Crisis Mitigation through Cash Assistance to Increase Local Consumption Levels—A Case Study of a Bimonetary System in Barcelona, Spain
Bibliographic record
Abstract
Subsidies in the form of direct transfers from the government to citizens constitute a powerful mechanism for crisis mitigation and for the alleviation of economic inequalities. However, the connection between direct transfers of cash assistance to selected individual beneficiaries and the prosperity of their immediate surrounding local economy has not been sufficiently explored. This paper presents a case study which analyzes the effects of allocating cash assistance in the form of a local currency. It shows that, under certain conditions, such a transfer not only provides the beneficiaries with additional purchasing power to satisfy their needs but also that the monetary injection benefits local SMEs by generating additional turnover. Using transactional data from the system, some indicators are proposed to analyze the properties of the system, namely, user satisfaction, total and average income generated by local businesses, the local multiplier, the recirculation of the local currency, and the velocity of its circulation. Our findings indicate that cash assistance provided in the REC local currency could contribute to local economic development and financial stability by sustaining local commerce, while preserving most of the original positive effects of cash assistance in a legal tender.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".