Reconceptualizing Tax Compliance Behavior: A Theoretical Matrix Approach
Bibliographic record
Abstract
Tax compliance behavior is a multifaceted and extensively explored phenomenon within behavioral economics. However, due to its intricate nature, achieving a full grasp of this topic remains a challenge. This paper is motivated by the lack of coherent structure and lucidity in the existing theoretical frameworks employed to elucidate tax compliance behavior. Firstly, this paper puts forth a novel perspective that seeks to transcend the traditional binary framework of taxpayer behavior, which typically categorizes taxpayers as either compliant or non-compliant. Instead, this paper posits that taxpayer behavior may exist along a spectrum spanning from complete compliance to absolute non-compliance. This conceptual shift aims to provide a more nuanced understanding of taxpayer behavior by acknowledging the potential for varying degrees of compliance. Secondly, to address the existing gaps in the theoretical foundations of factors influencing tax compliance behaviour, the paper introduces a theoretical matrix. This matrix is intended to serve as an organized framework that succinctly encapsulates the prevalent theories underpinning studies concerning the determinants of tax compliance behavior. By methodically categorizing these theories based on ‘types of compliance’ and ‘types of factors’ dimensions, the matrix provides researchers and practitioners with a cohesive overview of the diverse factors influencing taxpayer behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".