Consumption Loan Augmented Divisia Monetary Index and China Monetary Aggregation
Bibliographic record
Abstract
Simple sum monetary aggregates are based on accounting conventions and have no aggregation theoretic foundations in economic theory. In contrast, Divisia monetary aggregates are directly derived from aggregation and index number theory. Credit card services cannot be included in simple sum monetary aggregates since accounting conventions cannot aggregate over assets and liabilities. However, microeconomic aggregation theory aggregates over service flows, not stocks, regardless of whether from assets or liabilities. As a result, it has recently been shown that Divisia monetary aggregates can be augmented to include credit card services and are available from the Center for Financial Stability in New York City. Other sources of consumer credit cannot be included in Divisia monetary aggregates for the United States since other sources of consumer credit in the United States are linked to specific groups of consumer goods and hence, violate the weak separability condition for the existence of an aggregator function. However, China produces a unique opportunity to broaden the Divisia monetary aggregates since sources of consumer credit, not limited to credit cards, are applicable to all consumption purchases and hence, do not violate the existence condition for an aggregator function. We report initial results with a broader Chinese Divisia monetary aggregate, including not only credit card services but also other broadly acceptable consumer loan services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".