Bibliographic record
Abstract
What is the relation between monetary policy and inequalities in income and wealth? This question has received insufficient attention, especially in light of the unconventional policies introduced since the 2008 financial crisis. The article analyzes three ways in which the concern central banks show for inequalities in their official statements remains incomplete and underdeveloped. First, central banks tend to care about inequality for instrumental reasons only. When they do assign intrinsic value to containing inequalities, they shy away from trade-offs with the standard objectives of monetary policy that such a position entails. Second, central banks play down the causal impact monetary policy has on inequalities. When they do acknowledge it, they defend their actions by claiming that it is an unintended side effect, that it is temporary, and/or that any alternative policy would fare even worse. The article appeals to the doctrine of double effect to criticize these arguments. Third, even if one accepts that inequalities should be contained and that today’s monetary policies exacerbate them, is it both desirable and feasible to make containing inequalities part of the mandate of central banks? The article analyzes and rejects three attempts on the part of central banks to answer this question negatively.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".