Revisiting the Pasinetti Index: Understanding Its Cyclical and Long-Term Features and Its Important Implications for Macroeconomic Policy
Bibliographic record
Abstract
This article explores the long-term and cyclical effects of the Pasinetti Index (PI) and their implications for income distribution and macroeconomic policy in a post-pandemic environment marked by inflationary pressures and increasingly restrictive central bank policies. It discusses the relevance of adopting a zero PI as monetary policy framework as either a short-term target or a long-term norm. The research underscores the importance of coordinating monetary and fiscal policies to achieve a balanced mix of short-term and long-term macroeconomic goals, as per the Pasinetti rule, aimed at stabilizing income distribution between rentier and non-rentier groups without compromising on a Keynesian full employment commitment. By analyzing historical data and employing a SVAR model for Canada and the United States, the study highlights the significant cyclical impact of PI fluctuations on unemployment and income distribution. The findings challenge the efficacy of rigid monetary policy rules and support a macroeconomic policy that reconciles short-term employment objectives and long-term distributional goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".