Geographic Coverage in Data – Why National vs. Regional vs. Local Data Can Lead to Very Different Conclusions

One of the most common mistakes in data analysis is assuming that geographic boundaries remain the same over time. This can lead to misinterpretations of trends, especially when using Metropolitan Statistical Area (MSA) data or Census tract data.

The Problem: Changing Geographic Definitions

Many analysts track MSA employment, population, or economic growth without checking whether the MSA's boundaries have changed. This can create a false sense of growth or decline if new counties were added (or removed) from the MSA definition.

Example 1: MSA Growth – Real or Just a Redefinition?

• If an MSA adds new counties, the population and job numbers may increase, but this does not necessarily mean economic expansion—it may just reflect a larger geographic area.
• If analysts don’t adjust historical data to reflect consistent MSA definitions, they might mistakenly attribute boundary changes to economic trends.

Example 2: The Changing Cincinnati MSA
The Cincinnati, OH-KY-IN Metropolitan Statistical Area (MSA) is a prime example of how geographic boundary changes can impact data interpretation.

• The Cincinnati MSA was first defined in the 1950 Census and included only three counties: Campbell (KY), Kenton (KY), and Hamilton (OH).
• Over the decades, additional counties were added, expanding the geographic scope of the MSA:
  • 1963: Dearborn (IN), Boone (KY), Clermont (OH), and Warren (OH) counties were added.
  • 1983: Butler (OH) County was transferred into the MSA.
  • 1992 - 2003: Multiple counties, including Brown (OH), Grant (KY), and Franklin (IN), were added.
  • 2013 & 2018: Further adjustments were made, including removing and re-adding Franklin County (IN) and incorporating Union County (IN).

Why This Matters for Cincinnati MSA Data

• If you compare Cincinnati’s employment, population, or economic output from 1950 to today, the numbers will naturally be much larger—but part of that growth is due to boundary changes, not economic expansion.
• When evaluating economic trends, it’s essential to use historical data that aligns with the current MSA definition or adjust for past boundary changes.

Why This Matters

National vs. Local Data Can Show Different Trends

• National data smooths out regional fluctuations, which can mask local economic booms or recessions.
• MSA or county-level data offers more precision but is affected by boundary shifts, industry concentration, and local policies.

Misinterpretation Risks:

• Overstating growth – Thinking an MSA is booming when it just expanded its boundaries.
• Understating trends – Failing to account for economic shifts within a stable geography.
• Policy Missteps – Decision-makers relying on inaccurate trends for economic development or funding allocation.

How to Avoid Geographic Data Pitfalls

·      Check whether MSA boundaries have changed when analyzing data over time.

·      Use historical datasets that apply consistent MSA definitions (e.g., BLS-adjusted MSA data for comparisons).

·      Be cautious when using Census tract data over time, since past data is not adjusted to reflect current boundaries.

·      Compare national, state, and local trends together to get a fuller picture.

·      Always read the data source documentation to understand any adjustments.

Pro Tip: The Census Bureau provides official MSA delineation files that list boundary changes over time. You can access them here: Census Bureau MSA Delineation Files

Key Takeaway

Before making conclusions about regional growth, always ask:
Is this trend real, or is it just a change in how the area is defined?

Stay tuned for more insights on making sense of data!