Bibliographic record
Abstract
The U.S. Federal Reserve has been following a tight money policy, defined by growth in the quantity of money compared to nominal GDP growth since the first quarter of 2004. The Fed has also increased the federal funds rate 17 times in a row by August 8, 2006. Normally, this degree of tightening would be reflected in a slowing of real economic activity by mid-2006, with subsequent lowering of inflation pressures. Yet evidence of a slowdown only materialized in the second quarter of 2006. The housing sector illustrated signs of softening as the inventory numbers started to rise. Are there different factors influencing the effectiveness of monetary policy in this tightening cycle from prior tightening cycles in the Greenspan era? Our thesis is that the linkage between money and credit has become weaker in this cycle. Money appeared to be tight over the relevant time period, while credit was loose. Normally the two move in the same direction – when monetary policy tightens, credit conditions also tighten. But that didn’t occur until very late in the tightening cycle, as credit remained plentiful. Long term interest rates remained low, compared to prior tightening cycles over the cycle. This divergence, in the assessment of the authors, is due to three factors: 1) an increase in monetary base velocity, 2) large net inflows of capital into the U.S., in particular from the Far East – Japan and China, and 3) the expansion of the markets for securitized assets. Rising incomes and high saving rates in the Far East combined with a relaxation of international capital controls resulted in a flood of savings washing up on America’s shores. The securitization of bank-originated assets—originally home mortgages, but now including auto finance loans and credit card debt—has loosened the link between bank reserves and the level of credit in the economy. These factors combined to explain why credit is loose in the U.S. while money appeared tight. A U.S. economy with these characteristics explains in part why the connection between domestic money policy and credit market conditions has been weakened.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".