On Business Cycles and Countercyclical Policies
Bibliographic record
Abstract
Since the third quarter of 2000, the U.S. economy began to experience a slowdown in its rate of growth. This slowdown serves as a reminder that the business cycle is still alive and raises the following questions: What do we know about the driving forces behind the business cycle? What should policymakers do in the face of economic fluctuations? ; The authors examine two explanations for business cycles that are well-known in academic circles: the animal spirits theory and the real business cycle theory. The former is closely connected with the Keynesian economic tradition and identifies market participants' mood swings as the key source of economic fluctuations. The second explanation is rooted in the classical economic tradition and views productivity shocks as the driving force behind economic fluctuations. The article then looks at what these theories suggest about countercyclical policies, which try to eliminate business cycle fluctuations or insulate market participants from their effects. The authors conclude that neither theory makes an unambiguous case supporting countercyclical policies. ; This conclusion may come as a surprise to government and business economists who have an ingrained belief in the benefits of such policies. It is important to remember, however, that attempts to understand business cycles and the effects and desirability of policies that may (or may not) moderate them are still at a very early stage.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".