Eight lessons for teaching macroeconomic policy after COVID-19: a heterodox perspective
Bibliographic record
Abstract
In this paper, we advance eight lessons that COVID-19, and before that, the Global Financial Crisis, imply for the teaching of macroeconomics to undergraduates. These lessons all pertain to fiscal and monetary policy, which we argue are central to macroeconomics and which should take a greater focus in our teaching. They touch upon key theoretical perspectives which underpin the policies considered, and they challenge the way that mainstream economics has approached these policy questions. They are, therefore, useful ideas around which to generate debate in undergraduate teaching about orthodox perspectives. Debate, we argue, is always intellectually healthy and makes teaching more instructive and enjoyable for students. This paper summarizes the main themes of traditional mainstream thinking about fiscal and monetary policies, restates some problems with this thinking identified by heterodox economics, reflects on some key features of the policy responses to COVID-19 and the Global Financial Crisis, and translates those reflections into eight lessons that could be used to shape the teaching of macroeconomic policy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".