The choice of the social discount rate and the opportunity cost of public funds
Bibliographic record
Abstract
The decades-old literature on the correct method for choosing and estimating a social discount rate (SDR) has resulted in two, largely opposing viewpoints. This note seeks to clarify the key sources of disagreement between these two camps. One view advocates that the choice should be based chiefly on the social opportunity cost of the return to foregone private capital investment (SOC), and suggests a SDR of around 7%. The other viewpoint, expressed by the authors, argues that the choice should be based on the social rate of time preference (STP), the rate at which society is willing to trade present for future consumption, suggesting a SDR of around 3.5%. Because of the fundamentally normative basis of the SDR choice, neither approach generates testable hypotheses that would allow falsification. For government project evaluation, the choice ultimately depends on the opportunity cost of public funds, which in turn depends on how fiscal policy actually operates. The STP approach contends that governments set targets for deficits and public debt, so that a marginal government project will be tax-financed, largely crowding out current consumption. The SOC belief is that governments set revenue targets, so that any government project will be deficit-financed on the margin, which will largely crowd out private investment. The authors also argue that a SDR based on the STP approach is appropriate for: benefit-cost analysis of government regulations, self-financing government projects, and government cost-effectiveness studies.
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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 | 0.036 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".