Does Financial Depth Improve Aggregate Savings Performance? Further Cross‐Country Evidence
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
The paper examines whether financial depth can encourage savings. The main issue concerns how best to measure financial depth. A variety of proxies have been used in the past, mostly in the form of financial intermediation ratios (FIRs). A second issue concerns specification. Misspecification in earlier work may have overstated the importance of financial depth. A final issue concerns the effect of outliers, which are dealt with using robust estimation techniques. Based on a broadly specified lifecycle regression model and data from 122 countries, it is concluded that, although financial depth has a positive influence on savings, its strength continues to be open to question. Only one FIR and a non‐FIR proxy (bank offices per person) are unambiguously significant. These results suggest that further work could be fruitful, especially if directed toward improving the accuracy and consistency of existing FIR data.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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