Determinants of Tax Compliance: A Review of Factors and Conceptualizations
Bibliographic record
Abstract
This paper aims at providing a review of the factors that determine taxpayer compliance from a social marketing point of view. Data was obtained from 18 empirical studies published between 1985 and 2012 from across the globe. The findings made several revelations. First, too many and different explanatory factors have been proposed in the literature making comparison of findings across several studies difficult. Second, several researchers proceed without a theoretical framework to help guide the selection of independent factors. Since the use of theory enhances understanding of the major factors that affect a phenomenon, this deficiency has left the tax literature without a meaningful convergence on the key determinants. Third, aggregate analysis showed that attitudinal, normative and subjective control variables were on the overall good predictors of tax compliance. The findings suggest the following implications for research and policy action. First, it is recommended that future studies should seek to develop a few theory based set of relevant determinants of tax compliance that can yield accurate predictions. Second, tax policy makers are advised to desist from exclusive use of the conventional coercive methods (subjective control factors) normally used to compel tax compliance; instead they should take a balanced approach to tax enforcement that will also encourage voluntary compliance through change of attitudes and norms.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".