Preliminary. Comments welcome. The Possible Unemployment Cost of Average Inflation below a Credible Target *
Bibliographic record
Abstract
The Riksbank in 1993 announced an official target for annual CPI inflation of 2 percent to apply from 1995. Over the 15 years since then, 1997-2011, average CPI inflation has equaled 1.4 percent and has thus fallen short of the target by 0.6 percentage points. In contrast, in Australia, Canada, and the U.K., which have had a fixed inflation target as long as Sweden, average inflation has been on or very close to the target. Has this undershooting of the inflation target in Sweden had any costs in terms of higher average unemployment? This depends on whether the long-run Phillips curve in Sweden is vertical or not. During 1997-2011, average inflation expectations have been close to the target. The inflation target has thus been credible. If inflation expectations are anchored to the target also when average inflation deviates from the target, the long-run Phillips curve is no longer vertical but downward-sloping. Then average inflation below the credible target means that average unemployment is higher than it would have been if average inflation had been on target. The data indicate that the average unemployment rate has been about 0.8 percentage points higher. This is a large unemployment cost of undershooting the inflation target. Some simple robustness tests indicate that the estimate of the unemployment cost is rather robust, but the
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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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.538 | 0.291 |
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".