How To Calculate Msa

Calculate key economic indicators for any U.S. metropolitan area using official BLS and Census Bureau methodologies

Comprehensive Guide: How to Calculate MSA (Metropolitan Statistical Area) Metrics

Metropolitan Statistical Areas (MSAs) are geographic entities defined by the U.S. Office of Management and Budget (OMB) for use by federal statistical agencies in collecting, tabulating, and publishing federal statistics. Understanding how to calculate key MSA metrics is essential for economists, urban planners, businesses, and policymakers.

What is an MSA?

An MSA is defined as:

• At least one urbanized area of 50,000 or more population
• Plus adjacent territory that has a high degree of social and economic integration with the core as measured by commuting ties

As of 2023, there are 392 MSAs in the United States, covering approximately 86% of the U.S. population according to the U.S. Census Bureau.

Key MSA Calculation Methodologies

1. Population Density Calculation

The most fundamental MSA metric is population density, calculated as:

Population Density = Total Population / Land Area (square miles)

For example, the New York-Newark-Jersey City MSA has:

• Population: 20.1 million
• Land area: 4,669 sq mi
• Density: 20,100,000 ÷ 4,669 ≈ 4,305 people/sq mi

2. Economic Output Metrics

MSA economic performance is typically measured by:

1. Gross Metropolitan Product (GMP): The MSA equivalent of GDP, representing total economic output
2. GMP per capita: GMP divided by total population
3. Economic growth rate: Year-over-year percentage change in GMP

MSA	2022 GMP (billions)	Population (2023)	GMP per capita	5-Year Growth (%)
New York-Newark-Jersey City	$1,986	20,100,000	$98,806	12.4%
Los Angeles-Long Beach-Anaheim	$1,183	12,500,000	$94,640	10.8%
Chicago-Naperville-Elgin	$771	9,500,000	$81,158	9.2%
Dallas-Fort Worth-Arlington	$624	7,600,000	$82,105	15.3%
Houston-The Woodlands-Sugar Land	$578	7,100,000	$81,408	8.7%

Source: Bureau of Economic Analysis (2023)

3. Labor Market Calculations

Key labor metrics for MSAs include:

• Labor Force Participation Rate: (Employed + Unemployed) / Working-Age Population × 100
• Employment-Population Ratio: Employed / Working-Age Population × 100
• Unemployment Rate: Unemployed / Labor Force × 100

For example, if an MSA has:

• Working-age population: 5,000,000
• Employed: 4,750,000
• Unemployed: 200,000

Then:

• Labor Force = 4,750,000 + 200,000 = 4,950,000
• Labor Force Participation Rate = (4,950,000 / 5,000,000) × 100 = 99.0%
• Employment-Population Ratio = (4,750,000 / 5,000,000) × 100 = 95.0%
• Unemployment Rate = (200,000 / 4,950,000) × 100 = 4.0%

4. Housing Market Analysis

Critical housing metrics include:

• Homeownership Rate: (Owner-occupied units / Total occupied units) × 100
• Vacancy Rate: (Vacant units / Total housing units) × 100
• Median Home Value: Middle value in distribution of home values
• Price-to-Income Ratio: Median home price / Median household income

MSA	Median Home Value (2023)	Median Household Income	Price-to-Income Ratio	Homeownership Rate
San Francisco-Oakland-Berkeley, CA	$1,300,000	$125,000	10.4	56.2%
San Jose-Sunnyvale-Santa Clara, CA	$1,600,000	$150,000	10.7	57.8%
Austin-Round Rock-Georgetown, TX	$550,000	$90,000	6.1	59.4%
Phoenix-Mesa-Chandler, AZ	$450,000	$75,000	6.0	62.1%
Atlanta-Sandy Springs-Roswell, GA	$380,000	$72,000	5.3	60.8%

Source: U.S. Census Bureau (2023) and Zillow Research

Advanced MSA Calculation Techniques

Location Quotient Analysis

The Location Quotient (LQ) measures an industry’s concentration in an MSA compared to the national average:

LQ = (MSA Industry Employment / MSA Total Employment) / (National Industry Employment / National Total Employment)

• LQ = 1: Industry has same concentration as national average
• LQ > 1: Industry is more concentrated in MSA
• LQ < 1: Industry is less concentrated in MSA

Example: If the finance industry represents:

