Is your locality impacted by changes to the 2023 Core Based Statistical Area definitions?

Key Takeaways

• MSA updates can affect how labor market data appears, so teams should check geography before interpreting movement in wages, unemployment, employment, or population.
• Official MSA definitions support consistent federal reporting, but custom regions often provide a better fit for workforce, education, hiring, and economic development decisions.
• Defensible regional analysis requires clear source timing, county composition, and geography choices that stakeholders can understand and verify.

MSA updates can alter how a locality appears in labor market data, even when the underlying local economy has not moved in the same way. That matters for workforce boards, economic developers, educators, site selectors, and employers because a boundary revision can affect comparisons, wage benchmarks, unemployment analysis, commuting patterns, and stakeholder reports.

The 2023 Core Based Statistical Area definitions were released by OMB on July 21, 2023, and they updated the 2020 delineations using 2020 Census standards and Census Bureau population and commuting data. Chmura’s published analysis also notes that the 2023 definitions are incorporated into JobsEQ and includes specific examples of local additions, removals, and name revisions, including several county-level updates in Virginia.

What a metropolitan statistical area measures

A metropolitan statistical area measures a county-based labor market tied to an urban center of at least 50,000 people. It is a statistical geography, not a local identity, service area, sales territory, school district, or economic development region. Its value comes from standardization, but that same standardization can limit local interpretation.

A county can be included because its workers commute into the core area or because workers from the core area commute into that county. That is why an MSA often includes places that feel outside the central city’s daily economy while excluding nearby places that matter to a specific employer, campus, or workforce program.

The practical question is not simply, “What is a metropolitan statistical area?” The better question is, “Does this geography match the workforce question you need to answer?” A wage benchmark, grant narrative, hiring market comparison, or program viability analysis can all rely on MSA data, but only when the MSA reflects the labor pool, commuting area, or stakeholder region being discussed.

Why 2023 CBSA redefinitions affect local comparisons

The 2023 CBSA redefinitions affect comparisons because a region’s statistical boundary can shift between reporting periods. A reported gain or loss can reflect a revised county mix instead of a true local economic shift. Readers need to separate boundary effects from actual movement in employment, wages, unemployment, population, or commuting.

Chmura’s Virginia example shows the issue clearly. Floyd County was added to the Blacksburg-Christiansburg-Radford MSA, Surry County was added to the Virginia Beach-Chesapeake-Norfolk MSA, and Southampton County was removed from that same Virginia Beach area. The data story changes when the counties in the geography change.

That distinction matters when a board member asks why an unemployment rate moved, or when an employer compares wage pressure across locations. A region can look stronger, weaker, larger, smaller, higher wage, or lower wage after a boundary update. The safest practice is to check the geography before interpreting the metric.

How OMB sets metropolitan statistical area boundaries

OMB sets metropolitan statistical area boundaries using published standards applied to Census Bureau data on population and commuting. The goal is a consistent national statistical framework for collecting, tabulating, and publishing federal data. It is not designed to define every local labor market, service area, or policy region.

The basic structure starts with a core urban area. Central counties contain all or a substantial portion of that core. Outlying counties can qualify when commuting ties connect them to the central counties. Census guidance also explains that delineations are reviewed after each decennial census, with updates between censuses based on population estimates and commuting-to-work data.

That process gives analysts a reliable standard, but it does not remove judgment from local work. A site selector evaluating a 45-minute commute area, a college assessing graduate job markets, and a workforce board reporting to a service area can all need geographies that differ from the official MSA. OMB boundaries are a strong starting point, not the only valid lens.

Which BLS datasets rely on MSA definitions

Several BLS programs publish data using metropolitan statistical areas, so CBSA revisions affect more than population tables. Local Area Unemployment Statistics, Occupational Employment and Wage Statistics, and Current Employment Statistics all have metropolitan reporting practices that depend on official statistical geography in different ways.

The timing also differs by dataset. BLS says the Local Area Unemployment Statistics program implemented the 2020 Census-based delineations on March 17, 2025, and reconstructed civilian labor force and unemployment series back to their beginnings, generally January 1990. OEWS updated metropolitan area estimates with the release of May 2024 estimates in April 2025.

That means users should not assume every dataset reflects the same geography at the same time. A workforce snapshot can combine unemployment, wages, industries, and postings, but the analyst still needs to confirm how each source defines place. The MSA name alone will not answer that question.

When MSA boundaries can distort regional analysis

MSA boundaries can distort analysis when the official statistical area does not match the functional region behind the decision. A county-based MSA can be too broad for a downtown hiring question, too narrow for a labor-shed study, or poorly matched to an education service area.

A health system hiring nurses across a 60-minute commute area will need a different view than a published MSA. A community college assessing program alignment will care about the counties where students live, and employers hire graduates. An economic development team responding to a prospect may need zip codes, drive times, and specific counties instead of the named metro area.

The distortion is usually subtle. The data looks official, so it feels final. The risk is using a clean geography for a messy operational question. Strong analysis starts by asking which geography best fits the decision, then uses MSA data where the fit is sound.

A defensible geography check should confirm:

• Which counties were added or removed from the named area
• Which dataset and release date are being used
• Which time series has been revised or reconstructed
• Which audience will rely on the finding
• Which custom region better reflects the actual question
  

How custom regions improve local workforce analysis

Custom regions improve local workforce analysis because they match data to the geography that stakeholders actually use. A custom region can reflect a labor shed, service area, commute zone, grant area, campus footprint, business attraction zone, or hiring market that does not line up with an official MSA.

A practical example is a manufacturer comparing 2 possible sites near the edge of a metro area. The official MSA can include counties that workers will not realistically commute from while excluding nearby counties across a state line. A custom region can compare talent supply, wages, job postings, and employer presence across the area that matters to the hiring plan.

This is where execution matters. JobsEQ supports custom regional analysis so teams can compare official MSAs with zip codes, counties, drive times, and labor-shed views inside the same workflow. That helps the analyst explain why one geography supports a public benchmark while another supports the actual workforce decision.

What regional teams should check before using MSA data

Regional teams should check the definition date, county composition, data program, comparison period, and use case before relying on MSA data. These checks prevent teams from mixing geographies, misreading trends, or presenting a metric that stakeholders cannot verify.

A grant writer might use MSA unemployment data for context, county-level data for eligibility, and a custom service area for program need. A people analytics team might use MSA wages for a first benchmark, then narrow the analysis to a labor shed before setting pay ranges. Both teams need clarity about which number answers which question.

The most common mistake is treating the named MSA as stable across every chart and dataset. The name can remain familiar while the county mix, source timing, and reporting rules shift underneath it. A defensible report should state the geography, confirm the source period, and explain any boundary update that affects interpretation.

How better geography supports defensible workforce decisions

Better geography supports defensible workforce decisions because it connects the analysis to the actual question being answered. Official MSAs provide consistency, but custom regions provide fit. Strong regional work uses both carefully, then explains the choice in plain language.

That discipline changes the quality of the recommendation. A board presentation becomes easier to defend when the geography matches the stakeholder question. A hiring plan becomes more credible when the labor pool reflects actual commute behavior. A program analysis becomes clearer when the region reflects where students and employers interact.

Chmura’s role in this work is practical: help teams move from a named statistical area to the geography that best supports the decision. The 2023 CBSA updates are a reminder that place-based analysis depends on more than the label on a map. The strongest workforce reports do not stop at the official boundary. They make the boundary part of the reasoning.