On the Optimal Allocation of Policy-Making
Bibliographic record
Abstract
How should society allocate policy-making between the legislative and the executive branches of government?We analyze a model in which biased and polarized policymakers set policy in response to shocks.We show that policy issues for which the policy-maker bias is small relative to the degree of polarization should be delegated to the legislature, while policy issues where the bias is large should be delegated to the executive.Moreover, when executive delegation is preferred, it is optimal to leave little discretion and impose a narrow mandate.This finding contrasts with conventional wisdom that executive delegation allows for greater flexibility.The main difference between the two institutional settings is the ability to restrict ex post bargaining under executive delegation.Thus, when the bias is large, executive delegation is preferred because it can effectively constrain policymakers' choices.In contrast, when the bias is small, the ability to bargain ex post allows for flexible responses to severe shocks while limiting political risk.We also study the credibility of these institutions and show that while delegating to the legislature is typically credible, executive delegation is typically not when the bias is exogenous but can be when the bias arises from time inconsistency problems.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 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".