IB Economics SL Extended Essay Example

The research question is clearly framed as an evaluative Economics investigation and is anchored to a country and time period. The phrase “to what extent” is appropriate because it invites judgement rather than a purely descriptive answer, which gives the essay a clear investigative purpose.

The objectives are useful, but the scope is still quite broad. The student could make the investigation stronger by narrowing the “negative externalities” focus to one or two measurable indicators, so the analysis can be more directly tied to a defensible causal claim.

The methods named here are not, by themselves, sufficiently robust for the causal claim being made. With only three annual observations, the regressions cannot meaningfully isolate the IRA’s effect, so the student should acknowledge the method as exploratory rather than confirmatory.

What would the conclusion look like if 2022 is treated as a transition year rather than a clean “before/after” split? That distinction matters because part of the data may reflect only partial policy exposure, not a full post-policy outcome.

This is a strong source choice because the student uses an official agency with clearly relevant indicators. The selection of EPA data makes the investigation more credible and shows informed awareness of what counts as appropriate evidence for environmental externalities.

The student has chosen a relevant dataset, but the justification overstates what it can prove. A one-year pre-IRA baseline is too short to establish a reliable counterfactual, so the method should be presented as a short-run comparison rather than “precise” evidence of policy impact.

The student demonstrates sound disciplinary knowledge by accurately defining the core concept before applying it to fossil fuels. This clear conceptual grounding helps the later analysis of market failure and social cost to remain understandable.

The diagram is linked well to the written explanation here, so the student is not leaving the theory isolated from the argument. This is effective because it shows how the market-failure model is meant to explain the later policy intervention.

This definition is acceptable, but the later application is less precise because “renewable,” “clean,” and “low-carbon” energy are sometimes treated as interchangeable. The student should keep these categories distinct, since different fuels and technologies do not create identical externalities or policy effects.

The mechanism is broadly correct, but the explanation is too compressed for a strong Economics essay. The student should spell out which IRA incentives affect production, which affect consumer demand, and why each one shifts the relevant curve, rather than treating all incentives as a single generic stimulus.

The table would be clearer if the student explicitly defined why nuclear is grouped with clean energy here. A short note distinguishing “low-carbon” from “renewable” would improve conceptual precision and prevent confusion in the later interpretation.

This conclusion is too strong for the evidence presented. The student identifies a rise in investment, but the essay does not yet rule out other drivers, so the claim should be softened to “associated with” unless stronger causal evidence is added.

The extrapolated trendline is being treated as a counterfactual, but a short three-year series cannot justify that inference reliably. The student should avoid presenting the gap as proof of causation and instead discuss it as a tentative indication that needs stronger controls.

This is a useful analytical move because the student does not simply list figures; the data are interpreted in relation to the economic model. The cautious wording “modest transition” is more defensible than an absolute claim and helps the argument stay evidence-based.

The phrase “indicating a causation” is not justified by a short time comparison alone. The student should explain alternative explanations and treat the data as correlation unless the method can better isolate the policy’s independent effect.

This interpretation is too broad because the table also shows PM2.5 rising in 2023, and the essay itself notes that air quality is affected by other factors such as wildfires. The student should evaluate the mixed evidence more carefully rather than presenting the pollutant trend as uniformly downward.

The regression results would be more persuasive if the student explained why the slope can be interpreted cautiously given the tiny sample size. Adding a limitation on statistical power would show stronger evaluation and prevent over-reading the equations.

The conclusion overstates the certainty of the findings. Because the essay itself identifies serious limits to causal attribution, the final judgement should be more qualified and should distinguish between evidence of association and evidence of direct policy causation.

If these confounding factors are significant, how can the final conclusion still claim that the IRA “has indeed reduced” negative externalities with confidence? The student should reconcile the limitations with the final judgement more explicitly.

The report follows a clear Extended Essay structure with a contents page and logically ordered sections. This helps the reader track the argument and supports the overall readability of the investigation.

The inclusion of explicit research objectives improves the organization of the essay because it shows how the investigation is being directed. This makes the later analysis easier to follow and gives the report a purposeful structure.

The theoretical framework is extensive, but it could be trimmed slightly so that more space is devoted to interpreting the evidence. A more economical balance between theory and analysis would make the report feel more tightly focused on the research question.

The subdivision of the analysis into focused sub-sections is effective because it keeps the essay organized around the main strands of evidence. This structure makes it easier to compare investment, output, and pollution outcomes separately before drawing a conclusion.

The regression table is hard to read as presented in the transcription, and the discussion relies on it heavily. The student should make sure the final report labels variables and outputs clearly so the reader can understand what each equation is intended to show.