I think one simple and effective idea is tying EA to marginal decreasing utility. Decreasing marginal utility is often a Econ-101 topic as it explains the downward sloping nature of demand curves. It is also a fundamental part of why donating money overseas rather than domestically is more impactful (a foundational EA insight).

People living in the West are most likely in the top 10% of global incomes, and because of that a single $/£/Euro will be purchase significantly less wellbeing than for someone in a low-income country. This is basically the 'drowning-child' argument in a nutshell, tied to a 101 Econ principle, and a good starting point before exploring more contentious/less intuitive EA ideas. 

I think another topic that can springboard into EA type ideas is the idea of discount rates, as this brings up the subject of how much we should care about the future. The question of discounting is central to Longtermism, and a ongoing discussion within economics, with plenty of different perspectives to consider. 

Perhaps just introduce the idea of quantifying welfare? Or prioritization between programs in the context of NGOs and gov programs?

Teaching counterfactual reasoning in economics education

A crucial EA concept for high school economics is counterfactual reasoning – systematically asking "what would have happened if agent X had not done action Y?" This is essential for understanding the actual impact of interventions.

Why it matters:

• Many interventions don't create as much value as they appear because something similar would have happened anyway
• The true impact is only the additional change caused by the intervention – the difference between what actually happened and what would have happened in the counterfactual scenario
• It's counterintuitive – our brains naturally credit actions without considering what would have occurred otherwise

Methods to evaluate counterfactual impact:

Randomized controlled trials (RCTs): Randomly assign some groups to receive an intervention and others not, then compare outcomes. The control group approximates what would have happened without the intervention.

Before-and-after with comparison groups: Compare changes in a treated group to changes in a similar untreated group over the same period. This helps account for broader trends that would have occurred anyway.

Trend analysis: Plot pre-intervention trends and project them forward. If post-intervention outcomes match the projected trend, the intervention may have had little counterfactual impact.

Natural experiments: Find situations where an intervention occurred in one place but not another similar place due to arbitrary reasons, allowing comparison.

Classroom applications:

• Analyze case studies using these methods (e.g., evaluating a job training program's effectiveness)
• Have students design simple evaluation plans for school or community interventions
• Critique news articles that claim causation without proper counterfactual analysis

This teaches students both to think counterfactually and to evaluate causal claims empirically.

(Comment made in collaboration with generative AI)