Reallocation of Credit, a Measure of Financial Activity, Has Yet To Bounce Back
Bibliographic record
Abstract
is composed of four zones, each of which is centered around one of the four main cities: Little Rock, Louisville, Memphis and St. Louis. ach year, while thousands of businesses grow and succeed, many others weaken and shut down. These dynamics, in turn, are reflected in the flow of factors of production (i.e., labor and capital) that are constantly being reallocated among businesses. As stated by University of Maryland economics professor John Haltiwanger, the sorting of successful business endeavors from unsuccessful ones is a central and necessary part of our market economy, and it is essential that the public and policymakers understand this process. 1 Our previous studies show that the reallocation of employment has been low in the current recovery compared with what happened in past recoveries. 2 Business Employment Dynamics data from the Bureau of Labor Statistics reveal that employment turnover was significantly lower following the Great Recession than following the former two recessions, in 2001 and 1990. The same trend appears in the creation of startups. 3 By the first quarter of 2010, business closings declined to prerecession levels for both the nation and the Eighth District, but business formations were slower to recover. Although these studies analyze the behavior of the labor market and small firms (those entering and exiting), little is known about reallocation of resources among larger, more-established firms. This article concentrates on credit flows among publicly traded firms at the national level and also examines a sample of firms headquartered in the Eighth District. Studying the reallocation of financial resources (e.g., credit)
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".