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Understanding CAGR

Further understanding Compound Annual Growth Rate as it relates to QS HolonIQ datasets

Updated today

What is CAGR?

CAGR stands for Compound Annual Growth Rate.

In very simple terms, CAGR tells us:

“On average, how fast did something grow each year over a period of time?”

Instead of looking at lots of yearly changes, CAGR summarises growth into one clear, easy-to-compare number.

You can think of it as the smooth, long-term growth speed of a metric.

Imagine an occupation had:

  • 1,000 employees five years ago

  • 1,500 employees today

CAGR answers the question:

“What steady yearly growth rate would explain going from 1,000 to 1,500 employees over five years?”

It does not mean growth happened evenly every year. It simply gives a clear summary of the overall trend.


Why CAGR is used in QS HolonIQ datasets?

Datasets focus on long-term performance and trends, not just single-year results.

CAGR is useful because it:

1. Shows long-term progress

CAGR highlights:

  • sustained improvement

  • consistent strategic growth

  • long-term direction


2. Allows fair comparison

Markets differ greatly in size and context.

CAGR helps compare:

  • large and small regions

  • mature and emerging markets

  • fast-growing and stable markets

Because CAGR is a percentage, it focuses on rate of growth, not raw size.


3. Why not just use year-over-year (YoY) growth?

Year-over-year growth can be misleading because:

  • one exceptional year can distort results

  • data can fluctuate due to reporting changes

  • external events (e.g. COVID, funding cycles) can cause temporary spikes or drops

CAGR solves this by:

  • smoothing short-term ups and downs

  • reducing the impact of one-off events

  • reflecting the overall trend, not noise

This leads to more stable, fair, and meaningful comparisons.


Common QS HolonIQ datasets where CAGR may be applied

You will find CAGR:

-In the Labour Data & Reports from the Labour Market Intelligence Dataset

-In the Flows Data & Reports from the Global Student Flows Dataset


Key takeaway

CAGR helps analysis by:

  • summarising growth in a simple way

  • focusing on long-term performance

  • avoiding over-reaction to short-term changes

In short, CAGR helps ensure rankings reward consistent progress, not just temporary success.

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