Income inequality and financial crises: evidence from the bootstrap rolling window
Bibliographic record
Abstract
This study aims to investigate the validity of the Rajan hypothesis, which argues that increasing income inequality plays a key role in the outbreak of financial crises. The relationship between income inequality and credit booms are examined in 10 developed countries: Australia, Canada, Denmark, Finland, France, the United Kingdom, Japan, Norway, Sweden, and the United States. In doing so, a bootstrap rolling-window estimation procedure is used to detect any possible causal link between inequality and credit booms in financial crisis sub-periods. The results reveal that the Rajan hypothesis is supported for the 1989 crisis in Australia, the 1991 and 2007 crises in the United Kingdom, and the 1929 and 2007 crises in the United States. Therefore, increasing income inequality has positive predictive power on credit booms in Anglo-Saxon countries. However, the hypothesis is not confirmed for Scandinavian and continental European countries. Our study is novel in its use of the bootstrap rolling-window procedure, which allows us to detect the possible relationship between inequality and credit booms in financial crises. The findings suggest that a progressive taxation policy or investments to accumulate human capital and increase the labor force are more beneficial than temporary solutions.
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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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".