Coercion, Persuasion, and Tax Compliance: The Case of Large Corporate Taxpayers
Bibliographic record
Abstract
To induce tax compliance, two opposite approaches are used: the coercive and the persuasive. Little attention has been paid in the literature to the comparative success of these two approaches. This article uses original survey data to assess the effectiveness of three coercive and three persuasive instruments used by the Large Taxpayer Unit of the Bangladesh National Board of Revenue to promote compliance by large corporate taxpayers. Using logistic regressions, we find that when instruments of either coercion or persuasion are used separately, they are less likely to improve the tax compliance of large corporate taxpayers than when both types of instruments are used in combination, although coercion seems the more powerful of the two. The findings may be relevant in other countries that rely heavily on tax revenue collected from large corporations, including Canada. Limitations of the study include the measurement of some variables using self-reported data and the assumption that no important causal constructs exist between the instruments of coercion and persuasion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".