Ups and downs in finance, ups without downs in inequality
Bibliographic record
Abstract
Abstract The upswing in finance in recent decades has led to rising inequality, but do downswings in finance lead to a symmetric decline in inequality? We analyze the asymmetry of the effect of ups and downs in finance, and the effect of increased capital requirements and the bonus cap on national earnings inequality. We use administrative employer–employee-linked data from 1990 to 2019 for 12 countries and data from bank reports, from 2009 to 2017 in 13 European countries. We find a strong asymmetry in the effect of upswings and downswings in finance on earnings inequality, a weak, if any, mitigating effect of capital requirements on finance’s contribution to inequality, and a restructuring but no absolute effect of the bonus cap on financiers’ earnings. We suggest that while rising financiers’ wages increase inequality in upswings, they are resilient in downswings and thus downswings do not contribute to a symmetric decline in inequality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".