Defaults, Mandates, and Taxes: Policy Design with Active and Passive Decision-Makers
Bibliographic record
Abstract
Growing evidence suggests that many people are surprisingly responsive to unconventional policy tools, such as defaults or choice-framing, yet unresponsive to conventional ones, such as taxes or subsidies. This article studies the optimal choice of policy instrument in settings characterized by such features. We utilize a simple binary-choice model in which decision-makers are either active or passive; active choosers make their decisions by comparing perceived costs and benefits whereas passive choosers select whichever option is the default. From this simple model, a number of results emerge. First, manipulating the default option is preferable to imposing a mandate when active choosers tend to make correct decisions. Second, taxes and defaults are complements, not substitutes; employing the two types of instruments in conjunction can yield better results than utilizing either one alone. Finally, the optimal combination of taxes and defaults is typically preferable to a mandate even in settings where active choosers are prone to biases. The results establish important limits on the range of settings in which mandates are an efficient policy response to decision-maker errors.
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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 | 0.035 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".