The Role of Central Bank Independence and Policy Transparency in Inflation Targeting: A Comparative Empirical Analysis of Five Countries
Bibliographic record
Abstract
Inflation targeting has become a cornerstone framework for contemporary monetary policy governance, yet its effectiveness varies significantly across countries. This study employs panel data from five countries—New Zealand, Canada, South Korea, Poland, and South Africa—over the period 2010 to 2020 to empirically examine the impact mechanisms of central bank independence and policy transparency on inflation dynamics. The findings indicate that institutional independence of central banks significantly contributes to curbing inflation levels, enhancing policy credibility and implementation stability. Meanwhile, policy transparency effectively reduces inflation volatility by stabilizing public expectations and strengthening market communication. Macroeconomic control variables, including the share of foreign exchange reserves and GDP growth rate, also play a moderating role in inflation fluctuations. Regression results reveal that the two core variables are statistically significant at the 1% level, with robust explanatory power of the model. Further analysis incorporating institutional heterogeneity demonstrates path-dependent differences in policy design and outcomes between developed and transition economies. Building on these findings, this paper proposes relevant policy recommendations that underscore the importance of strengthening central bank independence, enhancing policy transparency, improving exchange rate management frameworks, and promoting the gradual implementation of inflation targeting—particularly for emerging economies such as China. The conclusions provide theoretical support and empirical evidence for optimizing monetary policy institutions and macroeconomic regulation strategies.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".