Adverse Selection and Risk Aversion in Capital Markets
Bibliographic record
Abstract
We generalize Boadway and Keens model of adverse selection in capital markets to allow for risk aversion on the part of entrepreneurs. We use the new model to analyze two types of policies. We first consider policies that would allow entrepreneurs to use a greater fraction of their total wealth in financing their projects, thus allowing them to reduce reliance on debt or equity finance by outside investors. We show that such policies may not be welfare-improving, because they expose entrepreneurs to more downside risk. This result highlights the importance of allowing for risk aversion, since policies that aim at alleviating inefficiencies associated with adverse selection may increase risk exposure and ultimately reduce welfare. We then consider how the tax treatment of losses affects social welfare. We show that if a society places a high value on distributional equity or if entrepreneurs are sufficiently risk-averse, a full-loss-offset system may be desirable even when there is excessive investment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".