Income inequality and financial crises: evidence from the bootstrap rolling window
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it