Open-Market Operations, Asset Distributions, and Endogenous Market Segmentation
Bibliographic record
Abstract
This paper investigates the long-run effects of open-market operations on the distributions of assets and prices in the economy. It offers a theoretical framework to incorporate multiple asset holdings in a tractable heterogeneous-agent model, in which the central bank implements policies by changing the supply of nominal bond and money. This model features competitive search, which produces distributions of money and bond holdings as well as price dispersion among submarkets. At a high enough bond supply, the equilibrium shows segmentation in the asset market; only households with good income shocks participate in the bond market. When deciding whether to participate in the asset market, households compare liquidity services provided by money with returns on bond. Segmentation in the asset market is generated endogenously without assuming any rigidities or frictions in the asset market. In an equilibrium with a segmented asset market, open-market operations affect households’ participation decisions and, therefore, have real effects on the distribution of assets and prices in the economy. Numerical exercises show that the central bank can improve welfare by purchasing bonds and supplying money when the asset market is segmented.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".