Understanding the Great Recession: Some Fundamental Keynesian and Post-Keynesian Insights, with an Analysis of Possible Mechanisms to Achieve a Sustained Recovery
Bibliographic record
Abstract
Fears of deflation and long-term stagnation have become more commonplace since the Great Recession. Yet, within the mainstream, economists are divided into two camps: those who see the benefits of downward wage and price adjustment, as a private sector stabilizer, and those who fear deflationary pressures because of their destabilizing consequences. This paper reviews this theoretical literature using a simple New Consensus framework of analysis and it also seeks to describe how mainstream and heterodox economists analyzing the consequences of deflationary pressures come to very different conclusions on the nature of private sector stabilizers in a recessionary environment. After reviewing the different perspectives, the paper undertakes a comparative analysis of the experience of both the Great Depression and the Great Recession by looking at the behavior of certain key variables in three countries: Canada, the United States and the United Kingdom. The paper concludes that, if it was not for the quick actions of governments in stabilizing the economy through activist macroeconomic policies during the Great Recession, private sector stabilizers were actually less significant during the recent crisis than they were during the 1930s.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".