June 2026
The 2025 VCE Economics exam showed how easily students can lose marks when they treat data as background information rather than assessment material.
Economics is not only about knowing theory. Students must be able to calculate, interpret, compare, qualify and apply data under exam conditions. This includes multiple-choice calculations, demand and supply diagrams, macroeconomic statistics, terms of trade movements, unemployment data and policy-relevant figures.
The 2025 Examination Report repeatedly highlighted the importance of reading data carefully. High-scoring responses did not simply mention numbers. They used those numbers to support economic reasoning.
That is the key distinction.
In VCE Economics, data should not sit beside the answer.
It should help build the answer.
Multiple-choice questions rewarded careful calculation
Section A included several questions where the correct answer depended on careful interpretation rather than broad theory.
Question 14 was one of the clearest examples. Students were given an import prices index of 100 and an export prices index of 200. They were asked to calculate the terms of trade index.
The correct formula is:
Terms of trade index = export price index ÷ import price index × 100
Using the figures in the question:
200 ÷ 100 × 100 = 200
The correct answer was therefore 200.
The Examination Report noted that many students found this challenging. Some reversed the formula by dividing the import price index by the export price index. Others forgot to multiply by 100.
This is avoidable mark loss.
The concept itself is not especially complex, but the calculation must be exact. The export price index is the numerator. The import price index is the denominator. The result must be multiplied by 100 to express it as an index.
In Economics, formula knowledge must be operational. Students need to be able to use the formula accurately, not merely recognise it.
Unemployment data required interpretation
Question 5 in Section A gave labour market data for a hypothetical economy:
2023: 200,000 employed people and a labour force of 300,000
2024: 400,000 employed people and a labour force of 500,000
Students had to determine which statement about the unemployment rate was correct.
The unemployment rate is calculated as:
unemployed people ÷ labour force × 100
In 2023, unemployed people = 300,000 − 200,000 = 100,000.
So the unemployment rate was:
100,000 ÷ 300,000 × 100 = 33.3%
In 2024, unemployed people = 500,000 − 400,000 = 100,000.
So the unemployment rate was:
100,000 ÷ 500,000 × 100 = 20%
The unemployment rate was therefore lower in 2024 than in 2023.
This question shows why students must be careful with labour market data. The number of unemployed people was the same in both years, but the unemployment rate changed because the labour force changed.
That distinction matters.
Economics often assesses ratios, rates and proportions. Students who only compare raw figures can misread the economy.
“Close to capacity” changed the answer
Question 10 in Section A asked what would happen if the Australian economy was operating at a level close to capacityand policies increased aggregate demand.
Only around half the cohort selected the correct answer.
The phrase “close to capacity” was decisive. It did not mean the economy was already at full productive capacity. It meant there was still some spare capacity, allowing higher aggregate demand to increase employment, real GDP and inflation.
If the question had said the economy was operating at full productive capacity, the answer would have been different. Additional aggregate demand would be more likely to create inflationary pressure without a significant increase in output.
This is data interpretation at the level of wording.
Students needed to interpret the economic condition described by the phrase, not simply react to the word “capacity”.
The best students read qualifiers carefully because qualifiers change economic outcomes.
Graphs needed final equilibrium interpretation
Question 2a in Section B asked students to explain the movement in the global equilibrium market price and quantity for coffee beans using two non-price factors.
The graph showed demand shifting right and supply shifting left. The new equilibrium price increased, while the new equilibrium quantity decreased.
The report noted that some students did not clearly state the movement in equilibrium price and quantity, despite both being visible on the diagram.
That is a common problem.
Students often explain why curves shift, but they do not finish the graph analysis. In a demand and supply question, the explanation must end with the market outcome.
A complete answer would explain that unfavourable climatic conditions could reduce supply by lowering crop yields, shifting supply left. It would also explain that rising global incomes or stronger preferences for coffee could increase demand, shifting demand right. The combined effect shown in the graph is an increase in equilibrium price and a decrease in equilibrium quantity.
The graph is part of the question. It is not merely illustrative.
High-scoring responses use the graph’s actual movement.
Quantity supplied and equilibrium quantity had to be separated
The Examination Report also noted that students confused quantity supplied with equilibrium quantity.
This distinction is small in wording but large in economic meaning.
Quantity supplied is the amount producers are willing and able to supply at a particular price. It refers to a point on the supply curve.
Equilibrium quantity is the market-clearing quantity where demand equals supply.
In Question 2a, the market moved from one equilibrium to another. The correct language was therefore equilibrium quantity, not merely quantity supplied.
Using the wrong term suggests a weaker understanding of the graph. It also makes the answer less precise.
VCE Economics rewards students who can use technical terms carefully. Demand, quantity demanded, supply, quantity supplied and equilibrium quantity are related, but they are not interchangeable.
Elasticity data was about responsiveness
Question 2b asked why the price elasticity of supply for coffee beans is likely to change over time.
Although this was not a numerical calculation question, it was still a data-handling question in a broader sense. Students needed to understand what elasticity measures.
Price elasticity of supply measures the responsiveness of quantity supplied to a change in price.
A response about the law of supply was not enough. The law of supply explains that quantity supplied usually increases as price rises. Elasticity asks how responsive that quantity supplied is.
For coffee beans, supply is likely to be relatively inelastic in the short run because coffee takes time to grow. Producers cannot immediately increase output when price rises. Over time, supply may become more elastic if producers invest in improved technology, reduce growing times, improve storage or develop spare capacity.
