Does Information and Communication Technology Influence the Shadow Economy? A Panel Data Analysis for EU Countries
Bibliographic record
Abstract
Abstract Information and communication technology (ICT) adoption has emerged as a driving force in reshaping tax systems and global economic practices. This study addresses the implications of digitalization in reducing tax avoidance in the European Union (EU) Member States, where the time frame of analysis spans over a 10-year period between 2013 and 2022. This research aims to highlight correlations between tax avoidance and digitization. The means used for this cross-sectional and temporal dataset are based on the application of a regression on panel data, where we used the dependent variable tax avoidance represented by the shadow economy and the proxy for ICT services as independent variable. The study extends the literature by analyzing the influence of ICT on the shadow economy in the European Union and the contribution brings the innovative use of Internet server security and Internet access as factors influencing the shadow economy for this sample, through the System GMM method. We emphasize the results by replacing the shadow economy estimated using the classic MIMIC method with that estimated using the abnormal energy consumption method for robustness checks. The results confirm that increasing digitalization in EU countries leads to a reduction of the shadow economy, where the worst performing economies are in the South-East of the European Union. Finally, this study provides recommendations for increasing investments in the ICT services sphere and for developing effective tax policies to increase tax transparency.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".