An Assessment of Important Issues Concerning the Application of Benefit-Cost Analysis to Social Policy
Bibliographic record
Abstract
Abstract Benefit-cost analysis (BCA) provides a framework for systematically assessing the efficiency of public policies. Increasingly, BCA is being applied to social policies, ranging from preschool interventions to prison reentry programs. These applications offer great potential for helping to identify policies that offer the best returns on public investments aimed at helping the disadvantaged or otherwise improving social life. However, applying BCA to social policies pose a number of challenges. The need for a comprehensive approach to assessing social policies generally requires making predictions based on data from multiple sources and using available shadow prices. As these predictions and shadow prices are inherently uncertain, special effort must be made to explicitly address the resulting uncertainty of predictions of net benefits. Prediction and valuation are complicated by behaviors, such as addiction, that do not clearly satisfy the assumptions of neoclassical welfare economics. As distributional goals are often an explicit motivation for social policies, BCA may be an incomplete framework for public policy purposes unless analysts can find ways to incorporate people's willingness to pay for changes in the distribution of consumption across society. If BCA is to reach its potential for contributing to good social policy, analysts must be aware of these challenges and researchers must help address them.
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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 | 0.078 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".