More appropriate discounting: the rate of social time preference and the value of the social discount rate
Bibliographic record
Abstract
Recently, a number of authors, including Burgess and Zerbe, have recommended the use of a real social discount rate (SDR) in the range of 6–8% in benefit-cost analysis (BCA) of public projects. They derive this rate based on the social opportunity cost of capital (SOC) method. In contrast, this article argues that the correct method is to discount future impacts based on the rate of social time preference (STP). Flows in or out of private investment should be multiplied by the shadow price of capital (SPC). Using this method and employing recent United States data, we obtain an estimate of the rate of STP of 3.5% and an SPC of 2.2. We also re-estimate the SDR using the SOC method and conclude that, even if analysts continue to use this method, they should use a considerably lower rate of about 5%.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 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".