Persistence and Pervasiveness of Tax Evasion: An Evolutionary Analytical Framework
Bibliographic record
Abstract
There is considerable evidence that heterogeneity in tax compliance behavior is persistent and pervasive. This paper develops an evolutionary analytical framework in which taxpayers periodically choose between to comply or not to comply with their tax obligations. Aggregate demand formation arising from private and public expenditures depends on the frequency distribution of tax compliance behavior across taxpayers, so that the macrodynamic of the rates of capacity utilization and output growth is coevolutionarily coupled to the microdynamic of tax compliance across individuals. The analytical framework set forth here replicates several pieces of empirical evidence on tax evasion. First, the proportion of non-complying taxpayers (and hence the volume of tax evasion) depends on the tax rate and the expected cost of tax evasion. Second, heterogeneity in tax compliance behavior across taxpayers is evolutionarily persistent instead of temporary. Third, the immediate impact of a change in the proportion of tax evading individuals on the rates of capacity utilization and output growth is non-linear. Fourth, the proportion of non-complying taxpayers and the rates of capacity utilization and output growth vary positively with the tax rate in the evolutionary equilibrium.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".