Fiscal Risk and the Portfolio of Government Programs
Bibliographic record
Abstract
In this paper, we develop a new model for government cost-benefit analysis in the presence of risk.In our model, a benevolent government chooses the scale of a risky project in the presence of two key frictions.First, there are market failures, which cause the government to perceive project payoffs differently than private households do.This gives the government a "social risk management" motive: projects that ameliorate market failures when household marginal utility is high are appealing.The second friction is that government financing is costly because of tax distortions.This creates a "fiscal risk management" motive: incremental spending that occurs when total government spending is already high is particularly unattractive.A first key insight is that the government's need to manage fiscal risk frequently limits its capacity for managing social risk.A second key insight is that fiscal risk and social risk interact in complex ways.When considering many potential projects, government cost-benefit analysis thus acquires the flavor of a portfolio choice problem.We use the model to explore how the relative attractiveness of two technologies for promoting financial stability-bailouts and regulation-varies with the government's fiscal burden and characteristics of the economy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".