation holds specific economic meaning.
LQ Greater Than One (LQ > 1)
An LQ greater than one signifies a higher concentration of the industry compared to the national average. This result suggests the industry is specialized and likely produces goods or services in excess of local demand, meaning they are exported outside the region. These are categorized as “basic” industries that bring new money into the local economy, fueling growth.
LQ Less Than One (LQ < 1)
An LQ less than one indicates the industry is underrepresented locally relative to the national distribution. This suggests local demand for the goods or services provided by that industry is likely satisfied by imports from other regions. These are generally considered “non-basic” or supporting sectors.
LQ Equal to One (LQ = 1)
An LQ equal to one means the industry’s presence in the region is proportional to its national presence. The industry is neither specialized nor significantly deficient, suggesting its output largely meets the internal needs of the local population.
Practical Applications of Location Quotients
Location Quotient analysis helps policymakers make informed decisions about economic strategy.
Identifying Competitive Advantages
One primary use is identifying industries that represent the region’s existing competitive advantages, which are often the sectors with an LQ significantly greater than one. This identification allows economic developers to target their resources toward supporting these established, specialized industries for further growth.
Assessing Workforce Needs
Regional planners frequently use the LQ to assess potential workforce training needs. If an analysis reveals a high LQ in advanced manufacturing, it signals a long-term need for specialized technical education programs to maintain the local labor supply for that sector. This proactive approach ensures that the local workforce remains aligned with the specialized demands of the regional economy.
Informing Business Attraction
The LQ also informs target marketing efforts aimed at attracting new businesses to a region. A high LQ in a particular supply chain component signals to related industries that the necessary infrastructure, supplier network, and skilled labor already exist locally.
Data Requirements for Location Quotient Analysis
Accurate Location Quotient analysis depends on consistent, reliable data inputs, primarily focused on employment figures. The necessary data is typically sourced from government agencies responsible for economic statistics, such as the Bureau of Labor Statistics (BLS) or the Census Bureau. These sources provide detailed employment counts broken down by specific industries and geographic boundaries.
It is necessary that the local and national data utilize a consistent system for classifying industries, with the North American Industry Classification System (NAICS) being the standard in the United States. Maintaining consistency in both the industrial definition and the geographic definition of the local area is paramount.
Limitations of Location Quotient Analysis
While Location Quotient analysis is a powerful tool, it does possess inherent limitations that analysts must consider. The metric primarily relies on employment data, which means it overlooks other significant economic factors like wages, total output, or capital investment. Two regions might have the same LQ for an industry, but the quality of jobs (high-wage vs. low-wage) is not captured.
A significant assumption underlying the LQ model is that labor productivity is uniform across all regions and industries being compared. In reality, productivity levels can vary widely due to differences in technology adoption, capital intensity, or worker skill. Furthermore, the LQ model struggles to account for commuting patterns. If a significant portion of the workforce commutes into a city, local employment figures may artificially inflate the industry concentration.