Sellers' Inflation and Distributive Conflict: Lessons from the Post-COVID Recovery
Bibliographic record
Abstract
The paper offers a post-Keynesian explanation of the soaring inflation experienced during the post-COVID recovery, coherent both at the microeconomic and macroeconomic levels. The microeconomic argument is rooted on the premise that price-making firms consider both their costs and their desired share of profits when setting prices. To defend profit margins in the aftermath of the pandemic, the initial cost-push shock was passed to consumers through higher prices; in a second phase, some firms, particularly in more highly concentrated and systemically significant sectors, benefited from the post-pandemic permissive pricing environment to increase their price mark-ups, leading to temporary profit-fueled inflation following the cost-push shock. This microeconomic explanation is compatible with the macroeconomic notion of a stable inflation barrier. For a given quantity of real output, it is shown that if profit earners defend their share of income following a cost-push, this will produce a one-time price increase, with inflation becoming more persistent if the target adapts endogenously — i.e., if the aggregate mark-up changes. The paper contrasts the notion of a wage-price spiral with that of a profit-price sink, arguing that sellers' inflation is a real — albeit temporary phenomenon.
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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 | 0.004 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".