Risk in Public Sector Project Appraisal: It Mostly Does Not Matter!
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Public project appraisal using cost-benefit analysis (CBA) requires analysts to project risky net benefits and to convert these into present values using a social discount rate (SDR). We consider which types of risk matter for CBA. For small projects with only idiosyncratic risks, expected net benefits should be discounted at a risk-free SDR. If projects are large or expected net benefits are correlated with aggregate consumption, the alternatives are to replace expected net benefits with their certainty equivalents (CEs) and to discount these at a risk-free SDR, or to discount expected net benefits using a higher SDR that includes a risk premium. These methods are equivalent under special circumstances that are unlikely; the first approach is the correct one. We examine when replacing expected values with CEs will matter, and how this might be done. For most projects, analysts should discount expected values of net benefits at a risk-free SDR.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it