Bibliographic record
Abstract
How do financial frictions affect the response of an economy to aggregate shocks? In this paper, we address this question, focusing on liquidity constraints and uninsurable idiosyncratic risk. We consider a search model where agents use a liquid asset to smooth individual income shocks. We show that the response of this economy to aggregate shocks depends on the rate of return on liquid assets. When liquid assets pay a low return, agents hold smaller liquidity reserves and the response of the economy tends to be larger. In this case, agents expect to be liquidity constrained and, due to a precautionary motive, their consumption decisions respond more to changes in expected income. On top of this, there is a general equilibrium effect that magnifies the economy response. After a positive aggregate shock, agents' consumption increases and this raises income expectations, further reducing the precautionary demand for liquid assets. On the other hand, when liquid assets pay a large return, agents hold larger reserves and their consumption decisions are more insulated from income uncertainty. In this case, the equilibrium achieves the first-best allocation and the response to aggregate shocks tends to be smaller.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".