LIQUIDITY INSURANCE, CREDIT MARKET FRICTIONS, AND CORPORATE RESILIENCE: AN ASSESSMENT OF POST-FINANCIAL CRISIS LINE OF CREDIT FOR CANADIAN FIRMS
Bibliographic record
Abstract
This paper investigates the role of lines of credit (LOCs) in supporting Canadian firms’ financing and investment behavior in the post-financial crisis period, with particular attention to their function as liquidity insurance during systemic shocks such as the COVID-19 pandemic. Using firm-level data and econometric analysis, the study demonstrates that LOCs mitigate liquidity constraints, enhance corporate financial flexibility, and sustain investment capacity, although their effectiveness is moderated by firm size, leverage, and profitability. The findings further reveal that while large and financially robust firms gain significant benefits, smaller firms continue to face barriers in accessing LOC facilities. Moreover, systemic stress conditions highlight the dependence of LOC effectiveness on the stability of the banking sector and macroeconomic policy interventions. The study contributes to the growing body of empirical evidence by emphasizing the need for robust regulatory frameworks, enhanced banking sector resilience, and targeted policy interventions to ensure equitable access to liquidity insurance mechanisms. These insights hold significant implications for policymakers, banks, and firms seeking to strengthen corporate resilience and macro-financial stability in Canada’s evolving economic landscape.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".