“Just give me a number!” Practical values for the social discount rate
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it