“Just give me a number!” Practical values for the social discount rate
Bibliographic record
Abstract
Abstract A major reason the quality of cost‐benefit analysis (CBA) varies widely is inconsistent use of the social discount rate (SDR). This article offers guidance about the choice of the SDR. Namely, we recommend the following procedures: If the project is intragenerational (does not have effects beyond 50 years) and there is no crowding out of private investment, then discount all flows at 3.5 percent; if the project is intragenerational and there is some crowding out of investment, then weight investment flows by the shadow price of capital of 1.1 and then discount at 3.5 percent; if the project is intergenerational and there is no crowding out of investment, then use a time‐declining scale of discount rates; if the project is intergenerational and investment is crowded out, then convert investment flows during the first 50 years to consumption equivalents using a shadow price of 1.1, and then discount all of these flows at 3.5 percent, and discount all flows after the 50th year using time‐declining rates. We then compare current discounting practices of U.S. federal agencies with our estimates. Consistent use of the recommended rates would eliminate arbitrary choices of discount rates and would lead to better public sector decision‐making. © 2004 by the Association for Public Policy Analysis and Management.
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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 | 0.038 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".