The Moral Hazard Effects of Unlimited Deposit Insurance: Evidence from Canadian Credit Unions
Bibliographic record
Abstract
The objective of this study is to examine whether British Columbia’s adoption of unlimited deposit insurance in 2008 encouraged risk-taking behaviour among provincially-constituted credit unions after the financial crisis. The purpose of deposit insurance is to encourage confidence among depositors by covering some, or all, of their deposit in the case of bank failure. However, this policy can have side effects. These side effects, known as a moral hazard, can incentivize bank risk-taking behaviour and in so doing, paradoxically, compromise the very financial stability deposit insurance is meant to achieve. Therefore, deposit insurance can both enhance the depositor’s confidence and encourage risk-taking behaviour among financial institutions. While there is an abundance of empirical studies that examine whether the benefits of deposit insurance outweigh the drawbacks for banks, there have been very few such studies conducted on credit unions, particularly for Canadian credit unions. British Columbia’s adoption of unlimited deposit insurance (from $100,000 previously) in the midst of the financial crisis presents a unique opportunity to examine the influence of policy change on risk-taking behaviour in credit unions. To investigate this matter, this thesis applied the difference-in-difference and comparative interrupted time series methods for the period from 1997 to 2018, comparing the experience of British Columbia credit unions with those of credit unions in the province of Ontario, which kept its coverage constant at $100,000 until 2018. The results from both the methodologies failed to provide any evidence that the policy change contributed to an increase in risk-taking behaviour by British Columbia credit unions; in fact, I observed a decrease in risk-taking behaviour, contrary to what theory predicted. I argue that these findings might be explained by the unique features of credit unions, paired with strong regulatory oversight.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".