3 Key Considerations for Using Inflation-Adjusted Numbers in Data Analysis
By Annie Elliott, PhD
Have you ever wondered why $20 bought a week’s groceries decades ago but barely covers a single meal today? This shift isn’t just anecdotal—it’s the power of inflation in action.
As a Metopio data scientist, I frequently work with datasets that capture trends over time. A common mistake I’ve observed in data analysis is failing to adjust for inflation when comparing historical costs or income levels. Inflation is the gradual increase in prices over time, which erodes the purchasing power of money. To make accurate comparisons and derive meaningful insights, we must account for this economic reality.
Here are three key considerations for using inflation-adjusted numbers, using median household income as an example:
Avoid Misleading Comparisons Imagine analyzing median household income in 1990 versus 2020. Without adjusting for inflation, it might seem that income levels have dramatically increased. However, the apparent growth may be a result of price changes rather than an improvement in economic well-being. For example, if the median household income in 1990 was $30,000 and in 2020 it’s $60,000, it’s easy to assume a doubling of income. Yet, once adjusted for inflation, the real income increase might shrink or even vanish, revealing a different economic story
Assess True Purchasing Power Inflation-adjusted numbers reflect the real purchasing power of income, allowing us to understand how far a dollar stretches over time. For instance, a $30,000 income in 1990 might afford more housing, healthcare, and education than the same nominal amount today. By accounting for inflation, analysts can determine whether households are truly better off or simply earning more nominal dollars.
Policy and Business Decision-Making Policymakers and business leaders rely on accurate data to craft solutions for economic challenges. Misinterpreting trends in income or costs due to unadjusted figures can lead to misguided decisions. For example, without inflation adjustment, programs designed to support middle-class households might underestimate the financial pressures they face today compared to decades ago.
A Real-World Example: Income Trends in Georgia
Household income trends in Georgia reveal significant disparities between counties, with a notable divergence in economic well-being over recent years when adjusted for inflation.
Randolph County: Median income has declined significantly, dropping from around $39,881 in 2005-2009 to approximately $25,425 by 2019-2023.
Fulton County: In contrast, median incomes have risen from about $82,855 in 2005-2009 to roughly $91,490 by 2019-2023.
These discrepancies highlight the varying economic environments and the impact on local communities. Fulton County’s wealthier status may afford better services and opportunities compared to the financially struggling Randolph County. The stark economic differences between these counties underscore the need for targeted economic policies that address the unique challenges each community faces, aiming to bridge the income gap and enhance overall community well-being.
Putting It All Together
By using inflation-adjusted numbers, we can:
Present a clearer picture of economic health and growth.
Identify trends that reflect real improvements or challenges in living standards.
Provide a solid foundation for data-driven decision-making.
At Metopio, we’re committed to empowering our users with accurate, contextualized data. Our team delivered the 2023 ACS update in record time and is now available, inflation-adjusted, and ready for our clients to use. Whether you’re exploring trends in median household income or another monetary metric, inflation adjustment ensures your insights are both meaningful and actionable.
Curious about how inflation-adjusted data can transform your analysis? Explore Metopio’s platform today.