MétaCan
Menu
Back to cohort
Record W2178789818 · doi:10.19030/jber.v5i4.2533

Tight Money And Loose Credit In An Open Economy

2011· article· en· W2178789818 on OpenAlexaboutno aff
Michael H. Cosgrove, Daniel L. Marsh

Bibliographic record

VenueJournal of Business & Economics Research (JBER) · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsMonetary policyMonetary economicsBusiness cycleInflation (cosmology)Quarter (Canadian coin)Keynesian economics

Abstract

fetched live from OpenAlex

<p class="MsoNormal" style="text-align: justify; margin: 0in 34.2pt 0pt 0.5in;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">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.<span style="mso-spacerun: yes;">  </span>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.</span></span></p>

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.194
GPT teacher head0.335
Teacher spread0.141 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Business & Economics Research (JBER)Same topicGlobal Financial Crisis and PoliciesFrench-language works237,207