Indirect Tax Policy Acceptance Model: From the Perspective of the Public in Malaysia
Bibliographic record
Abstract
Given the strong rumour that the Goods and Services Tax (GST) may be re-introduced in Malaysia, Bernama (2022) reported that Malaysia is keen on reintroducing GST to expand its revenue base and carry the weight of public subsidies.Due to GST abolishment, Malaysia incurred an annual revenue loss of RM20 billion, shockingly, the reintroduction of SST contributed little to the government.Unfortunately, from the public's perspective, GST remains a very unpopular indirect tax reform due to its regressive nature, affecting both the poor and rich.Since the tax applies to every transaction regardless of the socioeconomic status of individuals, it places an undue burden, especially on poor households.The literature on the critical catalyst of the public acceptance of Malaysian indirect tax also lacks discussions on a specific public acceptance model for indirect tax policy implementation.This study, therefore, aims to bridge the gap by proposing an indirect tax acceptance model using the guiding principles of sound tax policy recommended by AICPA (2017).Using a quantitative approach, questionnaires were distributed to the B40, M40, and T20 household income earners throughout Malaysia.The results show that the guiding principles of the effectiveness of tax administrations, transparency and visibility, appropriate government revenue, neutrality, and simplicity are the variables that allow the assessment of public behaviour towards indirect tax.The findings can provide helpful feedback to relevant policymakers and tax authorities in designing a more acceptable indirect tax.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".