After the syllabus revision in 2023, the difficulty of data response questions has increased significantly, mainly due to higher requirements for understanding the materials and increased emphasis on evaluation.
I personally think that the current data response questions are better than before. Previous questions were really too simple, and students could even answer them without looking at the extract, which defeats the purpose of material-based questions. Students could rely solely on pre-prepared knowledge points to answer, and their ability for case analysis and critical thinking was not fundamentally examined. This also leads to teachers focusing more on teaching exam answering techniques and memorization of knowledge points rather than on thinking about and solving problems.
AS data response question structure:
2 marks: Data analysis or calculation
2 marks: Economic model analysis / chain of analysis / application with examples
4 marks: (3+1): Analysis of cause/impacts, application of models, 1m for evaluation
6 marks (4 + 2): Analysis of consequences/impacts/effectiveness of policy, 2m for evaluation
How to score?
To score high in data response, here are some key points to note:
Understand the question requirements and interpret them accurately. Although the focus is still on cause and consequence, the difficulty lies in the fact that the question may require you to explain specific causes leading to specific consequences, or specific impacts, etc. Unlike essays, the requirements are more specific, so you need to closely integrate the information provided in the extract.
Understand the economic logic behind the information in the passage and have a rough understanding of the overall economic situation of the country in the case. This allows you to provide more reasonable and targeted opinions and conclusions in the evaluation section.
Apply economic theories and models to explain specific problems. Some students may accurately find information relevant to the question in the passage, but they only copy it verbatim and cannot explain the economic logic behind it in their own words. This approach also does not lead to high scores.
Examples of AS Data Response Questions:
S23/22
(c) Consider the extent to which the removal of fuel subsidies in Nigeria would lead to an increase in inflation. [4]
From the information in the passage, factors contributing to inflation include higher food prices, removal of fuel subsidies, and an increase in the price of electricity. Therefore, in the evaluation, we can consider whether the removal of subsidies is the main factor, the extent of the subsidy itself, and whether there might be offset effects.
Evaluation:
The removal of fuel subsidies could indeed contribute to inflation, as it would lead to higher fuel prices, impacting transportation costs and production expenses. However, the extent to which it contributes depends on various factors, such as the magnitude of the subsidy and whether other factors like food prices and electricity costs play a more significant role in driving inflation. Additionally, there might be offset effects if the removal of subsidies leads to efficiency gains or if the government implements measures to mitigate the impact on inflation.
M24/22
(d) Assess the possible impact on unemployment in Japan as a result of the increased investment in AI. [6]
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For this question, the AO2 part can first analyze the positive and negative impacts of AI on employment. AO3, besides evaluating based on the information in the extract, can also consider short-term and long-term impacts. Additionally, we can assess based on our understanding of Japan's basic economic situation. For example, Japan has long been experiencing deflation and low consumption. To what extent does its unemployment rate depend on structural unemployment versus cyclical unemployment? Will AI exacerbate structural unemployment, or will it mainly address labor cost issues due to Japan's aging population?
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Evaluation:
Increased investment in AI could lead to both positive and negative impacts on employment in Japan. While AI adoption may create new job opportunities in the tech sector, it could also lead to job displacement in traditional industries. Moreover, Japan's aging population and existing structural issues in its labor market, such as a reliance on temporary workers, may influence the overall impact of AI on unemployment. In the short term, AI may contribute to structural unemployment, but in the long term, it could potentially address labor shortages and increase productivity, mitigating unemployment concerns.
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Conclusion
Imagine yourself as a policy maker or analyst placed in the context of the scenario, analyze the problem from the perspective of the country, and propose solutions applicable to that country. In fact, this is what economists are doing in reality.
Knowledge is vivid only when applied. We learn not only subject knowledge but also the unique ways of thinking of each subject. Exams are just one method of assessing abilities; they should not be the purpose or endpoint of learning. Data response questions can be very flexible. Not all questions in the world have a unique answer; good or bad often depends on perspective.
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