Bibliographic record
Abstract
Decentralized markets where assets are useful as medium of exchange are also usually subject to private information. In the first chapter, I analyze how adverse selection affects liquidity and prices in such markets by studying the Shi (1995) and Trejos and Wright (1995) model with Lucas trees under adverse selection. While most studies focus on either pooling or separating equilibrium, I adapt the undefeated equilibrium refinement to make the selection based on fundamentals. Under pooling, the high-quality asset accepts a pooled price, and under separating is willing to signal quality through retention. A negative shock to the quality or quantity of lemons implies a switch in regime from no-information (pooling) to information revelation (separating) which leads to a discontinuous fall in aggregate welfare. In the second chapter I apply insights from the first chapter to help account for the debasement puzzle by interpreting Lucas trees as commodity money of different weights. Debasements constitute a puzzle under standard price theory because people voluntarily exchanged heavy coins for lighter ones; the difference being kept as seigniorage. To resolve this, I adopt Velde, Weber and Wright (1999) featuring a decentralised market with private information on indivisible coins. To disentangle indivisibility from imperfect recognizability, I use a proxy for divisibility by allowing lotteries on coins in trade. Indivisibility accounts for debasement if agents need a medium of exchange for low-value goods, for which heavy coins would not be traded. And, even a small degree of imperfect recognizability provides incentives for debasement due to adverse selection. Finally, in the third chapter I model household's portfolio choice with an endogenous supply of assets under uncertainty to analyze its effect on real interest rates and unemployment. Asset returns typically reflect a risk and liquidity premium, the size of which depends on the state of the economy. This in turn affects unemployment as firms respond to interest rates. In this chapter, I explicitly model demand and supply of liquid assets under uncertainty. Households adjust their portfolios depending on their liquidity needs, and supply of liquid assets is affected by firms' entry decision. An increase in aggregate uncertainty raises interest rates thereby fewer firms enter, and if accompanied by a fall in productivity, unemployment rises by even more. A self-financed private asset purchase program can increase liquidity but leaves unemployment unchanged.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".