Digital Transformation and Corporate Tax Avoidance: Evidence from Moroccan Listed Firms
Bibliographic record
Abstract
This study aims to investigate the impact of digital transformation on corporate tax avoidance. In fact, this revolution has pervasively affected firms in different aspects and represents a significant opportunity to modernize their internal processes, bringing alongside a set of challenges that they must overcome. One hypothesis posits that digitalization enhances information transparency and internal control, reducing tax avoidance, while the other one suggests that the increase in digitalization leads to more complex and opaque transactions, leaving avenues for more aggressive tax strategies. This paper uses data of listed firms in the Casablanca Stock Exchange from 2020 to 2024, excluding the financial sector due to its specific tax regulation, leaving a final sample of 56 companies and 272 firm-year observations. It applies an OLS regression to assess the relation between the two variables, controlling for a set of firm and governance characteristics. The aim of the article is to address the scholarly debate by providing insights into an emerging economy where there is little research on the subject. The findings reveal that digital transformation contributes to the decrease in corporate tax avoidance in conjunction with governance variables like the presence of independent directors on the board and the duality of a CEO position, strongly supporting the first hypothesis. Notably, the OLS regression results show that an increase in digitalization by 1 point is associated with a decrease of 40.4755 in the book-tax differences, significant at the 5% level. The results provide high support for firms to invest in technologies in order to optimize their internal processes and improve their data quality; it also calls for tax authorities to strengthen their digital audit capacities and integrate data-driven tools to detect and interpret signals of potential tax-aggressive strategies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".