Aggregate and Welfare Effects of Redistribution of Wealth Under Inflation and Price Level Targeting” Working Papers 08-31
Bibliographic record
Abstract
Since the work of Doepke and Schneider (2006a) and Meh and Terajima (2007) we know that inflation causes major redistribution of assets –between, households and the government, between nationals and foreigners, between households within the same country. Two types of monetary policy regimes, inflation targeting (IT) and price level targeting (PT), have very different implications on the inflation path subsequent to a shock that results in an unexpected price increase, and consequently, have different redistributional properties which is what we explore in this paper. For Canada, which has a positive net asset position with respect to the rest of the world in Canadian dollars, we show that the magnitude of the effects of an unexpected price level increase of 2 % under IT is larger than under PT. Households ’ wealth loss to foreigners are 0.07 % and 0.04 % of GDP respectively under IT and PT. The combined effects on GDP (due to the wealth loss, the lower value of the debt and its associated fiscal adjustment (1.10 % and 0.34 % of GDP respectively) and the non-linear effects on work effort of the redistribution of wealth across households) are 0.44 % versus 0.12%. The weighted welfare of households worsens under both IT and PT but, again, the magnitudes are larger under IT. The welfare losses are 0.23 % of consumption under IT and 0.11 % under PT.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".