Does Inflation Targeting Reduce Inflation? An Analysis for the OECD Industrial Countries
Bibliographic record
Abstract
Despite of its popularity, empirical studies have failed to find evidence of the causal effect of a country's adoption of the Inflation Targeting regime on that country's inflation rate decline. This paper applies the multi-period differences-in-differences estimation to the quarterly CPI inflation rates from the first quarter of 1985 until the third quarter of 2002 to the 22 OECD industrial countries and finds two basic sets of results. The first set of evidences is that countries that have officially adopted Inflation Targeting experience a decrease in their average inflation rates that is not only due to a reversion to mean process. The second set of results is that (1) there seems to be no evidence that Inflation Targeting countries experienced a significant increase in the level of their real interest rates after they adopted the new regime and that (2) even after controlling for the level of real interest rates there is still a causal effect from the adoption of Inflation Targeting to the reduction in inflation rates. In other words, the empirical evidence rejects the idea that the better performance in the inflation rates of the Inflation Targeting countries is only due to a more "aggressive" monetary policy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".