Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore the ability of monetary policy to generate real effects in laboratory general equilibrium production economies. Design/methodology/approach – To understand why monetary policy is not consistently effective at stabilizing economic activity, the author vary the types of agents interacting in the economy and consider treatments where subjects are playing the role of households (firms) in an economy where automated firms (households) are programmed to behave rationally. Findings – While the majority of participants’ expectations respond to monetary policy in the direction intended, subjects do form expectations adaptively, relying heavily on past variables and forecasts in forming two-steps-ahead forecasts. Moreover, in the presence of counterparts that are boundedly rational, forecast accuracy worsens significantly. When interacting with automated households, updating firms’ prices respond modestly to monetary policy and significantly to anticipated marginal costs and future prices. The greatest deviations in behavior from theoretical predictions arise from human households (HH). Households persistent oversupply of labor and under-consumption is attributed to precautionary saving and debt aversion. The results provide evidence that the effects of monetary policy on decision making hinge on the distribution of indebtedness of households. Originality/value – The author present causal evidence of the effects of potential bounded rationality on agents’ consumption and labor decisions.
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 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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".