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Record W2922485425 · doi:10.34989/san-2017-13

Redistributive Effects of a Change in the Inflation Target

2020· article· en· W2922485425 on OpenAlexaffabout
Robert Amano, Thomas J. Carter, Yaz Terajima

Bibliographic record

VenueStaff Analytical Notes · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBank of Canada
Fundersnot available
KeywordsPolitical scienceHumanitiesInflation (cosmology)PhysicsPhilosophyTheoretical physics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.127
GPT teacher head0.269
Teacher spread0.142 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes2
Has abstractyes

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