The important word was responsiveness.
Students needed to explain how the degree of responsiveness changes over time.
Aggregate demand required understanding what is being measured
Question 3b asked why imports are subtracted from the aggregate demand equation:
AD = C + I + G + X − M
This question exposed a conceptual data issue. Students needed to understand what the equation measures.
Aggregate demand measures total spending on Australian-made goods and services over a period of time. Imports are subtracted because spending on imported goods and services may already be included in consumption, investment or government spending, but those goods and services are not produced in Australia.
For example, if an Australian consumer purchases an imported car, the spending may initially appear in consumption. However, it does not represent demand for Australian output. Subtracting imports removes that foreign-produced component and prevents aggregate demand from being overstated.
The report noted that many students explained imports as a leakage, but did not answer the specific calculation issue.
This is an important lesson.
Students need to understand what an economic measure includes, what it excludes and why.
Macroeconomic data had to support evaluation
Question 3d asked students to evaluate the extent to which the goal of full employment had been achieved in Australia over the past two years and the impact on living standards.
This question required current macroeconomic data.
The report noted that strong responses used statistics to show an understanding of unemployment movements over the past two years. They also interpreted the data in relation to full employment, the NAIRU, inflation and living standards.
A strong response might note that unemployment rose from very low levels in 2023 towards approximately 4.5 per cent by September 2025. This could indicate that the economy moved closer to the estimated NAIRU range, particularly as inflation moved closer to target. However, students could also qualify their judgement by discussing underemployment, labour underutilisation or whether employment outcomes were inclusive across different groups.
The data alone did not earn the evaluation marks.
The interpretation did.
A student who simply lists unemployment figures is not evaluating. A student who explains what those figures suggest about the achievement of full employment is moving into higher-scoring territory.
Living standards needed data-linked reasoning
The same question also asked students to explain the impact on living standards.
This is where data had to connect to people’s economic experience.
If unemployment is low, more people are earning incomes, which can improve material living standards by increasing access to goods and services. Employment can also support non-material living standards by improving confidence, purpose and social participation.
However, if unemployment is very low and the economy operates beyond sustainable capacity, wage pressures and inflation can reduce purchasing power. This may weaken material living standards, especially for households whose incomes do not keep pace with prices.
This is why full employment cannot be evaluated by looking at unemployment alone.
The data must be read alongside inflation, labour market spare capacity and living standards.
That is what makes the question evaluative.
Terms of trade data required careful explanation
Question 4 stated that Australia’s terms of trade index increased from 89.4 in the September quarter to 91.0 in the December quarter of 2024.
Students needed to define terms of trade and explain the possible effects of that movement.
The terms of trade refers to the ratio of export prices to import prices, expressed as an index.
A rise from 89.4 to 91.0 means that export prices increased relative to import prices, or import prices fell relative to export prices. This can improve national income if Australia receives higher prices for exports relative to what it pays for imports.
However, the inflation effect depends on why the terms of trade increased.
If the rise was driven by higher export prices, export incomes may rise, increasing national income and potentially aggregate demand. This could add demand inflationary pressure if the economy is close to capacity.
If the rise was driven by lower import prices, it may reduce imported inflation and lower production costs for firms using imported inputs.
This is how strong students handle data.
They do not treat a movement as automatically good or bad. They explain the possible economic pathways.
Budget data required prediction, not recall
Question 6a asked why the actual budget deficit for 2025–2026 may be smaller than the government’s estimated $42.1 billion budget deficit.
This required students to think about how budget outcomes can change when economic conditions differ from forecasts.
For example, if economic growth is stronger than expected, tax revenue may be higher than forecast because households, firms and companies earn more income. At the same time, government spending on welfare may be lower if unemployment is lower than expected. Both effects could reduce the size of the actual deficit.
This is data-based reasoning.
The figure of $42.1 billion was not there simply to be repeated. Students had to explain how actual outcomes can differ from estimates.
Economic forecasts are conditional. When economic activity, employment, incomes or prices differ from assumptions, budget outcomes shift.
Data should not replace explanation
One of the risks in Economics is using statistics as decoration.
Students often include figures because they have memorised them, but the numbers do not always strengthen the answer. A statistic only helps when it is used to support a claim.
For example, writing that unemployment was around 4.5 per cent in September 2025 is useful only if the response explains how that relates to full employment and the NAIRU. Writing that the terms of trade increased from 89.4 to 91.0 is useful only if the response explains what that movement means for export prices, import prices, income or inflation.
Data should sharpen the argument.
It should not interrupt it.
What future students should learn from the 2025 data questions
The 2025 VCE Economics exam shows that students need to handle data actively.
They should be able to:
- calculate unemployment rates
- calculate the terms of trade index
- interpret demand and supply diagrams
- distinguish equilibrium quantity from quantity supplied
- explain what aggregate demand measures
- use unemployment data to evaluate full employment
- interpret terms of trade movements
- explain how budget outcomes can differ from estimates
- connect statistics to economic goals and living standards
These are not side skills. They are central to the subject.
Economics rewards students who can turn data into reasoning.
How ATAR STAR approaches data in VCE Economics
At ATAR STAR, students are taught to use data as evidence, not ornament.
They practise calculations, diagram interpretation, macroeconomic data analysis and policy evaluation using current economic conditions. Most importantly, they learn how to explain what a number means in relation to the question.
The 2025 Examination Report confirms why this matters. High-scoring responses did not merely know the data. They interpreted it.
They made the numbers do economic work.