Aggregate data summarizes outcomes for groups by combining many individual observations into totals, averages, or rates.
Examples include GDP for a country, the unemployment rate for a region, or average wages by occupation.
How aggregation works
Aggregation applies a rule such as:
- sum (total output, total spending),
- mean (average wage),
- share/rate (unemployment rate, inflation rate).
These aggregates are essential for macroeconomic measurement and for public reporting (privacy and simplicity).
Why aggregation can mislead
Aggregate data can hide heterogeneity and nonlinear effects. Two common pitfalls are:
- ecological fallacy: relationships in group averages need not hold for individuals.
- composition effects: an “average” can move because the mix of people/firms changes, not because anyone’s outcome changed.
So aggregate series are powerful for economy-wide questions, but they are not a substitute for micro-level evidence when the mechanism operates at the individual or firm level.
Practical example
Average wages can rise during a recession if low-wage workers disproportionately lose jobs. The aggregate mean increases even though many individuals are worse off.
Related Terms
- Gross Domestic Product
- GDP
- Unemployment Rate
- Inflation
- Ecological Fallacy
- Panel Data
- Time Series Data