Liquidity-Saving through Obligation-Clearing and Mutual Credit: An Effective Monetary Innovation for SMEs in Times of Crisis
Bibliographic record
Abstract
During financial crises, liquidity tends to become scarce, a problem that disproportionately affects small companies. This paper shows that obligation-clearing is a very effective liquidity-saving method for providing relief in the trade credit market and, therefore, on the supply-side or productive part of the economy. The paper also demonstrates that when used in conjunction with a complementary currency system such as mutual credit as a liquidity source the effectiveness of obligation-clearing can be doubled. Real data from the Sardex mutual credit system show a reduction of net internal debt of the obligation network of approximately 25% when obligation-clearing is used by itself and of 50% when it is used together with mutual credit. These instruments are also relevant from the point of view of risk mitigation for lenders, based in part on the information on individual companies that the mutual credit circuit manager can provide to banks (upon the circuit member’s request) and in part on the relief that liquidity-saving provides especially to NPL companies. The paper concludes by outlining recommendations for how even greater savings could be achieved by including the tax authority as another node in the obligation network.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".