Bibliographic record
Abstract
In this paper, I examine the implications of collateral constraints in a production economy and demonstrate that collateral constraints may have a role to play in resolving two outstanding puzzles: the risk-free rate puzzle and the total factor productivity puzzle. The first puzzle, as noted by Mehra and Prescott (1985), Weil (1989) and others is simply that it is difficult to obtain plausible values of the risk-free real interest rate in production economies without assuming implausibly high values of risk-aversion. This paper demonstrates that the risk-free real interest is related to idiosyncratic productivity risk through the collateral constraint and that a low risk-free real interest rate can be obtained for small, and plausible, values of risk-aversion. The second puzzle is more recent - namely why has the risk-free real interest rate fallen while measured total factor productivity has risen during the 1990's in the United States? The argument put forth here is that the level and persistence of idiosyncratic productivity risk is related to measured aggregate total factor productivity and the risk-free real interest rate via the collateral constraint. Hence, increases in aggregate total factor productivity that occur in conjunction with decreases in the risk-free real interest rate may simply reflect unanticipated increases in the level (or persistence) of idiosyncratic productivity risk
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".