Mitigating the shadow: Exploring taxes as solutions
Bibliographic record
Abstract
Nations attempt to attract major enterprises to their territories by implementing lower tax rates while simultaneously enhancing tax collection efficiency within their jurisdictional boundaries. In this study, we scrutinize the correlation between the Baltic countries’ tax systems and the levels of the shadow economy inherent to their respective economic landscapes. Our analysis indicates that tax reform can substantially influence diminishing the corporate shadow economy within a society. More specifically, our research delves into how economic growth can mitigate the corporate shadow economy, primarily driven by shifts in tax collections within Lithuania. Utilizing quarterly data from 2002 to 2022, we use panel regression and causality analyses as the overall analytical approach. The analyses uncover a complex relationship between various effective taxes and the extent of the shadow economy. Notably, we find that while an increase in the effective income tax rate is associated with a growing shadow economy, an uptick in the effective corporate income tax rate has the opposite effect, reducing its scale. Additionally, a rise in the effective VAT rate is linked to an expanded shadow economy. However, the influence of these effective taxes on imports has limited significance in regulating the scope of the shadow economy, likely due to increased tax evasion incentives. Overall, this study contributes to our understanding of how tax reform can impact the shadow economy and underscores the need for more comprehensive strategies to address this issue.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".