The Effects of the Financing Facilitation Act after the Global Financial Crisis: Has the Easing of Repayment Conditions Revived Underperforming Firms?
Bibliographic record
Abstract
After the global financial crisis, the Japanese government enacted the Financing Facilitation Act in 2009 to help small and medium-sized enterprises (SMEs) that had fallen into unprofitable conditions. Under this law, when troubled debtors asked financial institutions to ease repayment conditions (e.g., extend repayment periods or bring down interest rates), the institution would have the obligation to meet such needs as best as possible. Afterward, the changing of loan conditions began to be utilized often in Japan as a means for supporting underperforming companies. Although many countries employed various countermeasures against the global financial crisis, the Financing Facilitation Act was unique to Japan. However, there is criticism that it did not become an opportunity for companies to substantially reform their businesses, and that there was a moral hazard on the company’s side. This paper analyses whether the easing of repayment conditions revived underperforming firms and who were likely to recover, by using the “Financial Field Study After the End of the Financing Facilitation Act”, carried out by the Research Institute of Economy, Trade and Industry (RIETI) in Oct 2014. We found that the act was successful in that about 60% of companies whose loan conditions were changed recovered their performance after the loan condition changed, and the attitude that financial institutions had towards support was an important factor in whether performance recovered or not. In sum, the act might be effectual when financial institutions properly support firms, although previous studies tend to emphasize its problems.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".