Interest-free monetary policy and its impact on inflation and unemployment rates
Bibliographic record
Abstract
Purpose This paper aims to examine the effects of interest-free and interest-based monetary policy on inflation and unemployment rates for two groups of countries where in one group, interest-free monetary policy (IFMP) was pursued, while in the other group, interest-based monetary policy (IBMP) was followed. Design/methodology/approach This study involves a sample of 23 developed countries divided into two groups. The authors measure economic performance by misery index (MI), and MI is calculated as unemployment rate plus inflation rate. A group of countries, where MI is lower, performs better compared to the other group where MI is relatively higher. Findings The results reveal that in group of 12 countries where IFMP is adopted, the MI is lower and thus performs better compared to a group of countries where IBMP is pursued. Research limitations/implications The findings of this study have profound implications for the policymakers and government leaders who look for a solution to maintain both low inflation and unemployment rates. The findings in this study clearly portray that such ideal situations can only be achieved by pursuing IFMP. No wonder the countries which have been historically pursuing IFMP such as Japan, Switzerland, Sweden, the Netherlands and Denmark have been able to contain both inflation and unemployment rates compared to their counterparts among the English-speaking countries. Originality/value This is one of the most recent tests on the differences in economic performance between IFMP and IBMP. These results have significant value for policymakers and central bankers who have been struggling to maintain lower MI for decades.
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 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".