Where do the Pasinetti Rule and the Pasinetti Index Come from?
Bibliographic record
Abstract
The article describes the origins of the Pasinetti index and Pasinetti rule and links both notions to debates over concerns regarding which socio-economic income group in a modern capitalist economy was being hurt and which broad income class was benefiting from the high interest rate policy, especially during the Volcker era of the 1980s. These were statistical constructs as well as distinct normative concepts that resulted from a close reading of Pasinetti’s original writings published in 1980–81. The purpose of the Pasinetti index was to measure and monitor the actual transfer between the rentier and non-rentier sectors of an economy both cyclically and over the long term. As discussed in some detail, both authors of this article had directly participated in those debates and had basically initiated the early discussions around these concepts among post-Keynesians and other heterodox economists. The first part of our article, after the introduction, is primarily focused on what came to be called the Pasinetti rule, while the following section of the article recounts how the different variants of the Pasinetti index came to be constructed and used. We conclude with a discussion of the possible continued relevance of the Pasinetti norm in today’s world.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".