• 12% of NYC MSA employment
• 5% of national employment

Then NYC’s finance LQ = 0.12 / 0.05 = 2.4 (highly concentrated)

Shift-Share Analysis

This technique decomposes MSA employment growth into three components:

1. National Growth Effect: Growth due to national industry trends
2. Industry Mix Effect: Growth due to MSA’s industry composition
3. Regional Competitive Effect: Growth due to MSA’s competitive advantages

The formula is:

Total Change = National Growth + Industry Mix + Competitive Effect

Economic Base Multiplier

This calculates how basic (export-oriented) employment supports non-basic employment:

Multiplier = Total Employment / Basic Employment

For example, if an MSA has:

• Total employment: 5,000,000
• Basic employment: 1,250,000

Then the multiplier = 5,000,000 / 1,250,000 = 4.0

This means each basic job supports 3 additional non-basic jobs in the local economy.

Data Sources for MSA Calculations

Primary sources for MSA data include:

• U.S. Census Bureau: Population, housing, and demographic data (census.gov)
• Bureau of Economic Analysis: GDP, personal income, and economic growth (bea.gov)
• Bureau of Labor Statistics: Employment, unemployment, and wage data (bls.gov)
• Federal Housing Finance Agency: Housing price indices
• Local MPOs: Metropolitan Planning Organizations provide transportation and land use data

Common Challenges in MSA Calculations

1. Boundary Changes: MSAs are redefined every decade after the census, requiring data adjustments
2. Commuting Patterns: Accurately measuring cross-county commuting flows is complex
3. Data Lag: Some economic data is released with 1-2 year lags
4. Seasonal Adjustments: Many MSA economies have strong seasonal components
5. Comparability: Ensuring consistent methodologies across different MSAs

Best Practices for MSA Analysis

• Always use the most current MSA definitions from OMB
• Verify data sources and collection methodologies
• Consider both absolute numbers and per capita metrics
• Analyze trends over time (5-10 years minimum)
• Compare to peer MSAs of similar size and composition
• Account for regional cost-of-living differences
• Use multiple data sources to cross-validate findings

Emerging Trends in MSA Analysis

Several new approaches are enhancing MSA analysis:

• Big Data Integration: Using cellphone mobility data to track real-time commuting patterns
• Machine Learning: Predictive models for economic forecasting
• Geospatial Analysis: GIS mapping of economic activity
• Real-time Indicators: Credit card spending, job postings, and other high-frequency data
• Climate Risk Assessment: Incorporating environmental vulnerability metrics

Case Study: Calculating MSA Economic Impact

Let’s examine how to calculate the economic impact of a new manufacturing plant in a mid-sized MSA:

Scenario: A $200 million automotive parts plant locating in the Columbus, OH MSA

Direct Impacts:

• 500 new jobs at $60,000 average salary = $30 million annual payroll
• $200 million capital investment

Indirect Impacts (using regional multipliers):

• Employment multiplier of 1.8 → 500 × 1.8 = 900 total jobs
• Income multiplier of 2.1 → $30M × 2.1 = $63M total income impact
• Output multiplier of 2.5 → $200M × 2.5 = $500M total economic output

Fiscal Impacts:

• New property tax revenue: $200M × 2.5% = $5M annually
• Income tax revenue: $63M × 3.5% = $2.2M annually
• Sales tax from employee spending: $30M × 7% = $2.1M annually

Total annual fiscal impact: ~$9.3 million

Tools for MSA Calculation

Professional tools for MSA analysis include:

• IMPLAN: Input-output modeling software
• REMI: Regional economic modeling
• RIMS II: BEA’s regional input-output modeling system
• ArcGIS: Geographic information system for spatial analysis
• Tableau/Power BI: Data visualization tools
• R/Python: Statistical programming for custom analysis

Future of MSA Calculations

The field of MSA analysis is evolving with:

• More granular real-time data sources
• Integration of alternative data (satellite imagery, sensor data)
• Enhanced predictive analytics using AI
• Greater focus on equity and inclusion metrics
• Climate change and resilience modeling
• Cross-MSA comparative analysis tools

As urban areas continue to drive the majority of economic activity (MSAs account for 90% of U.S. GDP according to the Brookings Institution), the importance of accurate MSA calculations will only grow. Mastering these methodologies provides valuable insights for business location decisions, economic development strategies, and public policy formulation.