🔬 Economic Models
Big Idea: An economic model is a simplified representation of reality used to understand, explain, and predict economic behaviour. Think of it like a map — it doesn't show every tree and building, but it helps you find your way.
Why do we need models?
The real world is incredibly complex. Models strip away that complexity to focus on the key relationships.
- Models help us isolate and understand specific relationships (e.g. how price affects demand)
- They use assumptions to keep things manageable
- They allow us to make testable predictions
- Common IB models: demand/supply diagrams, PPC, AD/AS model, circular flow of income
Example: The demand and supply model assumes a competitive market with many buyers and sellers. It simplifies reality — but it helps predict that when demand increases, price rises.
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🔒 Ceteris Paribus — All Else Equal
Key Assumption: Ceteris paribus is a Latin phrase meaning 'all other things being equal.' Economists use it to isolate the effect of ONE variable while holding everything else constant.
- It allows economists to study one relationship at a time
- Example: 'If price rises, ceteris paribus, quantity demanded falls' — this only works if income, tastes, etc. stay the same
- Without ceteris paribus, we couldn't untangle cause and effect in a complex world
When drawing diagrams in exams, you can strengthen your answer by stating your ceteris paribus assumption: 'Assuming all other factors remain constant, an increase in income will shift the demand curve to the right.'
Key assumptions economists make
- Rational behaviour — consumers maximise utility (satisfaction), firms maximise profit
- Ceteris paribus — only one variable changes at a time
- Perfect information — buyers and sellers know all relevant information
- Many buyers and sellers — no single agent can influence the market
These assumptions are often UNREALISTIC — and that's okay! Models don't need to be perfectly realistic to be useful. But acknowledging their limits is great for evaluation marks.
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📈 Data in Economics
Economists use data to test their models and support their arguments. But interpreting data requires care.
Correlation vs causation
- Correlation — two variables move together (both go up, or one goes up while the other goes down). This does NOT prove one causes the other
- Causation — one variable actually CAUSES a change in another
- Economists must be careful not to confuse correlation with causation
Example: Ice cream sales and drowning deaths both rise in summer. Does ice cream cause drowning? Of course not! Both are caused by a third variable — hot weather. This is the classic 'correlation ≠ causation' trap.
Challenges with economic data
- Data may be inaccurate or incomplete (e.g. GDP doesn't count the informal economy)
- Time lags — economic data often comes out weeks or months after the events
- Many variables change at once — it's hard to isolate the effect of just one
- People change their behaviour when they know they're being measured
In Paper 2, you'll often be given data (tables, graphs) to interpret. Always ask: What does this data actually show? Could there be other explanations?