Policy Forum: Cognitive Bias as a Factor in Determining the Efficiency of Sliding Scales
Bibliographic record
Abstract
In tax law, sharp lines occur where a minimal change in a taxpayer's circumstances results in significantly different legal treatment. Sharp-line tests are criticized because they encourage taxpayers to alter their optimal behaviour for purely tax reasons, thereby producing deadweight loss. The most obvious alternatives to sharp-line tests are sliding scales, which operate by imposing tax proportionately on the basis of where taxpayers fall along a continuum. Scholars, including Edward Fox and Jacob Goldin, have suggested that the adoption of sliding scales in determining tax liability as opposed to the use of sharp-line tests could reduce deadweight loss in many contexts. However, the adoption of sliding scales generally comes at the cost of greater complexity, which can lead taxpayers to make systematic errors in selecting optimal behaviours and can also introduce cognitive biases. This article argues that smoothing existing sharp-line tests may not bring about predicted efficiency gains if such predictions rely on taxpayers behaving optimally. Rather, a more accurate determination of the change in deadweight loss occasioned by a shift from sharp lines to sliding scales would also account for the effects of cognitive bias. In order to illustrate the concepts dealt with herein, this article establishes a simplified example based on tax residence to explain the concepts of deadweight loss, sharp lines, and sliding scales in the context of Fox and Goldin's suggestion that sliding scales would be more efficient than sharp lines in many circumstances.
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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 | 0.030 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".