Impact of Mandatory Audits of Small- and Medium-Sized Enterprises on Their Income Tax Compliance: Evidence from the Egyptian Small- and Medium-Sized Enterprise Stock Market
Bibliographic record
Abstract
Small- and medium-sized enterprises are essential to the economies of nearly all countries, as they directly influence the GDP and tax revenue. In 2019, the European Federation of Accountants and Auditors for SMEs surveyed SME account users, revealing that tax authorities were the most common recipients of the company’s accounts, accounting for 61.40% of cases. This study, from a macroeconomic perspective, aims to uncover evidence of the impact of mandatory audits of Egyptian SMEs on their income tax compliance. It also seeks to explore the Egyptian SME tax environment to fill the knowledge gap by exploring perceptions of SMEs’ tax performances and their levels of tax. This study provides evidence that the taxpayers’ tax compliance behavior is an essential and decisive factor in the compliance of Egyptian SMEs with income tax. The results show that SME management is less persuaded by the potential benefits of mandatory audits on tax compliance than auditors and academics. Also, the study found experimental evidence confirming that the mandatory auditing of SMEs positively impacts their compliance with income tax. Additionally, this study developed a tax compliance scale (the RTRP scale) that effectively suits the nature and characteristics of SMEs, enabling the quantitative measurement of their compliance levels with income tax, as well as comparisons between SMEs.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".