Redistributive Effects of a Change in the Inflation Target
Bibliographic record
Abstract
In light of the financial crisis and its aftermath, several economists have argued that inflation-targeting central banks should reconsider the level of their inflation targets. While the appropriate level for the inflation target remains an open question, it’s important to note that any transition to a new target would entail certain costs. In this note, we consider one dimension of these costs, namely, the redistributive effects stemming from the fact that financial contracts are often written in nominal terms and would thus experience changes in real value following the announcement of a new target. We use Canadian data on the distribution of nominal assets and liabilities to predict the redistribution of wealth that would occur following a permanent 1-percentage-point increase in the rate of inflation, both across sectors and between various demographic cohorts. We find that this change would trigger a large redistribution of wealth from the household sector to government, mainly through a reduction in the real value of government bonds and unindexed pensions. However, losses are unevenly distributed across the household sector, with a disproportionate share falling on middle-class and wealthier households. We also use a macro model to explore potential implications for output and find that these depend critically on the particular use to which the government directs its windfall.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".