Multidimensional tax compliance attitude
Bibliographic record
Abstract
This paper theorizes that individuals’ tax compliance attitudes are characterized not only by interpersonal heterogeneity but also by intrapersonal heterogeneity. Utilizing three online surveys, we develop a multidimensional taxpayer typology based on factor and cluster analysis. Our findings underscore that taxpayers can be classified into two categories: (a) moralists and (b) rationalists. Notably, rationalists consistently exhibit lower tax compliance levels than their moralist counterparts. We introduce a questionnaire labeled the Tax Compliance Attitude Inventory (TCAI) alongside a classification algorithm. These tools enable users to categorize individuals in any dataset, applying the TCAI, as moralists and rationalists. The heterogeneity in taxpayer attitudes can primarily be attributed to differences in four key factors: (i) morale, (ii) monetary benefit, (iii) deterrence, and (iv) authority. Lastly, to demonstrate the practical application of our findings, we present an online experiment that tests our results using incentivized and out-of-sample data. Overall, this work provides an instrument for assessing taxpayer attitudes, predicting individuals’ tax compliance intentions, and personalizing behavioral interventions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".