Government Guarantees and the Risk-Taking of Financial Institutions: Evidence from a Regulatory Experiment
Bibliographic record
Abstract
The potential dark side of government guarantees, introduced to mitigate concerns about financial stability during economic downturns, is that they may create incentives for excessive risk-taking. In a low-interest rate environment, this effect may be even stronger as financial institutions try to “reach for yield”. In this paper, we use the 2008 introduction of unlimited deposit insurance for all credit unions in the province of British Columbia, Canada, to examine the effect of government guarantees on financial institutions’ earnings uncertainty. We find that the policy change resulted in an economically and statistically significant decrease in earnings uncertainty. In addition, although deposits grew following the policy change, lending did not increase and instead capitalization ratios improved. Overall, our results suggest that the provincial government guarantee boosted depositor confidence and increased the flow of funds to the insured financial institutions. We do not find support for the risk-taking hypothesis but instead show that risk management improved following the policy change. Finally, the effect of the policy change was stronger for smaller, more levered credit unions as well as those with fewer members and smaller market share.
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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".